International Journal of Reasoning-based Intelligent Systems (64 papers in press)
Social Network Analysis: Friendship inferred by chosen courses, Commuting time and Student Performance at University
by Lionel Khalil, Marie Khair
Abstract: Our Social Network Analysis (SNA) evaluates the performance of students taking courses with a group of friends versus students used to take courses alone. We evaluate the probability to be friend by comparing the number of courses shared by students with the probability to be assigned in the same classroom randomly based on curriculum constraints. A minimum of courses taken in common is used as a criterion to identify students belonging to a tribe of friends. The main findings are that students in tribes over perform other students by about half point of GPA, and are dropping and repeating fewer courses. Considering student without friends, we measured the impact of the commuting distance on GPA and drop off rate: students with very low GPA and high drop off are mostly students with significant higher commuting time.
Keywords: Social Network Analysis; Friendship; Student Performance; GPA; drop off; commuting time.
Design of Public Bicycle Scheduling Model based on Data Mining Algorithm
by Xia Wendong, Liu Yuanfeng, Chen Deli
Abstract: Guangzhou Public Bicycle System has been the largest bike-sharing program in the world. Software of the system was developed by our research team. To meet the fluctuating demand for bicycles and for vacant lockers at each station, employees need to actively shift bicycles between stations by a fleet of vehicles. Hybrid GRA-SP metaheuristic which incorporate a path-relinking procedure have been successfully applied for different combinatorial problems. Data mining is a process that uses a variety of data analysis tools to discover patterns and relationships in data. In this paper, a new hybrid data mining metaheuristic combines GRA-SP which incorporate path-relinking procedure with data mining process is proposed and some improvement are made.
Keywords: public resource; scheduling; mining algorithm; model.
Parallel K-Means Algorithm based on Two Stage Clustering of Large Data
by Xia Wendong, Liu Yuanfeng, Chen Deli
Abstract: Aiming at the fact that the algorithm communication time occupation ratio is too high and the practical application value is limited under the Mapreduce mechanism, a Hadoop-based two stage parallel c-Means clustering algorithm is proposed to solve the classification problem of super large data. First, the Hadoop-based two stage parallel fuzzy c-Means clustering algorithm is proposed to process the clustering of large data; and a protocol-based group typical individual reduction strategy is used to improve the time complexity of the MPI communication model of Mapreduce, so as to improve the overall efficiency of the algorithm; secondly, the interference of bad data items can be effectively eliminated by the selective group reduction algorithm, so that the algorithm in this paper has higher operating efficiency and clustering success rate. In terms of parallel rate and speedup ratio, the parallel rate and speedup ratio of the proposed algorithm in this paper on the large data set is better than the performance of the small data set, which means that the algorithm in this paper can adjust itself according to the amount of data in real time. The simulation results show that the performance of PGR-PFCM algorithm is better in the processing of large data.
Keywords: parallel algorithm; fuzzy clustering; K-Means; big data; two stages.
Decision Support for Grape Crop Protection Using Ontology
by Archana Chougule, Vijay Kumar Jha, Debajyoti Mukhopadhyay
Abstract: Weather based decision support for managing pests and diseases of crops requires use of Information Technology. This paper details a system developed using ontology, semantic web rule language and image processing techniques for management of pests and diseases on wines, particularly in hot tropical region of India. It aims at minimizing use of pesticides and fungicides by forecasting pests and diseases occurrence using information about meteorological conditions and its relation with pest and disease occurrence. It is named as PDMGrapes. For system knowledge base, knowledge available in different formats on grape pests and diseases is converted to ontology. Favourable meteorological conditions for pest and disease occurrences are mentioned by SWRL rules. Grapes disease identification is done using image processing techniques. The system helps grape growers to minimize side effects of pesticides on environment. The developed system is validated and verified for accuracy and performance.
Keywords: decision support; ontology building; decision tree; semantic web rule language; grapes; nutrition management.
Double PWM Coordinated Control Based on Model Predictive Algorithm and Power Compensation
by Bo Fan, Ke Wang, Bowen Ding, Ning Guo
Abstract: With analysis on double PWM structure though systems energy flow theory, active power and reactive power of rectifier are controlled by model predictive algorithm. System adopts the method of combine dynamic powers compensation with static powers compensation for system power in order to reduce system power error. A new power compensation algorithm is proposed to designs a new controller to replace PI controller of systems voltage loop, which can restrain fluctuation of DC bus voltage when load power suddenly varies and reduces DC-links capacity of capacitance. As for inverter side, system uses the method of rotor flux linkage oriented to control three phase asynchronous motor. Results of simulation can show that, system can realize the smallest tracking error of active power and reactive power by model predictive control, and restrain fluctuation of DC bus when load power suddenly changes. As the same time, the gird currents waveform is well.
Keywords: Model Prediction; Tracking Error; Power Compensation; Coordination Control.
Psychological Cognition Behavior Model Based on Reinforcement Learning
by Shiyong LIU, Ruosong CHANG, Sang Fu
Abstract: The trust relations in open systems are essentially one of the most complex social relationships, involving a variety of factors, such as hypotheses, expectations, behaviors and environment, etc., which are very difficult to have quantitative expression and forecast accuratly. The goal of the paper is to propose effectively illustrate psychological cognition behaviors using the reinforcement learning. Combined with the trust behavior of human society, an reinforcement learning model based on human trust habits is put forward: (1) Self-adaptive overall knowability decision-making method based on the historical evidence window is constructed, which not only has overcome the subjective judgment method for the determination of weights commonly used in existing models, but also can solve the knowability forecast problem when the direct evidence is insufficient; (2) The concept of reinforcement learning weighted averaging (hereinafter referred to as RLWA for short) operator is introduced, and the direct trust forecast model based on the RLWA operator is established, which can be sued to solve the problem of insufficient dynamic adaptability of the traditional forecast model. The experimental results show that, compared with the existing models, the proposed model has more robust dynamic adaptability and also significant improvement in the forecast accuracy of the model.
Keywords: Distributed System; Information Security; Reinforcement Learning Model; Reinforcement Learning Weighted Averaging Operator.
Cloud Platform Load Balancing Based on Bee Colony Algorithm
by Fan Xue, Zhijian Wu
Abstract: In order to shorten the time needed to execute tasks in cloud system and maximize the utilization of available resources in the system, this article proposes the cloud platform load balancing design under the background of bee colony algorithm (ABC algorithm). First of all, puts forward the designed mathematical model, and then gives the basic algorithm of load balancing based on bee colony algorithm. In addition, in the design of the process, three experiments are respectively carried out. The first set of experiments results show that the result is stochastic and stable and the system overhead will affect the system performance; the second set of experiments results show that there is the presence of outliers, algorithm can guarantee the system to complete the system task implementation within a limited time, and the system consumption continuously rises; the third set of experiments results show that the algorithm has stability and independence, and the algorithm has stable efficiency in the range that the virtual machine can withstand; if it exceeds the range, the results will be unstable. Overall, the ABC algorithm has an effective implementation effect.
Keywords: Bee colony algorithm; Cloud platform; Load balancing.
Visual Automatic Obstacle Avoidance Technology Research in Unmanned Vehicles
by Bo Liu, Liguang Li, Piqiang Tan, Rui Jia, Qing Liu
Abstract: In view of the problems of low obstacle avoidance and low efficiency in traditional unmanned vehicle in automatic obstacle avoidance, multi-feature fusion automatic obstacle avoidance method in unmanned vehicle is proposed. Optimize unmanned vehicle obstacle avoidance objective function, measure target obstacle distance, and calculate the braking distance. Based on this, unmanned vehicle dynamics model is established and the obstacle is located and determined. Multi-feature fusion design of unmanned vehicle visual automatic obstruction steps is made and finally the process of obstacle avoidance is analyzed. Experimental results show that the use of improved visual automatic obstacle avoidance technology has certain advantages in target obstacle positioning and obstacle avoidance accuracy, which are superior to those of traditional obstacle avoidance technology.
Keywords: Vehicle; Unmanned; Visual; Automatic; Obstacle avoidance technology.
Special Issue on: ICEST'2015 Information, Communication and Energy Systems and Technologies
An approach to transformation of data into knowledge for power control in smart homes
by Ivaylo Atanasov, Anastas Nikolov, Evelina Pencheva
Abstract: Internet of things (IoT) encompasses information and networking technologies which allow connected devices gathering data from their environment to exchange information with network applications. The increased number of diverse devices and the variety of multimodal data make interoperability a challenging task. Synthesis of semantic information from raw IoT data enables sharing of common data models between different applications. The paper presents an approach to modelling semantic annotation for power control in smart homes and then converting it to knowledge. The approach includes context aware models as well as a knowledge base describing behaviour of an autonomous agent. The context aware models representing remote device management are formalised and verified using the concept of bisimulation. Temporal logic is used for specifying the agent behaviour and reasoning about power control of home appliances.
Keywords: internet of things; IoT; semantic annotation; remote device management; formal model verification; weak bisimulation; autonomous service model; temporal logic.
Solving medical classification problems with RBF neural network and filter methods
by Jasmina D. Novakovic, Alempije Veljovic
Abstract: This paper evaluates classification accuracy of radial basis function (RBF) neural network and filter methods for feature selection in medical datasets. To improve the diagnostic procedure in the daily routine and to avoid wrong diagnosis, machine learning methods can be used. Diagnosis of tumours, heart disease, hepatitis, liver and Parkinson's diseases are a few of the medical problems which we have used in artificial neural networks. The main objective of this paper is to show that it is possible to improve the performance of the system for inductive learning rules with RBF neural network for medical classification problems, using the filter methods for feature selections. The aim of this research is also to present and compare different algorithm approach for the construction system that learns from experience and makes decisions and predictions and reduce the expected number or percentage of errors.
Keywords: medical classification problems; classification accuracy; feature selection; filter methods; machine learning; RBF neural network.
Comparison of vertical projection profile, moment-based and initial skew rate algorithm for text skew estimation
by Darko Brodić, Ivo R. Draganov, Zoran N. Milivojević, Viša Tasić
Abstract: This paper makes analysis and evaluation of vertical projection profile, moment-based and initial skew rate algorithm for the text skew. Among these three methods, the initial skew rate method is newly developed, while moment-based is adapted compared to its initial version. The vertical projection profile method is used in its native form, which is widely accepted for text skew estimation. The comparison is based on the experiment which includes testing of the algorithm with a dataset that consist of the printed text image samples. These image samples are given in the resolution of 25, 50 and 300 dpi. Tested algorithms obtain different skew accuracy for different resolution of text images. At the end, the moment-based method has the smallest accuracy declination, which demonstrates its benefit over the other two methods. Furthermore, this contributes to its robustness in applications, which use low resolution images.
Keywords: binarisation; initial skew rate; ISR; moment-based method; printed text documents; projection profiles methods; text skew.
Experimental determination of soil electrical parameters for the creation of a computer model of a grounding system for lightning protection
by Rositsa F. Dimitrova, Marinela Y. Yordanova, Margreta P. Vasileva, Milena D. Ivanova
Abstract: The paper presents multifactor experimental studies for determining the apparent soil resistivity and the dielectric permittivity depending on the frequency of the electromagnetic field, the multilayered structure, moisture content and density of the soil. The gravimetric method for considering the soil moisture during the experimental researches was chosen. The received experimental results were statistically processed and a mathematical modelling of the controlled parameters was performed considering the specifics of the examined soil. These analytically obtained results of the dependences would contribute to more precise sizing of the grounding systems and could be used for creation of accurate simulation models for the study of wave processes in them.
Keywords: soil electrical parameters; soil resistivity; dielectric permittivity; multilayered soil; gravimetric method; mathematical modelling; grounding system; grounding rod; lightning protection.
A performance analysis of computing the LU and the QR matrix decompositions on the CPU and the GPU
by Dušan B. Gajić, Radomir S. Stanković, Miloš Radmanović
Abstract: We present an analysis of time efficiency of five different implementations of the LU and the QR decomposition of matrices performed on central processing unit (CPUs) and graphics processing units (GPUs). Three of the considered implementations, developed using the Eigen C++ library, Intel MKL, and MATLAB are executed on a multi-core CPU. The remaining two implementations are processed on a GPU and employ MATLAB's Parallel Computing Toolbox and Nvidia CUDA augmented with the cuSolver library. Computation times are compared using randomly generated single- and double-precision floating-point matrices. The experiments for the LU decomposition show that the two GPU implementations offer best performance for matrices that can fit into the GPU global memory. For larger LU decomposition problem instances, Intel MKL on the CPU is found to be the fastest approach. Furthermore, Intel MKL also proves to be the fastest method for computing QR decomposition for all considered sizes of matrices.
Keywords: performance comparison; LU decomposition; QR decomposition; parallel computing; general-purpose algorithms on graphics processing unit; GPGPU; MATLAB; Intel MKL; Compute Unified Device Architecture; CUDA.
Special Issue on: Challenges in Smart Reasoning Intelligent Systems
Clustering Algorithm for Wireless Sensor Network to Improve the Efficiency of Acnode
by Youwei Shao
Abstract: In the vehicular ad hoc networks VANETs, the topological structure with high dynamic and frequent cracked link challenge the vehicle to vehicle communication. By taking VANETs city scene as the background, the Thesis proposes routing VAC-BNR (Vector-angle-cluster and bridge nodes-based routing) protocol based o clustering of direction vector angle and bridge nodes. The VAC - BNR protocol at first divides a road into the intersection area and straight line sections between or among intersections. In the straight line sections, according to the moving direction of vehicles, the vehicles are divided into different clusters, later on, utility value of the nodes in each cluster are calculated, and then priority of node forwarding data packages is arranged according to utility values of nodes; In the intersection area, the stability factor of the vehicles is calculated, which includes relative velocity and distance between the vehicle from surrounding vehicles, and the most stable vehicle in the intersection area is chosen as the forwarding node. The experimental data shows that the proposed VAC - BNR protocol can effectively improve data transmission in the urban scene environment.
Keywords: Topological structure; Clustering routing; Vector angle; Sensor network; Routing protocol.
Fuzzy Neural Network Learning based on Hierarchical Agglomerative T-S Fuzzy Inference
by Tao Duan, Ang Wang
Abstract: It is well-known that the accuracy of classification prediction is relatively high, but the prediction result is obscure in concept since result is given in two-value form (0 or 1) which says that red tide exists or does not exist. On the other hand, the accuracy of numerical prediction is relatively low, but it can offer density value of plankton which influences red tide. In order to combine characteristics of the above mentioned two methods, a prediction method for red tide which is mixed with integration model of hierarchical agglomerative T-S fuzzy inference is proposed. In the Thesis, through using the proposed prediction method mixed with integration model of hierarchical agglomerative T-S fuzzy inference, taking respective advantages of classification prediction and numerical prediction in prediction process for reference, and through experiment and comparison, it is proved that this algorithm is better than LMBP algorithm in prediction accuracy which shows the validity of the proposed algorithm. In the next step, it is mainly to further study the practical application of the algorithm, and to apply this prediction model to red tide warning system, and also to conduct experimental verification for a certain period by using actual marine environment.
Keywords: Fuzzy inference; Neural network; Hierarchical agglomerative; Prediction.
Key Data for Cloud Computing based on Ensemble Clustering Approximate Analysis
by Zou Yu, Qin Zhong Ping
Abstract: To realize multi-label classification of text and meanwhile reduce calculation complexity and keep classification precision, dimensionality-reduction clustering method for fuzzy association of text multi-label based on cluster classification has been proposed. In text classification, it usually involves enormous feature numbers, which may cause curse of dimensionality. In addition, classification region can not always keep convex characteristics. It can be non-convex region composed of several overlapping or intersecting sub-regions. Above mentioned automatic classification system may require enormous memory requirement or has poor classification performance. Hence, new multi-label text classification method is proposed to overcome these problems in combination with fuzzy association technology. Fuzzy association evaluation is adopted to transform high-dimension text to low-dimension fuzzy association vector, thus avoiding curse of dimensionality. Experiment results show that the proposed method can more effectively classify text multi-label problem.
Keywords: Fuzzy transformation; Key data; Integration clustering; Cloud data; Data analysis.
Design of Unsupervised Facial Expression Animation Based on Geometric Grid Measurement
by Niu Chunzhou, Zhu Yukai
Abstract: Many actual application images in the real world are formed by high dimensional data in most cases, while the manifold learning algorithm can explore the nonlinear information hidden in these high dimensional data. As most of manifold learning algorithms can only be defined in training cluster, it is impossible to project the sample on the lower dimensional space. In the Thesis, we introduce a kind of double manifold algorithm based on LLE and Isomap. Different from the traditional LLE algorithm, our algorithm learns two kinds of manifold information in which one group of data relates to many types and it compares two kinds of single LLE algorithm and Isomap algorithm through the setting of the appropriate nearest neighbor number K. No matter for the recognition rate or running time, it is obviously superior to the other two kinds of algorithms and it can effectively achieve the estimation of facial expression and significantly reduce the computation complexity.
Keywords: Facial expression; Geometric grid; Unsupervised; Manifold learning algorithm; K-nearest neighbor.
Factor analysis model of the result of hospitalized patients with neurosis
by Sun Shanhui, Li Hong, Li Zhuangzhuang, Zhang Bingqiu
Abstract: To study the diagnosis of hospitalized patients with neurosis and its influencing factors, this article, on the basis of the data of treating hospitalized patients with neurosis hospitalization, empirically analyzes the relationship between the treating effect and personal basic situation, personal social relations, personal original condition, and makes the corresponding regression analysis and factor analysis. The results show that the patient's social relationship and personal character are obviously related to the diagnosis of neurosis. There are obvious correlations between the original condition in the early diagnosis and patients with neurosis, so it is important to strengthen the understanding and analysis of the original condition. We should strengthen publicity and education of mental health knowledge, encourage people from all walks of life to actively participate in it and improve their awareness of neurosis, and thus to effectively reduce the bias in patients with neurosis. Untimely separation and unharmonious relation with parents are one of socio-psychological factors which cause adults suffer neurological disorders.
Keywords: Neurosis; Diagnosis; Factor Analysis Model;.
Multi Criteria Decision Making Method based on Analytic Hierarchy Process with Intuitionistic Fuzzy Preference Information
by Cai Liang
Abstract: An extension method of multi criteria decision making (MCDM) is proposed in the Thesis for selection and assessment of parting line in mold design. First of all, linguistic variable is used to express grade of alternative parting line scheme and weight of criterion significance; and then, the membership function of final fuzzy assessed value is determined based on these linguistic values; in the end, a new ordering method of maximum and minimum sets will be adopted for normalization of weight grading and defuzzification to clear value and for strength and weakness ordering of alternative scheme. The verification and contrast experiment show effectiveness of the method in the Thesis and the comparison with traditional fuzzy multi criteria decision making method shows the method in the Thesis is more applicable.
Keywords: Fuzzy number; Multi criteria decision making; Selection of parting line; Mold design; Ordering of maximum and minimum sets.
MapReduce Optimization Information Query Method for File Management System
by XuGuang Zhu, Yuzhi Shen
Abstract: MJQO problem is very complicated, query speed influences execution efficiency of database application software. To solve deficiencies such as low rate of convergence, etc of PSO algorithm and improve optimization efficiency of database multi-connection query, this Thesis proposes a MJQO method adapting to escape momentum particle swarm optimization aiming at deficiencies of particle swarm optimization such as early-maturing, partial optimization, etc, and it verifies effectiveness of SAEV-MPSO via emulation contrasted test, and this algorithm can obtain optimal query scheme of MJQO in relatively short time. Crossover mechanism is first introduced by this algorithm of genetic algorithm to particle swarm algorithm to maintain diversity of it and prevent early-maturing phenomenon, and then this Thesis introduces search track of momentum algorithm smoothness particle to accelerate convergence rate of particle swarm; finally this Thesis applies this algorithm to database multi-connection query optimization solution to achieve optimal database query scheme. Emulation result indicates this algorithm improves database query efficiency and shortens query response time.
Keywords: Database query; Archive information management; Genetic algorithm; Mapreduce; Particle swarm.
Dynamic Path Planning of Mobile Robot based on Ant Colony Algorithm
by Long Zhuo-Qun
Abstract: The Thesis makes vehicle in cross-country environment as research object, and uses improved ant colony algorithm to research and analyze the cross-county path planning. First, improved ant colony algorithm is used to research cross-county path planning of vehicle, then Slope Table and Roughness Table are introduced to analyze topographic Slope and land surface propertys affect on path planning, and path optimization algorithm is designed considering restriction of slop and Roughness. Simulation result shows that this algorithm can realize cross-county path planning with speediness and efficiency. Experimental result demonstrates that the improved ant colony algorithm has stronger feasibility and better searching capability.
Keywords: Path planning of robot; Land surface property; Optimal method; Ant colony.
Construction of Evaluation System of Sports Talent Training Scheme based on Data Mining
by Gong Xun, Lin Suxia
Abstract: In order to improve the effectiveness of the evaluation system construction for sports talent training program, this paper puts forward a kind of evaluation system construction method for sports talent training program based on data mining. It provides the description target of sports talent training program, the problem that the upper bound of itemsets only used by the traditional mining algorithm has unsatisfactory effect on the high-expectation weight and downward closure property of mining algorithm in design hierarchy, and presents the proof process, and this property can effectively reduce the processing capacity of candidate set in the premise of ensuring the accuracy, to construct the two-stage data mining process. By comparing with the training program of 2007 version, the proposed method can eliminate the setting compulsory course equivalent to individual professional elective course, to promote the diverse development of students.rnrn
Keywords: Two-stage; Apriori algorithm; Sports talent; Training program.
Design of College Students' Physique Monitoring and Service Platform based on Computer and Network
by Haiyan Wang
Abstract: The problem of college students physique has become a serious problem which restricts the cultivation of high-competent talents in China. In view of the existing deficiencies in the monitoring platform of students physique, we fully utilize the advanced technology of computer and Internet development and follow the basic principles of economical practicality, scalability, user-friendliness and real-time information exchange to establish monitoring and service platform for college students physique. It will have far-reaching practical significance to fulfill the expected goals in different levels such as teaching management, campus sports, and students' individual physical intervention in all universities through the basic and extended functions of platform such as basic assessment, testing organization, data management, information release, fitness guidance, and printout. The main design idea of the platform, the function realization, and the design expansion and operation method of each module will provide reference for universities to establish their own physique monitoring and service platform in line with their own requirements.
Keywords: Computer; College students; Physique; Monitoring; Service.
Decision Making Model of energy Consumption based on Multi Uncertain Factors
by Cai Liang
Abstract: An Energy Consumption Decision Model Method based on multi-factor Agent Fuzzy Game Compromise is proposed in this Article, to promote availability of energy consumption decisions. First, give the Fuzzy Decision Model for Energy System based on multi-agent methods, in which fuzzy decision Agent is the core of whole alliance, able to get the energy demands information of users from user layer Agent, and the energy supply conditions and energy supply availability inside the alliance from energy consumption layer Agent; secondly, give a fuzzy game compromise weight decision method to build a relation matrix based on fuzzy evaluation, and propose an assessment methods for grey Euclid model; and at last, by simulation experiment, verify the availability of energy consumption decision model based on the proposed multi-factor Agent Fuzzy Game Compromise.
Keywords: Multi-factor; Agent model; Fuzzy compromise; Energy consumption; Decision model.
WSNs Heterogeneous Cluster Routing based on Distributed Fuzzy Logic Inference
by Tang JunYong, Chen Xiang
Abstract: Targeted at the wireless sensor network in heterogeneous distribution composed of solar energy supply nodes and zero energy supply nodes, the cluster routing algorithm based on node density and energy size is proposed. In this Thesis, the wireless sensor network of heterogeneous non-uniform distribution is studied, and sun energy harvesting model ISHE based on intensity of illumination is proposed. In IISHE model, at first, the light intensity sensor is used to read solar illuminance under the current environment, and then, energy size obtained by solar energy supply node at specified period can be estimated based on corresponding relation between illuminance and irradiance. The model is characteristic of strong timelines and high calculation accuracy. The second innovation point is to propose cluster routing algorithm of the wireless sensor network of heterogeneous non-uniform distribution based on node intensity and energy size in the Thesis. The experiment results show that invalidation round rate of the first node LEACH in the algorithm proposed in the Thesis is 86.2% higher than the average value and invalidation round rate of the last node is 65.9% higher than the average value; network coverage rate of the algorithm in 86% of lifetime is 38% higher than LEACH; network throughput of the algorithm proposed in the Thesis at round 2000 is 5 times as that of LEACH.
Keywords: Fuzzy inference; Distributed; Wireless sensor network; Cluster routing; Non-uniform distribution.
Facial Feature Extraction based on Principal Component Analysis and Class Independent Kernel Sparse Representation
by Xin Xiong, Li Kefeng
Abstract: Robust Principal Component Analysis (RPCA) and kernel sparse representation technology which have been proposed in recent years provide a new idea for solving problems of the above three aspects. In this Thesis, classification algorithm of kernel sparse representation has been proposed based on robust principal component analysis by using RPCA technology to generate redundant dictionary and kernel sparse representation to structure classifier, and has been used for face recognition. Basic idea of this algorithm is to generate base dictionary and error dictionary by using RPCA technology, and to realize face recognition through classifier structured by kernel sparse representation. Firstly, each training sample matrix has been decomposed into a low rank matrix and a sparse error matrix by using RPCA technology, so as to structure base dictionary and error dictionary by using the low rank matrix and error matrix respectively, and generate redundancy dictionary of sparse representation of test samples. Then, Kernel regularized Orthogonal Matching Pursuit (KROMP) algorithm has been proposed to get sparse representation coefficient which has been used to complete classification and recognition of test samples. Compared with similar algorithms, algorithm in the Thesis is of a high recognition rate for face recognition, and has a strong ability to adapt to noise and error interference.rnrn
Keywords: Principal component analysis (PCA); Image recognition; Sparse representation; Face recognition; Facial feature; Feature extraction.
An Arabic Natural Language Interface for Querying Relational Databases Based on Natural Language Processing and Graph Theory Methods
by Bais Hanane, Mustapha Machkour, Lahcen Koutti
Abstract: Nowadays, databases represent a great source of information. To extract information from these databases, the user needs to write queries using database query languages, such as Structured Query Language(SQL). Generally, for using this type of language, this user must know the structure of the database. However, this task can be difficult for non-expert users. In that, the using of natural language to extract data from the database can be a very important and efficient method. The problems in using natural language query are that it doesn't give any specification about the path access correspond to the required data. For that, many previous works are deal with the problems of freeing users from knowing the detailed structure of the database. But, Almost of this works are designed to the English language. Whereas, for the Arabic language which is the subject of this paper, there is not any proposed system. For that, we present in this paper a model of a natural language interface for databases. This interface allows the user to access data stored in the database by using Arabic language and it obviates the need for users to know the internal structure of the underlying databases. Also, it can function independently of database domain, and it can to improve its knowledge base through experience.
Keywords: Arabic language processing; DataBase; Graph theory; Dijkstra Algorithm; Extended Context Free Grammar.
Glossary Applications Model for Financial Terms with Boyer-Moore-Horspool Method Based on Mobile Application
by Nazori Agani, Arfian Maulidan
Abstract: String Search or the string matching is one aspect that is very important in terms of data processing, in addition the problem string matching is also one of the problems that are well known in the world of informatics. Some examples of the implementation of the string matching problem is in matching a string in a text editor application such as Microsoft Word, or in the case of bigger, ie matching website by entering key words as it has been implemented on search engines such as Google Inc. The general process in the searching a string is looking for a string that consists of some of the characters (called pattern) in a large amount of text. Search string is also used to look for patterns of bits in a large number of binary files. Problems begin to arise if the search process occurs in a lot of data and complex, of course, this would be very time-consuming and resources owned, so the search technique effectively and efficiently will be needed. The purpose of this study is to utilize a String Matching algorithm that is by using the Boyer-Moore-Horspool in the search data is a list of financial terms. Boyer-Moore algorithm-Horspool used to search for any string precision (word or phrase) entered with a high degree of accuracy of search, because it uses pattern matching from right to left. In addition to the HTML5-based application model, application model matching string can later be run multiplatform not only on mobile devices, but also be able to run on a desktop browser.
Keywords: String Matching; Search Engine; Boyer-Moore-Horspool; Mobile; Browser.
Special Issue on: Application of Multimedia Technology in Intelligent Manufacturing
A Effective System Layout Planning Method for Railway Logistics Center in the Background of Big Data
by Jie Li
Abstract: In the background of big data, railway logistics has become the inevitable trend of freight transportation. This paper puts forward a new method of logistics center function area layout. System layout planning (SLP) method is firstly used to analyze functional domains to construct a comprehensive correlation chart of the functional domains according to certain weights. Manhattan Distance and circuitous path are used to express the distances among the functional domains and to construct a double-object function with minimized total trucking expense and maximized total integrated relations. In the practical application of R.Muther line chart method, the proposed method can get the feasible scheme of layout of functional areas, and it has good application value.
Keywords: Big Data; Railway Logistics Center; Layout of Functional Domains; Manhattan Distance; Circuitous Path.
Personalized ranking online reviews based on user individual preferences
by Wei Song, Shiwei Zhang, Lizhen Liu, Hanshi Wang
Abstract: With the development of e-commerce sites, online reviews have become important data resources for e-customers. However, the quality of review varies a lot, with positive reviews always intermingled with negative ones, seriously interfering with their accuracy and credibility and making it very difficult for consumers to make the right purchase decision. Nowadays, there have been many literatures on the category of reviews category or ranking for public. The thing is, they only satisfy common preferences, and ignore personalized preferences of individual users. In view of this phenomenon, this paper is trying to put forward a ranking method for individual preferences. It begins with collecting the rules of user preferences by showing reviews to them to let them mark the reviews they like. Then it combines the common rules with user personalized rules to get the range of features. Finally, after calculating the optimal solution of features, the paper strives to structure a ranking model to rank reviews with the set of optimal solution.
Keywords: attribute word; user preference rule; hill climbing algorithm; ranking.
An Effective Foggy Image Acquisition Algorithm in Multimedia Big Data Era
by Jinxing Niu
Abstract: Outdoor images are often degraded by fog weather conditions in the era of multimedia big data , which affect computer vision applications severely. In this paper, an effective fog image acquisition algorithm based on big data analysis is proposed in the big data environment, and single image defogging algorithm based on histogram equalization and dark channel prior methods is proposed. The transmission and air light of the fog image need to be estimated by the dark channel prior theory methods, then clear images can be received after defogging and keep the original color. The experimental results show that the image by fog removal dark channel prior method can get clear images and keep the original color, the treatment effect is better than that of the histogram equalization method.
Keywords: Multimedia Big Data ,Foggy Image; Effective Foggy Image Acquisition Algorithm ; Image Restoration; Big Data Analysis.
Bi-level Optimization Model for Greener Transportation with Intelligent Transport System
by Kun Liu
Abstract: In this paper, we propose a bi-level optimization model (BLOM) with three algorithms. BLOM is intended for fuel saving and carbon dioxide emission reduction in both upper-level and lower-level model with Intelligent Transport System. Traffic signal schemes are optimized for minimizing total fuel consumption passing through a road intersection in unit time in the upper-level model. At the same time, traffic signal information data are sent to the lower-level model in which vehicle motion states are optimized for greener transportation. Three algorithms include hybrid genetic algorithm and particle swarm optimization in upper-level model with hybrid genetic algorithm and particle swarm optimization in lower-level model (GA-PSO/GA-PSO), GA in upper-level model with PSO in lower-level model (GA/PSO) and GA in both level model (GA/GA) are realized to compare and improve the performance of the model. The simulation results derive GA-PSO/GA-PSO hybrid algorithm converges faster with the best resolution and least calculation time than other GA/PSO and GA/GA algorithms.
Keywords: Bi-level optimization; greener transportation; Intelligent Transport System.
The Development and Popularization of Network Platform of College Sports Venues in Intelligent Manufacturing
by Han Kaiyan, Wang Qin
Abstract: With the promotion of the national fitness campaign, the number of physical exercise shows explosive growth in China.The question that blocks the development of sports exposed which is a lack of public sports venues. This paper focuses on building a network platform of all college sports venues resources which can reach the goal to serve national fitness, and proposes an Improved Parallel Heuristic Map Reduce Algorithm (IPHMRA). The experimental results show the stability, concurrency and feasibility of the network platform of college sports venues in big data era.
Keywords: Intelligent manufacturing; college sports venues; national fitness; Improved Parallel Heuristic Map Reduce Algorithm (IPHMRA); stability and concurrency.
A Medical Big Data Analysis Algorithm Based on Access Control System
by Chen Yegang
Abstract: in the latest progress in healthcare, the continuous creation of digital medical information is an important basis for the analysis of large medical data. This paper proposes a medical big data analysis algorithm based on access control system that provides reliable security protection to big medical data(BMD) by considering various quantitative parameters. The proposed algorithm calculates the reputation values of different users, which correspond to each parameter.Based on the calculated reputation values, the proposed algorithm grants access authority to each user. Simulation results are performed to verify the effectiveness of security system solving protection of the sensitive personal information of patients.
Keywords: Medical Big Data Analysis Algorithm; Big Medical Data; Access Control System; Sensitive Personal Information.
Fuzzy Self-Learning Control of Glass Tempering and Annealing Temperature Based on the Optimized Genetic Big Data Analysis Algorithm
by Xiaokan Wang
Abstract: The temperature control of glass tempering and annealing process has the problems of the time varying parameters and time lag characteristic. In order to solve this problem, this paper proposes a self-learning fuzzy controller based on improved genetic algorithm and big data analysis.The proposed algorithm can quickly search the global optimal factor by using the big data temperature. Thus the fuzzy control rules are perfected and corrected. The simulation results demonstrate that the proposed control algorithm is suitable for systems with time varying parameters and time lag characteristic.
Keywords: Annealing Temperature; Big Data Analysis; Fuzzy Control; Self Learning,Improved Genetic Algorithm.
Special Issue on: Artificial Intelligent Techniques Applied to the Study of Engineering Applications
Exploiting Ontology to map requirements derived from informal descriptions
by Murugesh Sundaram, Jayal A
Abstract: Requirements are narration of the services which a software system should make available along with the constraints that should be satisfied when the system operates. Software requirements have to be arrived from descriptions that are often incomplete, inconsistent, informal and ambiguous . Such informal descriptions have to be preprocessed and information constructs have to be extracted. This article deals with use of an ontology specific to Automatic Teller Machine(ATM) operations domain that contains the concepts, the relationships that exists among the concepts and the focus is to decide on the feasibility of the requirement by mapping the extracted requirement with the requirement defined in the background ontology. The developed ontology is queried using Simple Protocol and RDF Query Language (SPARQL), if the derived requirement is present in the ontology it is said to be feasible; else decision may be taken to eliminate the requirements that are invalid and infeasible. Ontology is a formal specification of concepts with their attributes and relationship in a particular domain. As standard description formalism, the Web Ontology Language (OWL) that is based on Resource Description Framework (RDF) is to be used.
Keywords: Ontology; Requirements elicitation; Simple Protocol And RDF Query Language (SPARQL); Web Ontology Language (OWL); Resource Description Framework (RDF); Unstructured documents; Natural Language Processing (NLP).
Application of mutation inspired Constrained Factor PSO considering voltage stability and losses by locating and rating TCSC during N-1 Contingency
by Jayachitra S, Baskar G, Feridinand T
Abstract: This paper describes, a strategy for optimal placement and setting of series FACTS controller- Thyristor Controlled Series Capacitor (TCSC) under single line contingency (N -1) using Particle Swarm Optimization(PSO), Constriction Factor PSO(CFPSO), Cauchy mutation- CFPSO(CM-CFPSO) & Gaussian Mutation CFPSO(GM-CFPSO) algorithm in order to reduce over loading and power loss in transmission lines and to improve voltage stability of a power system. In this proposed CM-CFPSO & GM-CFPSO methods, a new-fangled position equation is framed and the features of the Constriction Factor Approach (CF) is incorporated with the proposed approach. To detect the most severe transmission line, Composite Severity Index (COSI) is calculated under N-1 contingency and top three severe lines are taken for this research work. To validate the consequence of proposed approach, simulation studies are carried out on a standard IEEE 30-bus network. Appraisals are made in provisions of eminence solution, execution time and stable convergence behaviour
Keywords: Particle Swarm Optimization; Composite Severity Index; Mutation; Contingency; Optimal placement; Constraint Factor; Thyristor Controlled Series Capacitor;.
Survey on Data Analytics Techniques in Healthcare Using IOT Platform
by GOKULNATH CHANDRA BABU, SHANTHARAJAH S P
Abstract: The large amount of data generated by IOT has high impact values, the mining algorithms with IOT used to get the meaningful information that has been hidden in the data. In this paper designed a system that reviews the data in the knowledge view, technique view and application view, including clustering, classification, time series, association and outlier analysis and the application used in mining algorithm are surveyed. As the many devices connected with the IOT it produces large volumes of data and it also analyzed. So the algorithm should be modified so it can be used for mining algorithms. At last architecture has been suggested for big data mining system.
Keywords: Internet of Things (IOT); RFID; FMMEA; Near Field Communication (NFC); Prognostics and Health Management (PHM); CBM; Time to Failure (TTF); SVM; Ultra-Wide Bandwidth (UWB); DFT.
Octagonal Picture Languages
by Ramya Govindaraj, Anand M
Abstract: A picture grammar is the generation of pictures through description of words. The picture is represented in matrix form of finite alphabet using various grammars .Picture grammar can be achieved through context free grammar or regular expressions. Here we extend hexagonal picture language to octagonal picture language thereby introducing Octagonal Wang System (OWS) and octagonal picture language. Previously hexagonal wang system coincides with hexagonal tile system. Here we determine the same using octagon that is to check the coincidence of octagonal wang system and octagonal tile system. We use octagons since, they form several interior angles that leads to the base for drawings and architectural planning.
Keywords: Formal languages;picture languages;Hexagonal picture language;octagonal picture language;octagonal tiling system;Octagonal wang system.
FlowForensic: FlowRule Enforcement for Control Plane Attacks in Software Defined Networking
by NITHYA SAMPATH, Jayakumar Chinnappan
Abstract: Due to the lack of security in the traditional network, a new reprogrammable network called Software Defined Networking has been introduced. It is a layered abstraction network with easy programmable, flexible, and extensible by managing the networks by segregating the control plane from the data plane. This separation provides a way for developing more complex and advanced applications efficiently. OpenFlow is an interface between switches and controllers. It simplifies network management and programming of the network devices. The landscape of digital threats and cyber-attacks is evolving tremendously. The impact of various network attacks in software defined network environment is studied and implemented.The throughput results are compared and analyzed between normal packet and spoofed packet. In accordance with the analysis of spoofed packet, rules are enforced for protection.
Keywords: Software Defined Networking; Attacks; POX controller; rule; Mininet; throughput.
Trusted computing in Social Cloud
by Priya Govindaraj, Jaisankar Natarajan
Abstract: Social Cloud is a new paradigm which allows a user to share the system resources for computing purposes when it is not otherwise being used. This paper focuses on the computing model Social Cloud where the computing nodes are managed by the social bonds determined from a trust retaining social graph. It can be considered as a situation of a computing method in which cloud users gather a group of resources to do computational tasks for the sake of a social friend. So whenever a task is given by a user, the burden of computing is passed to the friend, i.e. to the nodes which are directly connected to them in the social network. We have used two different scheduling algorithms namely FCFS and Round Robin to check the performance statistics of the Social Cloud and state which algorithm is best suitable for the implementation. Apart from this we have proposed a new index called Trust Index for calculating the trust value of a user and also to find out whether a user is trustworthy or not. The proposed Trust Index mainly fills the gap of human interaction in evaluating a users trustworthiness, since here a user decides whether or not to trust another user. To show the efficiency of this trust index we have created a social network with a feature of verifying a friend, by using which we can verify our friends and make them trustworthy.
Keywords: Trust; Trust index; Scheduling; Social cloud; Social graph.
Energy Efficient Data Compression and Aggregation Technique for Wireless sensor Networks[TELSOB MOTES]
by Karthikeyan B, Kumar R, Srinivasa Rao Inabathini
Abstract: This paper present and analyze an energy efficient data compression and data aggregation algorithm which results in the whole network lifetime prolonged by about 24% . In this paper, a new idea is proposed for sensor values compression based on a technique that involves feedback mechanism. In this technique, the base node in the sensor network generates Huffman code for the sensor data that needs to be compressed and broadcast the Huffman code in to the sensor network. All nodes in the sensor network receives Huffman code, compress the sensor data and transmit to base node. For data aggregation, secure data aggregation algorithm is used which does not necessitate additional phase for data integrity verification and also it eludes extra transmissions and computational overhead on the sensor nodes to reduce the amount of energy used up by the network. The whole idea was tested on TelosB sensor network platform, programmed in nesC language and also analyses the performance of the algorithm in the Contiki OS- simulator Cooja. A comparison is also done with existing compression algorithms in terms of lifetime of the sensor network.
Keywords: Sensor Network; Huffman code; Secure Hierarchical aggregation; Cooja; Telosb.
An Intelligent Neuro-Genetic Framework for Effective Intrusion Detection
by Rama Prabha Krishnamoorthy Pakkirisamy, Jeyanthi N
Abstract: Intrusion detection systems are useful for improving the network performance by safeguarding the networks from attacks including flooding attacks. Intrusion detection systems can be developed by identifying the important features from the network data to be analysed and by classifying the network traffic using the most contributing and important features. In this paper, a new intelligent neuro-genetic framework is proposed for detecting the intruders in networks by analysing their behaviour. For this purpose, a new Genetic Algorithm based Feature Selection Algorithm (GAFSA) and a Neuro-Genetic Fuzzy Classification Algorithm (NGFCA) have been proposed in this paper which are used to identify the malicious users through classification of user behaviours. The main advantage of this proposed framework is that it reduces the attacks by identifying the intruders with high accuracy and reduced false positive rate. This work has been tested through simulations and also using bench mark dataset for analysing the performance of the proposed algorithms. From the experiments conducted in this work using full features and selected features by applying the existing classification algorithms as well as the proposed classification algorithm, it is proved that the proposed framework detects the intruders more accurately and reduces the attacks leading to increase in packet delivery ratio and reduction in delay.
Keywords: Intrusion Detection System; Feature Selection; Classification; GAFSA; NGFCA; false positive rate; neuro-genetic framework.
Cluster Based EA-PATM Protocol for Energy Consumption in Hierarchical WSNs
by Meenatchi SS, Prabu Sevagan
Abstract: Consumption of energy by the sensor node in wireless sensor node is the main criteria affecting the wireless sensor network. The message transmission of wireless sensor network requires high power consumption and quality of service, which affects the energy consumption in WSN. To overcome the criteria the energy consumption of node is reduced by the proposed EA-PATM protocol. The proposed protocol consists of pillar k-mean clustering method to cluster the network in to set of nodes. For the selection of cluster heads, the ant lion optimization algorithm check the clustered node for effective QoS parameters. Ant lion is a nature inspired optimization algorithm proposed in this paper to generate cluster head for the evaluation of energy consumption in the WSN. The TDMA based MAC protocol is proposed in the paper to evaluate the energy consumed in transmission of information from one cluster node to other during routing. The proposed formulation offer a stable definition for estimating the quality of service performance of network and hence by consuming less energy in wireless sensor network. The proposed technique is carried out in network simulator and the results are plotted in terms of processing energy, nodes remaining energy and QoS parameters such as packet delivery ratio, packet loss ratio, delay, latency, throughput and overheads. From the concluded results, it clearly mentioned that the proposed EA-PATM protocol is an efficient method for consumption of sensor node processing energy and quality of service in wireless sensor networks.
Keywords: Wireless Sensor Networks (WSN); Ant Lion Optimization Algorithm; Pillar K- means Clustering; TDMA based MAC; Network Simulator; Quality of Service (QoS).
Real Time implementation of Multivariable Centralized FOPID controller for TITO process
by Lakshmanaprabu Sk
Abstract: The development and real time implementation of multivariable centralized FOPID (MC-FOPID) controllers for two interacting frustum conical tank level process (TICFTLP) is presented. The Modeling and control of TICFTLP is difficult due to its dynamic coupling between inputs and outputs. The black box model is developed from the open loop experimental data using process reaction curve method (PRC). The multivariable centralized FOPID (MC-FOPID) controller with five tuning parameters is designed based on the steady state gain matrix of the process and then the controller parameters are tuned using bat optimization algorithm. The comparison of proposed MC-FOPID controller with multiloop PID controller is demonstrated in the simulation study. The simulation results of the controllers are compared in terms of settling time and integral error criteria. It is found that the MC-FOPID controller has better servo and regulatory response than multiloop PID control. The real time implementation of MC-FOPID is done in MATLAB/SIMULINK using USB based DAQ module.
Keywords: Multiloop PID control; Centralized Control; Fractional order control; FOPID; BAT optimization algorithm; Two input Two Output Process; Two Interacting frustum conical tank process.
A novel feature extraction approach for tumor Detection and Classification of data based on Hybrid SP Classifier
by Nandha Gopal, Roheet Bhatnagar
Abstract: This paper deals with how to identify the cancer affected region of the brain. There have been many tools and techniques such as SOM (Self Organizing Map), PSVM (Proximal Support Vector Machine) classifiers, discovered to find out the cancer affected region in the brain. There is a rapid growth in the brain tumour cases in the recent past. Technology failed to find out the root cause behind it. Recent reports reveal that different types of brain tumours can be treated either through surgery or in rare cases, with radiation. The role of image segmentation in identifying and the treatment of brain tumours are enormous, because image segmentation will help to find out the volume and the growth of the tumours using the techniques like human edge correction, outer edge coloring and inter active thresholds holdings. In order to reduce the human error and to get the accurate results in MRI images there is an urgent need to find out an automatic or semi automatic method for the classification of brain tumor images .Finally a system called Hybrid SP classifier has been developed for the detection and classification of brain cancer, this type of mechanisms uses a system dependent actions to find the block and different types of brain tumours. It also makes the use of some of the mechanisms like Image enhancement, segmentation and Equalization of histogram.
Keywords: Image processing; Segmentation; Medical Image;.
DESIGN AND IMPLEMENTATION OF ENERGY EFFICIENT RECONFIGURABLE NETWORKS (WORN-DEAR) FOR BAN IN IOT ENVIRONMENT (BIOT)
by Kumaresan P, Prabukumar M
Abstract: Embedded Systems are pervasive with the advent of Internet of Things. This has led smart devices to be omnipotent. In future, this will convert any object (living, non-living, smart devices) into smarter devices which finds applications in an unimaginable way. Even though the technology becomes omnipotent, several research problems arise in design and implementation. Problems such as energy consumption, security, quality of information, performance and intelligence are needed to be addressed when it is applied in the health care system, wireless communication, defence, agriculture and so on. Here, we have concentrated on the Low Power Health Care System based on Body Area Networks (BAN) in which the technology embeds the Body Area Networks with Internet of Things (IOT) which can be jointly coined as BIOT (BAN Internet of Things). BIOT finds application as wearable devices for monitoring and care giving systems for patients. Due to BIOTs nature of omnipotent application in the health care, maintaining its life time remains in the darker side of the research. To overcome this problem, a new algorithm for the BIOT called WORN (Wake - On Reconfigurable Networks) has been proposed. The proposed algorithm works on the DEAR (Distance Energy Adaptive Rule) rule sets. This algorithm calculates distance based on RSSI and selects frequency using DEAR rule sets for minimum energy. It has been tested with different transceivers on different architectures. The results obtained from different testbeds have shown a 20-30%of increase in lifetime of the BIOT network. By increasing the life time of the devices, BIOT with WORN-DEAR power model will be the bridge between the human and the machine Interface.
Keywords: BAN; IOT; BIOT; WORN; DEAR; RECONFIGURABLE NETWORKS; NETWORK LIFETIME.
A HIGH PERFORMANCE COGNITIVE FRAMEWORK (SIVA SELF INTELLIGENT VERSATILE AND ADAPTIVE) FOR HETEROGENOUS ARCHITECTURE IN IOT ENVIRONMENT
by Yokesh Babu Sundaresan, Saleem Durai M A
Abstract: The advent of the Internet of Things in todays technology brings automation to the footsteps of every human. But still the technology is in darker side when it is required to implement machine learning on Internet of Things for the intelligent detection. Several Machine learning algorithms like Artificial Neural Networks, Support Vector Machines, Deep learning Algorithms are applied for bringing the Cognitive aspects in the Internet of Things. But these machine learning algorithms finds their application in face recognition, emotion recognitions etc., on the hardware. Still there is a need for developing low power, high accurate, more intelligent machine learning framework for embedded architectures when they are used for dynamic inputs in health care solutions. Hence we propose a framework named SIVA (Self Intelligent Versatile and Adaptive) for dynamic inputs in IOT based health care solutions. This framework is based on Neural Network and Cognitive rule sets for self learning and adaptability. The proposed learning algorithm works on self adaptive principles which make the framework suitable for the biomedical wearable devices for dynamic inputs. This framework has been evaluated for different biomedical sensors and embedded heterogeneous architectures. Various performance parameters viz. recognition rate, accuracy, execution time and energy are measured and analysed. The results indicate that the framework not only have superiority on complexity, but also have low power consumption over existing neural network and svm algorithms.
Keywords: SIVA; iot; svm; cognitive rule sets; deep learning; self-adaptive.
Encrypted Image based Data Hiding Technique Using Elliptic Curve ElGamal Cryptography
by JAYANTHI RAMASAMY, John Singh K
Abstract: Most of the data hiding techniques used RSA based encryption algorithms for encrypting the images and the messages. However, the security provided by elliptic key cryptography is higher with a lower size key than the RSA algorithm. Therefore, a new image encryption scheme which can be reversed during decryption is proposed in this paper which uses an elliptic curve key based ElGamal encryption scheme for effective data hiding in images. Moreover, it uses the difference scheme available in the existing work for data hiding of images. Form the experiments conducted in this work, it is proved that the proposed scheme is more efficient with respect to security and it reduces the computation complexity when it is compared with other related schemes.
Keywords: Elliptic key cryptography; ElGamal encryption; Data hiding; Difference expansion; Encrypted image; Public key cryptography.
Design of CMOS Full Subtractor using 10T for Object Detection Application
by M. Mahaboob Basha, K. Venkata Ramanaiah, P. Ramana Reddy
Abstract: This paper presents the design of full subtractor (FS), which is able to operate at low voltage and low power. In this method, 2 XOR gates with 1 MUX circuit are used to design the 10T full subtractor in 45nm CMOS technology. In this paper, Low Cost Thresholded Full Subtractor (LCTFS) method is presented to utilize the subtractor circuit with minimum number of transistors, which is mostly used in digital circuits and high-speed applications. Multi threshold CMOS (MTCMOS) circuit is introduced in FS to avoid the thresholding problem. From this subtractor, Restoring Array Divider (RAD) is designed for object detection application. Simulation results have shown that with the help of LCTFS circuit, area, power, delay and power delay product have minimized in LCTFS, RAD, and object detection application with compared to the conventional methods.
Keywords: Full subtractor; Multi threshold CMOS; Integer restoring divider; Area; Power; Delay;.
An Efficient Raindrop Parameter Estimation using Image Processing
by Pandharinath Appasaheb Ghonge, Kushal R. Tuckley
Abstract: Nowadays, image processing algorithms play a key role in the rain drop distribution estimation. This paper deals with Number of drops and Drop size distribution and its volume in particular time. We are using the raindrop image to calculate the amount of rainfall in a particular time. The proposed Image Processing based Rain Drop Parameter Estimation (IPRDPE) by using Double-Density Dual-Tree DWT (DDDT DWT) and thresholding based segmentation. By using effective image fusion technique, rain drop images from different angles are fused and using segmentation and morphological operations raindrop parameters estimated. To get better fused output max-based effective image fusion rules are used. The system using advanced image fusion technique and estimation for rain drop parameter, produce more accuracy and error free system compared to the existing techniques and also achieved better accuracy with respect to the real-time measurement.
Keywords: Raindrop image; Double-Density Dual-Tree Discrete wavelet transform; segmentation; image processing based rain drop parameter estimation;.
Special Issue on: Recent Trends in Reasoning-based Intelligence Systems
Prediction and Optimal Allocation of Agricultural Non-point Source Pollution based on Chaos Theory
by Li Chenyang, Cheng Na, Sun Nan
Abstract: In order to promote the accuracy of agricultural pollution source prediction algorithm, an agricultural pollution source prediction algorithm based on chaotic differential evolution algorithm neural network is proposed in the Article. Firstly, it initializes the population of differential evolution algorithm with Chaos theory, to promote the diversity for initial population solution; then it improves the differential evolution algorithm by using of mean entropy and perturbation variation method, to promote its optimize performance; secondly, it optimizes the neural network parameter learning process by using of the improved differential evolution algorithm, to promote the accuracy of parameters optimization; at last, it applies the proposed algorithm into the example of local agricultural pollution source prediction and the results showed that the proposed method could effectively the accuracy of agricultural pollution source prediction.
Keywords: chaos algorithm; differential evolution; neural network; agricultural pollution source; prediction.
Image Watermarking Algorithm based on DCT and Arnold Transform
by Xu Zhe, Xuan Yinan
Abstract: At present, to make medical information storage and transmission easily and safely are discussed hotly. According to the safety of the medical image in medical information system, the robust watermarking method is put forward, and it is suitable for medical image authentication and protection. Firstly, the visual feature vectors of medical image are obtained through DCT transformation, which is used to make watermarks embedding and extraction. Then, chaotic encryption technique is used in watermark to improve the security of watermark information. At last, the zero watermarking concepts are combined to get the watermark with the qualities against conventional watermark attacks and geometric attacks. The experimental results show that the method can effectively extract the watermark information, and has good invisibility and robustness. In addition, compared with the existing watermarking technology, this method can not only reduce the complexity of the watermark embedding, but can improve the capacity of watermark embedding and it has better flexibility and practicability.
Keywords: Medical image; Feature vector; Zero watermarks; Invisibility; Robust watermark.
Harmonic Analysis and Detection of Power System based on Double Moment Wavelet Transform
by Liang Kangyou, Yuan Ling, Tan Yuhang
Abstract: The influence of unbalanced installation of single-phase DG unit with harmonic compensation function in three-phase and three-wire public power grid shows that there will be risk for the unbalanced installation to increase the three-phase current of the power grid. In order to overcome this problem, balanced triple-frequency harmonic function is proposed for the purpose of avoiding the increase in three-phase power grid current. In addition, compared with the traditional method, such method is also capable of reducing the rated power of harmonic current compensation. In order to verify the effectiveness of the method proposed, the balance and unbalanced conditions are verified in experiment and the experimental circuit is a simplified system based on actual three-phase three-wire system. In order to avoid the increase in the harmonic current, a balanced third harmonic suppression method is proposed. Finally, performance of the algorithm proposed has been verified through experiment.
Keywords: Balance; Harmonic suppression; Power system; Harmonic detection; Wavelet transform.
A Knowledge graph-based Content Selection Model for Data-driven Text Generation
by Gong Jun-peng, Cao Juan, Zhang Peng-zhou
Abstract: Content selection is a critical task for natural language generation. A novel approach based on knowledge graph is proposed. Structure data is mapping to the graph and combined with user defined knowledge. The model analyzes the content selection features on the graph, and automatically learns the content selection rules. The model was evaluate in the domain of weather forecasting.
Keywords: content selection; ontology; knowledge graph.
Cloud Computing Resource Scheduling and Leasing Algorithm based on Extreme Price Filter
by Liu Xiaoming, Li Zonghui, Wang Junjie, Xu Xujiang
Abstract: VRS problem provides virtual resource of different types and different prices. CSP should select the most profitable leasing strategy during confirmation of virtual resource in VRS. However, there is no algorithm at present that considers about virtual resources selection of price and type. Because the price fluctuations of different types of resource are inconsistent, resource with the biggest profit can vary among different price intervals. A cloud computing virtual resource leasing algorithm considering about extreme price filtering has been proposed in this paper, which realizes optimal selection for handling leasing price for task virtual machine. First, three-function module cloud composed of virtual resource provider, cloud service provider and final user has been adopted to calculate environment and list calculation target for virtual resource leasing profit; second, distribution and task urgency of price has been fully considered. For weakly stationary price sequence, outlier detection method has been adopted to make extreme price filtering. At the same time, weak equilibrium operator has been designed, exponential function has been used to control the overall shape of curve, non-uniform mutation operator has been used to make local operating adjusting, realize effective prediction for price in the future and optimal selection and make optimal selection for handling leasing price of task virtual machine.
Keywords: extreme price; maximization; filtering algorithm; cloud computing; resource scheduling.
Training Project Arrangement for Tennis Athletes based on BP Neural Network Model
by Wang Hao, Yuan Hong
Abstract: In order to improve the prediction accuracy of athletes tennis training effect, a kind of prediction method for athletes tennis training effect of RBF (Boundary Value Constraints Radial Basis Function, BVC-RBF) neural network with boundary value constraints is proposed. Firstly, the internal and external factors that influence the athletes tennis training effect is analyzed, and the influence models of 12 indexes including quantitative load heart rate and body fat percentage are predicted and analyzed emphatically; secondly, the RBF neural network algorithm with boundary value constraints is built to solve the boundary value constraint equation, so as to obtain the compensation function, and the least square method is used to train traditional RBF neural network, which achieves the improvement of prediction algorithm performance; finally, the simulation experiment shows that the proposed method provides higher prediction accuracy, which has a certain guiding value for tennis training.
Keywords: tennis training; boundary value constraint; rbf neural network; least squares; prediction accuracy.
Algorithm of Key Data Ensemble Clustering and Approximate Analysis in Cloud Computing
by Xia Wendong, Liu Yuanfeng, Chen Deli
Abstract: One collaborative data fusion recommendation algorithm (SFS-TOPSIS) based on customer satisfaction degree and characteristic approximation has been designed to improve recommended execution efficiency of data fusion algorithm as well as the reliability of recommendation result, so as to recommend service that meets more personalized demands to user. First, it starts from calculation efficiency and recommended precision angle that improves service recommendation algorithm, makes improvement for similarity evaluation by combining user attribute and satisfaction degree for service, makes real-time updating and algorithm improvement for it by combining time-varying weight method TOPSIS fusion algorithm and designs a collaborative data fusion recommendation algorithm based on customer satisfaction degree and characteristic approximation. Second, for the problem of inadequate definition of traditional similarity for resolution, improvements have been made based on user evaluation confidence, interest preference and characteristic similarity evaluation; make it more suitable for the real experience of user by combining the similarity substitution of user attribute for user. Last, time-varying weight method has been adopted to improve standard TOPSIS fusion, improve time-varying attribute of TOPSIS decision fusion and realize effective attribute fusion of user similarity data; through making simulation comparison on two standard testing sets of and , it indicates that the service recommendation performance of SFS-TOPSIS is more superior. The proposed SFS-TOPSIS algorithm can improve service recommendation accuracy effectively and it is with certain application value.
Keywords: big data; similarity; approximate analysis; clustering; cloud computing; decision recommendation.
Parallel Cluster Analysis of Multi City Congestion based on Spatial Temporal Potential Correction in Mobile Phone APP
by Shi Jin
Abstract: To improve urban road congestion detection and governance efficiency of city, this paper puts forward spatial-temporal analysis method for urban road congestion of multiprocessor parallel clustering based on potential field correction, establishes spatial-temporal model of urban road congestion based on temporal data of GIS four-dimensional space and constructs multiprocessor parallel clustering method by utilizing potential field correction method and designs parallel multiprocessing detection method of urban road congestion of distance matrix, neighborhood radius and density function. Experimental result verifies effectiveness of method mentioned and it shows that method mentioned can realize fast and effective detection analysis of urban road congestion and provides data support for urban road congestion management.
Keywords: spatial-temporal analysis; parallel clustering; urban road congestion; GIS four-dimensional space; spatial-temporal model.
Cluster Analysis Algorithm based on Key Data Integration for Cloud Computing
by Li Dong-rui
Abstract: In order to improve scheduling efficiency and resource utilization ratio of cloud computing platform, a kind of cloud task scheduling algorithm of improved fuzzy cluster has been proposed. Firstly, cloud task scheduling algorithm of improved fuzzy cluster has been introduced, which mainly uses fuzzy FCM algorithm to complete resource cluster to three resource sets including computing type, storage type and bandwidth type in the context of using parallel processing to ensure the efficiency. On this basis, Min-min algorithm has been improved towards Qos so that the resource of cluster set with the shortest time in completion of task allocation will not be idle and the resource of cluster set with the longest time in completion will be liberated from the busy schedule to improve the utilization ratio of resources, ensure load balance, reduce execution costs and enhance customer satisfaction; secondly, tasks have been allocated to each cluster through heuristic algorithm and the results have been adjusted according to set threshold to obtain the better scheduling results. The experimental results show that the proposed algorithm is superior to the traditional algorithm without cluster in terms of execution time. Compared with the algorithm without adjustment of the threshold, the algorithm is better in performance and load balance which is a more efficient cloud task scheduling algorithm.
Keywords: big data; cloud computing; approximate data; fuzzy cluster; resource scheduling; intelligent algorithm.
Two Echelon Supply Chain Model of Agricultural Products based on Stochastic Fuzzy Process of Cost Demand
by Gao Jie
Abstract: Stochastic fuzziness existed in supply chain process has important influence on inventory maintenance and decision processing of normal system operation, especially under the situation of coupling existed in two echelon supply chain, this influence will be amplified to some extent. To process this potential influencing factor effectively, one two echelon supply chain model based on stochastic fuzzy process of cost demand has been designed, which can improve the rationality of supply arrangement. First, for the studied two echelon supply chain objects, joint cost has been taken as target to make optimal model design; Second, considering about the stochastic fuzziness existed in two echelon supply chain, the demand rate of market for products as well as supply and distribution time have been selected as main research parameters, two echelon supply stochastic demand model based on triangular fuzzy number has been constructed and then model solving process has been deduced according to the characteristics of design model; Last, verification for the performance of proposed model has been made based on parameter influence experiment and horizontal contrast. This paper has made full use of research results of above literature in single echelon supply chain and focused on study the IPP inventory management integration system, which is composed of single manufacturer and single demand customer. For real application scenario, two echelon supply chain meets the distribution demand and it the most common.
Keywords: joint cost; fuzzy demand; demand rate; two echelon supply chain; distribution; logistics.
Parameter Tuning of Boiler Thermal Process based on SVM Neural Net Optimization
by He Peng
Abstract: Because of complex characteristics, such as multivariable coupling in boiler thermal process of circulating fluid bed, parameter turning, there is relatively large difficulty in automatic accurate control so that a kind of Self-adaptive Controller Algorithm is put forward. Fuse fuzzy control and equivalent method of BP neural net usage structure to fuzzy BP neural net and bring in weight of Genetic Algorithm optimization BP neural net by aiming at defects, such long convergence time of neutral net and realize self-adaptive accuracy control to boiler thermal process of circulating fluid bed by feed-forward compensation decoupling device. It is showed from experiment results that the Algorithm can adapt to working condition of variable parameter boiler thermal process of circulating fluid bed and it has realized uncoupling of bed temperature and main steam pressure.
Keywords: boiler thermal process of circulating fluid bed; thermal self-adaptive control; fuzzy control; bp neural net; genetic algorithm.
Design of Human-Computer Interaction Interface Considering User Friendliness
by Chen Hong
Abstract: Analysis and modeling for interactions on self-service terminal interface have been made in this paper based on distributed cognition theory, which is to confirm the relationship between interactions and information presentation in human-computer interaction and propose interface interaction design method of self-service terminal based on user cognitive ability. With adoption of this method, designers need to make research on users and identify the user group that needs to be taken care of firstly, and then make analysis of cognitive ability, set up user cognitive load model, describe the interactive behavior of users, confirm basic interaction frame, and then establish interaction design matrix with universal usability design model and propose interactive design program. We take hotel self-service terminal as example, adopt this method to make design program and then verify the effectiveness of the proposed design method through making comparisons with design program formed by traditional method. This interactive design method can help designers develop self-service terminal interface that is suitable for people to understand and use, decrease the cognitive load of users and meet the diversified demands of self-service terminal users for cognition.
Keywords: universal usability; cognition theory; user friendliness; interactive design; interface system.
Complex Electromechanical System Condition Monitoring based on Improved Particle Swarm Optimization RBF for Audio Visual Fusion
by Xu Jiangwei, Li Tiejun, Zhang Liang
Abstract: To improve transient stability of multi-generator power system, continuous high-order sliding mode excitation control strategy is put forward. Power angle deviation of each generator is variable of sliding mode. Nonlinear and uncertain high-order sliding mode control of multi-generator power system is transferred into limited time stability problem of uncertain integral chain system. Realize limited time convergence of system condition and overcome uncertainty, such as unmodeled dynamics of system, measuring error and external disturbance etc. through combination of controller and geometric homogeneous continuous control law and second-order sliding mode super-twisting algorithm. Observe power angle differential with precise robust differentiator. Analyze and verify limited time stability of closed-loop system theoretically. High-order sliding mode excitation controller designed can keep voltage stability at generator terminal and improves transient stability of power system effectively. Simulation result aimed at 3-generator system verifies effectiveness of control method mentioned.
Keywords: multi-generator power system; transient stability; continuous high-order sliding mode; excitation control; condition monitoring.