International Journal of Innovative Computing and Applications (35 papers in press)
Optimal Placement of Unified Power Flow Controller using Differential Search Algorithm
by Dhiman Banerjee, Sriparna Bhattacharya, Provas Kumar Roy
Abstract: Optimal power flow (OPF) problem plays a crucial role to run an economically efficient and well-planned power system. It is a strenuous and challenging task for the power system researchers to cope with the ever-increasing load-demand while getting the minimum system loss. The development of flexible ac transmission system (FACTS) has added a new dimension both to the system operation and research. Unified power flow controller (UPFC) is the most reliable FACTS controller, having its operational capability as series and shunt compensator. As a matter of fact, UPFC can reliably control the different power system parameters. In this article, UPFC is incorporated into the modified IEEE 5-bus and modified IEEE 30-bus test system. Differential search algorithm (DSA) is proposed and implemented to run the OPF with and without UPFC , and the results are listed, analyzed and compared with the same that is obtained by genetic algorithm (GA) and BAT search algorithm.
Keywords: Differential search algorithm; Optimal power flow; FACTS devices; UPFC.
Development of Fuzzy Logic Controller for Improved Interline Unified Power Quality Conditioner
by Ravindranath Tagore Yadlapalli, Rajanand Patnaik Narasipuram, Anusha Dodda
Abstract: This paper presents the improved interline Unified Power Quality Conditioner (iUPQC) and its controlling aspects for nonlinear loads. The nonlinear loads are the major sources of harmonics and raise the power quality issues. However, the iUPQC compensates the harmonics that are generated by a nonlinear load besides reactive power support. This in turn minimizes the harmonic distortion both in the source current as well as voltage. Furthermore, it also provides the current and voltage imbalance compensations, reactive power, frequency and voltage support at grid. The simulation of entire power system is fulfilled using proposed FUZZY controller and compared with the conventional PI controller. The performance of both the controllers is sifted in terms of %Total Harmonic Distortion (THD) by considering different case studies having the 3-ϕ diode rectifier connected to R, RL & RLE loads. The MATLAB/Simulink version R2012b is used for accomplishing the in depth simulation studies.
Keywords: interline dynamic voltage restorer,IDVR; improved interline Unified Power Quality Conditioner; iUPQC; interline Voltage Controller; IVOLCON; Fuzzy logic Controller (FLC); Proportional Integral (PI) controller; Total Harmonic Distortion; THD%.
New stochastic synchronization condition of neutral-type Markovian chaotic neural networks under impulsive perturbations
by Cheng-De Zheng
Abstract: This paper investigates the globally stochastic synchronization problem for a class of neutral-type chaotic neural networks with Markovian jumping parameters under impulsive perturbations in mean square. By virtue of drive-response concept and time-delay feedback control techniques, by using the Lyapunov functional method, vector Wirtinger-type inequality, a novel reciprocal convex lemma and the free-weight matrix method, a novel sufficient condition is derived to ensure the asymptotic synchronization of two identical Markovian jumping chaotic delayed neural networks with impulsive perturbation. The proposed results, which do not require the differentiability and monotonicity of the activation functions, can be easily checked via Matlab software. Finally, a numerical example with their simulations is provided to illustrate the effectiveness of the presented synchronization scheme.
Keywords: Stochastically asymptotic synchronization; chaotic neural networks; Markovian jump; impulse; reciprocal convex.
The Multi-Object Tracking Algorithm Research using Kalman Filtering Method
by Shuqing Liu
Abstract: Aiming at the tracking failure caused by occlusion between objects, interleaving or target drift in multi-object tracking process, the new improved algorithm of occlusion prediction tracking based on Kalman filtering and spatiograms was proposed. By combining the color histogram and the distribution of color in space, spatiograms can be used to distinguish between different objects so that we can track the object when interleaving or occlusion between objects occurs. The state of the object can be predicted by the Kalman filtering, and the occlusion mark is used for the object which overlaps with other objects, so that the occluded object which is undetected can be tracked in the next frame video. The 2D MOT 2015 dataset was used in the experimental procedure, and the average accuracy of tracking was 34.1%. The experimental results have shown that the proposed algorithm can improve the performance of multi-object tracking process.
Keywords: Multi-Object Tracking; Kalman Filtering; Spatiograms; Occlusion Prediction.
Intelligent Recognition Method of Color Multidimensional Image Boundary Feature Based on Improved Neural Network
by Jie Ding, Guotao Zhao, Huibing Hao, Chunping Li
Abstract: In order to improve the intelligent edge feature recognition ability of super-pixel color multidimensional images, an improved neural network based edge feature recognition algorithm for color multidimensional images is proposed. A color multi-dimensional image imaging model based on super-pixel fusion is constructed, and the edge contour of the multi-dimensional color image is detected by using the color block region fusion and segmentation method. The improved neural network adaptive optimization method is used to segment and match the blocks of multi-dimensional color images, and to extract the color boundary information features of the multi-dimensional color images. According to the RGB value of color multi-dimensional image and neighborhood mean, the adaptive fusion segmentation of color multi-dimensional image is realized. The simulation results show that this method can effectively realize the intelligent recognition of super-pixel color multi-dimensional image information boundary, the recognition accuracy is higher, and the ability of image recognition and target detection is improved.
Keywords: improved neural network; image; segmentation; detection; edge feature extraction.
A survey on partitional clustering using single-objective metaheuristic approach
by Preeti Pragyan Mohanty, Subrat Kumar Nayak, Usha Manasi Mohapatra, Debahuti Mishra
Abstract: Clustering is one of the important functions of data mining, which is used to analyse a large amount of data. It groups these set of data according to some similarity property such that data within the cluster are similar to each other and data between the clusters are dissimilar to each other. To obtain an optimal clustering result with the help of an optimization algorithm is an emerging trend in data mining. The partitional clustering is one of the popularly used types of clustering algorithm. These algorithms often land in local optimum and number of clusters needs to be predefined. To encounter the above problem, optimization algorithms such as metaheuristic algorithms are used as a suitable problem-solving paradigm. This paper presents an overview of single-objective metaheuristic algorithms used for partitional clustering problem and their applications. This paper even presents the research issues which can be dealt with in future.
Keywords: partitional clustering; metaheuristic approach; evolutionary algorithm; swarm optimization algorithm; physics-inspired algorithm; bio-inspired algorithm.
Evaluating Nondeterministic Signal Machine Relative Complexity: A case study on Dominating Set Problem
by Sahar Ardalan, Sama Goliaei, Ayaz Isazadeh
Abstract: Signal machine is an abstract geometrical model of computation, which can be viewed as a continous space and time generalisation of cellular automata. Almost all studies that have been made are about deterministic signal machines. In spite of few studies that have been made on nondeterministic signal machines, the present paper shows their high efficiency in solving problems using a well-known combinatorial problem. We provide a method to solve the graph dominating set problem using nondeterministic signal machines. First we show how to design a signal machine for each specific instance of the dominating set problem. Then we propose a signal machine which solves the dominating set problem for any instance of the problem, and show how to reduce the space complexity of solution using nondeterminism.
Keywords: Abstract Geometrical Computation; Nondeterministic Signal Machines; Dominating Set Problem; Computational Model.
Sentiment Analysis: An empirical comparison between various training algorithms for Artificial Neural Network
by Ankit Thakkar, Dhara Mungra, Anjali Agrawal
Abstract: The proliferated increase in the commercial benefits of sentiment analysis has accumulated a huge interest in the domain of sentiment classification. Sentiment analysis categorizes a given text into positive or negative class. With the availability of a significant amount of electronic data, machine learning is becoming popular for text classification. In this paper, we present an empirical comparison between different training algorithms Gradient Descent (GD), Gradient Descent with Momentum backpropagation (GDM), Gradient descent adaptive learning rate backpropagation (GDA), Gradient descent with momentum and adaptive learning rate backpropagation (GDX), and Levenberg-Marquardt backpropagation (LM), used for training the neural network for the domain of sentiment classification. The performance of all the methods is compared and evaluated using three balanced binary datasets from various domains with different features using various performance metrics such as accuracy, precision, recall, $f$-score, mean squared error (mse), and training time. The experiments are performed $5$ times with different random seed values using 10-fold cross-validation. The values for the minimum, maximum, mean, median, standard deviation (SD), and the top-three values of $5250$ classification accuracies indicate that GDX and LM outperform other methods in terms of classification accuracy. The paper also outlines the effectiveness of these methods regarding the limitations, advantages, and accuracy for different domains.
Keywords: Sentiment Analysis; Artificial Neural Network; Training Algorithms; Binary class; Different Domains.
Slime Mould Foraging: An inspiration for algorithmic design
by Anthony Brabazon, Sean McGarraghy
Abstract: The metaphor of `foraging as search' provides a rich source of inspiration for the design of optimisation algorithms. An extensive literature has resulted in computer science over the past twenty years based on this, with algorithmic families such as ant colony optimisation and honeybee optimisation amongst others, being successfully applied to a wide range of real-world problems. Of course, all organisms must forage to acquire necessary resources and in recent years, increasing attention has been paid to the mechanisms by which nonneuronal organisms, in other words organisms without a central nervous system, forage. The vast majority of living organisms, encompassing some 99.5% of all biomass on earth, are nonneuronal. In this paper we introduce the plasmodial slime mould Physarum polycephalum. Inspiration has been drawn from some of its foraging behaviours to develop algorithms for graph optimisation and exemplars of these algorithms along with some suggestions for future research are presented in this paper.
Keywords: Slime mould algorithms; Foraging-inspired algorithms; Graph optimisation; Nonneuronal organisms.
Hybrid metaheuristic for generalized assignment
by Salim Haddadi
Abstract: This paper investigates the classical generalized assignment problem (GAP), a challenging combinatorial optimization problem that arises in numerous applications and that has attracted a great deal of research. For solving it we propose a hybrid metaheuristic combining guided search (GS), iterated local search (ILS), and very large-scale neighborhood search (VLSN). The hybrid method is iterative. It starts with a random assignment, and in every iteration it acts in the following way: (i) The best current assignment is perturbed. (ii) An exponential size neighborhood of the perturbed assignment is constructed. It is the feasible solution set of a special GAP where only two fixed machines can execute a job. The neighborhood construction is guided by arntechnique penalizing poor machine/job selections. (iii) The exponential neighborhood is searched for improvement. Exploring the neighborhood amounts to solving a monotone binary program (BP) a monotone BP is one with two non-zero coefficients of opposite sign per column. We prove that the proposed metaheuristic runs in polynomial-time when applied to a variation of GAP. Goodrncomputational results in terms of solution quality, as well as of computation speed, are obtained with two new best values on hard instances.
Keywords: Generalized assignment problem; hybrid metaheuristic; very large scale neighborhood; iterated local search; guided search; variable-fixing.
Implementation of Fuzzy Logic Controller based Quadratic Buck Converter for LED Lamp Driver Applications.
by Ravindranath Tagore Yadlapalli, Anuradha Kotapati
Abstract: This paper focuses mainly on design of quadratic buck converter (QBC) for LED lamp driver applications. The LED current regulation is the critical issue in the family of LED lamp drivers. The continuous mode based QBC is well designed for 60V/20mA at 100 kHz. The QBC performance is analysed with Digital average current mode control (ACMC) based QBC and fuzzy logic control-ACMC based QBC. The simulation is fulfilled using MATLAB/Simulink software.
Keywords: Pulse Width Modulation; Amplitude modulation (FLC); Organic LEDs.
Special Issue on: Recent Advances in Bio-inspired Computing Paradigms for Security and Privacy of Innovative Computing
An Out-of-Band Mobile Authenticating Mechanism for Controlling Access to data outsourced in the Mobile cloud environment
by Sumit Kumar Yadav, Nisha Saroha, Kavita Sharma
Abstract: Mobile Cloud Computing (MCC), a cloud environment, formed by mobile users at the client-side and cloud servers at the back-end enables users to store and pervasively access a huge amount of data via different mobile devices (smartphones, tablets, PDAs, etc.) in a distributive manner. The "mobile cloud", though meant to resolve the space and processing constraints of mobile devices, increases the risk of data abuse, since data is outsourced on the distrusted cloud servers. Hence, to ensure the security of user's data, of all the security principles (confidentiality, integrity and access control) we argue that controlling access of data with appropriate authentication methods can strive for data protection and integrity. The drawbacks of the current frameworks, such as overloaded computations in key distribution, reduced flexibility, and scalability, are unable to achieve fine-graininess and confidentiality. Moreover, some are not even compatible with MCC environment due to their static nature. Thus, we propose an access control mechanism which is lightweight with minimal computational overhead and provides fine-grained access control for sharing data using out-of-band (OOB) mobile authentication. In this, we perform client-side encryption and decryption using simple hash functions and concatenation operator and achieve dynamic scalability. The encryption algorithms, sharing mechanism, and the use of OOB (out-of-band) mobile authentication are extensively analyzed to prove its efficiency and applicability.
Keywords: Mobile Cloud Computing (MCC); Access Control; Security; Confidentiality; Integrity; Out-Of-Band (OOB) Mobile Authentication; Data Sharing.
Parkinsons Diagnosis Using Ant-Lion Optimization Algorithm
by Prerna Sharma, Rishabh Jain, Moolchand Sharma, Deepak Gupta
Abstract: Parkinsons disease (PD) is a long term progressive disorder of the central nervous system that mainly affects the movement of the body. But there are several limitations in detecting PD at an early stage. In this paper, a binary variant of the recently proposed Ant Lion Optimization (ALO) algorithm has been proposed and implemented for diagnosing patients for Parkinsons disease at early stages. ALO is a recently proposed bio-inspired algorithm, which imitates the hunting patterns of ant-lions or doodlebugs. The proposed algorithm is used to find a minimum number of features that result in higher accuracy using machine learning classifiers. The proposed modified version of ALO extracts the optimal features for the two different Parkinsons Datasets with improved accuracy and computational time. The maximum accuracy achieved by the classifiers after optimal feature selection is 95.91%. The proposed algorithm results have been compared with other related algorithms for the same datasets.
Keywords: Ant Lion Optimization Algorithm; Feature Extraction; Bio-Inspired Algorithm; Parkinson’s disease.
An Improved Chaotic-Based African Buffalo Optimization Algorithm
by Chinwe Peace IGIRI, Yudhveer Singh, Ramesh Chandra Poonia
Abstract: Optimization remains inevitable in any organization as the need to maximize the limited resources persists. It justifies the seemingly endless research in this area. This study explores the effectiveness of chaos to mitigate false or premature convergence problem in African Buffalo Optimization (ABO) algorithm. Chaos employs the ergodic and stochastic properties to handle this limitation. Three resourceful chaotic functions in the literature are evaluated to find the best strategy for ABO improvement. The same strategy is applied across the algorithms under study to provide an unbiased judgment. The study validates the proposed systems performance with a range of nonlinear test functions. The proposed systems result is compared with standard ABO, Particle Swarm Optimization (PSO), and chaotic Particle Swarm Optimization (CPSO) algorithms. Although chaotic ABO (CABO) gave 92% performance in comparison with standard ABO, chaotic PSO, and standard PSO; it requires further investigation. To be more explicit, the reason for no significant difference between chaotic-ABO and standard ABO in some functions calls for further research attention. The present study also highlights the research future scope. In all, the study gives insight to researchers on the appropriate algorithm for a real-world problem.
Keywords: chaotic-optimization; premature convergence; African buffalo optimization; bio-inspired algorithm; nonlinear benchmark optimization problems.
Image Integrity Verification via Reversible Predictive Hiding and Elliptic Curve Diffie-Hellman
by Siddharth Agarwal, J. Jennifer Ranjani
Abstract: This paper presents a medical image integrity verification algorithm which will be able to detect any changes made in the pixel value or the size of a medical image. At the same time, it also provides a secure way of transmitting images over the public domain. This algorithm can not only ensure integrity of the medical image, but also checks if the sender of the image is authentic, making it useful for archiving medical images and remote medical diagnosis. This algorithm essentially has three modules: hashing, data embedding and image encryption. Initially, hash signatures are extracted from the image and are embedded inside the medical image. In the embedding phase, the image is divided into blocks of uneven size. In the raster scanning order, the blocks are embedded with multiple pixels depending on its smoothness. At the receiver end, the signatures are extracted and it can be used to verify the integrity of the medical images. The effectiveness of the embedding algorithm is verified in terms of peak signal to noise ratio, structural similarity index metrics, correlation coefficient. $L^2-$norm is used for verifying the integrity of the images.
Keywords: Reversible data hiding; Elliptic Cryptography; Diffie-Hellman; Image encryption; Integrity verification.
BIO-INSPIRED ALGORITHMS FOR DIAGNOSIS OF BREAST CANCER
by Moolchand Sharma, SHUBBHAM GUPTA, Prerna Sharma, Deepak Gupta
Abstract: Most commonly found cancer among women is Breast cancer. Roughly 12% of women grow breast cancer during their lifetime. It is the second prominent fatal cancer among women. Breast Cancer Diagnosis is necessary during its initial phase for the proper treatment of the patients to lead constructive lives for an extensive period. Many different algorithms are introduced to improve the diagnosis of Breast Cancer, but many have less efficiency. In this work, we have compared different bio-inspired algorithms including Artificial Bee Colony Optimization, Particle Swarm Optimization, Ant Colony Optimization, and Firefly Algorithm. The performances on these algorithms have been measured for UCI Dataset of Wisconsin Diagnostic Breast Cancer, and the results have been calculated using different classifiers on the selected features. After the experiment, it is seen that BPSO has shown maximum accuracy of 96.45% and BFA has shown considerable results of 95.81% with 6 features which is minimum of all algorithms
Keywords: Breast Cancer; Bio-inspired Algorithms; Artificial Bee Colony Optimization; Particle Swarm Optimization; Ant Colony Optimization; Firefly Algorithm; Feature selection; Decision Tree; Linear Support Vector Machines; K-Nearest Neighbor; Random Forest Classifiers.
A Robust Approach to Detect Video Based Attacks to Enhance Security
by Shefali Arora, M.P.S. Bhatia
Abstract: Face authentication has become widespread on smart devices and in various applications these days. Real time detection of human faces in video surveillance systems is challenging due to variations in expression and background conditions. Thus, precise detection of spoofed faces is important to make such security systems robust against potential attacks. Several deep learning based techniques involving the use of Convolutional Neural Networks have proven to be excellent in detection of spoofed faces. In this paper, we have proposed the combined use of spatial and temporal information from facial images using CNN and Long Short Term Memory Networks. We have tested the approach on Idiap Replay-Attack benchmark and compared the results with the application of pre-trained models like InceptionV3, VGGNet and ResNet models to detect replay attacks during video surveillance. Our approach proves to be robust and more efficient for detection of security breaches in real time situations.
Keywords: Video attacks; Replay attacks; Deep Learning; Convolutional Neural Networks; Biometrics; Long Short-Term Memory; Face Spoofing; Real-time detection.
Increased PSNR with Improved DWT Digital Watermarking Technique
by IIfra I. Khan, Khaleel Ahmad, M.A. Rizvi, Khairol Amali Bin Ahmad
Abstract: Today, the world is moving towards a digital era in every walk of life. Every business unit, government and private sectors, and research organization use the digital image, as a transferring mode for every critical data. These images over the Internet are not secure. Therefore, there is a need for image security. Currently, various image security techniques like encryption, watermarking, steganography, etc. are being used. In this work, a new technique of image security is proposed using digital watermarking which is more efficient and robust than the existing technique of Discrete Wavelet Transformation (DWT). Use of DWT along with the Least Significant Bit (LSB) technique in the watermarking mechanism is intended to make the technique more efficient. The technique is tested against the PSNR of several images. The average PSNR calculated of all the tested images was compared to other techniques and found that the new technique is better than the existing techniques. This new technique is a novel technique which is highly robust and provides better and faster results. The main component is the transformation of a single image into an invisible watermarked image. The whole mechanism is greatly reliable on the Human Visual System, as the watermark is not visible to the human eye. This increases the security of the hidden image or messages. This technique can be used for security of multimedia data over the Internet.
Keywords: Image Watermarking; DWT Watermarking; Digital Watermarking; PSNR; HVS; Copyright Protection.
Secure Provenance based Communication using Visual Encryption
by Kukatlapalli Kumar, Ravindranath Cherukuri
Abstract: Explicit specification of the historical record of an instance or a data item is called data provenance. It has many applications in various fields with regards to its importance on capturing data flow mechanisms. However, on the other hand, there are good number of security mechanisms in place to withstand the cyber-attacks. Almost all of these algorithms uses complex mathematical calculations in providing security for the systems. Visual cryptography is a peculiar approach which uses concept of secret sharing by dividing the message into transparencies as encryption process. Upon superimposing transparencies one obtains the original message. In this paper, we propose secret sharing as a notion of security onto data provenance. Main inference of this writing is to throw a model combining above two mentioned aspects which gives away an indigenous solution in the area of information security. This proposed model is implemented with specific use case scenarios for substantiation and related analysis. Simulated results and discussion of the same is presented in the paper.
Keywords: Data Provenance; Visual Cryptography; Communication; Shares; Cyber Security.
Special Issue on: Cognitive Computing for Emerging Internet of Things
HOG Features and Connected Region Analysis-Based Workpiece Object Detection Algorithm
by Yu Ting, Tian Maoyi
Abstract: In order to solve the problem of bearing workpiece object, namely, the insuffi-cient detection ability of the algorithm caused by the complex edge features and inconspicuousness of the surface as well as the uncertainty and interference of the background, a HOG features and connected region analysis-based workpiece object detection algorithm is proposed in this paper starting from the calculation of HOG features, the image gradient direction, the connected region analysis and object detection. The image is processed in accordance with the color chroma-tography of foreign matters to separate the foreign matter from the background. Firstly, the target images of standard workpiece in the training set are meshed to calculate the pixel gradient in the grid, count the gradient histogram and com-plete the extraction and training of HOG features. Then interval division of the single peak threshold is refined, and a two-threshold segmentation mechanism is proposed to convert the two-valued image into a label image by combining the connected region analysis, and the evaluation of pixel attribute and the filtering of interference is conducted to achieve the purpose of accurately detecting the workpiece object. The experimental results show that the bearing workpiece ob-ject detection algorithm in this paper has higher accuracy and stability.
Keywords: Workpiece object detection; Image gradient; Chromatography; Edge feature; Connected region; Meshing; Histogram.
SLFNs Interpolation Fingerprint Particle Filter-Based Shared Bicycle Tracking Algorithm
by Cao Honghua, Yan Xiaoyan, Li Yan
Abstract: In order to improve the performance of traditional fingerprint detection method in the process of tracking the shared bicycle, the inertial sensor is used for data measurement. The particle filter (PF) method is a widely used sensor fusion al-gorithm, but the initialization and weighting processes are problematic in shared bicycle positioning systems. In this paper, a new PF scheme is proposed, and it can produces smooth and stable localized knowledge. However, the feed-forward network that uses the single hidden layer is used to simulate the estimation and improvement of the performance of multiple probability to achieve the distinc-tion of similar fingerprints. At the same time, the random sample consensus al-gorithm (RANSAC) is used to initialize the algorithm so as to reduce the conver-gence time. Experiments show that the tracking error of this scheme is less than 1.2m, which is superior to the selected comparison method.
Keywords: Feed-forward network; Particle filter; Shared bicycle; Tracking algorithm; Strength indicator of signal.
Markov Model-Based Low delay Data Aggregation tree Algorithm
by Huang Luyu
Abstract: The data aggregation technology can save resources of wireless sensor networks, but it can also add extra delays. To this end, specific to the special scenario where data transmission must be completed under specified delay constraints, the Markov model-based low delay data aggregation tree (MLDGT) algorithm is proposed. Firstly, the formal expression of the problem of constructing data ag-gregation tree under delay constraints is given. This problem has been confirmed as a NP problem. Then, the Markov approximate model is used to find a subop-timal solution, and further obtain the low delay data aggregation tree. Finally, the effectiveness of the MLDGT algorithm is analyzed by simulation and compari-son. The experimental results show that the MLDGT algorithm can reduce the data aggregation delay.
Keywords: Wireless sensor network; Aggregation tree; Data aggregation; Formal expression; Markov model.
MeTis Meshing-Based Bayes 3D Ship Model Geometry Reconstruction
by Yue Jingya
Abstract: In order to improve the compression efficiency of 3D model geometry reconstruction process, a MeTiS meshing-based Bayes 3D ship model geometry reconstruction algorithm is proposed. The original 3D mesh is subnetted by the MeTiS method at the coding end, and the geometrical shape of the subnet is coded by a random linear matrix, and the neighbor node of the boundary node is considered to construct the data sequence by the pseudo random number generator; then the Bayes algorithm is used to design the geometric model reconstruction algorithm, and the learning rules for the mean, variance matrix and model parameter are theoretically given, realizing the geometric reconstruction of 3D model; finally, on the 3D model standard test library and 3D ship model, the simulation comparison with the GFT, LSM and CSGFT and other algorithms show that the proposed method has a relatively high bit rate compression index and a low reconstruction error, leading to significantly improved computational efficiency.
Keywords: 3D vessel model; Geometric reconstruction; MeTiS meshing; Bayes; Neighbor node.
Dynamic Node Adaptive Incremental Interaction Optimization in Micro-blogging Community
by Fei Shang, Xiaobo Nie
Abstract: Most community discovery methods are based on network topology and edge density for best community determination, but these methods have very high computational complexity and are very sensitive to the form and type of network. In order to solve these problems, this paper proposes a micro-blogging community interaction optimization algorithm based on dynamic node adaptive increment model, which is based on optimizing the interaction of members in each community, and uses greedy algorithm to search the best candidate for the optimal community effectively without traversing all nodes. The model can quickly and accurately measure the interaction difference between the community and the community. Finally, the simulation tests on the datum test network and the Sohu micro-blogging platform show that the proposed algorithm is better than the selected contrast algorithm in the index of recall, accuracy, algorithm calculation time and network coverage.
Keywords: Complex network; Edge density; Community discovery; Self- adaptive; Interaction optimization; Incremental model.
Quantitative Structure-Activity Analysis of Predicted Drug Targets Based on Adaboost-SVM
by Fujun Gao
Abstract: This paper first constructs two sets of data sets to demonstrate the effectiveness of the proposed method, one data set consists of all human protein data, and the other is composed of human G protein-coupled receptor data, which accounts for a high proportion of drug targets. It extracts the corresponding primary structure, polypeptide characteristics and basic physicochemical properties of each protein in the data set, feature selection is used to reduce the learning burden of classifier as the feature space of training classifier. Then the data are preprocessed and the optimal classifier is constructed by adjusting the parameters of the model. Data sets are classified by SVM classifier and Adaboost-SVM classifier respectively in the experimental construction and analysis part, analyzed and compared the experimental results of two classifiers applied to two sets of data sets before and after data preprocessing, the classification results of the two groups were verified each other to increase the reliability of the classification results. The experimental results verify the effectiveness of the proposed method. At the same time, it shows that the method proposed in this paper can effectively predict drug targets, and provide a preliminary reference for drug research and development workers.
Keywords: Direct push type; Support vector machine; Predictive drugs; Target quantification; Structure-activity analysis.
Energy Sensing Streaming Media Data Transmission Protocol Based on Implicit Markov Algorithm in WSNs
by Guozhong Li
Abstract: Streaming media transmission protocols can be divided into traditional streaming media push protocol and streaming media pull protocol. Traditional streaming media push protocols such as RTP, whose server determines the channel state according to the RTP feedback from the client, and then decides to send data packets suitable for the current channel state. The pull protocol of streaming media sends data packets according to the content of the client when the transmission rate meets the requirement on the contrary. Streaming media pull protocol greatly reduces the complexity of servers in streaming media transmission technology, it can also support the application of different adaptive algorithms in the transmission process compared with the traditional universal server transmission algorithm mechanism. Therefore, this kind of pull protocol cannot only improve the quality of service of streaming media transmission, but also meet the requirements of different channel states and different clients.
Keywords: WSNs; Data aging; Mesh area; Energy aware; Data transmission protocol.
Multi-Feature Fusion Energy-Saving Routing in Internet of Things Based on Hybrid Ant Colony Algorithm
by Ren Xiao-Li, Yang Jian-Wei, Li Nai-Qian
Abstract: This paper analyzes the research status of sensor networks and several improved LEACH protocols. It is known that there are some shortcomings in current low-energy clustering protocols:The problem of uneven network cluster and unequal energy consumption of each node in the cluster group leads to excessive energy consumption of some nodes, the whole network life cycle is also greatly shortened. This paper proposes a multi-feature fusion energy-saving routing algo-rithm based on hybrid ant colony algorithm to optimize and upgrade LEACH energy-saving routing model for the Internet of Things on the basis of LEACH.(NPCHS-Leach)to improve the problems of short lifetime and low energy utilization caused by existing clustering routing protocols,it improves and prolongs the network life cycle. Finally, the effectiveness of the proposed algorithm is verified by simulation experiments.
Keywords: Ant colony algorithm; Multi-feature fusion; Internet of things; Energy efficient routing.
Energy-Saving Algorithm for Data Center Network Based on Genetic Algorithm
by Shu Yang, Hua Yang, Hua Yang, Wen Chai, Wen Chai, Zehui Liu, Zehui Liu
Abstract: This thesis focuses on network equipments in the data center which hasrncaused rapidly growth of energy consumption recent years. The switches account for the largest proportion of energy consumption of network equipments, so turning off unneeded switches can reduce energy consumption effectively. Based on this point, we develop an high-efficient routing algorithm based on genetic algorithm(GA) in order to improve energy consumption of network equipments. Genetic algorithm is a kind of a heuristic algorithm which solves the optimization problem rapidly by imitating the way of the natural selection, but to a certain degree, it reduces accuracy. Its a complicated problem to decide routing path in arnshort period, so we choose genetic algorithm to achieve our goals. In ourrnsimulation, we make some improvements of GA in order to fit our problem andrnraise the accuracy of its solution.
Keywords: Data center network; Energy efficient routing; Genetic algorithm.
Damage Prevention Analysis of Heavy-Duty Gear Body Based on Finite Element Neural Network
by Pei Weichi, Dong Jianwei, Long Haiyang, Ji Hongchao, Zhang Wenming, Li Yaogang
Abstract: The method of damage prevention analysis of heavy-duty gear body based on finite element neural network is proposed to improve the effectiveness of damage prevention analysis of heavy-duty gear body. Firstly, a design platform for gearbox gears of caterpillar tractors is developed based on finite element theory, the three-dimensional model of the gear is designed on this platform, and the bending and contact finite element analysis of the gear teeth is carried out, the bending stress and contact stress of the gears are obtained, which provides a basis for the parameter design and reliability of the gears. Secondly, a neural network algorithm is introduced to predict and analyze the impact of damage data of heavy-duty gear body. Finally, the effectiveness of the proposed algorithm is verified by simulation experiments.
Keywords: Finite element; Neural network; Heavy-duty gear body; Destruction prevention.
On-Demand Distance Vector Refinement-Based Car Networking Stable Routing
by Shutao Zhou, Chengxing Li, Hui Yu
Abstract: Due to the high-speed movement of vehicles and obstacles in urban scenes, the communication paths between vehicles become extremely fragile. Specific to the routing problem of VANETs, a path criterion-based on-demand distance vector PA-AODV routing algorithm is proposed. The PA-AODV routing algorithm makes full use of the characteristics of AODV routing, and improves its routing decision. By calculating the path criterion weight and preferentially selecting the path with small weight for data transmission, the path stability is thus improved. The experimental data show that the proposed PA-AODV routing reduces the data packets loss rate and also shortens the end-to-end transmission delay.
Keywords: Car networking; Distance vector refinement; On-demand distance vector; Communication path; Link quality.
Cognitive and Artificial Intelligence System for Logistics Industry
by Jing Zhao, Fengjie XIE
Abstract: With the continuous development of cognitive science, the impact on society is becoming more and more significant.Artificial intelligence is an important branch of cognitive science.Artificial intelligence has been applied to medical, education, security, logistics and other industries, which has broad prospects for development. Logistics industry uses artificial intelligence technology to complete intelligent search, face recognition, combined with large data calculation and planning reasonable path in warehousing, which plays an important role in the process of storage, transportation and distribution.Taking China's logistics industry as the research object, this paper analyzes the application of artificial intelligence technology in the logistics industry. In the warehousing process, artificial intelligence technologies including compile storage code, automatic picking with Automated Guided Vehicle, warehouse robot to improve work efficiency.Intelligent unmanned aerial vehicle (UAV) transport and intelligent sorting technology are implemented by artificial intelligence technology in the transportation link.Logistics distribution links use artificial intelligence technology to plan the best path, improve the recognition rate of express waybill that save a lot of labor.Artificial intelligence technology allocates logistics resources, optimizes logistics links, and improves logistics efficiency and other measures to promote the development of logistics informatization and automation.rn
Keywords: Cognitive Technology;Artificial Intelligence (AI); Logistics Industry.
Study on oceanic big data clustering based on incremental K-means algorithm
by Yongyi Li, Zhongqiang Yang, Kaixu Han
Abstract: With the increase of marine industry in the Beibu Gulf, data clustering has become an important task of intelligent ocean. Partition clustering methods are suitable for marine data. However, traditional K-means algorithm is not suitable for large scale data. Focusing on the characteristics of oceanic big data, we propose a clustering method based on incremental K-means (IKM) algorithm. First, a vector model is adopted to represent data sets, and the calculation model for mean values and centers is used to initialize arbitrary numbers of data points. Second, the input data vectors are iteratively calculated in an incremental vector form. Finally, by applying incremental vector and distance model, the large-scale data are clustered according to convergence condition. Experiments show that the algorithm can increase the clustering efficiency, reduce time and space complexity, and lower the missing data rate.
Keywords: cluster; K-means; incremental; oceanic big.
A fuzzy comprehensive evaluation model for Smart City Application
by Huaihui Liu, Zhiqing Zhang, Zhijie Sun
Abstract: As one of the basic social relationships in current world, the relationship between the police and citizens directly reflects the relationship between the authority and the public, which play an important role in the social stability. It has an essential significance to properly get to know, to deal with and to evaluate the police-citizen relationship. Firstly, we design a hierarchy evaluation index system model about the harmony degree between the police and the citizens, with the help of questionnaires, based on the principle of designing an evaluation index system and the five major factors that impact the harmony relationship between the police and the citizens. Secondly, we set up a fuzzy comprehensive evaluation model based on an improved analysis hierarchy process (AHP). And then we make an empirical research on the harmony degree between the police and the citizens with the help of the model we set up. Finally, based on the conclusion of the empirical research, we make a propose to the government and the security department about how to promote the construction of the harmony police-citizen relationship. The research enriches the methods and means of evaluation the harmony degree of the police and the citizens and exemplifies the empirical research.
Keywords: the harmony degree of the police and citizens; evaluation index system; the improved AHP; the fuzzy comprehensive evaluation mathematical model.
A Chameleon Hash Authentication Tree Optimization Audit for Data Storage Security in Cloud Calculation
by Yang Bo
Abstract: In order to improve the security of data storage in cloud calculation , a chameleon Hash authentication tree optimization audit method for data storage security in cloud calculation is proposed. First, an optimized public audit agreement is proposed. By storing homomorphic linear validator for user data on TPA sites, the response size of cloud storage server (CSS) is optimized. At the same time, the quasi-random function is used to optimize the query request to CSS; secondly, the chameleon hash and an improved chameleon authentication tree are used to perform efficient dynamic data updating on client data (cloud calculation ) to support block-level updating and fine-grained updating; finally, through thorough security and performance analysis, it is clearly verified that the proposed method is safe and efficient.
Keywords: Cloud calculation; Data storage; Chameleon authentication tree; Third part audit; Quasi random function.
Optimization of CoMP based Cellular Network design and its Radio network parameters for Next Generation HetNet using Taguchi
by Sarosh Dastoor, Upena Dalal, Jignesh Sarvaiya
Abstract: A heterogeneous network (HetNet) is a complex network made of variable cellular dimensions with different network topology. An erratic network design is valueless, unproductive and expensive. Research paper describes coordination of multipoint transmission, in which a collection of transmitting Base Stations (BS) dynamically harmonizes their transmission among themselves, enhancing the coverage to the edge users. The proposed cellular planning strategy uses variable radii cells forming a cluster in a given region to be dimensioned. For a given cluster, minimum distance (dmin) between two cells has been calculated and using proposed (1/3 d_min ) dimensioning technique, the coverage radius of cells in a cluster is made, forming a HetNet. By optimizing the network; coverage, cost and energy requirements could be minimized and optimization of network performance parameters like transmission power, tilt and azimuth angle of antenna with the radius of cell provides cost-efficient deployment of a network. The research paper proposes the mathematical dimensioning model for the design of a HetNet as well as its performance parameters using Taguchi
Keywords: heterogeneous network; optimization; orthogonal array; coordination; Multipoint transmission; Taguchiâ€™s Method; azimuth; tilt; energy conservation; throughput.