International Journal of Wireless and Mobile Computing (63 papers in press)
Multicast stable path routing protocol for wireless ad-hoc networks
by K.S. Saravanan, N. Rajendran
Abstract: Wireless Ad-Hoc Networks (WANETs) enable steady communication between moving nodes through multi-hop wireless routing path. The problem identified is how to improve the lifetime of the route and reduce the need for route maintenance. This helps to save bandwidth and reduce the congestion control available in the network. This paper aims to focus on redesign and development of multicast stable path routing protocol with special features that determine long-living routes in these networks. An extensive ns-2 simulation based performance has been analysed of three widely recognised stability oriented wireless ad-hoc network routing protocols, namely are Associativity Based Routing (ABR) protocol, Flow Oriented Routing Protocol (FORP) and Lifetime Route Assessment Based Routing Protocol (LRABP). The order of ranking of the protocols in terms of packet delivery ratio, average hop count per route, end-to end delay per packet and the number of route transitions is presented.
Keywords: wireless ad-hoc networks; multicast routing protocol; wireless communication; routing protocol.
Study on carbon footprint model and its parameter optimisation of wave soldering process based on response surface method
by Renwang Li, Haixia Liu, Jiaqi Li, Jinyu Song, Rong Jie
Abstract: In order to respond to low carbon manufacturing, from energy, materials and process carbon emissions, etc., this paper constructs a carbon footprint model for the wave soldering process in the module workshop of H Company. Based on this model, the carbon footprint value of the wave soldering process is calculated. On the basis of selecting appropriate parameter factors, a parameter optimisation model of the wave soldering process life-cycle carbon footprint is constructed, and the optimum parameters combination is analysed by the response surface method, which contains surface area, clip velocity, clipping angle, flux flux, purity of solder, temperature of tin furnace, height of wave peak, etc. The the response values obtained are verified. The experimental results show that the optimised parameters are used to process and manufacture the wave soldering process, and the carbon emissions produced by wave wave welding can be controlled from above 15 kg CO2 to 12 kg CO2.
Keywords: wave soldering; carbon footprint; calculation model; response surface method; parameter optimisation.
A trusted behaviour measurement scheme based on feedback and control in a trusted network
by Junxi Zhuang, Xu Zhao, Bei Gong, Jing Zhan
Abstract: A terminal is connected to a trusted network through authentication and integrity measurement. In order to solve the real-time trusted measurement of user behaviour in a trusted network, a new combination of dynamic and static, subjective and objective behaviour trusted measurement scheme is presented. By the combination of state measurement, behaviour measurement and behaviour feedback control, the scheme can guarantee the credibility of user behaviour. The scheme analyses the credibility of the real-time user state and the direct trusted measurement value of the user behaviour, and finally controls the user behaviour by the feedback measurement of the user behaviour. The research results show that when the ratio of untrusted nodes is 15%, 30%, or 50% in the trusted network, the trusted detection rate can reach more than 92%, and the untrusted node in the trusted network can be identified with the increase of time. Compared with traditional measurement schemes, this scheme can be more accurate and reasonable to calculate the credibility of user behaviour, and can reflect the changes of the credibility of users' actual behaviour at all times, and ultimately implement the feedback control on user behaviour. The scheme can more effectively prevent the virus from invading than the traditional scheme. The research results provide technical support for the continuous trusted assessment of user behaviour in trusted networks.
Keywords: trusted network; trusted measurement; state measurement; behaviour feedback control.
Torque calculation model and structural optimisation of axial magnetic drive mechanism
by Qiao Xu, Xiaolong Cui, Zhen Chen, Shunqi Mei, Qiaoling Ji
Abstract: A torque calculation model based on the magnetic energy theory is established to give a more effective calculation method of torque for axial magnetic mechanism. The relationship between the torque and the relative rotation angle, the gap, and the magnetic flux leakage coefficient is analysed. On this basis the structural optimisation model is established and solved. Its purpose is to use the minimum volume magnet to meet the torque requirement. The analysis shows that the gap and the magnetic flux leakage coefficient should be as small as possible under meeting the structural conditions, and there is a certain relative rotation angle to make the torque reach a maximum value. The experiment is carried out and experiment results compared with the calculation results. The results show that when the structure is reasonable, the calculated values of the torque are consistent with the measured values, and the calculation model and structural optimisation model are very effective.
Keywords: torque calculation; structural optimisation; magnetic drive; magnetic energy theory.
Modified JAYA algorithm for solving the flexible job shop scheduling problem considering worker flexibility and energy consumption
by Hongchan Li, Haodong Zhu, Tianhua Jiang
Abstract: This paper investigates a flexible job shop scheduling problem with worker flexibility and energy consumption. A modified JAYA algorithm (MJAYA) is developed to minimise the total energy consumption. In the MJAYA, three improvement strategies are used to improve the algorithms performance, such as modified individual updating method (MIU), adaptive mutation operator (AMO) and local search strategy (LSS). The MIU is developed to improve the exploration ability by adding a random term to the original updating equation. The AMO is used to keep the population diversity. In addition, The LSS is employed to enhance the local search capacity. Finally, extensive simulations are performed to validate the effectiveness of the proposed MJAYA algorithm. Experimental data show that the MJAYA algorithm is effective for solving the considered problem.
Keywords: flexible job shop; production scheduling problem; worker flexibility; energy consumption; modified JAYA algorithm.
Road recognition and motion target tracking based on infrared image
by Qingqing Wang, Li-Guo Zheng, Jia-Nan Meng
Abstract: Road recognition is a very important link in the safe driving of vehicles. In this paper, a region-based road recognition method is proposed for infrared road images. Noise is eliminated by mean filtering and threshold segmentation. Then, the road boundary segments are obtained by the threshold exhaustive method, and the road boundary segments are obtained by the second-order differential operator. The road boundary segments obtained by the two methods are fused to obtain the candidate road boundary segments. Finally, the real road boundary is obtained by optimising the line fitting. For the identified road, the moving target tracking based on interacting multi-mode Kalman filter is adopted. The simulation results show that the interacting multimode Kalman filter can achieve better trajectory fitting, and the errors in X and Y directions before and after filtering are small. This shows the excellent performance of interacting multimode Kalman filter in moving target detection.
Keywords: infrared image; road recognition; target tracking; Kalman filter; chain code tracking.
Numerical simulation of the pool fire behaviours in the aircraft cabin
by Yimeng Hao, Guanbing Cheng, Shuming Li
Abstract: The cabin pool fire is a complicated phenomenon and the cabin misfire may lead to a seriously hazardous accident. Thus, it is significant to study the pool fire behaviour in the cabin for aircraft safety. In this paper, one typical civil transport airplane is considered. The aircraft has a single aisle cabin. Using the fire dynamic simulator, the calculated and physical models of the cabin were constructed accordingly. Four slices along the vertical direction and seven detectors along the cabin aisle at several centimetres above the floor were placed in order to trace the dynamic changes in the significant parameters. The evolution of the parameters such as temperature, flow velocity and species concentrations (e.g. CO, soot, and oxygen together with CO2) were analysed via the parameters sequence diagrams and nephograms. The results show that the changes in those fire characteristic parameters include the transient and steady stages. At the transient stage, the temperature, flow velocity and species concentrations increase within several seconds. But the oxygen concentration decreases in this stage owing to oxygen consumption in the combustion process. In the steady stage, those parameters oscillate around one constant. In the first stage, all the fire characteristic parameters change more importantly in the zones far away from the fuselage doors. Although the seats produce turbulence to some extent, the air entrance from the doors predominantly reduces the combustion temperature and rates and also prolongs the transient period.
Keywords: aircraft cabin; fire behaviour; temperature; velocity; species’ concentration.
Modelling and simulation of a thermo-fluid system with one-dimensional distributed parameters on Modelica
by Yiming Yuan, Zefei Zhu, Guojin Chen, Chang Chen
Abstract: Thermo-fluid systems are applied to industrial production and daily life widely and influence human activities deeply. This paper describes a modelling and simulation method for a thermo-fluid system with one-dimensional parameters on Modelica, aiming at providing a framework and modelling process of a one-dimensional thermo-fluid system model. The method of lines is introduced to convert the partial differential equation, which is used to describe the one-dimensional thermo-fluid system, into a group of differential-algebraic equations. Based on the Newton-Gregory polynomial, the discretisation method and step is presented and the difference expression is deduced for the first and second order spatial derivative terms in PDE. Two illustrative examples, wave equation and human body heat loss model, are presented to confirm the veracity and accuracy of the proposed method.
Keywords: thermo-fluid system; one-dimensional distributed parameter; partial differential equation; differential algebra equation; Modelica.
Analysis and optimisation of server load balancing based multi-factor integrated algorithm
by Xiang Li, Anan Jin, Li Wei
Abstract: In distributed server application service deployment, load balancing is an important factor that affects service performance. However, the biggest factor affecting load balancing is the performance of the server and the performance ratio of the requested service execution. This paper uses the application of service-oriented architecture system as the embedding point, which based on the analysis and comparison with traditional load balancing algorithms, such as random load balancing algorithm, round-robin algorithm, and consistent hash algorithm. This paper is aimed to design a new load factor-based algorithm to solve some defects of traditional load balancing algorithm by improving minimum concurrency number and integrating various factors that affect server performance. This paper proposes an optimisation algorithm that can more fully reflect server load capacity. The experiment results show that the load balancing effect of this algorithm has a better effect than the traditional load balancing algorithm.
Keywords: load balancing; SOA structure; load factor; minimum concurrent number.
Semi-supervised learning of pose-specific detector for human lying-pose detection
by Xia Daoxun, Liu Haojie, Li Weian
Abstract: Under the superlow-altitude aerial image, human lying-pose detection is an important problem in object detection. This paper is mainly focused on the application study of an unmanned aerial vehicle (UAV) life detector after a disaster, and we study the problem of learning an effective pose-specific detector using weakly annotated images and a deep neural network. This typical approach 1) clusters a series of human poses for the human lying-pose and assigns an image-level label to all human lying-poses in each image and breaks them down into several categories; 2) trains multiple classifiers for each category using a deep neural network; and 3) uses the boosted semi-supervised CNN forest classifier to select a human lying-pose with high confidence scores as the positive instances for another round of training. Experiments on the XiaMen University Lying-Pose Dataset (XMULP) show that significant performance improvement can be achieved with our proposed method.
Keywords: human lying-pose detection; pose-specific detector; semi-supervised learning; object detection.
Survey on throughput enhancement techniques for real time wireless link deployment
by Thiyagarajan Krishnan, T. Vetrivel Chelian
Abstract: Over the recent decades, the emergence of advanced wireless technologies has attracted many researchers and industries to explore the field of next generation wireless links towards capacity optimisation with minimal energy requirements. This gave beginning to a new dimension of research in the field of green yet high capacity links, which can connect our physical world to the digital reality with support from environment friendly networks. The phenomenal growth in wireless applications and technology development also demands a step-up in link capacity. This paper focuses on reviewing recent concepts and methodologies to improve link capacity for the next generation wireless systems. The main aim of this paper is to provide a conceptual understanding about the impact of modulation techniques and error control codes towards capacity improvement.
Keywords: hierarchical modulation; OFDM; FBMC; LDPC; polar codes; turbo codes; spatial modulation.
QANet-based candidate answer rethink model for machine reading Comprehension
by Yong Wang, Chong Lei
Abstract: The current model applied to the span extraction reading comprehension task fuses the information of context and question, and outputs the index with the highest probability calculated in the context as the prediction span. In this process, the model discards all the remaining candidate answers, which results in a waste of the available information in the candidate answers. Our model is designed to simulate the behavior of human beings choosing multiple candidate answers and comprehensively judging the final answer in reading comprehension tasks. We propose the QANet-based candidate answer rethink model. The model interacts with and fuses multiple candidate answers with context and question, prompting the model to obtain a more accurate answer by synthesising these three aspects of information. Experiments show that our model has made new progress in performance.
Keywords: machine reading comprehension; candidate answer rethink; information interaction.
Mobile lung cancer early warning based on Windows Azure cloud computing
by Jun Lv, Pan Wang, Zijuan Zhao
Abstract: In view of the repeated investment in the current medical lung cancer early warning system, "information island" and the low rate of lung cancer early warning, this paper proposes a mobile lung cancer early warning system based on Windows Azure cloud computing with the application requirements of medical information system. The system is applied to the smart phones of windows system, and the windows azure Microsoft cloud platform is accessed through wireless network, using SQL azure to store medical data (such as lung CT image, ECG curve and high-resolution colour ultrasound image), embed the medical lung cancer early warning model based on deep learning in the cloud computing platform to process and analyze the patient's medical data, and realize the real-time synchronous update of doctors, patients and cloud platform data. The accuracy of lung cancer early-warning system on lidc-idri is high, and the detection results are uploaded to doctors and patients' smart phones in real time. The results show that the mobile lung cancer early warning system based on Windows azure cloud computing not only effectively integrates medical resources, but also provides patients with medical services and consultation at home, which integrates the advantages of all aspects and adapts to the development of the times.
Keywords: pulmonary nodule detection; cloud computing; mobile medicine; CT image of lung.
Image feature point matching method based on improved BRISK
by Qian Shi, Yong Liu, Yichun Xu
Abstract: In order to improve the accuracy of the BRISK algorithm of feature point matching, an improved BRISK feature point matching method is proposed by combining the SIFT algorithm idea with high matching accuracy. Firstly, the Gaussian image pyramid is established, then the FAST algorithm is used to detect the feature points of each image layer in the image pyramid, and the intensity centroid method is used to determine the direction of the feature points, so the feature points obtained have scale information and direction information. Then using the BRISK descriptor to describe the feature points, using the Hamming distance to measure the similarity of the feature descriptors, and calculating the matching accuracy and matching time. The experimental results show that the accuracy of the method proposed in this paper is higher than that of the BRISK algorithm and the time is better than that of the SIFT algorithm and the BRISK algorithm.
Keywords: image matching; feature point matching; intensity centroid; improved BRISK; image pyramid.
Research on pipe crack detection based on image processing algorithm
by Licheng Huang, Bo Tao, Donghai Chen, Xun Zhang, Gongfa Li
Abstract: The detection of pipe cracks based on machine vision is a new and effective technology. However, it requires high quality of the image. Moreover, images with adequately lighting, evident cracks, and clean backgrounds are difficult to obtain in practice. This paper proposes an algorithm for pipe crack detection in natural background. The algorithm performs filtering, background segmentation, edge detection, threshold segmentation, morphological contour extraction, and annotation on the image. This paper also proposes an adaptive threshold segmentation method to obtain the clear crack. By comparing the proposed algorithm with the DEE algorithm, the result shows that the proposed algorithm has certain advantages in experiments. The experiment results show that the algorithm proposed can also be used in the detection of significant pipe cracks.
Keywords: pipe cracks; noise reduce; Sobel operator; edge detection; image processing.
An optimal condition of robust low-rank matrices recovery
by Jianwen Huang, Sanfu Wang, Jianjun Wang, Feng Zhang, Hailin Wang, Jinping Jia
Abstract: In this paper we investigate the reconstruction conditions of nuclear norm minimisation for low-rank matrix recovery. We obtain a sufficient condition to guarantee the robust reconstruction or exact reconstruction of all rank matrices via nuclear norm minimisation. Furthermore, we not only show that when $t=1$, the upper bound is the same as the result of Cai and Zhang, but also demonstrate that the gained upper bounds concerning the recovery error are better. Moreover, we prove that the restricted isometry property condition is sharp. Besides, the numerical experiments are conducted to reveal the nuclear norm minimisation method is stable and robust for the recovery of low-rank matrix.
Keywords: low-rank matrix recovery; nuclear norm minimisation; restricted isometry property condition; compressed sensing; convex optimisation.
Analysis of influence factors on employment and entrepreneurship of ex-college soldiers
by Keqing Ni
Abstract: With the increasing number of ex-college soldiers, how to transform these high-quality human resources into social capital has become a hot topic of concern for government and academia. Based on social cognition theory, self-efficacy theory and ternary interaction theory, as well as combining with data mining technique, this paper studies the influence factors of employment and entrepreneurship of ex-college soldiers. Detailed analysis indicates that entrepreneurial self-efficacy has a significant positive effect on entrepreneurial behaviour, and external environmental factors such as external support and entrepreneurial education have a significant positive effect on entrepreneurial cognition. In addition, entrepreneurial cognition has a significant positive effect on entrepreneurial behaviour. In conclusion, there are significant differences of entrepreneurial experience, business experience and family income on entrepreneurial behaviour. This study complements the theoretical research on entrepreneurship of ex-college soldiers.
Keywords: entrepreneurial cognition; self-efficacy; entrepreneurial behaviour; structural equation; K-means clustering.
Multi-model fusion framework based on multi-input cross-language emotional speech recognition
by Guohua Hu, Qingshan Zhao
Abstract: Aiming at the limitations of the present time domain and frequency domain attribute characteristics and the emotion classification network in cross-language emotional speech recognition, a multi-model fusion framework based on multi-input cross-language emotional speech recognition is proposed. Firstly, happiness, sadness, neutrality and anger, which are common to the four corpus of Chinese, English, Urdu and German, were selected as experimental samples. Secondly, the acoustic features, MFCC spectrogram features and language spectrogram features of multilingual emotional speech signals were used as the inputs of the multi-model fusion framework, and then SVM, MobileNet26 and ResNet38 constituted the basic framework of multi-input corresponding multi-model fusion, the feature map after the convolution operation of MobileNet26 and ResNet38 models was global max pooling and global average pooling, so as to capture different features and double the diversity of the models, and finally forming a framework for fusion of five models. Finally, an experimental scheme was designed to verify the effectiveness of the multi-model fusion framework based on multi-input in cross-language emotional speech recognition. The experimental results show that, compared with the traditional acoustic features and the network model with a single feature, the multi-model fusion framework based on multi-input can not only effectively distinguish the emotion differences of multiple languages, but also realise the transfer learning of emotional speech recognition of small languages through the learning of large languages.
Keywords: multi-input; cross-language; emotional speech recognition; multi-model fusion; transfer learning.
A DV-Hop dynamic weight positioning model with genetic algorithm
by Penghong Wang, Tian Fan, Xingjuan Cai, Wuchao Li
Abstract: Wireless sensor networks (WSNs), as an important part of the Internet of Things (IoT), are widely deployed to the location estimation in WSNs. However, at present, the study of DV-Hop algorithm is still limited to the improvement of the sensor nodes in the ordinary region. In this paper, aiming at the complex areas under different application backgrounds, based on the analysis of the localisation principle of the traditional DV-Hop model based on optimisation algorithm, a dynamic weight positioning model based on genetic algorithm (DWMGA-DV-Hop) is proposed. The algorithm performs simulation experiments on three different types of test set by adopting dynamic weight calculation model. Experiment results demonstrate that, under different test sets, compared with the standard DV-Hop algorithm, TW-PSODV-Hop and OCS-DV-Hop algorithm, this algorithm has the highest positioning accuracy and stability.
Keywords: DV-Hop; genetic algorithm; dynamic weight positioning model; internet of things.
Application of many-objective particle swarm algorithm based on fitness allocation in WSN coverage optimisation
by Weiwei Yu, Chengwang Xie
Abstract: In order to improve the situation that the wireless sensor network (WSN) nodes in the random deployment is not uniform, the network coverage performance is improved. The traditional particle swarm optimisation has slow convergence speed and is easy to fall into local extremum. The many-objective Particle Swarm Algorithm based on Fitness Allocation (FAMPSO) is proposed by combining fuzzy information theory and new mutation methods. The algorithm combines the fuzzy information theory to associate the ideal solution with the Pareto solution and proposes a new fitness allocation method, which increases the pressure of population selection and enhances the convergence of the algorithm. The FAMPSO algorithm is compared with three other representative multi-objective evolution algorithms on the DTLZ series test function set. At the same time, the FAMPSO algorithm is applied to the coverage optimisation of WSN, and the simulation analysis is carried out. The simulation results show that the FAMPSO algorithm has a significant performance advantage in terms of convergence, diversity, and robustness. FAMPSO algorithm improves the coverage performance of WSN.
Keywords: wireless sensor network; network coverage;particle swarm optimisation; many-objective optimisation; fitness allocation;.
Finite impulse response low-pass digital filter based on particle swarm optimisation for image denoising
by Peng Shao, Le Yang, Xing Li
Abstract: Noise has very important influence on image quality. Finite Impulse Response (FIR) digital filter is an effective technique for removing image noise. But the effectiveness of FIR digital filter designed by traditional methods in image denoising needs to be improved further because of their demerits such as lower accuracy. Therefore, in order to suppress noise and improve image quality, the FIR low-pass digital filter designed by the improved particle swarm optimisation algorithm based on refraction principle (refrPSO) is employed to remove image noise. In the method, firstly, the coefficients of FIR low-pass digital filter are mapped to the candidate solution of refrPSO, and then the refrPSO is invoked to optimise it. Finally, the designed FIR low-pass digital filter is used to make images denoise. In the next experiments, two images with different sizes are tested and experimental results and analysis show that the FIR digital filter designed by the refrPSO has better effect on image denoising compared with FIR digital filters by traditional methods such as the window function method, the best equivalent ripple approximation method, and the particle swarm optimisation algorithm.
Keywords: particle swarm optimisation; refraction principle; FIR low-pass digital filter; image denoising.
Research on lightweight detection model of fake domain name
by Tao Ye, Jianbiao Zhang, Fengbiao Zan
Abstract: In recent years, phishing websites and other fake domain name attacks have become more frequent, posing a serious threat to the security of society and individuals. Fake domain name detection has thus become an important part of network protection. At present, fake domain name detection is mainly for public domain names, and detection methods are mainly based on edit distance. It is difficult to express the visual characteristics of domain names fully. Based on that, this paper studies the lightweight detection strategy of domain name string for the educational fake domain names and improves the detection efficiency by comprehensively considering the effect of character position, character similarity and operation type on the vision of domain name. Experimental results show that our method has a higher precision rate and recall rate on the position of the character, the character similarity, the type of operation than the traditional edit distance algorithms.
Keywords: educational domain name; fake domain name; edit distance; visual similarity.
Experimental study on modal characteristics of flame tube in a can-type combustor in an aero-engine
by Guanbing Cheng, Yinsheng Chai
Abstract: The flame tube is a key component in the combustor of gas turbines. The fuel air mixtures burn efficiently in the tube and control the temperature distribution in both the radial and circumferential directions by adjusting the air entrance from the various geometrical holes in the liner. Thus, understanding the dynamic characteristics of the flame tube structure is one of the key problems in understanding performance of the combustor and turbine. The present paper, from both FEM and experimental aspects, studied the vibrating modal characteristics of a flame tube in a can-type combustor. First, the tube FEM model was constructed by Solidworks and analysed in ANSYS Workbench. Then, its first six orders modal parameters, such as frequency and mode, were obtained. Afterwards, the modal experiments were effectuated by the classical hammering method and the resonant frequencies and modes of the tube were identified. Finally, we compared the calculated frequencies in FEM with the experimental ones. The results show that in the calculated modal of the flame tube, the flame tube vibrates along the x and y directions. The periodic tangential vibration with several circumferential waves and few horizontal half waves was observed. The tube's lower order resonant frequencies varied from 145 Hz to 600 Hz, and its higher order frequencies are on the order of 1000 Hz. In the tubes experimental modal, the first order frequency is about 100 Hz, its second and third order vibrating frequencies are about 400 Hz. The last three orders frequencies vary around 1000 Hz. The damping ratio is higher in the first order case than in the other orders. In the FEM and experimental methods, the relative maximum amplitude of the flame tube still occurs at its rear part. Finally, the first three orders frequencies of the tube in the experiment are lower by about 30% than those by the FEM method. This difference probably results from the constraint condition of the swirl section. The calculated last three orders frequencies are consistent with the experimental ones.
Keywords: flame tube; can-type combustor; vibration mode; resonant frequency; FEM; experimental modal analysis method.
New transformation method in continuous particle swarm optimisation for feature selection
by Kangshun Li, Dunmin Chen, Zhaolian Zeng, Guang Chen, James Tin-Yau Kwok
Abstract: Feature selection is a very important task in many real-world problems. Because of its powerful search ability, particle swarm optimisation (PSO) is widely applied to feature selection. However, PSO was originally designed for continuous problems, and therefore, the transformation between continuous particles and binary solutions is needed. This paper proposes a new transformation methods-based PSO (PSOS) in which the related feature subset of a particle is decided by a sine function rather than comparing with a single threshold. To further upgrade the performance of the proposed method, an extra increment generated by the Gaussian distribution is added to the marginal positions (PSOSI). The experimental results show that PSOS and PSOSI can select smaller feature subsets with higher classification accuracy than all the other algorithms compared in this paper. Furthermore, in most cases, the performance of the second method is better than the first one.
Keywords: particle swarm optimisation; feature selection; classification; sine function; Gaussian distribution; transformation method.
Research of small fabric defects detection method based on deep learning network
by Siqing You, Kexin Fu, Peiran Peng, Ying Wang
Abstract: For quality improvement of textile products, fabric defects detection is significant. In this paper, the detection capacity of SSD for small defects was studied. The loss of feature information was reduced through the reduction of layers of SSD network; then the size of the default box was adjusted based on the K-means clustering algorithm, and the adaptive histogram equalisation algorithm was applied to enhance the defect features and effectively improve the detection accuracy. The improved SSD network model was tested to verify the fabric defects dataset, which further improved the accuracy of detection. In addition, the two-stage algorithm was compared to find the optimal algorithm for small object detection. According to the test results, the subsequent improvement method for small object detection with SSD was proposed.
Keywords: fabric defects detection; default box; feature enhancement; SSD; faster RCNN.
Field theory trusted measurement model for IoT transactions
by Meng Xu, Bei Gong, Wei Wang
Abstract: The Internet of Things (IoT) allows the concept of connecting billions of tiny devices to retrieve and share information regarding numerous applications, such as healthcare, environment, and industries. Trusted measurement technology is crucial for the security of the sensing layer of the IoT, especially the trusted measurement technology oriented to transaction IoT nodes. In the traditional trust management system, historical behaviour data are considered to predict the trust value of the network entity, while the nodes' trust between network entities is rarely considered. This paper proposes a novel field theory trusted measurement model of the sensing layer network, which can well adapt to the transaction scenarios of the IoT.
Keywords: field theory; internet of things; trust measurement; transaction scenario.
Experimental and numerical study for dynamic characteristics of truck fuel tank based on fluid-structure interaction
by Xianfu Cheng, Junjie Liu, Rongqing Bao, Jing Li
Abstract: It was found that the fuel tank containing 50% fuel was prone to damage in truck road test. Thence, numerical simulation and experiment are combined to investigate the vibration features of the fuel tank with different liquid-filling ratios. The Fluid-Structure Interaction (FSI) vibration of the fuel tank concerns service life and dynamic characteristics. The FSI model is used for modal analysis of natural frequencies and mode shapes of the fuel tank, where corresponding vibration response behaviours are obtained. Modal analysis indicates that the modal frequency of the fuel tank with 50% fuel fluctuated significantly, subsequently random vibration analysis aims at the fuel tank with 50% fuel. The horizontal direction is determined as the harsh direction by extracting the welded joints stress of horizontal, longitudinal and vertical directions. Hereafter conducting the random vibration simulation and experiment with the RMS of 2g, 4g and 6g respectively, it is turned out that the calculation error is within 5% compared with the experimental data, which verifies the correctness of the proposed numerical simulation.
Keywords: fluid-structure interaction; modal frequency; random vibration; dynamic characteristics; fuel tank.
Research on point cloud generation algorithm of virtual depth camera
by Ke Li, Shilin Wu, Qinxia Huang, Zhen Zheng
Abstract: In this paper, based on the example of insulator taken by Realsence camera, a virtual camera is designed using OpenGL to take a picture of the STL model of the insulator, and the surface information of the model is extracted to obtain the three-dimensional point cloud data of the insulator. The experimental results show that the virtual depth camera can replace the solid camera to collect point cloud data from the STL model of the object.
Keywords: virtual depth camera; insulator; STL model.
3D object detection based on synthetic RGB image
by Chao Xu, Zeshen Li, Du Jiang, Juntong Yun, Ying Liu, Yibo Liu, Dongxu Bai, Ying Sun
Abstract: With the rapid development of computer vision technology and the maturity of 2D object detection technology, 3D object detection has attracted more and more attention of scholars. As an extension of 2D object detection, 3D object detection can not only detect the object category and position in the image, but also output the spatial position and pose angle of the object in a certain three-dimensional coordinate system. It has a considerable application prospect in automatic driving, robot operation and augmented reality. However, 3D object detection is much more difficult than 2D object detection, and it is very difficult to make and label the dataset, which requires high hardware requirements. At present, there is no dominant algorithm, and scholars use a variety of methods. In order to solve the above problems, this paper proposes a method of making and synthesising a 3D object detection dataset. Based on the improved 2D object detection algorithm, 3D object detection of a single RGB image is realised.
Keywords: 3D object detection; RGB image; synthetic dataset.
Effects of fuel pool on temperature profiles of fire in one engine nacelle
by Yicun Chen, Guanbing Cheng, Shuming LI
Abstract: The pool geometries, configuration and position have significant influence on the pool fire behaviour in an enclosed compartment. An attempt is made in the present paper to investigate the effects of a fuel pool on the fire temperature in an engine nacelle. We established the nacelle physical model based on classical turbofan engine CFM56 by AutoCAD, then introduced the Pyrosim to construct its numerical model. Three pool areas, A0404, A0303 and A0202, three pool shapes, S0404, S0208 and S0802, and three pool positions, front, middle and rear locations, were considered. The slice and four detectors were installed in the middle plane vertical to the nacelle longitudinal direction in order to obtain the temperature evolution and cloud charts at the left-right sides and top-bottom of the nacelle. The results indicate that for the pools with different areas, shapes and positions the temperature evolution divides into both an increasing stage within a few seconds and a steady stage, with an oscillation around an average temperature. For the pools with different areas, the increase of the pool areas contributes to the temperature augmentation at the left-right sides or the top and bottom of the nacelle. This change reveals that more fuel participates in the chemical reaction. More combustion heat boosts the fluid temperature in the nacelle by convection and radiation. For the pools with different shapes, the temperature at the left and top sides of the nacelle is higher for the pools farther away from the fire source. But it is always slightly higher at the right and bottom of the nacelle for the pools closer to the inner cylinder. For the pools with different positions, the temperature at the nacelle left and top sides is higher for the front and middle pools. However, it is always higher at the right and bottom of the nacelle for the front and rear pools.
Keywords: engine nacelle; pool area; pool shape; pool position; fire temperature; FDS.
Rating of catering enterprises based on fuzzy hierarchy and K-means clustering
by Xiaoyang Zheng, Yafeng Chen, Chengting Lin, Wei Zhang, Xinyi Zhou
Abstract: This paper firstly collects the credit
Keywords: catering enterprises; fuzzy analytic hierarchy process; K-means; rating.
Edge computing enabled services identification algorithm of elastic optical networks
by Lin Liu, Huifeng Bai, Jie Zhang, Chao Huo
Abstract: As internet services newly emerge with real-time and fine-granularity QoS demands, great challenges are presented to the elastic optical networks (EON) to satisfy diverted requirements of services. An edge computing enabled services identification scheme of EONs is proposed in this article, where the edge computing model is built and an Edge-Computing Echo-State-Network (EC-ESN) is also presented. This EC-ESN is able to transform the numerical output into the probabilistic one to determine the QoS strategy of EON. Moreover, the implemented approach of the proposed service identification scheme is also depicted. Test results show that the edge computing enabled service identification algorithm can greatly enhance the matching degree between services and EON.
Keywords: edge computing; echo-state-network; services identification; elastic optical network.
Emotion analysis method for elderly living alone based on CNN-BGRU neural network
by Qingqing Wang, Jianglin Luo, Jianwen Song
Abstract: China has entered a serious ageing society. The psychological needs of the elderly who live alone and need to be accompanied are a common concern of society. On the basis of affective computing technology and deep learning, this paper proposes an emotional analysis method for the elderly who are alone. In the big data environment, their daily emotional changes are analysed and forewarned remotely. However, there are some problems in text classification, such as difficult to extract semantic key features and poor classification effect. Therefore, this paper proposes a hybrid neural network model based on CNN-BGRU to solve the problem of accurate classification. In this algorithm, firstly, the convolution neural network is used to extract the local features of the input text vector, and then BGRU is used to obtain the information before and after this layer, and then the global features are obtained. Finally, the emotion classification results are obtained by Softmax classifier. The experimental results show that the accuracy of the proposed algorithm is 92.8%, the lowest loss rate is 0.2, and the trend is stable. It can be seen that this model can not only obtain more semantic information between texts, but also better capture the dependence of specific emotions in the whole text, so as to more effectively identify the emotional polarity in different aspects of the text.
Keywords: elderly alone; aged-care at home; convolutional neural network; BGRU; emotional analysis; deep learning.
Adaptive genetic algorithm for scheduling problem in flexible workshop with low carbon constraints
by Haixia Liu, Renwang Li, Yichao He
Abstract: Taking the completion time, energy carbon emission and total machine load as independent time factors into consideration, a flexible workshop scheduling model is established to minimise the maximum completion time, energy carbon emissions and the total machine load. The population could be initialised by greedy algorithm and random number method, and this model could be solved by the crossover probability and genetic probability adaptive method. The feasibility and effectiveness of the improved genetic algorithm are verified by testing the dataset and comparing several single-objective data and normalised multi-objective data.
Keywords: genetic algorithm; flexible workshop scheduling; carbon emissions; adaptive.
Electromagnetic pulse response prediction of intelligent wireless sensors based on NARX
by Cui Hao, Wenbai Chen, Hao Wu, Changjian Jiang
Abstract: the artificial neural network algorithm can represent all functions at any accuracy through learning the observed data and training parameters. Compared with conventional methods such as analytical methods, which could be limited in accuracy, or numerical modelling methods, which could be time-consuming, the artificial neural network algorithm is attractive for providing fast and accurate answer in the modelling of electromagnetic pulse response prediction of intelligent wireless sensors. According to the characteristics of input and output, nonlinear autoregressive with external input (NARX) neural network was chosen in this paper. It can obtain the current output value depends on its own previous output values and the input values. In order to verify the accuracy of the model, the electromagnetic pulse experiments of intelligent wireless sensors with protection circuit and without protection circuit were done. The results showed that the input-output curve estimated by the NARX neural network model is in good agreement with the experiment results. After two groups of simulation, the NARX model has high fitting ability, which suggests that the NARX model has good generalisation ability.
Keywords: electromagnetic pulse; intelligent wireless sensor; NARX neural network; signal line; transient voltage suppressor.
Image tracking and matching algorithm of semi-dense optical flow method
by Tao Song, Bo L. Cao, Fu M. Zhao, Hang Y. Luo, Xin Yang, Shuai Liu
Abstract: The traditional optical flow method is based on the assumption of spatial consistency of optical flow field. It is easy to reduce the tracking quality and even lead to the loss of target tracking in the areas of image feature missing, boundary and occlusion. A semi-dense optical flow method is proposed to realise stable tracking of image features. Firstly, the feature points are preserved by calculating the pixel points with a large change of pixel gradient in the image. Secondly, according to the principle of grey level invariance, the grey level difference function between the corresponding feature points of adjacent frames is constructed. Finally, the gradient descent principle is used to optimise the grey difference function and realise the accurate matching of feature points of adjacent frames. The results show that compared with the traditional LK optical flow method, this algorithm can effectively improve the feature tracking capability, and at the same time can effectively retain the useful information in the image. Compared with the traditional feature point matching method, the algorithm presented in this paper has an efficient operation rate.
Keywords: optical flow method; half dense; image processing; feature tracking; image matching.
The actual traffic prediction method based on glowworm swarm optimization
by Ke Chen
Abstract: In order to mitigate congestion caused by the rapid growth of computer network, a novel traffic prediction algorithm PGS (Prediction method based on Glowworm Swarm) is proposed by glowworm swarm optimisation method. In this algorithm, the arrival flow is regarded as glowworm swarm and the node service rate is regarded as attractiveness at first, and in order to improving the prediction accuracy, the optimal position and attractiveness are obtained with the individuals' moving operation and random flying operations. Then, a simulation with OPENT and MATLAB was conducted to research on the key factors of prediction error for PGS. Compared with wavelet transform prediction method, the prediction error is decreased 1.08%. The result shows that PGS has better adaptability.
Keywords: congestion; prediction; accuracy; glowworm swarm.
Cloud logistics and risk assessment design platform based on service modularisation
by Yuyan Shen, Yan Qian
Abstract: This paper intended to develop a cloud logistics service model from the three dimensions of service modularisation namely design structure, interface and standards. A case study research method was employed to analyse the service modularisation of the Star Expresss cloud logistic platform based on service process modularisation, service function modularisation and service object modularisation and propose the realisation method of the modularisation service mode of the logistics service of the e-commerce platform. Further, this study conducted a risk assessment of the modularisation service quality of the cloud logistics platform, through the cloud logistics platform risk assessment model based on the OWA operator. Researh findings revealed that cloud logistics service model and risk assessment analysis leads to decreasing the maintenance costs, and improve the service quality and optimise the modularisation service of the cloud logistics platform.
Keywords: service modularisation; cloud logistics; modularisation design; risk assessment; Star Express.
Flow field simulation and structural parameter optimisation of vacuum adsorption system for textiles fabrics
by Shunqi Mei, Qiao Xu, Zhenghui Wang, Yichuang Gu, Quan Zheng
Abstract: Vacuum adsorption and grabbing for textile fabrics is one key technology for intelligent garment processing. Owing to the softness and air permeability of textile fabrics, the design of the vacuum adsorption grab device has lacked an effective method. In this paper, the standard k-epsilon turbulence model is used to analyse the flow field in the suction cavity of vacuum adsorption device for textile fabrics, the optimization design model of structural parameters is established and solved by the Fluent software, and the verification experiment is carried out. The experimental results show that the suction mechanism with optimised parameters can effectively absorb and grasp the fabric, and the negative pressure required is minimum. The research results show that the structure parameters, such as the thickness of the suction cup cavity, the diameter of the suction hole, and the depth of the suction hole, affect the adsorption performance of the vacuum adsorption device.
Keywords: textile fabric; vacuum adsorption; Fluent simulation; parameter optimisation.
Long short-term memory with compensation method for text classification
by Wei Huang, Mengyu Liu, Wenqian Shang, Haibin Zhu, Weiguo Lin, Chunjie Zhang
Abstract: As a foundational task, text classification is widely used in the field of natural language processing. In the recent research on text classification, neural network-based methods have produced promising results. Nevertheless, most previous works ignore the fact that information may be lost or misinterpreted after the calculation of the neural network. In the research of this paper, we avoid such problems by using historical information, such as the original information of a text and the output information of the hidden layers, then perform text classification. This proposed method is called long short-term memory with compensation, or simply, LSTM-Com, which dynamically selects the important historical information as compensation for the neural network. In the classification experiment, the improved algorithm showed excellent performance compared with the baseline.
Keywords: text classification; long short-term memory; neural networks; compensation mechanism.
Dynamic time warping-based evolutionary robotic vision for gesture recognition in physical exercises
by Quan Wei, Kubota Naoyuki, Ahmad Lotfi
Abstract: In this paper, we propose a three-dimensional posture evaluating system from two-dimensional images, which can be implemented in physical exercises for elderly people. In this system, two-dimensional coordinates of human joints are first captured and calculated, then our proposed Dynamic Time Warping Steady State Genetic algorithm (DTW-based SSGA) is used for the evaluation of three-dimensional rotational variables from RGB images for the human arm. Finally, these predicted rotational variables would be compared with the template of sample posture by Dynamic Time Warping (DTW) to check the complement of physical exercises. The experimental result shows that our proposed DTW-based SSGA performs with higher accuracy than other evolutionary algorithms, such as standard Steady State Genetic Algorithm (SSGA) and Particle Swarm Optimisation (PSO) when evaluating human joint variables with templates, especially in the physical exercises for rehabilitation.
Keywords: gesture recognition; forward kinematics; evolutionary computing; dynamic time warping.
Research on trusted SDN network construction technology
by Fazhi Qi, Zhihui Sun, Yongli Yang
Abstract: In this paper, we combine trusted computing with SDN. By active measurement of the SDN controller when it is starting and running, we can guarantee the trust of the SDN controller. By actively measuring the behaviour of the SDN data transponder in the domain, we can guarantee trust of the SDN data transponder. When the cross-domain data interaction is involved, by trusted network connection mechanism, we can guarantee the trust of the transmission of data in different domains so as to build a trusted SDN network as a whole.
Keywords: trusted computing; SDN; active measurement.
A research framework for constructing the knowledge database of public security information
by Han Zhong, Shiqiang Zhang, Jianli Liu
Abstract: At present, the public security organs in China have accumulated a great deal of public security data. These data have broad sources, complex structures, and large and increasing scales. How to effectively integrate, manage and mine these data has become a new problem faced by all public security organs. This paper proposes a research framework for constructing the knowledge database of public security information. Based on this multi-dimension framework, data features can be effectively extracted and modelled for improving the management and use of public safety data.
Keywords: public security information; feature extraction; knowledge architecture.
Research on small target pedestrian detection based on improved YOLO
by Xing Xu, Kaiyao Wang, Yun Zhao
Abstract: Aiming at the problems of low detection accuracy and speed for small target pedestrians in traffic scenes, the YOLO-SP based on YOLO-v4 is proposed. Firstly, the KITTI and INRIA datasets are used to make the new dataset, and the k-means algorithm is used to cluster the anchor box. Secondly, in the feature fusion phase (Neck), the number of fused channels is increased and the number of output channels is simplified. Finally, the opitimised loss function GIOU is used to calculate the coordinate loss, and focal is used to calculate the confidence loss. The test shows that all the improvement measures show better effect on small and overlapping pedestrians, the final detection accuracy is increased by 4.0%, and the detection speed is accelerated by 11.3%. YOLO-SP has a certain practicality in the small target pedestrian detection.
Keywords: small target; pedestrian detection; YOLO; deep learning.
Bi-GRU model based on pooling and attention for text classification
by Hu Yu-lan, Qin-Shan Zhao
Abstract: Aiming at the problems that most of the text classification models based on neural network are easy to overfit and ignore keywords in sentences in the training process, an improved text classification model is proposed. This text classification model is a bi-direction gated recurrent unit (Bi-GRU) model based on pooling and attention mechanism. The model solves the above problems in the following ways. First, the bidirectional gated recurrent unit is used as the hidden layer to learn the deep semantic representation. Second, max-pooling is adopted to extract text features and the self-attention mechanism is adopted to obtain information about the influence of words and sentences for text classification. Third, the model uses the splicing results of the two to classify texts. The experiment chooses two common Chinese datasets, which are Fudan Set and THUCNews, on Pytorch deep learning framework. The experimental results show that the proposed model is better than the Text-CNN model and Bi-GRU_CNN model, such as precision, recall rate and Fscore. Compared with the optimal model, the precision, recall rate and F-score are respectively increased by 5.9%, 5.8%, and 4.6% for Fudan Set, which is the longer Chinese text dataset.
Keywords: text classification; bi-direction gated recurrent unit; max pooling; self-attention mechanism.
Real-time road transportation safety risk evaluation model based on data-mining
by Wenhui Luo, Xingkai Meng, Fengtian Cai, Chuna Wu
Abstract: To realise real-time road transport safety risk evaluation is the basis of transportation safety production, and also a key node for road transportation safety risk control. The shortcoming of the massive amount of manual work required and the resulting delay on the cognitive level of experts means the process cant be implemented dynamically. A real-time road transportation safety risk evaluation model based on data-mining is proposed in this paper. The model is divided into four steps. First, the pre-processing of unstructured text contains process of adding custom item dictionary, deletion of stop words, word segmentation of text at the beginning process, then dynamic risk factors identification using TF-IDF on processed text. Secondly, accident chains extraction by cue words and causal sentence structure construction. Thirdly, the relevance mining among risk factors or accident states through Apriori algorithm. Finally, real-time risk assessment is realised by classification of the product of obtained probability and severity degree result using K-means. To verify the validity of the proposed model, experiments are conducted on text dataset, the result show that the proposed risk identification model can identify risk factors of text. The Apriori algorithm of risk relevance mining based on accident chain extraction can obtain more accurate probability and using k-means can realise real-time classification of risk levels. The accuracy is 88.57%, which is an effective real-time road transportation safety risk evaluation model.
Keywords: road engineering; risk real-time evaluation model; data-mining; safety risk; Apriori; K-means.
Abnormal sound detection of washing machines based on convolution neural network in production environment
by Jingkai Ma, Nan Li, Yong Jiang, Tao Feng
Abstract: Convolutional Neural Networks (CNN) have been shown to have great advantages equally in the fields of image and audio. In the field of abnormal sound detection of household appliances in the production environment, the fundamental difficulty is to extract and recognise the features that can represent abnormal sounds effectively. However, owing to the lack of knowledge reserve and the wide variety of data volume, appropriate feature extraction is not easy in the actual production process of home appliance products. In this paper, an end-to-end CNN deep model framework is designed for washing machine type rotating machinery data analysis, which can perform adaptive mining on the features existing in the original rotating mechanical data even under the influence of different rotational speeds and considerable noise. By validating on real datasets with different characteristics, the results show that the method can realise online fast training learning and offline testing. The test time is shorter than one second, and the highest test classification accuracy is 99.3%.
Keywords: household appliances; convolutional neural networks; deep neural networks; audio feature; abnormal sound detection.
A method of spatial place representation based on visual place cell firing
by Naigong Yu, Hui Feng
Abstract: Constructing a model of visual place cells (VPCs), which produce sensitive firing to visual information, is of great significance for studying bionic positioning and bionic navigation. Based on the physiological research of place cells and the analysis of existing VPC generation models, a firing model of VPCs based on the distance perception of landmarks by the agent is proposed in the paper. Based on the firing activity of VPCs, a spatial place representation method is proposed. The method mainly includes exploring the environment and detecting landmarks, calculating the firing rate of VPCs, adding VPCs and constructing the map of VPCs. Through simulation experiments, the reliability of the positioning performance of the proposed method is verified, and the influence of various parameters in the model on the accuracy of spatial representation of the VPCs map is analysed.
Keywords: visual place cell; spatial representation; bionic positioning; bionic navigation.
Five-dimensional model research of complex product assembly driven by Digital Twin
by YuJin Zou, Renwang Li, Xiang Zhang, Jinyu Song
Abstract: This paper describes an analysis of the connotation of process optimization driven by Digital Twin (DT) and puts forward the framework design of a five-dimensional assembly system driven by DT. Based on the assembly framework, the DT technology is constructed based on key features from the exploration of physical space and virtual space. A method for optimising the assembly process is put forward through assembly hierarchy division, process preparation and information collection, and process execution process feedback.
Keywords: Digital Twin; product assembly; information model; process optimisation.
Research and analysis of psychological data based on machine learning methods
by Guangshun Chen, Wei Lv, Junwei Ma, Yanchun Liang
Abstract: The integration of psychology and computer science has become a mainstream contemporary research method on psychological data. Weibo, Chinas largest open platform for communication and information sharing between users, has many emotional contents hidden in its data. According to the current trend, the Weibo data are segmented by machine learning to obtain a psychological portrait of Weibo users. This design uses long- and short-term memory networks (LSTMs) and convolutional neural networks (CNNs) to perform sentiment classification on Weibo data. The classification results are analysed using word frequency analysis and the latent Dirichlet allocation model (LDA) to obtain portraits of Weibo users sentiment and an analysis of the results. The results are displayed in the form of word clouds. According to the clustering results of the word clouds, the main factors affecting different polar emotions can be analysed.
Keywords: recurrent neural network; short-term memory network; convolutional neural network; emotion analysis; LDA.
Point cloud registration algorithm based on 3D-NDT algorithm and ICP algorithm
by Jiangge Huang, Bo Tao, Fei Zeng
Abstract: The purpose of point cloud registration is to minimise the difference of spatial position between point clouds. In addition, the point cloud registration process needs to be performed with high efficiency and accuracy. This paper combines the high efficiency of the 3D normal distribution transformation (3D-NDT) algorithm with the high precision of the iterative nearest point (ICP) algorithm, and proposes a fusion registration algorithm. At the same time, the fusion algorithm can still keep high efficiency and high precision registration. First, the 3D-NDT algorithm is used to select appropriate parameters, so that the point cloud to be registered is closer to the target. It provides an excellent initial position for the ICP algorithm to complete coarse registration. Secondly, in order to improve the efficiency of solving transformation matrix in ICP algorithm, kd-tree is introduced for acceleration. The experimental results show that the fusion point cloud registration algorithm proposed in this paper is better than the 3D-NDT algorithm and the ICP algorithm in efficiency and accuracy. The method proposed in this paper has more obvious advantages in dealing with larger point clouds.
Keywords: 3D-NDT algorithm; ICP algorithm; point cloud registration; point cloud search.
Community-based 3-SAT formulas with a predefined solution
by Yamin Hu, Wenjian Luo, Junteng Wang
Abstract: It is crucial to generate crafted SAT formulas with predefined solutions for the testing and development of SAT solvers because many SAT formulas from real-world applications have solutions. Although some generating algorithms have been proposed to generate SAT formulas with predefined solutions, community structures of SAT formulas are not considered in these algorithms. Consequently, we propose a 3-SAT formula generating algorithm that not only guarantees the existence of a predefined solution, but also simultaneously considers community structures and clause distributions. The proposed 3-SAT formula generating algorithm controls the quality of community structures through controlling (1) the number of clauses whose variables have a common community, which we call intra-community clauses, and (2) the number of variables that belong to only one community, which we call intra-community variables. For a SAT formula, the more intra-community clauses and intra-community variables, the higher the quality of community structures. To study the combined effect of community structures and clause distributions on the hardness of SAT formulas, we measure solving runtimes of two solvers, gluHack (a leading CDCL solver) and CPSparrow (a leading SLS solver), on the generated SAT formulas under different groups of parameter settings. Through extensive experiments, we obtain some noteworthy observations on the SAT formulas generated by the proposed algorithm: (1) The community structure has little or no effect on the hardness of SAT formulas with regard to CPSparrow but a strong effect with regard to gluHack. (2) Only when the proportion of true literals in a SAT formula in terms of the predefined solution is 0.5, SAT formulas are hard-to-solve with regard to gluHack; when this proportion is below 0.5, SAT formulas are hard-to-solve with regard to CPSparrow. (3) When the ratio of the number of clauses to that of variables is around 4.25, the SAT formulas are hard-to-solve with regard to both gluHack and CPSparrow.
Keywords: SAT generator; community structure; predefined solution.
Research on the method of vision online distance measurement of laser holes in solar base cell
by Huaiguang Liu, Wanghui Xiao, Yu Cai
Abstract: As the efficiency of the current offline laser hole distance method for solar cells is low, an online distance measurement method of laser holes based on machine vision is proposed. This method combines the distribution characteristics of laser holes and uses edge fitting and local positioning methods to quickly obtain the sub-pixel centre of the holes. Firstly, the forward irradiation imaging method of the surface light source is designed to obtain the image of the solar cell. Then the straight line expression in the image coordinate system is obtained by detecting and fitting the edge of the image. Then, the sub-pixel coordinates of feature points are obtained according to the laser point extraction algorithm based on the local area, and the distance between the laser hole and edge is measured. Finally, the actual distance is output according to the conversion relation between the measured pixel and millimetre calibrated by the system. The experimental results show that this method can accurately measure the laser hole spacing of solar cells, is more efficient than the traditional method, and is more accurate than the existing sub-pixel corner detection algorithm, which verifies the feasibility and accuracy of this method.
Keywords: laser hole ranging; machine vision; image centre moment; solar cells.
Intelligent garage system based on human and vehicle identification
by Xing Wang, Gongfa Li, Ying Liu, Juntong Yun
Abstract: At present, the number of city cars has greatly increased, and fewer garages, parking difficulties and other problems seriously affect people's travel. In this paper, an intelligent garage system is designed, which uses the matching information of face recognition and licence plate recognition to store and retrieve cars, Firstly, the car owner's face is collected through the camera and face image is extracted. Meanwhile, the licence plate information is collected and image pre-processing, licence plate positioning, character segmentation and character recognition are carried out. Face and licence plate information are stored correspondingly. Secondly, according to the tracking car combined with communication, automatic car storage is realised. Face recognition is used to match licence plate or input licence plate information when extracting vehicles. Finally, the design of the garage interface, at any time feedback parking information, to achieve the software reservation, greatly improves the service efficiency.
Keywords: face recognition; licence plate recognition; intelligent garage; template matching; real-time feedback.
Research on public opinion reversal phenomenon of network mass events modelling and simulation
by Liqun Cheng, Yanan Liu
Abstract: At the present, network mass incidents occur frequently, and the network public opinion of the non-directional reversal causes great concern. Based on the JA model, this paper focuses on the evolution process of the dynamic attitude reversal of the network group, taking the important information of the outside world as a variable, exploring the influence of the important information release on the whole public opinion, and using the multi-agent Monte Carlo method for experimental simulation. This paper takes the Guizhou bus crash incident as an example to make an empirical analysis.
Keywords: group polarisation; reversal of public opinion; mass events; information interaction.
Research on power regulation service data transmission path optimisation method
by Lei Yan, Zhengshang Yan, Zhifang He, Xiuli Zhang, Xincong Shi, Ting Wang, Zijuan Zhao
Abstract: Owing to the large amount of transmission data and transmission paths between equipment and applications in the power grid control centre, it is easy to cause problems such as packet loss and incomplete data transmission. An improved Apriori algorithm is proposed to mine multi-dimensional data association rules when multiple service devices are running in parallel, then optimise the discontinuous data path transmission based on the ant colony algorithm. The ant colony migration probability is used to calculate the pheromone concentration value of the ant on the discontinuous data path in a fixed period, plan the ant travel path, use the direction factor to adjust the discontinuous data transmission path appropriately, determine the optimal path, and realise the mining optimisation. Simulation results show that the proposed method can effectively improve the convergence speed of discontinuous data transmission, reduce the delay time of the data transmission network, and ensure the integrity of data transmission.
Keywords: power dispatch data centre; cloud computing; Apriori algorithm; association rules; route optimisation.
Hardware design methodology of multilayer feedforward neural network for spectrum sensing in cognitive radio
by Swagata Roy Chatterjee, Jayanta Chowdhury, Supriya Dhabal, Mohuya Chakraborty
Abstract: This paper aims to design a simple hardware architecture of Multilayer Feedforward Neural Network (MFNN) and verify its performance in the detection of vacant/busy state of channels. A single neuron with tansigmoid activation function is proposed utilising the rule of matrix multiplication for simplification in computation. The proposed hardware module of the single neuron, utilising parallel processing, is assembled to obtain the architecture of desired MFNN. The area optimised hardware architecture of MFNN is achieved by reutilising the hardware resources. The hardware module of the single neuron is compared with the allied design methods which exhibits its outperformance in terms of mean square error and accuracy over the existing ones. The proposed optimised MFNN provides almost 62% reduction in hardware resources as compared with standard non-optimised MFNN. Further, the performance analyses of proposed hardware architectures demonstrate almost 90% accuracy in the detection of both vacant and busy states of channels.
Keywords: cognitive radio; hardware architecture; multilayer feedforward neural network; vacant band detection; spectrum sensing.
A new-one-time-pad stream cipher: new design, hardware implementation and its application in security of UAVs
by Mohamed Nabil, Ashraf A.M. Khalaf, Sara M. Hassan
Abstract: UAVs are used in military and civilian fields. Security of UAV Data Link is still unsatisfactory especially if a strong adversary model is considered. This paper describes a new design of AENOTP, tested for security in UAVs Data Link. AENOTP algorithm combines NOTP with AE technique to ensure encryption and authentication along with data integrity. AE is a technique that ensures the security of the transported data. The design and analysis of AENOTP gives a fast and enhanced stream cipher AE architecture based on the unpredictability concept. Implementation of AENOTP measures encryption and decryption of any sent and received data. AENOTP shows high speed of generating the key stream bits of the OTP stream cipher and high speed of the proposed algorithm of 75 Mbits/sec. It achieves high level of security for any transmitted data. It can easily resist different attacks such as linear and differential attacks.
Keywords: security of UAVs; authenticated encryption; OTP algorithm; NOTP; AENOTP; unpredictability.
The effect of entrepreneurial social capital on dynamic capability: the moderating role of the entrepreneurial spirit
by Junjie Wu, Su Chen, Xin Geng
Abstract: Entrepreneurs' social capital is one of the key factors for success of enterprise innovation. The resources mobilised by entrepreneurs through networks provide guarantees for enterprise innovation. However, the theory of entrepreneurs' social capital is still not mature enough and needs to be re-examined and clarified through empirical research. Based on the empirical study of 173 private enterprises, this paper explores the moderating effect of entrepreneurship on the relationship between entrepreneurs' social capital and dynamic ability. It is found that the structural dimension of positive regulation of entrepreneurship has no moderating effect on the dynamic capabilities of technology and the market. Meanwhile, entrepreneurship plays a positive moderating role in the relationship dimension, in the cognitive dimension of entrepreneurs' social capital, in the technological dynamic capabilities and in the market dynamic capabilities of enterprise. Effective resource mobilisation of entrepreneurs' social capital is essential to maintain sustainable development of organisational dynamic capacity.
Keywords: entrepreneurship; social capital; dynamic ability.
Research on the current situation and norms of public welfare crowdfunding
by Bo Peng
Abstract: Public welfare crowdfunding is a new type of 'internet+' public welfare crowdfunding. It has the characteristics of wide audience, low cost and high influence, which is conducive to solving the difficulties of the public and exerting the spirit of mutual help. The basic process of public welfare crowdfunding in China was analysed, and the status and trends of public welfare crowdfunding development were studied. At same time, specific aspects were proposed from multiple dimensions, such as the public welfare crowdfunding sponsor, crowdfunding platform, third-party regulatory agencies, and government recommendations for healthy development of public welfare crowdfunding, in order to make public welfare crowdfunding a healthy development.
Keywords: public welfare; situation; standard; independent third party supervision; improving the laws and regulations.
Coexistence between 5G wireless sensor communication system and Wi-Fi IEEE802.11n networks
by Ehab M. Shaheen
Abstract: This paper investigates the coexistence between the fifth generation (5G) wireless sensor communication systems and Wi-Fi communication networks. The 5G wireless sensor communication system is considered as the victim system and is modelled as the Poly-Phase Network (PPN) Filter Bank Multi-Carrier (FBMC) Offset Quadrature Amplitude Modulation (OQAM) communication system, whereas the Wi-Fi communication network is considered as the interference signal. It is modelled as the Orthogonal Frequency Division Multiplexing (OFDM) based 2 × 2 Multi-Input Multi-Output (MIMO) IEEE802.11n communication signal. A closed form analytical formula to the bit error rate performance of 5G PPN FBMC OQAM communication system in presence of IEEE802.11n interference signal in additive white Gaussian noise channel is evaluated. The obtained analytical results are validated by the aid of extensive simulation results. The simulation results are experimentally done using the Software Defined Radio (SDR) Keysight SystemVue software. It will be shown that the impact of the Wi-Fi interference signal has a severe effect on the performance of the 5G PPN FBMC communication system.
Keywords: 5G; Wi-Fi IEEE802.11n wireless networks; interference; PPN FBMC OQAM; SDR SystemVue software.
Research on the DouDiZhu's playing strategy based on XGBoost
by Hengyang Cao, Shuqin Li
Abstract: In recent years, imperfect information games have received extensive attention in computer game research. As a three-person imperfect information game in China, DouDiZhu not only has competitive relationship but also has cooperative relationship between the players, which makes the model more complicated, hence it has high research value. In this paper, the DouDiZhu's playing strategy is converted into 182 legal playing multi-classification problems, by extracting the characteristics of winning data of different players, using the XGBoost model and setting reasonable model parameters. In addition, to make the strategy of playing cards more reasonable through phased training, character training, and rule correction methods. In this paper, a new data representation method of DouDiZhu game is proposed. The XGBoost model is innovatively used to solve the poker game problem. By adjusting the parameters and multi model training, a better card playing strategy is obtained. The experimental results show that the playing strategy predicted by the playing strategy model proposed in this paper is basically consistent with the human playing strategy, and has a good playing strategy for different situations. The method in this paper has achieved a third-place excellent result in the Chinese University Student Computer Game Contest 2019.
Keywords: DouDiZhu; computer game; imperfect information game; XGBoost.
A modified quantum-inspired evolutionary algorithm for minimising network coding operations
by Zhijian Qu, Tiantian Li, Xiao Tan, Panjing Li, Xiaohong Liu
Abstract: Network coding operations will benefit the multicast network performances in improving both the transmission throughput and the reliability. Meanwhile, the network coding operations can also bring some additional resource consumption and transmission delay into the multicast network. Thus, minimising the network coding operations is worthy of in-depth studying. To address this resource optimisation problem, an adaptive evolution mechanism-based modified quantum-inspired evolutionary algorithm is presented in this paper. Three evaluation operators were defined and added into the algorithm to improve the global optimisation ability. In the modified quantum-inspired evolutionary algorithm, the state of each population was jointly determined by these three operators. In the algorithm evolution process, the evolution parameters of the algorithm can be determined by the state of each population. To illustrate the effectiveness of the modified algorithm, it was applied to resolve the function optimisation and the network coding recourse minimisation problems respectively. The experiment results indicated that our adaptive evolution mechanism based modified quantum-inspired evolutionary algorithm has better performances both in searching global optimal solution and convergence speed.
Keywords: multicast network; resource optimisation; network coding; evolutionary algorithm.