Vol.62, No.3, 2020-Table of Contents
  • Free Vibration Analysis of FG-CNTRC Cylindrical Pressure Vessels Resting on Pasternak Foundation with Various Boundary Conditions
  • Abstract This study focuses on vibration analysis of cylindrical pressure vessels constructed by functionally graded carbon nanotube reinforced composites (FG-CNTRC). The vessel is under internal pressure and surrounded by a Pasternak foundation. This investigation was founded based on two-dimensional elastic analysis and used Hamilton’s principle to drive the governing equations. The deformations and effectivemechanical properties of the reinforced structure were elicited from the first-order shear theory (FSDT) and rule of mixture, respectively. The main goal of this study is to show the effects of various design parameters such as boundary conditions, reinforcement distribution, foundation parameters, and aspect ratio on the free… More
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  • Hybrid Clustering Algorithms with GRASP to Construct an Initial Solution for the MVPPDP
  • Abstract Mobile commerce (m-commerce) contributes to increasing the popularity of electronic commerce (e-commerce), allowing anybody to sell or buy goods using a mobile device or tablet anywhere and at any time. As demand for e-commerce increases tremendously, the pressure on delivery companies increases to organise their transportation plans to achieve profits and customer satisfaction. One important planning problem in this domain is the multi-vehicle profitable pickup and delivery problem (MVPPDP), where a selected set of pickup and delivery customers need to be served within certain allowed trip time. In this paper, we proposed hybrid clustering algorithms with the greedy randomised adaptive… More
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  • A Review of Data Cleaning Methods for Web Information System
  • Abstract Web information system (WIS) is frequently-used and indispensable in daily social life. WIS provides information services in many scenarios, such as electronic commerce, communities, and edutainment. Data cleaning plays an essential role in various WIS scenarios to improve the quality of data service. In this paper, we present a review of the state-of-the-art methods for data cleaning in WIS. According to the characteristics of data cleaning, we extract the critical elements of WIS, such as interactive objects, application scenarios, and core technology, to classify the existing works. Then, after elaborating and analyzing each category, we summarize the descriptions and challenges… More
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  • Ameliorate Security by Introducing Security Server in Software Defined Network
  • Abstract Software Defined Network (SDN) deals with huge data processing units which possess network management. However, due to centralization behavior ensuring security in SDN is the major concern. In this work to ensure security, a security server has been at its aid to check the vulnerability of the networks and to keep an eye on the packet according to the screening policies. A Secure Shell Connection (SSH) is established by the security server which does a frequent inspection of the network’s logs. Malware detection and the Intrusion Detection System policies are also incorporated in the server for the effective scanning of… More
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  • High Speed Network Intrusion Detection System (NIDS) Using Low Power Precomputation Based Content Addressable Memory
  • Abstract NIDS (Network Intrusion Detection Systems) plays a vital role in security threats to computers and networks. With the onset of gigabit networks, hardware-based Intrusion Detection System gains popularity because of its high performance when compared to the software-based NIDS. The software-based system limits parallel execution, which in turn confines the performance of a modern network. This paper presents a signature-based lookup technique using reconfigurable hardware. Content Addressable Memory (CAM) is used as a lookup table architecture to improve the speed instead of search algorithms. To minimize the power and to increase the speed, precomputation based CAM (PBCAM) can be used,… More
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  • Stabilization for Equal-Order Polygonal Finite Element Method for High Fluid Velocity and Pressure Gradient
  • Abstract This paper presents an adapted stabilisation method for the equal-order mixed scheme of finite elements on convex polygonal meshes to analyse the high velocity and pressure gradient of incompressible fluid flows that are governed by Stokes equations system. This technique is constructed by a local pressure projection which is extremely simple, yet effective, to eliminate the poor or even non-convergence as well as the instability of equal-order mixed polygonal technique. In this research, some numerical examples of incompressible Stokes fluid flow that is coded and programmed by MATLAB will be presented to examine the effectiveness of the proposed stabilised method. More
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  • Stochastic Numerical Analysis for Impact of Heavy Alcohol Consumption on Transmission Dynamics of Gonorrhoea Epidemic
  • Abstract This paper aims to perform a comparison of deterministic and stochastic models. The stochastic modelling is a more realistic way to study the dynamics of gonorrhoea infection as compared to its corresponding deterministic model. Also, the deterministic solution is itself mean of the stochastic solution of the model. For numerical analysis, first, we developed some explicit stochastic methods, but unfortunately, they do not remain consistent in certain situations. Then we proposed an implicitly driven explicit method for stochastic heavy alcohol epidemic model. The proposed method is independent of the choice of parameters and behaves well in all scenarios. So, some… More
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  • Access Control Policy Based on Friend Circle
  • Abstract Nowadays, the scale of the user’s personal social network (personal network, a network of the user and their friends, where the user we call “center user”) is becoming larger and more complex. It is difficult to find a suitable way to manage them automatically. In order to solve this problem, we propose an access control model for social network to protect the privacy of the central users, which achieves the access control accurately and automatically. Based on the hybrid friend circle detection algorithm, we consider the aspects of direct judgment, indirect trust judgment and malicious users, a set of multi-angle… More
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  • A Performance Fault Diagnosis Method for SaaS Software Based on GBDT Algorithm
  • Abstract SaaS software that provides services through cloud platform has been more widely used nowadays. However, when SaaS software is running, it will suffer from performance fault due to factors such as the software structural design or complex environments. It is a major challenge that how to diagnose software quickly and accurately when the performance fault occurs. For this challenge, we propose a novel performance fault diagnosis method for SaaS software based on GBDT (Gradient Boosting Decision Tree) algorithm. In particular, we leverage the monitoring mean to obtain the performance log and warning log when the SaaS software system runs, and… More
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  • Classification and Research of Skin Lesions Based on Machine Learning
  • Abstract Classification of skin lesions is a complex identification challenge. Due to the wide variety of skin lesions, doctors need to spend a lot of time and effort to judge the lesion image which zoomed through the dermatoscopy. The diagnosis which the algorithm of identifying pathological images assists doctors gets more and more attention. With the development of deep learning, the field of image recognition has made longterm progress. The effect of recognizing images through convolutional neural network models is better than traditional image recognition technology. In this work, we try to classify seven kinds of lesion images by various models… More
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  • Automatic Detection of Aortic Dissection Based on Morphology and Deep Learning
  • Abstract Aortic dissection (AD) is a kind of acute and rapidly progressing cardiovascular disease. In this work, we build a CTA image library with 88 CT cases, 43 cases of aortic dissection and 45 cases of health. An aortic dissection detection method based on CTA images is proposed. ROI is extracted based on binarization and morphology opening operation. The deep learning networks (InceptionV3, ResNet50, and DenseNet) are applied after the preprocessing of the datasets. Recall, F1-score, Matthews correlation coefficient (MCC) and other performance indexes are investigated. It is shown that the deep learning methods have much better performance than the traditional… More
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  • Prison Term Prediction on Criminal Case Description with Deep Learning
  • Abstract The task of prison term prediction is to predict the term of penalty based on textual fact description for a certain type of criminal case. Recent advances in deep learning frameworks inspire us to propose a two-step method to address this problem. To obtain a better understanding and more specific representation of the legal texts, we summarize a judgment model according to relevant law articles and then apply it in the extraction of case feature from judgment documents. By formalizing prison term prediction as a regression problem, we adopt the linear regression model and the neural network model to train… More
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  • Empirical Comparisons of Deep Learning Networks on Liver Segmentation
  • Abstract Accurate segmentation of CT images of liver tumors is an important adjunct for the liver diagnosis and treatment of liver diseases. In recent years, due to the great improvement of hard device, many deep learning based methods have been proposed for automatic liver segmentation. Among them, there are the plain neural network headed by FCN and the residual neural network headed by Resnet, both of which have many variations. They have achieved certain achievements in medical image segmentation. In this paper, we firstly select five representative structures, i.e., FCN, U-Net, Segnet, Resnet and Densenet, to investigate their performance on liver… More
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  • Research on the Application of Super Resolution Reconstruction Algorithm for Underwater Image
  • Abstract Underwater imaging is widely used in ocean, river and lake exploration, but it is affected by properties of water and the optics. In order to solve the lower-resolution underwater image formed by the influence of water and light, the image super-resolution reconstruction technique is applied to the underwater image processing. This paper addresses the problem of generating super-resolution underwater images by convolutional neural network framework technology. We research the degradation model of underwater images, and analyze the lower-resolution factors of underwater images in different situations, and compare different traditional super-resolution image reconstruction algorithms. We further show that the algorithm of… More
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  • An Efficient Steganalysis Model Based on Multi-Scale LTP and Derivative Filters
  • Abstract Local binary pattern (LBP) is one of the most advanced image classification recognition operators and is commonly used in texture detection area. Research indicates that LBP also has a good application prospect in steganalysis. However, the existing LBP-based steganalysis algorithms are only capable to detect the least significant bit (LSB) and the least significant bit matching (LSBM) algorithms. To solve this problem, this paper proposes a steganalysis model called msdeLTP, which is based on multi-scale local ternary patterns (LTP) and derivative filters. The main characteristics of the msdeLTP are as follows: First, to reduce the interference of image content on… More
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  • A Novel Bidirectional LSTM and Attention Mechanism Based Neural Network for Answer Selection in Community Question Answering
  • Abstract Deep learning models have been shown to have great advantages in answer selection tasks. The existing models, which employ encoder-decoder recurrent neural network (RNN), have been demonstrated to be effective. However, the traditional RNN-based models still suffer from limitations such as 1) high-dimensional data representation in natural language processing and 2) biased attentive weights for subsequent words in traditional time series models. In this study, a new answer selection model is proposed based on the Bidirectional Long Short-Term Memory (Bi-LSTM) and attention mechanism. The proposed model is able to generate the more effective question-answer pair representation. Experiments on a question… More
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  • Phase Field Modelling Allotropic Transformation of Solid Solution
  • Abstract Based on multiphase field conception and integrated with the idea of vectorvalued phase field, a phase field model for typical allotropic transformation of solid solution is proposed. The model takes the non-uniform distribution of grain boundaries of parent phase and crystal orientation into account in proper way, as being illustrated by the simulation of austenite to ferrite transformation in low carbon steel. It is found that the misorientation dependent grain boundary mobility shows strong influence on the formation of ferrite morphology comparing with the weak effect exerted by misorientation dependent grain boundary energy. The evolution of various types of grain… More
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  • Wind-Induced Vibration Control for Substation Frame on Viscous Damper
  • Abstract In order to study the wind-induced vibration control effect of the viscous damper on the large-scale substation frame, this paper takes the large-scale 1000 kV substation frame of western Inner Mongolia as an example. The time-history sample of pulsating wind load is simulated by harmonic superposition method based on Matlab software. 6 kinds of viscous damper arrangement schemes have been designed, and SAP2000 finite element software is used for fine modeling and input wind speed time history load for nonlinear time history analysis. The displacement and acceleration of a typical node are the indicators of wind vibration control. The wind-induced… More
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  • DDoS Attack Detection via Multi-Scale Convolutional Neural Network
  • Abstract Distributed Denial-of-Service (DDoS) has caused great damage to the network in the big data environment. Existing methods are characterized by low computational efficiency, high false alarm rate and high false alarm rate. In this paper, we propose a DDoS attack detection method based on network flow grayscale matrix feature via multiscale convolutional neural network (CNN). According to the different characteristics of the attack flow and the normal flow in the IP protocol, the seven-tuple is defined to describe the network flow characteristics and converted into a grayscale feature by binary. Based on the network flow grayscale matrix feature (GMF), the… More
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  • Consensus of Multi-Agent Systems with Input Constraints Based on Distributed Predictive Control Scheme
  • Abstract Consensus control of multi-agent systems has attracted compelling attentions from various scientific communities for its promising applications. This paper presents a discrete-time consensus protocol for a class of multi-agent systems with switching topologies and input constraints based on distributed predictive control scheme. The consensus protocol is not only distributed but also depends on the errors of states between agent and its neighbors. We focus mainly on dealing with the input constraints and a distributed model predictive control scheme is developed to achieve stable consensus under the condition that both velocity and acceleration constraints are included simultaneously. The acceleration constraint is… More
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  • Service Scheduling Based on Edge Computing for Power Distribution IoT
  • Abstract With the growing amounts of multi-micro grids, electric vehicles, smart home, smart cities connected to the Power Distribution Internet of Things (PD-IoT) system, greater computing resource and communication bandwidth are required for power distribution. It probably leads to extreme service delay and data congestion when a large number of data and business occur in emergence. This paper presents a service scheduling method based on edge computing to balance the business load of PD-IoT. The architecture, components and functional requirements of the PD-IoT with edge computing platform are proposed. Then, the structure of the service scheduling system is presented. Further, a… More
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  • Defend Against Adversarial Samples by Using Perceptual Hash
  • Abstract Image classifiers that based on Deep Neural Networks (DNNs) have been proved to be easily fooled by well-designed perturbations. Previous defense methods have the limitations of requiring expensive computation or reducing the accuracy of the image classifiers. In this paper, we propose a novel defense method which based on perceptual hash. Our main goal is to destroy the process of perturbations generation by comparing the similarities of images thus achieve the purpose of defense. To verify our idea, we defended against two main attack methods (a white-box attack and a black-box attack) in different DNN-based image classifiers and show that,… More
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  • Secrecy Outage Probability Analysis Based on Cognitive Decodeand-Forward Relaying
  • Abstract Wireless communications have to face to several different security issues in practice due to the nature of broadcast. The information theory is well known to provide efficient approaches to address security issues in wireless communications, which attracts much attention in both industry and academia in recent years. In this paper, inspired by information theory, we study the outage probability of the opportunistic relay selection based on cognitive decode-and-forward relaying with the secrecy consideration. Specifically, the closed-form expression of the outage probability is proposed. Moreover, the asymptotic performance evaluation on the basis of the analytical results is investigated. The simulation results… More
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  • Evaluating the Topology Coverage of BGP Monitors
  • Abstract BGP monitors are currently the main data resource of AS-level topology measurement, and the integrity of measurement result is limited to the location of such BGP monitors. However, there is currently no work to conduct a comprehensive study of the range of measurement results for a single BGP monitor. In this paper, we take the first step to describe the observed topology of each BGP monitor. To that end, we first investigate the construction and theoretical up-limit of the measured topology of a BGP monitor based on the valley-free model, then we evaluate the individual parts of the measured topology… More
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  • An Extended Approach for Generating Unitary Matrices for Quantum Circuits
  • Abstract In this paper, we do research on generating unitary matrices for quantum circuits automatically. We consider that quantum circuits are divided into six types, and the unitary operator expressions for each type are offered. Based on this, we propose an algorithm for computing the circuit unitary matrices in detail. Then, for quantum logic circuits composed of quantum logic gates, a faster method to compute unitary matrices of quantum circuits with truth table is introduced as a supplement. Finally, we apply the proposed algorithm to different reversible benchmark circuits based on NCT library (including NOT gate, Controlled-NOT gate, Toffoli gate) and… More
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  • A Novel DDoS Attack Detection Method Using Optimized Generalized Multiple Kernel Learning
  • Abstract Distributed Denial of Service (DDoS) attack has become one of the most destructive network attacks which can pose a mortal threat to Internet security. Existing detection methods cannot effectively detect early attacks. In this paper, we propose a detection method of DDoS attacks based on generalized multiple kernel learning (GMKL) combining with the constructed parameter R. The super-fusion feature value (SFV) and comprehensive degree of feature (CDF) are defined to describe the characteristic of attack flow and normal flow. A method for calculating R based on SFV and CDF is proposed to select the combination of kernel function and regularization… More
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  • CNN Approaches for Classification of Indian Leaf Species Using Smartphones
  • Abstract Leaf species identification leads to multitude of societal applications. There is enormous research in the lines of plant identification using pattern recognition. With the help of robust algorithms for leaf identification, rural medicine has the potential to reappear as like the previous decades. This paper discusses CNN based approaches for Indian leaf species identification from white background using smartphones. Variations of CNN models over the features like traditional shape, texture, color and venation apart from the other miniature features of uniformity of edge patterns, leaf tip, margin and other statistical features are explored for efficient leaf classification. More
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  • Investigating the Use of Email Application in Illiterate and SemiIlliterate Population
  • Abstract The use of electronic communication has been significantly increased over the last few decades. Email is one of the most well-known means of electronic communication. Traditional email applications are widely used by a large population; however, illiterate and semi-illiterate people face challenges in using them. A major population of Pakistan is illiterate that has little or no practice of computer usage. In this paper, we investigate the challenges of using email applications by illiterate and semiilliterate people. In addition, we also propose a solution by developing an application tailored to the needs of illiterate/semi-illiterate people. Research shows that illiterate people… More
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