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Two linear subpattern dimensionality reduction algorithms

Journal of Harbin Institute of Technology(New Series) 2012年第5期19卷 47-53页

作者：贲晛烨孟维晓王泽王科俊 School of information Science and Engineering Shandong University School of electronics and information Engineering Harbin Institute of Technology School of Transportation Science and Engineering Harbin Institute of Technology college of Automation Harbin Engineering University

This paper presents two novel algorithms for feature extraction-Subpattern Complete Two Dimensional Linear Discriminant Principal Component Analysis (SpC2DLDPCA) and Subpattern Complete Two Dimensional Locality Preserving Principal Component Analysis (SpC2DLPPCA). The modified SpC2DLDPCA and SpC2DLPPCA algorithm over their non-subpattern version and Subpattern Complete Two Dimensional Principal Component Analysis (SpC2DPCA) methods benefit greatly in the following four points: (1) SpC2DLDPCA and SpC2DLPPCA can avoid the failure that the larger dimension matrix may bring about more consuming time on computing their eigenvalues and eigenvectors. (2) SpC2DLDPCA and SpC2DLPPCA can extract local information to implement recognition. (3)The idea of subblock is introduced into Two Dimensional Principal Component Analysis (2DPCA) and Two Dimensional Linear Discriminant Analysis (2DLDA). SpC2DLDPCA combines a discriminant analysis and a compression technique with low energy loss. (4) The idea is also introduced into 2DPCA and Two Dimensional Locality Preserving projections (2DLPP), so SpC2DLPPCA can preserve local neighbor graph structure and compact feature expressions. Finally, the experiments on the CASIA(B) gait database show that SpC2DLDPCA and SpC2DLPPCA have higher recognition accuracies than their non-subpattern versions and SpC2DPCA.

关键词： subpattern dimensionality reduction Subpattern Complete Two Dimensional Linear Discriminant Principal Component Analysis (SpC2DLDPCA) Subpattern Complete Two Dimensional Locality Preserving Principal Component Analysis (SpC2DLPPCA) gait recognition

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An Intelligent Approach for Intrusion Detection in Industrial Control System

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Computers, Materials & Continua 2023年第11期77卷 2049-2078页

作者： Adel Alkhalil Abdulaziz Aljaloud Diaa Uliyan Mohammed Altameemi Magdy Abdelrhman Yaser Altameemi Aakash Ahmad Romany Fouad Mansour Department of information and Computer Science College of Computer Science and EngineeringUniversity of Ha’ilHa’il81481Saudi Arabia Applied college University of Ha’ilHa’il81481Saudi Arabia college of Education New Valley UniversityEl-Kharga72511Egypt college of Art University of Ha’ilHa’il81481Saudi Arabia School of Computing and Communications Lancaster UniversityLeipzig04109Germany college of Science New Valley UniversityEl-Kharga72511Egypt

Supervisory control and data acquisition(SCADA)systems are computer systems that gather and analyze real-time data,distributed control systems are specially designed automated control system that consists of geographically distributed control elements,and other smaller control systems such as programmable logic controllers are industrial solid-state computers that monitor inputs and outputs and make logic-based decisions.In recent years,there has been a lot of focus on the security of industrial control systems.Due to the advancement in information technologies,the risk of cyberattacks on industrial control system has been drastically increased.Because they are so inextricably tied to human life,any damage to them might have devastating consequences.To provide an efficient solution to such problems,this paper proposes a new approach to intrusion detection.First,the important features in the dataset are determined by the difference between the distribution of unlabeled and positive data which is deployed for the learning process.Then,a prior estimation of the class is proposed based on a support vector machine.Simulation results show that the proposed approach has better anomaly detection performance than existing algorithms.

关键词： Industrial control system anomaly detection intrusion detection system protection

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A Blockchain-Based Framework for Secure Storage and Sharing of Resumes

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Computers, Materials & Continua 2022年第9期72卷 5395-5413页

作者： Huanrong Tang Changlin Hu Tianming Liu Jianquan Ouyang Key Laboratory of Intelligent Computing and information Processing Ministry of EducationComputer Science College of Xiangtan UniversityXiangtanHunan ProvinceChina Department of Computer Science The University of GeorgiaAthensGeorgiaUSA

In response to problems in the centralized storage of personal resumes on third-party recruitment platforms,such as inadequate privacy protection,inability of individuals to accurately authorize downloads,and inability to determine who downloaded the resume and when,this study proposes a blockchain-based framework for secure storage and sharing of resumes.Users can employ an authorized access mechanism to protect their privacy rights.The proposed framework uses smart contracts,interplanetary file system,symmetric encryption,and digital signatures to protect,verify,and share resumes.Encryption keys are split and stored in multiple depositories through secret-sharing technology to improve the security of these keys.Corresponding key escrow incentives are implemented using smart contracts to automatically verify the correctness of keys and encourage the active participation of honest key escrow parties.This framework combines blockchain and searchable symmetric encryption technology to realize multikeyword search using inverted indexing and Bloom filters and verify search results on the chain.Escrow search service fees are charged through contracts.Only after the search results are verified can the search service provider obtain the search fee,thus ensuring fair and efficient search for encrypted resumes.The framework is decentralized,secure,and tamper-evident,and achieves controlled sharing while protecting personal privacy and information security.

关键词： Resume blockchain interplanetary file system secret sharing searchable encryption

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Criss-Cross Attentional Siamese Networks for Object Tracking

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Computers, Materials & Continua 2022年第11期73卷 2931-2946页

作者： Zhangdong Wang Jiaohua Qin Xuyu Xiang Yun Tan Neal N.Xiong college of Computer Science and information Technology Central South University of Forestry&TechnologyChangsha410004China Department of Mathematics and Computer Science Northeastern State UniversityTahlequah74464OKUSA

Visual object tracking is a hot topic in recent years.In the meanwhile,Siamese networks have attracted extensive attention in this field because of its balanced precision and speed.However,most of the Siamese network methods can only distinguish foreground from the non-semantic background.The fine-tuning and retraining of fully-convolutional Siamese networks for object tracking(SiamFC)can achieve higher precision under interferences,but the tracking accuracy is still not ideal,especially in the environment with more target interferences,dim light,and shadows.In this paper,we propose crisscross attentional Siamese networks for object tracking(SiamCC).To solve the imbalance between foreground and non-semantic background,we use the feature enhancement module of criss-cross attention to greatly improve the accuracy of video object tracking in dim light and shadow environments.Experimental results show that the maximum running speed of SiamCC in the object tracking benchmark dataset is 90 frames/second.In terms of detection accuracy,the accuracy of shadow sequences is greatly improved,especially the accuracy score of sequence HUMAN8 is improved from 0.09 to 0.89 compared with the original SiamFC,and the success rate score is improved from 0.07 to 0.55.

关键词： Criss-cross attention object-tracking siamese-network

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Enhanced performance triboelectric nanogenerator based on porous structure C/MnO_(2)nanocomposite for energy harvesting

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Nano Research 2022年第8期15卷 7163-7171页

作者： Honghao Zhang Ping Zhang Pengfei Li Lu Deng Weikang Zhang Baocheng Liu Zhengchun Yang School of Electrical and information Engineering Tianjin UniversityTianjin 300072China School of Integrated Circuit Science and Engineering Adv.Mater.and Printed Electronics CenterTianjin Key Laboratory of Film Electronic&Communication DevicesTianjin University of TechnologyTianjin 300384China

Triboelectric nanogenerator(TENG)can realize a variety of mechanical energy collections in the environment,which has great potential in the field of wearable energy.However,many TENGs could rarely be satisfactory to wearable electronics promotion because of their expensive raw materials and complex manufacturing processes.In this study,a type of porous structure carbon powder/manganese dioxide(C/MnO_(2))nanocomposite is introduced.The material adopts low-cost,high-yield carbon powder,can be prepared in one step through a simple,economical,and environmentally friendly hydrothermal preparation process,and has high economic practicality.Superior power generation performance was obtained by modulating the charge trapping ability and storage capacity of polydimethylsiloxane@C/MnO_(2)(PDMS@C/MnO_(2))film based on variations in the weight-loading of C/MnO_(2).The maximum output voltage of carbon powder/manganese dioxide TENG(CM-TENG)is 63 V,which is 2.1 times that of PDMS-TENG and 1.86 times that of carbon powder TENG(C-TENG)and can easily light up 53 LEDs.Furthermore,CM-TENG can convert biological motion energy into electrical signals to detect human hand movements.The CM-TENG self-powered system can successfully drive various microelectronic devices,such as electronic watches,liquid crystal displays(LCDs),and calculators.This study provides a reliable,low-cost,high-performance,and widely applicable electronic system that shows great potential in future fields such as wearable devices and micro-sensing systems.

关键词： porous structure carbon powder/manganese dioxide(C/MnO_(2)) nanocomposite triboelectric nanogenerator widely applicable microelectronic devices

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Intelligent Slime Mould Optimization with Deep Learning Enabled Traffic Prediction in Smart Cities

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Computers, Materials & Continua 2022年第12期73卷 6563-6577页

作者： Manar Ahmed Hamza Hadeel Alsolai Jaber S.Alzahrani Mohammad Alamgeer Mohamed Mahmoud Sayed Abu Sarwar Zamani Ishfaq Yaseen Abdelwahed Motwakel Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of information Systems College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Computer Science and Bioinformatics Singhania UniversityPacheri BariDistrict JhnujhunuRajasthanIndia Department of Architectural Engineering Faculty of Engineering and TechnologyFuture University in EgyptNew Cairo11835Egypt

Intelligent Transportation System(ITS)is one of the revolutionary technologies in smart cities that helps in reducing traffic congestion and enhancing traffic quality.With the help of big data and communication technologies,ITS offers real-time investigation and highly-effective traffic management.Traffic Flow Prediction(TFP)is a vital element in smart city management and is used to forecast the upcoming traffic conditions on transportation network based on past data.Neural Network(NN)and Machine Learning(ML)models are widely utilized in resolving real-time issues since these methods are capable of dealing with adaptive data over a period of time.Deep Learning(DL)is a kind of ML technique which yields effective performance on data classification and prediction tasks.With this motivation,the current study introduces a novel Slime Mould Optimization(SMO)model with Bidirectional Gated Recurrent Unit(BiGRU)model for Traffic Prediction(SMOBGRU-TP)in smart cities.Initially,data preprocessing is performed to normalize the input data in the range of[0,1]using minmax normalization approach.Besides,BiGRUmodel is employed for effective forecasting of traffic in smart cities.Moreover,the novelty of the work lies in using SMO algorithm to effectively adjust the hyperparameters of BiGRU method.The proposed SMOBGRU-TP model was experimentally validated and the simulation results established the model’s superior performance in terms of prediction compared to existing techniques.

关键词： Smart cities traffic flow prediction slime mould optimization algorithm deep learning intelligent models

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Foreground Segmentation Network with Enhanced Attention

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Journal of Shanghai Jiaotong university(Science) 2023年第3期28卷 360-369页

作者：姜锐朱瑞祥蔡萧萃苏虎 college of information Engineering Shanghai Maritime UniversityShanghai 201306China Research Center of Precision Sensing and Control Institute of AutomationChinese Academy of SciencesBeijing 100190China

Moving object segmentation (MOS) is one of the essential functions of the vision system of all robots,including medical robots. Deep learning-based MOS methods, especially deep end-to-end MOS methods, are actively investigated in this ﬁeld. Foreground segmentation networks (FgSegNets) are representative deep end-to-endMOS methods proposed recently. This study explores a new mechanism to improve the spatial feature learningcapability of FgSegNets with relatively few brought parameters. Speciﬁcally, we propose an enhanced attention(EA) module, a parallel connection of an attention module and a lightweight enhancement module, with sequentialattention and residual attention as special cases. We also propose integrating EA with FgSegNet_v2 by taking thelightweight convolutional block attention module as the attention module and plugging EA module after the twoMaxpooling layers of the encoder. The derived new model is named FgSegNet_v2 EA. The ablation study veriﬁesthe effectiveness of the proposed EA module and integration strategy. The results on the CDnet2014 dataset,which depicts human activities and vehicles captured in different scenes, show that FgSegNet_v2 EA outperformsFgSegNet_v2 by 0.08% and 14.5% under the settings of scene dependent evaluation and scene independent evaluation, respectively, which indicates the positive effect of EA on improving spatial feature learning capability ofFgSegNet_v2.

关键词： human-computer interaction moving object segmentation foreground segmentation network enhanced attention convolutional block attention module

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Performance Analysis of Multi-Channel CR Enabled IoT Network with Better Energy Harvesting

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Computers, Materials & Continua 2022年第4期71卷 183-197页

作者： Afiya Kiran Ahmad Karim Yasser Obaid Alharbi Diaa Mohammed Uliyan Department of Computer Science BZ UniversityMultan6000Pakistan Department of information Technology BZ UniversityMultanPakistan college of Computer Science and Engineering University of Ha’ilHailSaudi Arabia

Wireless Sensor Networks(WSNs)can be termed as an autoconfigured and infrastructure-less wireless networks to monitor physical or environmental conditions,such as temperature,sound,vibration,pressure and motion etc.WSNs may comprise thousands of Internet of Things(IoT)devices to sense and collect data from its surrounding,process the data and take an automated and mechanized decision.On the other side the proliferation of these devices will soon cause radio spectrum shortage.So,to facilitate these networks,we integrate Cognitive Radio(CR)functionality in these networks.CR can sense the unutilized spectrum of licensed users and then use these empty bands when required.In order to keep the IoT nodes functional all time,continuous energy is required.For this reason the energy harvested techniques are preferred in IoT networks.Mainly it is preferred to harvest Radio Frequency(RF)energy in the network.In this paper a region based multi-channel architecture is proposed.In which the coverage area of primary node is divided as Energy Harvesting Region and Communication Region.The Secondary User(SU)that are the licensed user is IoT enabled with Cognitive Radio(CR)techniques so we call it CR-enabled IoT node/device and is encouraged to harvest energy by utilizing radio frequency energy.To harvest energy efficiently and to reduce the energy consumption during sensing,the concept of overlapping region is given that supports to sense multiple channels simultaneously and help the SU to find best channel for transmitting data or to harvest energy from the ideal channel.From the experimental analysis,it is proved that SU can harvest more energy in overlapping region and this architecture proves to consume less energy during data transmission as compared to single channel.We also show that channel load can be highly reduced and channel utilization is proved to be more proficient.Thus,this proves the proposed architecture cost-effective and energy-efficient.

关键词： Wireless sensor network multi-channel sensing energy harvesting cognitive radio IoT network

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A Robust Approach for Detection and Classification of KOA Based on BILSTM Network

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Computer Systems Science & Engineering 2023年第11期47卷 1365-1384页

作者： Abdul Qadir Rabbia Mahum Suliman Aladhadh Computer Science Department UETTaxilaPakistan Department of information Technology College of ComputerQassim UniversityBuraydahSaudi Arabia

A considerable portion of the population now experiences osteoarthritis of the knee,spine,and hip due to lifestyle changes.Therefore,early treatment,recognition and prevention are essential to reduce damage;nevertheless,this time-consuming activity necessitates a variety of tests and in-depth analysis by physicians.To overcome the existing challenges in the early detection of Knee Osteoarthritis(KOA),an effective automated technique,prompt recognition,and correct categorization are required.This work suggests a method based on an improved deep learning algorithm that makes use of data from the knee images after segmentation to detect KOA and its severity using the Kellgren-Lawrence(KL) classification schemes,such as Class-I,Class-II,Class-III,and Class-IV.Utilizing ResNet to segregate knee pictures,we first collected features from these images before using the Bidirectional Long Short-Term Memory(BiLSTM)architecture to classify them.Given that the technique is a pre-trained network and doesn’t require a large training set,the Mendeley VI dataset has been utilized for the training of the proposed model.To evaluate the effectiveness of the suggested model,cross-validation has also been employed using the Osteoarthritis Initiative(OAI)dataset.Furthermore,our suggested technique is more resilient,which overcomes the challenge of imbalanced training data due to the hybrid architecture of our proposed model.The suggested algorithm is a cuttingedge and successful method for documenting the successful application of the timely identification and severity categorization of KOA.The algorithm showed a cross-validation accuracy of 78.57%and a testing accuracy of 84.09%.Numerous tests have been conducted to show that our suggested algorithm is more reliable and capable than the state-of-the-art at identifying and categorizing KOA disease.

关键词： KOA image classification knee osteoarthritis deep learning neural networks human computer interaction(HCI) medical imaging

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Theoretical investigations on a variable-coefficient generalized forced–perturbed Korteweg–de Vries–Burgers model for a dilated artery,blood vessel or circulatory system with experimental support

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Communications in Theoretical Physics 2023年第11期75卷 49-55页

作者： Xin-Yi Gao Yong-Jiang Guo Wen-Rui Shan State Key Laboratory of information Photonics and Optical Communications and School of ScienceBeijing University of Posts and TelecommunicationsBeijing 100876China college of Science North China University of TechnologyBeijing 100144China

Recent theoretical physics efforts have been focused on the probes for nonlinear pulse waves in,for example,variable-radius arteries.With respect to the nonlinear waves in an artery full of blood with certain aneurysm,pulses in a blood vessel,or features in a circulatory system,this paper symbolically computes out an auto-B?cklund transformation via a noncharacteristic movable singular manifold,certain families of the solitonic solutions,as well as a family of the similarity reductions for a variable-coefficient generalized forced–perturbed Korteweg–de Vries–Burgers equation.Aiming,e.g.,at the dynamical radial displacement superimposed on the original static deformation from an arterial wall,our results rely on the axial stretch of the injured artery,blood as an incompressible Newtonian fluid,radius variation along the axial direction or aneurysmal geometry,viscosity of the fluid,thickness of the artery,mass density of the membrane material,mass density of the fluid,strain energy density of the artery,shear modulus,stretch ratio,etc.We also highlight that the shock-wave structures from our solutions agree well with those dusty-plasma-experimentally reported.

关键词： dynamics in blood-filled artery or blood vessel variable-coefficient generalized forced-perturbed Korteweg-de Vries-Burgers equation solitons with experimental support Bäcklund transformation and similarity reductions singular manifold

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