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Explainable-AI-based two-stage solution for WSN object localization using zero-touch mobile transceivers

Science China(Information Sciences) 2024年第7期67卷 27-45页

作者： Kai FANG Junxin CHEN Han ZHU Thippa Reddy GADEKALLU Xiaoping WU Wei WANG School of Mathematics and Computer Science Zhejiang A&F University School of Software Dalian University of Technology Faculty of Applied Sciences Macao Polytechnic University Department of electrical and Computer engineering Lebanese American University School of Information engineering Huzhou University Guangdong-Hong Kong-Macao Joint Laboratory for Emotional Intelligence and Pervasive computing Artificial Intelligence Research Institute Shenzhen MSU-BIT University

Artificial intelligence technology is widely used in the field of wireless sensor networks(WSN).Due to its inexplicability, the interference factors in the process of WSN object localization cannot be effectively eliminated. In this paper, an explainable-AI-based two-stage solution is proposed for WSN object localization. In this solution, mobile transceivers are used to enlarge the positioning range and eliminate the blind area for object localization. The motion parameters of transceivers are considered to be unavailable,and the localization problem is highly nonlinear with respect to the unknown parameters. To address this,an explainable AI model is proposed to solve the localization problem. Since the relationship among the variables is difficult to fully include in the first-stage traditional model, we develop a two-stage explainable AI solution for this localization problem. The two-stage solution is actually a comprehensive consideration of the relationship between variables. The solution can continue to use the constraints unused in the firststage during the second-stage, thereby improving the performance of the solution. Therefore, the two-stage solution has stronger robustness compared to the closed-form solution. Experimental results show that the performance of both the two-stage solution and the traditional solution will be affected by numerical changes in unknown parameters. However, the two-stage solution performs better than the traditional solution, especially with a small number of mobile transceivers and sensors or in the presence of high noise. Furthermore,we have also verified the feasibility of the proposed explainable-AI-based two-stage solution.

关键词： explainable AI object localization semidefinite relaxation mobile transceiver two-stage solution closed-form solution

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Advancements and applications of position-sensitive detector(PSD): a review

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Optoelectronics Letters 2024年第6期20卷 330-338页

作者： Shaher Dwik G.Sasikala S.Natarajan Department of Electronics&Communication engineering School of Electrical&CommunicationVel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyChennai 600062India Department of Computer Science and engineering Amrita School of ComputingAmrita Vishwa VidyapeethamChennai 601103India1

This paper presents a review of the position-sensitive detector(PSD) sensor, covering different types of PSD and recent works related to this field. Furthermore, it explains the theoretical concepts and provides information about its structure and principles of operation. Moreover, it includes the main information about the available commercial PSDs from different companies, along with a comparison between the common modules. The PSD features include high position resolution, fast response, and a wide dynamic range. These features make it suitable for various fields and applications, such as imaging, spectrometry, spectroscopy and others.

关键词： Advancements and applications of position-sensitive detector a review PSD

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Traffic Control Based on Integrated Kalman Filtering and Adaptive Quantized Q-Learning Framework for Internet of Vehicles

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Computer Modeling in engineering & Sciences 2024年第3期138卷 2103-2127页

作者： Othman S.Al-Heety Zahriladha Zakaria Ahmed Abu-Khadrah Mahamod Ismail Sarmad Nozad Mahmood Mohammed Mudhafar Shakir Hussein Alsariera Faculty of Electronic and Computer engineering Universiti Teknikal Malaysia Melaka(UTeM)Durian TunggalMalaysia College of computing&Informatics Saudi Electronic UniversityRiyadhSaudi Arabia Department of electrical Electronic and System EngineeringUniversiti Kebangsaan MalaysiaUKM BangiSelangorMalaysia Computer Technology engineering College of Engineering TechnologyAl-Kitab UniversityKirkukIraq Faculty of engineering and Electronic engineering Universiti Tun Hussein Onn Malaysia(UTHM)Batu PahatMalaysia 6Research Center DepartmentThe University of MashreqBaghdadIraq

Intelligent traffic control requires accurate estimation of the road states and incorporation of adaptive or dynamically adjusted intelligent algorithms for making the *** this article,these issues are handled by proposing a novel framework for traffic control using vehicular communications and Internet of Things *** framework integrates Kalman filtering and *** smoothing Kalman filtering,our data fusion Kalman filter incorporates a process-aware model which makes it superior in terms of the prediction *** traditional Q-learning,our Q-learning algorithm enables adaptive state quantization by changing the threshold of separating low traffic from high traffic on the road according to the maximum number of vehicles in the junction *** evaluation,the model has been simulated on a single intersection consisting of four roads:east,west,north,and south.A comparison of the developed adaptive quantized Q-learning(AQQL)framework with state-of-the-art and greedy approaches shows the superiority of AQQL with an improvement percentage in terms of the released number of vehicles of AQQL is 5%over the greedy approach and 340%over the state-of-the-art ***,AQQL provides an effective traffic control that can be applied in today’s intelligent traffic system.

关键词： Q-learning intelligent transportation system(ITS) traffic control vehicular communication kalman filtering smart city Internet of Things

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Cyberbullying Sexism Harassment Identification by Metaheurustics-Tuned eXtreme Gradient Boosting

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Computers, Materials & Continua 2024年第9期80卷 4997-5027页

作者： Milos Dobrojevic Luka Jovanovic Lepa Babic Miroslav Cajic Tamara Zivkovic Miodrag Zivkovic Suresh Muthusamy Milos Antonijevic Nebojsa Bacanin Technical Faculty Singidunum UniversityBelgrade11000Serbia Informatics and computing Singidunum UniversityBelgrade11000Serbia Business Economics Singidunum UniversityBelgrade11000Serbia computing and Informatics Sinergija UniversityBijeljina76300Bosnia and Herzegovina Department for Information Systems and Technologies University“Union Nikola Tesla”Cara DusanaBelgrade11080Serbia Department for Computer Science and Informatics School of Electrical EngineeringUniversity of BelgradeBelgrade11000Serbia Department of electrical and Electronics engineering Kongu Engineering College(Autonomous)PerunduraiErode638060India Department of Mathematics Saveetha School of Engineering(Deemed to be University)SIMATS ThandalamChennai602105India MEU Research Unit Middle East UniversityAmman11831Jordan

Cyberbullying is a form of harassment or bullying that takes place online or through digital devices like smartphones,computers,or *** can occur through various channels,such as social media,text messages,online forums,or gaming *** involves using technology to intentionally harm,harass,or intimidate others and may take different forms,including exclusion,doxing,impersonation,harassment,and ***,due to the rapid growth of malicious internet users,this social phenomenon is becoming more frequent,and there is a huge need to address this ***,the main goal of the research proposed in this manuscript is to tackle this emerging challenge.A dataset of sexist harassment on Twitter,containing tweets about the harassment of people on a sexual basis,for natural language processing(NLP),is used for this *** algorithms are used to transform the text into a meaningful representation of numbers for machine learning(ML)input:Term frequency inverse document frequency(TF-IDF)and Bidirectional encoder representations from transformers(BERT).The well-known eXtreme gradient boosting(XGBoost)ML model is employed to classify whether certain tweets fall into the category of sexual-based harassment or ***,with the goal of reaching better performance,several XGBoost models were devised conducting hyperparameter tuning by *** this purpose,the recently emerging Coyote optimization algorithm(COA)was modified and adjusted to optimize the XGBoost ***,other cutting-edge metaheuristics approach for this challenge were also implemented,and rigid comparative analysis of the captured classification metrics(accuracy,Cohen kappa score,precision,recall,and F1-score)was ***,the best-generated model was interpreted by Shapley additive explanations(SHAP),and useful insights were gained about the behavioral patterns of people who perform social harassment.

关键词： Coyote optimization algorithm NLP TF-IDF BERT XGBoost online harassment and cyberbullying metaheuristics

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AI-Driven Learning Management Systems:Modern Developments, Challenges and Future Trends during theAge of ChatGPT

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Computers, Materials & Continua 2024年第8期80卷 3289-3314页

作者： Sameer Qazi Muhammad Bilal Kadri Muhammad Naveed Bilal AKhawaja Sohaib Zia Khan Muhammad Mansoor Alam Mazliham Mohd Su’ud Department of Computer Science Faculty of EngineeringScience and TechnologyIQRA UniversityKarachi75500Pakistan College of Computer and Information Sciences Prince Sultan UniversityRiyadh11586Saudi Arabia Faculty of engineering Department of Electrical EngineeringIslamic University of MadinahP.O.BOX 170Madinah41411Saudi Arabia Faculty of engineering Department of Mechanical EngineeringIslamic University of MadinahP.O.BOX 170Madinah41411Saudi Arabia Faculty of computing Riphah International UniversityIslamabad46000Pakistan Faculty of computing and Informatics(FCI)Multimedia University Cyberjaya63100Malaysia Faculty of engineering and Information Technology School of Computer ScienceUniversity of Technology SydneySydneyNSW 2007Australia

COVID-19 pandemic restrictions limited all social activities to curtail the spread of the *** foremost and most prime sector among those affected were schools,colleges,and *** education system of entire nations had shifted to online education during this *** shortcomings of Learning Management Systems(LMSs)were detected to support education in an online mode that spawned the research in Artificial Intelligence(AI)based tools that are being developed by the research community to improve the effectiveness of *** paper presents a detailed survey of the different enhancements to LMSs,which are led by key advances in the area of AI to enhance the real-time and non-real-time user *** AI-based enhancements proposed to the LMSs start from the Application layer and Presentation layer in the form of flipped classroom models for the efficient learning environment and appropriately designed UI/UX for efficient utilization of LMS utilities and resources,including AI-based *** layer enhancements are also required,such as AI-based online proctoring and user authentication using *** extend to the Transport layer to support real-time and rate adaptive encrypted video transmission for user security/privacy and satisfactory working of *** also needs the support of the Networking layer for IP-based geolocation features,the Virtual Private Network(VPN)feature,and the support of Software-Defined Networks(SDN)for optimum Quality of Service(QoS).Finally,in addition to these,non-real-time user experience is enhanced by other AI-based enhancements such as Plagiarism detection algorithms and Data Analytics.

关键词： Learning management systems chatbots ChatGPT online education Internet of Things(IoT) artificial intelligence(AI) convolutional neural networks natural language processing

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Research on Carbon Emission for Preventive Maintenance of Wind Turbine Gearbox Based on Stochastic Differential Equation

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Energy engineering 2024年第4期121卷 973-986页

作者： Hongsheng Su Lixia Dong Xiaoying Yu Kai Liu School of Automation and electrical engineering Lanzhou Jiaotong UniversityLanzhou730070China

Time based maintenance(TBM)and condition based maintenance(CBM)are widely applied in many large wind farms to optimize the maintenance issues of wind turbine gearboxes,however,these maintenance strategies do not take into account environmental benefits during full life cycle such as carbon emissions ***,this article proposes a carbon emissions computing model for preventive maintenance activities of wind turbine gearboxes to solve the *** on the change of the gearbox state during operation and the influence of external random factors on the gearbox state,a stochastic differential equation model(SDE)and corresponding carbon emission model are established,wherein SDE is applied to model the evolution of the device state,whereas carbon emission is used to implement carbon emissions *** simulation results indicate that the proposed preventive maintenance cannot ensure reliable operation of wind turbine gearboxes but reduce carbon emissions during their *** with TBM,CBM minimizes unit carbon emissions without influencing reliable operation,making it an effective maintenance method.

关键词： Stochastic differential equation(SDE) condition-based maintenance(CBM) carbon emissions

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Robust facial expression recognition via lightweight reinforcement learning for rehabilitation robotics

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Optoelectronics Letters 2024年

作者： CHEN Yifan FAN Weiming GAO Hongwei YU Jiahui JU Zhaojie School of computing， University of Portsmouth School of Automation and electrical engineering， Shenyang Ligong University Department of Biomedical engineering Zhejiang Univeristy

The Facial Expression Recognition (FER) capability could endow machines with a more accurate evaluation of rehabilitation robots， making rehabilitation robots’ experience more harmonious and credible. How to obtain robust facial expression representation features has yet to be fully explored. Moreover， the convolutional networks often used in facial expression recognition usually have many parameters， which is not conducive to the online diagnosis of rehabilitation robots. Therefore， this paper proposes a Lightweight Reinforcement Network (LRN) and Auxiliary Label Distribution Learning (ALDL) based robust FER method. Our designed representation reinforcement network mainly comprises two modules， i.e.， the Representation Reinforcement (RR) module and the Auxiliary Label Space Construction (ALSC) module. RR module highlights key feature messaging nodes in feature maps， and ALSC allows multiple labels with different intensities to be linked to one expression. Therefore， LRN has a more robust feature extraction capability when model parameters are greatly reduced， and ALDL is proposed to contribute to the training effect of LRN in the condition of ambiguous training data. We tested our method on FER-Plus， and RAF-DB data sets， and the experiment demonstrates the feasibility of our method in practice during rehabilitation robots.

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Endoscopy-assisted lightweight diagnosis system based on transformers for colon polyp detection

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Optoelectronics Letters 2024年

作者： FAN Weiming YU Jiahui JU Zhaojie School of Automation and electrical engineering， Shenyang Ligong University Department of Biomedical engineering， Zhejiang University School of computing， University of Portsmouth

The integration of endoscopy has significantly propelled the diagnosis and treatment of gastrointestinal diseases， with colonoscopy establishing itself as the primary method for early diagnosis and preventive care in colorectal cancer. Although deep learning holds promise in mitigating missed polyp rates， modern endoscopy examinations pose additional challenges， such as image blurring and atomizing. This study explores lightweight yet powerful attention mechanisms， introducing the Spatial-Channel Transformer—an innovative approach that leverages spatial channel relationships for attention weight calculation. The method utilizes rotation operations for inter-dimensional dependencies， followed by residual transformation， encoding inter-channel and spatial information with minimal computational overhead. Extensive experiments on the CVC-ClinicDB polyp detection dataset， addressing endoscopy pitfalls， underscore the superiority of our Spatial-Channel Transformer over other state-of-the-art methods. The proposed model maintains high performance， even in challenging scenarios.

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Game Theory based Optimal Defensive Resources Allocation with Incomplete Information in Cyber-physical Power Systems against False Data Injection Attacks

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Protection and Control of Modern Power Systems 2024年第2期9卷 115-127页

作者： Bingjing Yan Zhenze Jiang Pengchao Yao Qiang Yang Wei Li Albert Y.Zomaya College of electrical engineering Zhejiang University College of Control Science and engineering Zhejiang University College of electrical engineering Zhejiang UniversityHangzhou 310027China School of Information Technologies at The University of Sydney School of Information Technologies

Modern power grid is fast emerging as a complex cyber-physical power system(CPPS)integrating physical current-carrying components and processes with cyber-embedded computing,which faces increasing cy-berspace security threats and *** this paper,the state(i.e.,voltage)offsets resulting from false data injection(FDI)attacks and the bus safety characterization are applied to quantify the attack *** state offsets are obtained by the state estimation method,and the bus safety characterization considers the power net-work topology as well as the vulnerability and connection relationship of *** the indeterminacy of attacker’s resource consumption and reward,a zero-sum game-theoretical model from the defender’s perspective with incomplete information is explored for the optimal allocation of limited defensive *** attacker aims to falsify measurements without triggering threshold alarms to break through the protection,leading to load shedding,over-voltage or *** defender attempts to ensure the estimation results to be as close to the actual states as possible,and guarantee the system’s safety and efficient defensive resource *** proposed solution is extensively evaluated through simu-lations using the IEEE 33-bus test network and real-time digital simulator(RTDS)based testbed experiments of the IEEE 14-bus *** results demonstrate the effec-tiveness of the proposed game-theoretical approach for optimal defensive resource allocation in CPPS when lim-ited resources are available when under FDI *** Terms—Optimal strategy,game theory,Nash equilibrium,CPPS,FDI attack.

关键词： Optimal strategy game theory Nash equilibrium CPPS FDI attack

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AI-Based Helmet Violation Detection for Traffic Management System

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Computer Modeling in engineering & Sciences 2024年第10期141卷 733-749页

作者： Yahia Said YahyaAlassaf Refka Ghodhbani Yazan Ahmad Alsariera Taoufik Saidani Olfa Ben Rhaiem Mohamad Khaled Makhdoum Manel Hleili Department of electrical engineering College of EngineeringNorthern Border UniversityArar91431Saudi Arabia Department of Civil engineering College of EngineeringNorthern Border UniversityArar91431Saudi Arabia Faculty of computing and Information Technology Northern Border UniversityRafha91911Saudi Arabia College of Science Northern Border UniversityArar91431Saudi Arabia Department of Mathematics Faculty of Sciences of TabukUniversity of TabukTabuk71491Saudi Arabia

Enhancing road safety globally is imperative,especially given the significant portion of traffic-related fatalities attributed to motorcycle accidents resulting from non-compliance with helmet *** the critical role of helmets in rider protection,this paper presents an innovative approach to helmet violation detection using deep learning *** primary innovation involves the adaptation of the PerspectiveNet architecture,transitioning from the original Res2Net to the more efficient EfficientNet v2 backbone,aimed at bolstering detection *** rigorous optimization techniques and extensive experimentation utilizing the India driving dataset(IDD)for training and validation,the system demonstrates exceptional performance,achieving an impressive detection accuracy of 95.2%,surpassing existing ***,the optimized PerspectiveNet model showcases reduced computational complexity,marking a significant stride in real-time helmet violation detection for enhanced traffic management and road safety measures.

关键词： Non-helmet use detection traffic violation safety deep learning optimized PerspectiveNet

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