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Congress on Evolutionary Computation

作者： Xunfeng Wu Songbai Liu Junkai Ji Lijia Ma Victor C. M. Leung College of Computer Science and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Electrical and Computer Engineering The University of British Columbia Vancouver Canada

ISBN: (数字)9798350308365

ISBN: (纸本)9798350308372

Surrogate-assisted evolutionary algorithms (SAEAs) have demonstrated promising optimization performance in addressing expensive dynamic optimization problems or expensive multimodal optimization problems. However, none of existing SAEAs are designed specifically for tackling expensive dynamic multimodal optimization problems (EDMMOPs). Therefore, in this paper, a first SAEA for tackling EDMMOPs is proposed. First, a nearest density clustering is designed to divide the population into a number of subpopulations, enhancing the diversity of the population. Then, a surrogate-assisted evolutionary optimizer is developed to construct surrogate models for each subpopulation and evolve all solutions in subpopulations by means of the built surrogate models, accelerating the population's converge towards several optimal solutions rapidly. Finally, a transfer learning-based prediction is devised to generate initial samples for next environment by leveraging the stored training samples in the previous environments. To assess the performance of our proposed algorithm, a set of complex benchmark problems is adopted, and the experimental results confirm its superior performance over several competitive algorithms on most test cases.

关键词： Training Heuristic algorithms Computational modeling Sociology Clustering algorithms Evolutionary computation Benchmark testing

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Computation of an Emptiable Minimal Siphon in a Subclass of Petri Nets Using Mixed-Integer Programming

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IEEE/CAA Journal of Automatica Sinica 2021年第1期8卷 219-226页

作者： Shouguang Wang Wenli Duo Xin Guo Xiaoning Jiang Dan You Kamel Barkaoui MengChu Zhou the School of Information and Electronic Engineering Zhejiang Gongshang UniversityHangzhou 310018China the Macao Institute of Systems Engineering Macao University of Science and TechnologyMacao 999078China the Department of Computer Science Conservatoire National des Arts et MétiersParis 75141France the Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102 USA the Institute of Systems Engineering and Collaborative Laboratory for Intelligent Science and Systems Macao University of Science and TechnologyMacao 999078China

Deadlock resolution strategies based on siphon control are widely *** computational efficiency largely depends on siphon ***-integer programming(MIP)can be utilized for the computation of an emptiable siphon in a Petri net(PN).Based on it,deadlock resolution strategies can be designed without requiring complete siphon enumeration that has exponential *** to this reason,various MIP methods are proposed for various subclasses of *** work proposes an innovative MIP method to compute an emptiable minimal siphon(EMS)for a subclass of PNs named S^(4)*** particular,many particular structural characteristics of EMS in S4 PR are formalized as constraints,which greatly reduces the solution *** results show that the proposed MIP method has higher computational ***,the proposed method allows one to determine the liveness of an ordinary S^(4)PR.

关键词： Automated manufacturing systems deadlocks discrete event system mixed-integer programming(MIP) Petri nets(PN) siphons

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Incentive Design for Eco-driving in Urban Transportation Networks

Incentive Design for Eco-driving in Urban Transportation Net...

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European Control Conference (ECC)

作者： M. Umar B. Niazi Jung-Hoon Cho Munther A. Dahleh Roy Dong Cathy Wu Department of Electrical Engineering and Computer Science Laboratory for Information and Decision Systems Massachusetts Institute of Technology Cambridge MA USA Department of Civil and Environmental Engineering Laboratory for Information and Decision Systems Massachusetts Institute of Technology Cambridge MA USA Department of Industrial and Enterprise Systems Engineering University of Illinois at Urbana-Champaign Urbana IL USA

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

Eco-driving emerges as a cost-effective and efficient strategy to mitigate greenhouse gas emissions in urban transportation networks. Acknowledging the persuasive influence of incentives in shaping driver behavior, this paper presents the ‘eco-planner,’ a digital platform devised to promote eco-driving practices in urban transportation. At the outset of their trips, users provide the platform with their trip details and travel time preferences, enabling the eco-planner to formulate personalized eco-driving recommendations and corresponding incentives, while adhering to its budgetary constraints. Upon trip completion, incentives are transferred to users who comply with the recommendations and effectively reduce their emissions. By comparing our proposed incentive mechanism with a baseline scheme that offers uniform incentives to all users, we demonstrate that our approach achieves superior emission reductions and increased user compliance with a smaller budget.

关键词： Transportation Europe Greenhouse gases

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Transformative effects of ChatGPT on modern education: Emerging Era of AI Chatbots

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Internet of Things and Cyber-Physical systems 2024年第1期4卷 19-23页

作者： Gill, Sukhpal Singh Xu, Minxian Patros, Panos Wu, Huaming Kaur, Rupinder Kaur, Kamalpreet Fuller, Stephanie Singh, Manmeet Arora, Priyansh Parlikad, Ajith Kumar Stankovski, Vlado Abraham, Ajith Ghosh, Soumya K. Lutfiyya, Hanan Kanhere, Salil S. Bahsoon, Rami Rana, Omer Dustdar, Schahram Sakellariou, Rizos Uhlig, Steve Buyya, Rajkumar School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Raygun Performance Monitoring Wellington New Zealand Center for Applied Mathematics Tianjin University Tianjin China Department of Science Kings Education London United Kingdom Cymax Group Technologies BC Canada QM Academy Queen Mary University of London London United Kingdom Jackson School of Geosciences University of Texas at Austin AustinTX United States Centre for Climate Change Research Indian Institute of Tropical Meteorology Pune India Microsoft Hyderabad India Institute for Manufacturing Department of Engineering University of Cambridge Cambridge United Kingdom Faculty of Computer and Information Science University of Ljubljana Ljubljana Slovenia Machine Intelligence Research Labs AuburnWA United States Faculty of Computing and Data Science FLAME University Maharashtra Pune India Department of Computer Science and Engineering Indian Institute of Technology Kharagpur India Department of Computer Science University of Western Ontario London Canada Sydney Australia School of Computer Science University of Birmingham United Kingdom School of Computer Science and Informatics Cardiff University Cardiff United Kingdom Distributed Systems Group Vienna University of Technology Vienna Austria Department of Computer Science University of Manchester Oxford Road Manchester United Kingdom Laboratory School of Computing and Information Systems The University of Melbourne Australia

ChatGPT, an AI-based chatbot, offers coherent and useful replies based on analysis of large volumes of data. In this article, leading academics, scientists, distinguish researchers and engineers discuss the transformative effects of ChatGPT on modern education. This research discusses ChatGPT capabilities and its use in the education sector, identifies potential concerns and challenges. Our preliminary evaluation shows that ChatGPT perform differently in different subject areas including finance, coding, maths, and general public queries. While ChatGPT has the ability to help educators by creating instructional content, offering suggestions and acting as an online educator to learners by answering questions, transforming education through smartphones and IoT gadgets, and promoting group work, there are clear drawbacks in its use, such as the possibility of producing inaccurate or false data and circumventing duplicate content (plagiarism) detectors where originality is essential. The often reported "hallucinations" within GenerativeAI in general, and also relevant for ChatGPT, can render its use of limited benefit where accuracy is essential. What ChatGPT lacks is a stochastic measure to help provide sincere and sensitive communication with its users. Academic regulations and evaluation practices used in educational institutions need to be updated, should ChatGPT be used as a tool in education. To address the transformative effects of ChatGPT on the learning environment, educating teachers and students alike about its capabilities and limitations will be crucial. © 2023 The Authors

关键词： Machine learning

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Road Signs Recognition by Using YOLOv8 Model

Road Signs Recognition by Using YOLOv8 Model

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International Conference on Intelligent systems: Theories and Applications (SITA)

作者： Abdellah Nabou Atheer L. Salih Ibtissam Ben Rabbaa El-Hassan Abdelwahed Mouaad Ait Ahmed Ouhamou Computer Science Dept Computing Systems Engineering Laboratory (LISI) Cadi Ayyad University Marrakesh Morocco Electrical & Communications Engineering Department DHBW University - Campus Friedrichshafen Germany Computer Science Dept Cadi Ayyad University Marrakesh Morocco

Efficient Road sign recognition is key to improving road safety, navigation and to enhance driver assistance for an intelligent transportation system (ITS). However, achieving efficient road sign and traffic recognition is accompanied by many challenges, in our last few years, several deep learning algorithms have been deployed for responding to these limitations. With the exponential increase in the number of vehicles in Morocco, the need for accurate and real-time detection and classification of road signs has become essential. In this paper, we propose an advanced approach for recognizing Moroccan road signs utilizing the YOLOv8 (You Only Look Once) model that very known for its efficiency and effectiveness in object detection tasks. the model is trained and evaluated under a comprehensive dataset that comprising diverse Moroccan road signs. The test of YOLOv8 model give an encouraging result in different performance metrics such as accuracy, recall, precision and mAP which have respectively an average of 0.95, 0.94, 0.96 and 0.97.

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An Optimized Design of Antenna Arrays for the Smart Antenna systems

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computers, Materials & Continua 2021年第11期69卷 1979-1994页

作者： Fahd N.Al-Wesabi Murad A.A.Almekhlafi Huda G.Iskandar Adnan Zain Saleh Alzahrani Mohammed Alamgeer Nadhem Nemri Sami Dhabi Mohammad Medani Ali.M.Al-Sharafi Department of Computer Science King Khalid UniversityMuhayel AseerKingdom of Saudi Arabia Faculty of Computer and IT Sana’a UniversitySana’aYemen Department of Electrical Engineering–Communication Engineering Sana’a UniversityYemen School of Computing Faculty of EngineeringUniversiti Teknologi MalaysiaMalaysia Department of Electronics and Communication Engineering Faculty of EngineeringUniversity of AdenYemen Department of Information Systems King Khalid UniversityMuhayel AseerKingdom of Saudi Arabia Department of Computer Science College of Computers and Information TechnologyUniversity of BishaKSA

In recent years,there has been an increasing demand to improve cellular communication services in several *** aspect that received the most attention is improving the quality of coverage through using smart antennas which consist of array *** paper investigates the main characteristics and design of the three types of array antennas of the base station for better coverage through simulation(MATLAB)which provides field and strength patterns measured in polar and rectangular coordinates for a variety of conditions including broadsides,ordinary End-fire,and increasing directivity End-fire which is typically used in smart *** method of analysis was applied to twenty experiments of process design to each antenna type separately,so sixty results were obtained from the radiation pattern indicating the parameters for each radiation ***,nineteen design experiments were described in this *** is hoped that the results obtained from this study will help engineers solve coverage problems as well as improve the quality of cellular communication networks.

关键词： Smart antenna antenna arrays patterns construction coverage

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MaDEVIoT: Cyberattacks on EV Charging Can Disrupt Power Grid Operation

MaDEVIoT: Cyberattacks on EV Charging Can Disrupt Power Grid...

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Innovative Smart Grid Technologies (ISGT)

作者： Samrat Acharya Hafiz Anwar Ullah Khan Ramesh Karri Yury Dvorkin Pacific Northwest National Laboratory Richland WA USA Department of Electrical and Computer Engineering New York University Brooklyn NY USA Department of Electrical and Computer Engineering Department of Civil and Systems Engineering Ralph O’Connor Sustainable Energy Institute Johns Hopkins University Baltimore MD USA

This paper examines the feasibility of demand-side cyberattacks on power grids launched via internet-connected high-power EV Charging Stations (EVCSs). By distorting power grid frequency and voltage, these attacks can trigger system-wide outages. Our case study focuses on Manhattan, New York, and reveals that such attacks will become feasible by 2030 with increased EV adoption. With a single EVCS company dominating Manhattan, compromising a single EVCS server raises serious power grid security concerns. These attacks can overload power lines and trip over-frequency (OF) protection relays, resulting in a power grid blackout. This study serves as a crucial resource for planning authorities and power grid operators involved in the EV charging infrastructure roll-out, highlighting potential cyberthreats to power grids stemming from high-power EVCSs.

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Machine learning predicts long-term mortality after acute myocardial infarction using systolic time intervals and routinely collected clinical data

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Intelligent Medicine 2024年第3期4卷 170-176页

作者： Bijan Roudini Boshra Khajehpiri Hamid Abrishami Moghaddam Mohamad Forouzanfar Department of Systems Engineering School of Advanced TechnologyUniversity of QuebecMontrealCanada Machine Vision and Medical Image Processing(MVMIP)Laboratory Faculty of Electrical and Computer EngineeringK.N.Toosi University of TechnologyTehranIran Research Center of the Montreal University Institute of Geriatrics MontrealCanada

Background Precise estimation of current and future comorbidities of patients with cardiovascular disease is an important factor in prioritizing continuous physiological monitoring and new *** learning(ML)models have shown satisfactory performance in short-term mortality prediction in patients with heart disease,whereas their utility in long-term predictions is *** study aimed to investigate the performance of tree-based ML models on long-term mortality prediction and effect of two recently introduced biomarkers on long-term *** This study used publicly available data from the Collaboration Center of Health Information Appli-cation at the Ministry of Health and Welfare,Taiwan,*** collected data were from patients admitted to the cardiac care unit for acute myocardial infarction(AMI)between November 2003 and September *** collected and analyzed mortality data up to December *** records were used to gather demo-graphic and clinical data,including age,gender,body mass index,percutaneous coronary intervention status,and comorbidities such as hypertension,dyslipidemia,ST-segment elevation myocardial infarction,and non-ST-segment elevation myocardial *** the data,collected from 139 patients with AMI,from medical and demographic records as well as two recently introduced biomarkers,brachial pre-ejection period(bPEP)and brachial ejection time(bET),we investigated the performance of advanced ensemble tree-based ML algorithms(random forest,AdaBoost,and XGBoost)to predict all-cause mortality within 14 years.A nested cross-validation was performed to evaluate and compare the performance of our developed models precisely with that of the conventional logistic regression(LR)as the baseline *** The developed ML models achieved significantly better performance compared to the baseline LR(C-Statistic,0.80 for random forest,0.79 for AdaBoost,and 0.78 for XGBoost,vs.0.77 for LR)(PRF<0.001,PAdaBoost<0.001,a

关键词： Electrocardiogram Predication Mortality prediction Machine learning

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Impact and Mitigation of Electromagnetic Interference Between HVTL and Pipelines

Impact and Mitigation of Electromagnetic Interference Betwee...

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IEEE Southeastcon

作者： Hossam Hussein Abd El-Latif S. Ahmed Sabry Mousa El-Sayed M. El-Refaie Osama A. Mohammed Department of Electrical and Computer Engineering Energy Systems Research Laboratory Florida International University Miami USA Department of Electrical Power and Machines Engineering Faculty of Engineering Helwan University Cairo Egypt

High-voltage transmission lines transmit bulk power from one place to another and may pass through densely populated areas or forests, causing an economic challenge. While following the right-of-way (ROW) rules, high-voltage transmission lines are forced to share the same corridors with others, like metallic gas pipelines. In this regard, the electromagnetic fields from high-voltage transmission lines will affect neighboring pipelines using inductive, conductive, and capacitive effects. As a result, there will be an induced voltage across the metallic pipes, which will have negative repercussions. An example of this includes corrosion in the pipeline itself and those in direct contact with the pipeline via touch and step voltages. This paper studies the electromagnetic interference for the El-Fayoum-Dahshour gas pipeline, which shares the same route for 72 km with three overhead transmission lines, two of which are 500 kV and the third is 220 kV. Several study cases were done to show the impact of soil resistivity variation on the pipeline-induced voltage. The results conclude that the LG fault is the worst-case scenario. Moreover, mitigating the total induced voltage to the recommended safe limits was achieved by applying the polarization cell method.

关键词： Power transmission lines Pipelines Electromagnetic interference Voltage High-voltage techniques Forestry Soil

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Hybrid deep learning model for risk prediction of fracture in patients with diabetes and osteoporosis

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Frontiers of Medicine 2022年第3期16卷 496-506页

作者： Yaxin Chen Tianyi Yang Xiaofeng Gao Ajing Xu Department of Pharmacy Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of MedicineShanghai 200240China Shanghai Key Laboratory of Scalable Computing and Systems Department of Computer Science and EngineeringShanghai Jiao Tong UniversityShanghai 200240China Clinical Research Unit Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of MedicineShanghai 200240China

The fracture risk of patients with diabetes is higher than those of patients without diabetes due to hyperglycemia,usage of diabetes drugs,changes in insulin levels,and excretion,and this risk begins as early as *** factors including demographic data(such as age,height,weight,and gender),medical history(such as smoking,drinking,and menopause),and examination(such as bone mineral density,blood routine,and urine routine)may be related to bone metabolism in patients with ***,most of the existing methods are qualitative assessments and do not consider the interactions of the physiological factors of *** addition,the fracture risk of patients with diabetes and osteoporosis has not been further studied *** this paper,a hybrid model combining XGBoost with deep neural network is used to predict the fracture risk of patients with diabetes and osteoporosis,and investigate the effect of patients’physiological factors on fracture risk.A total of 147 raw input features are considered in our *** presented model is compared with several benchmarks based on various metrics to prove its ***,the top 18 influencing factors of fracture risks of patients with diabetes are determined.

关键词： XGBoost deep neural network healthcare risk prediction

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