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13th IEEE Congress on Evolutionary Computation, CEC 2024

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

ISBN: (纸本)9798350308365

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. © 2024 IEEE.

关键词： Digital elevation model

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A Secure and Cost-Effective Training Framework Atop Serverless Computing for Object Detection in Blasting

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computer Modeling in engineering & sciences 2024年第5期139卷 2139-2154页

作者： Tianming Zhang Zebin Chen Haonan Guo Bojun Ren Quanmin Xie Mengke Tian Yong Wang Department of Computer and Science Shanghai Jiaotong UniversityShanghai200240China Aerospace System Engineering Shanghai Shanghai200240China State Key Laboratory of Precision Blasting Jianghan UniversityWuhan430056China Beijing Microelectronics Technology Institute Beijing100076China

The data analysis of blasting sites has always been the research goal of relevant *** rise of mobile blasting robots has aroused many researchers’interest in machine learning methods for target detection in the field of *** Computing can provide a variety of computing services for people without hardware foundations and rich software development experience,which has aroused people’s interest in how to use it in the field ofmachine *** this paper,we design a distributedmachine learning training application based on the AWS Lambda *** on data parallelism,the data aggregation and training synchronization in Function as a Service(FaaS)are effectively *** also encrypts the data set,effectively reducing the risk of data *** rent a cloud server and a Lambda,and then we conduct experiments to evaluate our *** results indicate the effectiveness,rapidity,and economy of distributed training on FaaS.

关键词： Serverless computing object detection blasting

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Position Measurement Based Slave Torque Feedback Control for Teleoperation systems With Time-Varying Communication Delays

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IEEE/CAA Journal of Automatica Sinica 2023年第2期10卷 388-402页

作者： Xian Yang Jing Yan Changchun Hua Xinping Guan the Institute of Information Science and Engineering Yanshan University the Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao 066004China IEEE the Institute of Electrical Engineering Yanshan UniversityQinhuangdao 066004China the Department of Automation Shanghai Jiao Tong UniversityShanghai 200240China

Bilateral teleoperation system is referred to as a promising technology to extend human actions and intelligence to manipulating objects *** the tracking control of teleoperation systems,velocity measurements are necessary to provide feedback ***,due to hardware technology and cost constraints,the velocity measurements are not always *** addition,the time-varying communication delay makes it challenging to achieve tracking *** paper provides a solution to the issue of real-time tracking for teleoperation systems,subjected to unavailable velocity signals and time-varying communication *** order to estimate the velocity information,immersion and invariance(I&I)technique is employed to develop an exponential stability velocity *** the proposed velocity observer,a linear relationship between position and observation state is constructed,through which the need of solving partial differential and certain integral equations can be ***,the mean value theorem is exploited to separate the observation error terms,and hence,all functions in our observer can be analytically *** the estimated velocity information,a slave-torque feedback control law is presented.A novel Lyapunov-Krasovskii functional is constructed to establish asymptotic tracking *** particular,the relationship between the controller design parameters and the allowable maximum delay values is ***,simulation and experimental results reveal that the proposed velocity observer and controller can guarantee that the observation errors and tracking error converge to zero.

关键词： Asymptotic stability bilateral teleoperation time-varying delay velocity observer

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Sentiment Analysis in Indonesian Trading using Lexicon-based and Support Vector Machine

Sentiment Analysis in Indonesian Trading using Lexicon-based...

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2023 International Conference on computer science, Information Technology and engineering, ICCoSITE 2023

作者： Oetama, Raymond Sunardi Yanfi, Yanfi Ikhsan Assiddiq Up, Masagus M. Muhamad Isa, Sani Universitas Multimedia Nusantara Information System DepartmentFaculty of Engineering and Informatics Jakarta Indonesia Bina Nusantara University BINUS Graduate Program Computer Science School of Computer Science Computer Science Department Jakarta Indonesia Bina Nusantara University BINUS Graduate Program Computer Science Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350320954

Many individuals now trade online utilizing trading software in the digital world. Binomo is one of Indonesia's most popular trading platforms. This is because some influencers made several promises to Binomo customers. Since many customers were deceived, this case became quite popular. This study was executed to see how Indonesians felt about the Binomo application after the case went viral. The solution taken was in the form of sentiment analysis because there had been no previous research on sentiment analysis that discussed the Binomo case. The data was scanned using Netlytic tools, a cloud-based text and social network analyzer capable of identifying any talks on social media sites such as Twitter. The sentiment analysis of Binomo trading tweets by using the Multi-Perspective Question Answering lexicon utilized the KNIME tool. But unfortunately, the accuracy of sentiment analysis results is low. Furthermore, the Support Vector Machine technique is also being conducted. The Term Frequency-Inverse Document Frequency method is applied to perform feature extraction whilst the chi-square approach is utilized to identify features that are thought to be useful for inclusion in the classification process and to exclude features that are irrelevant to the target class. The obtained accuracy is 86%. The study proposes that words from the algorithm's outputs can be utilized to improve the quality of sentiment analysis using the lexicon. As an outcome of the algorithm, positive and negative terms are added to the lexicon, increasing the accuracy of sentiment analysis using the new vocabulary from 58.984% to 71.146%. © 2023 IEEE.

关键词： Support vector machines

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Smart Material Selection: Leveraging Machine Learning for Sustainable and Cost-Effective Building Design 2

Smart Material Selection: Leveraging Machine Learning for Su...

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2nd Jordanian International Biomedical engineering Conference, JIBEC 2024

作者： Hashim, Wassan Adnan Khoshaba, Lina Sami Kareem, Shahab Wahhab Khoshaba, Farah Sami Al-Qalam University College Medical Device Engineering Department Kirkuk Iraq Lebanese French University College of Engineering and Computer Science Architecture Engineering Department Erbil Iraq Polytechnic University Technical Engineering College Information System Engineering Department Erbil Iraq

ISBN: (纸本)9798331539542

In modern-day architecture, the choice of building material plays an important role in sustainability, cost-value engineering, and design integrity. Moreover, the traditional methods for selecting materials tended to make use of manual approaches, which at times led to drawbacks by not focusing on the basics of integration and proper data management. The paper is a very interesting approach of how a complex material selection process can be optimized with simple machine learning algorithms. In that sense, the current research developed a framework from environmental sustainability to economic cost, durability, and aesthetic appeal, with an intention for its use in guiding the design final decision process. The proposed machine learning model suggests ideal prediction for material combination from different material datasets concerning the properties and performance metrics applicable to architecture. The research showed that the approach had a hand in getting thoughtful, analytic-based decisions while assuring, at the same time, that an architect would be able to satisfy the project goals and budget constraints with environmentally safe execution. It elaborates on the cases, or rather case studies, that have applied the model and gives illustrations of great improvements not only in efficiency in design but also in the sustainability of the project coupled with better strategies for saving costs. The current research is opening an entirely new field of innovation in architecture that emphasizes the role of technology for the future form of building practice. © 2024 IEEE.

关键词： Budget control

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Dual-State-Driven Evolutionary Optimization for Expensive Optimization Problems with Continuous and Categorical Variables 5

Dual-State-Driven Evolutionary Optimization for Expensive Op...

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5th International Conference on Data-Driven Optimization of Complex systems, DOCS 2023

作者： Xie, Lindong Li, Genghui Lin, Kangnian Wang, Zhenkun School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen China School of System Design and Intelligent Manufacturing Southern University of Science and Technology Department of Computer Science and Engineering Shenzhen China

ISBN: (纸本)9798350393521

The surrogate-assisted evolutionary algorithm (SAEA) is one of the most efficient approaches for addressing expensive continuous or combinatorial optimization problems. However, it encounters significant challenges in expensive mixed-variable optimization problems (EMVOPs). To overcome this limitation, a dual-state-driven evolutionary optimization (called DSDEO), integrating a surrogate-assisted mixed-variable evolutionary optimization stage (MVEOS) and a surrogate-assisted continuous-variable evolutionary optimization stage (CVEOS), is proposed in this paper. Specifically, MVEOS employs global and local search to enhance the exploration and exploitation of the mixed-variable space. Global and local searches are alternately executed if one search fails to yield a better solution. CVEOS utilizes a continuous-improvement strategy to refine the continuous variables of the best solution obtained so far. Experimental results demonstrate the advantages of DSDEO compared to some state-of-the-art SAEAs on many benchmark problems. © 2023 IEEE.

关键词： Evolutionary algorithms

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Light Fidelity and Internet of Things for Medical Applications 4

Light Fidelity and Internet of Things for Medical Applicatio...

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4th International Conference of science and Information Technology in Smart Administration, ICSINTESA 2024

作者： Adi, Puput Dani Prasetyo Sariningrum, Ros Rahmatullah, Rizky Novita, Hessy Situngkir, Tiar Lina Adji, Priyo Hartono Lusiastuti, Angela Mariana Wibowo, Antonia Rahayu Rosaria Prastiyono, Yudi Adiprabowo, Tjahjo Yassir, Muhammad Maulana, Fairuz Iqbal Department of Telecommunication Bandung Indonesia Veterinary Research Center Bandung Indonesia Singaperbangsa University Faculty of Economics Department of Management Karawang Indonesia Bandung Indonesia Langlangbuana University Faculty of Engineering Department of Electrical Engineering Bandung Indonesia Institut Teknologi Dan Bisnis Nobel Department of System and Information System Makassar Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350376111

This study focuses on transmitting data utilizing medical sensors that are vital for patients in hospitals and other settings. In this situation, Light Fidelity refers to transmitting medical sensor data or other data comprising the Transmitter (Tx) and the Receiver (Rx). If the Light Fidelity Principle is used, the Transmitter transmits LEDs or light to the Receiver. At the same time, the Receiver contains a receiver or light detection component known as the Light Dependent Resistor (LDR);using programming and this basic working principle, data can be sent from the LED Transmitter to the LDR Receiver using C++ Programming and its Library to send Medical sensor data. Sensors can include pulse sensors, like in prior research, and additional sensors such as SPO2 or blood pressure sensors. For medical data, a modest bit rate is sufficient, but LiFi applications can be created in such a way that they can be hybridized with WiFi and other RF devices such as LoRa and the Internet of Things. © 2024 IEEE.

关键词： C++ (programming language)

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Semantic Segmentation and YOLO Detector over Aerial Vehicle Images

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computers, Materials & Continua 2024年第8期80卷 3315-3332页

作者： Asifa Mehmood Qureshi Abdul Haleem Butt Abdulwahab Alazeb Naif Al Mudawi Mohammad Alonazi Nouf Abdullah Almujally Ahmad Jalal Hui Liu Faculty of Computing and AI Air UniversityIslamabad44000Pakistan Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Information Systems College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Cognitive Systems Lab University of BremenBremen28359Germany

Intelligent vehicle tracking and detection are crucial tasks in the realm of highway ***,vehicles come in a range of sizes,which is challenging to detect,affecting the traffic monitoring system’s overall *** learning is considered to be an efficient method for object detection in vision-based *** this paper,we proposed a vision-based vehicle detection and tracking system based on a You Look Only Once version 5(YOLOv5)detector combined with a segmentation *** model consists of six *** the first step,all the extracted traffic sequence images are subjected to pre-processing to remove noise and enhance the contrast level of the *** pre-processed images are segmented by labelling each pixel to extract the uniform regions to aid the detection phase.A single-stage detector YOLOv5 is used to detect and locate vehicles in *** detection was exposed to Speeded Up Robust Feature(SURF)feature extraction to track multiple *** on this,a unique number is assigned to each vehicle to easily locate them in the succeeding image frames by extracting them using the feature-matching ***,we implemented a Kalman filter to track multiple *** the end,the vehicle path is estimated by using the centroid points of the rectangular bounding box predicted by the tracking *** experimental results and comparison reveal that our proposed vehicle detection and tracking system outperformed other state-of-the-art *** proposed implemented system provided 94.1%detection precision for Roundabout and 96.1%detection precision for Vehicle Aerial Imaging from Drone(VAID)datasets,respectively.

关键词： Semantic segmentation YOLOv5 vehicle detection and tracking Kalman filter SURF

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Scar-Aware Late Mechanical Activation Detection Network for Optimal Cardiac Resynchronization Therapy Planning 22

Scar-Aware Late Mechanical Activation Detection Network for ...

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22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Xing, Jiarui Wang, Shuo Petal, Amit R. Bilchick, Kenneth C. Epstein, Frederick H. Zhang, Miaomiao School of Medicine University of Virginia Health System United States University of Virginia Health System Department of Biomedical Engineering United States University of Virginia Department of Electrical and Computer Engineering United States University of Virginia Department of Computer Science United States

ISBN: (纸本)9798331520526

Accurate identification of late mechanical activation (LMA) regions is crucial for optimal cardiac resynchronization therapy (CRT) lead implantation. However, existing approaches using cardiac magnetic resonance (CMR) imaging often over-look myocardial scar information, which may be mistakenly identified as delayed activation regions. To address this issue, we propose a scar-aware LMA detection network that simultaneously detects myocardial scar and prevents LMA localization in these scarred regions. More specifically, our model integrates a pre-trained scar segmentation network using late gadolinium enhancement (LGE) CMRs into a LMA detection network based on highly accurate strain derived from displacement encoding with stimulated echoes (DENSE) CMRs. We introduce a novel scar-aware loss function that utilizes the segmented scar information to discourage false-positive detections of late activated areas. Our model can be trained with or without paired LGE data. During inference, our model does not require the input of LGE images, leveraging learned patterns from strain data alone to mitigate false-positive LMA detection in potential scar regions. We evaluate our model on subjects with and without myocardial scar, demonstrating significantly improved LMA detection accuracy in both scenarios. Our work paves the way for improved CRT planning, potentially leading to better patient outcomes. © 2025 IEEE.

关键词： Cardiac resynchronization therapy

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Sigma-Mixed Unscented Kalman Filter-Based Fault Detection for Traction systems in High-Speed Trains

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Chinese Journal of Electronics 2023年第5期32卷 982-991页

作者： CHENG Chao WANG Weijun MENG Xiangxi SHAO Haidong CHEN Hongtian Department of Computer Science and Technology Changchun University of Technology Department of Mathematics and Statistics Changchun University of Technology Institute of System Research China Industrial Control Systems Cyber Emergency Response Team Department of Mechanical and Vehicle Engineering Hunan University Department of Chemical and Materials Engineering University of Alberta

Fault detection(FD) for traction systems is one of the active topics in the railway and academia because it is the initial step for the running reliability and safety of high-speed trains. Heterogeneity of data and complexity of systems have brought new challenges to the traditional FD methods. For addressing these challenges, this paper designs an FD algorithm based on the improved unscented Kalman filter(UKF) with consideration of performance degradation. It is derived by incorporating a degradation process into the state-space *** network topology of traction systems is taken into consideration for improving the performance of state estimation. We first obtain the mixture distribution by the mixture of sigma points in UKF. Then, the Lévy process with jump points is introduced to construct the degradation model. Finally, the moving average interstate standard deviation(MAISD) is designed for detecting *** the proposed methods via a traction systems in a certain type of trains obtains satisfactory results.

关键词： Degradation Network topology Fault detection Rail transportation State-space methods Safety Kalman filters

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