Optimizing resource allocation in industrial IoT with federated machine learning and edge computing integration by Ala'a R. Al-Shamasneh, Faten Khalid Karim, Yu Wang

“…This algorithm adeptly balances resource expenditures with model quality, employing Lyapunov-driven optimization theory to convert long-term stochastic challenges into short-term deterministic resolutions. …”
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Multi-Energy Optimal Dispatching of Port Microgrids Taking into Account the Uncertainty of Photovoltaic Power by Xiaoyong Wang, Xing Wei, Hanqing Zhang, Bailiang Liu, Yanmin Wang

“…Firstly, based on the improved Iterative Self-organizing Data Analysis Techniques Algorithm (ISODATA) clustering algorithm and backward reduction method, a set of typical scenarios that represent the uncertainties of PV and load is generated. …”
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A study on improved random forest-based anomaly detection of regional tariff data under distributed photovoltaic access by Shujun Ji, Kai Liu, Bo Ling, Jiadong Li, Jinteng Wang, Xun Ma

“…To ensure the reliability of the measurement and calculation results for electric energy and electricity charges, it is necessary to accurately determine the anomalous data in the regional electricity pricing datasets and consequently propose an anomaly detection method for regional electricity price data based on an improved random forest algorithm under distributed photovoltaic access. …”
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Data-driven automated job shop scheduling optimization considering AGV obstacle avoidance by Qi Tang, Huan Wang

“…To solve the model, we design an improved particle swarm algorithm combining genetic operators, crossover operators and elite retention operator. …”
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Coordinated Optimal Control of Secondary Cooling and Final Electromagnetic Stirring for Continuous Casting Billets by Dongsheng Wu, Zhenping Ji, Jian Yang, Hongwei Gao, Jiahui Yu, Zhaojie Ju

“…The solidification and heat transfer model are developed for the computation of billet temperature and the solidification, and the adaptive grid method is used to improve the diversity and robustness of optimal solutions. …”
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Dual Strategy of Reconfiguration with Capacitor Placement for Improvement Reliability and Power Quality in Distribution System by Ali Nasser Hussain, Wathiq Rafa Abed, Mohanad Muneer Yaqoob

“…These scenarios were tested on typical 33 and 69 bus IEEE RDS using the binary salp swarm algorithm (BSSA) based on the multiobjective functions (MOFs), in order to identify the most effective scenario performance that achieved the highest power quality and system reliability. …”
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Optimization of Central Pattern Generator-Based Torque-Stiffness-Controlled Dynamic Bipedal Walking by William Suliman, Chadi Albitar, Lama Hassan

“…This reduction enables the employment of the particle swarm algorithm to find the optimal values of these parameters which lead to different solutions with different performance criteria. …”
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Multi-objective optimization placement strategy for SDN security controller considering Byzantine attributes by Tao WANG, Hongchang CHEN

“…By giving the software defined network distributed control plane Byzantine attributes, its security can be effectively improved.In the process of realizing Byzantine attributes, the number and location of controllers, and the connection relationship between switches and controllers can directly affect the key network performance.Therefore, a controller multi-objective optimization placement strategy for SDN security controllers considering Byzantine attributes was proposed.Firstly, a Byzantine controller placement problem (MOSBCPP) model that comprehensively considered interaction delay, synchronization delay, load difference and the number of controllers was constructed.Then, a solution algorithm based on NASG-II was designed for this model, which included the initialization function, the mutation function, the fast non-dominated sorting function and the elite strategy selection function.Simulation results show that this strategy can effectively reduce interaction delay, synchronization delay, load difference and the number of controllers, while improving control plane security.…”
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Coordinated Optimal Dispatch of Distribution Grids and P2P Energy Trading Markets by Jing Deng, Fawu He, Qingbin Zeng, Jie Yan, Rangxiong Liu, Dongsheng He, Song Zhou

“…To address the nonlinear, high‐dimensional optimization challenges, an improved Convex‐Soft Actor‐Critic (C‐SAC) algorithm is developed, combining deep reinforcement learning with convex optimization to achieve privacy‐preserving distributed coordination. …”
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RE-YOLO: An apple picking detection algorithm fusing receptive-field attention convolution and efficient multi-scale attention. by Jinxue Sui, Li Liu, Zuoxun Wang, Li Yang

“…Finally, the loss function of YOLOv8 is improved using the Wise Intersection over Union (WIOU) function, which not only simplifies the gradient gain assignment mechanism and improves the ability to detect targets of different sizes, but also accelerates the model optimization. …”
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Preamble Allocation and Access Delay Reduction for Improved Massive Random Access in 5G Beamforming Networks by Gangwoo Lee, Jaewook Jung, Jong-Moon Chung

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Application of BITCN-BIGRU Neural Network Based on ICPO Optimization in Pit Deformation Prediction by Yong Liu, Cheng Liu, Xianguo Tuo, Xiang He

“…To enhance the prediction of pit deformation and improve accuracy and precision, an Improved Crown Porcupine Optimization Algorithm (ICPO) based on a Bidirectional Time Convolution Network–Bidirectional Gated Recirculation Unit (BITCN-BIGRU) is developed. …”
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A multi objective optimization framework for smart parking using digital twin pareto front MDP and PSO for smart cities by Dinesh Sahu, Priyanshu Sinha, Shiv Prakash, Tiansheng Yang, Rajkumar Singh Rathore, Lu Wang

“…Evaluation outcomes also show that the proposed algorithm is better than Round Robin, Random Allocation, and Threshold Based algorithms in terms of 25% improvement in the search time, 18% better energy usage, and 30% less traffic congestion. …”
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IMPROVE OF UNCERTAIN MICROSATELLITE MAGNETIC CLEANLINESS BASED ON MAGNETIC FIELD SPATIAL HARMONICS COMPENSATION by Б.І. Кузнецов, Т.Б. Нікітіна, І.В. Бовдуй, К.В. Чуніхін, В.В. Коломієць, Б.Б. Кобилянський

“…Both vector game solution calculated based on particles multi-swarm optimization (PMSO) algorithms from Pareto optimal solutions taking into account binary preference relations. …”
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Bayesian Optimized GridDehazeNet for Adaptive Haze Removal in Real-World Applications by Sungkwan Youm

“…By employing our algorithm, we effectively identify the best architectural parameters, achieving higher Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) compared to models without this optimization. …”
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Improvement of the Diagnostics of the Fetus Heart Anomalies During a Routine Screening Ultrasound Examination by Markin L., Medvjedjeva O.

“…So, the study of fetal heart is one of the most important stages of screening ultrasound in the second trimester of pregnancy.…”
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Enhancing Sustainable Manufacturing in Industry 4.0: A Zero-Defect Approach Leveraging Effective Dynamic Quality Factors by Rouhollah Khakpour, Ahmad Ebrahimi, Seyed Mohammad Seyed Hosseini

“…The methodology follows these steps:</p> <p style="text-align: left;">Step 1: Analysing effective dynamic factors of product quality</p> <p style="text-align: left;">Step2: Evaluating Triple Bottom Line (TBL) criteria</p> <p style="text-align: left;">Step 3: Measuring current sustainability state</p> <p style="text-align: left;">Step 4: Implementing ZDM strategies</p> <p style="text-align: left;">Step 5: Measuring improvements in sustainability</p> <p style="text-align: left;">&nbsp;</p> <p style="text-align: left;"><strong>Results</strong></p> <p style="text-align: left;">&nbsp;<strong>Effects</strong> <strong>of Single Unit Defective Product on TBL Sustainability State in Value Stream</strong></p> <p style="text-align: left;">&nbsp;</p> <p style="text-align: left;">&nbsp;</p> <p style="text-align: left;">Summary of current sustainability state</p> <table style="float: left;" width="479"> <tbody> <tr> <td width="64"> <p>Product model</p> </td> <td width="56"> <p>Daily schedule (set)</p> </td> <td width="61"> <p>Defective product rate (%)</p> </td> <td width="58"> <p>Number of defective products (set)</p> </td> <td width="85"> <p>Environmental sustainability</p> <p>State</p> </td> <td width="78"> <p>Social sustainability</p> <p>state</p> </td> <td width="78"> <p>Economic sustainability</p> <p>state</p> </td> </tr> <tr> <td width="64"> <p>Refrigerator</p> </td> <td width="56"> <p>480 set</p> </td> <td width="61"> <p>3%</p> </td> <td width="58"> <p>15</p> </td> <td width="85"> <p>Wasted material: 15 set</p> <p>&nbsp;</p> <p>Wasted energy: 239.25 kwh</p> </td> <td width="78"> <p>Waste of manpower: 1650 pmin</p> </td> <td width="78"> <p>Wasted costs:</p> <p>3265.65 $</p> </td> </tr> </tbody> </table> <p style="text-align: left;"><strong>&nbsp;</strong></p> <p style="text-align: left;">&nbsp;</p> <p style="text-align: left;">Future TBL sustainability state</p> <table style="float: left;" width="486"> <tbody> <tr> <td width="67"> <p>Product model</p> </td> <td width="59"> <p>Daily schedule (set)</p> </td> <td width="56"> <p>Defective product rate (%)</p> </td> <td width="16"> <p>&nbsp;</p> </td> <td width="61"> <p>Number of defective products (set)</p> </td> <td width="83"> <p>Environmental sustainability</p> <p>state</p> </td> <td width="82"> <p>Social sustainability state</p> </td> <td width="62"> <p>Economic sustainability state</p> </td> </tr> <tr> <td width="67"> <p>Refrigerator</p> </td> <td width="59"> <p>480 set</p> </td> <td width="56"> <p>0.2%</p> </td> <td width="16"> <p>&nbsp;</p> </td> <td width="61"> <p>1</p> </td> <td width="83"> <p>Wasted material: 1 set</p> <p>&nbsp;</p> <p>Wasted energy: 15.95 kwh</p> </td> <td width="82"> <p>Waste of manpower: 110 pmin</p> </td> <td width="62"> <p>Wasted costs:</p> <p>217.71 $</p> </td> </tr> </tbody> </table> <p style="text-align: left;"><strong>&nbsp;</strong></p> <p style="text-align: left;">&nbsp;</p> <p style="text-align: left;">&nbsp;</p> <p style="text-align: left;">&nbsp;</p> <p style="text-align: left;">&nbsp;</p> <p style="text-align: left;">&nbsp;</p> <p style="text-align: left;">&nbsp;</p> <p style="text-align: left;">&nbsp;</p> <p style="text-align: left;">&nbsp;</p> <p style="text-align: left;">&nbsp;</p> <p style="text-align: left;"><strong>Discussion and conclusion</strong></p> <p style="text-align: left;">&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Implementing the proposed approach aimed at achieving zero-defect products and enhancing TBL sustainability as its ultimate goal has provided valuable insights for practitioners and tangible improvements in the case study of this research. …”
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Reverse Logistics Network Optimization for Retired BIPV Panels in Smart City Energy Systems by Cimeng Zhou, Shilong Li

“…Meanwhile, the multi-level recycling network which covers “building points-regional transfer stations-specialized distribution centers” is designed in the research, the Pareto solution set is solved by the improved NSGA-II algorithm, a “1 + 1” du-al-core construction model of distribution center and transfer station is developed, so as to minimize the total cost and life cycle carbon footprint of the logistics network. …”
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A study of parameter aggregation algorithms for virtual power plant terminal decentralized resource scheduling characteristics in alpine regions by Yan Wang, Ruizhi Zhang, Ying Wang, Wen Xiang, Lu Wang

“…This approach enhances scheduling flexibility and resolves the grid-side secondary dispatch issue. An improved ant colony algorithm based on continuous optimization was used to solve the aggregation parameters. …”
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Optimization of the Operation Plan of Airport Express Train with Consideration of Train Departure Time Window by Jin He, Yinzhen Li, Yuhong Chao, Ruhu Gao

“…This paper proposes an optimization model for the train operation scheme of the Airport Express Line (AEL) based on the expected arrival time of passengers by the introduction of the train departure time to cope with the time-dependent passenger flow and provide better prompt train service according to passengers’ demand. …”
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