PID Control and Input Shaping for Quadrotor UAV Stabilization and Payload Swing Reduction by Liyana Ramli, Izzuddin M. Lazim, Amalin Aisya Awi, Aina Syazwin Shukor

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Information-guided adaptive learning approach for active surveillance of infectious diseases by Qi Tan, Chenyang Zhang, Jiwen Xia, Ruiqi Wang, Lian Zhou, Zhanwei Du, Benyun Shi

“…Based on a probabilistic model, we evaluate the information gain of monitoring a spatio-temporal target and design a greedy selection algorithm for monitoring targets selection. …”
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Dynamic Control and Analysis of Dual Active Bridge Converters in Grid-Connected PV-BESS by Mehmet Can Sekanli, Samet Yalçin, Okan Bingol

“…The Perturb & Observe (P&O) Maximum Power Point Tracking (MPPT) algorithm optimized power extraction from the PV array, while the DAB converter employed Single Phase Shift (SPS) control for efficient, bidirectional energy management. …”
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Multi-Depot Pickup and Delivery Problem with Resource Sharing by Yong Wang, Lingyu Ran, Xiangyang Guan, Yajie Zou

“…Finally, optimization results of a real-world logistics network from Chongqing confirm the applicability of the mathematical model and the designed solution algorithm. …”
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An Intelligent System for Management of Medical Equipment Maintenance by Abbas Izadi, Mohamad Amin Bakhshali, Hadi Ghasemifard, Omid Sarrafzadeh

“…Results: The results demonstrate that the proposed system can accurately predict equipment failures, schedule maintenance tasks efficiently, and manage spare parts inventory effectively. This improves equipment availability and reliability, reduces maintenance costs, and ensures that spare parts are available when needed without incurring excessive inventory costs.Conclusion:Overall, the proposed intelligent system for managing medical equipment maintenance is an effective solution for healthcare facilities to optimize maintenance operations, reduce costs, and ensure patient safety.…”
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Power Allocation Technology of Long Time Multi-Star Hopping Beam for LEO Satellite by Ziyi LIU, Xiaoning ZHANG, Zesong FEI

“…LEO satellites have superior development prospects due to their low cost, low latency and small path loss, and are widely used in IoT, B5G and other fields.For the LEO satellite and its coverage area will be in a moving state, a convex optimization-based long-time multi-star beam hopping power allocation algorithm was proposed to maximize the system capacity.Focused on the multi-star hopping beam scenario over a period of time, a system model was developed based on the long-time co-orbital multi-star hopping beam scenario and the long-time heterodyne multi-star hopping beam scenario respectively.The resource allocation algorithm was designed for the two long-time multi-star hopping beams with the weighted objective function as the optimization objective, considered the influence factors of inter-star interference, load balancing and inter-star resource allocation priority, a long-time skipping beam resource allocation algorithm based on convex optimization was proposed.The simulation results showed that the proposed scheme could improve the resource utilization of the system compared with the conventional schemes.…”
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Manufacturing engineering production line scheduling management technology integrating availability constraints and heuristic rules by Gu Yun

“…The above results indicate that the proposed model and hybrid algorithm have good performance and effectiveness, which can help improve the quality of engineering production line scheduling management.…”
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Optimising Connectivity and Energy: The Future of LoRaWAN Routing Protocols for Mobile IoT Applications by Izzah N. Buang, K. Zen, Syahrul N. Junaini

“…However, the mobility of IoT devices introduces challenges in optimizing energy efficiency. This study provides a comprehensive review of energy-efficient routing algorithms for LoRaWAN in mobile IoT applications. …”
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Myocarditis Diagnosis Using Semi-Supervised Generative Adversarial Network and Differential Evolution by Haifeng Gui, Na Zhang

“…To minimize reliance on hyperparameters, the Random Key method is employed, optimized using the DE algorithm. The efficacy of the model is demonstrated on the Z-Alizadeh Sani myocarditis dataset, with further validation achieved through experiments on the EMIDEC dataset, assessing transfer learning (TL) effects. …”
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Vehicle Routing Problem for Collaborative Multidepot Petrol Replenishment under Emergency Conditions by Guangcan Xu, Qiguang Lyu

“…As a method to solve the model, genetic variation of multiobjective particle swarm optimization algorithm is considered. …”
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Joint Allocation of Power and Subcarrier for Low Delay and Stable Power Line Communication by Zhixiong Chen, Zhihui Yang, Zeng Dou

“…The optimal power allocation under the condition of a single slot is realized by subcarrier diversity grouping and improved genetic algorithm, and then the subcarrier power below the rate threshold is recycled and allocated to the slot with good prediction performance. …”
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Outdoor location scheme with fingerprinting based on machine learning of mobile cellular network by Zhichao ZHOU, Yi FENG, Xiaohan XIA, Yuyao FENG, Chao CAI, Jiahui QIU, Lihui YANG, Yunxiao WU

“…The positioning scheme based on mobile cellular network technology is one of the important technical approaches to provide network optimization, emergency rescue, police patrol and location services.The traditional positioning scheme based on cell base station location information has low positioning accuracy and large positioning error, so it cannot meet the requirements of some positioning applications.The scheme based on fingerprint location can greatly improve the location accuracy, save computational cost and enhance the usability based on the coarse location scheme of the cell and become the hotspot of the research.Rasterization and non-rasterization of outdoor fingerprint location scheme based on machine learning were studied and analyzed to meet the business requirements of outdoor fingerprint location.By means of parameter weighting, data fitting and other methods, large-scale fingerprint data were cleaned to improve the effectiveness of data sources.Through the realization of sub-modules such as demarcating research area, rasterizing, constructing fingerprint database, training model, correcting model, non-rasterizing, rough positioning coupling, matching parameter and training parameter, the operation efficiency and positioning accuracy of the algorithm were analyzed and optimized, and the key indexes affecting the algorithm performance were determined.Then, the performance of two fingerprint-based localization schemewas analyzed based on the simulation results.Finally, the typical scenarios of the fingerprint location scheme based on machine learning in practical application were presented.…”
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Similar Instances Reuse Based Numerical Control Process Decision Method for Prismatic Parts by Changhong XU, Shusheng ZHANG, Jiachen LIANG, Rui HUANG, Rong BIAN

“…The NC process decision efficiency is improved by 84.6%. On the other hand, the manufacturing cost of the optimal NC process scheme is 16.6% lower.Conclusions The experimental results showed that the proposed approach can generate optimal NC process schemes for parts effectively and automatically, decrease production costs, and shorten the development cycle. …”
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Spatio‐temporal dynamic navigation for electric vehicle charging using deep reinforcement learning by Ali Can Erüst, Fatma Yıldız Taşcıkaraoğlu

“…A recently proposed on‐policy actor–critic method, phasic policy gradient (PPG) which extends the proximal policy optimization algorithm with an auxiliary optimization phase to improve training by distilling features from the critic to the actor network, is used to make EVCS decisions on the network where EV travels through the optimal path from origin node to EVCS by considering dynamic traffic conditions, unit value of EV owner and time‐of‐use charging price. …”
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A dynamic service migration strategy based on mobility prediction in edge computing by Lanlan Rui, Shuyun Wang, Zhili Wang, Ao Xiong, Huiyong Liu

“…Furthermore, we build a network model and propose a based on Lyapunov optimization method with long-term cost constraints. …”
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Research on collaborative scheduling strategies of multi-agent agricultural machinery groups by Ziyi Wang, Fan Zhang, Shiji Ma, Hailong Wang, Shunyao Zhang, Xiaozhong Gao

“…Subsequently, the network model is trained to improve its solution capabilities using the policy gradient algorithm (REINFORCE). …”
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Integrated Planning for Shared Electric Vehicle System Considering Carbon Emission Reduction by Xiaohui Sun, Yumei Mi, Askar Ahtam, Zhi Zuo

“…By applying these models to the Chicago Sketch network and using a genetic algorithm to solve the models, it is concluded that the optimal outlet location solution considering carbon emission reduction will increase the outlet construction cost and user travel time cost. …”
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Distributed Multi-Energy Trading in Energy Internet: An Aggregative Game Approach by Jingwei Hu, Enhui An, Qiuye Sun, Bonan Huang

“…Since each WE only needs to communicate with its neighbors to exchange information, this distributed process reduces communication burden and improves information security. Furthermore, a multi-energy transmission optimization model is established to determine the transmission path of the transmission energy, which can minimize the transmission cost. …”
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Distributed Collaborative Control Strategy for Intra-regional AGC Units in Interconnected Power System with Renewable Energy by Lei ZHANG, Xiaowei MA, Manliang WANG, Li CHEN, Bingtuan GAO

“…Then, considering the frequency regulation economy, a power dynamic allocation optimization model is constructed, with the objective of minimizing the quadratic cost function of active power variation constrained by active power output and power balance. …”
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Double-layer energy transaction strategy of multi-microgrids and distribution network with leased shared energy storage by WANG Hui, WU Zuohui, LI Xin, ZOU Zhichao, ZHOU Kerui

“…In profit allocation of microgrid alliance, an asymmetric Nash bargaining method is proposed that fairly distribute profits according to the contribution size of each member in providing energy. Finally, an improved particle swarm optimization algorithm combined with the alternating direction multiplier method is adopted to solve the hybrid game model. …”
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