Optimal Scheduling Method for Power Generation of Cascade Reservoirs Based on RLDE Algorithm by CHEN Jia-wen, ZHU Xin, TANG Zheng-yang, SHEN Ke-yan, CHEN Xiao-lin, QIN Hui

“…To validate the feasibility and effectiveness of the RLDE algorithm, it was applied to optimize the power generation scheduling model for four major cascade reservoirs (Wudongde, Baihetan, Xiluodu, and Xiangjiaba) on the lower Jinsha River. …”
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Optimization and Application of Fuzzy Neural Network by LI Hao-nan, LIU Yong

“…In order to improve the generalization ability，convergence speed and reduce the error of the fuzzyneural network，the method of using momentum gradient descent algorithm and ＲMSprop optimization algorithm to optimize the fuzzy neural network is proposed． …”
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Post-Processing Optimization of the Global 30 m Land Cover Dynamic Monitoring Product by Zhehua Li, Xiao Zhang, Wendi Liu, Tingting Zhao, Weitao Ai, Jinqing Wang, Liangyun Liu

“…Post-processing optimization refers to the refinement of land cover products by applying specific rules or algorithms to minimize erroneous changes in land cover types caused by classification uncertainty or interannual phenological variations. …”
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Analysis and simulation of interception characteristics of broad-leaved forest canopy based on parameter optimization: Dianchi basin case by Qi Yi, Shengfang Hou, Xiaodong Dou, Yuting Gao, Zhongbin Li, Yiyan Liu

“…This study investigated the rainfall redistribution characteristics of three typical subtropical semi-humid evergreen broad-leaved forests in the Dianchi Basin of China. The model structure was improved by incorporating both canopy density and leaf area index (LAI) as indicators, and optimized the parameters using the Gauss-Newton algorithm, with appropriate threshold settings for absorption and additional interception. …”
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Research on kd-tree Cache Optimization Based on Particle Index Sorting Algorithm by 张挺, 林震寰, 杨丁颖, 王宗锴, 陈轶凡

“…Consequently, the cache access pattern is optimized, leading to fewer cache misses and improved search times. …”
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An Improved KF-RBF Based Estimation Algorithm for Coverage Control with Unknown Density Function by Lei Zuo, Maode Yan, Yaoren Guo, Wenrui Ma

“…Compared with the other estimation algorithms, a novel sampling regulation mechanism is designed to improve the estimation performance and reduce the computational load. …”
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Multi-Objective Optimization of Chiral Metasurface for Sensing Based on a Distributed Algorithm by Xianglai Liao, Lili Gui, Shulei Bi, Ang Gao, Zhenming Yu, Kun Xu

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FPGA multi-unit parallel optimization and implementation of post-quantum cryptography CRYSTALS-Kyber by Bin LI, Xiaojie CHEN, Feng FENG, Qinglei ZHOU

“…In lattice-based post-quantum cryptography, polynomial multiplication is complicated and time-consuming.In order to improve the computational efficiency of lattice cryptography in practical applications, an FPGA multi-unit parallel optimization and implementation of post-quantum cryptography CRYSTALS-Kyber was proposed.Firstly, the flow of Kyber algorithm was described and the execution of NTT, INTT and CWM were analyzed.Secondly, the overall structure of FPGA was given, the butterfly arithmetic unit was designed by pipeline technology, and the Barrett modulus reduction and CWM scheduling optimization were used to improve the calculation efficiency.At the same time, 32 butterfly arithmetic units were executed in parallel, which shortens the overall calculation cycle.Finally, the multi-RAM channel was optimized to improve the memory access efficiency with alternate data access control and RAM resource reuse.In addition, with the loosely coupled architecture, the overall operation scheduling was realized by DMA communication.The experimental results and analysis show that the proposed scheme implemented can complete NTT, INTT and CWM operations within 44, 49, and 163 clock cycles, which is superior to other schemes and has high energy efficiency ratio.…”
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Investigating the Performance of Open-Vocabulary Classification Algorithms for Pathway and Surface Material Detection in Urban Environments by Kauê de Moraes Vestena, Silvana Phillipi Camboim, Maria Antonia Brovelli, Daniel Rodrigues dos Santos

“…Our approach uses large language models (LLMs) to improve the accuracy of classifying different pavement types. …”
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Engine Optimization Model for Accurate Prediction of Friction Model in Marine Dual-Fuel Engine by Mina Tadros

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MILP Modeling and Optimization of Multi-Objective Three-Stage Flexible Job Shop Scheduling Problem With Assembly and AGV Transportation by Shiming Yang, Leilei Meng, Saif Ullah, Biao Zhang, Hongyan Sang, Peng Duan

“…To solve this problem, a mixed-integer linear programming model (MILP) is developed and the optimal Pareto front for small-scale instances are solved by using the <inline-formula> <tex-math notation="LaTeX">$\varepsilon $ </tex-math></inline-formula>-method. …”
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An automated software algorithm for optimizing microwave ablation parameters for treatment of liver tumors by Sabrina Q. R. Liew, Nathaniel Rex, Celina Hsieh, Hyeonseon Kim, Scott A. Collins, Grayson L. Baird, DaeHee Kim, Aaron W. P. Maxwell

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Optimization of external container delivery and pickup scheduling based on appointment mechanism. by Pengfei Huang, Hao Wang, Fangjiao Tan, Yuyue Jiang, Jinfen Cai

“…Through case studies, we have demonstrated the superior performance of this algorithm in addressing relevant problems. The results show that, in terms of truck operational costs, the improved algorithm reduces costs by 10.96% and 3.02% compared to traditional Ant Colony Optimization and Variable Neighborhood Search algorithms, respectively, and by 4.89% compared to manual scheduling. …”
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Adaptive Particle Swarm Optimization with Landscape Learning for Global Optimization and Feature Selection by Khalil Abbal, Mohammed El-Amrani, Oussama Aoun, Youssef Benadada

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A Review: The Application of Path Optimization Algorithms in Building Mechanical, Electrical, and Plumbing Pipe Design by Ruijun Deng, Xiaoliang Li, Yuhua Tian

“…This review systematically integrates recent advancements in path optimization algorithms for the automated layout of mechanical, electrical, and plumbing (MEP) systems within complex building environments. …”
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Deep Reinforcement Learning-Based Distribution Network Planning Method Considering Renewable Energy by Liang Ma, Chenyi Si, Ke Wang, Jinshan Luo, Shigong Jiang, Yi Song

“…Based on the proximal policy optimization algorithm, an actor-critic-based autonomous generation and adaptive adjustment model for DNP is constructed. …”
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Autonomous Decision-Making for Air Gaming Based on Position Weight-Based Particle Swarm Optimization Algorithm by Anqi Xu, Hui Li, Yun Hong, Guoji Liu

“…As the complexity of air gaming scenarios continues to escalate, the demands for heightened decision-making efficiency and precision are becoming increasingly stringent. To further improve decision-making efficiency, a particle swarm optimization algorithm based on positional weights (PW-PSO) is proposed. …”
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Research on location algorithm of opencast mine vehicle based on improved adaptive H ∞ CKF and IAGA by Zhen Yang, Liwen Ji, Xin Li, Haoyuan Liu, Jianxiong Li, Dianxing Sun, Ruiheng Sun

“…Improved Adaptive Genetic Algorithm (IAGA) updates the optimal preservation strategy of traditional genetic algorithm and redefines the adaptive cross rate and variation rate. …”
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Microgrid Load Forecasting Based on Improved Long Short-Term Memory Network by Qiyue Huang, Yuqing Zheng, Yuxuan Xu

“…In this paper, a load-forecasting algorithm for microgrid based on improved long short-term memory neural network (LSTM) is proposed. …”
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Daily Runoff Prediction Model Based on Multivariate Variational Mode Decomposition and Correlation Reconstruction by DING Jie, TU Peng-fei, FENG Yu, ZENG Huai-en

“…Finally, the integrated prediction combining fluctuation and random terms under condition 5 yielded R2 of 0.87 and 0.93 for the overall prediction at Ankang and Baihe stations, respectively, demonstrating excellent model performance. [Conclusions](1) The MVMD decomposition method can control the number of decomposition layers, ensuring complete signal feature extraction without overfitting while improving processing speed.(2) Pearson correlation coefficient method enhances prediction accuracy through decomposed data classification.(3) The MEA-BP can improve signal-to-noise ratio, adapt to complex environments, enhance learning efficiency and generalization ability, and reduce computational complexity.(4) The GWO-ELM algorithm integrates grey wolf optimizer with extreme learning machine, providing a fast and adaptive solution for time-series prediction with reduced overfitting and improved efficiency.(5) The overall combined model can efficiently and stably process large amount of data while ensuring high accuracy.…”
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