Traffic light detection and recognition based on deep learning for autonomous-rail rapid tram by XIONG Qunfang, LIN Jun, YUAN Xiwen, XU Yanghan, YUE Wei, LI Yuanzhengyu

“…These abnormal image data were saved and utilized for further training and optimizing the model. Experimental results reveals that the proposed approach effectively detected and recognized ART traffic lights, achieving an average detection precision of 84.76% on designated roads during the daytime while exhibiting good real-time performance.…”
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Intelligent Dynamic Trajectory Planning of UAVs: Addressing Unknown Environments and Intermittent Target Loss by Zhengpeng Yang, Suyu Yan, Chao Ming, Xiaoming Wang

“…Additionally, considering future practical applications, the predictor and planner adopt a parallel optimization and correction algorithm structure to ensure planning accuracy and computational efficiency. …”
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Mission Sequence Model and Deep Reinforcement Learning-Based Replanning Method for Multi-Satellite Observation by Peiyan Li, Peixing Cui, Huiquan Wang

“…Both phases are formulated as Markov Decision Processes (MDPs) and optimized using the PPO algorithm. Extensive simulations demonstrate that our method significantly outperforms state-of-the-art approaches, achieving a 15.27% higher request insertion revenue rate and a 3.05% improvement in overall mission revenue rate, while maintaining a 1.17% lower modification rate and achieving faster computational speeds. …”
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Tactical Coordination-Based Decision Making for Unmanned Combat Aerial Vehicles Maneuvering in Within-Visual-Range Air Combat by Yidong Liu, Dali Ding, Mulai Tan, Yuequn Luo, Ning Li, Huan Zhou

“…Finally, an improved particle swarm optimization algorithm (I-PSO) is proposed, which enhances the optimization ability and real-time performance through the design of local social factor iterative components and adaptive adjustment of inertia weights. …”
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Lightweight Anomaly Detection for Wireless Sensor Networks by Pu Cheng, Minghua Zhu

“…The main challenge of anomaly detection algorithm in WSNs is identifying anomalies with high accuracy while consuming minimal resource of the network. …”
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Cell Outage Detection and Compensation in Two-Tier Heterogeneous Networks by Wenqian Xue, Hengzhi Zhang, Yong Li, Dong Liang, Mugen Peng

“…Power adjustment on each resource block is carried out via Lagrange optimizing algorithm to compensate the breakdown cell. …”
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Improved Variational Mode Decomposition in Pipeline Leakage Detection at the Oil Gas Chemical Terminals Based on Distributed Optical Fiber Acoustic Sensing System by Hongxuan Xu, Jiancun Zuo, Teng Wang

“…This paper employs a distributed fiber optic sensing system to collect pipeline leakage signals and processes these signals using the traditional variational mode decomposition (VMD) algorithm. While traditional VMD methods require manual parameter setting, which can lead to suboptimal decomposition results if parameters are incorrectly chosen, our proposed method introduces an improved particle swarm optimization algorithm to automatically determine the optimal parameters. …”
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The interpretable machine learning model for depression associated with heavy metals via EMR mining method by Site Xu, Mu Sun

“…We constructed 5 ML models to detect depression based on heavy metal exposure and assessed their performance using 10 discrimination metrics. The optimal model was selected after parameter tuning with a Genetic Algorithm (GA). …”
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Subspace-based local compilation of variational quantum circuits for large-scale quantum many-body simulation by Shota Kanasugi, Yuichiro Hidaka, Yuya O. Nakagawa, Shoichiro Tsutsui, Norifumi Matsumoto, Kazunori Maruyama, Hirotaka Oshima, Shintaro Sato

“…Here, we propose a hybrid quantum-classical algorithm, called local subspace variational quantum compilation (LSVQC), for compiling the time-evolution operator of quantum many-body systems. …”
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DECISION SUPPORT SYSTEM IN PLANNING INVESTMENT PROJECTS by Viktor Kosenko

“…The following results were obtained: it is shown that the efficiency of the investment process is related to the assessment and selection of the most attractive investment projects from a number of alternatives, which will provide the maximum profit in the future; the strategic tasks of investing in the development of organizations and enterprises are singled out; the main finances that can be involved in carrying out an investment project are considered; the concepts of an investment project, a subject and an object of the investment activity were defined; the above concepts were formalized using the theory of sets and the system approach; the basic conditions under which the project should be implemented are formulated; the generalized diagram of the investment project implementation is developed. While developing the method of selecting the optimal variant of the investment project, the types of source information about the project and the organization which will use the finances are involved are determined. …”
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Research into prediction and influential factors of circuit breaker closing time using BFGS-NN by Longcheng Dai, Jiaying Yu, Zhihui Huang, Hui Ni, Yifan Zhang, Junting Dou

“…On-site operational data were analyzed to build a circuit breaker action time database. The BFGS algorithm trained on these data generated a closing time prediction model, achieving rapid convergence and optimal fit during learning. …”
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HiCoACR: A reconfiguration decision-making model for reconfigurable security protocol based on hierarchically collaborative ant colony by Zhuo Yi, Ying Liao, Xuehui Du, Xin Lu, Lifeng Cao

“…The Explorer controls directions of protocol reconfiguration and generates abstract scheduling sub-goals which are conveyed from the Worker. While the Worker schedules most suitable cryptogram resources for each sub-goal received and produces the optimal reconfiguration solution which is verified and re-optimized by a Lévy process–based stochastic gradient descent algorithm. …”
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Task allocation and path planning for multi-robot systems in intelligent warehousing by CHU Jing, TIAN Yiqiu, YUE Qi, HUANG Yong

“…Additionally, a two-layer path planning strategy is proposed: the outer layer conducts global path planning based on the Floyd algorithm, while the inner layer resolves various collision issues through traffic rule constraints, achieving local optimal path planning. …”
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Embedded Hardware-Efficient FPGA Architecture for SVM Learning and Inference by B. B. Shabarinath, Muralidhar Pullakandam

“…While Sequential Minimal Optimization (SMO) has enhanced the efficiency of SVM training, traditional implementations still suffer from high computational cost. …”
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Multi-time Scale Voltage-Reactive Power Control for Distribution Network with Distributed Generation by LIU Haotian, ZHENG Chuyu, LENG Yang, LI Qin, ZHANG Hua, CHEN Shidu, LI Jiayong

“…In day-ahead stage, an optimization model is established to minimize the network loss and voltage deviation, by controlling traditional equipment like tap changers and capacitor banks, which is efficiently solved by linearization and second-order cone algorithm. …”
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Growth Curve Models and Clustering Techniques for Studying New Sugarcane Hybrids by Carlos David Carretillo Moctezuma, María Guzmán Martínez, Flaviano Godínez-Jaimes, José C. García-Preciado, Ramón Reyes Carreto, José Terrones Salgado, Edgar Pérez Arriaga

“…According to the Connectivity and Dunn indexes, the DBSCAN algorithm provides the best clustering structure for materials in the plant cycle, while for the ratoon cycle, the k-means algorithm offers the best clustering structure. …”
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Forest cover and canopy health mapping in Australian subalpine landscape: supervised machine learning models for Sentinel-2 and Landsat images by Weerach Charerntantanakul, Marta Yebra, Hilary Rose Dawson, Adrienne Beth Nicotra, Saul Alan Cunningham, Matthew Theodore Brookhouse

“…We tested random-forest (RF), support vector machine (SVM), and multiple linear regression (MLR) to find the algorithm that provides the best accuracy. Cross-validation experiments were undertaken to optimize the model configurations. …”
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Designing diverse and high-performance proteins with a large language model in the loop. by Carlos A Gomez-Uribe, Japheth Gado, Meiirbek Islamov

“…We present a protein engineering approach to directed evolution with machine learning that integrates a new semi-supervised neural network fitness prediction model, Seq2Fitness, and an innovative optimization algorithm, biphasic annealing for diverse and adaptive sequence sampling (BADASS) to design sequences. …”
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Pig Aggression Tracking and Analysis Application Based on A RPMeMOTR Method by Shuqin Tu, Haoxuan Ou, Aqing Yang, Yun Liang, Jiaying Du, Yuefei Cao, Ruilin He, Fang Yuan

“…The management of pig aggression in group-housed environments is crucial for ensuring animal welfare and optimizing production efficiency in the global agricultural industry. …”
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Innovative Lightweight Detection for Airborne Remote Sensing: Integrating G-Shuffle and Dynamic Multiscale Pyramid Networks by Ruofei Liang, Yigang Cen, Linna Zhang, Fugui Zhang, Yansen Huang, Fei Gan

“…To address the challenges of significant scale variations and difficulty in extracting discriminative features for small targets in airborne remote sensing object detection, while considering the constraints and efficiency requirements of onboard systems, this article proposes a lightweight detection algorithm. …”
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