Classification-based point cloud denoising and 3D reconstruction of roadways by Denghong CHEN, Ning PANG, Wen NIE, Juqiang FENG, Jiliang KAN, Jinjing ZHANG

“…This algorithm delivered adaptive optimization performance, yielding types Ⅰ and Ⅱ errors of 1.54 % and 5.37 %, respectively. …”
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Obstacle Avoidance Control of Autonomous Undersea Vehicle Based on DVFH+ in Ocean Current Environment by Zhongben ZHU, Jiahao ZHANG, Yifan XUE, Hongde QIN

“…Additionally, by considering the drift angle compensation in the real ocean current environment, the obstacle avoidance algorithm was optimized to improve its robustness and adaptability. …”
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A novel pulse-current waveform circuit for low-energy consumption and low-noise transcranial magnetic stimulation by Xinhua Tan, Ao Guo, Jiasheng Tian, Yingwei Li, Yingwei Li, Jian Shi

“…This study proposes a novel, non-resonant, high-frequency switching design controlled by high-frequency pulse-width modulation (PWM) voltage excitation to achieve ideal pulse-current waveforms that minimize both clicking noise and heat generation from the TMS coil.MethodFirst, a particle swarm optimization algorithm was used to optimize the pulse-current waveform, minimizing both the resistance loss and clicking noise (vibration energy) generated by the TMS coils. …”
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Hybrid A*-Guided Model Predictive Path Integral Control for Robust Navigation in Rough Terrains by Joonyeol Yang , Minhyeong Kang , Seulchan Lee, Sanghyun Kim

“…These computed paths are then used to define the mean control input for the MPPI algorithm, which performs localized optimization while adhering to the terrain-aware trajectory. …”
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Evaluation of Multi-Class Classification Performance Lung Cancer Through K-NN and SVM Approach by Muh. Indra Endriartono Saputra Troy, Sitti Rahmah Jabir, Siska Anraeni

“…Therefore, the results show that the SVM algorithm is superior to the KNN algorithm. The KNN and SVM methods were implemented for multi-class classification of lung cancer, allowing identification of various subtypes of lung cancer with optimal accuracy.…”
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Digital three-stage recursive-separable image processing filter with variable sizes of scanning multielement aperture by A. V. Kamenskiy, T. M. Akaeva, D. A. Grebenshchikova

“…The study established that the processing time of a test image using the developed filter is on average 5 times less than the time taken by the classical two-dimensional convolution algorithm. The optimal coefficients for magnifying the central element and raising the positive part of the aperture of a digital filter were determined, enabling the efficiency of its use to be enabled.Conclusions. …”
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Game Theoretic Non‐cooperative Dynamic Target Tracking for Directional Sensing‐Enabled Unmanned Aerial Vehicles by Peng Yi, Ge Jin, Wenyuan Wang

“…Game theory is employed to determine the optimal confrontation paths for defenders against the intruders. …”
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A hybrid blockchain-based solution for secure sharing of electronic medical record data by Gang Han, Yan Ma, Zhongliang Zhang, Yuxin Wang

“…This algorithm not only streamlines the signature process across chains but also strengthens system security and optimizes storage efficiency, addressing a key challenge in multi-chain systems. …”
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Maintenance Scheduling Strategy for MMCs Within an MVDC System Using Sensitivity Analysis by Tae-Yang Nam, Dong-Il Cho, Joong-Woo Shin, Kwang-Hoon Yoon, Jae-Chul Kim, Won-Sik Moon

“…The proposed algorithm optimizes the maintenance timing and priority by performing a sensitivity analysis of the system reliability indicators, from both the consumer and operator perspectives. …”
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GTrXL-SAC-Based Path Planning and Obstacle-Aware Control Decision-Making for UAV Autonomous Control by Jingyi Huang, Yujie Cui, Guipeng Xi, Shuangxia Bai, Bo Li, Geng Wang, Evgeny Neretin

“…Experimental results in the AirSim drone simulation environment demonstrate that compared to PPO and SAC algorithms, GTrXL-SAC achieves more precise policy exploration and optimization, enabling superior control of drone velocity and attitude for stabilized flight while accelerating convergence speed by nearly 20%.…”
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Aircraft Flight Autonomous Decision-Making Method Based on Target Predicted Trajectory and Markov Decision Process by Yang Zhou, Xinmin Tang, Xuanming Ren

“…Secondly, we adopt the Markov Decision Process as the autonomous decision-making method for the own aircraft, proposing a method by which to calculate the optimal decision sequence based on the prediction scenarios of the IMM algorithm; that is, in both multi-step prediction and single-step prediction scenarios, the payoff values of different action strategies at each decision moment are calculated to obtain the optimal decision sequence. …”
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Analysis of Encrypted Network Traffic for Enhancing Cyber-security in Dynamic Environments by Faeiz Alserhani

“…User selection is accomplished through robust Deep Reinforcement Learning with the Tabu Search (DRL-TS) algorithm, while channel selection is optimized through rigorous training employing Proximal Policy Optimization (PPO). …”
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A Hybrid Dynamic Path-Planning Method for Obstacle Avoidance in Unmanned Aerial Vehicle-Based Power Inspection by Zheng Huang, Chengling Jiang, Chao Shen, Bin Liu, Tao Huang, Minghui Zhang

“…Simulation results show that, compared to traditional algorithms, the proposed method achieves an 8% to 12% optimization in path length, more than 50% in node optimization, and over 95% in planning time optimization. …”
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Bayesian Q-learning in multi-objective reward model for homophobic and transphobic text classification in low-resource languages: A hypothesis testing framework in multi-objective... by Vivek Suresh Raj, Ruba Priyadharshini, Saranya Rajiakodi, Bharathi Raja Chakravarthi

“…Most Reinforcement Learning (RL) algorithms optimize a single-objective function, whereas real-world decision-making involves multiple aspects. …”
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Adaptive dimensionality reduction for neural network-based online principal component analysis. by Nico Migenda, Ralf Möller, Wolfram Schenck

“…While the continuous update of the principal components is widely studied, the available algorithms for dimensionality adjustment are limited to an increment of one in neural network-based and incremental PCA. …”
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A Centralized Multi-Agent DRL-Based Trajectory Control Strategy for Unmanned Aerial Vehicle-Enabled Wireless Communications by Getaneh Berie Tarekegn, Rong-Terng Juang, Belayneh Abebe Tesfaw, Hsin-Piao Lin, Huan-Chia Hsu, Robel Berie Tarekegn, Li-Chia Tai

“…The trajectory of the aerial base stations is then continuously adjusted through a centralized multi-agent deep reinforcement learning algorithm to optimize communication performance. We evaluated our proposed system using real channel measurement data, i.e., amplitude and phase signal information. …”
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Local Search-Based Metaheuristic Methods for the Solid Waste Collection Problem by Haneen Algethami

“…The routing solver in the Google OR-tools solver is utilised with three well-known metaheuristic methods for neighbourhood exploration: a guided local search (GLS), a tabu search (TS), and simulated annealing (SA), with two initialisation strategies, Clarke and Wright’s algorithm and the nearest neighbour algorithm. Results showed that optimal solutions are found in faster computational times than using only an IP solver, especially for large instances. …”
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Research on the cooperative offloading strategy of sensory data based on delay and energy constraints by Peiyan YUAN, Saike SHAO, Ran WEI, Junna ZHANG, Xiaoyan ZHAO

“…The edge offloading of the internet of things (IoT) sensing data was investigated.Multiple edge servers cooperatively offload all or part of the sensing data initially sent to the cloud center, which protects data privacy and improves user experience.In the process of cooperative offloading, the transmission of the sensing data and the information exchange among edge servers will consume system resources, resulting in the cost of cooperation.How to maximize the offloading ratio of the sensing data while maintaining a low collaboration cost is a challenging problem.A joint optimization problem of sensing data offload ratio and cooperative scale satisfying the constraints of network delay and system energy consumption was formulated.Subsequently, a distributed alternating direction method of multipliers (ADMM) via constraint projection and variable splitting was proposed to solve the problem.Finally, simulation experiments were carried out on MATLAB.Numerical results show that the proposed method improved the network delay and energy consumption compared to the fairness cooperation algorithm (FCA), the distributed optimization algorithm (DOA), and multi-subtasks-to-multi-servers offloading scheme (MTMS) algorithm.…”
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A Time- and Space-Integrated Expansion Planning Method for AC/DC Hybrid Distribution Networks by Yao Guo, Shaorong Wang, Dezhi Chen

“…A modified graph attention network (MGAT)-based deep reinforcement learning (DRL) algorithm is used for optimization, balancing economic and reliability objectives. …”
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Utilizing weak graph for edge consolidation-based efficient enhancement of network robustness by Wei Ding, Zhengdan Wang

“…We compare the proposed algorithm with optimal and approximate algorithms across graphs of varying scales. …”
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