Degeneralization Algorithm for Generation of Büchi Automata Based on Contented Situation by Laixiang Shan, Jun Qin, Mingshi Chen, Zheng Qin

“…These ideas are implemented in a conversion algorithm used to build a property automaton corresponding to the given LTL formulae. We compare our method to previous work and show that it is more efficient for four sets of random formulae generated by LBTT.…”
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Bio-inspired cryptography based on proteinoid assemblies. by Panagiotis Mougkogiannis, Essam Ghadafi, Andrew Adamatzky

“…We present an innovative cryptographic technique inspired by the self-assembly processes of proteinoids-thermally stable proteins that form spontaneously under prebiotic conditions. By emulating the deterministic yet complex interactions within proteinoid assemblies, the proposed method generates secure encryption keys and algorithms. …”
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Time-Delayed Feedback Control in the Multiple Attractors Wind-Induced Vibration Energy Harvesting System by Qin Guo, Zhongkui Sun, Ying Zhang, Wei Xu

“…In the deterministic case, the time delay can control efficiently the birhythmic properties; thus one can realize a dividing line in the parameter plane that separates the space into two subspaces of generically distinct nature. …”
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A Proposed Stochastic Finite Difference Approach Based on Homogenous Chaos Expansion by O. H. Galal

“…Galerkin projection is used in converting the original stochastic partial differential equation (PDE) into a set of coupled deterministic partial differential equations and then solved using finite difference method. …”
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Geological modeling of carbonate fracture-cavity reservoir: case study of Shunbei fault zone No. 5 by Bolin Li, Yunfeng He, Wen Chen, Haojie Shang, Lixin Wang

“…First, deterministic modeling methods were employed to establish fault-controlled body contour models through seismic attribute fusion (structure tensor, energy gradient, and variance attributes) calibrated with well-log data. …”
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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

“…The method also achieved a 40.5% improvement in computational efficiency and a 30-50% reduction in system costs, demonstrating its practicality and scalability. …”
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Accelerated Transfer Learning for Cooperative Transportation Formation Change via SDPA-MAPPO (Scaled Dot Product Attention-Multi-Agent Proximal Policy Optimization) by Almira Budiyanto, Keisuke Azetsu, Nobutomo Matsunaga

“…The MADDPG (Multi-Agent Deep Deterministic Policy Gradient) method is popularly used for recognized environments. …”
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A Novel Multiobjective Optimization Approach for EV Charging and Vehicle-to-Grid Scheduling Strategy by Muhammad Aurangzeb, Yifei Wang, Sheeraz Iqbal, Md Shafiullah, Sultan Alghamdi, Zahid Ullah

“…These enhancements make our method more effective, flexible, and capable of supporting the transition to a sustainable and efficient energy system.…”
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Joint Resource Allocation for V2X Sensing and Communication Based on MADDPG by Zhiyong Zhong, Zhangyou Peng

“…In this paper, we propose a joint resource allocation method for V2X communication and sensing, aiming to optimize both communication rate and sensing performance. …”
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Probabilistic Load Flow of an Islanded Microgrid with WTGS and PV Uncertainties Containing Electric Vehicle Charging Loads by Neeraj Gupta, B. Rajanarayan Prusty

“…For an objective analysis of microgrids with large grid parity of renewable energy sources, probabilistic methods are required along with the already established deterministic methods. …”
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A Deep Reinforcement Learning Framework for Adaptive Resiliency Enhancement in Smart Power Grids by Boyu Wang, Aliasghar Baziar, Mohammad Reza Askari

“…To overcome these limitations, we propose a deep reinforcement learning framework based on the Deep Deterministic Policy Gradient (DDPG) algorithm, optimized using the Root Mean Square Propagation (RMSprop) method for stable and efficient training. …”
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Path planning algorithm for logistics autonomous vehicles at Cainiao stations based on multi-sensor data fusion. by Yan Chen

“…In view of the shortcomings of existing methods in multi-sensor data fusion and path optimization, this paper proposes a path planning model based on multi-sensor image fusion, named DynaFusion-Plan. …”
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A Novel Dynamic Lane-Changing Trajectory Planning Model for Automated Vehicles Based on Reinforcement Learning by Cenxin Yu, Anning Ni, Jing Luo, Jinghui Wang, Chunqin Zhang, Qinqin Chen, Yifeng Tu

“…This study develops a lane-changing model using the deep deterministic policy gradient method, which can simultaneously control the lateral and longitudinal motions of the vehicle. …”
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Quality of Experience Optimization for AR Service in an MEC Federation System by Huong Mai do, Tuan Phong Tran, Myungsik Yoo

“…We propose an improved deep deterministic policy gradient algorithm for efficient solution exploration. …”
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Longitudinal Hierarchical Control of Autonomous Vehicle Based on Deep Reinforcement Learning and PID Algorithm by Jialu Ma, Pingping Zhang, Yixian Li, Yuhang Gao, Jiandong Zhao

“…A hierarchical longitudinal control system that integrates deep deterministic policy gradient (DDPG) and proportional–integral–derivative (PID) control algorithms was proposed in this paper to ensure safe and efficient vehicle operation. …”
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Optimal Design of Interior Permanent Magnet Synchronous Motor Considering Various Sources of Uncertainty by Giacomo Guidotti, Dario Barri, Federico Soresini, Massimiliano Gobbi

“…Finally, the results of the robust optimization are compared with those of the deterministic optimization. Due to the small margin of improvement in robustness, both methods lead to similar results.…”
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Solving the Traveling Salesman Problem Based on The Genetic Reactive Bone Route Algorithm whit Ant Colony System by Majid Yousefikhoshbakht, Nasrin Malekzadeh, Mohammad Sedighpour

“…Since the performance of the Metaheuristic algorithms is significantly influenced by their parameters, Taguchi Method is used to set the parameters of the proposed algorithm. …”
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Measuring Component Importance for Network System Using Cellular Automata by Li He, Qiyan Cao, Fengjun Shang

“…This paper concentrates on the component importance measure of a network whose arc failure rates are not deterministic and imprecise ones. Conventionally, a computing method of component importance and a measure method of reliability stability are proposed. …”
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Flexible Load Stochastic Optimal Control of Wind Power-Based Hydrogen Production and Ammonia Synthesis Systems Based on the Itô Process by Guoshan YANG, Jie ZHU, Wenqin SONG, Yiwei QIU, Buxiang ZHOU

“…Then, the stochastic optimization problem is transformed into a deterministic second-order cone programming by the trajectory sensitivity decomposition and solved by the stochastic model predictive control (SMPC) in a rolling-horizon manner, avoiding the disadvantages of high computational complexity and low efficiency of the traditional sampling-based stochastic control methods. …”
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Optimization of distribution networks using quantum annealing for loss reduction and voltage improvement in electrical vehicle parking management by Naser Rashnu, Babak Mozafari, Reza Sharifi

“…The results demonstrate the method’s scalability and effectiveness in handling real-time EV integration while reducing operational costs and supporting voltage stability. …”
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