In this paper, a method of single-phase grounding fault line selection based on high frequency energy proportion of zero sequence current and polarity clustering is proposed. According to the energy proportion of the ...
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As quantum computing progresses, the need for optimized task allocation and scheduling becomes paramount to efficiently harness quantum resources. Simultaneously, ensuring the security of communications in quantum sys...
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Among all electrified transportation instruments, electric vehicles (EVs) garnered high mark lately and are becoming increasingly popular trends because they can both vie with and surpass vehicles powered by fossil fu...
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Radio Frequency Identification (RFID) door lock systems are at the forefront of modern access control technology, combining security, convenience, and scalability. This paper presents a comprehensive review of RFID-ba...
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The proceedings contain 298 papers. The topics discussed include: design and implementation of artificial intelligence-driven network intrusion detection system;comparative study of intelligent algorithms in fault dia...
ISBN:
(纸本)9798331529246
The proceedings contain 298 papers. The topics discussed include: design and implementation of artificial intelligence-driven network intrusion detection system;comparative study of intelligent algorithms in fault diagnosis of aviation mechanical systems;design and implementation of network intrusion detection system based on machine learning;evaluation of teaching quality in higher mathematics courses based on artificial neural networks;research on mechanical automation control based on bidirectional long short-term memory;intelligent mapping technology for single line diagram and layered diagram of intelligent distribution network;a comprehensive analysis of knowledge graph-based social recommendation system;and networks assessment of regional economic development level prediction with deep graph convolutional network.
The proceedings contain 56 papers. The topics discussed include: contrastive learning regularization method for information extraction of floating raft aquaculture;fractional-order adaptive sliding mode projective syn...
ISBN:
(纸本)9798331516147
The proceedings contain 56 papers. The topics discussed include: contrastive learning regularization method for information extraction of floating raft aquaculture;fractional-order adaptive sliding mode projective synchronization of different hyperjerk systems;intelligent supplier evaluation and selection module for fast-moving consumer goods;filtered sliding mode tracking controller for robot joint using linear matrix inequalities design;optimization algorithm for global k-means algorithm;enhancing product bundling with large language models;hyperspectral image analysis using maximum abundance classification;invariant set analysis and control synthesis of multi-equilibrium switched systems under control constraints;and a framework for solving quadratic knapsack problem based on deep reinforcement learning.
The complexity of current optimization methods for HVAC systems is increasing, resulting in relatively lower computational efficiency, particularly in more complex systems. This difficulty makes real-time optimization...
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The complexity of current optimization methods for HVAC systems is increasing, resulting in relatively lower computational efficiency, particularly in more complex systems. This difficulty makes real-time optimization and control challenging in practice. Therefore, there is an urgent need to simultaneously improve both system energy efficiency and computational efficiency to enhance system robustness. Present optimization methods predominantly emphasize enhancing system energy efficiency, often overlooking computational efficiency. Consequently, these methods become infeasible or unstable when implemented in practical systems. In our research, a multi-agent-based collaborative optimization method is proposed to solve the global optimization problem of complex HVAC systems. Under the multi-agent framework, the global optimization problem is decomposed into multiple sub-optimization problems considering the interaction characteristics among components, thus reducing the complexity of the global optimization problem in HVAC systems. The proposed AH-AFSA algorithm supports the solution of optimization problems containing hybrid decision variables (continuous and discrete variables) and can directly search for optimal discrete variables in the binary space. This feature is suitable for searching the optimal ON/OFF sequence and setpoints simultaneously during the global optimization process. The results demonstrate that the proposed method can save 18.9 % of electricity consumption with an average computing time of 12.2 s for each operating condition, saving about 54 % of the time cost compared to centralized methods. The methodology used in our research holds significant theoretical and practical value for enhancing the computational efficiency and productivity of optimization methods in complex HVAC systems.
The rapid advances in Artificial Intelligence (AI), microservices architecture, and cloud computing highly impact airlines in optimizing reservation systems. This review paper attempts to analyze the ongoing trend, ch...
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Diabetes, a chronic health condition, is witnessing a steady rise in cases annually, highlighting the importance of timely diagnosis and early detection. Recent innovations in ontology-based techniques and machine lea...
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The purpose of this paper is to propose a panoramic human-machine collaborative training system that can adapt to new energy grid-connected operation conditions, simulate various complex situations, and provide regula...
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ISBN:
(数字)9781510688902
ISBN:
(纸本)9781510688896
The purpose of this paper is to propose a panoramic human-machine collaborative training system that can adapt to new energy grid-connected operation conditions, simulate various complex situations, and provide regulators with simulation exercises, intelligent deductions, decision-making references, Q&A services, which provide intelligent reference solutions for precise regulation under new energy access scenarios with high percentage of new energy under a new type of power system. In order to enhance training effectiveness and operational efficiency, it is need to apply intelligent technology and data-driven models instead of experience and human labor. Interactive Q&A application such as Chat GPT has subversively changed the implementation mode of the training process, making the traditional teaching of theory and basic knowledge easier and faster. However, these commercial Q&A systems still have limitations in terms of accuracy, security, as well as professionalism, which makes them inefficient in professional learning area. In this paper, we use the idea of parallel control and the transformer model to construct a human-computer cooperative training system adapted to new energy grid-connected electric power systems, which realize a human-computer cooperative training model that highly integrates the Q&A services with the real trainer, the real trainees, the computer simulation system. By constructing a large model of human-computer system, a training computing experiment platform, as well as a system with a closed loop of training reality, the parallel training will help training program planning, training teaching design, training arrangements, teaching interaction and other key training links, and realize automated and intelligent training design and execution. As a training and management model adapted to the situation of artificial intelligence, parallel training will bring brand new possibilities for the development of the training industry in the intellige
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