Internet technology advancement has led to an exponential surge in text data. Among the pressing data types today, natural language data stands out. Leveraging natural language processing (NLP) technology, computers c...
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With the proportion of renewable energy in the construction sector increasing and the structure of energy systems becoming increasingly complex, it is crucial to optimize the operation strategy of building energy syst...
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With the proportion of renewable energy in the construction sector increasing and the structure of energy systems becoming increasingly complex, it is crucial to optimize the operation strategy of building energy systems with thermal energy storage for improving multi -performance. However, conventional heuristic optimization algorithm exhibits low searching speed and converges slowly, which is hard for operation optimization of such systems. In this study, with a binaryparticleswarm optimization (BPSO) algorithm coupled with experience-based searching guiding strategy, the operation of a ventilated floor heating system in a nearly-zero-energy building was optimized . System performance under different optimization objectives and combinations, including reducing operation cost and carbon emission fee, meanwhile increasing wind power consumption and thermal comfort time, was investigated. Results show that with the experiencebased accelerating strategy, the BPSO algorithm converged fast and found more reasonable solutions. In contrast, multi -objective optimization yielded better and diverse feasible solutions were found than singleobjective optimization. By optimizing operating costs, wind power consumption, and environmental costs, the best comprehensive solution was obtained. The cost range of energy consumption and carbon emissions within 10 days is 19.24-22.17 yuan and 17.45-20.11 yuan, respectively. The proportion of thermal comfort time is 95.39-95.82%. However, due to high thermal insulation level, thermal comfort time under different optimization objectives showed little difference.
Attack graph is a common tool for qualitative analysis of Sensor network security and it can provide an important basis for security administrators to prevent malicious intrusion. In order to conduct Sensor network se...
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Attack graph is a common tool for qualitative analysis of Sensor network security and it can provide an important basis for security administrators to prevent malicious intrusion. In order to conduct Sensor network security assessment and active defense, this paper proposed a Sensor network security defense strategy based on attack graphs and improved binary PSO. Based on each intrusion action in the attack graph, it constructed a set of weighted defense strategies to emphasize the defense cost. In order to prevent Sensor network malicious intrusion with minimum cost, the strategy introduced and improved binaryparticleswarm optimization algorithm and obtains the minimum key strategy set of attack graph. Based on the principle of M-IDS combined with game theory and attack pattern mining algorithm of Markov Decision Process (MDP), the optimal protection strategy is determined by game theory, and MDP is used to predict future attacks and design corresponding protection strategies. Simulation experiments show that compared with the ant colony algorithm and greedy algorithm, the proposed strategy can effectively obtain the optimal solution of the minimum key strategy set and it is more efficient.
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