Systems Science & Control Engineering系统科学与控制工程
Systems Science & Control Engineering(英文缩写 SYST SCI CONTROL ENG),ISSN 2164-2583,eISSN 2164-2583,中文译名:系统科学与控制工程 是一本学术期刊。本页汇总该期刊的最新影响因子、分区信息以及最新收录于 PubMed 的文献,帮助您快速了解期刊全貌。
发文量统计区间：2025-11-01 至 2026-10-31，按本站收录文献的发表日期统计。
期刊介绍
Systems Science & Control Engineering 最新收录文献
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1. Deep Reinforcement Learning Sensor Scheduling for Effective Monitoring of Dynamical Systems.
PMID:日期:2024-04-23Advances in technology have enabled the use of sensors with varied modalities to monitor different parts of systems, each providing diverse levels of information about the underlying system. However, resource limitations and computational power restrict the number of sensors/data that can be processed in real-time in most complex systems. These challenges necessitate the need for selecting/scheduling a subset of sensors to obtain measurements that guarantee the best monitoring objectives. This paper focuses on sensor scheduling for systems modeled by hidden Markov models. Despite the development of several sensor selection and scheduling methods, existing methods tend to be greedy and do not take into account the long-term impact of selected sensors on monitoring objectives. This paper formulates optimal sensor scheduling as a reinforcement learning problem defined over the posterior distribution of system states. Further, the paper derives a deep reinforcement learning policy for offline learning of the sensor scheduling policy, which can then be executed in real-time as new information unfolds. The proposed method applies to any monitoring objective that can be expressed in terms of the posterior distribution of the states (e.g., state estimation, information gain, etc.). The performance of the proposed method in terms of accuracy and robustness is investigated for monitoring the security of networked systems and the health monitoring of gene regulatory networks.
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2. Covid-19 Diagnosis by WE-SAJ.
2. 通过WE-SAJ进行Covid-19诊断PMID:日期:2022-03-06With a global COVID-19 pandemic, the number of confirmed patients increases rapidly, leaving the world with very few medical resources. Therefore, the fast diagnosis and monitoring of COVID-19 are one of the world's most critical challenges today. Artificial intelligence-based CT image classification models can quickly and accurately distinguish infected patients from healthy populations. Our research proposes a deep learning model (WE-SAJ) using wavelet entropy for feature extraction, two-layer FNNs for classification and the adaptive Jaya algorithm as a training algorithm. It achieves superior performance compared to the Jaya-based model. The model has a sensitivity of 85.47±1.84, specificity of 87.23±1.67 precision of 87.03±1.34, an accuracy of 86.35±0.70, and F1 score of 86.23±0.77, Matthews correlation coefficient of 72.75±1.38, and Fowlkes-Mallows Index of 86.24±0.76. Our experiments demonstrate the potential of artificial intelligence techniques for COVID-19 diagnosis and the effectiveness of the Self-adaptive Jaya algorithm compared to the Jaya algorithm for medical image classification tasks.
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3. Heart rate control using first- and second-order models during treadmill exercise.
3. 跑步机运动期间使用一阶和二阶模型的心率控制PMID:日期:2021-09-16Heart rate control using first- and second-order models was compared using a novel control design strategy which shapes the input sensitivity function. Ten participants performed two feedback control test series on a treadmill with square wave and constant references. Using a repeated measures, counterbalanced study design, each series compared controllers C1 and C2 based on first- and second-order models, respectively. In the first series, tracking accuracy root-mean-square tracking error (RMSE) was not significantly lower for C2: 2.59 bpm vs. 2.69 bpm (mean, C1 vs. C2), = 0.79. But average control signal power was significantly higher for C2: vs. , . In the second series, RMSE was also not significantly lower for C2: 1.99 bpm vs. 1.94 bpm, = 0.39; but average control signal power was again significantly higher for C2: vs. , = 0.045. The results provide no evidence that controllers based on second-order models lead to better tracking accuracy, despite the finding that they are significantly more dynamic. Further investigation using a substantially larger sample size is warranted.