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Chinese Intelligent systems Conference, CISC 2020

作者： Zhang, Qing Tong, Jiahui Li, Haifeng Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China Beijing Electromechanical Engineering Institute Beijing China

ISBN: (纸本)9789811584497

A common training simulation system is proposed for the general-purpose and efficient control training needs of the mid-and highend military UAV system. The general modeling technology and processconfigurable interface protocol modeling method are adopted to realize the rapid construction of flight simulation subsystem of various aircraft models. A scheme based on general bus monitoring and fast extraction equipment is designed to realize the fast embedded real-time control training of UAV with different physical devices. A self-correctable control and evaluation method of UAV training based on multi-sample statistics is proposed to enhance the targeted strengthening setting of training subjects for trainees, so as to improve training efficiency and training effect. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021.

关键词： Extraction

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Thermal Design of Active Phased Array Antenna for GEO Communication Satellite Based on Structure and Thermal control Integration Method 8th

Thermal Design of Active Phased Array Antenna for GEO Commun...

引用

8th Asia International Symposium on Mechatronics, AISM 2021

作者： Yang, Shuanggen Cheng, Wenlong Wang, Lu Zhao, Rui Ning, Bo Deng, Qi Department of Thermal Science and Energy Engineering University of Science and Technology of China Hefei230027 China East China Research Institute of Electronic Engineering Hefei230031 China Beijing Key Laboratory of Space Thermal Control Technology Beijing Institute of Spacecraft System Engineering Beijing100094 China

ISBN: (纸本)9789811913082

With the further development of space communication satellite payload technology, the demand for active phased array antenna (APAA) is increasing, and heat dissipation is a key technical problem affecting the performance of APAA. In order to ensure an ideal temperature range and reduce temperature difference in orbit, a thermal design method of a high power APAA of GEO communication satellite is proposed. The heat pipes are embedded in the active satellite installing plate to solve the heat collection, uniformly distributing temperature and heat transfer, and the active satellite installing plate is not only the structural support of the antenna but also the cooling plate of the antenna. The heat of APAA is taken away by integrated thermal coupling with the satellite platform, which is exchanged between both sides of the active satellite installing plate and the cold plate based liquid cooling, and afterwards is transmitted to the thermal radiators of satellite through single-phased fluid loop. The simulation analysis results show that the temperature and the temperature difference of the APAA meet the requirements of thermal reliability and design performance. The thermal control method used provides a feasible design reference for the thermal design of similar GEO orbit communication satellite APAA. © 2022, Science Press.

关键词： Heat pipes

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Multi-scale feature fused stacked autoencoder and its application for soft sensor modeling

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Chinese Journal of Chemical engineering 2025年 81卷 241-254页

作者： Li, Zhi Xia, Yuchong Long, Jian Liu, Chensheng Zhang, Longfei State Key Laboratory of Industrial Control Technology East China University of Science and Technology Shanghai 200237 China Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education East China University of Science and Technology Shanghai200237 China Huzhou Institute of Industrial Control Technology Huzhou313099 China Engineering Research Center of Process System Engineering Ministry of Education East China University of Science and Technology Shanghai200237 China

Deep Learning has been widely used to model soft sensors in modern industrial processes with nonlinear variables and uncertainty. Due to the outstanding ability for high-level feature extraction, stacked autoencoder (SAE) has been widely used to improve the model accuracy of soft sensors. However, with the increase of network layers, SAE may encounter serious information loss issues, which affect the modeling performance of soft sensors. Besides, there are typically very few labeled samples in the data set, which brings challenges to traditional neural networks to solve. In this paper, a multi-scale feature fused stacked autoencoder (MFF-SAE) is suggested for feature representation related to hierarchical output, where stacked autoencoder, mutual information (MI) and multi-scale feature fusion (MFF) strategies are integrated. Based on correlation analysis between output and input variables, critical hidden variables are extracted from the original variables in each autoencoder's input layer, which are correspondingly given varying weights. Besides, an integration strategy based on multi-scale feature fusion is adopted to mitigate the impact of information loss with the deepening of the network layers. Then, the MFF-SAE method is designed and stacked to form deep networks. Two practical industrial processes are utilized to evaluate the performance of MFF-SAE. Results from simulations indicate that in comparison to other cutting-edge techniques, the proposed method may considerably enhance the accuracy of soft sensor modeling, where the suggested method reduces the root mean square error (RMSE) by 71.8%, 17.1% and 64.7%, 15.1%, respectively. © 2025 Chemical Industry and engineering Society of China, and Chemical Industry Press Co., Ltd.

关键词： Frequency estimation

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LiFePO4 Battery SOC Estimation under OCV-SOC Curve Error Based on Adaptive Multi-Model Kalman Filter

arXiv

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arXiv 2024年

作者： Paizulamu, Daniyaer Cheng, Lin Zhuang, Yingrui Xu, Helin Qi, Ning Ci, Song The State Key Laboratory of Power System Operation and Control Department of Electrical Engineering Tsinghua University Beijing100084 China The Department of Earth and Environmental Engineering Columbia University New YorkNY10027 United States

LiFePO4 batteries are widely used in electric vehicles and energy storage systems due to long cycle life and high safety performance. However, the OCV-SOC curve (OSC) of these batteries features a long plateau region, making state of charge (SOC) estimation highly sensitive to OSC error, which arises due to aging and temperature. To address this, we propose an SOC estimation method that accounts for error in OSC. First, we establish battery equivalent circuit model (ECM) and introduce a parameters identification algorithm based on adaptive recursive least squares. Next, we derive the relationship between the innovation's cross-correlation matrix (CCM)/auto-correlation matrix (ACM) of the Kalman filter and the OSC error. We then develop an adaptive multi-model Kalman filter (AMMKF), which dynamically adjusts the measurement model parameters of each filter based on the sign of the OSC error. By assigning a probability to each filter according to its predicted voltage distribution function, the optimal filter is selected. The proposed method is tested under various OSC error types and operating conditions. Results demonstrate that the proposed method achieves high accuracy and robustness, with an RMSE of less than 3%, which is more than 10% lower than the estimation error of traditional method. Copyright © 2024, The Authors. All rights reserved.

关键词： Adaptive filters

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Interior Scene Intelligent Generated for Smart Homes Based on GAN-ID Algorithms

Journal of Network Intelligence

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Journal of Network Intelligence 2024年第2期9卷 1046-1055页

作者： Zhang, Fu-Quan Ma, Lin-Juan Zhou, Jun-Xian Wang, Run-Hong Zhang, Guang-Zhen College of Computer and Control Engineering Minjiang University Fuzhou China Digital Media Art Key Laboratory of Sichuan Province Sichuan Conservatory of Music Chengdu China Fuzhou Technology Innovation Center of Intelligent Manufacturing Information System Minjiang University Fuzhou China Fujian Province University Fuzhou China School of Computer Science and Technology Beijing Institute of Technology Beijing China Minjiang University Fuzhou China Information Media School of Tianjin Technician Institute of Mechanical & Electrical Technology Tianjin China

The purpose of this paper is to present a new GAN-ID algorithm to gener-ate interior scene intelligently based on Artificial Intelligence of Things (AIoT). Although great progress has been made when GAN algorithm applied in smart homes and smart cities, the issue of the trade-off between the accuracy and the efficiency in interior scene has been overlooked. A number of experiments were performed to check the performance and it is indicated that the results of the proposed GAN-ID has great advantages in model sample generation than other common methods when generating interior scene intelligently, which is similar to state-of-the-art. It is meaningful when it can be applied in smart cities and smart homes filed based on AIoT in the future. © 2024, J. Network Intell. All rights reserved.

关键词： Automation

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Risk Prediction of Cavitation Erosion of Venturi Tube Based on a New Erosion Indicator

SSRN

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SSRN 2024年

作者： Ma, Junqi Wang, Xiaoyu Wang, Tian Sun, Qiang School of Mechanical Engineering and Automation Northeastern University Shenyang110819 China Key Laboratory of Vibration and Control of Aero-Propulsion System Ministry of Education Northeastern University Shenyang110819 China

Cavitation erosion would reduce the performance of the fluid machinery. In order to improve the reliability and prolong the life span of fluid machinery, it is necessary to study the mechanism of cavitation erosion and predict the possible erosion aera. Since the erosion power to be measured and calculated is closer to the actual state of cavitation, a new cavitation erosion indicator model based on erosion power is proposed, which can reflect the size and region of the erosion generated by cavitation more precisely. Based on the two models of axisymmetric nozzles and venturi tubes, the prediction of cavitation erosion location based on the newly proposed indicator is illustrated. It is found that the location of cavitation erosion mainly occurs near the maximum location boundary of the cavitation region. This research lays a foundation for the study of possible erosion state of fluid machinery in cavitation environment. © 2024, The Authors. All rights reserved.

关键词： Cavitation

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SC-NeRF: Self-Correcting Neural Radiance Field with Sparse Views

arXiv

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arXiv 2023年

作者： Song, Liang Wang, Guangming Liu, Jiuming Fu, Zhenyang Miao, Yanzi Wang, Hesheng Engineering Research Center of Intelligent Control for Underground Space Ministry of Education School of Information and Control Engineering Advanced Robotics Research Center China University of Mining and Technology Xuzhou221116 China Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai200240 China

In recent studies, the generalization of neural radiance fields for novel view synthesis task has been widely explored. However, existing methods are limited to objects and indoor scenes. In this work, we extend the generalization task to outdoor scenes, trained only on object-level datasets. This approach presents two challenges. Firstly, the significant distributional shift between training and testing scenes leads to black artifacts in rendering results. Secondly, viewpoint changes in outdoor scenes cause ghosting or missing regions in rendered images. To address these challenges, we propose a geometric correction module and an appearance correction module based on multi-head attention mechanisms. We normalize rendered depth and combine it with light direction as query in the attention mechanism. Our network effectively corrects varying scene structures and geometric features in outdoor scenes, generalizing well from object-level to unseen outdoor scenes. Additionally, we use appearance correction module to correct appearance features, preventing rendering artifacts like blank borders and ghosting due to viewpoint changes. By combining these modules, our approach successfully tackles the challenges of outdoor scene generalization, producing high-quality rendering results. When evaluated on four datasets (Blender, DTU, LLFF, Spaces), our network outperforms previous methods. Notably, compared to MVSNeRF, our network improves average PSNR from 19.369 to 25.989, SSIM from 0.838 to 0.889, and reduces LPIPS from 0.265 to 0.224 on Spaces outdoor scenes. © 2023, CC BY.

关键词： Rendering (computer graphics)

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Low-Earth Orbital Lifetime Prediction Based on Parameter Sensitivity Analysis and Deep Learning

引用

IEEE Transactions on Aerospace and Electronic systems 2025年

作者： Chen, Shun-Yi Yang, Zhen Wang, Hua Lu, Lin State Key Laboratory of Space System Operation and Control Changsha410073 China National University of Defense Technology College of Aerospace Science and Engineering Changsha410073 China China Astronaut Research and Training Center Beijing100094 China

This article proposes an intelligent prediction method for the orbital lifetime of resident space objects (RSOs) in low Earth orbit. This method is intended to satisfy the computational efficiency and accuracy requirements when predicting the large-scale RSOs. It helps to analyze the distribution and uncertainty of RSOs' lifetime. First, based on the semi-analytical orbit prediction model, a parameter sensitivity analysis method for the orbital lifetime is proposed, and the parameters that affect the orbital lifetime are investigated. Second, using a fully connected neural network, an orbital lifetime prediction model is established based on deep learning. The hyperparameters of the deep learning model are optimized using the black-winged kite algorithm. Finally, by combining the sensitivity analysis with the deep learning model, an intelligent prediction method is proposed for the rapid prediction of orbital lifetime. Simulation results show that the proposed intelligent prediction method accurately determines the orbital lifetime of RSOs and has higher prediction efficiency than the traditional prediction method. The research results provide an effective calculation and analysis tool for predicting the orbital lifetime of large-scale RSOs in the future. © 2025 IEEE.

关键词： Prediction models

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Synthesis of Temporally-Robust Policies for Signal Temporal Logic Tasks using Reinforcement Learning

Synthesis of Temporally-Robust Policies for Signal Temporal ...

引用

IEEE International Conference on Robotics and Automation (ICRA)

作者： Siqi Wang Shaoyuan Li Li Yin Xiang Yin Department of Automation and Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai China Institute of Systems Engineering Macau University of Science and Technology Taipa China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

This paper investigates the problem of designing control policies that satisfy high-level specifications described by signal temporal logic (STL) in unknown, stochastic environments. While many existing works concentrate on optimizing the spatial robustness of a system, our work takes a step further by also considering temporal robustness as a critical metric to quantify the tolerance of time uncertainty in STL. To this end, we formulate two relevant control objectives to enhance the temporal robustness of the synthesized policies. The first objective is to maximize the probability of being temporally robust for a given threshold. The second objective is to maximize the worst-case spatial robustness value within a bounded time shift. We use reinforcement learning to solve both control synthesis problems for unknown systems. Specifically, we approximate both control objectives in a way that enables us to apply the standard Q-learning algorithm. Theoretical bounds in terms of the approximations are also derived. We present case studies to demonstrate the feasibility of our approach.

关键词： Measurement Uncertainty Q-learning Approximation algorithms Robustness Logic Task analysis

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Bottlenecks and Countermeasures of High-Penetration Renewable Energy Development in China

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engineering 2021年第11期7卷 1611-1622页

作者： Jizhen Liu Qinghua Wang Ziqiu Song Fang Fang State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power UniversityBeijing 102206China School of Control and Computer Engineering North China Electric Power UniversityBeijing 102206China

China has become the world’s largest producer and consumer of energy,and ranks first in its wind and solar power installation ***,serious wind and solar curtailment in China has significantly hindered the development and utilization of renewable *** address problems in the consumption of renewable energy,this paper analyzes four key factors affecting the capacity of power generated from renewable energy sources:power balance,power regulation performance,transmission capacity,and load *** on these bottlenecks,we propose seven solutions:centralized and distributed development of renewable energy,improving the peak-load regulation flexibility of thermal power,increasing the proportion of gas turbines and pumped-hydropower storage,construction of transmission channels and a flexible smart grid developing demand response and virtual power plants,adopting new energy active support and energy storage,and establishing appropriate policies and market *** Chinese Government and energy authorities have issued a series of policies and measures,and in the past three years,China has had remarkable achievements in the adoption of renewable *** rate of idle wind capacity decreased from 17%in 2016 to 7%in 2018,and that of solar decreased from 10%in 2016 to 3%in 2018.

关键词： Bottlenecks Countermeasures Idle wind and solar power Renewable energy

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