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A non-probabilistic reliability topology optimization method based on aggregation function and matrix multiplication considering buckling response constraints

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Applied Mathematics and Mechanics(English Edition) 2024年第2期45卷 321-336页

作者： Lei WANG Yingge LIU Juxi HU Weimin CHEN Bing HAN Institute of Solid Mechanics School of Aeronautic Science and EngineeringBeihang UniversityBeijing 100191China Aircraft and Propulsion Laboratory Ningbo Institute of TechnologyBeihang UniversityNingbo 315100Zhejiang ProvinceChina State Key Laboratory of Navigation and Safety Technology Shanghai Ship and Shipping Research InstituteShanghai 200135China National Engineering Research Center of Ship&Shipping Control System Shanghai Ship and Shipping Research InstituteShanghai 200135China

A non-probabilistic reliability topology optimization method is proposed based on the aggregation function and matrix *** expression of the geometric stiffness matrix is derived,the finite element linear buckling analysis is conducted,and the sensitivity solution of the linear buckling factor is *** a specific problem in linear buckling topology optimization,a Heaviside projection function based on the exponential smooth growth is developed to eliminate the gray *** aggregation function method is used to consider the high-order eigenvalues,so as to obtain continuous sensitivity information and refined structural *** cyclic matrix programming,a fast topology optimization method that can be used to efficiently obtain the unit assembly and sensitivity solution is *** maximize the buckling load,under the constraint of the given buckling load,two types of topological optimization columns are *** variable density method is used to achieve the topology optimization solution along with the moving asymptote optimization *** vertex method and the matching point method are used to carry out an uncertainty propagation analysis,and the non-probability reliability topology optimization method considering buckling responses is developed based on the transformation of non-probability reliability indices based on the characteristic ***,the differences in the structural topology optimization under different reliability degrees are illustrated by examples.

关键词： buckling topology optimization aggregation function uncertainty propagation analysis non-probabilistic reliability

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research on Smart Photovoltaic Power Station control system Based on DCS Architecture

Research on Smart Photovoltaic Power Station Control System ...

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2024 International Conference on Computer Communication, Networks and Information Science, CCNIS 2024

作者： Zhan, Lei Chn Energy Zhishen Control Technology Co. Ltd. Beijing Engineering Research Center of Power Station Automation State R&D Center of Control System and Information Security Technologies for Energy Industry Beijing102211 China

ISBN: (纸本)9798331507046

Aiming at the operation and maintenance management and control issues of large-scale photovoltaic power stations, a smart photovoltaic power station control system based on DCS architecture was studied. In terms of system architecture design, the design idea based on the 'cloud-edge-end' architecture is adopted, and some core equipment and data storage devices in the site are placed in the cloud to achieve centralized management and storage of data. At the same time, some computationally intensive tasks are processed in the cloud to reduce the computing pressure on edge devices. Applying this system to the control of photovoltaic power plants can achieve precise control of operation and maintenance data, and enable the identification and accurate processing of different faults. Comparing the fault identification algorithm used in this system with other algorithms, the results prove that the fault identification accuracy of this algorithm can reach 100℅, creating a large amount of economic benefits for the stable operation of photovoltaic power plants. © 2024 IEEE.

关键词： Ferroelectric RAM

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research on head way control of a guided transport system based on intersection condition evaluation

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Transportation Safety and Environment 2023年第4期5卷 29-35页

作者： Xiao Yu Yuan Cao Yongkui Sun Signal and Communication Research Institute China Academy of Railway Sciences Corporation LimitedBeijing 100081China State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong UniversityBeijing 100044China National Engineering Research Center of Rail Transportation Operation and Control System Beijing Jiaotong UniversityBeijing 100044China

ln order to ensure the safety and efficiency of the section tracking operation of a guided transport system,a safety headway control method of section tracking based on intersection conditions is proposed in this *** the difference of signal phase,the evaluation model of road conditions was established based on a fuzzy comprehensive evaluation method,*** on the artificial potential field method,the time-varying hybrid artificial potential field(TH-APF)method was proposed,and the tracking headway control algorithm was designed to realize the dynamic control of the tracking headway of the guide transport *** simulation results verified the effectiveness and applicability of the evaluation model of intersection road conditions;the tracking headway can be maintained at about 120 *** tracking headway control algorithm of guided transport vehicles can respond to the road conditions and avoid the local optimum of the artificial potential field method,thus improving the operating efficiency.

关键词： guided transport system intersection condition evaluation artificial potential field method tracking headway control

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High-performance self-powered ultraviolet to near-infrared photodetector based on WS_(2)/InSe van der Waals heterostructure

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Nano research 2023年第5期16卷 7851-7857页

作者： Jinping Chen Zhen Zhang Yi Ma Jiying Feng Xiaoyu Xie Xiaoxuan Wang Aoqun Jian Yuanzheng Li Zhuxin Li Heng Guo Yizhi Zhu Qiannan Cui Zengliang Shi Chunxiang Xu State Key Laboratory of Bioelectronics School of Biological Science and Medical EngineeringSoutheast UniversityNanjing 210096China Micro Nano System Research Center Key Laboratory of Advanced Transducers and Intelligent Control System of the Ministry of Education&College of Information Engineering and ComputerTaiyuan University of TechnologyTaiyuan 030024China Key Laboratory of UV-Emitting Materials and Technology of Ministry of Education Northeast Normal UniversityChangchun 130024China

van der Waals heterostructures(vdWHs)based on two-dimensional(2D)materials without the crystal lattice matching constraint have great potential for high-performance optoelectronic ***,a WS_(2)/InSe vdWH photodiode is proposed and fabricated by precisely stacking InSe and WS_(2)flakes through an all-dry transfer *** WS_(2)/InSe vdWH forms an n–n heterojunction with strong built-in electric field due to their intrinsic n-type semiconductor characteristics and energy-band alignments with a large Fermi level offset between WS_(2)and *** a result,the device displays excellent photovoltaic behavior with a large open voltage of 0.47 V and a short-circuit current of 11.7 nA under 520 nm light ***,a fast rising/decay time of 63/76μs,a large light on/off ratio of 105,a responsivity of 61 mA/W,a high detectivity of 2.5×10^(11) Jones,and a broadband photoresponse ranging from ultraviolet to near-infrared(325–980 nm)are achieved at zero *** study provides a strategy for developing high-performance self-powered broadband photodetectors based on 2D materials.

关键词： two-dimensional materials van der Waals heterostructure self-powered photodetector photocurrent

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Understanding microstructure-property relationships of HPDC Al-Si alloy based on machine learning and crystal plasticity simulation

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Advances in Manufacturing 2024年第3期12卷 497-511页

作者： Qiang-Qiang Zhai Zhao Liu Ping Zhu State Key Laboratory of Mechanical System and Vibration Shanghai Jiao Tong UniversityShanghai200240People’s Republic of China National Engineering Research Center of Automotive Power and Intelligent Control Shanghai Jiao Tong UniversityShanghai200240People’s Republic of China School of Design Shanghai Jiao Tong UniversityShanghai200240People’s Republic of China

Al-Si alloys manufactured via high-pressure die casting(HPDC)are suitable for a wide range of ***,the heterogeneous microstructure and unpredictable pore distribution of Al-Si high-pressure die castings result in significant variations in the mechanical properties,thus leading to a complicated microstructure-property relationship that is difficult to ***,a computational framework incorporating machine learning and crystal plasticity method is *** framework aims to provide a systematic and comprehensive understanding of this relationship and enable the rapid prediction of macroscopic mechanical properties based on the ***,we select eight variables that can effectively characterize the microstructural features and then obtain their statistical ***,based on 160 samples obtained via the Latin hypercube sampling method,representative volume elements are constructed,and the crystal plasticity fast Fourier transformation method is executed to obtain the macroscopic mechanical ***,the yield strength,elastic modulus,strength coefficient,and strain-hardening exponent are used to characterize the stress-strain curve,and Gaussian process regression models and microstructural variables are ***,sensitivity and univariate analyses based on these machine-learning models are performed to obtain insights into the microstructure-property relationships of the HPDC Al-Si *** results show that the Gaussian process regression models exhibit high accuracy(R^(2) greater than 0.84),thus confirming the viability of the proposed *** results of sensitivity analysis indicate that the pore size exerts the most significant effect on the mechanical ***,the proposed framework can not only be transferred to other alloys but also be employed for material design.

关键词： High-pressure die casting(HPDC) Machine learning Crystal plasticity Aluminum alloys

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Quasi-Parallel Resonant Switched Capacitor DC-DC Converter with Continuous Voltage Regulation Capability

Quasi-Parallel Resonant Switched Capacitor DC-DC Converter w...

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2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Qi, Jingjing Wu, Xuezhi Xu, Wenzheng Sun, Kai Tsinghua University State Key Laboratory of Power System Operation and Control Department of Electrical Engineering Beijing China Beijing Jiaotong University National Active Distribution Network Technology Research Center Beijing China

ISBN: (纸本)9798350376067

Resonant switched capacitor converters have the advantages of small size, light weight and high efficiency, but have poor voltage regulation ability. To overcome this problem, a quasi-parallel resonant switched capacitor DC-DC converter is proposed in this paper, which mainly incorporates a resonant switched capacitor converter with soft-switching and variable voltage ratio and a Buck converter. Most of the power can be transmitted by the resonant switched capacitor converter, while the Buck converter is responsible for output voltage regulation. In addition, the voltage ratio of the resonant switched capacitor converter can be flexibly changed base on the input voltage, which is beneficial for Buck converter to always handle a small portion of power. Furthermore, input voltage feedforward control is incorporated to suppress the input voltage disturbance. The proposed converter can not only continuously regulate the voltage over a wide range, but also perform high efficiency and achieve rapid dynamic response. The operating principle of the converter is introduced, the circuit characteristic is analyzed, and the correctness of the theoretical analysis is verified by experiments. © 2024 IEEE.

关键词： Capacitor bank

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Real-Time Traffic Management of High-Speed Trains: A Novel Approach Combining Optimization with Machine Learning

Real-Time Traffic Management of High-Speed Trains: A Novel A...

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2024 China Automation Congress, CAC 2024

作者： Li, Chong Yin, Jiateng Ma, Lin Tang, Tao State Key Laboratory of Advanced Rail Autonomous Operation Beijing Jiaotong University Beijing China National Engineering Research Center of Rail Transportation Operation and Control System Beijing Jiaotong University Beijing China

ISBN: (纸本)9798350368604

The real-time traffic control of high-speed trains (HSTs) is an important task in railway networks to reduce the delay time of trains in case of unexpected disruptions. Traditional approaches are based on mixed-integer programming (MILP) formulations to be solved by optimization methods such as branch-and-bound (B&B), which however require a very long time in real-world problems. Our study introduces a novel methodology that employs machine learning (ML) techniques to accelerate the optimization of MILPs. Specifically, our approach develops a feedforward network, which can be trained off-line, to predict the optimal MILP parameter configurations for the control of HSTs. Additionally, we develop a differential evolution (DE) algorithm, which fine-tunes these parameters in response to dynamic conditions. We conduct numerical experiments based on the real-world data of Beijing-Zhangjiakou high-speed railway (BZHR). The results demonstrate that our ML-accelerated approach significantly outperforms traditional optimization methods, improving the solution performance by 21.47%. © 2024 IEEE.

关键词： Integer programming

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Face Detection on Thermal Infrared Images Combined with Visible Images 16th

Face Detection on Thermal Infrared Images Combined with Visi...

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16th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2021

作者： Chen, Yujia Wang, Liqing Xu, Guangda School of Mathematics and Statistics Xi’an Jiaotong University Xi’an China Infrared Division Wuhan Huazhong Numerical Control Co. Ltd. Wuhan China National Numerical Control System Engineering Research Center Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9789811912528

Due to COVID-19, intelligent thermal imagers are widely used all over the world. Since intelligent thermal imagers usually require real-time temperature measurement, it is significant to find a method to quickly and accurately detect human faces in thermal infrared images. This paper mainly proposes two different methods. One is to use image processing methods and face detected from visible images to determine the position of the face in the infrared image, while the other is to use target detection algorithms on infrared images, including YOLOv3 and Faster R-CNN. This paper uses the two methods on a self-collected dataset containing 944 pairs of visible and infrared images and observes the robustness of methods by adding random noise to images. Experiments show that the first one has much lower latency and the latter one has higher accuracy in both cases. © 2022, Springer Nature Singapore Pte Ltd.

关键词： Face recognition

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Adaptive Parametric Impedance Model Order Reduction Method for Grid-tied Renewable Energy Dominated Microgrid

Adaptive Parametric Impedance Model Order Reduction Method f...

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2024 International Exhibition and Conference for Power Electronics, Intelligent Motion, Renewable Energy and Energy Management, PCIM Asia 2024

作者： Jiang, Xun Mao, Meiqin Xie, Bao Chang, Liuchen Wang, Haijiao Research Center for Photovoltaic System Engineering of Ministry of Education Hefei University of Technology China University of New Brunswick Canada State Key Laboratory of Operation and Control of Renewable Energy & Storage Systems China Electric Power Research Institute China

ISBN: (纸本)9783800764150

The high-precision reduced order impedance model can significantly reduce the computation burden in the small-signal oscillatory instability detection process of grid-tied renewable energy dominated microgrids (REMGs). However, due to improper interpolation strategies, the existing model order reduction method cannot build the high-precision parametric reduced order impedance model (PROIM) for REMG. To fill these gaps, this paper proposes the adaptive parametric model order reduction method to build a high-precision PROIM for REMGs. In this method, the adaptive optimization of system parameters and frequency interpolation points is achieved by introducing the evaluation indexes including convergence error, cumulative error, and projection matrix rank. The error performance of the obtained PROIM by the proposed method and its efficiency in actual oscillatory stability analysis are validated by a small-scale REMG with 4 different converter-based renewable generators (CREGs) and a real-time simulation model using a large-scale REMG of 4.2 MW with 18 different CREGs. © VDE VERLAG GMBH.

关键词： Microgrids

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Trajectory Prediction of High-Speed Train Group Tracking Based on LSTM-KF Hybrid Model 27

Trajectory Prediction of High-Speed Train Group Tracking Bas...

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27th IEEE International Conference on Intelligent Transportation systems, ITSC 2024

作者： Yang, Boyu Lv, Jidong Liu, Hongjie Chai, Ming Zhang, Qinglong Tang, Tao Lv, Jiahui National Engineering Research Center of Rail Transportation Operation and Control System Beijing Jiaotong University Beijing China School of Civil and Resource Engineering University of Science and Technology Beijing China Beijing Jiaotong University The Associate Director of the Rail Traffic Control and Safety State Key Laboratory China

ISBN: (纸本)9798331505929

Operating multiple trains as a group under Train-to-Train communication is an efficient way in sparse railway networks. However, it is a barrier to accurate tracking for follower trains with the uncertain status of the preceding trains under communication delay. Therefore, we proposed a train trajectory prediction approach, aiming to obtain an accurate state of preceeding trains in a long time horizon. Firstly, we established a hybrid prediction model combined Long Short-Term Memory (LSTM) with the Kalman Filter (KF) and using the sliding window mechanism to enhance future prediction accuracy. Secondly, to fusion LSTM prediction data and the Kalman calculation model, we proposed the Hybrid Train Trajectory Prediction algorithm by adopting real-time calculation of the covariance matrix using real data and predicted data. Finally, we apply an experiment considering network characteristics in Western China. The result shows that the Mean Absolute Error (MAE) of series prediction in the longer time domain is 44% of the Recurrent Neural Network (RNN) model and 88% of the traditional LSTM model. © 2024 IEEE.

关键词： Kalman filters

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