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第40届中国控制会议

作者： Jilong Zhuang Jian Ge Kanjian Zhang Haikun Wei Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education School of AutomationSoutheast University

With the development of energy storage technology, more and more battery energy storage stations（BESSs） are connected to the power grid to restrain fluctuation and intermittency of renewable energy generation on the stability of the power grid. However, the connection with BESS will change the topology and power flow direction of the power grid. Therefore, based on the actual geographical wiring diagram and related data, this paper describes a grid model to analyze the influence of BESS connected to the power grid on power flow and node voltage. When the voltage of each node is stable, the genetic algorithm is applied to study how to select the access position and control the power of centralized or distributed BESSs to minimize the active power loss of the power grid. It provides guidance and reference for future location planning and control strategy of BESSs in the power grid.

关键词： Battery Energy Storage Station Power Flow Calculation Optimization

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TEMPORAL-SPATIAL REPRESENTATION LEARNING TRANSFORMER FOR EEG-BASED EMOTION RECOGNITION

arXiv

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arXiv 2022年

作者： Wang, Zhe Wang, Yongxiong Hu, Chuanfei Yin, Zhong Song, Yu School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology China Key Laboratory of Measurement and Control of CSE Ministry of Education Southeast University China Tianjin Key Laboratory for Control Theory and Applications in Complicated Systems Tianjin University of Technology China

Both the temporal dynamics and spatial correlations of Electroencephalogram (EEG), which contain discriminative emotion information, are essential for the emotion recognition. However, some redundant information within the EEG signals would degrade the performance. Specifically, the subjects reach prospective intense emotions for only a fraction of the stimulus duration. Besides, it is a challenge to extract discriminative features from the complex spatial correlations among a number of electrodes. To deal with the problems, we propose a transformer-based model to robustly capture temporal dynamics and spatial correlations of EEG. Especially, temporal feature extractors which share the weight among all the EEG channels are designed to adaptively extract dynamic context information from raw signals. Furthermore, multi-head self-attention mechanism within the transformers could adaptively localize the vital EEG fragments and emphasize the essential brain regions which contribute to the performance. To verify the effectiveness of the proposed method, we conduct the experiments on two public datasets, DEAP and MAHNOB-HCI. The results demonstrate that the proposed method achieves outstanding performance on arousal and valence classification. Copyright © 2022, The Authors. All rights reserved.

关键词： Emotion Recognition

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A Classification Method of Photovoltaic Modules Shaded Area Based on Weighted K-nn

A Classification Method of Photovoltaic Modules Shaded Area ...

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第40届中国控制会议

作者： Lin Xia Chenxi Li Kanjian Zhang Haikun Wei Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of EducationSchool of AutomationSoutheast University

In the actual operation of photovoltaic power stations,various types of shading,including leaves,bird’s droppings,and clouds,will inevitably *** shadings will affect the output power of the *** them,if a small area of shading is not removed in time,hot spots may be formed,accelerating the aging of photovoltaic modules,and not conducive to the healthy and stable operation of the overall ***,different shading objects cover different shading areas of photovoltaic modules,which cause different *** paper mainly analyzes the influence of different areas of shading on the current-voltage curve of photovoltaic modules,and proposes a classification algorithm based on weighted K-nn to classify the shading area according to the current-voltage curve characteristics of the module,which is helpful for subsequent identification of shading types and troubleshooting.

关键词： Photovoltaic Modules Shading Area K-Nearest Neighbor Classification

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Vital signs detection based on differential evolution and variational mode decomposition algorithm

Vital signs detection based on differential evolution and va...

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第33届中国控制与决策会议

作者： Yuhao Chen Haiyang Hu Zhenxing Sun Shihua Li Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education School of Automation Southeast University

Aiming at the problem that human breathing and heartbeat signals are difficult to detect in a low signal-to-clutter environment,this paper proposes a signal processing method that combines differential evolution algorithm and variational modal decomposition *** order to achieve non-contact detection of vital signs,this paper uses ultra-wideband radar to collect the signal reflected by the human body,and suppresses the interference of the background signal by removing the background of the signal,and then extracts the signal reflected by the human chest in the fast time *** that,according to the frequency range of breathing and heartbeat signals,low-pass filter and band-pass filter are used to filter useless *** at the problem that the variational modal decomposition algorithm needs to manually adjust parameters in advance,this paper takes the maximum effective frequency band energy ratio as the objective function,and uses differential evolution algorithm to automatically search for the best parameter combination of variational modal decomposition,which reduces the workload of parameter ***,variational modal decomposition by the best parameter combination is used again to decompose the signal,and the breathing and heartbeat signals are reconstructed from the decomposed modal *** show that the method proposed in this paper can measure the human body's respiratory rate and heartbeat rate with high accuracy.

关键词： UWB radar Differential evolution algorithm Variational modal decomposition algorithm Maximum effective frequency band energy ratio

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Dynamic Multi-Person Mesh Recovery from Uncalibrated Multi-View Cameras

arXiv

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arXiv 2021年

作者： Huang, Buzhen Shu, Yuan Zhang, Tianshu Wang, Yangang Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing China

Dynamic multi-person mesh recovery has been a hot topic in 3D vision recently. However, few works focus on the multi-person motion capture from uncalibrated cameras, which mainly faces two challenges: the one is that inter-person interactions and occlusions introduce inherent ambiguities for both camera calibration and motion capture;The other is that a lack of dense correspondences can be used to constrain sparse camera geometries in a dynamic multi-person scene. Our key idea is incorporating motion prior knowledge into simultaneous optimization of extrinsic camera parameters and human meshes from noisy human semantics. First, we introduce a physics-geometry consistency to reduce the low and high frequency noises of the detected human semantics. Then a novel latent motion prior is proposed to simultaneously optimize extrinsic camera parameters and coherent human motions from slightly noisy inputs. Experimental results show that accurate camera parameters and human motions can be obtained through one-stage optimization. The codes will be publicly available at https://***. Copyright © 2021, The Authors. All rights reserved.

关键词： Mesh generation

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Finite-time disturbance Observer Based Active Anti-disturbance control for Disturbed Inverted Pendulum System

Finite-time disturbance Observer Based Active Anti-disturban...

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第40届中国控制会议

作者： Xueying Lu Huazhou Hou Shihua Li School of Automation Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education

The balance control problem of disturbed inverted pendulum systems is *** of the existing advanced control methods for disturbed inverted pendulum systems are developed only to suppress constant or bounded *** results in a degradation of system performance when meeting severe *** this end,a finite-time disturbance observer is developed to estimate time-varying ***,an integral terminal sliding mode controller is designed for the inverted pendulum *** on the finite-time disturbance observer and the integral terminal sliding mode technique,a composite controller with finite-time convergence is proposed,which rejects the time-varying disturbances and alleviates the *** simulation results on the inverted pendulum system are presented to demonstrate the advantage and effectiveness of the proposed method.

关键词： Anti-disturbance Finite-time Disturbance Observer Integral Terminal Sliding Mode control

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Subgroup State Prediction under Different Noise Levels Using MODWT and XGBoost

引用

Journal of Healthcare engineering 2023年第1期2023卷

作者： Zhao, Xin Nie, Xiaokai School of Mathematics Southeast University Nanjing211189 China School of Automation Southeast University Nanjing210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing210096 China Shenzhen Research Institute Southeast University Shenzhen518057 China

In medical states prediction, the observations of different individuals are generally assumed to follow an identical distribution, whereas precision medicine has a rigorous requirement for accurate subgroup analysis. In this research, an aggregated method is proposed by means of combining the results generated from different subgroup models and is compared with the original method for different denoising levels as well as the prediction gaps. The results using real data demonstrate the effectiveness of the aggregated method exhibiting superior performance such as 0.95 in AUC, 0.87 in F1, and 0.82 in sensitivity, particularly for the denoising level that is set to be 2. With respect to the variable importance, it is shown that some variables such as heart rate and lactate arterial become more important when the denoising level increases. © 2023 Xin Zhao and Xiaokai Nie.

关键词： Forecasting

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Fault Detection Method of Photovoltaic System Based on Power Change of Array

Fault Detection Method of Photovoltaic System Based on Power...

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2021年第四届算法、计算和人工智能国际会议

作者： Chao Tan Chenxi Li Kanjian Zhang Haikun Wei Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of EducationSchool of AutomationSoutheast University

The fault detection of photovoltaic power generation system is of great significance in photovoltaic plant management. The conventional fault detection method of photovoltaic power system requires additional sensors, and the fault detection scheme needs to be redesigned if the parameters of the photovoltaic plant ***, for large photovoltaic plants, it is necessary to develop a method that does not require the use of external sensors or the detailed parameters of the photovoltaic plant. This paper proposes a photovoltaic system fault detection method based on photovoltaic array current and voltage data. This method does not require additional sensor data such as irradiance and temperature. The failure of photovoltaic system can be detected through the change of power,and the abnormal situation of photovoltaic power station can still be detected under cloudy and shaded conditions. The advantages of this method are that there is no need for complex and diverse sensor equipment, the calculation is simple and the fault detection ability is accurate. The experimental data of Padang Poverty Power Station of State Grid has been verified.

关键词： Photovoltaic System(PVS) Fault detection algorithm Electrical data Sensorless Power characteristics

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An Improved Faster R-CNN for Railway Fastening System Detection 21

An Improved Faster R-CNN for Railway Fastening System Detect...

引用

5th International Conference on Computer Science and Application engineering, CSAE 2021

作者： Peng, Xinfeng Tong, Shuzhen Lu, Xiaobo Wei, Yun School of Automation Southeast University Nanjing210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing210096 China Beijing Mass Transit Railway Operation Corporation Limited Beijing100044 China

ISBN: (纸本)9781450389853

In the automatic railway anomaly inspection technology based on image processing and deep learning, an effective algorithm used for high-precision detection of the fastening system is very important, especially in turnout sections. It is challenging because the background of the turnout sections is complicated with various types of targets. This paper improved the Faster R-CNN model, used multi-scale feature map fusion for small targets. And modified predefined anchor to generate region proposals, added attention module to make the network focus on meaningful feature. Besides, this paper used cross-entropy function and SmoothL1 loss function for training and labeled 1200 image samples as dataset. Compared with the original Faster R-CNN model, the experimental results (AP) of the improved model in this paper increased from 96.3% to 98.9%, which effectively reduced the fault detection and missed detection and improved the accuracy of location.. © 2021 Association for Computing Machinery. All rights reserved.

关键词： Deep learning

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Distilling Object Detectors with Efficient Logit Mimicking and Mask-Guided Feature Imitation

SSRN

引用

SSRN 2023年

作者： Lu, Xin Cao, Yichao Chen, Shikun Li, Weixuan Zhou, Xin Lu, Xiaobo School of Automation Southeast University Nanjing210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing210096 China Nanjing Enbo Technology Co. Ltd. Nanjing210007 China

Knowledge distillation (KD) is a promising approach to learning compact models for object detection with information inherited from intricate teacher networks. In this paper, we raise some shortcomings of existing KD methods for object detectors, e.g., ignoring knowledge selection, coarse feature imitation mask, etc. To address these issues, we present a novel KD framework to train efficient object detectors via Logit Mimicking and Feature Imitation (LMFI). First, we put forward a novel logit mimicking method for classification and localization heads. On the one hand, it first proposes to mimic the classification logits of one-category object detectors. On the other hand, we exploit the localization knowledge from teacher predictions and ground truths, which dynamically guides the learning of student's regression outputs by stages. Second, an adaptive positive teacher selection (APTS) strategy is designed to obtain high-quality teacher samples during distillation, which reduces the transmission of inferior knowledge. Moreover, we heuristically introduce a soft metric and fine-grained mask to reconcile the discrepancy between teacher and student features in a position-wise manner. Extensive experiments show that LMFI outperforms the state-of-the-art KD frameworks for object detection. It can significantly boost the performance of various detectors on different benchmarks, e.g., 2.83% and 2.58% MR-2 reduction of Cascade R-CNN on the S and All sets of CityPersons, and an improvement of ResNet-50 based ATSS from 36.7% to 40.5% mAP on the COCO benchmark. © 2023, The Authors. All rights reserved.

关键词： Object detection

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