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第34届中国控制与决策会议

作者： Ran Xie Ling Xiong Binbin Dan Zeyu Ren Engineering Research Center for Metallurgical Automation and Measurement Technology of Ministry of Education Institute of Robotics and Intelligent Systems Wuhan University of Science and Technology Key Laboratory of Metallurgical Equipment and Control of Ministry of Education School of Physics and Electronic Science Hunan Institute of Science and Technology

To solve the problem that early fault features of rotating machinery are difficult to extract,an adaptive k-value hierarchical variational mode decomposition(H-VMD) combined with optimized maximum second-order cyclostationarity blind deconvolution(CYCBD) fault feature extraction method is proposed in this *** decomposition of the vibration signal is performed with *** then the noise dominant component is denoised by wavelet threshold denoising(WTD).Furthermore,the improved autocorrelation-weighted correlated kurtosis(ACK)when CYCBD enhances the periodic shock component of the denoised signal is the fitness function of ChOA,and the envelope demodulation analysis of the feature-enhanced signal is performed using the teager energy operator(TEO).Simulation analysis and experimental results show that the interference of background noise can be effectively removed and the periodic shock component of the vibration signal be enhanced by the proposed method,which is a new feature extraction method for the fault diagnosis of rotating machinery.

关键词： Variational mode decomposition Maximum second-order cyclostationarity blind deconvolution Wavelet threshold denoising Teager energy operator Fault feature extraction

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Quantitative Method for Security Situation of the Power Information Network Based on the Evolutionary Neural Network

arXiv

引用

arXiv 2022年

作者： Yuan, Quande Pi, Yuzhen Kou, Lei Zhang, Fangfang Ye, Bo Changchun Institute of Technology Changchun China National Local Joint Engineering Research Center for Smart Distribution Grid Measurement and Control with Safety Operation Technology Changchun Institute of Technology Changchun China Qingdao China State Grid Xinjiang Electric Power Research Institute Urumqi China

Cybersecurity is the security cornerstone of digital transformation of the power grid and construction of new power systems. The traditional network security situation quantification method only analyzes from the perspective of network performance, ignoring the impact of various power application services on the security situation, so the quantification results cannot fully reflect the power information network risk state. This study proposes a method for quantifying security situation of the power information network based on the evolutionary neural network. First, the security posture system architecture is designed by analyzing the business characteristics of power information network applications. Second, combining the importance of power application business, the spatial element index system of coupled interconnection is established from three dimensions of network reliability, threat, and vulnerability. Then, the BP neural network optimized by the genetic evolutionary algorithm is incorporated into the element index calculation process, and the quantitative model of security posture of the power information network based on the evolutionary neural network is constructed. Finally, a simulation experiment environment is built according to a power sector network topology, and the effectiveness and robustness of the method proposed in the study are *** Codes 68T99 Copyright © 2022, The Authors. All rights reserved.

关键词： Cybersecurity

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Real-time Seamless Image Stitching in Autonomous Driving

Real-time Seamless Image Stitching in Autonomous Driving

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International Conference on Information Fusion

作者： Christian Kinzig Irene Corté s Carlos Ferná ndez Martin Lauer Institute of Measurement and Control Systems Karlsruhe Institute of Technology (KIT) Karlsruhe Germany Department of Computer Science and Engineering Universidad Carlos III de Madrid (UC3M) Madrid Spain

ISBN: (数字)9781737749721

ISBN: (纸本)9781665489416

Autonomous vehicles depend on an accurate perception of their surroundings. For this purpose, different approaches are used to detect traffic participants such as cars, cyclists, and pedestrians, as well as static objects. A commonly used method is object detection and classification in camera images. However, due to the limited field of view of camera images, detecting in the entire environment of the ego-vehicle is an additional challenge. Some solutions include the use of catadioptric cameras or clustered surround view camera systems that require a large installation height. In multi-camera setups, an additional step is required to merge objects from overlapping areas between cameras. As an alternative to these systems, we present a real-time capable image stitching method to improve the horizontal field of view for object detection in autonomous driving. To do this, we use a spherical camera model and determine the overlapping area of the neighboring images based on the calibration. Furthermore, lidar measurements are used to improve image alignment. Finally, seam carving is applied to optimize the transition between the images. We tested our approach on a modular redundant sensor platform and on the publicly available nuScenes dataset. In addition to qualitative results, we evaluated the stitched images using an object detection network. Moreover, the real-time capability of our image stitching method is shown in a runtime analysis.

关键词： Runtime Laser radar Pipelines Merging Object detection Cameras Real-time systems

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Beat Signal Analysis for Terahertz Frequency Modulation Continuous Wave

Beat Signal Analysis for Terahertz Frequency Modulation Cont...

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IEEE International Conference on Information Communication and Signal Processing (ICICSP)

作者： Chao Yang Yi Yang Defeng Liu Nai Lin Wei Liu Jie Zhuang Beijing Changcheng Aeronautic Measurement and Control Technology Research Institute China School of Information and Communication Engineering University of Electronic Science and Technology of China (UESTC) Chengdu China The 10th Research Institute of CETC Chengdu Sichuan China

The terahertz radar system can realize accurate detection, high-resolution imaging, and feature recognition of targets due to its small size, lightweight, and high resolution. Based on this special-shaped terahertz frequency-modulated continuous-wave radar, this paper studies the slope control of the transmitter and the local oscillator signal source and the change rule of the difference frequency signal and the effective acquisition interval. A short-time Fourier transform is used to simulate and verify the effective acquisition interval of the received intermediate frequency signal. It can be seen from the experimental results that under the special-shaped structure, controlling the FM continuous-wave slope at the transmitting and receiving ends can obtain the desired frequency-varying frequency signal, which can verify the correctness of the theoretical derivation.

关键词： Time-frequency analysis Terahertz wave imaging Frequency modulation Fourier transforms Transmitters Target recognition Radar

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Context-aware Emotion Recognition Based on Vision-Language Pre-trained Model

Context-aware Emotion Recognition Based on Vision-Language P...

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International Conference on Advanced Robotics and Mechatronics (ICARM)

作者： XingLin Li Xinde Li Chuanfei Hu Huaping Liu School of Automation Southeast University Nanjing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing China Nanjing Center for Applied Mathematics Nanjing China Southeast University Shenzhen Research Institute Shenzhen China Department of Computer Science and Technology Tsinghua University Beijing China

ISBN: (数字)9798350385724

ISBN: (纸本)9798350385731

Given the difficulty of recognizing ambiguous emotions in facial expression recognition tasks, we propose a visual-language model named CAER-CLIP to address this challenge. The proposed CAER-CLIP standed for Context-Aware Emotion Recognition (CAER), and were incorporated structure of the Contrastive Language–Image Pre-training (CLIP) model as promising alternative to classifier. There are two parts in CAER-CLIP model. In the visual part, facial expressions and contextual information of the image are simultaneously extracted to obtain the final feature embeddings, which are then used as a learnable “class” token for text-image pairing with desired module. In the textual part, we use text labels for emotion recognition classes as input. The outputs were merged to participate the comparative study to generated parameters of the model. The experiments demonstrate the effectiveness of the proposed method and show that our CAER-CLIP outperforms the state-of-the-art results on the CAER benchmark. The ablation experiment verified the effectiveness of both the classifier-based and text-based (ours without classifier) models, demonstrating that our method with the CAER-CLIP structure performed better, and the incorporation of a text encoder in the deep network model architecture effectively enhancing recognition accuracy.

关键词： Training Emotion recognition Visualization Accuracy Text recognition Face recognition Benchmark testing Feature extraction Data mining Context modeling

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Trajectory tracking of 4-DOF assembly robot based on quantification factor and proportionality factor self-tuning fuzzy PID control

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International Journal of Wireless and Mobile Computing 2020年第4期18卷 361-370页

作者： Li, Cuiqiao Sun, Ying Li, Gongfa Jiang, Du Zhao, Haoyi Jiang, Guozhang Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education Wuhan University of Science and Technology Wuhan Hubei China Research Centre for Biomimetic Robot and Intelligent Measurement and Control Wuhan University of Science and Technology Wuhan Hubei China Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education Institute of Precision Manufacturing Wuhan University of Science and Technology Wuhan China Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education Institute of Precision Manufacturing Wuhan University of Science and Technology Wuhan Hubei China Research Centre for Biomimetic Robot and Intelligent Measurement and Control Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering Wuhan University of Science and Technology Wuhan Hubei China Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education Research Centre for Biomimetic Robot and Intelligent Measurement and Control Wuhan University of Science and Technology Wuhan China Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering Wuhan University of Science and Technology Wuhan Hubei China

When the manipulator performs the assembling task, the performance of the system is easily affected by external interference and parameter changes, resulting in slow tracking response speed and low tracking accuracy. In order to improve the stability and robustness of the controller, a self-tuning fuzzy PID controller with quantification factor and proportionality factor is designed based on the fuzzy PID control. The parameters of the PID controller are dynamically fine-tuned by the fuzzy controller 1, and the quantification factor and proportionality factor are adjusted online by the modifying fuzzy controller 2 to fine-tune the parameters of PID controller. The control effect and robustness of the improved fuzzy PID controller to the manipulator system are simulated and verified on the MATLAB/Simulink platform. The simulation results show that the proposed method significantly improves the transient response speed, tracking accuracy and follower characteristics of the system, and has good dynamic performance. Copyright © 2020 Inderscience Enterprises Ltd.

关键词： Manipulators

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A Lightweight Object Detection Network for Industrial Robot Based YOLOv5

A Lightweight Object Detection Network for Industrial Robot ...

引用

Chinese Automation Congress (CAC)

作者： Tao Zhong Yahui Gan Zao Han Huan Gao Ang Li Key Laboratory of Measurement and Control of Complex Systems of Engineering School of Automation Southeast University Ministry of Education NanJing China Nanjing YingQi Intelligent Technology Co. Ltd. Intel Joint Research Institute on Intelligent Edge Computing NanJing China

With the rapid development of deep learning, it has been widely applied in fields such as computer vision, natural language processing, and robotics. Despite the superior performance of deep learning in object detection, most industrial vision robots still rely on traditional object detection methods due to computational constraints of robot controllers. In order to improve the performance of object detection for vision robots, a lightweight 3D object detection network based on You Only Look Once version 5 (YOLOv5) is proposed for satisfying industrial production. YOLOv5 work to efficiently object detection using deep convolutional networks. In terms of model deployment, we adopt a novel OpenVINO-based model deployment approach. The OpenVINO framework significantly enhances the inference speed of models by model optimization and compression. Our model achieves a 70% reduction in inference time compared to the baseline model on CPU. The effectiveness of the proposed method is demonstrated through experiments.

关键词：

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Cyanometallate Framework Templated Synthesis of Hierarchically Porous La(Oh)3 for High-Efficient and Stable Phosphorus Removal from Tailwater

SSRN

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SSRN 2023年

作者： Jing, Xiaoxu Zhang, Jing Li, Yungui Li, Qingqing Shen, Yi Zhang, Suorong Fang, Qile Advanced Institute of Natural Sciences Beijing Normal University Zhuhai519087 China Sichuan Provincial Sci-Tech Cooperation Base of Low-cost Wastewater Treatment Technology Department of Environmental Engineering Southwest University of Science and Technology Mianyang621010 China Key Laboratory of Microbial Technology for Industrial Pollution Control of Zhejiang Province College of Environment Zhejiang University of Technology Hangzhou310032 China Chongqing Feiyang Measurement and Control Technology Research Institute Co. Ltd Chongqing402760 China

Lanthanum (La)-based hydroxides have been recognized as promising candidates for dephosphorization due to their high affinity and selectivity for phosphate adsorption. However, the development of porous La(OH)3 materials with enriching active sites and high structural stability remains a challenge in tailwater treatment. Here we developed a novel self-templating method based on an ion-exchange mechanism to construct hierarchically porous La(OH)3, where the OH− of an alkali conversion reagent replaces the [Fe(CN)6]4− cluster within the LaFe cyanometalate (CM) precursor, simultaneously completing the hydroxide conversion and pore-creating processes. The obtained hierarchically porous La(OH)3 shows excellent structural stability with no ion leaching and can maintain its primary microsphere structure and secondary nanoneedle morphology after multiple cycles of application. Due to the abundant micropores/mesopores, there are fully exposed active sites inside and outside the porous La(OH)3 microsphere, giving it an excellent phosphate adsorption capacity (maximum capacity of 163.6 mg P/g). Its high selectivity and environmental adaptability make it a favorable adsorbent for aquaculture tailwater treatment that can rapidly reduce the dissolved inorganic phosphorus level to below 0.05 mg P/L to meet the mariculture tailwater discharge standard in a wide range of salinities and temperatures. The information gleaned in this study provides valuable guidance for the materials design of porous La-based hydroxides for phosphorus removal. © 2023, The Authors. All rights reserved.

关键词： Aquaculture

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Smart palm-size optofluidic hematology analyzer for automated imaging-based leukocyte concentration detection

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Opto-Electronic Science 2023年第12期2卷 1-8页

作者： Deer Su Xiangyu Li Weida Gao Qiuhua Wei Haoyu Li Changliang Guo Weisong Zhao Innovation Photonics and Imaging Center School of Instrumentation Science and EngineeringHarbin Institute of TechnologyHarbin 150080China Department of Control Science and Engineering Harbin Institute of TechnologyHarbin 150081China Department of Neurosurgery The Second Affiliated Hospital of Harbin Medical UniversityHarbin 150086China Institute of Optical Measurement and Intellectualization Harbin Institute of TechnologyHarbin 150080China Beijing Institute of Collaborative Innovation Beijing 100094China State Key Laboratory of Membrane Biology Beijing Key Laboratory of Cardiometabolic Molecular MedicineInstitute of Molecular MedicineNational Biomedical Imaging CenterSchool of Future TechnologyPeking UniversityBeijing 100871China

A critical function of flow cytometry is to count the concentration of blood cells,which helps in the diagnosis of certain ***,the bulky nature of commercial flow cytometers makes such tests only available in hospitals or laboratories,hindering the spread of point-of-care testing(POCT),especially in underdeveloped ***,we propose a smart Palm-size Optofluidic Hematology Analyzer based on a miniature fluorescence microscope and a microfluidic platform to lighten the device to improve its *** gadget has a dimension of 35×30×80 mm and a mass of 39 g,less than 5%of the weight of commercially available flow ***,automatic leukocyte concentration detection has been realized through the integration of image processing and leukocyte counting *** compared the leukocyte concentration measurement between our approach and a hemocytometer using the Passing-Bablok analysis and achieved a correlation coefficient of *** Bland-Altman analysis,we obtained the relationship between their differences and mean measurement values and established 95%limits of agreement,ranging from−0.93×10^(3)to 0.94×10^(3)cells/μ*** anticipate that this device can be used widely for monitoring and treating diseases such as HIV and tumors beyond hospitals.

关键词： hematology analyzer miniature fluorescence microscope microfluidics leukocyte concentration

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A Novel Regenerative Braking Energy Recuperation System For Electric Vehicles Based on Driving Style

SSRN

引用

SSRN 2023年

作者： Qun, Qiu Chengqun Wan, Xinshan Wang, Na Cao, Sunjia Ji, Xinchen Wu, Kun Hu, Yaoyu Meng, Mingyu School of Automotive and Traffic Engineering Jiangsu University Zhenjiang212013 China Jiangsu Province Intelligent Optoelectronic Devices and Measurement-Control Engineering Research Center Yancheng Teachers University Jiangsu Yancheng224007 China School of Mechanical Engineering Yancheng Institute of Technology Jiangsu Yancheng224051 China Department of Mathematical Sciences Tsinghua University Beijing100084 China Interdisciplinary Graduate School of Science & Engineering Tokyo Institute of Technology Yokohama2268502 Japan

The regenerative braking energy recovery system of pure electric vehicle is to recover and reuse the consumed driving energy under the premise of ensuring the braking safety. In this paper, the regenerative braking energy recovery system of pure electric vehicle was optimized based on driving style, and the driver model is constructed and the parameters that characterize driving style are determined. BLSTM (Bidirectional Long Short Term Memory) neural network model method was introduced for deep self-learning, and IDP (Iterative dynamic programming)-BLSTM based regenerative braking energy recovery management control strategy was established. Through theoretical analysis and numerical model of the system, the results of parameter representation of the energy system were preliminarily evaluated and then 170,000 km road test was carried out. The results of real vehicle test show that compared with PSO (Particle Swarm Optimization) and SVR (Support Vector Regression), IDP-BLSTM method can meet the personalized requirements of various drivers, improve driving experience and safety, and recover braking energy efficiently. © 2023, The Authors. All rights reserved.

关键词： Recovery

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