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第35届中国控制与决策会议

作者： Yuan Shibo Duan Guangxin Wang Qingyu Wang Yanmin School of Electrical Engineering and Automation Harbin Institute of Technology Architectural Design and Research Institute of HIT

ISBN: (纸本)9798350334722

In order to solve the problem of transmission delay located in DC/DC converter systems,a novel compensation approach is proposed by combining sliding mode(SM) control and predictive *** an n-phase interleaving DC/DC buck converter as an example to simulate the signal transmission in such systems like the distributed energy and DC micro-grid types,the discrete model is established,following the design of discrete SM controller based on Lyapunov *** the random property of the transmission delay,a buffer zone is adopted to change the delay time into a fixed value,and further,a multistep prediction approach is used to estimate the length of the delay *** using the robustness of SM controller,the influence of transmission delay can be compensated automatically,following the analysis of the system *** validate the proposed approach.

关键词： transmission delay sliding mode control DC/DC converter predictive control stability

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Hot spot temperature prediction and operating parameter estimation of racks in data center using machine learning algorithms based on simulation data

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Building Simulation 2023年第11期16卷 2159-2176页

作者： Xianzhong Chen Rang Tu Ming Li Xu Yang Kun Jia School of Civil and Resources Engineering University of Science and Technology BeijingBeijing100083China School of Automation and Electrical Engineering University of Science and Technology BeijingBeijing100083China China Electronics Engineering Design Institute CO. LTD.Beijing100142China

In this paper,models to predict hot spot temperature and to estimate cooling air’s working parameters of racks in data centers were established using machine learning algorithms based on simulation ***,simulation models of typical racks were established in computational fluid dynamics(CFD).The model was validated with field test results and results in literature,error of which was less than 3%.Then,the CFD model was used to simulate thermal environments of a typical rack considering different factors,such as servers’power,which is from 3.3 kW to 20.1 kW,cooling air’s inlet velocity,which is from 1.0 m/s to 3.0 m/s,and cooling air’s inlet temperature,which is from 16℃ to 26℃ The highest temperature in the rack,also called hot spot temperature,was selected for each ***,a prediction model of hot spot temperature was built using machine learning algorithms,with servers’power,cooling air’s inlet velocity and cooling air’s inlet temperature as inputs,and the hot spot temperatures as ***,based on the prediction model,an operating parameters estimation model was established to recommend cooling air’s inlet temperatures and velocities,which can not only keep the hot spot temperature at the safety value,but are also energy saving.

关键词： data center CFD simulation hot spot temperature machine learning algorithm prediction and estimation models

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A linkage mechanism with high mechanical advantage for robotic grippers

A linkage mechanism with high mechanical advantage for robot...

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2024 International Conference on Industrial Robotics and Advanced Manufacturing Technology, IRAMT 2024

作者： Zhu, Yonghui Sun, Xiantao Wang, Zhiyong Chen, Wenjie Xie, Gang Chen, Weihai School of Electrical Engineering and Automation Anhui University Hefei China Wuhan Second Ship Design and Research Institute Wuhan China School of Automation Science and Electrical Engineering Beihang University Beijing China

In this paper, a compact linkage mechanism with high mechanical advantage and stroke is designed. The mechanism converts a small input force into a large output force using a toggle mechanism. Its feasibility for application in large gripping force manipulators has been verified. In the paper, the kinematic and static analyses of the mechanism are carried out. The effects of the mechanism parameters on its characteristics are investigated. Then, a dynamic mechanism simulation is carried out using ADAMS to obtain its mechanical gain and gripping force during motion. Finally, the processed double toggle mechanism is analyzed experimentally to verify its applicability. The simulation and experimental results show that the linkage mechanism performs better in the case of large gripping force demand. It can provide effective help for the design of mechanical grippers with large gripping force. © 2024 Institute of Physics Publishing. All rights reserved.

关键词： Grippers

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E-textile Sleeve with Graphene Strain Sensors for Arm Gesture Classification of Mid-Air Interactions 24

E-textile Sleeve with Graphene Strain Sensors for Arm Gestur...

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18th International Conference on Tangible, Embedded, and Embodied Interaction, TEI 2024

作者： Li, Yangfangzheng Zhou, Yi Shen, Cheng Stewart, Rebecca Dyson School of Design Engineering Imperial College London London United Kingdom School of Automation Science and Electrical Engineering Beihang University Beijing China

ISBN: (纸本)9798400704024

Arm gestures play a pivotal role in facilitating natural mid-air interactions. While computer vision techniques aim to detect these gestures, they encounter obstacles like obfuscation and lighting conditions. Alternatively, wearable devices have leveraged interactive textiles to recognize arm gestures. However, these methods predominantly emphasize textile deformation-based interactions, like twisting or grasping the sleeve, rather than tracking the natural body *** study bridges this gap by introducing an e-textile sleeve system that integrates multiple ultra-sensitive graphene e-textile strain sensors in an arrangement that captures bending and twisting along with an inertia measurement unit into a sports sleeve. This paper documents a comprehensive overview of the sensor design, fabrication process, seamless interconnection method, and detachable hardware implementation that allows for reconfiguring the processing unit to other body parts. A user study with ten participants demonstrated that the system could classify six different fundamental arm gestures with over 90% accuracy. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Smart textiles

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Autonomous Conscious Humanoid Robot System 5

Autonomous Conscious Humanoid Robot System

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5th International Conference on Robotics, Intelligent Control and Artificial Intelligence, RICAI 2023

作者： Zhang, Zhijun Yu, Haomin Wang, Wentong Luo, Yamei School of Automation Science and Engineering South China University of Technology Guangzhou China School of Automation Science and Engineering School of Design South China University of Technology Guangzhou China

ISBN: (纸本)9798350357950

In order to enable intelligent robots to have autonomous consciousness and make decisions autonomously, and generate behaviors safely and ethically, an autonomous conscious humanoid robot (ACHR) architecture is proposed and designed in this paper. The ACHR is mainly composed of mental system, environment perception system and behavior generation system. When the robot is stimulated externally in an environmental interaction, or generates an internal motivation due to an internal need, the stimulation is analyzed and goals of the robot are generated. Then goals are decomposed and logically sorted through the mental system, and sent into the behavioral decision-making system. Finally, behavior decision that meet moral ethics and safety principle is transferred to the behavior generation module so that the robot can generate behaviors. Compared with existing humanoid robots, the ACHR proposed in this paper combines large-scale models with generative intelligence technology and it is more autonomous. The framework of autonomous conscious humanoid robot proposed in this paper can promote the development of conscious robot in the future and provide a new idea for the development of intelligent robots. © 2023 IEEE.

关键词： Autonomous conscious behavior generation environment perception mental system moral ethics

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Investigation of Data Transmission Between Construction Machines and Attachment Tools via OPC UA Technology

Investigation of Data Transmission Between Construction Mach...

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International Conference on Construction Logistics, Equipment, and Robotics, CLEaR 2023

作者： Land, Kathrin Vogel-Heuser, Birgit Krüger, Marius Chen, Jan‐Kristof Fottner, Johannes Institute of Automation and Information Systems School of Engineering and Design Technical University of Munich Munich Germany Chair of Materials Handling Material Flow Logistics School of Engineering and Design Technical University of Munich Munich Germany

ISBN: (纸本)9783031440205

Construction sites require improved information logistics in order to obtain an efficient overview of the construction machine and attachment tools available on the construction site as well as their maintenance states. To enable data transfer to the central back office, an information technology integration of the machine and its tools is necessary. OPC UA is considered a potential solution to connect the devices and gather the information required. In this paper, a communication architecture using OPC UA is proposed to connect construction machine and attachment tools on the construction site with the back office. To evaluate the runtime performance of the communication architecture proposed, an experimental setup, which covers the communication on construction sites, is designed to measure the transmission time and data loss of the communication considering different PLC cycle times and data sizes. This paper contributes an order of magnitude for the transmission time and data loss in dependence of the performance class and cycle time of hardware devices. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Data transfer

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Experiments and modeling of microstructural and mechanical behaviors of laser-welded Ni-based superalloy at high temperatures

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Chinese Journal of Aeronautics 2025年第1期38卷 99-114页

作者： Ting HOU Yong LI Degang WANG Yuelin WANG Marko VILOTIC School of Mechanical Engineering and Automation Beihang UniversityBeijing 100191China Department of Structure Shenyang Aircraft Designand Research InstituteShenyang 110000China Engineering Center Shenyang Aircraft CorporationShenyang 110000China Faculty of Technical Science University of Novi SadNovi Sad 21102Serbia

The performance of welded Ni-based superalloys at high temperatures is essential to be evaluated due to their particular service environment for aero-engines and high-speed *** tensile properties and related microstructural evolutions such as the carbide precipitate and grain of a laser-welded Ni-based alloy were experimentally and numerically investigated at different temperatures(20,300,500,800℃).The results show that at room temperature,the strength of the Base Material(BM)was slightly smaller,with a difference of less than 1%,than the Welded Material(WM),which can be attributed to the more uniformly distributed needle-shaped carbide precipitates in the WM than those nonuniformly coarser spherical ones in the *** at 300℃ and 500℃,the strength of WM decreased more obviously compared with that of BM due to the more apparent growth of grain:13.52%loss in yield strength in WM alloys as compared with BM alloys at 300℃,and 16.57% at 500℃.At 800℃,the strength of BM and WM both decreased to a similar level due to Dynamic Recrystallization(DRX).However,a much higher elongation was observed for the BM than WM(less than 50%of BM),which can be attributed to the enhanced dislocation accumulation capability of the large spherical carbides along grain boundaries on the fracture surface in ***,a unified model considering the welding effects on both microstructures(dislocation,carbides,and grain)and mechanical properties evolutions at different temperatures was developed and *** on this model,the key temperature ranges(20–600℃)where apparent weakening of strength and uniform plasticity occurs for welded structures were identified,providing a direct guidance for potential structure and process design.

关键词： Ni-based superalloy Microstructure Mechanical properties Constitutive model High temperature

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Information space of sensor networks:Lagrangian,energy-momentum tensor,and applications

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Chinese Journal of Aeronautics 2023年第3期36卷 271-284页

作者： Mo TAO Shaoping WANG Hong CHEN Han PAN Jian SHI Yuwei ZHANG School of Automation Science and Electrical Engineering Beihang UniversityBeijing 100083China Wuhan Second Ship Design and Research Institute Wuhan 430205China School of Aeronautics and Astronautics Shanghai Jiao Tong UniversityShanghai 200240China

It is a challenge to investigate the interrelationship between the geometric structure and performance of sensor networks due to the increasingly complex and diverse architecture of *** paper presents two new formulations for the information space of sensor networks,including Lagrangian and energy–momentum tensor,which are expected to integrate sensor networks target tracking and performance evaluation from a unified *** proposed method presents two geometric objects to represent the dynamic state and manifold structure of the information space of sensor *** on that,the authors conduct the property analysis and target tracking of sensor *** the best of our knowledge,it is the first time to investigate and analyze the information energy-momentum tensor of sensor networks and evaluate the performance of sensor networks in the context of target *** and examples confirm the competitive performance of the proposed method.

关键词： Energy-momentum tensor Information science Information space Sensor networks Target tracking

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Marine Navigation Radar Target Detection with Multi-scale Spatiotemporal Feature Fusion-based Deep Learning 7

Marine Navigation Radar Target Detection with Multi-scale Sp...

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7th International Symposium on Autonomous Systems, ISAS 2024

作者： Liu, Yangyang Huang, Xiao Tao, Hao Xia, Zhiwei Xu, Xinfeng Liu, Chun Shanghai University School of Mechatronic Engineering and Automation Shanghai China China Ship Development and Design Center Wuhan China

ISBN: (纸本)9798350363173

This article endeavors to tackle the challenge of radar target detection in dynamic scenarios characterized by strong maneuvering, such as those involving rotating radar carriers or high-speed moving targets. A deep learning model, termed the multi-scale spatiotemporal fusion network (MSSF-Net), is proposed to effectively tackle the aforementioned scenarios. The proposed method encompasses several modules, with a notable inclusion being the improved Faster R-CNN module, which incorporates attention and spatial transformation mechanisms. This integration enables the extraction of spatial offset features from the current frame's echo image, facilitating the suppression of sea clutter and noise. Concurrently, the spatiotemporal feature fusion module within the network is designed to extract spatiotemporal information from historical frame echo images. It conducts multi-scale and multi-level feature fusion with the feature maps generated by other modules. This enables the capture of profound spatiotemporal features from a global perspective, thereby mitigating the decline in detection accuracy induced by the spatial shift of echo information resulting from strong maneuvering. Other modules within the method serve to manage the input and output of the model. Simulation results validate the enhanced accuracy of radar target detection achieved by the method under conditions of strong maneuvering. © 2024 IEEE.

关键词： Deep learning

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A Cross-Entropy-Based Convolutional Neural Network Approach to Composite Power System Risk Assessment 3

A Cross-Entropy-Based Convolutional Neural Network Approach ...

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3rd International Conference on Power Systems and Electrical Technology, PSET 2024

作者： Tang, Yi Geng, Lian Changshu Institute of Technology School of Electrical Engineering and Automation Suzhou China Institute Company Suzhou Electric Power Design Secondary Design Department Suzhou China

ISBN: (纸本)9798350353204

In the realm of data-driven power system risk assessment, the training datasets derived from randomly sampling high-reliability power systems exhibit a significant bias. This bias adversely impacts the efficacy of model training. Furthermore, the conventional application of convolutional neural networks (CNNs) to power system risk assessment encounters the issue of vanishing gradients, thereby diminishing prediction accuracy. To address these challenges, this study introduces a novel training data construction algorithm, leveraging the cross-entropy technique. This algorithm is designed to automatically identify and prioritize system states critical to risk indices, thereby enhancing the proportion of effective samples within the training dataset. Concurrently, we incorporate a batch normalization layer into the CNN architecture tailored for power system risk assessment. This modification aims to bolster the model's generalization capabilities. Through a comprehensive case study, our findings demonstrate that the proposed methodology significantly elevates both the prediction accuracy and the precision of risk assessment conducted by neural network models. © 2024 IEEE.

关键词： batch normalization convolutional neural network cross entropy power system risk assessment

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