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3rd IEEE International Conference on electronic technology, Communication and information, ICETCI 2023

作者： Pan, Jian Li, Zhe Wei, Yi Chen, Zhibin Nong, Yingxiong Zhou, Bin Huang, Wuqing Zou, Jun Pan, Zilang Liu, Weiwei Guangxi Industrial Co. Ltd Information Center China Tobacco Nanning China Shenzhen Huafu Technology Co. Ltd Research and Development Center Shenzhen China

ISBN: (纸本)9798350398410

Regular security facilities check plays a key role in preventing potential dangers in production safety. Those facilities generally include helmets, protective suits, and fire extinguishers. Amongst them, fire extinguishers are essential facilities for fire hazard prevention and fire extinguishing. However, manual checks of fire extinguishers' presence may suffer from low efficiency and are error-prone, so we developed a real-time deep-learning framework, called FEDNet (Fire Extinguisher Detection Network), for the automatic detection of fire extinguishers on the scenes via cameras. Based on YOLOv5, which is a well-known one-stage object detection framework, we developed our FEDNet by introducing the state-of-the-art techniques including our proposed attention module, transformer-like modules and label assignment strategy. Our developed FEDNet showed much more advantageous performance in terms of detection accuracy. On a private dataset, our proposed framework achieved a mAP@0.5 of 94.0% with an FPS (Frames Per Second) of 74, which surpassed the original YOLOv5 by a big margin. Compared to other existing methods, our method also showed overwhelming performance in terms of speed and accuracy. More importantly, the developed framework can be extended for other object detection scenarios. © 2023 IEEE.

关键词： Deep learning

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TT@CIM: A Tensor-Train In-Memory-Computing Processor Using Bit-Level-Sparsity Optimization and Variable Precision Quantization

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IEEE Journal of Solid-State Circuits 2023年第3期58卷 852-866页

作者： Guo, Ruiqi Yue, Zhiheng Si, Xin Li, Hao Hu, Te Tang, Limei Wang, Yabing Sun, Hao Liu, Leibo Chang, Meng-Fan Li, Qiang Wei, Shaojun Yin, Shouyi Tsinghua University Beijing National Research Center for Information Science and Technology School of Integrated Circuits and the Beijing Advanced Innovation Center Beijing100084 China Southeast University National ASIC System Engineering Research Center School of Electronic Science and Engineering Nanjing210096 China Department of Electrical Engineering Hsinchu300 Taiwan Chengdu610054 China

Computing-in-memory (CIM) is an attractive approach for energy-efficient deep neural network (DNN) processing, especially for low-power edge devices. However, today's typical DNNs usually exceed CIM-static random access memory (SRAM) capacity. The introduced off-chip communication covers up the benefits of CIM technique, meaning that CIM processors still encounter the memory bottleneck. To eliminate this bottleneck, we propose a CIM processor, called TT@CIM, which applies the tensor-Train decomposition (TTD) method to compress the entire DNN to fit within CIM-SRAM. However, the cost of storage reduction by TTD is to introduce multiple serial small-size matrix multiplications, resulting in massive inefficient multiply-And-Accumulate (MAC) and quantization operations (QuantOps). To achieve high energy efficiency, three optimization techniques are proposed in TT@CIM. First, TTD-CIM-matched dataflow is proposed to maximize CIM utilization and minimize additional MAC operations. Second, a bit-level-sparsity-optimized CIM macro with high bit-level-sparsity encoding scheme is designed to reduce the power consumption of one MAC operation. Third, a variable precision quantization method and a lookup table-based quantization unit are presented to improve the performance and energy efficiency of QuantOp. Fabricated in 28-nm CMOS and tested on 4/8-bit decomposed DNNs, TT@CIM achieves 5.99-To-691.13-TOPS/W peak energy efficiency depending on the operating voltage. © 1966-2012 IEEE.

关键词： Deep neural networks

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Investigation into the willingness to accept of electric vehicle (EV) users to engage in vehicle-to-grid (V2G) participation

Investigation into the willingness to accept of electric veh...

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2023 International Conference on Computer Application and information Security, ICCAIS 2023

作者： Wang, Bo Nie, Fuhua Sun, Kaining Yuan, Jiucun School of Management and Economics Beijing Institute of Technology Beijing China Research Center for Sustainable Development & Intelligent Decision Beijing Institute of Technology Beijing China Center for Energy & Environmental Policy Research Beijing Institute of Technology Beijing China State Grid Xinjiang Electronic Power Co. Ltd. Xinjiang Urumqi China

ISBN: (纸本)9781510675032

Electric vehicle users play a crucial role in the interaction between electric vehicles and the power grid. Therefore, it is highly significant to design a discharge subsidy method that aligns with their willingness to accept compensation, thereby enhancing the incentive mechanism for vehicle-to-grid interaction. This study utilizes questionnaire data from electric vehicle users in a specific city and employs the choice experiment method to construct a model for their participation in grid interaction based on six attributes: discharge season, discharge time, discharge period, discharge amount, and discharge frequency. The random parameter Logit model is utilized to analyze the willingness to accept (WTA) of EV users towards different discharge schemes. These findings provide valuable insights for improving the compensation mechanism associated with EV integration into the power grid. © 2024 SPIE.

关键词： Vehicle-to-grid

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YOLO-H: A lightweight object detection framework for helmet wearing detection 3

YOLO-H: A lightweight object detection framework for helmet ...

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3rd International Conference on Image Processing and Intelligent Control, IPIC 2023

作者： Pan, Jian Li, Zhe Wei, Yi Huang, Cong Liang, Dong Lu, Tong Chen, Zhibin Nong, Yingxiong Zhou, Bin Liu, Weiwei Information Center China Tobacco Guangxi Industrial Co. LTD Nanning China Research and Development Center Shenzhen Huafu Technology Co. LTD Shenzhen China

ISBN: (纸本)9781510668140

In construction, coal mining, tobacco manufacturing and other industries, wearing helmets is crucial safety measure for workers, and the monitoring of helmet wearing plays a significant role in maintaining production safety. However, manual monitoring demands substantial human, material and financial resources, and will suffer from low efficiency and are error-prone. Therefore, we proposed a lightweight real-time deep learning based detection framework called YOLO-H, for automatic helmet wearing detection. Our YOLO-H model was developed on the foundation of YOLOv5-n by introducing the state-of-the-art techniques such as re-parameterization, decoupled head, label assignment strategy and loss function. Our proposed YOLO-H performed more efficiently and effectively. On a private dataset, our proposed framework achieved 94.5% mAP@0.5 and 65.2% mAP@0.5:0.95 with 82 FPS (Frames Per Second), which surpassed YOLOv5 by a large margin. Compared to other methods, our framework also showed overwhelming performance in terms of speed and accuracy. More importantly, the developed framework can be applied to other object detection scenarios. © 2023 SPIE.

关键词： Object detection

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Exploiting Deep Reinforcement Learning for Multi-AUV Assisted VoI-Maximum Data Collection in UWSNs

Exploiting Deep Reinforcement Learning for Multi-AUV Assiste...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Gu, Xuan Tang, Jiarun Huang, Xiao He, Jianhua Xu, Jing Huazhong University of Science and Technology School of Electronic Information and Communications Wuhan China China Ship Development and Design Center Wuhan China University of Essex School of Computer Science and Electronic Engineering ColchesterCO4 3SQ United Kingdom

ISBN: (纸本)9798350351255

Reliable and timely data collection is an important and challenging problem for underwater wireless sensor networks (UWSNs), partly due to the very slow underwater communication and the difficulty in recharging the sensors. In this paper, we exploit the use of autonomous underwater vehicles (AUVs) for UWSN data collection task. Specifically, we investigate how to maximize the value of information (VoI) for data collection through joint optimization of cluster head (CH) selection and multi-AUV path planning. We formulate the joint optimization problem for the task, taking into account the energy constraints of sensor nodes. To solve the problem, we propose a deep reinforcement learning algorithm based on an encoder-decoder architecture. The entire UWSN system is fed into the encoder network, followed by a composite decoder consisting of an AUV selection decoder and a cluster access sequence decoder to obtain the cluster access sequence for each AUV. Based on the determined sequences, we further utilize the dynamic programming algorithm to achieve optimal CH selection. Finally, we obtain the sequence of AUVs accessing the selected CHs. Simulation results demonstrate that the proposed learning-based approach converges and achieves a higher VoI than the existing benchmark algorithms. © 2024 IEEE.

关键词： Decoding

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Roles, applications, and research designs of robots in science education: a systematic review and bibliometric analysis of journal publications from 1996 to 2020

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Interactive Learning Environments 2024年第5期32卷 1834-1859页

作者： Chiu, Min-Chi Hwang, Gwo-Jen Tu, Yun-Fang Graduate Institute of Applied Science and Technology National Taiwan University of Science and Technology Taipei Taiwan Graduate Institute of Digital Learning and Education National Taiwan University of Science and Technology Taipei Taiwan Department of Library and Information Science Research and Development Center for Physical Education Health and Information Technology Fu Jen Catholic University New Taipei City Taiwan

This research reviews 56 robots in science education (RSE) research articles published in SSCI (Social Sciences Citation Index) journals in the Web of Science (WoS) database. The analysis results showed that there were three clusters of RSE studies: robots in elementary science education, robot-based STEM activities, and robot-facilitated computational thinking. In terms of the research issues, more studies were conducted across-disciplines. Regarding thematic classifications, “general benefits of educational robots” was the most frequently investigated issue, while “diversity and broadening participation,” and “creativity and motivation” were less frequently discussed. The most adopted research methods were quantitative and mixed methods, focusing on measuring learners’ cognition and affect. Most of the interventions in the empirical studies adopted pedagogical interventions. In terms of the interaction issue, LEGO was adopted the most, followed by self-made robots;moreover, robots frequently played the role of tools in science education. From the perspective of specific robotics content knowledge, “control” (e.g. programming) comprised the majority. The most adopted learning strategy was project-based learning, followed by issue-based discussion. Regarding assessment issues, the most adopted evaluation approaches to evaluating students’ performance were questionnaires, observation, verbal interviews, and artifacts to assess the quality of learning activities. © 2022 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Bibliometric analysis robots science education STEM systematic review

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A Wide Beam and Circularly Polarized Antenna with Parasitic Element

A Wide Beam and Circularly Polarized Antenna with Parasitic ...

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2023 IEEE MTT-S International Microwave Workshop Series on Advanced Materials and Processes for RF and THz Applications, IMWS-AMP 2023

作者： Li, Hao Huang, Jianming Zhang, Naibo Cui, Yansong Zhu, Ronghua School of Electronic Engineering Beijing University of Posts and Telecommunications Beijing China Beijing Research and Development Center The 54th Research Institute of China Electronics Technology Group Corporation Beijing China Kash Institute of Electronics and Information Industry Xinjiang China

ISBN: (纸本)9798350308761

A circularly polarized wide beam microstrip patch antenna is proposed for the low earth orbit(LEO) satellite communication. The proposed antenna consists of a radiation patch, a parasitic metal and the feed network. Wide beam radiation patterns can be obtained from 27.5 to 30.5GHz by applying a square ring parasitic elements around the radiation patch. The 3 dB beamwidth of the antenna in the 2-D space is in the range between 93° and 104°. The simulated results show that S11 is less than -10dB at the frequency range of 25.1-31.4GHz and axial ratio (AR) is less than 3dB at the frequency range of 26-30.8GHz. © 2023 IEEE.

关键词： circularly polarized millimeter wave patch antenna wide beam

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Test-Time training for domain adaption in IP geolocation 4

Test-Time training for domain adaption in IP geolocation

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4th International Conference on Artificial Intelligence, Robotics, and Communication, ICAIRC 2024

作者： Yu, Yue Jin, Qianwen Liu, Chunqi Wang, Liuqiang Liu, Yan Liu, Xueting Wang, Xiaohan Wu, Wei Xinjiang Production and Construction Corps Information Technology Service Center Urumchi China Xinjiang Internet Security Management Center Urumchi China Xinjiang Uygur Autonomous Region Branch Agricultural Development Bank of China Urumchi China University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu China AIWEN TECH Zhengzhou China

ISBN: (纸本)9798331531225

IP geolocation is a fundamental task in network management and cybersecurity, enabling services such as targeted advertising and content delivery optimization. However, the performance of geolocation models often deteriorates due to distribution shifts between training and testing data, such as changes in network topologies, new IP address allocations, or the emergence of previously unseen geographical regions. These challenges make it difficult for traditional geolocation models to generalize effectively in real-world scenarios. To tackle this issue, we introduce TTAGeo, a novel approach that applies test-time adaptation (TTA) techniques to dynamically adjust model parameters during inference. Our method integrates empirical risk minimization and self-supervised learning (SSL) to adapt geolocation models in a flexible and efficient manner. TTAGeo is particularly designed to handle the graph-structured nature of IP geolocation data, which involves complex feature correlations and topological relationships that are crucial for accurate predictions. Extensive experiments conducted on real-world benchmark datasets show that TTAGeo significantly enhances geolocation accuracy compared to baseline models. The results highlight its robustness and scalability for deployment in real-world network environments. TTAGeo represents a promising step toward more reliable and adaptive IP geolocation methods capable of meeting the challenges of evolving network landscapes. © 2024 IEEE.

关键词： Self-supervised learning

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research on Single-Event Burnout Hardening of Super-junction MOSFET

Research on Single-Event Burnout Hardening of Super-junction...

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2022 International Conference on Optoelectronic Materials and Devices, ICOMD 2022

作者： Wang, Zhengjiang Song, Jichang Hu, Dongqing The Faculty of Information Technology Beijing University of Technology Chaoyang Beijing China Research and Development Center Shenzhen Jihua Micro Special Electronics Company Ltd Guangdong Shenzhen China

ISBN: (纸本)9781510663268

When superjunction power devices work in spacecraft, they have to face extremely high radiation intensity, among which the single event effect is very serious, it will cause different types of damage to power devices, and then have catastrophic consequences for spacecraft, and With the increasing complexity and device integration of spacecraft systems, the harm of single event effects will become more serious. Therefore, it is very necessary to conduct research on anti-single event burnout of superjunction MOSFETs in aerospace applications. In order to solve the problem of single-event burnout of super-junction MOSFET under heavy-ion strike, the single event radiation effect and radiation hardening of traditional super-junction MOSFET devices are studied by using sentaurus TCAD simulation tool. The simulation results show that the device is most sensitive to the single-event effect when heavy-ion are vertically incident at the middle of the gate of the super-junction device, and the opening of the parasitic bipolar junction transistor is an important reason for the single-event burnout [1]. A 200 V radiation-hardened super-junction power MOSFET device structure is designed. The experimental results show that the designed super-junction power MOSFET device with a buffer layer structure relieves the strong electric field on the backside during heavy-ion strike, has good resistance to single-event burnout, and improves the robustness of the device in aerospace applications [2]. © 2023 SPIE.

关键词： Radiation hardening

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Fracture Analysis of QBe2.0 Beryllium Bronze Conductive Spring 14

Fracture Analysis of QBe2.0 Beryllium Bronze Conductive Spri...

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14th International Conference on Reliability, Maintainability and Safety, ICRMS 2023

作者： Li, Ziyang Zhang, Kun Zhu, Gang Zhao, Hao Zhang, Yingjie Sun, Lixian Reliability Research and Analysis Center China Electronic Product Reliability and Environmental Testing Research Institute Guangzhou China Science and Technology on Reliability Physics and Application of Electronic Component Laboratory China Electronic Product Reliability and Environmental Testing Research Institute Guangzhou China Guangxi Key Laboratory of Information Materials Guilin University of Electronic Technology Guilin China

ISBN: (纸本)9798350329988

Beryllium bronze conductive elastic materials are widely used in aerospace electronics, and the reliability of this material is critical for aircraft equipment A batch of conductive springs suffered from fracture failure during assembly. The fractography, chemical composition, microstructure, and microhardness of the spring were investigated, and the causes of fracture and improvement measures were discussed. The fractographs of the spring show an intergranular fracture accompanied with a dimple fracture. The chemical composition of the raw material conforms to the technical requirements. The sizes of the precipitates in the failed spring were primarily 1-2 um, while the size of the precipitates in the un-failed spring were less than 0.5 um. The grain boundary of the failed spring was wider, with larger particle phases distributed at the grain boundaries. Additionally, the average hardness of the fracture spring was 394.4 HV, which exceeds the technical requirements. The hardness of the un-failed spring met the requirements. According to the results of microstructure and hardness, improper aging treatment led to material embrittlement, and the spring suffered an intergranular fracture during assembly. © 2023 IEEE.

关键词： Brinell Hardness

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