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Segment differential aggregation representation and supervised compensation learning of ConvNets for human action recognition

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Science China(Technological Sciences) 2024年第1期67卷 197-208页

作者： REN ZiLiang ZHANG QieShi CHENG Qin XU ZhenYu YUAN Shuai LUO DeLin School of Computer Science and Technology Dongguan University of TechnologyDongguan 523808China Shenzhen Institute of Advanced Technology Chinese Academy of SciencesShenzhen 518055China School of Electronic Engineering and Automation Guilin University of Electronic TechnologyGuilin 541004China Department of the mechanical engineering Erlangen-Nuremberg UniversityErlangen 91508Germany School of Aerospace Engineering Xiamen UniversityXiamen 361102China

With more multi-modal data available for visual classification tasks,human action recognition has become an increasingly attractive ***,one of the main challenges is to effectively extract complementary features from different modalities for action *** this work,a novel multimodal supervised learning framework based on convolution neural networks(Conv Nets)is proposed to facilitate extracting the compensation features from different modalities for human action *** on information aggregation mechanism and deep Conv Nets,our recognition framework represents spatial-temporal information from the base modalities by a designed frame difference aggregation spatial-temporal module(FDA-STM),that the networks bridges information from skeleton data through a multimodal supervised compensation block(SCB)to supervise the extraction of compensation *** evaluate the proposed recognition framework on three human action datasets,including NTU RGB+D 60,NTU RGB+D 120,and *** results demonstrate that our model with FDA-STM and SCB achieves the state-of-the-art recognition performance on three benchmark datasets.

关键词： action recognition segment frame difference aggregation supervised compensation learning ConvNets

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Response Characteristics and Regulation Feasibility of DC Charging Station Controlled by GFM/GFL Virtual Inertia for Grid Frequency Stability

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IEEE Transactions on Transportation Electrification 2025年第3期11卷 7743-7758页

作者： Ke, Song Ding, Leyan Shi, Xingye Fan, Peixiao Wang, Hongli Chen, Lei Yang, Jun Chung, Chi Yung Wuhan University School of Electrical Engineering and Automation Hubei Engineering and Technology Research Center for AC/DC Intelligent Distribution Network China Wuhan University School of Electrical Engineering and Automation Wuhan430072 China Department of Electrical and Electronic Engineering Hong Kong Hong Kong

To investigate the feasibility and differences between the grid-forming (GFM) and grid-following (GFL) control strategies for the frequency response (FR) in DC charging stations (CSs), this paper first identifies the characteristics of the virtual inertia of GFM and GFL and establishes an analytical model of the system frequency response considering FR in CSs. Second, the virtual inertia response control model of CSs are proposed under GFM and GFL controls, respectively. And their FR characteristics are analyzed. Finally, the frequency response characteristics of CS FR under variations of time delay, FR capacity, and virtual inertia are analyzed in the improved IEEE 3-machine 9-node and 10-machine 39-node benchmarks. The results show that the frequency response of the GFM CS is less affected by time delay and can affect the grid frequency in the form of inertia when the amount of electric vehicles (EVs) required in the CS groups is about 300-400. Whereas the overcapacity of the GFL CS will cause the system frequency to oscillate. Therefore, the choice of virtual inertia control strategy adopted by the CS is closely related to the number of EVs in the station and its adjustable margin. © 2015 IEEE.

关键词： Vehicle-to-grid

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Design and analysis of an intra-abdominal wall-lifting device for vNOTES

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Biomimetic Intelligence & Robotics 2023年第1期3卷 74-85页

作者： Jie Wang Yue Zhou Xiao Xiao Jiaole Wang Li Liu Department of Electronic Engineering The Chinese University of Hong KongHong Kong 999077China Department of Biomedical Engineering Tsinghua UniversityBeijing 100084China Department of Electronic and Electrical Engineering Southern University of Science and TechnologyShenzhen 518055China Department of Mechanical Engineering and Automation Harbin Institute of Technology(Shenzhen)Shenzhen 518055China

Natural orifice transluminal endoscopic surgery(NOTES)associated with less pain,shorter hospital stay,and outstanding cosmetic results has drawn considerable attention from the academic *** NOTES,pneumoperitoneum is an essential technology to obtain intraperitoneal surgical space,which,however,can have some serious adverse hemodynamic *** this paper,we propose a novel intra-abdominal wall-lifting device that can replace the pneumoperitoneum functionally for transvaginal NOTES(vNOTES).The device consists mainly of two layers of elastic strips,namely,the outer strips that are used to lift the abdominal wall for surgical exposure and the inner strip with a camera mounted that can rotate to provide a wide surgical field of *** the design,gasless vNOTES can be carried out with 82.74% of the operative field provided by pneumoperitoneum,eliminating all complications associated with gas *** design principle and kinematic analysis of the device are discussed in detail and validated by ***,a prototype is fabricated to demonstrate the feasibility of the proposed concept.

关键词： Gasless NOTES Transvaginal NOTES Abdominal lifting device Large deflection compressive column

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Positioning system design of closed-loop magnetically driven laser steering manipulator for endoscopic microsurgery

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Biomimetic Intelligence & Robotics 2023年第4期3卷 40-46页

作者： Qi Zhang Han Xu Li Zhu Li Liu School of Electronic Engineering and Automation Guilin University of Electronic TechnologyGuilin 541004China Shenzhen Key Laboratory of Robotic Perception and Intelligence Department of Electronic and Electrical EngineeringSouthern University of Science and TechnologyShenzhen 518055China

This study presents a closed-loop magnetically driven laser steering manipulator positioning system for endoscopic microsurgery.A multimagnetic field strength sensor circuit is embedded in a 16−mm diameter analog laser steering *** magnetic field distribution of a cylindrical permanent magnet in three-dimensional space is first modeled using an integral model to overcome the large error induced by the magnetic dipole model when the sensor and permanent magnet are *** integral in the model is then decomposed using the Gauss–Legendre quadrature to improve the computational efficiency of the ***,five commonly used global search optimization methods are ***,the algorithm with the fastest computational rate among these five algorithms,the tree-seed algorithm,is fused with the Levenberg–Marquardt algorithm,which performs well in local search,to obtain a hybrid optimization ***,it is demonstrated through static and dynamic experiments that the system based on the hybrid algorithm can obtain satisfactory computational errors while maintaining a high computational rate.

关键词： Magnetic localization Minimally invasive surgery Hybrid optimization algorithm Robot-assisted surgery

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Silk-Aloe Vera Composite Piezoelectric Film: A New Paradigm in Eco- Friendly Piezoelectrics

IEEE Journal on Flexible Electronics

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IEEE Journal on Flexible electronics 2024年第7期3卷 292-299页

作者： Bhagavathi, K. Aditya Bonam, Satish Joseph, Jose Rao, Karri Trinadha Singh, Shiv Govind Vanjari, Siva Rama Krishna Indian Institute of Technology Hyderabad Department of Electrical Engineering Hyderabad502285 India Digital University Kerala School of Electronic Systems and Automation Trivandrum695317 India

Piezoelectric materials derived from biological sources are given greater consideration in the present era due to their advantages, such as biocompatibility, sustainability, and potential for tailoring properties through chemical modification and genetic engineering. Silk, a bio-piezopolymer, derived from the silk glands of various species of silkworms, is extensively studied for its piezoelectric properties and applications thereof. This article presents the development of a composite bio-piezopolymer, amalgamating the material properties of silk and the extracts taken from a tropical desert plant, aloe vera. A comprehensive material characterization protocol is embraced to assess the performance of the thin film formed from the silk-aloe vera (SAV) composite solution. In addition to an improvement in the piezoelectric characteristics, the composite film exhibited excellent surface uniformity and crystallinity, proving its potential for real-time applications. Pressure sensors were fabricated with SAV composite films and characterized using a custom-built test setup. The characterization results surpassed the performance achieved by pressure sensors fabricated using only silk thin film. © 2022 IEEE.

关键词： Pressure sensors

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Data-driven Transient Stability Assessment Using Sparse PMU Sampling and Online Self-check Function

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CSEE Journal of Power and Energy systems 2023年第3期9卷 910-920页

作者： Guozheng Wang Jianbo Guo Shicong Ma Xi Zhang Qinglai Guo Shixiong Fan Haotian Xu Department of Electrical Engineering Tsinghua UniversityBeijing 100084China Department of Power System China Electric Power Research InstituteBeijing 100192China School of Automation Beijing Institute of TechnologyBeijing 100192China

Artificial intelligence technologies provide a newapproach for the real-time transient stability assessment (TSA)of large-scale power systems. In this paper, we propose a datadriven transient stability assessment model (DTSA) that combinesdifferent AI algorithms. A pre-AI based on the time-delay neuralnetwork is designed to locate the dominant buses for installingthe phase measurement units (PMUs) and reducing the datadimension. A post-AI is designed based on the bidirectionallong-short-term memory network to generate an accurate TSAwith sparse PUM sampling. An online self-check function of theonline TSA’s validity when the power system changes is furtheradded by comparing the results of the pre-AI and the *** IEEE 39-bus system and the 300-bus AC/DC hybrid systemestablished by referring to China’s existing power system areadopted to verify the proposed method. Results indicate that theproposed method can effectively reduce the computation costswith ensured TSA accuracy as well as provide feedback forits applicability. The DTSA provides new insights for properlyintegrating varied AI algorithms to solve practical problems inmodern power systems.

关键词： Artificial intelligence phasor measurement units recurrent neural networks transient stability assessment

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Segmentation of Fish in the Natural Habitat Using Deep Learning for Fish Size Measurement Application 7

Segmentation of Fish in the Natural Habitat Using Deep Learn...

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7th International Conference on Green technology and Sustainable Development, GTSD 2024

作者： Nguyen, Quang-Huy Le, Hoang-Minh-Quang Tran, Thi-Thao Nguyen, Tuan Ninh Pham, Van-Truong School of Electrical and Electronic Engineering Hanoi University of Science and Technology Department of Automation Engineering Hanoi Viet Nam

ISBN: (纸本)9798350386073

Assessing the size of fish in their natural habitat could provide crucial information for managing and monitoring these resources, thereby enabling the development of sustainable fishing and resource conservation strategies. However, manual methods are time-consuming, labor-intensive, and can have adverse effects on the health of the fish. Therefore, this study proposes a novel automated monitoring method by utilizing fish image segmentation, which allows for easier estimation of their size. Our proposed model namely AquaAttSeg has Ushape architecture with an encoder and a decoder. AquaAttSeg utilizes the PVTv2-B3 model as the backbone of encoder to enhance the ability to extract semantic information from input. In contrast to conventional U-shape models that directly connect low-level features from the encoder to high-level features in the decoder, leading to information imbalance, we proposed the HFIM module, which includes two our proposed modules SAM and CAM to replace conventional skip connections in order to enhance important information from the encoder to the decoder and enhance the processing of low-level features to extract more semantic information before combine with high-level features to reduce the information imbalance. In the decoder stage, we also proposed the Convmix Transpose Custom block, which can effectively synthesize rich and multidimentional information between the encoder and decoder to efficiently recover lost information and enhance the performance of the model. The performance of our proposed model has been tested on two datasets including Deepfish dataset and SUIM dataset. Additionally, we have compared the results of our model with other state-of-the-art models, demonstrating that our model produces highly competitive and promising outcomes. © 2024 IEEE.

关键词： Fish

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MCST-Net: A Multi-Cross-Spatial Attention U-Net with Transformer Block for Skin Lesion Segmentation 13th

MCST-Net: A Multi-Cross-Spatial Attention U-Net with Transf...

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13th International Conference on Information technology and its Applications, CITA 2024

作者： Vu, Manh-Hung Tran, Ngoc-Du Le, Hoang-Minh-Quang Tran, Thi-Thao Pham, Van-Truong Department of Automation Engineering School of Electrical and Electronic Engineering Hanoi University of Science and Technology Hanoi Viet Nam

ISBN: (纸本)9783031741265

Applying deep learning to skin lesion image segmentation has grown in popularity over the past few years. It is simpler to understand the injuries and administer the proper care using segmented images. In this research, we proposed a new model, by building a multi-cross-attention block, combining it with the transformer block and Unet model. In particular, our multi-cross-spatial attention block is highly effective when it comes to extracting features from multiple layers, helping to increase efficiency in the image upsample process. The transformer block that is widely used in natural language processing (NLP) is incorporated into the model, which helps to improve performance with processing the highest features. Additionally, our model has the benefit of having a small number of parameters in addition to providing great performance on the two well-known datasets, ISIC 2018 and PH2. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Image segmentation

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Finite Time Tracking Control of Uncertain Robotic Manipulator with Unknown Disturbance 13

Finite Time Tracking Control of Uncertain Robotic Manipulato...

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13th International Conference on Control, automation and Information Sciences, ICCAIS 2024

作者： Nguyen, Tuan T. Nguyen, Nam H. School of Electrical and Electronic Engineering Hanoi University of Science and Technology Department of Automation Engineering Hanoi Viet Nam

ISBN: (纸本)9798331542047

Stability with finite-time convergence rate has been recently studied and applied for various systems. It guarantees that the system is not only stable but also it has the settling time as small as expected. This improves the control performance of the system. In this paper, fixed-time and arbitrary-time tracking controllers are designed for a class of uncertain robotic systems with input disturbance. Some numerical simulations with twolink manipulator were taken to verify the proposed controllers and compare them. It can be concluded that the fixed-time controller provided better performance than the other ones in term of overshoot and control signal. © 2024 IEEE.

关键词： Manipulators

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A Lightweight-Based Architecture for Nuclei Segmentation in Histopathology Images 13th

A Lightweight-Based Architecture for Nuclei Segmentation in...

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13th International Conference on Information technology and its Applications, CITA 2024

作者： Pham, Viet-Tien Vu, Ngoc-Tu Pham, Van-Truong Tran, Thi-Thao Department of Automation Engineering School of Electrical and Electronic Engineering Hanoi University of Science and Technology Hanoi Viet Nam

ISBN: (纸本)9783031741265

The segmentation of histopathology images is crucial in diagnosing and assessing various cell types, including cancer cells, normal cells, and bacteria. However, analyzing these images manually is time-consuming and can lead to subjective results. To address this, automated segmentation methods based on deep learning and AI techniques have been developed. Supervised learning methods with encoder-decoder architectures, such as U-Net, have shown promising results in medical image segmentation. However, these models often struggle with the imbalance of information in heterogeneous medical images. To overcome this, new models like Unet++ and ResUnet were introduced, combining skip connections and dense connections to balance semantic information. Transformer architectures, such as ViT, have emerged as a new paradigm in computer vision and have shown excellent results when combined with Unet in the medical field. However, these models are computationally expensive and not feasible for mobile devices. To address this, LiReNet, a lightweight U-Net++-based architecture, was proposed to reduce storage and training costs while maintaining good learning and prediction capabilities. Our model LiReNet achieves superior results compared to other state of the arts, with the Dice scores up to 92.3% on the Data Science Bowl 2018 datset, and 88.3% on the glaS dataset. Code is available at https://***/PVTHust/LiReNet. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Medical imaging

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