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Retraction Note: Prediction and detection of harvesting stage in cotton fields using deep adversarial networks

Soft Computing 2024年第1期28卷 45-45页

作者： Gangadhar, Ch. Reji, R. Bhanudas, Musmade Bhausaheb Shinde, Swati Vijay Kumar, Devendra Kavitha, D. Department of Electronics and Communication Engineering Prasad V. Potluri Siddhartha Institute of Technology Vijayawada India Department of Computer Science and Engineering Carmel College of Engineering and Technology Alappuzha India Department of Instrumentation and Control Engineering D Y Patil College of Engineering Pune India Department of Computer Engineering Pimpri Chinchwad College of Engineering Pune India Department of Computer Applications ABES Engineering College Ghaziabad India Department of Computer Science and Engineering Easwari Engineering College (Autonomous) Chennai India

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Reliability Modeling Methods Using Field Operation Data of Smart Electricity Meters Based on Wiener Process

Reliability Modeling Methods Using Field Operation Data of S...

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Prognostics and System Health Management Conference (PHM-Qingdao)

作者： Dong Xianguang Dai Yanjie Wang Qing Chen Zhiru Du Yan Wang Pingxin Qi Jia State Grid Shandong electric power company electric power research institute Jinan China School of Measurement and Communication Engineering Harbin University of Science and Technology Jinan China

ISBN: (数字)9781728159461

ISBN: (纸本)9781728159478

The reliability of smart electricity meters was significantly affected by environment, field operation testing of smart electricity meters was conducted in high altitude locations, and one year operation data including power error were collected. This paper investigated the degradation behaviors of smart electricity meters with the help of Wiener process. The variation coefficients were introduced to analyze the characteristics of unknown parameters in the degradation model. Reliability estimates were computed according to the maximum likelihood and were compared to the results from different parameter characteristics. A case study validates that the reliability modelling method and provide guidelines for design, maintenance and applications of smart electricity meters.

关键词： Meters Degradation Maximum likelihood estimation Computational modeling Gaussian distribution Data models Reliability

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Tumor Detection, Segmentation and Classification Challenge on Automated 3D Breast Ultrasound: The TDSC-ABUS Challenge

arXiv

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arXiv 2025年

作者： Luo, Gongning Xu, Mingwang Chen, Hongyu Liang, Xinjie Tao, Xing Ni, Dong Jeong, Hyunsu Kim, Chulhong Stock, Raphael Baumgartner, Michael Kirchhoff, Yannick Rokuss, Maximilian Maier-Hein, Klaus Yang, Zhikai Fan, Tianyu Boutry, Nicolas Tereshchenko, Dmitry Moine, Arthur Charmetant, Maximilien Sauer, Jan Du, Hao Bai, Xiang-Hui Raikar, Vipul Pai Montoya-Del-Angel, Ricardo Martí, Robert Luna, Miguel Lee, Dongmin Qayyum, Abdul Mazher, Moona Guo, Qihui Wang, Changyan Awasthi, Navchetan Zhao, Qiaochu Wang, Wei Wang, Kuanquan Wang, Qiucheng Dong, Suyu School of Computer Science and Technology Harbin Institute of Technology Harbin150001 China Department of Mathematics Faculty of Science National University of Singapore Singapore National-Regional Key Technology Engineering Laboratory for Medical Ultrasound School of Biomedical Engineering Shenzhen University Medical School Shenzhen University Shenzhen China Laboratory Shenzhen University Shenzhen China School of Biomedical Engineering and Informatics Nanjing Medical University Nanjing China Pohang Korea Republic of Heidelberg Division of Medical Image Computing Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Germany Heidelberg Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Karlsruhe Heidelberg Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Germany Department of Biomedical Engineering and Health KTH Royal Institute of Technology Stockholm Sweden France FathomX Singapore Saw Swee Hock School of Public Health National University of Singapore Singapore Philips Research University of Girona Spain Department of Robotics and Mechatronics Engineering DGIST Korea Republic of Department of Interdisciplinary Studies of Artificial Intelligence DGIST Korea Republic of National Heart and Lung Institute Faculty of Medicine Imperial College London London United Kingdom Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom Lab School of Communication and Information Engineering Shanghai University Shanghai China Faculty of Science Mathematics and Computer Science Informatics Institute University of Amsterdam Amsterdam1090 GH Netherlands Department of Biomedical Engineering and Physics Amsterdam UMC Amsterdam1081 HV Netherlands Xi’an Jiaotong-Liverpool University China Department of Ultrasound Harbin Medical University Cancer Hospital No. 150 Haping Road Nangang

Breast cancer is one of the most common causes of death among women worldwide. Early detection helps in reducing the number of deaths. Automated 3D Breast Ultrasound (ABUS) is a newer approach for breast screening, which has many advantages over handheld mammography such as safety, speed, and higher detection rate of breast cancer. Tumor detection, segmentation, and classification are key components in the analysis of medical images, especially challenging in the context of 3D ABUS due to the significant variability in tumor size and shape, unclear tumor boundaries, and a low signal-to-noise ratio. The lack of publicly accessible, well-labeled ABUS datasets further hinders the advancement of systems for breast tumor analysis. Addressing this gap, we have organized the inaugural Tumor Detection, Segmentation, and Classification Challenge on Automated 3D Breast Ultrasound 2023 (TDSC-ABUS2023). This initiative aims to spearhead research in this field and create a definitive benchmark for tasks associated with 3D ABUS image analysis. In this paper, we summarize the top-performing algorithms from the challenge and provide critical analysis for ABUS image examination. We offer the TDSC-ABUS challenge as an open-access platform at https://***/ to benchmark and inspire future developments in1algorithmic research. © 2025, CC BY.

关键词： Image segmentation

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Online Non-Contact Weighing Duck Carcasses Using 2d Images and Deep Learning

SSRN

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SSRN 2022年

作者： Zhao, Yuliang Tian, Kaixuan Chen, Ruoyu Yang, Yongliang School of Control Engineering Northeastern University Hebei Qinhuangdao066001 China Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology Qinhuangdao066004 China School of Cyber Security University of Chinese Academy of Sciences Beijing100049 China State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110016 China

In the poultry meat processing industry, non-contact weighing methods based on computer vision have become increasingly popular due to their low costs and high efficiency. However, the accuracy of the traditional 2D image weighing techniques is reduced by the overlap and sway of carcasses. In this paper, we propose a more accurate regression model, Vggx, to weigh duck carcasses through image processing and deep learning. We adopt an image segmentation technique to obtain 2D images of a single duck section under a complex background. The images of the same duck are fused together to estimate its 3D information. Then, we use a convolutional neural network regression model to estimate the weight of duck carcass with the acquired 3D information. The Mean Absolute Percentage Error of our model is 1.46% and the average weight error of our model is 23.1 gram. Experimental results demonstrate that our method is high performance. © 2022, The Authors. All rights reserved.

关键词： Image segmentation

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Deep visual odometry with adaptive memory

arXiv

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arXiv 2020年

作者： Xue, Fei Wang, Xin Wang, Junqiu Zha, Hongbin School of Electrical Engineering and Computer Sciences Peking University Beijing100871 China Beijing Changcheng Aeronautical Measurement and Control Technology Research Institute Beijing10081 China

We propose a novel deep visual odometry (VO) method that considers global information by selecting memory and refining poses. Existing learning-based methods take the VO task as a pure tracking problem via recovering camera poses from image snippets, leading to severe error accumulation. Global information is crucial for alleviating accumulated errors. However, it is challenging to effectively preserve such information for end-to-end systems. To deal with this challenge, we design an adaptive memory module, which progressively and adaptively saves the information from local to global in a neural analogue of memory, enabling our system to process long-term dependency. Benefiting from global information in the memory, previous results are further refined by an additional refining module. With the guidance of previous outputs, we adopt a spatial-temporal attention to select features for each view based on the co-visibility in feature domain. Specifically, our architecture consisting of Tracking, Remembering and Refining modules works beyond tracking. Experiments on the KITTI and TUM-RGBD datasets demonstrate that our approach outperforms state-of-the-art methods by large margins and produces competitive results against classic approaches in regular scenes. Moreover, our model achieves outstanding performance in challenging scenarios such as texture-less regions and abrupt motions, where classic algorithms tend to fail. Copyright © 2020, The Authors. All rights reserved.

关键词： Computer vision

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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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Dome-shaped tactile sensor for detecting object stiffness and potential

Dome-shaped tactile sensor for detecting object stiffness an...

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IEEE International Conference on Unmanned Systems (ICUS)

作者： Yue Feng Kehang Zhong Zhilong Zhou Yanhui Han Science and Tehnology on Electromechanical Dynamic Control Labortary School of Mechatronical Engineering Beijing Institute of Technology Beijing China Beijing Orient Insititute of Measurement and Test Beijing China

ISBN: (数字)9781728180250

ISBN: (纸本)9781728180267

Multi-functional tactile sensors, capable of measuring multi-physical quantities in a limited space by one structure, have aroused great research interest. This paper proposes a monostable dome-shaped sensor for the simultaneous measurement of the stiffness and surface potential of the object at the same position. In the proposed sensor, the top electrode is a flexible spherical stainless steel dome, and the bottom electrode is a circular sheet covered with an electret film, providing a bias electric field. Output Voltages between two electrodes are generated because of the electrostatic effect, and resulting waveforms can be measured as the proposed sensor contacts and releases the object. While the proposed sensor approaches the object, the surface potential of the object determines the voltage waveform. After contacting the object, the voltage waveform indicates a direct comparison of stiffness between the object and top electrode, based on the monostable buckling of the dome. Here, we present theoretical and experimental relationships between measured voltages obtained from the proposed sensor and a charged electret film on the surface of the object. The proposed sensor makes it possible to measure the stiffness and surface potential of the object using voltage waveforms.

关键词： TV Radio frequency Erbium

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Jamming Recognition Based on AC-VAEGAN

Jamming Recognition Based on AC-VAEGAN

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International Conference on Signal Processing Proceedings (ICSP)

作者： Yan Tang Zhijin Zhao Xueyi Ye Shilian Zheng Lijun Wang School of Communication Engineering Hangzhou Dianzi University Hangzhou China State Key Lab of Information Control Technology in Communication System The 36th Research Institute of China Electronics Technology Group Corporation Jiaxing China

ISBN: (数字)9781728144801

ISBN: (纸本)9781728144818

To solve the performance deterioration of jamming recognition method based on deep learning in the case of small sample set, a jamming recognition method based on AC-VAEGAN is presented. Interference signal time-frequency diagrams are used as the small sample training set. AC-VAEGAN network is designed by modifying the structure of ACGAN network, that is, adding the core idea of VAE in the ACGAN network, so that the latent space of a continuous and meaningful sample set can be obtained through the encoder. By combining of regularization loss, reconstruction loss, source loss and classification loss, loss functions of three parts of AC-VAEGAN are obtained, and the three parts are trained to make the AC-VAEGAN network more efficient. The simulation results show that when the jamming-to-noise ratio is -10dB~10dB, the AC-VAEGAN network has a higher correct recognition rate for the five kinds of jamming than ACGAN and CNN networks in the case of small sample datasets.

关键词： Jamming Interference Time-frequency analysis Convolution Feature extraction Training Deep learning

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Entanglement filter with Rydberg atoms

arXiv

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arXiv 2022年

作者： Ye, Gen-Sheng Xu, Biao Chang, Yue Shi, Shuai Shi, Tao Li, Lin MOE Key Laboratory of Fundamental Physical Quantities Measurement Hubei Key Laboratory of Gravitation and Quantum Physics PGMF Institute for Quantum Science and Engineering School of Physics Huazhong University of Science and Technology Wuhan430074 China Beijing Automation Control Equipment Institute Beijing100074 China Quantum Technology R&D Center of China Aerospace Science and Industry Corporation Beijing10019 China Institute of Theoretical Physics Chinese Academy of Sciences P.O. Box 2735 Beijing100190 China CAS Center for Excellence in Topological Quantum Computation University of Chinese Academy of Sciences Beijing100049 China

Devices capable of deterministically manipulating the photonic entanglement are of paramount importance, since photons are the ideal messengers for quantum information. Here, we report a Rydberg-atom-based entanglement filter that preserves the desired photonic entangled state and deterministically blocks the transmission of the unwanted ones. Photonic entanglement with near-unity fidelity can be extracted from an input state with an arbitrarily low initial fidelity. The protocol is inherently robust, and succeeds both in the Rydberg blockade regime and in the interaction-induced dissipation regime. Such an entanglement filter opens new routes toward scalable photonic quantum information processing with multiple ensembles of Rydberg atoms. © 2022, CC BY.

关键词： Atoms

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Commensurate and Incommensurate Chern Insulators in Magic-angle Bilayer Graphene

arXiv

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arXiv 2024年

作者： Zhang, Zaizhe Yang, Jingxin Xie, Bo Feng, Zuo Zhang, Shu Watanabe, Kenji Taniguchi, Takashi Yang, Xiaoxia Dai, Qing Liu, Tao Liu, Donghua Liu, Kaihui Song, Zhida Liu, Jianpeng Lu, Xiaobo International Center for Quantum Materials School of Physics Peking University Beijing100871 China National Engineering Research Center of Electromagnetic Radiation Control Materials University of Electronic Science and Technology of China Chengdu611731 China School of Physical Science and Technology ShanghaiTech University Shanghai201210 China State Key Laboratory for Mesoscopic Physics Frontiers Science Centre for Nano-optoelectronics School of Physics Peking University Beijing100871 China CAS Key Laboratory of Nanophotonic Materials and Devices CAS Key Laboratory of Standardization and Measurement for Nanotechnology CAS Center for Excellence in Nanoscience National Center for Nanoscience and Technology Beijing100871 China Research Center for Electronic and Optical Materials National Institute of Material Sciences 1-1 Namiki Tsukuba305-0044 Japan Research Center for Materials Nanoarchitectonics National Institute of Material Sciences 1-1 Namiki Tsukuba305-0044 Japan School of Materials Science and Engineering Shanghai Jiao Tong University Shanghai200240 China School of Materials and Energy University of Electronic Science and Technology of China Chengdu611731 China Collaborative Innovation Center of Quantum Matter Beijing100871 China Hefei National Laboratory Hefei230088 China

The interplay between strong electron-electron interaction and symmetry breaking can have profound influence on the topological properties of materials1–4. In magic angle twisted bilayer graphene (MATBG), the flat band with a single SU(4) flavor associated with the spin and valley degrees of freedom gains non-zero Chern number when C2z symmetry or C2zT symmetry is broken. Electron-electron interaction can further lift the SU(4) degeneracy, leading to the Chern insulator states5–14. Here we report a complete sequence of zero-field Chern insulators at all odd integer fillings (ν = ±1, ±3) with different chirality (C = 1 or -1) in hBN aligned MATBG which structurally breaks C2z symmetry. The Chern states at hole fillings (v = -1, -3), which are firstly observed in this work, host an opposite chirality compared with the electron filling scenario. By slightly doping the v = ±3 states, we have observed new correlated insulating states at incommensurate moiré fillings which is highly suggested to be intrinsic Wigner crystals according to our theoretical calculations. Remarkably, we have observed prominent Streda-formula violation around v = -3 state. By doping the Chern gap at v = -3 with notable number of electrons at finite magnetic field, the Hall resistance Ryx robustly quantizes to ~ h/e2 whereas longitudinal resistance Rxx vanishes, indicating that the chemical potential is pinned within a Chern gap, forming an incommensurate Chern insulator. By providing the first experimental observation of zero-field Chern insulators in the flat valence band, our work fills up the overall topological framework of MATBG with broken C2z symmetry. Our findings also demonstrate that doped topological flat band is an ideal platform to investigate exotic incommensurate correlated topological states. © 2024, CC BY-NC-SA.

关键词： Degrees of freedom (mechanics)

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