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End to End Multitask Joint Learning Model for Osteoporosis Classification in CT Images

Computational intelligence and neuroscience 2023年第1期2023卷 3018320页

作者： Kun Zhang Pengcheng Lin Jing Pan Peixia Xu Xuechen Qiu Danny Crookes Liang Hua Lin Wang School of Electrical Engineering Nantong University Nantong Jiangsu 226001 China. Nantong Key Laboratory of Intelligent Control and Intelligent Computing Nantong Jiangsu 226001 China. Nantong Key Laboratory of Intelligent Medicine Innovation and Transformation Nantong Jiangsu 226001 China. Department of Radiology Affiliated Hospital 2 of Nantong University Nantong Jiangsu 226001 China. College of Mechanical Engineering Donghua University Shanghai 201620 China. School of Electronics Electrical Engineering and Computer Science Queen's University Belfast Belfast BT7 1NN UK.

Osteoporosis is a significant global health concern that can be difficult to detect early due to a lack of symptoms. At present, the examination of osteoporosis depends mainly on methods containing dual-energyX-ray, quantitative CT, etc., which are high costs in terms of equipment and human time. Therefore, a more efficient and economical method is urgently needed for diagnosing osteoporosis. With the development of deep learning, automatic diagnosis models for various diseases have been proposed. However, the establishment of these models generally requires images with only lesion areas, and annotating the lesion areas is time-consuming. To address this challenge, we propose a joint learning framework for osteoporosis diagnosis that combines localization, segmentation, and classification to enhance diagnostic accuracy. Our method includes a boundary heat map regression branch for thinning segmentation and a gated convolution module for adjusting context features in the classification module. We also integrate segmentation and classification features and propose a feature fusion module to adjust the weight of different levels of vertebrae. We trained our model on a self-built dataset and achieved an overall accuracy rate of 93.3% for the three label categories (normal, osteopenia, and osteoporosis) in the testing datasets. The area under the curve for the normal category is 0.973; for the osteopenia category, it is 0.965; and for the osteoporosis category, it is 0.985. Our method provides a promising alternative for the diagnosis of osteoporosis at present.

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False Noise Attack Detection for differentially-private distributed control of microgrids

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Automatica 2025年 179卷

作者： Feng Ye Xianghui Cao Lin Cai Mo-Yuen Chow School of Automation Southeast University Nanjing 210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing 210096 China Department of Electrical and Computer Engineering University of Victoria Victoria BC V8W 3P6 Canada UM-SJTU Joint Institute Shanghai Jiao Tong University Shanghai 200240 China

Privacy preserving in distributed control is getting more attention, and differential privacy (DP) is the common tool to protect data privacy, in which additive noise is applied in the algorithm function. However, DP can be leveraged by false noise (FN) attacks because attack vectors can be disguised as artificial noise in DP. FN attacks are a concern as the stealth attacks are hard to detect. Moreover, DP in distributed control makes FN attack detection more difficult. Hence, detecting FN attacks in privacy-preserving distributed control is critical and challenging. In this paper, taking distributed energy management systems as the control object, we propose a novel peer-to-peer attack detection approach, named False Noise Attack Detection (FNAD). In FNAD, each device observes the power decisions of its neighbors based on the data from its two-hop neighbors, estimates the power decisions of its neighbors by a Kalman filter, and updates the detection index of each neighbor according to the residues of the Kalman filter at each iteration. The detection index is developed based on information entropy, without any prior knowledge of the FN attacks. If a device’s detection index is out of well-defined thresholds, its neighbors can perform a majority vote to decide whether it is malicious. We theoretically prove the detection effect of FNAD against three representative attacks in the literature and analyze the advantages of FNAD compared with the traditional methods. The effectiveness of FNAD is demonstrated by extensive simulations.

关键词： State estimation

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Dynamic Modeling for Dielectric Elastomer Actuators Based on LSTM Deep Neural Network

Dynamic Modeling for Dielectric Elastomer Actuators Based on...

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International Conference on Advanced Robotics and Mechatronics (ICARM)

作者： Huai Xiao Jundong Wu Wenjun Ye Yawu Wang School of Automation Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems China University of Geosciences Wuhan Hubei China Gina Cody School of Engineering and Computer Science Concordia University Montreal Quebec Canada

ISBN: (数字)9781728164793

ISBN: (纸本)9781728164809

This paper proposes a dynamic model for dielectric elastomer actuators (DEAs) based on the long-short term memory (LSTM) deep neural network. The fabrication of the DEA and the framework of the experimental platform are introduced firstly. The behaviors of the DEA are analyzed through several sets of experiments, which shows the DEA has obvious memory behavior (i.e., the hysteresis behavior and creep behavior), where the hysteresis behavior is a symmetry and rate-dependence. Considering that the traditional neural network is difficult to describe the memory property, the LSTM deep neural network is constructed as the dynamic model of the DEA. Then, such neural network is trained according to the experimental data. Finally, the comparation results of the experimental data and the model output verify the effectiveness as well as the generalization ability of the dynamic model.

关键词： Mathematical model Neural networks Hysteresis Actuators Logic gates Soft robotics

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Attribute Prototype Network for Any-Shot Learning

arXiv

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arXiv 2022年

作者： Xu, Wenjia Xian, Yongqin Wang, Jiuniu Schiele, Bernt Akata, Zeynep State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China Max Planck Institute for Informatics Saarland Informatics Campus Saarbrücken Germany Computer Vision Laboratory ETH Zurich Switzerland University of Tübingen Tübingen Germany Max Planck Institute for Intelligent Systems Tübingen Germany City University of Hong Kong Hong Kong Hong Kong Aerospace Information Research Institute Chinese Academy of Sciences China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences China

Any-shot image classification allows to recognize novel classes with only a few or even zero samples. For the task of zero-shot learning, visual attributes have been shown to play an important role, while in the few-shot regime, the effect of attributes is underexplored. To better transfer attribute-based knowledge from seen to unseen classes, we argue that an image representation with integrated attribute localization ability would be beneficial for any-shot, i.e. zero-shot and few-shot, image classification tasks. To this end, we propose a novel representation learning framework that jointly learns discriminative global and local features using only class-level attributes. While a visual-semantic embedding layer learns global features, local features are learned through an attribute prototype network that simultaneously regresses and decorrelates attributes from intermediate features. Furthermore, we introduce a zoom-in module that localizes and crops the informative regions to encourage the network to learn informative features explicitly. We show that our locality augmented image representations achieve a new state-of-the-art on challenging benchmarks, i.e. CUB, AWA2, and SUN. As an additional benefit, our model points to the visual evidence of the attributes in an image, confirming the improved attribute localization ability of our image representation. The attribute localization is evaluated quantitatively with ground truth part annotations, qualitatively with visualizations, and through welldesigned user studies. Copyright © 2022, The Authors. All rights reserved.

关键词： Image representation

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Application of Iterative Learning control in X-Y Precision Planar Motion Platform

Application of Iterative Learning Control in X-Y Precision P...

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Chinese Automation Congress (CAC)

作者： Hongyu Yan Danping Jia Tao Zhang Hualiang Zhang Yingjie Xiong Xu Lu Yang Liu School of Artificial Intelligence Shenyang University of Technology Shenyang China School of Information Science and Engineering Shenyang University of Technology Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China School of Electrical Engineering Dalian University of Technology Dalian China College Of Information Science and Engineering Northeastern University Shenyang China

ISBN: (纸本)9781665426480

The size of the tracking error is one of the important indicators to measure the control system of the X-Y Precision Planar Motion Platform. Iterative learning control (ILC) can obtain higher tracking accuracy through multiple iterations. However, it is found in experiments that the tracking error of the XY precision motion stage using ordinary PD-type iterative learning will eventually fluctuate in a relatively range, and some high-frequency Noise will seriously affect the stability of the system after many iterations. Aiming at the above problems, a PD-type variable gain open-closed loop ILC with a low-pass filter is proposed. This control scheme can effectively remove high-frequency noise and reduce tracking errors. Finally, a hardware-in-the-loop real-time simulator is used to conduct a comparative experiment. Through comparison with PID controller and ordinary PD-type ILC, the experimental consequence show that this arithmetic has a smaller tracking error than the other two algorithms.

关键词： Tracking loops Tracking Low-pass filters control systems Size measurement Stability analysis Real-time systems

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Gauss-Newton unrolled neural networks and data-driven priors for regularized PSSE with robustness

arXiv

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arXiv 2020年

作者： Yang, Qiuling Sadeghi, Alireza Wang, Gang Giannakis, Georgios B. Sun, Jian State Key Lab of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China Department of Electrical and Computer Engineering University of Minnesota MinneapolisMN55455 United States

Renewable energy sources, elastic loads, and purposeful manipulation of meter readings challenge the monitoring and control of today’s power systems (PS). In this context, fast and robust state estimation (SE) is timely and of major importance to maintain a comprehensive view of the system in real time. Conventional PSSE solvers typically entail minimizing a nonlinear and nonconvex least-squares cost using e.g., the Gauss-Newton method. Those iterative solvers however, are sensitive to initialization, and may converge to local minima. To overcome these hurdles, the present paper adapts and leverages recent advances on image denoising to introduce a PSSE approach with a regularizer capturing a deep neural network (DNN) prior. For the resultant regularized PSSE objective, a "Gauss-Newton-type" alternating minimization solver is developed first. To accommodate real-time monitoring, a novel end-to-end DNN is constructed subsequently by unrolling the proposed alternating minimization solver. The deep PSSE architecture can further account for the power network topology through a graph neural network (GNN) based prior. To further endow the physics-based DNN with robustness against bad data, an adversarial DNN training method is put forth. Numerical tests using real load data on the IEEE 118-bus benchmark system showcase the improved estimation and robustness performance of the proposed scheme compared with several state-of-the-art alternatives. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

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Multi-dimensional Parameter Estimation in RIS-aided MU-MIMO Channels

arXiv

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arXiv 2025年

作者： Mo, Linlin Song, Yi Saggese, Fabio Lu, Xinhua Wang, Zhongyong Popovski, Petar Academy for Electronic Information Discipline Studies Nanyang Institute of Technology Nanyang473000 China Key Laboratory of Grain Information Processing and Control Ministry of Education Henan Engineering Research Center of Grain Condition Intelligent Detection and Application Henan University of Technology Zhengzhou450001 China School of Electrical and Information Engineering Zhengzhou University Zhengzhou450001 China Department of Electronic Systems Aalborg University Aalborg9220 Denmark

We address the channel estimation problem in reconfigurable intelligent surface (RIS) aided broadband systems by proposing a dual-structure and multi-dimensional transformations (DS-MDT) algorithm. The proposed approach leverages the dual-structure features of the channel parameters to assist users experiencing weaker channel conditions, thereby enhancing estimation performance. Moreover, given that the channel parameters are distributed across multiple dimensions of the received tensor, the proposed algorithm employs multidimensional transformations to effectively isolate and extract distinct parameters. The numerical results demonstrate the proposed algorithm reduces the normalized mean square error (NMSE) by up to 10 dB while maintaining lower complexity compared to state-of-the-art methods. © 2025, CC BY.

关键词： Channel estimation

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Stability and Neimark-Sacker Bifurcation of Leslie-Gower Predator-Prey System with Two Delays

Stability and Neimark-Sacker Bifurcation of Leslie-Gower Pre...

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第三十九届中国控制会议

作者： Wei Yin Weiqi Qin Xiaowei Jiang Xiangyong Chen Ming Chi School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation of Complex Systems Affiliated Hospital of Shandong Medical College School of Automation and Electrical Engineering Linyi University Huazhong University of Science and Technology

Neimark-Sacker bifurcation of a delay differential equation modeling two-enterprise interaction mechanism is investigated. For the demands of computer simulation applications, the discrete-time two-enterprise interaction model is proposed by using the forward Euler scheme. By discussing the characteristic equation of linearized part, the local stability criterion of the positive fixed point is presented. Time lag is selected as the bifurcation parameter. Then it is demonstrated that variation of time lag may result in Neimark-Sacker bifurcation. Furthermore, derivations for the directions and stability of Neimark-Sacker bifurcation are also given. Finally, we simulate the main results.

关键词： Leslie-Gower model Delay Stability Neimark-Sacker bifurcation

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Abnormal Data Cleaning for Wind Turbines by Image Segmentation Based on Active Shape Model and Class Uncertainty

SSRN

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SSRN 2022年

作者： Liang, Guoyuan Su, Yahao Wu, Xinyu Ma, Jiajun Long, Huan Song, Zhe Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Province Shenzhen China Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Province Shenzhen China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Province Shenzhen China School of Electrical Engineering Southeast University Jiang Su Province Nanjing China School of Business Nanjing University Jiang Su Province Nanjing China

Wind power curve describes the relationship between wind speed and output power of wind turbine, which may be contaminated due to various unexpected factors. Following the idea of image segmentation in our previous work, in this paper, we propose a novel image-based abnormal data cleaning algorithm aiming to provide high quality data for wind power curve modeling and relevant applications. For image binarization, a multiscale space is built to determine the optimal scale at which the most stable as well as informative shape representations are extracted. The Active Shape Model is utilized to model the power curve shape and generate a set of segmentation candidates for filtering abnormal data. To measure the cost of each candidate, the Class Uncertainty map based on dual feature images is built. And eventually the optimal segmentation boundary is determined by the optimization in the energy space. A series of comparative experiments conducted over the real-world datasets of 37 wind turbines have validated the superior performance of the proposed algorithm. © 2022, The Authors. All rights reserved.

关键词： Wind power

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Mechanical Analysis and Performance Optimization for the Lunar Rover’s Vane-Telescopic Walking Wheel

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engineering 2020年第8期6卷 936-943页

作者： Lu Yang Bowen Cai Ronghui Zhang Kening Li Zixian Zhang Jiehao Lei Baichao Chen Rongben Wang Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical EngineeringTianjin University of TechnologyTianjin 300384China National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin University of TechnologyTianjin 300384China Guangdong Key Laboratory of Intelligent Transportation System School of Intelligent Systems EngineeringSun Yat-sen UniversityGuangzhou 510275China College of Transportation Jilin UniversityChangchun 130025China Department of Mechanical Science and Engineering Tokyo Institute of TechnologyTokyo 152-8550Japan Armour College of Engineering Illinois Institute of TechnologyChicagoIL 60616USA China Academy of Space Technology Beijing 100029China

It is well-known that optimizing the wheel system of lunar rovers is ***,this is a difficult task due to the complex terrain of the moon and limited resources onboard lunar *** this study,an experimental prototype was set up to analyze the existing mechanical design of a lunar rover and improve its ***,a new vane-telescopic walking wheel was proposed for the lunar rover with a positive and negative quadrangle suspension,considering the complex terrain of the ***,the performance was optimized under the limitations of preserving the slope passage and minimizing power *** was achieved via analysis of the wheel force during ***,the effectiveness of the proposed method was demonstrated by several simulation *** newly designed wheel can protrude on demand and reduce energy consumption;it can be used as a reference for lunar rover development engineering in China.

关键词： intelligent vehicle Vane-telescopic walking wheel Performance optimization Vane spring Lunar rover

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