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arXiv 2021年

作者： Wu, Haiwen Jayawardhana, Bayu De Marina, Hector Garcia Xu, Dabo Engineering and Technology Institute Groningen Faculty of Science and Engineering University of Groningen Groningen9747 AG Netherlands Department of Computer Architecture and Automatic Control Faculty of Physics Universidad Complutense de Madrid Madrid28040 Spain School of Automation Nanjing University of Science and Technology Nanjing210094 China

We present three classes of distributed formation controllers for achieving and maintaining the 2D/3D formation shape of manipulator end-effectors to cope with different scenarios due to availability of modeling parameters. We firstly present a distributed formation controller for manipulators whose system parameters are perfectly known. The formation control objective is achieved by assigning virtual springs between end-effectors and by adding damping terms at joints, which provides a clear physical interpretation of the proposed solution. Subsequently, we extend it to the case where manipulator kinematic and system parameters are not exactly known. An extra integrator and an adaptive estimator are introduced for gravitational compensation and stabilization, respectively. Simulation results with planar manipulators and with seven degree-of-freedom humanoid manipulator arms are presented to illustrate the effectiveness of the proposed approach. Copyright © 2021, The Authors. All rights reserved.

关键词： Manipulators

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Species-Shared and -Specific Structural Connections Revealed by Dirty Multi-task Regression 23rd

Species-Shared and -Specific Structural Connections Revealed...

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23rd International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2020

作者： Zhang, Tuo He, Zhibin Jiang, Xi Guo, Lei Hu, Xiaoping Liu, Tianming Du, Lei School of Automation Northwestern Polytechnical University Xian China School of Life Science and Technology MOE Key Lab for Neuroinformation University of Electronic Science and Technology of China Chengdu China Department of Bioengineering UC Riverside RiversideCA United States Cortical Architecture Imaging and Discovery Lab Department of Computer Science and Bioimaging Research Center The University of Georgia AthensGA United States

ISBN: (纸本)9783030597276

Comparative studies across species, such as primates in this work, reveal the shared structural or functional patterns that might be inherited from the common ancestors and the specific ones that might be related to individualized evolution strategies. Both the shared or specific patterns could help promote the understanding of mechanisms of brain structural and functional architectures and brain dynamics. Many previous studies can be found to report the comparative results on species pairs. However, very few studies were found to perform a comparison of large-scale connectomes across multiple species via a data-driven method. To this end, we construct brain connectomes for three primates, macaque, chimpanzee and human, by using Brodmann areas as graphic nodes and diffusion MRI derived white matter fibers to define edges and edge weights. On these connectomes, a novel dirty multi-task regression method is developed in the attempt to automatically identified the species-shared and -specific connections. The concordance of the findings via our method and previous reports demonstrate the effectiveness and the promise of this framework. © 2020, Springer Nature Switzerland AG.

关键词： Magnetic resonance imaging

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OpenAPMax: Abnormal Patterns-based Model for Real-World Alzheimer’s Disease Diagnosis

arXiv

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arXiv 2023年

作者： Huang, Yunyou Guan, Xianglong Lu, Xiangjiang Liang, Xiaoshuang Miao, Xiuxia Xie, Jiyue Liu, Wenjing Ma, Li Tang, Suqin Zhang, Zhifei Zhan, Jianfeng The Key Lab of Education Blockchain and Intelligent Technology Ministry of Education The Guangxi Key Laboratory of Multi-Source Information Mining and Security Guangxi Normal University China The School of Electronic and Information Engineering School of Integrated Circuits Guangxi Normal University Guilin530015 China The Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University China The State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing100086 China The Department of Physiology and Pathophysiology Capital Medical University Beijing100069 China The Guilin Medical University Guilin541001 China The Guangxi Key Lab of Multi-Source Information Mining & Security China

Alzheimer’s disease (AD) cannot be reversed, but early diagnosis will significantly benefit patients’ medical treatment and care. In recent works, AD diagnosis has the primary assumption that all categories are known a prior—a closed-set classification problem, which contrasts with the open-set recognition problem. This assumption hinders the application of the model in natural clinical settings. Although many open-set recognition technologies have been proposed in other fields, they are challenging to use for AD diagnosis directly since 1) AD is a degenerative disease of the nervous system with similar symptoms at each stage, and it is difficult to distinguish from its pre-state, and 2) diversified strategies for AD diagnosis are challenging to model uniformly. In this work, inspired by the concerns of clinicians during diagnosis, we propose an open-set recognition model, OpenAPMax, based on the anomaly pattern to address AD diagnosis in real-world settings. OpenAPMax first obtains the abnormal pattern of each patient relative to each known category through statistics or a literature search, clusters the patients’ abnormal pattern, and finally, uses extreme value theory (EVT) to model the distance between each patient’s abnormal pattern and the center of their category and modify the classification probability. We evaluate the performance of the proposed method with recent open-set recognition, where we obtain state-of-the-art results. Copyright © 2023, The Authors. All rights reserved.

关键词： Patient treatment

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Deep learning based Identification of Solid Waste Management in Smart Cities through Garbage Separation and Monitoring

Deep learning based Identification of Solid Waste Management...

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International Conference on Inventive Research in Computing Applications (ICIRCA)

作者： Abin George Sham H. Mankar N V S Suryanarayana Manish Gupta R Sathish A Priya Knowledge and technology management Division Council of Scientific & Industrial Research National Aerospace Laboratories Bangaluru Karnataka India Department of Mechanical Engineering Pimpri Chinchwad College of Engineering and Research Pune Maharashtra India Administrative Officer Central Tribal University of Andhra Pradesh Vizianagaram Andhra Pradesh India Division of Research and Development Lovely Professional University Phagwara Punjab India Department of Computer Science Shri Nehru Maha Vidayala College of Arts and Science Coimbatore Tamil Nadu India School of Architecture Koneru Lakshmaiah Educational Foundation Gundur Andhra Pradesh India

The solid waste management is the process of proper decomposition of waste materials within a period of time. This includes the collection of garbage's and then proceeded through certain measures for decomposition. There are various methods adopted in the garbage separation process. This includes the artificial intelligence techniques for the estimation and determination of the solid waste through automatic detection and separation of the garbage waste using control and sensing units. They are integrated with the internet of things to enable the two way communication system. This helps to visualize the functioning of the system adopting the digital platform. The proposed system is implemented through the smart dust bin held in every household that automatically senses the non-biodegradable and biodegradable waste materials. The classification of the waste materials are identified through the image processing techniques.

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Real-Time Measurement-Driven Reinforcement Learning Control Approach for Uncertain Nonlinear Systems

arXiv

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arXiv 2023年

作者： Abouheaf, Mohamed Boase, Derek Gueaieb, Wail Spinello, Davide Al-Sharhan, Salah College of Technology Architecture and Applied Engineering Bowling Green State University Bowling GreenOH43403-0001 United States School of Electrical Engineering and Computer Science University of Ottawa 800 King Edward Avenue OttawaONK1N 6N5 Canada Department of Mechanical Engineering 161 Louis Pasteur OttawaONK1N 6N5 Canada Machine Intelligence Research Labs AuburnWA98071-2259 United States

The paper introduces an interactive machine learning mechanism to process the measurements of an uncertain, nonlinear dynamic process and hence advise an actuation strategy in real-time. For concept demonstration, a trajectory-following optimization problem of a Kinova robotic arm is solved using an integral reinforcement learning approach with guaranteed stability for slowly varying dynamics. The solution is implemented using a model-free value iteration process to solve the integral temporal difference equations of the problem. The performance of the proposed technique is benchmarked against that of another model-free high-order approach and is validated for dynamic payload and disturbances. Unlike its benchmark, the proposed adaptive strategy is capable of handling extreme process variations. This is experimentally demonstrated by introducing static and time-varying payloads close to the rated maximum payload capacity of the manipulator arm. The comparison algorithm exhibited up to a seven-fold percent overshoot compared to the proposed integral reinforcement learning solution. The robustness of the algorithm is further validated by disturbing the real-time adapted strategy gains with a white noise of a standard deviation as high as 5%. © 2023, CC BY-NC-ND.

关键词： Reinforcement learning

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Assessment of PM2.5-associated mortality burden among the elderly: Insights into demographic, socio-economic, and geographic factors

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Air Quality, Atmosphere & Health 2024年第7期17卷 1535-1545页

作者： Sharma, Ayushi Hsiao, Hsi-yu Liu, Jia-Yi Lung, Shih-Chun Candice Su, Huey-Jen Shen, Ching-Fen Chen, Nai-Tzu Wu, Pei-Chih Lin, Cheng-Yu Liang, Sheng-Fu Cheng, Tain-Junn Chan, Ta-Chien Tsay, Yaw-Shyan Chung, Hsin-Ying Wang, Yu-Chun Department of Environmental Engineering College of Engineering Chung Yuan Christian University Taoyuan Taiwan Department of Civil Engineering College of Engineering Chung Yuan Christian University Taoyuan Taiwan Research Center for Environmental Changes Academia Sinica Taipei City Taiwan Department of Environmental and Occupational Health National Cheng Kung University Tainan City Taiwan National Cheng Kung University Hospital Pediatrics Office Tainan City Taiwan Department of Green Energy and Environmental Resources Chang Jung Christian University Tainan City Taiwan Sleep Medicine Center College of Medicine National Cheng Kung University Hospital National Cheng Kung University Tainan Taiwan Department of Computer Science and Information Engineering & Institute of Medical Informatics National Cheng Kung University Tainan City Taiwan Department of Neurology and Occupational Medicine Chi Mei Medical Center Tainan City Taiwan School of Medicine College of Medicine National Sun Yat-Sen University Kaohsiung City Taiwan Research Center for Humanities and Social Sciences Academia Sinica Taipei City Taiwan Department of Architecture National Cheng Kung University Tainan City Taiwan Department of Plant Industry National Pingtung University of Science and Technology Tainan City Taiwan

Since long-term exposure to PM2.5 is a growing concern in Taiwan, therefore, our study aimed to investigate the association between different PM2.5 concentrations and cause-specific mortalities, as well as explore the influence of demographic, socio-economic, and geographic determinants on mortality. Utilizing a Distributed Lag Non-Linear Model (DLNM), we estimated the PM2.5-attributable mortality burden among the elderly population (above 65 years) in Taiwan from 2005 to 2018. Our analysis considered various thresholds defined by the World Health Organization (WHO) to assess the impact of PM2.5 concentrations, ranging from standard Air Quality Guidelines (AQG) levels (0–15 μg/m3) to higher interim targets (up to 80 μg/m3). Our study reveals that achieving AQG levels (< 15 μg/m3) in Taiwan could potentially prevent 5571 elderly deaths, including 889 deaths from CVDs, 934 deaths from RTIs, and 680 deaths from chronic diseases. We observed a rapid increase in attributable deaths at lower PM2.5 concentrations, with a subsequent marginal increase at higher levels of exposure. We also identified several factors that modify the effects of PM2.5 exposures among elderly. For example, we found that higher elderly population ratios and high-income households were associated with an elevated risk of PM2.5-related deaths. In contrast, higher social welfare expenditure, better availability of medical practitioners per square kilometer, and higher forest-to-land ratios were associated with positive effects, indicating their potential in reducing mortality risk. This study fills a knowledge gap by examining the health consequences of varying PM2.5 levels and explored the complex interplay between demographic, socio-economic, and geographic factors in relation to PM2.5-related mortality.

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DoS-Aware Quantized Control of Nonlinear Systems via Linearization ⁎

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IFAC-PapersOnLine 2020年第2期53卷 3054-3059页

作者： Rui Kato Ahmet Cetinkaya Hideaki Ishii Department of Computer Science Tokyo Institute of Technology Yokohama 226-8502 Japan Information Systems Architecture Science Research Division National Institute of Informatics Tokyo 101-8430 Japan

This paper deals with a quantized feedback stabilization problem of nonlinear networked control systems via linearization. In particular, we study circumstances where the communication channel is interrupted by Denial-of-Service (DoS) attacks and its data rate is limited. We employ a deterministic DoS attack model which constraints the amount of attacks only by their frequency and duration, allowing us to capture a large class of potential attacks. To achieve asymptotic stabilization, we propose a resilient dynamic quantizer in the sense that it does not saturate in the presence of packet losses caused by DoS attacks. A sufficient condition for stability is derived by restricting the average frequency and duration of attacks. Since our result only guarantees local stability, we explicitly investigate an estimate of the region of attraction, which may be reduced by attacks. A simulation example is presented for demonstration of our results.

关键词： DoS attacks quantized control nonlinear systems stability analysis linearization

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InceptionNeXt-Transformer: A novel multi-scale deep feature learning architecture for multimodal breast cancer diagnosis

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Biomedical Signal Processing and Control 2025年 110卷

作者： Ishak Pacal Omneya Attallah Department of Computer Engineering Faculty of Engineering Igdir University 76000 Igdir Turkey Department of Electronics and Information Technologies Faculty of Architecture and Engineering Nakhchivan State University Nakhchivan AZ 7012 Azerbaijan Department of Electronics and Communications Engineering College of Engineering and Technology Arab Academy for Science Technology and Maritime Transport Alexandria 21937 Egypt Wearables Biosensing and Biosignal Processing Laboratory Arab Academy for Science Technology and Maritime Transport Alexandria 21937 Egypt

Manual breast cancer diagnosis is time-consuming, error-prone, and requires skilled professionals, while deep learning-based computer-Aided Diagnosis (CAD) systems offer greater performance. Yet existing CAD systems often focus on single imaging modalities, leading to reduced accuracy when applied to different image types. Furthermore, the challenges faced by current deep learning approaches in balancing local feature extraction and global context understanding hinder the development of comprehensive diagnostic tools. To address these issues, we introduce the InceptionNext-Transformer, a novel hybrid deep learning architecture designed for multi-modal breast cancer image analysis. This innovative approach combines Convolutional Neural Networks and Vision Transformers in a unique four-stage design: two initial stages with InceptionNext blocks for multi-scale feature extraction, a third stage employing self-attention to capture global dependencies, and a final stage integrating local and global data for enhanced classification. The study presents a computationally efficient CAD for breast cancer detection applicable to resource-constrained clinical environments. The model exhibited outstanding performance when assessed on seven datasets encompassing histopathology, mammography, and ultrasound imaging modalities. In binary classification using the BreakHis histopathology dataset, it attained 100% accuracy. Using the same dataset, the method achieved 98.25% accuracy for multi-class classification. In the analysis of mammography utilizing the INbreast, MIAS, and DDSM datasets, it achieved an accuracy of 99.97%. On BUSI and BLUID ultrasound datasets, the model attained an accuracy of 92.86%. These findings validate the InceptionNeXt-Transformer’s enhanced capacity for generalization across various imaging modalities. It demonstrates superior diagnostic reliability and computational efficiency, rendering it suitable for resource-limited clinical environments.

关键词： Breast cancer diagnosis Convolutional neural networks Deep learning, multiscale feature learning Histopathology Mammograms Vision transformers

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MV-Sketch: A Fast and Compact Invertible Sketch for Heavy Flow Detection in Network Data Streams

MV-Sketch: A Fast and Compact Invertible Sketch for Heavy Fl...

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2019 IEEE Conference on computer Communications, INFOCOM 2019

作者： Tang, Lu Huang, Qun Lee, Patrick P. C. Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong State Key Lab of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences China

ISBN: (纸本)9781728105154

Fast detection of heavy flows (e.g., heavy hitters and heavy changers) in massive network traffic is challenging due to the stringent requirements of fast packet processing and limited resource availability. Invertible sketches are summary data structures that can recover heavy flows with small memory footprints and bounded errors, yet existing invertible sketches incur high memory access overhead that leads to performance degradation. We present MV-Sketch, a fast and compact invertible sketch that supports heavy flow detection with small and static memory allocation. MV-Sketch tracks candidate heavy flows inside the sketch data structure via the idea of majority voting, such that it incurs small memory access overhead in both update and query operations, while achieving high detection accuracy. We present theoretical analysis on the memory usage, performance, and accuracy of MV-Sketch. Trace-driven evaluation shows that MV-Sketch achieves higher accuracy than existing invertible sketches, with up to 3.38 × throughput gain. We also show how to boost the performance of MV-Sketch with SIMD instructions. © 2019 IEEE.

关键词： Data structures

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Influence of surface albedo and vegetation fractional coverage on thermal environment of mining area

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Harbin Gongye Daxue Xuebao/Journal of Harbin Institute of technology 2022年第12期54卷 117-126页

作者： Hou, Chunhua Li, Fuping He, Baojie Ma, Pengkun Song, Wen Zhao, Jingjing College of Mining Engineering North China University of Science and Technology Hebei Tangshan063210 China Department of Computer Science Tangshan Normal University Hebei Tangshan063000 China Faculty of Architecture and Urban Planning Chongqing University Chongqing400405 China Faculty of Built Environment University of New South Wales Sydney2052 Australia Institute of Urban Boundary Layer and Atmospheric Environment Institute of Urban Meteorology CMA Beijing100089 China Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing100101 China

The influence of the changes in surface albedo on the thermal environment of the mining area due to the destruction of the original vegetation caused by the high-intensity mining of mineral resources was analyzed. Taking Malanzhuang Iron Mine in Qian'an city of Hebei province as an example, the statistical model method was adopted to retrieve the surface albedo based on the visible light band of Landsat remote sensing image, and the pixel trisection model was used to retrieve the vegetation fractional coverage based on NDVI-DFI. By means of radiative transfer equation method, the land surface temperature (LST) was retrieved based on the thermal infrared band of Landsat remote sensing image. With the aid of superposition analysis method, correlation analysis method, and regression analysis method, the response of spatiotemporal heterogeneity of surface thermal environment to the changes in photosynthetic vegetation coverage (fPV) and surface albedo was quantitatively and visually explored. Results showed that the spatial distribution of surface thermal environment in the study area showed obvious heterogeneity and regularity. The high temperature zone was mainly distributed in the northern Shaheshan stope, central Liuheyu dump, and the unreclaimed Baimashan dump in the south. The moderate temperature zone was mainly located at the edge of the high temperature zone and other bare surfaces. The low temperature zone was mainly located in the northern reclaimed tailings pond and reclaimed waste dump, as well as the green land and water bodies in the area. The spatiotemporal variation of photosynthetic vegetation coverage and surface albedo resulted in a large heterogeneity of the surface thermal environment during the summer midday in the study area. The mean surface temperature of underlying surface in the third phase of the image was ranked as follows: mining area bare rock > dump slag > reclamation vegetation > water body. The regression analysis results showed that f

关键词： Vegetation

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