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4th IEEE Advanced Information Management, Communicates, Electronic and automation Control Conference, IMCEC 2021

作者： Pu, Yunwei Liu, Taotao Wu, Haixiao Guo, Jiang Kunming University of Science and Thechnology Faculty of Information Engineering and Automation Kunming650500 China Computer Center Kunming University of Science and Thechnology Kunming650500 China

ISBN: (纸本)9781728185347

Aiming at the time-consuming and inconspicuous features of artificially extracting radar emitter signal features, a recognition method based on deep learning convolutional neural network and ambiguity function main ridge coordinate transformation is proposed. The method extracts the main ridge of the ambiguity function of the signal through fast discrete fractional Fourier transform, and then uses the two-dimensional time-frequency diagram of the main ridge polar coordinate domain of the ambiguity function as the input of the convolutional neural network to realize the sorting and recognition of different radar signals. Experiments show that the method in this paper not only maintains a 100% recognition rate above 0dB, but also stabilizes the recognition accuracy rate above 90% at -6dB. Compared with traditional radar signal recognition methods and other deep learning model recognition methods, the proposed method has greatly improved recognition rate and robustness, and has certain engineering application value. © 2021 IEEE.

关键词： Convolutional neural networks

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A Dynamic Interval Multi-Objective Evolutionary Algorithm Based on Multi-Task Learning and Inverse Mapping

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IEEE Transactions on Evolutionary Computation 2025年

作者： Wang, Feimeng Sun, Jing Gan, Xingjia Gong, Dunwei Wang, Gaige Guo, Yongde Qingdao University of Science and Technology College of Automation and Electronic Engineering Qingdao Shandong266061 China Qingdao University of Science and Technology School of Data Science Qingdao Shandong266061 China Central South University School of Automation Changsha Hunan410083 China Ocean University of China Department of Computer Science and Technology Qingdao Shandong266071 China City University of Macau Faculty of Data Science Macau999078 China

Dynamic interval multi-objective optimization problems, such as those encountered in wireless sensor network scheduling and portfolio selection, are increasingly prevalent. However, they present significant challenges due to the inherent uncertainty and variability of their parameters. This paper proposes a dynamic interval multi-objective evolutionary algorithm in terms of multi-task learning and inverse mapping. The algorithm employs an interval-based MOEA/D as its foundational framework. Upon encountering environmental changes, prediction models for the midpoints and widths of intervals are developed using multi-task learning combined with a self-evolving fuzzy system. These models generate a predicted Pareto front (PF) in the objective space. Subsequently, inverse mappings of the objective functions at the new time are established to identify solutions that correspond to the predicted PF in the decision space. Finally, these solutions are expanded to form an initial population at the new time. Testing on eighteen benchmark optimization problems and an uncertain scheduling of underwater wireless sensor networks, the proposed algorithm demonstrates both competitiveness and superiority when compared to five state-of-the-art algorithms. © 1997-2012 IEEE.

关键词： Contrastive Learning

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Mako:A Graph-based Pattern Growth Approach to Detect Complex Structural Variants

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Genomics, Proteomics & Bioinformatics 2022年第1期20卷 205-218页

作者： Jiadong Lin Xiaofei Yang Walter Kosters Tun Xu Yanyan Jia Songbo Wang Qihui Zhu Mallory Ryan Li Guo Chengsheng Zhang The Human Genome Structural Variation Consortium Charles Lee Scott E.Devine Evan E.Eichler Kai Ye School of Automation Science and Engineering Faculty of Electronic and Information EngineeringXi’an Jiaotong UniversityXi’an 710049China MOE Key Lab for Intelligent Networks&Networks Security Faculty of Electronic and Information EngineeringXi’an Jiaotong UniversityXi’an 710049China Genome Institute the First Affiliated Hospital of Xi’an Jiaotong UniversityXi’an 710061China Leiden Institute of Advanced Computer Science Faculty of ScienceLeiden UniversityLeiden 2311EZNetherland School of Computer Science and Technology Faculty of Electronic and Information EngineeringXi’an Jiaotong UniversityXi’an 710049China The Jackson Laboratory for Genomic Medicine FarmingtonCT 06032USA Precision Medicine Center the First Affiliated Hospital of Xi’an Jiaotong UniversityXi’an 710061China Institute for Genome Sciences University of Maryland School of MedicineBaltimoreMD 21201USA Department of Genome Sciences University of Washington School of MedicineSeattleWA 98119USA Howard Hughes Medical Institute University of WashingtonSeattleWA 98195USA The School of Life Science and Technology Xi’an Jiaotong UniversityXi’an 710049China 不详

Complex structural variants(CSVs) are genomic alterations that have more than two breakpoints and are considered as the simultaneous occurrence of simple structural ***,detecting the compounded mutational signals of CSVs is challenging through a commonly used model-match *** a result,there has been limited progress for CSV discovery compared with simple structural ***,we systematically analyzed the multi-breakpoint connection feature of CSVs,and proposed Mako,utilizing a bottom-up guided model-free strategy,to detect CSVs from paired-end short-read ***,we implemented a graph-based pattern growth approach,where the graph depicts potential breakpoint connections,and pattern growth enables CSV detection without pre-defined *** evaluations on both simulated and real datasets revealed that Mako outperformed other ***,validation rates of CSVs on real data based on experimental and computational validations as well as manual inspections are around 70%,where the medians of experimental and computational breakpoint shift are 13 bp and 26 bp,***,the Mako CSV subgraph effectively characterized the breakpoint connections of a CSV event and uncovered a total of 15 CSV types,including two novel types of adjacent segment swap and tandem dispersed *** analysis of these CSVs also revealed the impact of sequence homology on the formation of *** is publicly available at https://***/xjtu-omics/Mako.

关键词： Next-generation sequencing Complex structural variant Pattern growth Graph mining Formation mechanism

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U-HRMLP: Refining Segmentation Boundaries in Histopathology Images

U-HRMLP: Refining Segmentation Boundaries in Histopathology ...

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IEEE International Symposium on Biomedical Imaging

作者： Tao Wang Kai Zhang Weijie Wang Mingrui Ma Ye Zhang He Zhao Guixia Liu College of Computer Science and Technology Jilin University China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University China School of Information Science and Engineering Shandong Normal University China Department of Information Engineering and Computer Science University of Trento Italy Faculty of Information Engineering and Automation Kunming University of Science and Technology China School of Information Science and Technology Northeast Normal University China

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Segmenting regions of interest in histopathology images is a critical prerequisite for biomedical applications. CNN-based models have advanced medical image segmentation, yet accurately delineating boundaries is still challenging. This challenge arises from the loss of spatial resolution due to downsampling and the complexity of capturing contextual information when upsampling to the original resolution. To address these issues, we design U-HRMLP, a U-shaped network based on a hybrid combination of HRNet and Multi-Layer Perceptron (MLP). Specifically, we propose a multi-scale feature fusion module to avoid the loss of image resolution in the encoding stage as much as possible. Simultaneously, we propose an MLP-based decoding block to recover image details and resolution progressively, alleviating the issues related to the inadequate segmentation of smaller objects and the blurring of tissue boundaries. Results on the MoNuSeg and GlaS datasets demonstrate that U-HRMLP outperforms conventional or transformer-based methods and achieves state-of- the-art performance.

关键词： Measurement Image segmentation Histopathology Biological system modeling Semantics Refining Transformers

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ROBUST: 221 Bugs in the Robot Operating System

arXiv

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arXiv 2024年

作者： Timperley, Christopher S. van der Hoorn, Gijs Santos, André Deshpande, Harshavardhan Wąsowski, Andrzej School of Computer Science Carnegie Mellon University United States Department of Cognitive Robotics faculty of Mechanical Maritime and Materials Engineering Delft University of Technology Netherlands VORTEX CoLab Portugal Fraunhofer Institute for Manufacturing Engineering and Automation IPA Germany Department of Computer Science IT University of Copenhagen Denmark

As robotic systems such as autonomous cars and delivery drones assume greater roles and responsibilities within society, the likelihood and impact of catastrophic software failure within those systems is increased. To aid researchers in the development of new methods to measure and assure the safety and quality of robotics software, we systematically curated a dataset of 221 bugs across 7 popular and diverse software systems implemented via the Robot Operating System (ROS). We produce historically accurate recreations of each of the 221 defective software versions in the form of Docker images, and use a grounded theory approach to examine and categorize their corresponding faults, failures, and fixes. Finally, we reflect on the implications of our findings and outline future research directions for the community. © 2024, CC BY-NC-SA.

关键词： Robot Operating System

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Cooperative Control Strategy of Dual Manipulators Based on Model Reference Adaptive Admittance Control

Cooperative Control Strategy of Dual Manipulators Based on M...

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automation, Control and Robotics engineering (CACRE), International Conference on

作者： Hongmin Wang Meng Jiang Longhua Wu Junlin Huang Zengxi Pan Dagang Li Yingying Song Hui Huang School of Mechanical and Automation Engineering Institute of Carbon Peaking and Carbon Neutralization Wuyi University Jiang Men China School of Mechanical and Automation Engineering Wuyi University Jiang Men China Robotics and Welding Automation Laboratory University of Wollongong Australia School of Computer Science and Engineering Macau University of Science and Technology Macau China Faculty of Innovation and Entrepreneurship Wuyi University Jiang Men China

ISBN: (数字)9798350350302

ISBN: (纸本)9798350350319

This study aims to solve the dual robotic arm collaboration problem by proposing a method based on an adaptive conductivity control algorithm for nondestructive grasping and handling of unknown manipulated objects by dual robotic arms. The method is capable of dynamically tracking the intended force of the unknown entity while maintaining the desired trajectory and compensating for the error in the state of the unknown object. In this paper, adopting a two-arm collaborative robotic arm motion planning method oriented to the manipulated object, the kinematics of a singular appendage is calculated by closed-loop constraints. The collaborative control strategy of the dual robotic arms for the operated object is established, which facilitating the pursuit of the intended force and the rectification of the positional path deviation induced by external interference. Through simulation and experimental verification, the method demonstrates satisfactory performance regarding the error in tracking position and the steady state deviation in force, offering a viable solution for attaining heightened precision in tracking both position and force.

关键词： Trajectory tracking Force Collaboration Kinematics Manipulators Trajectory Steady-state

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Advanced AI techniques for root disease classification in dental X-rays using deep learning and metaheuristic approach

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Intelligent Systems with Applications 2025年 26卷

作者： Enkvetchakul, Prem Khonjun, Surajet Pitakaso, Rapeepan Srichok, Thanatkij Luesak, Peerawat Kaewta, Chutchai Gonwirat, Sarayut Boonmee, Chawis Noowattana, Matus Srisuwandee, Thitinon Department of Information Technology Faculty of Science Buriram Rajabhat University Buriram31000 Thailand Artificial Intelligence Optimization SMART Laboratory Industrial Engineering Department Faculty of Engineering Ubon Ratchathani University Ubon Ratchathani34190 Thailand Department of Industrial Engineering Faculty of Engineering Rajamangala University of Technology Lanna Chiang Rai57120 Thailand Digital Innovation Faculty of Computer Science Ubon Ratchathani Rajabhat University Mueang Ubon Ratchathani34000 Thailand Department of Computer Engineering and Automation Kalasin University Kalasin46000 Thailand Department of Industrial Engineering Faculty of Engineering Chiang Mai University Chiang Mai Thailand Dental Department Samrong Hospital Samrong District Ubon Ratchathani34360 Thailand

Root dental diseases remain among the most diagnostically challenging conditions in oral healthcare, often leading to treatment delays and suboptimal outcomes. This study is motivated by the limitations of existing automated diagnostic systems, which tend to focus on superficial abnormalities and overlook complex root pathologies such as pulpal infections, periapical lesions, and progressive periodontitis. To bridge this critical gap, we propose an advanced AI-based classification model that integrates ensemble deep learning architectures with a hybrid metaheuristic optimization strategy-namely, the non-population-based Artificial Multiple Intelligence System (np-AMIS) for image augmentation and the population-based AMIS (pop-AMIS) for adaptive decision fusion. This dual-phase approach enhances feature diversity, classification robustness, and computational efficiency. The model was trained and validated on two proprietary datasets, TD-1 and TD-2, achieving classification accuracies of 98.87 % and 98.41 %, respectively. It was further implemented in a real-world application via the Automated Teeth Disease and Abnormality Classification System (A-TD-A-CS), demonstrating 98.95 % accuracy, a rapid response time of 1.5 s, and a System Usability Scale (SUS) score of 94.5 from dental professionals. The system's ability to accurately identify multiple root disease categories highlights its clinical viability and transformative potential. In addition to its current performance, this study lays the groundwork for future extensions to multi-center datasets and cross-modality diagnostics using cone-beam CT or intraoral scans, further advancing intelligent dental care. © 2025

关键词： Deep learning

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State of Health Estimation for Lithium-Ion Batteries Using Electrochemical Impedance Spectroscopy and a Multi-Scale Kernel Extreme Learning Machine

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World Electric Vehicle Journal 2025年第4期16卷 224-224页

作者： Peng, Jichang Gao, Ya Cai, Lei Zhang, Ming Sun, Chenghao Liu, Haitao School of Electric Power Engineering Nanjing Institute of Technology Nanjing211167 China Faculty of Computer Science and Engineering Xi’an University of Technology Xi’an710048 China School of Information Engineering Ordos Institute of Technology Ordos017000 China School of Electrical and Automation Engineering Nanjing Normal University Nanjing210023 China

An accurate state of health (SOH) estimation for lithium-ion batteries (LIBs) is crucial for reliable operations and extending service life. While electrochemical impedance spectroscopy (EIS) effectively characterizes LIBs degradation patterns, the high dimensionality of EIS data poses challenges for an efficient analysis. This study proposes a novel method that combines EIS with an equivalent circuit model (ECM) and distribution of relaxation time (DRT) analysis to extract low-dimensional health features from high-dimensional EIS data. A multi-scale kernel extreme learning machine (MS-KELM), optimized by the Sparrow Search Algorithm (SSA), estimates battery SOH with an average mean absolute error (MAE) of 1.37% and a root mean square error (RMSE) of 1.76%. In addition, compared with support vector regression (SVR) and Gaussian process regression (GPR), the proposed method reduces computational time by factors of 4 to 30 and lowers memory usage by approximately 18%. © 2025 by the authors.

关键词： Lithium-ion batteries

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Advanced Analysis of Electro-Oculographic Signal using Deep Neural Networks for Safety Purposes in automation and Production Systems

Advanced Analysis of Electro-Oculographic Signal using Deep ...

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International Conference on computer and automation engineering, ICCAE

作者： Ivan Kuric Vladimír Stenchlák Ivan Zajačko Martin Bohušík Dorota Więcek Department of Automation and Production Systems University of Žilina Žilina Slovakia Faculty of Mechanical Engineering and Computer Science University of Bielsko-Biala Bielsko-Biala Poland

This article deals with the problematics of obtaining and processing the EOG signals. This research describes the ways in which it is possible to measure and evaluate the measured bio-electrical signals of brain and muscle activity in more detail. The work also describes the analysis of the current state of this problematics and individual models of artificial intelligence are also described in the work - artificial neural networks that can be used for such advanced analysis. The experimental part of this work is performed in the MATLAB environment using the Deep Neural Network Toolbox. The experimental part of the work is focused on the analysis and processing of bio-electrical activity eye-motor muscles in the area of eye-tracking with the help of the OLIMEX EEG-SMT device. The information provided in this research can be used for improving safety and reliability and elimination of potential risks connected to an epilepsy disease and epilepsy seizures in automated production systems.

关键词： Deep learning Safety devices Production systems Voltage measurement automation Tracking Current measurement

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Evaluation of Convolutional Neural Network Model Architecture Performance

Evaluation of Convolutional Neural Network Model Architectur...

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International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS)

作者： Musthofa Galih Pradana Hilda Khoirunnisa I Wayan Rangga Pinastawa Faculty of Computer Science Universitas Pembangunan Nasional Veteran Jakarta Jakarta Indonesia Department of Automation Engineering and Mechatronics Bandung Polytechnic for Manufacturing Bandung Indonesia

The identification of image types can be facilitated through the use of algorithms and automation in their detection. In this research, detection is performed on fashion images, which encompass various types and categorizations. The process of identification and detection can utilize the Neural Network algorithm known as Convolutional Neural Network (CNN). The Convolutional Neural Network (CNN) algorithm consists of multiple layers, and its application involves setting values for pixels as well as arranging the sequence of layers within it. In this study, two Convolutional Neural Network (CNN) algorithm models are employed for image classification in the fashion dataset. These two models differ in the order of layers within the Convolutional Neural Network (CNN) and in the values of strides applied for modification. Model 1 employs ZeroPadding2D in the first layer with a smaller stride value of 1 compared to Model 2, which places the Conv2D layer in the first layer and applies strides with 1, 2, and 3 values. This research indicates that Model 1 outperforms Model 2, achieving the highest accuracy in epoch 20 with an accuracy value of 0.9711. This is attributed to the fact that the strides value in the Conv2D layer of Model 1 is set to a smaller value compared to Model 2.

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