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International Conference on Emerging Technologies and Factory automation (ETFA)

作者： Johannes Kühnel Timo Eißmann Christian Wiede Dorothea Schwung Anton Grabmaier Fraunhofer IMS Duisburg Germany Artificial Intelligence and Data Science in Automation Hochschule Düsseldorf - University of Applied Sciences Düsseldorf Germany Department of Electronic Circuits and Systems University of Duisburg-Essen Duisburg Germany

ISBN: (数字)9798350361230

ISBN: (纸本)9798350361247

Tiny ML, defined by its ability to integrate AI into the smallest devices, unlocks the potential for always-on AI solutions at the edge. This capability is essential in predictive maintenance, enabling smart sensors within the Industrial In-ternet of Things (1IoT). In factory automation, the ability to detect anomalies and adapt those anomaly detection models to varying operating conditions is crucial for the efficiency and longevity of machinery. However, the challenge of semi-supervised anomaly detection with domain adaptation across diverse working conditions has not been sufficiently addressed in TinyML. This paper presents Shared Encoder Domain Adaptation (SEDA) to overcome these limitations. SEDA is a multi-target domain adaptation method for anomaly detection tailored to TinyML applications. Our approach facilitates the transfer of knowledge from existing domains to new ones, ensuring reliable anomaly detection in all domains with minimal data under new operational conditions. This is particularly relevant for IloT applications, where sensors must perform condition monitoring in a self-learning manner. The effectiveness of our method is evaluated through an extensive parameter study on the Toy-ADMOS 2 data set and compared to various baseline methods. The results show an improvement of up to 0.212 on the Area Under the Receiver Operating Characteristic Curve (ROC-AUC) score. It demonstrates that our method significantly enhances performance in multi-target domain adaptation. Importantly, it does this while remaining efficient for resource-constrained systems. This method has the potential to enable self-learning sensor systems with adaptation strategies to different working conditions while requiring only a few data points from these new domains.

关键词： Employee welfare Training Tiny machine learning Training data Sensor phenomena and characterization Sensor systems artificial intelligence Anomaly detection Manufacturing automation Signal to noise ratio

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Decomposition-based Multi-objective Genetic Programming for Feature Learning in Image Classification

Decomposition-based Multi-objective Genetic Programming for ...

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2024 Genetic and Evolutionary Computation Conference Companion, GECCO 2024 Companion

作者： Zhang, Tuo Bi, Ying Liang, Jing Xue, Bing Zhang, Mengjie School of Electrical and Information Engineering Zhengzhou University China State Key Laboratory of Intelligent Agricultural Power Equipment School of Electrical and Information Engineering Zhengzhou University Luoyang China School of Electrical and Information Engineering Zhengzhou University School of Electrical Engineering and Automation Henan Institute of Technology State Key Laboratory of Intelligent Agricultural Power Equipment China School of Engineering and Computer Science Centre for Data Science and Artificial Intelligence Victoria University of Wellington New Zealand

ISBN: (纸本)9798400704956

Image classification is challenging due to the high dimensionality and large variations of the image data. As an effective method in image classification, feature learning can be considered a multiobjective problem of maximizing the classification accuracy and minimizing the number of learned features. Existing multi-objective genetic programming (MOGP) methods directly apply the multiobjective techniques to GP without considering the characteristics of feature learning tasks. Therefore, this paper proposes a decomposition-based MOGP approach with a global replacement strategy to feature learning in image classification. The proposed approach is compared with existing MOGP methods and the experimental results demonstrate the effectiveness of the proposed approach. © 2024 Copyright held by the owner/author(s).

关键词： Contrastive Learning

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Evolving temporal convolutional neural networks for modeling turntable servo systems

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Applied Soft Computing 2025年 172卷

作者： Xie, Cheng Xue, Bing Zhang, Mengjie Chen, Songlin Liu, Yang Control and Simulation Center School of Astronautics Harbin Institute of Technology Heilongjiang Harbin150001 China Centre for Data Science and Artificial Intelligence & School of Engineering and Computer Science Victoria University of Wellington Wellington6140 New Zealand Shenyang Institute of Automation Chinese Academy of Sciences Liaoning Shenyang110169 China

Turntable servo systems are important experimental equipment used for semi-physical simulation and testing of aircraft, which have very strict requirements for tracking performance. To achieve high-precision servo performance, some advanced model-based control methods have been used recently, but the control performance is greatly influenced by the accuracy of the model. Therefore, this paper proposes evolving temporal convolutional networks (TCNs) for accurately modeling turntable servo systems. Considering the advantages of TCNs in capturing local dependencies in sequence data and parallel computing, a TCN is adopted to compensate the unmodeled dynamics to significantly improve the accuracy of the existing dynamics model. Then, an evolutionary algorithm (EA) based architecture optimization algorithm for the above TCN model is proposed, and the network performance is continuously improved by simulating the population evolution in nature. Finally, the weight inheritance method is utilized to evaluate the performance of individuals during the evolution process to effectively improve the execution efficiency of the whole algorithm. In addition, the subnets inherited from the supernet meet the real-time requirements of the actual tasks by setting real-time constraints on the supernet. The experimental results demonstrate the superiority of the proposed model against the peer competitors in terms of the prediction performance. In addition, the proposed optimization algorithm also shows higher execution efficiency as verified by ablation experiments. © 2025 Elsevier B.V.

关键词： Evolutionary algorithms (EAs) System identification and modeling Temporal convolutional networks (TCNs) Turntable servo systems

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Two-stream Dynamic Heterogeneous Graph Recurrent Neural Network for Multi-label Multi-modal Emotion Recognition

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IEEE Transactions on Affective Computing 2025年

作者： Wang, Jing Feng, Zhiyang Ning, Xiaojun Lin, Youfang Chen, Badong Jia, Ziyu Beijing Jiaotong University Beijing Key Laboratory of Traffic Data Mining and Embodied Intelligence School of Computer Science and Technology Beijing China Xi'an Jiaotong University Institute of Artificial Intelligence and Robotics Xi'an710049 China Beijing Jiaotong University School of Computer Science and Technology Beijing100044 China Chinese Academy of Sciences Brainnetome Center Institute of Automation Beijing100190 China

The study of the relationship between emotions and physiological signals of subjects under multimedia stimulation is an emerging field, and many important advances are made. However, there are still some challenges: 1) How to effectively utilize the complementarity among spatial-spectral-temporal domain information. 2) How to employ the heterogeneity and the correlation among multi-modal physiological signals simultaneously. 3) How to improve the robustness of the model dealing with missing channels. 4) How to model the dependency among different emotions. In this paper, we propose a novel two-stream Dynamic Heterogeneous Graph Recurrent Neural Network called DHGRNN. Specifically, DHGRNN consists of a spatial-temporal stream, a spatial-spectral stream, a fusion layer, and a multi-label classifier. Each stream is composed of a graph transformer network, evolved graph convolutional neural network, and gated recurrent units. We propose a graph-based two-stream structure to fuse the information of the spatial-spectral-temporal domain simultaneously. Graph transformer network and evolved graph convolutional neural network are used to model the heterogeneity and correlation of multi-modal physiological signals, respectively. To deal with the problem of robustness in the face of missing channel data, we transform it into the problem of dynamic graphs and use a dynamic graph neural network to improve the robustness. In addition, we propose a multi-label classifier to model the dependency among different emotion dimensions. Experiments on three public datasets demonstrate that our proposed model outperforms existing state-of-the-art methods. © 2010-2012 IEEE.

关键词： Graph neural networks

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A Wearable Eye-Tracking Approach for Early Autism Detection with Machine Learning: Unravelling Challenges and Opportunities

A Wearable Eye-Tracking Approach for Early Autism Detection ...

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International Joint Conference on Neural Networks (IJCNN)

作者： J. Lopez-Martinez Purificación Checa José Manuel Soto-Hidalgo Isaac Triguero Alberto Fernández Department of Computer Science and Artificial Intelligence University of Granada Spain Dept. of Educational and Evolutive Psychology University of Granada Spain Dept. of Comput. Eng. Automation and Robotics University of Granada Spain DaSCI Andalusian Institute in Data Science and Computational Intelligence School of Computer Science University of Nottingham Nottingham UK

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Early detection of Autism Spectrum Disorder (ASD) is crucial to facilitate timely interventions, improve outcomes, and enhance the quality of life for individuals on the spectrum. artificial intelligence and machine learning have significantly advanced the study of ASD by enabling sophisticated analyses of complex behavioural data, providing more accurate and timely detection methods. Nevertheless, existing research is mostly focused on either screening manual methods via psychological surveys, and/or the application of functional Magnetic Resonance Imaging. In the former case, there is a limitation due the subjectivity of the procedure; whereas in the latter the high cost represents a barrier to widespread use in diagnosis. In consideration of the aforementioned factors, this research contributes significantly to the field by integrating commodity wearable technology with psycho-healthcare, employing eye-tracking capabilities as a novel, non-invasive approach for early ASD diagnosis. Our methodology stands out by offering a comprehensive, four-step process that includes sophisticated image preprocessing, innovative feature extraction, precise classification, and a novel multi-instance aggregation strategy. We show its potential via a compelling case study that attests to the feasibility and efficacy of this innovative paradigm. Additionally, the work underscores prevailing challenges in the field, stressing some factors such as the acquisition of extensive and diverse datasets to allow for a multi-modal approach and the application of Trustworthy artificial intelligence.

关键词： Surveys Autism Neural networks Psychology Machine learning Gaze tracking Manuals

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Domain generalized solar cell defect segmentation based on shape-aware and multi-view meta-learning

Domain generalized solar cell defect segmentation based on s...

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第42届中国控制会议

作者： Kun Liu Ying Yang Jingkun Mao Weipeng Liu School of Artificial Intelligence and Data Science Hebei University of Technology Control Engineering Technology Innovation Center of Hebei Province Hebei University of Technology Department of Automation Tianjin University of Technology

Various defects are inevitably generated in the manufacturing process of solar *** learning-based methods for defect segmentation under closed situation have achieved remarkable *** to the difference of imaging condition and camera parameter under different production line,there are large differences in brightness distribution of solar cell *** model trained under closed situation cannot achieve good performance in opened situation such as multi-production *** this paper,a new shape-aware and multi-view meta-learning scheme is proposed to improve the model generalization performance for single-domain solar cell defect *** scheme roots in gradient-based *** view information is yielded from the generated augmented images,and explicitly simulating domain shift with virtual meta-train and meta-test during training to mitigate overfitting in single-source domain train and unstable ***,considering tiny and faint solar cell defects are not easily identified under different image brightness conditions,shape edge constraint loss is proposed to encourage shape compactness and shape smoothness of *** results show that the proposed method has high segmentation accuracy,which outperforms the state-of-the-art methods.

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Multi-scale attention semi-supervised network based on confidence iteration and pseudo-label optimization for COVID-19 CT segmentation

Multi-scale attention semi-supervised network based on confi...

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第42届中国控制会议

作者： Kun Liu Mei Chen JunfeiXie JingKun Mao WeiPeng Liu School of Artificial Intelligence and Data Science Hebei University of Technology Control Engineering Technology Innovation Center of Hebei Province Hebei University of Technology Department of Automation Tianjin University of Technology

Computed tomography(CT) image segmentation for COVID-19 lung infection has made great ***,accurate segmentation remains a major challenge due to the varying shape and size of COVID-19 infected areas,the more similar tissue of infected lung images to normal lung images,and the additional problem of a limited number of medical images annotated at the pixel *** this paper,we propose a semi-supervised segmentation network based on confidence iteration and pseudo-label optimization for COVID-19 CT *** addition,we develop a multi-scale attention module which can achieve attentional feature fusion at different *** experiment evaluated on the COVID-19 CT Segmentation dataset,demonstrate our method achieves higher segmentation accuracy than the state-of-art methods.

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Development of a Meta-Question Enhanced Retrieval-Augmented Generation Model and Its Application in Dermatology

Development of a Meta-Question Enhanced Retrieval-Augmented ...

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International Conference on Advanced Computer Theory and Engineering, ICACTE

作者： Changjin Li Jinsong Luo Fengshi Jing Yuxin Han Hao Ren Haobin Zhang Weibin Cheng Faculty of Data Science City University of Macau Macao SAR China School of Automation Central South University Changsha China Institute of Healthcare Artificial Intelligence Application Guangdong Second Provincial General Hospital Guangzhou China

ISBN: (数字)9798331539887

ISBN: (纸本)9798331539894

RAG (Retrieval Augmented Generation) provides a solution for enhancing domain-specific knowledge in large language models by interacting with external information. RAG functions similarly to a search engine, retrieving the most relevant knowledge or related conversation history to the user's query. It then combines this with the original query to create a detailed prompt, guiding the model to generate accurate outputs. However, traditional RAG methods match short queries with long texts in the knowledge base, leading to mismatched results despite apparent similarities due to length discrepancies. To address this issue, we propose the MQ-RAG (Meta Questions Retrieval Augmented Generation) method, a question-answer pair-based approach built upon RAG. The key difference in MQ-RAG lies in the retrieval and recall content: it uses a database of Meta Questions, which are pairs of questions and corresponding answers. When using MQ-RAG, queries are matched with Meta Questions to retrieve the most relevant answers, ensuring high compatibility between the query and the retrieved content. For evaluation, 100 multiple-choice questions from the Dermatology and Venereology Medical Licensing Examination database were selected. Results showed that, using Qwen2-7b and Qwen2-72b as base models, MQ-RAG improved accuracy by 17.14% and 2.27%, respectively, compared to traditional RAG. Our MQ-RAG method demonstrated higher accuracy across both large and small parameter models, with particularly significant improvements in smaller models.

关键词： Industries Accuracy databases Computational modeling Large language models Retrieval augmented generation Dermatology Knowledge based systems Oral communication Search engines

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Predictive Maintenance in Industrial Machinery using Machine Learning

Predictive Maintenance in Industrial Machinery using Machine...

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Intelligent Systems and Machine Learning Conference (ISML)

作者： Vanita Kshirsagar Digvijay G. Bhosale Shantanu Gilbile Anchal Kharade Harshal Sasane Pratik Dhembare Artificial Intelligence and Data Science Dr. D. Y. Patil Institute of Technology Pune India Automation and Robotics Dr. D. Y. Patil Institute of Technology Pune India

ISBN: (数字)9798350343878

ISBN: (纸本)9798350343885

Predictive maintenance is a proactive strategy that uses data and technology to forecast when machinery may fail. Which is important in preventing unexpected breakdowns, reducing downtime, lowering maintenance costs, and improving overall operational efficiency. When applied to gearboxes, it helps ensure their reliability, reducing unplanned downtime and extending their lifespan as Gearboxes play a critical role in various industries, and unplanned failures can lead to substantial downtime and operational losses. The project begins with a thorough study of gearbox operation, followed by the selection of appropriate sensors to monitor crucial parameters such as vibration, temperature, oil quality, and load. While newer gearboxes may have built-in sensors, the challenge lies in retrofitting sensors to older units. A custom retrofitting solution is developed to seamlessly integrate sensors, creating a unified data generation system. A robust data acquisition system captures real-time sensor data, which is then pre-processed and stored for analysis. Feature engineering techniques are employed to extract valuable insights from the sensor data, allowing for the identification of patterns associated with gearbox health. Machine learning models, including time-series analysis and anomaly detection algorithms, are trained to predict impending gearbox failures. In addition to predictive maintenance, the project also focuses on efficiency analysis. Ultimately, this project offers a holistic solution to gearbox maintenance and efficiency enhancement, with the potential to significantly benefit industrial holistic solution to gearbox maintenance and efficiency enhancement, with the potential to significantly benefit industrial operations by reducing downtime and increasing overall machine efficiency. Operations by reducing downtime and increasing overall machine efficiency downtime and increasing overall machine efficiency. Increasing overall machine efficiency.

关键词： Temperature sensors Costs Time series analysis Machine learning Feature extraction Sensor systems Sensors Maintenance Machinery Predictive maintenance

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Tiny noise, big mistakes: adversarial perturbations induce errors in brain–computer interface spellers

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National science Review 2021年第4期8卷 78-90页

作者： Xiao Zhang Dongrui Wu Lieyun Ding Hanbin Luo Chin-Teng Lin Tzyy-Ping Jung Ricardo Chavarriaga Ministry of Education Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and AutomationHuazhong University of Science and Technology School of Civil Engineering and Mechanics Huazhong University of Science and Technology Centre of Artificial Intelligence Faculty of Engineering and Information Technology University of Technology Sydney Swartz Center for Computational Neuroscience Institute for Neural ComputationUniversity of California San Diego Center for Advanced Neurological Engineering Institute of Engineering in Medicine University of California San Diego ZHAW Data Lab Zürich University of Applied Sciences

An electroencephalogram(EEG)-based brain–computer interface(BCI) speller allows a user to input text to a computer by thought. It is particularly useful to severely disabled individuals, e.g. amyotrophic lateral sclerosis patients, who have no other effective means of communication with another person or a *** studies so far focused on making EEG-based BCI spellers faster and more reliable; however, few have considered their security. This study, for the first time, shows that P300 and steady-state visual evoked potential BCI spellers are very vulnerable, i.e. they can be severely attacked by adversarial perturbations,which are too tiny to be noticed when added to EEG signals, but can mislead the spellers to spell anything the attacker wants. The consequence could range from merely user frustration to severe misdiagnosis in clinical applications. We hope our research can attract more attention to the security of EEG-based BCI spellers, and more broadly, EEG-based BCIs, which has received little attention before.

关键词： electroencephalogram brain-computer interfaces BCI spellers adversarial examples

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