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Multi-Scale Time Series Segmentation Network Based on Eddy Current Testing for Detecting Surface Metal Defects

IEEE/CAA Journal of Automatica Sinica 2025年第3期12卷 528-538页

作者： Xiaorui Li Xiaojuan Ban Haoran Qiao Zhaolin Yuan Hong-Ning Dai Chao Yao Yu Guo Mohammad S.Obaidat George Q.Huang the School of Intelligence Science and Technology University of Science and Technology Beijing the Beijing Advanced Innovation Center for Materials Genome Engineering the Key Laboratory of Intelligent Bionic Unmanned Systems and the Institute of Materials Intelligent Technology Liaoning Academy of Materials IEEE the Department of Computer Science Hong Kong Baptist University the School of Computer and Communication Engineering Key Laboratory of Advanced Materials and Devices for Post-Moore Chips Ministry of Education University of Science and Technology Beijing the Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology Beijing the School of Computer and Communication Engineering University of Science and Technology Beijing the King Abdullah Ⅱ School of Information Technology The University of Jordan the Department of Computational Intelligence the School of Computing SRM University the School of Engineering The Amity University The Hong Kong Polytechnic University

In high-risk industrial environments like nuclear power plants, precise defect identification and localization are essential for maintaining production stability and safety. However, the complexity of such a harsh environment leads to significant variations in the shape and size of the defects. To address this challenge, we propose the multivariate time series segmentation network(MSSN), which adopts a multiscale convolutional network with multi-stage and depth-separable convolutions for efficient feature extraction through variable-length templates. To tackle the classification difficulty caused by structural signal variance, MSSN employs logarithmic normalization to adjust instance distributions. Furthermore, it integrates classification with smoothing loss functions to accurately identify defect segments amid similar structural and defect signal subsequences. Our algorithm evaluated on both the Mackey-Glass dataset and industrial dataset achieves over 95% localization and demonstrates the capture capability on the synthetic dataset. In a nuclear plant's heat transfer tube dataset, it captures 90% of defect instances with75% middle localization F1 score.

关键词： Eddy current testing nondestructive testing semantic segmentation time series analysis

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A Music Recommender System to Identify the Emotional State of a Person Using Machine Learning Algorithms 4

A Music Recommender System to Identify the Emotional State o...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Muruganantham, A. Malarvizhi, C. Divya Vahini, S. Saranya, D. Uthiramoorthy, A. Karnataka Bengaluru India Psg Institute of Advanced Studies Department of Computer Science Coimbatore India Dr.Mahalingam College of Engineering and Technology Department of Computer Applications Tamil Nadu Pollachi India Sri Eshwar College of Engineering Department of Information Technology Coimbatore India Rathinam College of Arts and Science Department of Computer Science India

ISBN: (纸本)9798331523923

In today's digital world, customized recommendation algorithms can improve user experiences in many different areas. Within the sphere of intelligent human-technology interaction, this paper specifically examines a music recommender system driven by machine learning algorithms. It figures out what mood users are in and can provide an appropriate response in real time. The system being propounded in this essay uses advanced technology in facial expression recognition. With the help of a webcam, it aims to accurately detect user emotions, including joy, sadness, and anger. Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN) and Support Vector Machines (SVM) have been employed to effectively classify people's mood and recommend an appropriate song. Our extensive tests and examinations show that CNN, RNN, and SVM models are all very good at guessing users' moods - with high precision, recall, F1-score, and accuracy percentages. Furthermore, in-depth analyses like confusion matrixes illuminate how well each model serves as an emotion recognizer. This study's findings are representative of future prospects in affective computing around the globe with major implications for intelligent recommendation systems to which it's important that the models are sensitive. In conclusion, the suggested Music Recommender System is something new for taking a more personal view: We can be as deeply entertained as the web allows us to be - make tailored entertainment paths in the digital era. © 2025 IEEE.

关键词： Convolutional neural networks

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Adaptive Fine-Grained Feature Mining and RoI Feature Interaction Network for Small Object Detection in Aerial Images

Adaptive Fine-Grained Feature Mining and RoI Feature Interac...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Xie, Fangmin Xiong, Zhan Wang, Zhuoyue Yang, Kun Xiao, Guoqiang College of Computer and Information Science Southwest University Chongqing China Institute of Advanced Computer Science Leiden University Netherlands Department of Electrical Engineering and Computer Sciences University of California Berkeley Berkeley United States

ISBN: (纸本)9798350368741

Object detection in drone view images remains challenging due to small-scale objects distributed non-uniformly and exhibiting weak semantic features. Additionally, external factors such as lighting conditions, viewing angles, and background clutter further complicate the detection of small objects. To address these issues, we propose a small object detector called Adaptive Fine-grained Feature Mining Network (AFMNet) based on an anchor-free framework. Firstly, an Adaptive Fine-grained Feature Mining (AFFM) module is designed to generate Regions of Interest (RoI) on high-resolution feature maps and mine fine-grained information that benefits the representation of small objects. Secondly, an Inter-RoI Feature Interaction (IRFI) block is developed to efficiently integrate complementary cues-namely, deep layer semantic information and shallow layer spatial details-and enhance RoI features. Experiments on the VisDrone dataset demonstrate that our method achieves state-of-the-art performance. The source code of our AFMNet is available at https://***/xiefangmin/AFMNet. © 2025 IEEE.

关键词： Drone-View Image Feature Fusion Fine-grained Features Region of Interest Small object detection

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Buffer and Splitter Insertion for Adiabatic Quantum-Flux-Parametron Circuits

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IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2025年第3期44卷 975-988页

作者： Fu, Rongliang Wang, Mengmeng Kan, Yirong Chen, Olivia Yoshikawa, Nobuyuki Yu, Bei Ho, Tsung-Yi The Chinese University of Hong Kong Department of Computer Science and Engineering Hong Kong Yokohama National University Department of Electrical and Computer Engineering Yokohama240-8501 Japan Nara Institute of Science and Technology Division of Information Science Nara630-0192 Japan Kyushu University Department of Advanced Information Technology Fukuoka819-0395 Japan

The extremely low-bit energy characteristic of the adiabatic quantum-flux-parametron (AQFP) circuit makes it a promising candidate for highly energy-efficient computing systems. However, in contrast with conventional circuit design, general logic synthesis tools can not make sure that the circuit functionality of generated AQFP circuits is correct. AQFP circuits require buffer and splitter insertion for dataflow synchronization at all clock phases of the circuit and multifan-out driving. Notably, buffers and splitters inserted take up much area and delay in AQFP circuits, also causing a significant increase in energy dissipation. To address this problem, this article analyses in detail why buffer and splitter insertion is necessary for AQFP circuits and proposes a global optimization framework for this purpose. This framework consists of three parts: 1) logic level assignment;2) splitter tree generation;and 3) buffer insertion. An integer linear programming algorithm is proposed for the logic level assignment to estimate the globally optimal number of inserted buffers and splitters. Subsequently, a dynamic programming-based multiway search tree generation algorithm is proposed to construct an optimal splitter tree for each net of the input circuit. Moreover, three optimization strategies are proposed to further enhance the effectiveness and efficiency of our framework. Experimental results on ISCAS'85 and EPFL benchmarks demonstrate the effectiveness and efficiency of our proposed framework compared with the state-of-the-art, particularly with significant advantages on large circuits. © 2024 IEEE.

关键词： Global optimization

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Transformative Transfer Learning for MRI Brain Tumor Precision: Innovative Insights

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IEEE Access 2025年 13卷 31749-31761页

作者： Anwar, Raja Waseem Abrar, Mohammad Ullah, Faizan German University of Technology in Oman Department of Computer Science Muscat130 Oman Arab Open University Faculty of Computer Studies Muscat122 Oman Institute of Neuroscience and Medicine Forschungszentrum Jülich Jülich52428 Germany

Accurate brain tumor detection is essential for effective treatment and improved patient outcomes, yet conventional MRI classification methods often struggle with complex and variable imaging data. This study introduces the Transformative Transfer Learning (TTL) model, leveraging pre-trained deep neural networks fine-tuned for MRI brain tumor classification, using datasets from Nickparvar and Cheng. High-quality inputs were achieved through preprocessing, including resizing, normalization, noise reduction, and data augmentation. The TTL model, employing architectures such as VGG16, ResNet-50, InceptionV3, and DenseNet-121, demonstrated a substantial improvement over traditional methods, achieving an accuracy of 94.5%, sensitivity of 92.8%, and specificity of 93.1%. Additionally, the model yielded a precision of 93.2%, recall of 92.8%, F1-score of 93.0%, and true positive and true negative rates of 92.8% and 93.1%, respectively. Training and validation loss values of 0.15 and 0.18 confirm effective model convergence. These metrics underscore the model’s potential as a robust tool in clinical diagnostics, providing a pathway to more accurate and efficient MRI-based brain tumor detection. © 2025 The Authors.

关键词： Deep neural networks

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14-Bit SAR ADC with on-Chip Digital Bubble Sorting Calibration Technology

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Chinese Journal of Electronics 2025年第1期34卷 125-136页

作者： Hua Fan Zhuorui Chen Tongrui Xu Franco Maloberti Qi Wei Quanyuan Feng School of Integrated Circuit Science and Engineering(Exemplary School of Microelectronics) University of Electronic Science and Technology of China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Chongqing Institute of Microelectronics Industry Technology University of Electronic Science and Technology of China Department of Electrical Computer and Biomedical Engineering University of Pavia Department of Precision Instrument Tsinghua University Institute of Microelectronics Southwest Jiaotong University

This article designs a 14-bit successive approximation register analog-to-digital converter（SAR ADC）.A novel digital bubble sorting calibration method is proposed and applied to eliminate the effect of capacitor mismatch on the linearity of the SAR ADC. To reduce the number of capacitors, a hybrid architecture of a high 8-bit binary-weighted capacitor array and a low 6-bit resistor array is adopted by the digital-to-analog（DAC）. The common-mode voltage VCM-based switching scheme is chosen to reduce the switching energy and area of the DAC. The time-domain comparator is employed to obtain lower power consumption. Sampling is performed through a gate voltage bootstrapped switch to reduce the nonlinear errors introduced when sampling the input signal. Moreover, the SAR logic and the whole calibration is totally implemented on-chip through digital integrated circuit（IC） tools such as design compiler, IC compiler, etc. Finally, a prototype is designed and implemented using 0.18 μm bipolar-complementary metal oxide semiconductor（CMOS）-double-diffused MOS 1.8 V CMOS technology. The measurement results show that the SAR ADC with on-chip bubble sorting calibration method achieves the signal-to-noise-and-distortion ratio of 69.75 dB and the spurious-free dynamic range of 83.77 dB.

关键词： Semiconductor device measurement Capacitors Linearity Switches Calibration System-on-chip Frequency measurement Time-domain analysis Sorting Signal to noise ratio

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Unsupervised Meteorological Downscaling Based on Dual Learning and Subgrid-scale Auxiliary Information

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Advances in Atmospheric sciences 2025年第1期42卷 53-66页

作者： Jing HU Jialing MU Xiaomeng HUANG Xi WU Department of Computer Science Chengdu University of Information TechnologyChengdu 610225China Department of Earth System Science Ministry of Education Key Laboratory for Earth System ModelingInstitute for Global Change StudiesTsinghua UniversityBeijing 100084China

Climate downscaling is used to transform large-scale meteorological data into small-scale data with enhanced detail,which finds wide applications in climate modeling,numerical weather forecasting,and renewable *** deeplearning-based downscaling methods effectively capture the complex nonlinear mapping between meteorological data of varying scales,the supervised deep-learning-based downscaling methods suffer from insufficient high-resolution data in practice,and unsupervised methods struggle with accurately inferring small-scale specifics from limited large-scale inputs due to small-scale *** article presents DualDS,a dual-learning framework utilizing a Generative Adversarial Network–based neural network and subgrid-scale auxiliary information for climate *** a learning method is unified in a two-stream framework through up-and downsamplers,where the downsampler is used to simulate the information loss process during the upscaling,and the upsampler is used to reconstruct lost details and correct errors incurred during the *** dual learning strategy can eliminate the dependence on high-resolution ground truth data in the training process and refine the downscaling results by constraining the mapping *** findings demonstrate that DualDS is comparable to several state-of-the-art deep learning downscaling approaches,both qualitatively and ***,for a single surface-temperature data downscaling task,our method is comparable with other unsupervised algorithms with the same dataset,and we can achieve a 0.469 dB higher peak signal-to-noise ratio,0.017 higher structural similarity,0.08 lower RMSE,and the best correlation *** summary,this paper presents a novel approach to addressing small-scale uncertainty issues in unsupervised downscaling processes.

关键词： downscaling unsupervised deep learning dual learning auxiliary information

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Continual Learning Techniques to Reduce Forgetting: A Comparative Study 2

Continual Learning Techniques to Reduce Forgetting: A Compar...

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2nd International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2025

作者： Neeraj Nandal, Poonam Department of Computer Science & Engineering Manav Rachna International Institute of Research and Studies Haryana Faridabad India

ISBN: (纸本)9798331530389

A key aspect of artificial intelligence is continual learning. The capacity of a model to incrementally learn from a new task and adapt to it without losing the previous knowledge. A fundamental challenge that comes to successful continual learning is catastrophic forgetting;while learning a new task the model forgets the previously learned tasks. Various approaches to mitigate forgetting and enhance continual learning have been discussed across benchmark datasets such as MNIST, CIFAR-10, and Permuted MNIST. we focus mainly on replay, regularization, and modular architecture-based methods to prevent forgetting. Regularization methods penalize the significant change in parameters during the learning of new tasks while maintaining crucial parameters of previously completed tasks. Replay-based methods store samples of earlier tasks in explicit memory or generate pseudo-samples with the help of generative models. Modular architecture-based methods partition the neural architecture into task-specific modules to prevent interference between tasks, to effectively address catastrophic forgetting. This paper presents a comparative study of the key continual learning methods that reduce catastrophic forgetting. The findings demonstrate the trade-offs between different methods. We examine the efficiency and effectiveness of each method and propose a hybrid technique to prevent catastrophic forgetting. Our comparative analysis emphasizes the significance of choosing the best method depending on the specific requirement of the learning environment, the task complexity, and resource constraints. © 2025 IEEE.

关键词： artificial intelligence catastrophic forgetting continual learning

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AI-Enhanced Depression Detection System: Integrating Multimodal Analysis for Early Diagnosis

AI-Enhanced Depression Detection System: Integrating Multimo...

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2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Patidar, Aditya Gore, Dhruv Inje, Bhushan Alhat, Apurva Department of Computer Science SVKM's Narsee Monjee Institute of Management Studies Maharashtra Shirpur India

ISBN: (纸本)9798331512965

Depression is a prevalent mental health condition affecting millions globally, often going undiagnosed due to social stigma and limited accessibility to healthcare. This study presents an innovative AI-enhanced system for early depression detection, utilizing multimodal analysis integrating natural language processing (NLP) and patient health metrics. Two machine learning models form the system's core: a text classification model leveraging logistic regression to analyze user inputs and predict emotional tone and a health-data-based model that incorporates factors such as BMI, blood features, and comorbid conditions for comprehensive evaluation. The models were integrated into a web application offering a dual advantage of accurate screening and accessible mental health recommendations. Results demonstrated high efficacy with classification accuracy exceeding 91% across models, underscoring its potential to transform early-stage mental health interventions. Future enhancements include adopting advanced deep learning frameworks and expanding multilingual capabilities to broaden accessibility. © 2025 IEEE.

关键词： Electronic health record

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A Hybrid Approach for Stock Market Price Forecasting Using Long Short-Term Memory and Seahorse Optimization Algorithm

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Annals of Data science 2025年 1-27页

作者： Gülmez, Burak Department of Industrial Engineering Mudanya University Bursa16940 Turkey Leiden Institute of Advanced Computer Science Leiden University Leiden Netherlands

Stock market price forecasting is a challenging task due to the complexity and volatility of market dynamics. This paper proposes a novel approach that combines the strengths of Long Short-Term Memory (LSTM) networks and the Seahorse Optimization (SHO) algorithm for stock market price forecasting. The LSTM-SHO model is compared with other LSTM models optimized using Genetic Algorithm (GA) and different dimensionalities (1D, 2D, 3D), as well as an Artificial Neural Network (ANN) model. The results show that the LSTM-SHO model outperforms the other models in terms of mean squared error (MSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and coefficient of determination (R2). The hyperparameter tuning process using SHO significantly improves the forecasting accuracy of the LSTM model. The proposed approach demonstrates its potential in developing more accurate and robust stock market forecasting models, which can aid investors and analysts in making informed decisions. The findings of this study have important implications for investors, analysts, and policymakers, and contribute to the existing body of literature on stock market forecasting. © The Author(s) 2025.

关键词： Genetic algorithms

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