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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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Intelligent indoor metasurface robotics

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National Science Review 2023年第8期10卷 20-32页

作者： Hanting Zhao Shengguo Hu Hongrui Zhang Zhuo Wang Hao Dong Philipp del Hougne Tie Jun Cui Lianlin Li State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Peking University Center on Frontiers of Computing Studies School of Computer Science Peking University The French National Centre for Scientific Research(CNRS) University of Rennes State Key Laboratory of Millimeter Waves Southeast University

Intelligent indoor robotics is expected to rapidly gain importance in crucial areas of our modern society such as at-home health care and factories. Yet, existing mobile robots are limited in their ability to perceive and respond to dynamically evolving complex indoor environments because of their inherently limited sensing and computing resources that are, moreover, traded off against their cruise time and payload. To address these formidable challenges, here we propose intelligent indoor metasurface robotics(I2MR),where all sensing and computing are relegated to a centralized robotic brain endowed with microwave perception; and I2MR's limbs(motorized vehicles, airborne drones, etc.) merely execute the wirelessly received instructions from the brain. The key aspect of our concept is the centralized use of a computation-enabled programmable metasurface that can flexibly mold microwave propagation in the indoor wireless environment, including a sensing and localization modality based on configurational diversity and a communication modality to establish a preferential high-capacity wireless link between the I2MR's brain and limbs. The metasurface-enhanced microwave perception is capable of realizing low-latency and high-resolution three-dimensional imaging of humans, even around corners and behind thick concrete walls, which is the basis for action decisions of the I2MR's brain. I2MR is thus endowed with real-time and full-context awareness of its operating indoor environment. We implement, experimentally, a proof-of-principle demonstration at ～2.4 GHz, in which I2MR provides health-care assistance to a human inhabitant. The presented strategy opens a new avenue for the conception of smart and wirelessly networked indoor robotics.

关键词： intelligent metasurfaces intelligent robotics edge devices IoT 6G

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Optimized Low-Power FIR Filter Design Using Residue Number System for Energy-Efficient DSP Applications

Optimized Low-Power FIR Filter Design Using Residue Number S...

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International Conference on Inventive Computation Technologies (ICICT)

作者： Cheruku Navaneeth Kumar Goud Ch. Kavya S. Bharath JVR Ravindra Dept of Electronics and Communication Engineering Center for Advanced Computing Research Laboratory (C-ACRL) Vardhaman College of Engineering Hyderabad India

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

In modern digital signal processing (DSP) systems, power efficiency is a critical design consideration, especially for portable and battery-powered devices. This project presents an optimized low-power FIR (Finite Impulse Response) filter design using the Residue Number System (RNS) to achieve energy-efficient DSP applications. Traditional FIR filters, while effective, are often power-intensive due to the high computational complexity involved. The RNS offers a promising alternative by enabling parallelism and reducing the size of arithmetic operations, leading to lower power consumption without sacrificing performance. In this project, we explore the integration of RNS with FIR filter architecture, focusing on optimizing modular arithmetic operations to reduce power consumption while maintaining high throughput and precision. The design methodology includes theoretical analysis, hardware implementation, and performance evaluation, demonstrating the advantages of using RNS in reducing both dynamic and static power in DSP systems. Experimental results showcase significant power savings compared to conventional FIR filter designs, making this approach highly suitable for next-generation DSP applications in mobile, wireless communication, and IoT devices.

关键词： Performance evaluation Wireless communication Power demand Finite impulse response filters Digital signal processing Throughput Energy efficiency Real-time systems Delays Arithmetic

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Towards practical data alignment in production federated learning

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Frontiers of Computer Science 2025年第1期19卷 167-169页

作者： Yexuan SHI Wei YU Yuanyuan ZHANG Chunbo XUE Yuxiang ZENG Zimu ZHOU Manxue GUO Lun XIN Wenjing NIE State Key Laboratory of Complex&Critical Software Environment and Advanced Innovation Center for Future Blockchain and Privacy Computing Beihang UniversityBeijing 100191China Zhongguancun Pan Connected Mobile Communication Technology Innovation and Application Research Institute Beijing 100088China China Mobile Research Institute Beijing 100053China School of Data Science City University of Hong KongHong Kong 999077China

1 Introduction Federated learning has emerged as a promising par-adigm for collaborative model training that facilitates cooperation among multiple parties while ensuring data privacy[1].Successful alignment of data across parties is crucial for effective federated learning[2].This alignment involves harmonizing heterogeneous data from different parties to identify shared data for joint model *** set intersection(PSI)is a technique that allows the alignment of common entities between parties without revealing additional ***,efficiently performing data alignment with PSI in federated learning[3],especially when dealing with highly unbalanced data,remains challenging due to the low efficiency.

关键词： alignment dealing parties

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Adaptive Time Slot Resource Allocation in SWIPT IoT Networks

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Computer Modeling in Engineering & Sciences 2023年第9期136卷 2787-2813页

作者： Yunong Yang Yuexia Zhang Zhihai Zhuo Key Laboratory of Information and Communication Systems Ministry of Information IndustryBeijing Information Science and Technology UniversityBeijing100101China Key Laboratory of ModernMeasurement&Control Technology Ministry of EducationBeijing Information Science&Technology UniversityBeijing100101China Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing Beijing100101China

The rapid advancement of Internet of Things(IoT)technology has brought convenience to people’s lives;however further development of IoT faces serious challenges,such as limited energy and shortage of network spectrum *** address the above challenges,this study proposes a simultaneous wireless information and power transfer IoT adaptive time slot resource allocation(SIATS)***,an adaptive time slot consisting of periods for sensing,information transmission,and energy harvesting is designed to ensure that the minimum energy harvesting requirement ismet while the maximumuplink and downlink throughputs are ***,the optimal transmit power and channel assignment of the system are obtained using the Lagrangian dual and gradient descent methods,and the optimal time slot assignment is determined for each IoT device such that the sum of the throughput of all devices is *** results show that the SIATS algorithm performs satisfactorily and provides an increase in the throughput by up to 14.4%compared with that of the fixed time slot allocation(FTS)*** the case of a large noise variance,the SIATS algorithm has good noise immunity,and the total throughput of the IoT devices obtained using the SIATS algorithm can be improved by up to 34.7%compared with that obtained using the FTS algorithm.

关键词： Internet of Things simultaneous wireless information and power transfer adaptive time slot resource allocation

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Fostering a Thriving Virtual Organizational Culture: The Development of Metrics for Ten Soft Skills in the Case of XSEDE

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SN Computer Science 2024年第5期5卷 1-16页

作者： Kee, Kerk Jankowski, Harmony Knepper, Richard Snapp-Childs, Winona Brady, Rhett College of Media and Communication Texas Tech University 2500 Broadway Lubbock 79409 TX United States Pervasive Technology Institute Indiana University 2709 E Tenth Street Bloomington 47408 IN United States Center for Advanced Computing Cornell University Rhodes Hall Ithaca 14853 NY United States

The purpose of this study is to propose a framework through which an organization could identify, describe, and measure ten factors categorized as soft skills shown to undergird the thriving culture of XSEDE, NSF’s largest cyberinfrastructure project during 2011–2022. We conducted 54 interviews of XSEDE collaborators;these interviews proceeded in two phases, with the second functioning as member checks to ensure our findings accorded with respondents’ experiences. Based on a grounded theory analysis of these 54 semi-structured interviews with a range of XSEDE stakeholders, we identified 10 social and cultural factors contributing to XSEDE’s success: (1) communicating intentionally, (2) maintaining relationships, (3) helping colleagues and users, (4) trusting colleagues, (5) having thick skin, (6) assuming the best in others, (7) respecting colleagues, (8) remaining open-minded, (9) practicing transparency, and (10) showing appreciation. We believe these ten factors can be used to create a methodology for developing soft skills metrics for virtual organizations and cyberinfrastructure projects similar to XSEDE. This would allow stakeholders to proactively measure engagement in behaviors, practices, and mindsets conducive to generating a thriving culture for virtual collaborations. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Cyberinfrastructure projects Soft skills Thriving culture Virtual organizations XSEDE

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Multi-class Imbalanced Classification Problems in Network Attack Detections 1

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6th International Conference on Electrical, Control and Computer Engineering, InECCE 2021

作者： Fern, Soon Hui Amir, Amiza Azemi, Saidatul Norlyana Faculty of Electronic Engineering Technology Universiti Malaysia Perlis Perlis02600 Malaysia Advanced Computing Center of Excellence Universiti Malaysia Perlis Perlis02600 Malaysia Advanced Communication Engineering Center of Excellence Universiti Malaysia Perlis Perlis01000 Malaysia

ISBN: (数字)9789811686900

ISBN: (纸本)9789811686894

Nowadays, network anomaly activities can be detected by using machine learning. The performance of the machine learning algorithms depends on the distribution of the dataset. Network anomaly detection is one of the popular topics that contain vast numbers of data and faced imbalanced multiclass classification distribution. However, most researchers have neglected the problem of multiclass imbalanced classification when undergoing their research on network anomaly detection. It cannot be denied that some researchers proposed some methods to solve this problem. Nevertheless, the methods that have been proposed still contains some drawbacks. Hence, in this research, the performance of the machine learning due to the effect of a multiclass imbalanced dataset was studied. Seven machine learning algorithms from different families will be used in this study, and their ability to classify the majority and minority class instances and achieved high classification accuracy will be identified. Besides, seven different performance metrics will also be used to evaluate the performance of the classifiers used in this study. The results show that although the classification accuracy of the Bayesian machine learning algorithms is lower than other classifiers used in this research, they perform well in detecting the minority class instances. Besides, J48 has known as the classifier that can work well in every aspect of this research. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Machine learning

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Panel Submission: Cyberinfrastructure Innovations Supporting Research, Equity, Discovery 24

Panel Submission: Cyberinfrastructure Innovations Supporting...

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25th Annual International Conference on Digital Government Research, DGO 2024

作者： Knepper, Rich Kee, Kerk F. Chen, Yu-Che Xu, Shan Cornell Center for Advanced Computing Cornell University United States College of Media and Communication Texas Tech University Texas United States School of Public Administration/Digital Governance and Analytics Lab College of Public Affairs and Community Service University of Nebraska at Omaha OmahaNE United States

ISBN: (纸本)9798400709883

Cyberinfrastructure refers to the computational, data, networking and human resources that support scientific research. Frequently cyberinfrastructure is a strategic, nation-level investment in research and development resources that has long-term impacts in national competitiveness, innovation, and discovery. As such, cyberinfrastructure is an essential element of a cohesive national science policy. Our panel discusses the development and implementation of cyberinfrastructure resources that support science;how their management and makeup affects performance;metrics for assessment;and how well cyberinfrastructure service delivery reaches equity goals. © 2024 Copyright held by the owner/author(s).

关键词： Cyberinfrastructure digital innovation platform policy informatics science policy

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Boolean Variation and Boolean Logic BackPropagation

arXiv

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arXiv 2023年

作者： Nguyen, Van Minh Intelligent Computing and Communication Research Advanced Wireless Technology Laboratory Paris Research Center Huawei Technologies France

The notion of variation is introduced for the Boolean set and based on which Boolean logic backpropagation principle is developed. Using this concept, deep models can be built with weights and activations being Boolean numbers and operated with Boolean logic instead of real arithmetic. In particular, Boolean deep models can be trained directly in the Boolean domain without latent weights. No gradient but logic is synthesized and backpropagated through layers. Copyright © 2023, The Authors. All rights reserved.

关键词： Backpropagation

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RRCNN: Request Response-Based Convolutional Neural Network for ICS Network Traffic Anomaly Detection

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Computers, Materials & Continua 2023年第6期75卷 5743-5759页

作者： Yan Du Shibin Zhang Guogen Wan Daohua Zhou Jiazhong Lu Yuanyuan Huang Xiaoman Cheng Yi Zhang Peilin He Country School of Cybersecurity Chengdu University of Information TechnologyChengdu610225China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu610225China DAQSOFT CO. LTDChengdu610213China Communication and Information Technology Center Petro China Southwest Oil and Gas CompanyChengdu610057China School of Computing and Information University of PittsburghPittsburgh152607PAUSA

Nowadays,industrial control system(ICS)has begun to integrate with the *** the Internet has brought convenience to ICS,it has also brought severe security *** ICS network traffic anomaly detection methods rely on statistical features manually extracted using the experience of network security *** are not aimed at the original network data,nor can they capture the potential characteristics of network ***,the following improvements were made in this study:(1)A dataset that can be used to evaluate anomaly detection algorithms is produced,which provides raw network data.(2)A request response-based convolutional neural network named RRCNN is proposed,which can be used for anomaly detection of ICS network *** of using statistical features manually extracted by security experts,this method uses the byte sequences of the original network packets directly,which can extract potential features of the network packets in greater *** regards the request packet and response packet in a session as a Request-Response Pair(RRP).The feature of RRP is extracted using a one-dimensional convolutional neural network,and then the RRP is judged to be normal or abnormal based on the extracted *** results demonstrate that this model is better than several other machine learning and neural network models,with F1,accuracy,precision,and recall above 99%.

关键词： Industrial control system(ICS) dataset network traffic anomaly detection

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