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A Review and Benchmark on State-of-the-Art Steel Defects Detection

SN computer science 2024年第1期5卷 114页

作者： Chazhoor, Anthony Ashwin Peter Ho, Edmond S. L. Gao, Bin Woo, Wai Lok Computer and Information Science Department Northumbria University ELB 108 Ellison Pl Newcastle upon Tyne NE1 8ST United Kingdom School of Computing Science University of Glasgow 18 Lilybank Gardens Glasgow G12 8RZ United Kingdom School of Automation Engineering University of Electronic Science and Technology of China 4 1st Ring Rd East 2 Section Chenghua District Sichuan Chengdu 610056 China

Steel, a critical material in construction, automobile, and railroad manufacturing industries, often presents defects that can lead to equipment failure, significant safety risks, and costly downtime. This research aims to evaluate the performance of state-of-the-art object detection models in detecting defects on steel surfaces, a critical task in industries such as railroad and automobile manufacturing. The study addresses the challenges of limited defect data and lengthy model training times. Five existing state-of-the-art object detection models (faster R-CNN, deformable DETR, double head R-CNN, Retinanet, and deformable convolutional network) were benchmarked on the Northeastern University (NEU) steel dataset. The selection of models covers a broad spectrum of methodologies, including two-stage detectors, single-stage detectors, transformers, and a model incorporating deformable convolutions. The deformable convolutional network achieved the highest accuracy of 77.28% on the NEU dataset following a fivefold cross-validation method. Other models also demonstrated notable performance, with accuracies within the 70–75% range. Certain models exhibited particular strengths in detecting specific defects, indicating potential areas for future research and model improvement. The findings provide a comprehensive foundation for future research in steel defect detection and have significant implications for practical applications. The research could improve quality control processes in the steel industry by automating the defect detection task, leading to safer and more reliable steel products and protecting workers by removing the human factor from hazardous environments. © 2023, The Author(s).

关键词： Automated defect detection Deep learning models Steel defect detection Steel surface

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Perceptual Depth Quality Assessment of Stereoscopic Omnidirectional Images

arXiv

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arXiv 2024年

作者： Zhou, Wei Wang, Zhou The School of Computer Science and Informatics Cardiff University CardiffCF24 4AG United Kingdom The Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada

Depth perception plays an essential role in the viewer experience for immersive virtual reality (VR) visual environments. However, previous research investigations in the depth quality of 3D/stereoscopic images are rather limited, and in particular, are largely lacking for 3D viewing of 360-degree omnidirectional content. In this work, we make one of the first attempts to develop an objective quality assessment model named depth quality index (DQI) for efficient no-reference (NR) depth quality assessment of stereoscopic omnidirectional images. Motivated by the perceptual characteristics of the human visual system (HVS), the proposed DQI is built upon multi-color-channel, adaptive viewport selection, and interocular discrepancy features. Experimental results demonstrate that the proposed method outperforms state-of-the-art image quality assessment (IQA) and depth quality assessment (DQA) approaches in predicting the perceptual depth quality when tested using both single-viewport and omnidirectional stereoscopic image databases. Furthermore, we demonstrate that combining the proposed depth quality model with existing IQA methods significantly boosts the performance in predicting the overall quality of 3D omnidirectional images. © 2024, CC BY.

关键词： Stereo image processing

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Novel Bandpass and Periodic Frequency Responses in Non-Metallic Dielectric Metamaterials 12

Novel Bandpass and Periodic Frequency Responses in Non-Metal...

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12th International Electrical engineering Congress, iEECON 2024

作者： Malathong, Kritthanit Konpang, Jessada Boonthum, Direk Akkaraekthalin, Prayoot Chudpooti, Nonchanutt Thitimahatthanakusol, Phatsakul Rajamangala University Of Technology Krungthep Faculty Of Engineering Department Of Electrical And Telecommunication Engineering 2 Nanglingee Rd. Sathorn Thungmahamek Bangkok10120 Thailand Rajamangala University Of Technology Krungthep Division Of Physics Faculty Of Science And Technology 2 Nanglingee Rd. Sathorn Thungmahamek Bangkok10120 Thailand Department Of Electrical And Computer Engineering Faculty Of Engineering King Mongkut's University Of Technology North Bangkok 1518 Pracharat 1 Road Bangsue Bangkok10800 Thailand King Mongkut's University Of Technology North Bangkok Faculty Of Applied Science Department Of Industrial Physics And Medical Instrumentation Bangkok10800 Thailand Rajamangala University Of Technology Isan Faculty Of Engineering And Technology Department Of Telecommunication Engineering 744 Sura Narai Rd Nai-muang Mueang Nakhon Ratchasima Nakorn Ratchasima30000 Thailand

ISBN: (纸本)9798350383591

This study explores the remarkable properties of the Box-Ball Structure (BBS) within the 1GHz to 50GHz frequency range. The BBS structure demonstrates consistent bandpass frequencies with a periodic response and maintains a uniform 2.058GHz bandwidth at S11 = -6dB. Unlike metal-based structures, where changing C and L values can lead to frequency-dependent responses, the dielectric BBS structure exhibits stability. Its dielectric properties, including epsilon, mu, and refractive index (n), remain largely unaffected by frequency variations. Furthermore, the BBS structure is found to induce capacitance (C), as per the principles of virtual circuit theory, making it suitable for band-pass filter applications. The liquid state of epoxy resin allows for versatile structural designs, such as a hollow cylinder for omnidirectional antenna signal filtration. This research introduces the BBS as a dielectric resonator with unique properties and potential applications in various industries, particularly in the mobile phone sector. © 2024 IEEE.

关键词： Capacitance

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The Next Frontier: Legal Technology Systems for Document Retrieval in Court Proceedings

SSRN

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SSRN 2024年

作者： Kew, Stephanie Yen Nee Human Rights Lawyers Association 5 Chambers 7 Savoy Court LondonWC2R 0EX United Kingdom Department of Computer Science and Computer Engineering University of Arkansas FayettevilleAR72701 United States

The advancement of legal technology systems could potentially become promising tools for document retrieval in courtroom management, offering more streamlined legal procedures and facilitating access to court records. Within this framework, this research aims to present the current development of various legal technology systems, specifically applied to retrieving information. Through the analysis of existing literature and empirical studies, the main findings demonstrate an enhanced efficiency in processing lengthy court documents when utilizing a pre-trained language model, with a notable increment in accuracy by a margin of 6.59 points. In addition, among the various classifiers, it is revealed that the Principal Component Analysis (PCA)-based linear Support Vector Machines (SVMs) attained the greatest accuracy of up to 91.30% underscoring its effectiveness in retrieval tasks, vital for making informed decision-making in court proceedings. However, these technological advances also entail some key challenges that brought about future innovations, including the integration of facial recognition into the systems for end-to-end security with secure authentication. The overall trends in this judicial field are also highlighted which helps identify the direction of legal technology so that legal professionals can stay ahead of the latest developments, adopt new cutting-edge technologies, and put into law practices to remain competent in the evolving legal profession. © 2024, The Authors. All rights reserved.

关键词： Support vector machines

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Electromagnetic Channel Statistics for Continuous-Aperture Array (CAPA) Systems

arXiv

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arXiv 2025年

作者： Ouyang, Chongjun Zhao, Boqun Zhang, Xingqi Liu, Yuanwei School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom Department of Electrical and Computer Engineering University of Alberta EdmontonABT6G 2R3 Canada Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong

The channel statistics of a continuous-aperture array (CAPA)-based channel are analyzed using its continuous electromagnetic (EM) properties. The received signal-to-noise ratio (SNR) is discussed under isotropic scattering conditions. Using Landau’s theorem, the eigenvalues of the autocorrelation of the EM fading channel are shown to exhibit a step-like behavior. Building on this, closed-form expressions for the probability distribution of the SNR and the average channel capacity are derived. Numerical results are provided to validate the accuracy of the derivations. © 2025, CC BY.

关键词： Signal to noise ratio

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A Demo of Radar Sensing Aided Rotatable Antenna for Wireless Communication System

arXiv

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arXiv 2025年

作者： Dai, Qi Zheng, Beixiong Wang, Qiyao Xiong, Xue Shao, Xiaodan Zhu, Lipeng Zhang, Rui School of Microelectronics South China University of Technology Guangzhou511442 China Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore School of Science and Engineering The Chinese University of Hong Kong Shenzhen518172 China

As a new antenna architecture, rotatable antenna (RA) can flexibly adjust each antenna’s boresight/orientation to enhance communication performance cost-effectively. In this demonstration, we develop a prototype of radar sensing-aided RA system, where radar sensing module is applied to aid dynamic antenna orientation. The prototype consists of a transmitter (TX) module and a receiver (RX) module, both of which employ universal software radio peripherals (USRPs) for transmitting and receiving signals, respectively. Specifically, the RA-based TX first utilizes a laser radar to detect the RX’s location and collects its angle of arrival (AoA) information. Then based on the above information, the antenna servo is controlled by STM32 microcontroller to adjust the deflection angle, which enables the RA to align its boresight direction with the located RX. Experimental results verify the effectiveness of the prototype and indicate that the RA-based system significantly outperforms the conventional fixed-antenna system. Copyright © 2025, The Authors. All rights reserved.

关键词： Software prototyping

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Interactive and Explainable Optimized Learning for DDoS Detection in Consumer IoT Networks

引用

IEEE Transactions on Consumer Electronics 2024年

作者： Saiyed, Makhduma F Al-Anbagi, Irfan Hossain, M. Shamim University of Regina Faculty of Engineering and Applied Science ReginaSKS4S 0A2 Canada King Saud University College of Computer and Information Sciences Department of Software Engineering Riyadh12372 Saudi Arabia

The integration of Internet of Things (IoT) in consumer environments enhances convenience and security while increasing Human-computer Interaction (HCI). However,this increased interactivity has also increased the vulnerability of Consumer IoT (CIoT) networks to cyber threats,mainly Distributed Denial of Service (DDoS) attacks. The DDoS attacks,which vary in volume,present substantial challenges to these networks' operational integrity and customer trust. This paper introduces the Artificial Intelligence (AI)-driven (ADEPT) system that utilizes explainable and optimized deep-ensemble learning with pruning for DDoS detection. The system uses attention-based ensemble DL for DDoS detection,combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. To address the resource constraints of edge devices in CIoT networks,the system uses Differential Evolution (DE)-based pruning and quantization techniques,optimizing the model for efficient deployment on edge nodes while preserving high performance. An HCI interface is designed to allow network administrators and researchers to engage with the system through dynamic visualizations,facilitating complex data interpretation and empowering administrators to refine detection strategies. The interface,integrating SHapley Additive exPlanations (SHAP) and risk assessment,enhances model transparency and interpretability,highlighting the synergy of HCI and AI. The ADEPT system is evaluated using an experimental testbed and CIoT datasets and has demonstrated over 90% accuracy in detecting high-and low-volume DDoS attacks. © 1975-2011 IEEE.

关键词： Risk assessment

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Interpretable deep learning quantifies the impact of genomic variations on CD8+ T cell infiltration

Interpretable deep learning quantifies the impact of genomic...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yi He Liwen Xu Xiang Wang Shaoliang Peng College of Computer Science and Electronic Engineering Hunan University Changsha China Department of Thoracic Surgery 2nd Xiangya Hospital Central South University Changsha China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Immune checkpoint inhibitors (ICIs) have revolutionized cancer treatment, but challenges remain in identifying molecular determinants of clinical response. To address this, we developed a Transformer-based model, ImmRegVar, to predict CD8+ T cell infiltration using somatic genomic variations from 7972 solid tumor samples. ImmRegVar outperforms traditional regression methods in predictive accuracy and model performance. We introduce an innovative explanatory factor that combines Transformer attention weights with feature ablation techniques, allowing precise quantification of each gene mutation’s contribution. This factor offers an interpretable metric for understanding how specific mutations affect CD8+ T cell infiltration, shedding light on immune regulatory mechanisms. Validation through random permutation tests confirms its robustness and stability in high-dimensional sparse data. Our analysis also aligns with known immune regulatory genes, supporting the biological credibility of ImmRegVar. This research advances the understanding of key factors in immunotherapy and opens new pathways for exploring immune regulation and clinical applications. ImmRegVar and the explanatory factor are available at //***/Liwen-Liberty/ImmRegVar.

关键词： Solid modeling Genomics Predictive models Transformers Solids Stability analysis Regulation Bioinformatics Immune system Tumors

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Symptom-based drug prediction of lifestyle-related chronic diseases using unsupervised machine learning techniques

引用

computers in Biology and Medicine 2024年 174卷 108413-108413页

作者： Bhattacharjee, Sudipto Saha, Banani Saha, Sudipto Department of Computer Science and Engineering University of Calcutta JD-2 Sector-III Salt Lake Kolkata700098 India Department of Biological Sciences Bose Institute EN 80 Sector V Bidhan Nagar Kolkata700091 India

Background and objectives: Lifestyle-related diseases (LSDs) impose a substantial economic burden on patients and health care services. LSDs are chronic in nature and can directly affect the heart and lungs. Therapeutic interventions only based on symptoms can be crucial for prompt treatment initiation in LSDs, as symptoms are the first information available to clinicians. So, this work aims to apply unsupervised machine learning (ML) techniques for developing models to predict drugs from symptoms for LSDs, with a specific focus on pulmonary and heart diseases. Methods: The drug-disease and disease-symptom associations of 143 LSDs, 1271 drugs, and 305 symptoms were used to compute direct associations between drugs and symptoms. ML models with four different algorithms – K-Means, Bisecting K-Means, Mean Shift, and Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH) – were developed to cluster the drugs using symptoms as features. The optimal model was saved in a server for the development of a web application. A web application was developed to perform the prediction based on the optimal model. Results: The Bisecting K-means model showed the best performance with a silhouette coefficient of 0.647 and generated 138 drug clusters. The drugs within the optimal clusters showed good similarity based on i) gene ontology annotations of the gene targets, ii) chemical ontology annotations, and iii) maximum common substructure of the drugs. In the web application, the model also provides a confidence score for each predicted drug while predicting from a new set of input symptoms. Conclusion: In summary, direct associations between drugs and symptoms were computed, and those were used to develop a symptom-based drug prediction tool for LSDs with unsupervised ML models. The ML-based prediction can provide a second opinion to clinicians to aid their decision-making for early treatment of LSD patients. The web application (URL - http://***/ss

关键词： Machine learning

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IdeaSynth: Iterative Research Idea Development Through Evolving and Composing Idea Facets with Literature-Grounded Feedback 25

IdeaSynth: Iterative Research Idea Development Through Evolv...

引用

2025 CHI Conference on Human Factors in Computing Systems, CHI 2025

作者： Pu, Kevin Feng, K. J. Kevin Grossman, Tovi Hope, Tom Dalvi Mishra, Bhavana Latzke, Matt Bragg, Jonathan Chang, Joseph Chee Siangliulue, Pao Department of Computer Science University of Toronto Toronto ON Canada Human Centered Design & Engineering University of Washington Seattle WA United States Allen Institute for Artificial Intelligence Seattle WA United States AI2 Allen Institute for AI Seattle WA United States

ISBN: (纸本)9798400713941

Research ideation involves broad exploring and deep refining ideas. Both require deep engagement with literature. Existing tools focus primarily on broad idea generation, yet offer little support for iterative specification, refinement, and evaluation needed to further develop initial ideas. To bridge this gap, we introduce IdeaSynth, a research idea development system that uses LLMs to provide literature-grounded feedback for articulating research problems, solutions, evaluations, and contributions. IdeaSynth represents these idea facets as nodes on a canvas, and allow researchers to iteratively refine them by creating and exploring variations and combinations. Our lab study (N = 20) showed that participants, while using IdeaSynth, explored more alternative ideas and expanded initial ideas with more details compared to a strong LLM-based baseline. Our deployment study (N = 7) demonstrated that participants effectively used IdeaSynth for real-world research projects at various ideation stages from developing initial ideas to revising framings of mature manuscripts, highlighting the possibilities to adopt IdeaSynth in researcher's workflows. © 2025 Copyright held by the owner/author(s).

关键词： Human-AI Collaboration Research Ideation Scientific Literature

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