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Machine learning applied to property prediction of metal additive manufacturing products with textural features extraction

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International Journal of Advanced Manufacturing Technology 2024年第1-2期132卷 83-98页

作者： Chang, Lien-Kai Chen, Ri-Sheng Tsai, Mi-Ching Lee, Rong-Mao Lin, Ching-Chih Huang, Jhih-Cheng Chang, Tsung-Wei Horng, Ming-Huwi Electrical Motor Technology Research Center National Cheng Kung University Tainan700 Taiwan Department of Mechanical Engineering National Cheng Kung University Tainan700 Taiwan Department of Intelligent Automation Engineering National Chin-Yi University of Technology Taichung411 Taiwan Department of Computer Science and Information Engineering National Cheng Kung University Tainan700 Taiwan Academy of Innovative Semiconductor and Sustainable Manufacturing National Cheng Kung University Tainan700 Taiwan

Laser powder bed fusion (LPBF) is one of the common metal additive manufacturing technologies, which has been increasingly applied across various industries, including healthcare, manufacturing, and aerospace, owing to its advantages in customization and faster prototyping. However, acquiring accurate product properties necessitates repetitive and time-consuming measurements, which risk damaging the product. Thus, there is a pressing need to develop an automated method for predicting product properties. In this study, to forecast these properties, we documented details related to metal additive manufacturing products, encompassing both the process parameters and textural features. These features were extracted from layer-by-layer images using the gray-level co-occurrence matrix (GLCM). Subsequently, we employed machine learning (ML) models, such as support vector regression (SVR), XGBoost, and LightGBM, to predict product properties and compare their performance. The experimental results reveal stronger correlations between process parameters and texture features of three-dimensional co-occurrence matrices of the product images, compared to two-dimensional ones. Additionally, the models exhibit high predictive accuracy, especially XGBoost, and LightGBM, with R2 scores approaching 0.9 for all properties. These findings highlight the superiority and feasibility of the proposed approach. Moreover, this proposed approach holds promise in accurately predicting diverse product properties, meeting the demands of multiple application contexts. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： 3D printing

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Bamboo Forest Growth Optimization Algorithm for Night Image Enhancement 15th

Bamboo Forest Growth Optimization Algorithm for Night Image...

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15th International Conference on Genetic and Evolutionary Computing, ICGEC 2023

作者： Shi, Hao-Jie Pan, Jeng-Shyang Chu, Shu-Chuan Kong, Lingping Snášel, Václav College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China Innovative Information Industry Research Center Harbin Institute of Technology Shenzhen Graduate School Shenzhen China Faculty of Electrical Engineering and Computer Science VSB-Technical University of Ostrava Ostrava Czech Republic Department of Information Management Chaoyang University of Technology Taichung41349 Taiwan

ISBN: (纸本)9789819700677

The Bamboo Forest Growth Optimization(BFGO) algorithm is an algorithm proposed by the growth law of bamboo forest, which has the advantages of fast convergence and not easily falling into local optimum. In this paper we have experimented the BFGO with several other population intelligence optimization algorithms on 23 benchmark test functions and proved its superiority. Also we apply the bamboo forest optimization algorithm to nighttime image enhancement in this paper, and the algorithm achieves better results on this application. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Image enhancement

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Interference detection in radio astronomy: applying Shapiro–Wilks normality test, spectral entropy, and spectral relative entropy

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RAS Techniques and Instruments 2024年第1期3卷 535-547页

作者： Cao, Zhicheng Schmid, Natalia A. Bandura, Kevin Lorimer, Duncan R. Dameron, Morgan Crockett, Katelyn Grubick, Clayton Schmid, Andreas Zheng, Shaonan Xidian University School of Life Science and Technology Shaanxi 710126 China Lane Department of Computer Science and Electrical Engineering West Virginia University Morgantown 26506 WV United States Center for Gravitational Waves and Cosmology West Virginia University Chestnut Ridge Research Building Morgantown 26506 WV United States Department of Physics and Astronomy West Virginia University Morgantown 26506 WV United States Department of Electrical and Computer Engineering University of Illinois in Urbana-Champaign Urbana 61801 IL United States

Radio-frequency interference (RFI) is becoming an increasingly significant problem for most radio telescopes. Working with Green Bank Telescope data from PSR J1730+0747 in the form of complex-valued channelized voltages and their respective high-resolution power spectral densities, we evaluate a variety of statistical measures to characterize RFI. As a baseline for performance comparison, we use median absolute deviation (MAD) in complex channelized voltage data and spectral kurtosis (SK) in power spectral density data to characterize and filter out RFI. From a new perspective, we implement the Shapiro–Wilks (SW) test for normality and two information theoretical measures, spectral entropy (SE) and spectral relative entropy (SRE), and apply them to mitigate RFI. The baseline RFI mitigation algorithms are compared against our novel RFI detection algorithms to determine how effective and robust the performance is. Except for MAD, we find significant improvements in signal-to-noise ratio through the application of SE, symmetrical SRE, asymmetrical SRE, SK, and SW. These algorithms also do a good job of characterizing broad-band RFI. Time- and frequency-variable RFI signals are best detected by SK and SW tests. © 2024 The Author(s).

关键词： algorithms Machine learning normality tests PSR J1713+0747 pulsars spectral relative entropy

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Selective and Adaptive Incremental Transfer Learning with Multiple Datasets for Machine Fault Diagnosis

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computers, Materials & Continua 2024年第1期78卷 1363-1379页

作者： Kwok Tai Chui Brij B.Gupta Varsha Arya Miguel Torres-Ruiz School of Science and Technology Hong Kong Metropolitan UniversityHong Kong999077China Department of Computer Science and Information Engineering Asia UniversityTaichung41354Taiwan Center for Advanced Information Technology Kyung Hee UniversitySeoul02447Korea Symbiosis Centre for Information Technology Symbiosis International UniversityPuneMaharashtra411042India Department of Electrical and Computer Engineering Lebanese American UniversityBeirut1102Lebanon School of Computing Skyline University CollegeSharjah1797United Arab Emirates Department of Business Administration Asia UniversityTaichung41354Taiwan Center for Interdisciplinary Research University of Petroleum and Energy StudiesDehradun248007India Department of Computer Science&Engineering Chandigarh UniversityChandigarh160036India Centro de Investigación en Computación Instituto Politécnico NacionalUPALM-ZacatencoMexico City07320Mexico

The visions of Industry 4.0 and 5.0 have reinforced the industrial *** have also made artificial intelligence incorporated as a major *** machine faults has become a solid foundation for automatically recognizing machine failure,and thus timely maintenance can ensure safe *** learning is a promising solution that can enhance the machine fault diagnosis model by borrowing pre-trained knowledge from the source model and applying it to the target model,which typically involves two *** response to the availability of multiple datasets,this paper proposes using selective and adaptive incremental transfer learning(SA-ITL),which fuses three algorithms,namely,the hybrid selective algorithm,the transferability enhancement algorithm,and the incremental transfer learning *** is a selective algorithm that enables selecting and ordering appropriate datasets for transfer learning and selecting useful knowledge to avoid negative *** algorithm also adaptively adjusts the portion of training data to balance the learning rate and training *** proposed algorithm is evaluated and analyzed using ten benchmark *** with other algorithms from existing works,SA-ITL improves the accuracy of all *** studies present the accuracy enhancements of the SA-ITL,including the hybrid selective algorithm(1.22%-3.82%),transferability enhancement algorithm(1.91%-4.15%),and incremental transfer learning algorithm(0.605%-2.68%).These also show the benefits of enhancing the target model with heterogeneous image datasets that widen the range of domain selection between source and target domains.

关键词： Deep learning incremental learning machine fault diagnosis negative transfer transfer learning

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AlGaN-Based Deep-Ultraviolet Light-Emitting Diodes With Varied Thickness of Sidewall Passivation via Atomic Layer Deposition

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IEEE Transactions on Electron Devices 2023年第11期70卷 5727-5731页

作者： Peng, Kangwei Lai, Shouqiang Shen, Mengchun Li, Saijun Zheng, Lijie Dai, Yurong Chen, Jilan Zhu, Lihong Chen, Guolong Wang, Shuli Kuo, Hao-Chun Lu, Yijun Chen, Zhong Wu, Tingzhu Xiamen University Fujian Engineering Research Center for Solid-State Lighting Department of Electronic Science Xiamen361005 China Sanan Optoelectronics Company Ltd. Xiamen361005 China National Yang Ming Chiao Tung University College of Electrical and Computer Engineering Department of Photonics and Graduate Institute of Electro-Optical Engineering Hsinchu30010 Taiwan Hon Hai Research Institute Semiconductor Research Center Taipei11492 Taiwan Xiamen361005 China

In this study, we fabricated deep-ultraviolet light-emitting diodes (DUV-LEDs) with Al2O3 films of 0-, 130-, and 255-nm via atomic layer deposition (ALD). In addition, the effects of the passivation layers on the DUV-LEDs were systematically investigated. The current-voltage characteristics of these samples revealed the passivating effect of the Al2O3 ALD films on the DUV-LEDs. By analyzing the external quantum efficiency (EQE) at room temperature using the ABC + f,(n) model, the differences in the physical mechanisms of the Al2O3 ALD film on DUV-LEDs were determined. The measurements of the electroluminescence (EL) and far-field radiation patterns (EQE, light output power (LOP), and radiation patterns) showed that ALD can enhance the performance of DUV-LEDs, particularly DUV-LEDs with a 130-nm Al2O3 ALD passivation layer. Moreover, the aging test and failure ratio of these DUV-LEDs under humidity exposure (85 °C/85% RH) indicated the specific effect of these Al2O3 ALD films on the reliability of DUV-LEDs. © 1963-2012 IEEE.

关键词： Light emitting diodes

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An Investigation into Robust Video Watermarking Techniques 15

An Investigation into Robust Video Watermarking Techniques

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Deshpande, Vivek Vijayalakshmi, V.J. Ojha, Varun Arunadevi, R. Chaudhary, Anisha Manu, S.E. Vishwakarma Institute of Information Technology Department of Computer Engineering Maharashtra Pune India Karpagam Academy of Higher Education Department of Electrical and Electronics Engineering Coimbatore India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Chennai India Quantum University Research Center Quantum University India School of Engineering and Technology Civil Engineering Karnataka Bangalore India

ISBN: (纸本)9798350370249

this paper investigates techniques for ensuring the robustness of video watermarking algorithms. Many watermarking strategies awareness of performance measures like protection and imperceptibility;however frequently need to remember robustness, which is a vital element in creating a robust watermark that can preserve diverse types of signal noise and intentional attacks. The authors proposed an adaptive technique for the usage of movement estimation-primarily based watermark encoding and interpreting to assure robustness towards diverse noise and alteration assaults, along with white noise, contrast less noise, image filtering, JPEG compression, and video cropping. The authors in comparison the proposed algorithm with existing methods in phrases of height sign-to-noise ratio (PSNR), normalized correlation (NC), and robustness evaluation with four reference points (RP4). Effects showed that the proposed set of rules carried out higher in terms of PSNR, NC, and RP4, providing robust evidence of its robustness in comparison to present algorithms. Usual, the consequences from this paper recommended that the proposed adaptive method is a promising solution for robust video watermarking. The algorithm presents a strong overall performance in phrases of security, imperceptibility, and robustness towards various sorts of noise and intentional attacks. Future studies could discover the utility of this method to a much wider variety of video watermarking situations. © 2024 IEEE.

关键词： White noise

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Adaptive rapid defect identification in ECPT based on K-means and automatic segmentation algorithm

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Journal of Ambient Intelligence and Humanized Computing 2023年第11期14卷 1-18页

作者： Huang, Xuegang Yin, Chun Dadras, Sara Cheng, Yuhua Bai, Libing Hypervelocity Aerodynamics Institute China Aerodynamics Research and Development Center Mianyang621000 China School of Automation Engineering University of Electronic Science and Technology of China Sichuan Chengdu China Electrical and Computer Engineering Department Utah State University LoganUT84321 United States

To enhance the detection efficiency in eddy current pulsed thermography, an adaptive feature extraction algorithm for defect identification is developed in this paper. The proposed algorithm involves four stages, namely, the thermal image segmentation, the variable interval search, the distance correlation clustering analysis and the between-class distance decision making. The transient thermal responses (TTRs) with similar characteristics are collected into one data block. The thermal image segmentation and variable interval search can help reduce the repetitive calculation in defect identification by choosing local optimums in each data block. The global optimum that has the largest sum of the between-class distance, is derived by first classifying the local optimums and then calculating the correlation distance of the thermal responses with the center points of each class. Finally, the TTRs with the largest between-class distance are regarded as the typical ones which can be used to identify the discriminative defect features of infrared image sequence. Finally, the comparison experiments are carried out to demonstrate the effectiveness and advantages of the proposed approach. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： K-means clustering

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Advanced eHealth with Explainable AI: Secured by Blockchain with AI-Empowered Block Sensitivity for Adaptive Authentication 35

Advanced eHealth with Explainable AI: Secured by Blockchain ...

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35th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2024

作者： Dutta, Joy Eldeeb, Hossien B. Ho, Tu Dac Khalifa University C2PS Research Center Department of Computer Science Abu Dhabi United Arab Emirates University of Cambridge Electrical Engineering Division CambridgeCB3 0FA United Kingdom Faculty of Information Technology and Electrical Engineering Trondheim Norway

ISBN: (纸本)9798350362244

This paper presents an innovative, yet secure, eHealth framework that leverages Explainable Artificial Intelligence (XAI) and blockchain technology to enhance transparency and security in the IoT-edge-cloud continuum. The framework incorporates SHapley Additive exPlanations (SHAP) to provide real-time, model-agnostic explanations for AI predictions, enabling personalized health monitoring and informed decisionmaking in healthcare. To strengthen data security, a consortium blockchain is employed, and AI is utilized to identify block data sensitivity at the edge within blockchain-integrated IoT architectures using Random Forest (RF) algorithm. This approach achieves high accuracy in validating block sensitivity, enabling efficient selection of authentication mechanisms in the proof of authentication (PoAh) consensus within the consortium blockchain. This ensures heightened protection for sensitive data and contributes to improved overall blockchain performance. The proposed framework is evaluated in an edge computing environment and demonstrates significant potential for advancing security and authentication in eHealth, representing a substantial advancement in healthcare technology. © 2024 IEEE.

关键词： Electronic health record

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Seamless Distributed Power Management of DC Microgrid under Cyber Attacks with Bidirectional Power Flow

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IEEE Journal of Emerging and Selected Topics in Power Electronics 2025年

作者： Tran, Dat Thanh Kim, Kyeong-Hwa Lai, Jih-Sheng Seoul National University of Science and Technology Research Center for Electrical and Information Technology Department of Electrical and Information Engineering Seoul01811 Korea Republic of Department of Electrical and Computer Engineering BlacksburgVA24061 United States

A seamless distributed power management for the dc microgrid (DCMG) with bidirectional power flow is presented in this paper to achieve the overall system stabilization even under severe cyber attacks. First, a distributed secondary control (DSC) based on the V*-P droop curves is presented to ensure the power and voltage regulation for the DCMG system consisting of the electric vehicle (EV), wind turbine, battery, load, and utility grid agents under uncertain conditions. The proposed scheme automatically adjusts the utility grid droop curve to minimize electricity expenditure under electricity price change. To eliminate the negative effect of severe cyber attacks such as false data injection (FDI) and denial-of-service (DoS) in the distributed DCMG system, a resilient DSC based on the compensation term is utilized in the proposed scheme. In addition, by modifying the DSC structure of each power agent, the proposed scheme reduces the overshoot of the dc bus voltage even in the presence of the electricity price change, agent power variation, sudden utility grid disconnection, or critical state-of-charge (SOC) levels. Furthermore, the proposed distributed DCMG system utilizes only the unidirectional digital communication links (DCLs) to cut down the system cost and communication burden, which greatly simplifies communication structure. The efficiency and feasibility of the proposed distributed power management are validated by simulation and experimental results under various conditions. © 2025 IEEE.

关键词： Cyber attacks

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Encrypted Finite-Horizon Energy-to-Peak State Estimation for Time-Varying Systems Under Eavesdropping Attacks: Tackling Secrecy Capacity

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IEEE/CAA Journal of Automatica Sinica 2023年第4期10卷 985-996页

作者： Lei Zou Zidong Wang Bo Shen Hongli Dong Guoping Lu College of Information Science and Technology Donghua UniversityShanghai 201620 Engineering Research Center of Digitalized Textile and Fashion Technology Ministry of EducationShanghai 201620China College of Electrical Engineering and Automation Shandong University of Science and TechnologyQingdao 266590China Department of Computer Science Brunel University LondonUxbridgeMiddlesex UB83PHUK Intelligence Energy Research Institute Northeast Petroleum UniversityDaqing 163318 Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control Northeast Petroleum UniversityDaqing 163318 Sanya Offshore Oil&Gas Research Institute Northeast Petroleum UniversitySanya 572024China School of Electrical Engineering Nantong UniversityNantong 226019China

This paper is concerned with the problem of finitehorizon energy-to-peak state estimation for a class of networked linear time-varying *** to the inherent vulnerability of network-based communication,the measurement signals transmitted over a communication network might be intercepted by potential *** avoid information leakage,by resorting to an artificial-noise-assisted method,we develop a novel encryption-decryption scheme to ensure that the transmitted signal is composed of the raw measurement and an artificial-noise term.A special evaluation index named secrecy capacity is employed to assess the information security of signal transmissions under the developed encryption-decryption *** purpose of the addressed problem is to design an encryptiondecryption scheme and a state estimator such that:1)the desired secrecy capacity is ensured;and 2)the required finite-horizon–l_(2)-l_(∞)performance is *** conditions are established on the existence of the encryption-decryption mechanism and the finite-horizon state ***,simulation results are proposed to show the effectiveness of our proposed encryption-decryption-based state estimation scheme.

关键词： Artificial-noise-assisted technique eavesdropping encryption-decryption scheme energy-to-peak state estimation finitehorizon state estimation

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