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AI-based sensor layout for predicting thermal deformations of CFRP machine tools

CIRP Annals 2025年

作者： Finkeldey, Felix Kato, Makoto Wiederkehr, Petra Kakinuma, Yasuhiro Virtual Machining Department of Computer Science TU Dortmund University Dortmund Germany Lamarr Institute for Machine Learning and Artificial Intelligence Dortmund Germany Department of System Design Engineering Faculty of Science & Technology Keio University Yokohama Kohoku-ku Japan

Using data-based approaches, accurate predictions of thermal deformations, which can significantly affect the quality of manufactured components, can be enabled. However, a sufficient amount of data with maximised information content is necessary for efficient training. In this paper, an approach for optimising sensor configurations for predicting thermal deformations is presented. From initially 300 temperature sensors, the number of required sensors was significantly reduced while maintaining predictive accuracy. Furthermore, a pattern for sensor placement was identified, providing the potential for an efficient sensor layout that enables cost-effective data acquisition and improved monitoring of machining and wear progression of machine tool components. © 2025 The Authors

关键词： Metal working tools

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A predictive machine learning approach for early fault identification in solar photovoltaic systems

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Electric Power Systems Research 2025年 247卷

作者： Nora EL-Rashidy Zainab H. Ali Yara A. Sultan Department of Machine Learning and Information Retrieval Faculty of Artificial Intelligence Kafrelsheikh University El-Geish st Kafrelsheikh 33516 Egypt Department of Embedded Network Systems and Technology Faculty of Artificial Intelligence Kafrelsheikh University El-Geish st Kafrelsheikh 33516 Egypt Department of Electronics and Computer Engineering School of Engineering and Applied Sciences at Nile University Giza Egypt Mechatronics Department Faculty of Engineering Horus University-Egypt New Damietta Egypt

The reliability of photovoltaic (PV) panels plays a pivotal role in optimizing energy output, particularly as solar energy becomes an indispensable pillar in global initiatives aimed at achieving sustainable and decarbonized energy infrastructures. Since faults in PV panels can significantly impair system performance and reduce energy yield, early detection is essential to maintaining operational efficiency. This study proposes an advanced fault detection framework leveraging contemporary machine learning techniques to identify anomalies early, thereby preventing performance degradation and minimizing downtime. The proposed model incorporates single- and multi-objective feature selection strategies to enhance fault identification accuracy. Specifically, single-objective techniques utilize Information Gain (IG), Gini Ratio (GR), and Gini Importance (GI). At the same time, the Non-dominated Sorting Genetic Algorithm II (NSGA-II) is employed as a multi-objective method to select an optimal subset of fault-relevant features. The model was trained on a dataset comprising 600 instances. Results indicate that gradient boosting achieved 97.6 % accuracy using single-objective selection, whereas the NSGA-II-based approach reduced the feature set to 15 optimal variables and improved accuracy to 99.3 %. Implementing this model enhances the operational reliability of solar PV systems and supports the broader goal of advancing the sustainability of solar energy production.

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Deep learning models for enhanced in-field maize leaf disease diagnosis

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machine learning with Applications 2025年 20卷

作者： Nakatumba-Nabende, Joyce Murindanyi, Sudi Department of Computer Science College of Computing and Information Sciences Makerere University Uganda Marconi Machine Learning Lab College of Engineering Design Art and Technology Makerere University Uganda Makerere Center for Artificial Intelligence and Data Science Makerere University Uganda

Maize leaf diseases significantly threaten crop yields, and there is need for accurate, and accessible diagnostic tools. This research addresses this need by developing and evaluating deep learning (DL) and machine learning (ML) models for in-field classification and detection of four critical maize diseases: Maize Leaf Blight, Maize Lethal Necrosis, Maize Streak Virus, and Fall Armyworm damage. Utilizing field imagery captured via digital cameras and smartphones across Uganda, Tanzania, Ghana, and Namibia, we developed and compared custom Convolutional Neural Networks (CNNs), transfer learning (MobileNetV2, InceptionResNetV2), Vision Transformers (ViT), and classical ML models. For detection, a transformer-enhanced YOLOv10 architecture was implemented. Explainable AI (XAI) techniques (Grad-CAM, LIME) were incorporated to ensure model transparency. MobileNetV2 achieved the highest classification accuracy (97%), while the enhanced YOLOv10 reached a mean Average Precision (mAP) of 0.995 for object detection. The best models were integrated into a mobile application deployed on edge devices for real-time diagnosis by smallholder farmers in Uganda, with performance validated through field tests. This study demonstrates a powerful, interpretable, and field-deployable solution combining advanced DL, transformers, and XAI for effective maize health management. © 2025

关键词： Convolutional neural networks

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A hybrid monotone decision tree model for interval-valued attributes

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Advances in Computational intelligence 2021年第1期2卷 1-11页

作者： Chen, Jiankai Li, Zhongyan Wang, Xin Zhai, Junhai School of Control and Computer Engineering North China Electric Power University Beijing China Hebei Key Laboratory of Machine Learning and Computational Intelligence College of Mathematics and Information Science Hebei University Baoding China School of Mathematics and Physics North China Electric Power University Beijing China

The existing monotonic decision tree algorithms are based on a linearly ordered constraint that certain attributes are monotonously consistent with the decision, which could be called monotonic attributes, whereas others, called non-monotonic attributes. In practice, monotonic and non-monotonic attributes coexist in most classification tasks, and some attribute values are even evaluated as interval numbers. In this paper, we proposed a fuzzy rank-inconsistent rate based on probability degree to judge the monotonicity of interval numbers. Furthermore, we devised a hybrid model composed of monotonic and non-monotonic attributes to construct a mixed monotone decision tree for interval-valued data. Experiments on artificial and real-world data sets show that the proposed hybrid model is effective.

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DPA-2:a large atomic model as a multitask learner

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npj Computational Materials 2024年第1期10卷 185-199页

作者： Duo Zhang Xinzijian Liu Xiangyu Zhang Chengqian Zhang Chun Cai Hangrui Bi Yiming Du Xuejian Qin Anyang Peng Jiameng Huang Bowen Li Yifan Shan Jinzhe Zeng Yuzhi Zhang Siyuan Liu Yifan Li Junhan Chang Xinyan Wang Shuo Zhou Jianchuan Liu Xiaoshan Luo Zhenyu Wang Wanrun Jiang Jing Wu Yudi Yang Jiyuan Yang Manyi Yang Fu-Qiang Gong Linshuang Zhang Mengchao Shi Fu-Zhi Dai Darrin M.York Shi Liu Tong Zhu Zhicheng Zhong Jian Lv Jun Cheng Weile Jia Mohan Chen Guolin Ke Weinan E Linfeng Zhang Han Wang AI for Science Institute BeijingP.R.China DP Technology BeijingP.R.China Academy for Advanced Interdisciplinary Studies Peking UniversityBeijingP.R.China State Key Lab of Processors Institute of Computing TechnologyChinese Academy of SciencesBeijingP.R.China University of Chinese Academy of Sciences BeijingP.R.China HEDPS CAPTCollege of EngineeringPeking UniversityBeijingP.R.China Ningbo Institute of Materials Technology and Engineering Chinese Academy of SciencesNingboP.R.China CAS Key Laboratory of Magnetic Materials and Devices and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology Chinese Academy of SciencesNingboP.R.China School of Electronics Engineering and Computer Science Peking UniversityBeijingP.R.China Shanghai Engineering Research Center of Molecular Therapeutics&New Drug Development School of Chemistry and Molecular EngineeringEast China Normal UniversityShanghaiP.R.China Laboratory for Biomolecular Simulation Research Institute for Quantitative Biomedicine and Department of Chemistry and Chemical BiologyRutgers UniversityPiscatawayNJUSA Department of Chemistry Princeton UniversityPrincetonNJUSA College of Chemistry and Molecular Engineering Peking UniversityBeijingP.R.China Yuanpei College Peking UniversityBeijingP.R.China School of Electrical Engineering and Electronic Information Xihua UniversityChengduP.R.China State Key Laboratory of Superhard Materials College of PhysicsJilin UniversityChangchunP.R.China Key Laboratory of Material Simulation Methods&Software of Ministry of Education College of PhysicsJilin UniversityChangchunP.R.China International Center of Future Science Jilin UniversityChangchunP.R.China Key Laboratory for Quantum Materialsof Zhejiang Province Department of PhysicsSchool of ScienceWestlake UniversityHangzhouP.R.China Atomistic Simulations Italian Institute of TechnologyGenovaItaly State Key Laboratory of Physical Chemistry of Solid Surface iChEMCollege of Chemistry and Chemical EngineeringXiame

The rapid advancements in artificial intelligence(AI)are catalyzing transformative changes in atomic modeling,simulation,and ***-driven potential energy models havedemonstrated the capability to conduct large-scale,long-duration simulations with the accuracy of ab initio electronic structure ***,the model generation process remains a bottleneck for large-scale *** propose a shift towards a model-centric ecosystem,wherein a large atomic model(LAM),pretrained across multiple disciplines,can be efficiently fine-tuned and distilled for various downstream tasks,thereby establishing a new framework for molecular *** this study,we introduce the DPA-2 architecture as a prototype for ***-trained on a diverse array of chemical and materials systemsusing a multi-task approach,DPA-2demonstrates superior generalization capabilities across multiple downstream tasks compared to the traditional single-task pre-training and fine-tuning *** approach sets the stage for the development and broad application of LAMs in molecular and materials simulation research.

关键词： DPA establishing thereby

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Temporal latent variable structural causal model for causal discovery under external interferences

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Neurocomputing 2025年 640卷

作者： Cai, Ruichu Huang, Xiaokai Chen, Wei Li, Zijian Hao, Zhifeng School of Computer Science Guangdong University of Technology Guangzhou China Peng Cheng Laboratory Shenzhen China Machine Learning Department Mohamed bin Zayed University of Artificial Intelligence Abu Dhabi United Arab Emirates College of Mathematics and Computer Science Shantou University Shantou China

Inferring causal relationships from observed data is an important task, yet it becomes challenging when the data is subject to various external interferences. Most of these interferences are the additional effects of external factors on observed variables. Since these external factors are often unknown, we introduce latent variables to represent these unobserved factors that affect the observed data. Specifically, to capture the causal strength and adjacency information, we propose a new temporal latent variable structural causal model, incorporating causal strength and adjacency coefficients that represent the causal relationships between variables. Considering that expert knowledge can provide information about unknown interferences in certain scenarios, we develop a method that facilitates the incorporation of prior knowledge into parameters learning based on Variational Inference, to guide the model estimation. Experimental results demonstrate the stability and accuracy of our proposed method. © 2025 Elsevier B.V.

关键词： Variational techniques

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Analysing Blockchain-Based Cryptography: Enhancing Security, Transparency, and Practical Implementations

Analysing Blockchain-Based Cryptography: Enhancing Security,...

引用

Technological engineering and its Applications in Sustainable Development (ICTEASD), International Conference for

作者： Laith H. Jasim Anshoo Mishra Sanjeev Kumar Joshi I. B. Sapaev Primmia D R Suseela D Aadi Nagender Babu The Islamic University Najaf Iraq Department of Computer Science & Engineering IES Insititute of Technology and Management IES University Bhopal Madhya Pradesh India Department of Mechanical Engineering Uttaranchal Institute of Technology Uttaranchal University Dehradun India Tashkent Institute of Irrigation and Agricultural Mechanization Engineers National Research University Tashkent Uzbekistan the Department Physics and Chemistry Western Caspian University Scientific Researcher Baku Azerbaijan CSE Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Artificial Intelligence and Machine Learning Bannari Amman Institute of Technology GRIET Hyderabad Telangana India

ISBN: (数字)9798350336474

ISBN: (纸本)9798350336481

This paper explores the many uses of blockchain-based cryptography for building trust, improving transparency, and providing security in a variety of contexts. The article begins with a thorough Introduction that lays the groundwork for a thorough analysis of the cryptographic underpinnings of blockchain technology. In "Cryptographic Foundations," the fundamental components of blockchain security are thoroughly dissected. This includes a detailed examination of Digital Signatures (2.1.2), Cryptographic Keys (2.1.3), Data Encryption (2.1.4), Cryptographic Hashes (2.1.1), and Merkle Trees (2.1.5), explaining their vital functions in guaranteeing the validity and integrity of data on the blockchain. After that, the study goes into great length to analyse symmetric encryption in blockchain (3), highlighting how important it is to protecting the privacy of transaction data. Safe Communication (3.2), Privacy Protection (3.1), Next, Asymmetric Encryption in Blockchain (4) takes front stage, explaining how to improve security and trust in decentralised networks using Public and Private Keys (4.1), Secure Communication (4.2), Digital Signatures (4.3), Access Control (4.4), and Transaction Integrity (4.5). After that, the study delves into the specifics of Local Peer Network Components (5.1), illuminating the design and function of local peers in supporting blockchain transactions. Expanding on this basis, the section Cryptographic Foundations in Blockchain (6) goes over the essential cryptographic components, namely Cryptographic Hashing (6.1), Digital Signatures (6.2), and Cryptographic Keys (6.3), emphasising their critical role in the data integrity and immutability of blockchains. The actions that Internet of Things devices need to take in order to register with the Transaction Processing and Device Registration (7) process are described. A fascinating examination of Blockchain Success Stories (8) completes the article. Three noteworthy case studies are covered: Food Tra

关键词： Access control Data privacy Blockchains Encryption Registers Reliability Internet of Things

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Building an Italian-Chinese Parallel Corpus for machine Translation from the Web 20

Building an Italian-Chinese Parallel Corpus for Machine Tran...

引用

6th EAI International Conference on Smart Objects and Technologies for Social Good, GOODTECHS 2020

作者： Tse, Rita Mirri, Silvia Tang, Su-Kit Pau, Giovanni Salomoni, Paola School of Applied Sciences - Macao Polytechnic Institute Engineering Research Centre of Applied Technology on Machine Translation and Artificial Intelligence Ministry of Education - Macao China Department of Computer Science and Engineering University of Bologna Bologna Italy Computer Science Department - Ucla Los AngelesCA United States

ISBN: (纸本)9781450375597

In an increasingly globalized world, being able to understand texts in different languages (even more so in different alphabets and charsets) has become a necessity. This can be strategic even while moving and travelling across different countries, characterized by different languages. With this in mind, bilingual corpora become critical resources since they are the basis of every state-of-the-art automatic translation system;moreover, building a parallel corpus is usually a complex and very expensive operation. This paper describes an innovative approach we have defined and adopted to automatically build an Italian-Chinese parallel corpus, with the aim of using it for training an Italian-Chinese Neural machine Translation. Our main idea is to scrape parallel texts from the Web: we defined a general pipeline, describing each specific step from the selection of the appropriate data sources to the sentence alignment method. A final evaluation was conducted to evaluate the goodness of our approach and its results show that 90% of the sentences were correctly aligned. The corpus we have obtained consists of more than 6,000 sentence pairs (Italian and Chinese), which are the basis for building a machine Translation system. © 2020 ACM.

关键词： Neural machine translation

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Reproducing kernel Hilbert space, Mercer's theorem, eigenfunctions, Nyström method, and use of kernels in machine learning: Tutorial and survey

arXiv

引用

arXiv 2021年

作者： Ghojogh, Benyamin Ghodsi, Ali Karray, Fakhri Crowley, Mark Department of Electrical and Computer Engineering Machine Learning Laboratory University of Waterloo WaterlooON Canada Department of Statistics and Actuarial Science David R. Cheriton School of Computer Science Data Analytics Laboratory University of Waterloo WaterlooON Canada Department of Electrical and Computer Engineering Centre for Pattern Analysis and Machine Intelligence University of Waterloo WaterlooON Canada

This is a tutorial and survey paper on kernels, kernel methods, and related fields. We start with reviewing the history of kernels in functional analysis and machine learning. Then, Mercer kernel, Hilbert and Banach spaces, Reproducing Kernel Hilbert Space (RKHS), Mercer's theorem and its proof, frequently used kernels, kernel construction from distance metric, important classes of kernels (including bounded, integrally positive definite, universal, stationary, and characteristic kernels), kernel centering and normalization, and eigenfunctions are explained in detail. Then, we introduce types of use of kernels in machine learning including kernel methods (such as kernel support vector machines), kernel learning by semi-definite programming, Hilbert-Schmidt independence criterion, maximum mean discrepancy, kernel mean embedding, and kernel dimensionality reduction. We also cover rank and factorization of kernel matrix as well as the approximation of eigenfunctions and kernels using the Nyström method. This paper can be useful for various fields of science including machine learning, dimensionality reduction, functional analysis in mathematics, and mathematical physics in quantum mechanics. Copyright © 2021, The Authors. All rights reserved.

关键词： Functional analysis

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Restricted Boltzmann machine and Deep Belief Network: Tutorial and Survey

arXiv

引用

arXiv 2021年

This is a tutorial and survey paper on Boltzmann machine (BM), Restricted Boltzmann machine (RBM), and Deep Belief Network (DBN). We start with the required background on probabilistic graphical models, Markov random field, Gibbs sampling, statistical physics, Ising model, and the Hopfield network. Then, we introduce the structures of BM and RBM. The conditional distributions of visible and hidden variables, Gibbs sampling in RBM for generating variables, training BM and RBM by maximum likelihood estimation, and contrastive divergence are explained. Then, we discuss different possible discrete and continuous distributions for the variables. We introduce conditional RBM and how it is trained. Finally, we explain deep belief network as a stack of RBM models. This paper on Boltzmann machines can be useful in various fields including data science, statistics, neural computation, and statistical physics. Copyright © 2021, The Authors. All rights reserved.

关键词： Statistical Physics

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