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IEEE SSD International Multi-Conference on Systems, Signals and Devices

作者： Khadija Attouri Mansour Hajji Majdi Mansouri Mohamed-Faouzi Harkat Abdelmalek Kouadri Hazem Nounou Mohamed Nounou Higher institute of Applied Science and Technology of Kasserine University of Kairouan Kasserine Tunisia Electrical and Computer Engineering Program Texas A&M University at Qatar Doha Qatar

ISBN: (数字)9781728110806

ISBN: (纸本)9781728110813

To ensure high reliability of the Grid-Connected Photovoltaic (GCPV) systems, promptly faults detection, diagnosis and automatic process monitoring are essential tools to keep the PV and the grid network under optimal functioning. Regardless of fault types, incipient faults are usually more difficult to detect and accurately isolate. As an alternative and effective method, the principal components analysis (PCA) is proposed to extract and select more relevant features and support vector machines (SVM) technique is applied to quickly detect the faults that occur in a GCPV system. The T2 and squared weighted errors (SWE) statistics, generally used as fault detection indices, are appropriately extracted and selected within the PCA framework. Both of these features are fed to a SVM classifier handling the incipient fault detection. This task is carried out on a simulated GCPV operating under maximum power point trackers (MPPT) and matching realistic outdoor to demonstrate the effectiveness and robustness of the proposed technique.

关键词： Support vector machines Photovoltaic systems Maximum power point trackers Fault detection Feature extraction Robustness Principal component analysis

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Efficient fault detection and diagnosis of wind energy converter systems

Efficient fault detection and diagnosis of wind energy conve...

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IEEE SSD International Multi-Conference on Systems, Signals and Devices

作者： Zahra Yahyaoui Mansour Hajji Majdi Mansouri Mohamed-Faouzi Harkat Abdelmalek Kouadri Hazem Nounou Mohamed Nounou Higher institute of Applied Science and Technology of Kasserine University of Kairouan Kasserine Tunisia Electrical and Computer Engineering Program Texas A&M University at Qatar Doha Qatar

ISBN: (数字)9781728110806

ISBN: (纸本)9781728110813

Fault detection and diagnosis for modern wind turbines converter (WTC) systems have been received an important measure for improving the operation of these systems, in such a way to increase their reliability, availability and required safety. Therefore, this paper deals with the problem of fault detection and diagnosis (FDD) in WTC systems. The developed FDD approach uses feature extraction and selection, and fault classification tools for monitoring WTC system under different operating conditions. The developed FDD approach is addressed such that, the principal component analysis (PCA) technique is used for feature extraction purposes and the machine learning (ML) classifiers are applied for fault diagnosis. In the proposed FDD approach, an efficient features in PCA subspace that extract and select the most informative features from WTC data are provided. Besides, their statistical characteristics are also included. The ML classifiers are applied to the extracted and selected features to perform the fault diagnosis problem. The effectiveness and higher classification accuracy of the developed approach are demonstrated using simulated data extracted from different operating conditions of the wind turbine.

关键词： Fault diagnosis Fault detection Machine learning Feature extraction Wind turbines Monitoring Principal component analysis

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Automatic petrol and diesel engine sound identification based on machine learning approaches 1

Automatic petrol and diesel engine sound identification base...

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1st International Conference on Automotive, Manufacturing, and Mechanical engineering, IC-AMME 2018

作者： Frederick, Halim Winda, Astuti Solihin, Mahmud Iwan Automotive and Robotics Program Computer Engineering Department BINUS ASO School of Engineering Bina Nusantara University Jakarta11480 Indonesia Faculty of Engineering Technology and Built Environment UCSI University Cheras Kuala Lumpur56000 Malaysia

Petrol and diesel engine have a significantly different way to convert chemical energy into mechanical energy. In this work, the intelligent system approach is used to automatically identify the type of engine based on the sound of the engine. The combination of signal processing and machine learning technique for automatic petrol and diesel engine sound identification is presented in this work. After a signal preprocessing step of the engine sound, a Fast Fourier Transform (FFT)-based frequency characteristic modelling technique is applied as the feature extraction method. The resulting features extracted from the sound signal, in the form of frequency in the FFT matrix, are used as the inputs for the machine learning, the Support Vector Machine (SVM), step of the proposed approach. The experiment of FFT with SVM-based diesel and petrol engine sound identification has been carried out. The results show that the proposed approach produces a good accuracy in the relatively short training time. Experimental results show the training and testing accuracy of 100 % and 100 % respectively. They confirm the effectiveness of the proposed intelligent automatic diesel and petrol engine sound identification based on Fast Fourier Transform (FFT) and Support Vector Machines (SVMs). © 2019 The Authors, published by EDP Sciences.

关键词： Support vector machines

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Sensing fugitive hydrogen emissions

Nature Reviews Electrical Engineering

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Nature Reviews Electrical engineering 2024年第4期1卷 210-211页

作者： Yichen Cai Kuo-Wei Huang Sudipta Chatterjee Khaled N. Salama Lain-Jong Li Chemistry Program Division of Physical Sciences and Engineering and KAUST Catalysis Center King Abdullah University of Science and Technology Thuwal Saudi Arabia Agency for Science Technology and Research Institute of Materials Research and Engineering and Institute of Sustainability for Chemicals Energy and Environment Singapore Singapore Department of Chemistry Birla Institute of Technology and Science − Pilani K.K. Birla Goa Campus Zuarinagar India Advanced Membranes and Porous Materials Center and Division of Computer Electrical and Mathematical Science and Engineering King Abdullah University of Science and Technology Thuwal Saudi Arabia Department of Mechanical Engineering University of Hong Kong Hong Kong P. R. China

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DeepDTAGen: a multitask deep learning framework for drug-target affinity prediction and target-aware drugs generation

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Nature Communications 2025年第1期16卷 1-15页

作者： Shah, Pir Masoom Zhu, Huimin Lu, Zhangli Wang, Kaili Tang, Jing Li, Min School of Computer Science and Engineering Central South University Changsha China School of Computer Science and Technology Donghua University Shanghai China Research Program in Systems Oncology Faculty of Medicine University of Helsinki Helsinki Finland Department of Biochemistry and Developmental Biology Faculty of Medicine University of Helsinki Helsinki Finland Xiangjiang Laboratory Changsha China Furong Laboratory Central South University Changsha China

Identifying novel drugs that can interact with target proteins is a highly challenging, time-consuming, and costly task in drug discovery and development. Numerous machine learning-based models have recently been utilized to accelerate the drug discovery process. However, these existing methods are primarily uni-tasking, either designed to predict drug-target interaction (DTI) or generate new drugs. Through the lens of pharmacological research, these tasks are intrinsically interconnected and play a critical role in effective drug development. Therefore, the learning models must be utilized in such a manner to learn the structural properties of drug molecules, the conformational dynamics of proteins, and the bioactivity between drugs and targets. To this end, this paper develops a novel multitask learning framework that can predict drug-target binding affinities and simultaneously generate new target-aware drug variants, using common features for both tasks. In addition, we developed the FetterGrad algorithm to address the optimization challenges associated with multitask learning particularly those caused by gradient conflicts between distinct tasks. Comprehensive experiments on three real-world datasets demonstrate that the proposed model provides an effective mechanism for predicting drug-target binding affinities and generating novel drugs, thus greatly facilitating the drug discovery process. © The Author(s) 2025.

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A probabilistic model for pathway-guided gene set selection

A probabilistic model for pathway-guided gene set selection

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Inyoung Kim Sangseon Lee Youngkuk Kim Hugh Namkoong Sun Kim Artificial Intelligence Institute Seoul National University Seoul Korea Institute of Computer Technology Seoul National University Seoul Korea Seoul National University Seoul Korea Institute of Engineering Research Seoul National University Seoul Korea Interdisciplinary Program in Bioinformatics Seoul National University Seoul Korea

ISBN: (纸本)9781665429825

Breast cancer is classified into five intrinsic subtypes, with differing treatment methods and prognoses. Therefore, accurate identification of subtypes from patient transcriptome data is essential. Many gene signatures, including PAM50, have been developed to classify breast cancer subtypes. However, existing gene selection methods do not utilize biological pathways. Gene signature selection using biological pathways can explain signature genes in terms of biological functions. Thus, we propose a probabilistic model for pathway-guided gene set selection using gene expression data. First, we defined gene and pathway factors based on gene expression and pathway activation levels, and calculated the posterior probability. Second, we adopted the prediction strength to guide gene set selection. Third, the gene set was selected using the posterior probability and prediction strength values. Finally, on evaluating the selected gene set, it was experimentally confirmed that our gene set performed better on classification tasks than the PAM50 gene set, a gene set produced by the XGBoost classifier, and a random gene set. Among the genes selected by our method, it was confirmed that the genes included in the cell cycle and circadian rhythm pathways showed different expression patterns for each breast cancer subtype. Our selected gene set exhibited biological significance in terms of pathway activation.

关键词： Biological system modeling Conferences Probability Probabilistic logic Breast cancer Data models Circadian rhythm

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Energy Efficiency Project with Actions to Encourage the Rational and Sustainable Use of Electric Energy 19

Energy Efficiency Project with Actions to Encourage the Rati...

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19th IEEE International Conference on Environment and Electrical engineering and 2019 IEEE Industrial and Commercial Power Systems Europe, EEEIC/I and CPS Europe 2019

作者： Lemos, G.C. Faria, A.F. Viajante, G.P. Domingues, E.G. Domingos, J.L. Silva, S.B. Souto, O.C.N. Velloso, E.J.F. Franca, J.L. Graduate Program in Sustainable Process Technology IFG Goiânia-GO Brazil Graduate Program in Electrical and Computer Eng. UFG Goiânia-GO Brazil Graduate Program in Sustainable Process Technology IFG Itumbiara-GO Brazil Anima Projects Engineering Commerce Goiânia-GO Brazil

ISBN: (纸本)9781728106526

This work is the critical analysis of an Energy Efficiency Project (EEP) developed in an educational institution. The case-study is based on several fronts that synthesize the perspective of sustainability in a Solar Tree, an equipment to compose the landscape furniture with the capacity to generate electricity from a renewable source, symbolizing the project and acting as motivating agent in maintenance, and also contributing to the design of new actions with this bias. In the other fronts, the work includes the quantification of the level of energy efficiency of a building, besides promoting the retrofit of the lighting system and the installation of a photovoltaic generation system, all focusing on the rational use of electric energy, whose results show that the energy saved and the reduction in the peak of demand, as well as the installation of the photovoltaic plant, will bring the installation closer to the concept of energy selfsufficiency. © 2019 IEEE.

关键词： Sustainable development

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Scientific discovery in the age of artificial intelligence

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NATURE 2023年第7978期621卷 E33-E33页

作者： Wang, Hanchen Fu, Tianfan Du, Yuanqi Gao, Wenhao Huang, Kexin Liu, Ziming Chandak, Payal Liu, Shengchao Van Katwyk, Peter Deac, Andreea Anandkumar, Anima Bergen, Karianne Gomes, Carla P. Ho, Shirley Kohli, Pushmeet Lasenby, Joan Leskovec, Jure Liu, Tie-Yan Manrai, Arjun Marks, Debora Ramsundar, Bharath Song, Le Sun, Jimeng Tang, Jian Velickovic, Petar Welling, Max Zhang, Linfeng Coley, Connor W. Bengio, Yoshua Zitnik, Marinka Department of Engineering University of Cambridge Cambridge UK Department of Computing and Mathematical Sciences California Institute of Technology Pasadena CA USA NVIDIA Santa Clara CA USA Department of Computational Science and Engineering Georgia Institute of Technology Atlanta GA USA Department of Computer Science Cornell University Ithaca NY USA Department of Chemical Engineering Massachusetts Institute of Technology Cambridge MA USA Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA USA Department of Computer Science Stanford University Stanford CA USA Department of Physics Massachusetts Institute of Technology Cambridge MA USA Harvard-MIT Program in Health Sciences and Technology Cambridge MA USA Mila – Quebec AI Institute Montreal Quebec Canada Université de Montréal Montreal Quebec Canada HEC Montréal Montreal Quebec Canada CIFAR AI Chair Toronto Ontario Canada Department of Earth Environmental and Planetary Sciences Brown University Providence RI USA Data Science Institute Brown University Providence RI USA Center for Computational Astrophysics Flatiron Institute New York NY USA Department of Astrophysical Sciences Princeton University Princeton NJ USA Department of Physics Carnegie Mellon University Pittsburgh PA USA Department of Physics and Center for Data Science New York University New York NY USA Google DeepMind London UK Department of Computer Science and Technology University of Cambridge Cambridge UK Microsoft Research Beijing China Department of Biomedical Informatics Harvard Medical School Boston MA USA Broad Institute of MIT and Harvard Cambridge MA USA Harvard Data Science Initiative Cambridge MA USA Kempner Institute for the Study of Natural and Artificial Intelligence Harvard University Cambridge MA USA Department of Systems Biology Harvard Medical School Boston MA USA Deep Forest Sciences Palo Alto CA USA BioMap Beijing China Mo

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Feasibility study of Information technology Investment (a case study of ODOO ERP: Project Management Module Implementation in Indonesia Based Company) 10

Feasibility study of Information Technology Investment (a ca...

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10th International Conference on ICT for Smart Society, ICISS 2019

作者： Jingga, Fredy Kosala, Raymond Supangkat, Suhono Harso Ranti, Benny School of Information Systems Bina Nusantara University Computer Science Department BINUS Graduate Program-Doctor of Computer Science Information Systems Department Jakarta11480 Indonesia Bina Nusantara University Computer Science Department BINUS Graduate Program-Doctor of Computer Science Jakarta11480 Indonesia Bandung Institute of Technology School of Electrical Engineering and Informatics Bandung Indonesia

ISBN: (纸本)9781728148809

IT Governance are one of the needs in managing Enterprise Level IT. This study shows part of the decision domain of IT Governance Help, which are IT Investment and Prioritization. The purpose of this study is to determine feasibility of implementation open source ERP system Odoo project management module that replaces current system based on operational, economic, and technical aspects. The method used are Fit/Gap Analysis and Cost Benefit model using economic impact worksheet. The analysis is done by comparing the requirement with features owned by current system and Odoo ERP system. This research resulted that Odoo ERP System is feasible in the 3 aspects, the operational feasibility Odoo reaching 72% Fit, the economic feasibility with ROI of 84%, and technical feasibility indicating the technical requirement can be fulfilled by leasing AWS server. The conclusion is the open source ERP system Odoo project management module is feasible to be applied in terms of 3 aspects that have been studied, those are operational, economic, and technical. © 2019 IEEE.

关键词： Cost benefit analysis

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Multimodal deep learning framework for image popularity prediction on social media

arXiv

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arXiv 2021年

作者： Abousaleh, Fatma S. Cheng, Wen-Huang Yu, Neng-Hao Tsao, Yu Social Networks and Human-Centered Computing Program Taiwan International Graduate Program Institute of Information Science Research Center for Information Technology Innovation Academia Sinica Taipei11529 Taiwan Department of Computer Science National Chengchi University Taipei11605 Taiwan The Institute of Electronics Department of Electronics Engineering National Chiao Tung University Hsinchu30010 Taiwan The College of Design Department of Design National Taiwan University of Science and Technology Taipei10607 Taiwan The Research Center for Information Technology Innovation Academia Sinica Taipei11529 Taiwan

Billions of photos are uploaded to the web daily through various types of social networks. Some of these images receive millions of views and become popular, whereas others remain completely unnoticed. This raises the problem of predicting image popularity on social media. The popularity of an image can be affected by several factors, such as visual content, aesthetic quality, user, post metadata, and time. Thus, considering all these factors is essential for accurately predicting image popularity. In addition, the efficiency of the predictive model also plays a crucial role. In this study, motivated by multimodal learning, which uses information from various modalities, and the current success of convolutional neural networks (CNNs) in various fields, we propose a deep learning model, called visual-social convolutional neural network (VSCNN), which predicts the popularity of a posted image by incorporating various types of visual and social features into a unified network model. VSCNN first learns to extract high-level representations from the input visual and social features by utilizing two individual CNNs. The outputs of these two networks are then fused into a joint network to estimate the popularity score in the output layer. We assess the performance of the proposed method by conducting extensive experiments on a dataset of approximately 432K images posted on Flickr. The simulation results demonstrate that the proposed VSCNN model significantly outperforms state-of-the-art models, with a relative improvement of greater than 2.33%, 7.59%, and 14.16% in terms of Spearman’s Rho, mean absolute error, and mean squared error, respectively. Copyright © 2021, The Authors. All rights reserved.

关键词： Social networking (online)

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