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38th AAAI Conference on Artificial Intelligence, AAAI 2024

作者： Nguyen, Anh Duc Nguyen, Tuan Dung Nguyen, Quang Minh Nguyen, Hoang H. Nguyen, Lam M. Toh, Kim-Chuan Department of Mathematics National University of Singapore Singapore Department of Computer and Information Science University of Pennsylvania United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology United States School of Industrial and Systems Engineering Georgia Institute of Technology United States IBM Research Thomas J. Watson Research Center United States Institute of Operations Research and Analytics National University of Singapore Singapore

ISBN: (纸本)1577358872

This paper studies the Partial Optimal Transport (POT) problem between two unbalanced measures with at most n supports and its applications in various AI tasks such as color transfer or domain adaptation. There is hence a need for fast approximations of POT with increasingly large problem sizes in arising applications. We first theoretically and experimentally investigate the infeasibility of the state-of-the-art Sinkhorn algorithm for POT, which consequently degrades its qualitative performance in real world applications like point-cloud registration. To this end, we propose a novel rounding algorithm for POT, and then provide a feasible Sinkhorn procedure with a revised computation complexity of Oe(n2/Ε4). Our rounding algorithm also permits the development of two first-order methods to approximate the POT problem. The first algorithm, Adaptive Primal-Dual Accelerated Gradient Descent (APDAGD), finds an Ε-approximate solution to the POT problem in Oe(n2.5/Ε). The second method, Dual Extrapolation, achieves the computation complexity of Oe(n2/Ε), thereby being the best in the literature. We further demonstrate the flexibility of POT compared to standard OT as well as the practicality of our algorithms on real applications where two marginal distributions are unbalanced. Copyright © 2024, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Gradient methods

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A review: Challenges and opportunities for artificial intelligence and robotics in the offshore wind sector

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Energy and AI 2022年第2期8卷 177-212页

作者： Daniel Mitchell Jamie Blanche Sam Harper Theodore Lim Ranjeetkumar Gupta Osama Zaki Wenshuo Tang Valentin Robu Simon Watson David Flynn Smart Systems Group Institute of SensorsSignals and SystemsSchool of Engineering and Physical SciencesHeriot-Watt UniversityEdinburghEH144ASUnited Kingdom Centre for Mathematics and Computer Science Intelligent and Autonomous Systems GroupCWIAmsterdam1098 XG Netherlands Algorithms Group Faculty of Electrical EngineeringMathematics and Computer Science(EEMCS)Delft University of Technology(TU Delft)Delft2628 XE Netherlands The University of Manchester Department of Electrical and Electronic EngineeringOxford RoadManchesterM139PL United Kingdom James Watt School of Engineering University of GlasgowG128QQUnited Kingdom

The UK has set plans to increase offshore wind capacity from 22GW to 154GW by 2030. With such tremendous growth, the sector is now looking to Robotics and Artificial Intelligence (RAI) in order to tackle lifecycle service barriers as to support sustainable and profitable offshore wind energy production. Today, RAI applications are predominately being used to support short term objectives in operation and maintenance. However, moving forward, RAI has the potential to play a critical role throughout the full lifecycle of offshore wind infrastructure, from surveying, planning, design, logistics, operational support, training and decommissioning. This paper presents one of the first systematic reviews of RAI for the offshore renewable energy sector. The state-of-the-art in RAI is analyzed with respect to offshore energy requirements, from both industry and academia, in terms of current and future requirements. Our review also includes a detailed evaluation of investment, regulation and skills development required to support the adoption of RAI. The key trends identified through a detailed analysis of patent and academic publication databases provide insights to barriers such as certification of autonomous platforms for safety compliance and reliability, the need for digital architectures for scalability in autonomous fleets, adaptive mission planning for resilient resident operations and optimization of human machine interaction for trusted partnerships between people and autonomous assistants. Our study concludes with identification of technological priorities and outlines their integration into a new ‘symbiotic digital architecture’ to deliver the future of offshore wind farm lifecycle management.

关键词： Artificial intelligence Autonomous systems Digitalization Offshore renewable energy Offshore wind farms Robotics

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Diagnosis of Dynamic Eccentricity Fault in Inverter-Fed Induction Machine Using Three Phase Stator Currents Intersection Signal

Diagnosis of Dynamic Eccentricity Fault in Inverter-Fed Indu...

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Electronics, Energy and Measurement (IC2EM), International Conference on

作者： Hamid Khelfi Salim Hamouda Kamel Kassa Baghdouche Samir Hamdani Department of Electrical Engineering (DGE) Electrical and Industrial Systems Laboratory (EISL) Faculty of Electrical Engineering (FGE) University of Science and Technology Houari Boumediene (USTHB) Algiers Algeria

The current research presents a dependable and practical indirect approach for detecting and diagnosing Dynamic Eccentricity in inverter-feed induction machines operating at varied load circumstances. The spectral analysis of the Three Phase Currents Intersection Signal (TPCIS) and its positive envelope is the main focus of the suggested technique. The sinusoidal TPCIS signal is extracted from finding time and amplitude of each positive and negative intersection point in three-phase stator current signals rather than using the classical Zero Crossing Time Approach (ZCTA), and the TPCIS signal is formed through searching and interpolating all the crossing points. The positive envelope of TPCIS can be obtained through spline interpolation of all positive crossing points. The theoretical foundation of the suggested technique is presented, and it is then experimentally confirmed by testing two induction machines: one healthy for comparison, and the other with Dynamic Eccentricity fault (DEF). Each machine is exposed to two loads (low, and high load). The acquired experimental findings illustrate the resilience and usefulness of the suggested approach, as well as its capacity to distinguish defective cases from healthy ones under varied load conditions, even at low load.

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Ideation of engineering Solutions for Sustainable Development Goals: A Collaborative Course Between Thai and Japanese Students 38

Ideation of Engineering Solutions for Sustainable Developmen...

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38th IEEE Region 10 Conference, TENCON 2023

作者： Krutphong, Kodchakorn Leelawat, Natt Tang, Jing Ota, Eri Suzuki, Rie Murakami Taoka, Yuki Punyabukkana, Proadpran Environmental Engineering and Disaster Management Program Mahidol University Thailand Department of Industrial Engineering Faculty of Engineering Chulalongkorn University Thailand Disaster and Risk Management Information Systems Research Unit Chulalongkorn University Thailand International School of Engineering Chulalongkorn University Thailand Center for International Education Tokyo Institute of Technology Japan School of Environmet and Society Tokyo Institute of Technology Japan Department of Computer Engineering Faculty of Engineering Chulalongkorn University Thailand The Learning Innovation Center Chulalongkorn University Thailand

ISBN: (纸本)9798350302196

A collaborative course called Global Awareness for Technology Implementation was conducted by the faculty of engineering, Chulalongkorn University, Thailand, and the Tokyo Institute of Technology, Japan. The objective of this course was to facilitate the exchange of knowledge, perspectives, and experiences between Thai and Japanese students through project work, using an engineering design approach and the design thinking process to create innovation relevant to the Sustainable Development Goals (SDGs). During the class, experts shared their knowledge and experiences with the students. The students then worked on their respective projects to develop solution concepts using the design thinking process. The proposed ideas include the application for English class, smart water quality checker, hydrogen-based energy system for rural area, reduced wasted fashion solution, and auto waste sorting machine. Finally, this study concluded with a summary of the results and findings on the empathy process in the chosen SDGs, and of the commonalities and differences between Thailand and Japan in terms of ideation. © 2023 IEEE.

关键词： Students

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SPD Miscoordination in PV systems Due to Lightning Strikes

SPD Miscoordination in PV Systems Due to Lightning Strikes

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International Conference on Applied and Theoretical Electricity (ICATE)

作者： Osmel Pérez Baluja Bernardo Yaser León Avila Carlos A. García Vázquez Daniel Tudor Cotfas Petru Adrian Cotfas Orlys E. Torres Breffe Department of Electronics and Computers Faculty of Electrical Engineering and Computer Science Transilvania University of Braşov Brasov Romania Department of Electric Power Systems Faculty of Electrical Engineering Technological University of Havana Havana Cuba

ISBN: (数字)9798350388107

ISBN: (纸本)9798350388114

This study deals with the coordination of surge protection devices (SPD) in photovoltaic systems (PV). A PV farm model was developed in MATLAB Simulink with all the main components, solar panels, converter, grounding system, power transformer, cables in the DC and AC branches with their capacitances, and surge protection device throughout the installation. It was found that when lightning strikes the grounding system or in the vicinity of the PV installation, the coordination among SPD types 1, 2, and 3 is lost. Miscoordination can cause damage to several elements of the PV system, mainly the inverters. Finally, solutions were proposed to avoid SPD miscoordination during lightning strikes.

关键词： Grounding Software packages Lightning Surge protection Stars Switches Inverters Power transformers Solar panels Fault current limiters

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Intrusion Detection in 5G Cellular Network Using Machine Learning

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computer systems Science & engineering 2023年第11期47卷 2439-2453页

作者： Ishtiaque Mahmood Tahir Alyas Sagheer Abbas Tariq Shahzad Qaiser Abbas Khmaies Ouahada Knowledge Unit of Systems and Technology UMT Sialkot CampusSialkot51040Pakistan Department of Computer Science Lahore Garrison UniversityLahore54000Pakistan Faculty of Computer Science National College of Business Administration and EconomicsLahore54660Pakistan Department of Electrical and Computer Engineering COMSATS University IslamabadSahiwal CampusSahiwal57000Pakistan Faculty of Computer and Information Systems Islamic University of Madinah Madinah42351Saudi Arabia Department of Computer Science&IT University of SargodhaSargodha40100Pakistan Department of Electrical and Electronic Engineering Science University of JohannesburgP.O Box 524Auckland ParkJohannesburg2006South Africa

Attacks on fully integrated servers,apps,and communication networks via the Internet of Things(IoT)are growing *** devices’effectiveness harms end users,increases cyber threats and identity theft,raises costs,and negatively impacts income as problems brought on by the Internet of Things network go unnoticed for extended *** on Internet of Things interfaces must be closely monitored in real time for effective safety and *** the 1,2,3,and 4G cellular networks,the 5th generation wireless 5G network is indeed the great invasion of mankind and is known as the global advancement of cellular *** to this day,experts are working on the evolution’s sixth generation(6G).It offers amazing capabilities for connecting everything,including gadgets and machines,with wavelengths ranging from 1 to 10 mm and frequencies ranging from 300 MHz to 3 *** gives you the most recent *** countries have already established this technology within their *** is the most crucial aspect of using a 5G *** of the absence of study and network deployment,new technology first introduces new gaps for attackers and *** Protocol(IP)attacks and intrusion will become more prevalent in this *** efficient approach to detect intrusion in the 5G network using a Machine Learning algorithm will be provided in this *** research will highlight the high accuracy rate by validating it for unidentified and suspicious circumstances in the 5G network,such as intruder hackers/*** applying different machine learning algorithms,obtained the best result on Linear Regression Algorithm’s implementation on the dataset results in 92.12%on test data and 92.13%on train data with 92%precision.

关键词： Intrusion detection system machine learning confidentiality integrity availability

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Ergodic Performance Analysis of Intelligent Reflecting Surface Network with Phase Errors

Ergodic Performance Analysis of Intelligent Reflecting Surfa...

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International Conference on Advanced engineering Theory and Applications, AETA 2022

作者： Le, Si-Phu Van Nguyen, Minh-Sang Nguyen, Hong-Nhu Do, Dinh-Thuan Voznak, Miroslav Fazio, Peppino Faculty of Electrical Engineering and Computer Science VSB-Technical University of Ostrava 17. Listopadu 2172/15 Ostrava708 00 Czech Republic Faculty of Electronics Technology Industrial University of Ho Chi Minh City Ho Chi Minh City Viet Nam Faculty of Electronics and Telecommunications Saigon University Ho Chi Minh City Viet Nam Department of Electrical and Computer Engineering The University of Texas at Austin AustinTX78712 United States DSMN Ca’ Foscari University Via Torino 155 VE Mestre30172 Italy

ISBN: (纸本)9789819987023

In this paper, we investigate an application of the Intelligent Reflecting Surface (IRS). This technology has emerged as promising and low-cost technology for 6G networks improving wireless communications by collecting dispersed radio waves and redirecting them to the intended destination users. We characterize the achievable rate when multiple IRSs are utilized in the manner of Decode-and-Forward (DF) relaying. Our capacity performance analysis is based on the Rayleigh fading model with perfect Channel State Information (CSI) and we optimize the IRS configuration considering the ergodic capacity of the proposed scheme, derived by analyzing the statistics of CSI, which provides useful insights for practical design. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Rayleigh fading

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Automated Diagnosis of Heart-Lung Diseases in Chest X-ray Images 5

Automated Diagnosis of Heart-Lung Diseases in Chest X-ray Im...

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5th International Conference on engineering Technology and its Applications, IICETA 2022

作者： Alslatie, Mohammad Alquran, Hiam Mustafa, Wan Azani Abu-Qasmieh, Isam Alqudah, Ali Mohammad Alkhayyat, Ahmed The Institute of Biomedical Technology King Hussein Medical Center Royal Jordanian Medical Service Amman Jordan Yarmouk University Biomedical Systems and Medical Informatics Engineering Irbid Jordan Arau Faculty of Electrical Engineering Technology Perlis Malaysia University of Manitoba Biomedical Engineering Program Department of Electrical and Computer Engineering Canada The Islamic University Faculty of Engineering Najaf Iraq

ISBN: (纸本)9781665472159

The state of the art of artificial intelligence (AI) for various medical imaging applications leads to enhanced accuracy, analysis, visualization, and interpretation of chest Xray (CXR) images for diagnosis. Many diseases are diagnosed based on CXR images. In this paper, two types of abnormalities are diagnosed based on AI techniques. The two classes are atelectasis and cardiomegaly. The acquired images are segmented to localize the chest region and then enhanced using gray-level transformation methods. The enhanced images are passed to two pretrained convolutional neural networks (CNNs): shuffle and mobile net. The transfer learning approach is utilized in this stage. The automated features are extracted from the last fully connected layer. Each CNN deserves to have the two most representative features for the two classes. These four features are passed to support the vector machine classifier. The training accuracy reached 100% and the test accuracy was 96.7%. The proposed method can be extended to be a milestone in the classification of all heart-lung diseases that can be diagnosed using chest X-ray images. © 2022 IEEE.

关键词： Convolutional neural networks

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ModelPred: A Framework for Predicting Trained Model from Training Data

ModelPred: A Framework for Predicting Trained Model from Tra...

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Secure and Trustworthy Machine Learning (SaTML), IEEE Conference on

作者： Yingyan Zeng Jiachen T. Wang Si Chen Hoang Anh Just Ran Jin Ruoxi Jia Grado Department of Industrial and Systems Engineering Virginia Tech Blacksburg USA Department of Electrical and Computer Engineering Princeton University Princeton USA Bradley Department of Electrical and Computer Engineering Virginia Tech Blacksburg USA

In this work, we propose ModelPred, a framework that helps to understand the impact of changes in training data on a trained model. This is critical for building trust in various stages of a machine learning pipeline: from cleaning poor-quality samples and tracking important ones to be collected during data preparation, to calibrating uncertainty of model prediction, to interpreting why certain behaviors of a model emerge during deployment. Specifically, ModelPred learns a parameterized function that takes a dataset S as the input and predicts the model obtained by training on S. Our work differs from the recent work of Datamodels [1] as we aim for predicting the trained model parameters directly instead of the trained model behaviors. We demonstrate that a neural network-based set function class is capable of learning the complex relationships between the training data and model parameters. We introduce novel global and local regularization techniques to prevent overfitting and we rigorously characterize the expressive power of neural networks (NN) in approximating the end-to-end training process. Through extensive empirical investigations, we show that ModelPred enables a variety of applications that boost the interpretability and accountability of machine learning (ML), such as data valuation, data selection, memorization quantification, and model calibration.

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Enhancing 3D Human Pose Estimation Amidst Severe Occlusion with Dual Transformer Fusion

arXiv

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arXiv 2024年

作者： Ghafoor, Mehwish Mahmood, Arif Bilal, Muhammad Department of Computer Science Information Technology University Lahore Pakistan Center of Excellence in Intelligent Engineering Systems Department of Electrical and Computer Engineering Faculty of Engineering King Abdulaziz University Jeddah21589 Saudi Arabia

In the field of 3D Human Pose Estimation from monocular videos, the presence of diverse occlusion types presents a formidable challenge. Prior research has made progress by harnessing spatial and temporal cues to infer 3D poses from 2D joint observations. This paper introduces a Dual Transformer Fusion (DTF) algorithm, a novel approach to obtain a holistic 3D pose estimation, even in the presence of severe occlusions. Confronting the issue of occlusion-induced missing joint data, we propose a temporal interpolation-based occlusion guidance mechanism. To enable precise 3D Human Pose Estimation, our approach leverages the innovative DTF architecture, which first generates a pair of intermediate views. Each intermediate-view undergoes spatial refinement through a self-refinement schema. Subsequently, these intermediate-views are fused to yield the final 3D human pose estimation. The entire system is end-to-end trainable. Through extensive experiments conducted on the Human3.6M and MPI-INF-3DHP datasets, our method’s performance is rigorously evaluated. Notably, our approach outperforms existing state-of-the-art methods on both datasets, yielding substantial improvements. The code is available here: https://***/MehwishG/DTF. © 2024, CC BY-NC-ND.

关键词： 3D modeling

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