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International Conference on Renewable Energy Research and Applications (ICRERA)

作者： Nirashi Polwaththa Gallage Nihal Kularatna Alistair Steyn-Ross Dulsha Kularatna-Abeywardana School of Engineering University of Waikato Hamilton New Zealand Department of Electrical Computer and Software University of Auckland line 2-name of organization acronyms acceptable Auckland New Zealand

ISBN: (数字)9798350375589

ISBN: (纸本)9798350375596

Today solar or wind energy resources are coupled with the AC grid supply in the household using inverters and battery banks. However, the maximum power point tracking-based battery chargers and inverters have significant losses in the renewable-to-AC grid link. By using supercapacitor modules with extra low-frequency power converters, we can increase the end-to-end efficiency of whiteware such as refrigerators, washing machines, dryers, and heat pumps. Given that the modern inverter-driven whiteware carries brush-less DC motors driven by an inverter supplied by an internal DC bus derived from the line frequency AC supply, these systems can be easily powered by a direct DC source for the reduction of losses associated with the renewable energy link in a household. In addition, this could also reduce the converter losses within the white-good. The paper presents the theoretical and experimental details on how commercial whiteware can be easily modified to operate from a DC rail with a supercapacitor module used for the dual purpose of power conversion and energy buffering based on an example of a refrigerator.

关键词： Rails Renewable energy sources Wind energy Heat pumps Filtering Supercapacitors Frequency conversion Inverters Washing machines Power conversion

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Enhancing IoT-Based Smart Farming Security with a Hybrid GRU Intrusion Detection System

Enhancing IoT-Based Smart Farming Security with a Hybrid GRU...

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Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： Sivakumar Nagarajan S. Vijay S. Karthi RVS Praveen Ramesh Vijaya Babu K Burri Naresh Technical Architect I & I Software Inc Bethlehem USA Department of Data Science Sacred Heart College (Autonomous) Tamilnadu India Department of Computer Science and Engineering V.S.B. Engineering College Karur Tamilnadu India Director Digital Engineering & Assurance LTIMindtree Limited Hyderabad Telangana India SRKR Engineering College Bhimavaram Andhra Pradesh India Department of CSE - Cyber Security CVR College of Engineering Hyderabad Telangana India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

The growing use of IoT devices in smart agriculture makes the industry vulnerable to several cyber risks. This study introduces an innovative hybrid Intrusion Detection System (IDS) utilising Gated Recurrent Units (GRUs) to improve the security of IoT-enabled smart agriculture. compared to current LSTM and CNN-based intrusion detection systems, the proposed model effectively incorporates sequential data dependencies, hence minimising false positives and enhancing detection accuracy. Experimental findings indicate that the suggested model surpasses conventional techniques, with an accuracy of 97.5%, an F1 score of 0.96, and a recall of 0.97. These developments create a strong, efficient, and scalable solution for safeguarding IoT-based agricultural systems from cyber-attacks.

关键词： Smart agriculture Technological innovation Accuracy Computational modeling Scalability Intrusion detection Internet of Things Object recognition Protection Long short term memory

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Predicting bolt stress under uncertainty in offshore wind turbine structural connections using a gaussian process surrogate model 31st

Predicting bolt stress under uncertainty in offshore wind tu...

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31st International Ocean and Polar engineering Conference, ISOPE 2021

作者： Jorgensen, Jack Hodkiewicz, Melinda Cripps, Edward Hassan, Ghulam Mubashar Department of Mechanical Engineering University of Western Australia Crawley Australia Department of Mathematics and Statistics University of Western Australia Crawley Australia Department of Computer Science and Software Engineering University of Western Australia Crawley Australia

ISBN: (纸本)9781880653821

Fatigue assessment of bolted ring-flanges in offshore wind turbine structures is a critical step in the overall structural design process. Analytical and numerical methods are employed to predict bolt stress, offering the convenience of speed (analytical) or accuracy (numerical). In this paper, we investigate the application of a Gaussian Process (GP) surrogate model, leveraging the accuracy of numerical techniques with significant gains in computation times. We present the notion of predictive uncertainty available from the GP surrogate model. Finally, we demonstrate the capacity of the GP surrogate model to propagate input space uncertainty in a computationally efficient manner. © 2021 by the International Society of Offshore and Polar Engineers (ISOPE).

关键词： Finite element method

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Combinatorial optimization and reasoning with graph neural networks

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2023年第1期24卷 5922-5982页

作者： Quentin Cappart Didier Chételat Elias B. Khalil Andrea Lodi Christopher Morris Petar Veličković Department of Computer Engineering and Software Engineering Polytechnique Montréal Montréal Canada CERC in Data Science for Real-Time Decision-Making Polytechnique Montréal Montréal Canada Department of Mechanical & Industrial Engineering University of Toronto Toronto Canada Jacobs Technion-Cornell Institute Cornell Tech and Technion - IIT New York Department of Computer Science RWTH Aachen University Aachen Germany DeepMind London UK

Combinatorial optimization is a well-established area in operations research and computer science. Until recently, its methods have focused on solving problem instances in isolation, ignoring that they often stem from related data distributions in practice. However, recent years have seen a surge of interest in using machine learning, especially graph neural networks (GNNs), as a key building block for combinatorial tasks, either directly as solvers or by enhancing exact solvers. The inductive bias of GNNs effectively encodes combinatorial and relational input due to their invariance to permutations and awareness of input sparsity. This paper presents a conceptual review of recent key advancements in this emerging field, aiming at optimization and machine learning researchers.

关键词： combinatorial optimization graph neural networks reasoning

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Regularized over-parametrized neural networks learned by gradient descent can generalize well

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IEEE Transactions on Information Theory 2025年

作者： Kohler, Michael Krzyzak, Adam Technische Universität Darmstadt Fachbereich Mathematik Schlossgartenstr. 7 Darmstadt64289 Germany Department of Computer Science and Software Engineering MontrealQCH3G 1M8 Canada

Estimation of univariate regression function by a neural network with one hidden layer is considered, where the weight vector is determined by applying gradient descent to a regularized empirical L2 risk. Here the number of hidden neurons is allowed to be much larger than the sample size. It is shown that the estimate nevertheless generalizes well in case that the Fourier transform of the regression function decays suitably fast, and that in this case over-parametrization leads to a particular good rate of convergence. © 2025 IEEE.

关键词： Neural networks

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How to Combine Models? Principles and Mechanisms to Aggregate Fuzzy Cognitive Maps 23

How to Combine Models? Principles and Mechanisms to Aggregat...

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Proceedings of the Winter Simulation Conference

作者： Ryan Schuerkamp Philippe J. Giabbanelli Umberto Grandi Sylvie Doutre Department of Computer Science & Software Engineering Miami University Oxford USA Institut de Recherche en Informatique de Toulouse (IRIT) Université Toulouse Capitole Toulouse FRANCE

ISBN: (纸本)9798350369663

Fuzzy Cognitive Maps (FCMs) are graph-based simulation models commonly used to model complex systems. They are often built by participants and aggregated to compare the viewpoints of homogenous groups (e.g., anglers and ecologists) and increase the reliability of the FCM. However, the default approach for aggregation may propagate the errors of an individual participant, producing an aggregate FCM whose structure and simulation outcomes do not align with the system of interest. Alternative aggregation methods exist; however, there are no criteria to assess the quality of aggregation methods. We define nine desirable criteria for FCM aggregation algorithms and demonstrate how three existing aggregation procedures from social choice theory can aggregate FCMs and fulfill desirable criteria, enabling the assessment and comparison of FCM aggregation procedures to support modelers in selecting an aggregation algorithm. Moreover, we classify existing aggregation algorithms to provide structure to the growing body of aggregation approaches.

关键词：

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Predicting software Reusability Using Classification Techniques

Predicting Software Reusability Using Classification Techniq...

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International Conference on Information and Communication Systems (ICICS)

作者： Neda’a Alsalman Raed Shatnawi Ammar Jalamneh Omar AlZoubi Computer Science Jordan University of Science and Technology Irbid Jordan Software Engineering Department Jordan University of Science and Technology College of Arts & Science Applied Science University Bahrain

ISBN: (数字)9798331540760

ISBN: (纸本)9798331540777

This study predicts software reusability at the class level using machine learning and a dataset of 65 Java applications. The reuse rates were calculated using cohesion, coupling, complexity, inheritance, documentation, and size metrics. The data was segmented by project size (all, small, medium, and big). Several classifiers were tested once the labels were changed to multi-class labels (low: 0, moderate: 1, high: 2). On large projects, the voting model was 93% accurate. The results demonstrate how well machine learning predicts software reusability and provide recommendations for increasing software quality measures.

关键词： Q-factor Machine learning software quality Predictive models Complexity theory Maintenance software measurement

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Crowdsourced Requirements engineering Challenges and Solutions:A software Industry Perspective

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computer Systems Science & engineering 2021年第11期39卷 221-236页

作者： Huma Hayat Khan Muhammad Noman Malik Youseef Alotaibi Abdulmajeed Alsufyani Saleh Alghamdi Department of Software Engineering National University of Modern LanguagesIslamabadPakistan Department of Computer Science National University of Modern LanguagesIslamabadPakistan Department of Computer Science College of Computer and Information SystemsUmm Al-Qura UniversityMakkahSaudi Arabia Department of Computer Science College of Computers and Information TechnologyTaif UniversityPO Box 11099TaifSaudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaifSaudi Arabia

software crowdsourcing(SW CS)is an evolving software development paradigm,in which crowds of people are asked to solve various problems through an open call(with the encouragement of prizes for the top solutions).Because of its dynamic nature,SW CS has been progressively accepted and adopted in the software ***,issues pertinent to the understanding of requirements among crowds of people and requirements engineers are yet to be clarified and *** the requirements are not clear to the development team,it has a significant effect on the quality of the software *** study aims to identify the potential challenges faced by requirements engineers when conducting the SW–CS based requirements engineering(RE)***,solutions to overcome these challenges are also *** data analysis is performed on the interview data collected from software industry ***,20 SW–CS based RE challenges and their subsequent proposed solutions are devised,which are further grouped under seven *** study is beneficial for academicians,researchers and practitioners by providing detailed SW–CS based RE challenges and subsequent solutions that could eventually guide them to understand and effectively implement RE in SW CS.

关键词： software crowdsourced requirements engineering software industry software development survey challenges

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Context-Guided Spatio-Temporal Video Grounding

Context-Guided Spatio-Temporal Video Grounding

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Xin Gu Heng Fan Yan Huang Tiejian Luo Libo Zhang University of Chinese Academy of Sciences Beijing China Chinese Academy of Sciences Institute of Software Beijing China Department of Computer Science and Engineering University of North Texas Denton USA

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Spatio-temporal video grounding (or STVG) task aims at locating a spatio-temporal tube for a specific instance given a text query. Despite advancements, current methods easily suffer the distractors or heavy object appearance variations in videos due to insufficient object information from the text, leading to degradation. Addressing this, we propose a novel framework, context-guided STVG (CG-STVG), which mines discriminative instance context for object in videos and applies it as a supplementary guidance for target localization. The key of CG-STVG lies in two specially designed modules, including instance context generation (ICG), which focuses on discovering visual context information (in both appearance and motion) of the instance, and instance context refinement (ICR), which aims to improve the instance context from ICG by eliminating irrelevant or even harmful information from the context. During grounding, ICG, together with ICR, are deployed at each decoding stage of a transformer architecture for instance context learning. Particularly, instance context learned from one decoding stage is fed to the next stage, and leveraged as a guidance containing rich and discriminative object feature to enhance the target-awareness in decoding feature, which conversely benefits generating better new instance context to improve localization finally. Compared to existing methods, CG-STVG enjoys object information in text query and guidance from mined instance visual context for more accurate target localization. In experiments on HCSTVG-v1/-v2 and VidSTG, CG-STVG sets new state-of-the-arts in m_tIoU and m_vIoU on all of them, showing efficacy. Code is released at https://***/HengLan/CGSTVG.

关键词： Location awareness Degradation Visualization computer vision Codes Grounding computer architecture

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Video2Reward: Generating Reward Function from Videos for Legged Robot Behavior Learning

arXiv

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arXiv 2024年

作者： Zeng, Runhao Zhou, Dingjie Liang, Qiwei Liu, Junlin Li, Hui Huang, Changxin Li, Jianqiang Hu, Xiping Sun, Fuchun Artificial Intelligence Research Institute Shenzhen MSU-BIT University China College of Mechatronics and Control Engineering Shenzhen University China College of Computer Science and Software Engineering Shenzhen University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Department of Computer Science and Technology Tsinghua University China

Learning behavior in legged robots presents a significant challenge due to its inherent instability and complex constraints. Recent research has proposed the use of a large language model (LLM) to generate reward functions in reinforcement learning, thereby replacing the need for manually designed rewards by experts. However, this approach, which relies on textual descriptions to define learning objectives, fails to achieve controllable and precise behavior learning with clear directionality. In this paper, we introduce a new video2reward method, which directly generates reward functions from videos depicting the behaviors to be mimicked and learned. Specifically, we first process videos containing the target behaviors, converting the motion information of individuals in the videos into keypoint trajectories represented as coordinates through a video2text transforming module. These trajectories are then fed into an LLM to generate the reward function, which in turn is used to train the policy. To enhance the quality of the reward function, we develop a video-assisted iterative reward refinement scheme that visually assesses the learned behaviors and provides textual feedback to the LLM. This feedback guides the LLM to continually refine the reward function, ultimately facilitating more efficient behavior learning. Experimental results on tasks involving bipedal and quadrupedal robot motion control demonstrate that our method surpasses the performance of state-of-the-art LLM-based reward generation methods by over 37.6% in terms of human normalized score. More importantly, by switching video inputs, we find our method can rapidly learn diverse motion behaviors such as walking and running. Copyright © 2024, The Authors. All rights reserved.

关键词： Robots

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