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arXiv 2023年

作者： Muehlebach, Michael Jordan, Michael I. Learning and Dynamical Systems Max Planck Institute for Intelligent Systems Max-Planck-Ring 4 Baden-Wuerttemberg Tübingen72076 Germany Department of Electrical Engineering and Computer Science University of California Berkeley 387 Soda Hall BerkeleyCA94720 United States

We exploit analogies between first-order algorithms for constrained optimization and non-smooth dynamical systems to design a new class of accelerated first-order algorithms for constrained optimization. Unlike Frank-Wolfe or projected gradients, these algorithms avoid optimization over the entire feasible set at each iteration. We prove convergence to stationary points even in a nonconvex setting and we derive accelerated rates for the convex setting both in continuous time, as well as in discrete time. An important property of these algorithms is that constraints are expressed in terms of velocities instead of positions, which naturally leads to sparse, local and convex approximations of the feasible set (even if the feasible set is nonconvex). Thus, the complexity tends to grow mildly in the number of decision variables and in the number of constraints, which makes the algorithms suitable for machine learning applications. We apply our algorithms to a compressed sensing and a sparse regression problem, showing that we can treat nonconvex p constraints (p © 2023, CC BY.

关键词： Constrained optimization

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Automatic Concrete Surface Crack Recognition Using EfficientNetV2 Variants

Automatic Concrete Surface Crack Recognition Using Efficient...

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IEEE International Conference on System, Computation, Automation and Networking (ICSCAN)

作者： A. Rama Robertas Damaševičius S. Arunmozhi Mazin Abed Mohammed Ragheed Hussam V. Rajinikanth Department of CSE Saveetha School of Engineering SIMATS Chennai TN India Centre of Real Time Computer Systems Kaunas University of Technology Kaunas Lithuania Department of Electronics and Communication Engineering Manakula Vinayagar Institute of Technology Puducherry TN India Department of Artificial Intelligence College of Computer Science and Information Technology University of Anbar Anbar Iraq Medical instruments engineering techniques Al-farahidi University Baghdad Iraq Department of Computer Science and Engineering Division of Research and Innovation Saveetha School of Engineering SIMATS Chennai TN India

Building health and structural integrity are important to ensure occupant safety and long-term durability. Monitoring and assessing building condition regularly ensures that potential problems are identified early. This research aims to develop and implement a deep-learning (DL) supported tool to examine the concrete surface images collected using digital camera. The data gathering forms the first stage and the chosen DL algorithm is used for feature extraction followed by the feature selection and then fusion is done and finally a fivefold cross verification schema is applied for a binary classification. This work considered the EfficientNetV2 (ENV2) model with variants like small, medium and large for the examination. The investigation is implemented using individual and fused deep-features and the performance of the developed tool is confirmed using the achieved accuracy. The outcome of this research confirms that the proposed tool produces a betterment of >96% accuracy with individual-features and 100% accuracy with fused-features. These results confirm that the proposed scheme provides significant result during the crack detection.

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Strategic Planning and Knowledge Management Approaches as input to remote area strategic plans: A Case study in Aru Islands Regency Maluku Province 8

Strategic Planning and Knowledge Management Approaches as in...

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8th International Conference on ICT for Smart Society, ICISS 2021

作者： Irena Gutandjala, Ilona Harso Supangkat, Suhono Lumban Gaol, Ford Rianti, Benny Bina Nusantara University BINUS Graduate Program-Computer Science Computer Science Department Jakarta11480 Indonesia Bandung Institute of Technology Smart City and Community Innovation Center School of Electrical and Informatics Engineering Bandung Indonesia Information Systems Department BINUS Online Learning Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9781665416979

Knowledge is an important asset in an organization. Aru Islands District is one of the districts in Maluku Province. The Government of Aru Islands District Maluku has a vision and mission as outlined in the Regional Strategic Plan or commonly referred to as RENSTRA. This vision and mission are an activity that requires a policy direction that is planned and integrated with the strategic environment of all related aspects. The strategic plan is then realized with the implementation of the government with regional autonomy which is the modern governance in realizing quality public services, fast, easy, fair and unlimited for the welfare and progress of the region. In this research will be used a strategic approach as input from the analysis with knowledge management and assessment of the condition of Aru district. © 2021 IEEE.

关键词： Strategic planning

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Digital Twins for Smart Cities: Benefits, Enabling Technologies, Applications, and Challenges

Digital Twins for Smart Cities: Benefits, Enabling Technolog...

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IEEE Future Networks World Forum (FNWF)

作者： Ibrar Yaqoob Khaled Salah Latif U. Khan Raja Jayaraman Ala Al-Fuqaha Mohammed Omar Department of Electrical Engineering and Computer Science Khalifa University UAE Artificial Intelligence and Cyber Futures Institute Charles Sturt University Bathurst NSW Australia Department of Machine Learning Mohamed Bin Zayed University of Artificial Intelligence UAE Department of Industrial & Systems Engineering Khalifa University UAE College of Science and Engineering Hamad Bin Khalifa University (HBKU) Doha Qatar

Digital twins have immense potential to enhance the planning, management, and optimization of smart cities across diverse domains. A digital twin represents a real-world physical component through an integrated multiphysics, multiscale, and probabilistic simulation. By leveraging digital twins, smart cities can visualize designs in 3D, conduct virtual tests and simulations, and facilitate configuration, monitoring, diagnostics, and prognostics. Consequently, digital twins can contribute significantly to the development, sustainability, and maintenance of smart cities. In this paper, we explore the potential of digital twins in shaping smart cities to the next level. We highlight some key benefits associated with the adoption of digital twins in smart city contexts. We discuss the networking and communication technologies that provide connectivity support for digital twins. Furthermore, we provide insightful discussions on the role of digital twins in various domains of smart cities. Finally, we present some open challenges along with recommendations.

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Integration of SAP and Intelligent Robotic Process Automation

Integration of SAP and Intelligent Robotic Process Automatio...

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Next Generation Electronics (NEleX), International Conference on

作者： Dileep Kumar Yendluri Reshmi Tatikonda Ramya Thatikonda Jainath Ponnala M. Kempanna A. Bhuvanesh Department of Computer Science University of the Cumberlands Williamsburg USA Department of Computer Information Systems University of the Central Missouri Warrensburg USA Department of Information Technology University of the Cumberlands Williamsburg USA Department of ECE Jawaharlal Technological University Hyderbad India Department of Artificial Intelligence and Machine Learning Bangalore Institute of Technology Bengaluru India Department of Electrical and Electronics Engineering PSN College of Engineering and Technology Tirunelveli India

This article explores how implementing IRPA in enterprises could have a revolutionary effect. Businesses looking to maximize efficiency can find a partnership between IRPA and SAP systems to be a wise way to achieve their goals. This study investigates how several company operations including payroll processing, human administration, financial and accounting processes, and logistics are impacted by integration. This talk explores the benefits, challenges, and best practices for integrating IRPA with SAP, offering specific instances from real-world projects that highlight the effectiveness of this relationship. The future scope of the IRPA-SAP integration looks at enhanced analytics, hyper-automation, cutting-edge cognitive features, broader industry applications, and the growth of human-robot cooperation. A thorough analysis highlights tactics that businesses may use to use SAP's and IRPA's combined power to accelerate digital transformation and achieve peak performance.

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Modelling and Experimental Characteristics of Photovoltaic Modules in Typical Days at an Actual Photovoltaic Power Station 4

Modelling and Experimental Characteristics of Photovoltaic M...

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4th IEEE International Conference on Automation, Electronics and electrical engineering, AUTEEE 2021

作者： Lodhi, Ehtisham Yang, Pu Wang, Lina Lodhi, Zeeshan Khan, M. Adil Muhammad, Sijjad Tamir, Tariku Sinshaw Beihang University School of Automation Science and Electrical Engineering Beijing100191 China COMSATS University Islamabad Department of Electrical and Computer Engineering Abbottabad Campus 22060 Pakistan Chang'An University Department of Computer and Technology Xi'an710062 China Real Engineering Services Deans Trade Center Peshawar25000 Pakistan Institute of Automation Chinese Academy of Sciences The SKL For Management and Control of Complex Systems Beijing100190 China

ISBN: (纸本)9781665428392

Solar energy among the renewable energy sources (RES) plays an important role in fulfilling any country's energy demands. When the sunlight irradiates the photovoltaic (PV) modules, part of the light energy is directly converted into electric energy. Another part is converted into heat energy, which increases the working temperature and affects the output performance characteristics of the PV module. This paper proposes a dynamic mathematical model of PV modules working on solar irradiance and ambient temperature by calculating the PV module model parameters under different environmental conditions. The dynamic model of the PV module is suggested to simulate PV modules' output characteristics (voltage and current) under any environmental conditions. By employing MATLAB/Simulink, a PV output characteristics program is compiled. The whole day output characteristics of PV strings of a real PV power station are tested and compared with simulation results. The results depict that the established model can effectively reflect PV modules' output characteristics in actual operation and can be used for dynamic simulation of PV power generation. © 2021 IEEE.

关键词： Solar energy

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Opportunities and limitations of explaining quantum machine learning

arXiv

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arXiv 2024年

作者： Gil-Fuster, Elies Naujoks, Jonas R. Montavon, Grégoire Wiegand, Thomas Samek, Wojciech Eisert, Jens Dahlem Center for Complex Quantum Systems Freie Universität Berlin Berlin14195 Germany Fraunhofer Heinrich Hertz Institute Berlin10587 Germany Department of Mathematics and Computer Science Freie Universität Berlin Berlin14195 Germany BIFOLD - Berlin Institute for the Foundations of Learning and Data Berlin14109 Germany Department of Electrical Engineering and Computer Science Technische Universität Berlin Berlin14109 Germany Helmholtz-Zentrum Berlin für Materialien und Energie Berlin14109 Germany

A common trait of many machine learning models is that it is often difficult to understand and explain what caused the model to produce the given output. While the explainability of neural networks has been an active field of research in the last years, comparably little is known for quantum machine learning models. Despite a few recent works analyzing some specific aspects of explainability, as of now there is no clear big picture perspective as to what can be expected from quantum learning models in terms of explainability. In this work, we address this issue by identifying promising research avenues in this direction and lining out the expected future results. We additionally propose two explanation methods designed specifically for quantum machine learning models, as first of their kind to the best of our knowledge. Next to our pre-view of the field, we compare both existing and novel methods to explain the predictions of quantum learning models. By studying explainability in quantum machine learning, we can contribute to the sustainable development of the field, preventing trust issues in the future. © 2024, CC BY.

关键词： Adversarial machine learning

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InfoFlowNet: A Multi-head Attention-based Self-supervised learning Model with Surrogate Approach for Uncovering Brain Effective Connectivity

arXiv

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arXiv 2023年

作者： Chuang, Chun-Hsiang Fang, Shao-Xun Huang, Chih-Sheng Ding, Weiping Research Center for Education and Mind Sciences College of Education National Tsing Hua University Hsinchu Taiwan Institute of Information Systems and Applications College of Electrical Engineering and Computer Science National Tsing Hua University Hsinchu Taiwan Department of Education and Learning Technology National Tsing Hua University Hsinchu Taiwan Department of Computer Science and Engineering National Taiwan Ocean University Keelung Taiwan Department of Artificial Intelligence Research and Development Elan Microelectronics Corporation Hsinchu Taiwan College of Artificial Intelligence and Green Energy National Yang Ming Chiao Tung University Hsinchu Taiwan College of Electrical Engineering and Computer Science National Taipei University of Technology Hsinchu Taiwan School of Information Science and Technology Nantong University Nantong China Institute of Information Systems and Applications College of Electrical Engineering and Computer Science NTHU Treasure Taipei Chapter of IEEE Computational Intelligence Society Taiwan

Deciphering brain network topology can enhance the depth of neuroscientific knowledge and facilitate the development of neural engineering methods. Effective connectivity, a measure of brain network dynamics, is particularly useful for investigating the directional influences among different brain regions. In this study, we introduce a novel brain causal inference model named InfoFlowNet, which leverages the self-attention mechanism to capture associations among electroencephalogram (EEG) time series. The proposed method estimates the magnitude of directional information flow (dIF) among EEG processes by measuring the loss of model inference resulting from the shuffling of the time order of the original time series. To evaluate the feasibility of InfoFlowNet, we conducted experiments using a synthetic time series and two EEG datasets. The results demonstrate that InfoFlowNet can extract time-varying causal relationships among processes, reflected in the fluctuation of dIF values. Compared with the Granger causality model and temporal causal discovery framework, InfoFlowNet can identify more significant causal edges underlying EEG processes while maintaining an acceptable computation time. Our work demonstrates the potential of InfoFlowNet for analyzing effective connectivity in EEG data. The findings highlight the importance of effective connectivity in understanding the complex dynamics of the brain network. Copyright © 2023, The Authors. All rights reserved.

关键词： Electroencephalography

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Bayesian Optimization Framework for Efficient Fleet Design in Autonomous Multi-Robot Exploration

arXiv

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arXiv 2024年

作者： Concha, David Molina Li, Jiping Yin, Haoran Park, Kyeonghyeon Lee, Hyun-Rok Lee, Taesik Sirohi, Dhruv Lee, Chi-Guhn The Dynamic Optimization and Reinforcement Learning Laboratory The Department of Mechanical & Industrial Engineering University of Toronto TorontoONM5S3G8 Canada The Department of Electrical & Computer Engineering University of Toronto TorontoONM5S3G8 Canada The Division of Engineering Science The Faculty of Applied Science and Engineering University of Toronto TorontoONM5S3G8 Canada The Complex System Design Laboratory The Department of Industrial and Systems Engineering Korea Advanced Institute of Science and Technology Daejeon34141 Korea Republic of The Department of Industrial Engineering Inha University Incheon22212 Korea Republic of

This study addresses the challenge of fleet design optimization in the context of heterogeneous multi-robot fleets, aiming to obtain feasible designs that balance performance and costs. In the domain of autonomous multi-robot exploration, reinforcement learning agents play a central role, offering adaptability to complex terrains and facilitating collaboration among robots. However, modifying the fleet composition results in changes in the learned behavior, and training multi-robot systems using multi-agent reinforcement learning is expensive. Therefore, an exhaustive evaluation of each potential fleet design is infeasible. To tackle these hurdles, we introduce Bayesian Optimization for Fleet Design (BOFD), a framework leveraging multiobjective Bayesian Optimization to explore fleets on the Pareto front of performance and cost while accounting for uncertainty in the design space. Moreover, we establish a sub-linear bound for cumulative regret, supporting BOFD’s robustness and efficacy. Extensive benchmark experiments in synthetic and simulated environments demonstrate the superiority of our framework over state-of-the-art methods, achieving efficient fleet designs with minimal fleet evaluations. Copyright © 2024, The Authors. All rights reserved.

关键词： Multipurpose robots

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Role of RPA & AI in Optimizing Network Field Services

Role of RPA & AI in Optimizing Network Field Services

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International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS)

作者： Dileep Kumar Yendluri Jainath Ponnala Reshmi Tatikonda M. Kempanna Ramya Thatikonda A. Bhuvanesh Masters in Computer Science University of the Cumberlands USA Bachelors in ECE Jawaharlal Technological University Hyderbad India Masters in Computer Information Systems University of the Central Missouri Warrensburg USA Department of Artificial Intelligence and Machine Learning Bangalore Institute of Technology Bengaluru Karnataka India PhD in Information Technology University of the Cumberlands Williamsburg USA Department of Electrical and Electronics Engineering PSN College of Engineering and Technology Tirunelveli Tamil Nadu India

The paper investigates incorporating and implementing RPA and AI technologies within NFS to improve efficiency and boost service quality. Robotic Process Automation enables the streamlining of repetitive processes. It simplifies work processes and releases personnel for more critical projects. On the contrary, AI strengthens NFS providers via data-informed decision-making. Additionally, it facilitates anticipatory maintenance and preemptive network administration. The document explores particular RPA and cognitive computing instances, including AI-powered network issue resolution and AI-driven preventive maintenance for the network hardware. Furthermore, it emphasizes the advantages and constraints of adopting robotic process automation and artificial intelligence within the NFS framework. It considers aspects including data handling, growth potential, and moral implications. Prospects for the future related to RPA and AI within the NFS domain are also analyzed. It foresees self-governing network management, sophisticated predictive analytics, and merging with IoT and edge computing technologies. The article highlights the revolutionary capability of robotic process automation and artificial intelligence in transforming the field service sector, aiming for increased efficiency, reliability, and a customer-centric approach. It emphasizes embracing these technologies to keep up in the dynamic business sector.

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