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1st IEEE International Conference on Automation, Computing and Renewable systems, ICACRS 2022

作者： Abbas, A. Mohamed Hameed, M. Syed Shahul Balakrishnan, S. Anandh, K.S. University of Technology and Applied Sciences - SUR PO BOX: 484 PC 411 Oman Department of Computer Science and Business Systems Sri Krishna College of Engineering and Technology Tamilnadu Coimbatore India Department of Civil Engineering College of Engineering and Technology SRM Institute of Science and Technology SRM Nagar Kattankulathur India

ISBN: (纸本)9781665460842

In the era of digitalization, the assortment and exploration of great volumes of documents is becoming progressively significant for enterprises to increase their productions and practices. Optical Character Recognition (OCR) is a procedure of identifying text in scanned (image-based) documents. This paper aims to deliver seamless searching of documents in file systems using Optical Character Recognition (OCR) and Natural Language Processing (NLP). Our paper includes the following phases: "Text Identification (in terms of text files), Image Capturing, Image Enhancement, Image Identification, OCR, Data Extraction and Quality Assurance". In case of text files, the data extraction is done in the first phase itself. The document management system "processes both structured document images (ones which have a standard format) and unstructured document images" (ones which do not have a standard format). In the tagging phase, the document is divided into segments and the tags for each segment are generated using Natural Language Processing. © 2022 IEEE

关键词： Optical character recognition

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Adaptive Stiffness Control of Series Elastic Actuator Manipulators Based on Dynamic System

Adaptive Stiffness Control of Series Elastic Actuator Manipu...

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IEEE International Conference on Automation science and engineering (CASE)

作者： Kunlin Guo Chao Zeng Weiyong Si Ning Wang Chenguang Yang Key Laboratory of Autonomous Systems and Networked Control School of Automation Science and Engineering South China University of Technology Guangzhou China Department of Computer Science University of Liverpool Liverpool UK School of Computer Science and Electronic Engineering University of Essex Colchester Bristol Robotics Laboratory University of the West of England Bristol UK

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

In traditional control methods, Series Elastic Actuator (SEA) joint manipulators are limited by their hardware and can only perform simple tasks with low stiffness. To address this issue, we propose a stiffness adjustment control strategy for SEA manipulators based on Dynamic systems (DS) with good generalization performance. By enhancing the generalization capability of DS in complex tasks and combining posture control, the SEA manipulators can autonomously adjust control gains and postures according to task requirements to achieve higher stiffness at the end-effector. Experimental results involving the activation of air switches with different stiffness levels and installation angles have demonstrated the effectiveness of our proposed method. The results indicate that compared to traditional methods of adjusting the control gain and retraining DS for similar tasks, our approach exhibits superior generalization performance while maintaining end-effector stiffness during interactions.

关键词： Actuators computer aided software engineering Automation Adaptive systems Control systems End effectors Hardware Dynamical systems Manipulator dynamics

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Seamless Decision-Making in the Big Data Era: A Modular Approach to Integrating IoT, Cloud Computing, and Data Lakes 4

Seamless Decision-Making in the Big Data Era: A Modular Appr...

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4th International Conference on Electrical, Communication and computer engineering, ICECCE 2023

作者： Zemmouri, Mohamed Laalam, Fatma Zohra Kazar, Okba Himeur, Yassine Oulefki, Adel Toumi, Chahrazad Mansoor, Wathiq Attala, Shadi Artificial intelligence and Information Technologies Laboratory Faculty of New Information and Communication Technologies Kasdi Merbah Ouargla University Ouargla30000 Algeria College of Engineering and Information Technology University of Dubai Dubai United Arab Emirates Department of Computer Science University of Sharjah Sharjah United Arab Emirates Mohamed Khider University Department of Computer Science LINFI Laboratory Biskra07000 Algeria United Arab Emirate University Department of Information Systems and Security College of Information Technology Abu Dhabi United Arab Emirates

ISBN: (纸本)9798350369694

The integration of big data with cutting-edge technologies like IoT and Cloud Computing has profoundly influenced various aspects of modern life, including everyday service processes. To facilitate data-driven decision-making, big data analytics—focused on identifying patterns, trends, and correlations in large data sets—is indispensable. While traditional statistical techniques are useful, new tools and infrastructures such as Hadoop, Spark, and NoSQL are essential to tackle big data challenges. However, modifying the existing environment can be impractical, especially in production settings, due to the need for significant investment and specialized expertise. This article presents a novel computational paradigm that adds a decision-making layer atop existing systems for data analysis, eliminating the need to alter the environment. The approach treats the current information system as a data lake and introduces a new data recovery layer through web services, drawing inspiration from big data technologies like MapReduce. This system offers the advantage of being modular, reusable, and universally compatible, making it an independent decisional framework that can work with any information system or data source. © 2023 IEEE.

关键词： Big data

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Development of a Model for Discriminating Electroencephalograms of Patients with Mild Cognitive Impairment and Healthy Individuals Using a Support Vector Machine 13

Development of a Model for Discriminating Electroencephalogr...

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Joint 13th International Conference on Soft Computing and Intelligent systems and 25th International Symposium on Advanced Intelligent systems, SCIS and ISIS 2024

作者： Tanabe, Akifumi Nakatani, Kyosuke Harachi, Kento Yamamoto, Yusuke Muramatsu, Ayumi Nagahara, Hajime Takemura, Noriko Shimojo, Shinji Furihata, Daisuke Mizuno-Matsumoto, Yuko Kamae-Shigenobu, Kazue Shinosaki, Kazuhiro Graduate School of Information Science University of Hyogo Kobe Japan School of Life Science and Technology Tokyo Institute of Technology Yokohama Japan Aino University Department of Medical Engineering Ibaraki Japan Institute for Datability Science Osaka University Suita Japan Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Iizuka Japan Aomori University Faculty of Software and Information Technology Aomori Japan Osaka University Cyber Media Center Suita Japan Asakayama General Hospital Sakai Japan

ISBN: (纸本)9798350373332

We performed machine learning using spectrum analysis and power spectra of electroencephalograms (EEGs) of patients with mild cognitive impairment (MCI). Spectrum analysis was performed on the EEGs of MCI patients and healthy individuals, and the power spectrum values of the two groups were calculated and compared. Based on the power spectrum values of the theta and alpha bands, models for discriminating MCI patients were created using a support vector machine (SVM), and their accuracies were compared. In the spectrum analysis, the theta band showed significantly higher power spectrum values in the MCI group. In the comparison of the accuracies of SVM models, the model using the theta band showed higher values in parameters, such as accuracy rate. These results suggested that the power spectrum values of the theta band are useful for discriminating MCI patients. © 2024 IEEE.

关键词： Electroencephalography

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On-Demand Model and Client Deployment in Federated Learning with Deep Reinforcement Learning

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IEEE Internet of Things Journal 2025年

作者： Chahoud, Mario Sami, Hani Mourad, Azzam Otrok, Hadi Bentahar, Jamal Guizani, Mohsen Concordia University Concordia Institute for Information Systems Engineering Montreal Canada Mohammad Bin Zayed University of Artificial Intelligence Abu Dhabi United Arab Emirates Lebanese American University Artificial Intelligence and Cyber Systems Research Center Department of CSM Beirut Lebanon Khalifa University KU6G Research Center Department of Computer Science United Arab Emirates Department of Computer Science Abu Dhabi United Arab Emirates

In Federated Learning (FL), the limited accessibility of data from diverse locations and user types poses a significant challenge due to restricted user participation. Expanding client access and diversifying data enhance models by incorporating diverse perspectives, thereby improving adaptability. However, in dynamic and mobile environments, the availability of FL clients fluctuates as devices may become inaccessible, leading to inefficient client selection and reduced model performance. Current solutions often fail to adapt quickly to these changes, creating a gap in achieving real-time client availability and efficient data utilization. To address this, we propose a Deep Reinforcement Learning (DRL) On-Demand solution, deploying new clients using Docker Containers on-the-fly. Our On-Demand solution, employing DRL, targets client availability and selection while considering data shifts and container deployment complexities. It employs an autonomous end-to-end approach for handling model deployment and client selection. The DRL strategy leverages a Markov Decision Process (MDP) framework, with a Master Learner and a Joiner Learner to optimize decision-making. The designed cost functions account for the complexity of dynamic client deployment and selection, ensuring effective resource management and service reliability. Simulated tests show that our architecture can easily adapt to changes in the environment and respond to On-Demand requests while reducing the number of learning rounds used by 20-50 % compared with existing approaches. This highlights its ability to improve client availability, capability, accuracy, and learning efficiency, surpassing heuristic and traditional reinforcement learning methods. © 2014 IEEE.

关键词： Federated learning

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Two-Player Incomplete Games of Resilient Multiagent systems

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IFAC-PapersOnLine 2023年第2期56卷 258-263页

作者： Yurid E. Nugraha Tomohisa Hayakawa Hideaki Ishii Ahmet Cetinkaya Quanyan Zhu Department of Electrical Engineering Institut Teknologi Sepuluh Nopember Surabaya 60111 Indonesia Department of Systems and Control Engineering Tokyo Institute of Technology Tokyo 152-8552 Japan Department of Computer Science Tokyo Insitute of Technology Yokohama 226-8502 Japan Department of Functional Control Systems Shibaura Institute of Technology Tokyo 135-8548 Japan Department of Electrical and Computer Engineering New York University Brooklyn NY 11201 USA

Evolution of agents’ dynamics of multiagent systems under consensus protocol in the face of jamming attacks is discussed, where centralized parties are able to influence the control signals of the agents. In this paper we focus on a game-theoretical approach of multiagent systems where the players have incomplete information on their opponents’ strength. We consider repeated games with both simultaneous and sequential player actions where players update their beliefs of each other over time. The effect of the players’ optimal strategies according to Bayesian Nash Equilibrium and Perfect Bayesian Equilibrium on agents’ consensus is examined. It is shown that an attacker with incomplete knowledge may fail to prevent consensus despite having sufficient resources to do so.

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ALPINE: An Interpretable Approach for Decoding Phenotypes from Multi-condition Sequencing Data 29th

ALPINE: An Interpretable Approach for Decoding Phenotypes ...

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29th International Conference on Research in Computational Molecular Biology, RECOMB 2025

作者： Lee, Wei-Hao Li, Lechuan Dannenfelser, Ruth Yao, Vicky Systems Synthetic and Physical Biology Graduate Program Rice University Houston United States Department of Computer Science Rice University Houston United States Ken Kennedy Institute Rice University Houston United States Rice Synthetic Biology Institute Rice University Houston United States

ISBN: (纸本)9783031902512

Advancements in sequencing technology have expanded data availability, capturing diverse phenotypic traits and biological perturbations. However, increased resolution also raises complexity, as studies now examine multiple dimensions, including donor phenotypes, anatomical regions, cell types, and time points. Integrating datasets across studies promises insights into health and disease beyond the scope of individual experiments, but this requires methods that can separate technical artifacts from meaningful biological signals while providing interpretable insights into condition-related genetic factors. Existing approaches tend to focus on either integration or interpretability, rarely addressing both simultaneously. To overcome these challenges, we introduce ALPINE, a joint supervised-unsupervised non-negative matrix factorization framework that disentangles technical and biological variation while directly identifying condition-associated genes. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Matrix factorization

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Modeling Atomistically Assembled Diffractive Optics in Solids

arXiv

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arXiv 2024年

作者： Kling, Trevor Na, Dong-Yeop Hosseini, Mahdi Department of Electrical and Computer Engineering Applied Physics Program Northwestern University EvanstonIL60208 United States Department of Electrical Engineering Pohang University of Science and Technology Pohang37673 Korea Republic of Elmore Family School of Electrical and Computer Engineering Purdue University West LafayetteIN47907 United States

We develop a model describing long-range atom-atom interactions in a two-dimensional periodic or a-periodic lattice of optical centers inside a solid-state host material. We consider realistic environmental and technical conditions such as frequency and position broadening. Even when considering a significant frequency broadening in the ensemble (∼300 GHz), we observe up to a three-fold increase in directional scattering from the resonant lattice in a system. The model can be used to scalably design quantum optical elements, e.g. a quantum lens, harnessing atomistic engineering (e.g. via ion implantation) of collective interactions in materials to enhance quantum properties. © 2024, CC BY.

关键词： Nanocrystals

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GOOSE Algorithm: A New Bio-Inspired Metaheuristic Algorithm for Solving Optimization Problems Based on Goose Behavior

SSRN

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SSRN 2023年

作者： Hamad, Rebwar Khalid Rashid, Tarik A. Department of Information Systems Engineering Erbil Technical Engineering College Erbil Polytechnic University Erbil Iraq Computer Science and Engineering Department University of Kurdistan Hewler Erbil Iraq

This study proposes the GOOSE algorithm as a novel metaheuristic algorithm based on the goose's behavior during rest and foraging. The goose stands on one leg and keeps his balance to guard and protect other individuals in the flock. The GOOSE algorithm is benchmarked on 19 well-known benchmark test functions, and the results are verified by a comparative study with genetic algorithm (GA), particle swarm optimization (PSO), dragonfly algorithm (DA), and fitness dependent optimizer (FDO). In addition, the proposed algorithm is tested on 10 modern benchmark functions, and the gained results are compared with three recent algorithms, such as the dragonfly algorithm, whale optimization algorithm (WOA), and salp swarm algorithm (SSA). Moreover, the GOOSE algorithm is tested on 5 classical benchmark functions, and the obtained results are evaluated with six algorithms, such as fitness dependent optimizer (FDO), FOX optimizer, butterfly optimization algorithm (BOA), whale optimization algorithm, dragonfly algorithm, and chimp optimization algorithm (ChOA). The achieved findings attest to the proposed algorithm's superior performance compared to the other algorithms that were utilized in the current study. The technique is then used to optimize Welded beam design and Economic Load Dispatch Problem, three renowned real-world engineering challenges, and the Pathological IgG Fraction in the Nervous System. The outcomes of the engineering case studies illustrate how well the suggested approach can optimize issues that arise in the real-world. © 2023, The Authors. All rights reserved.

关键词： Particle swarm optimization (PSO)

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Myopically Verifiable Probabilistic Certificates for Safe Control and Learning

arXiv

引用

arXiv 2024年

作者： Wang, Zhuoyuan Jing, Haoming Kurniawan, Christian Chern, Albert Nakahira, Yorie The Department of Electrical and Computer Engineering Carnegie Mellon Universty PA15213 United States The Information Systems Technology and Design Pillar Singapore University of Technology and Design Singapore487372 Singapore The Department of Computer Science and Engineering University of California San DiegoCA92093 United States

This paper addresses the design of safety certificates for stochastic systems, with a focus on ensuring long-term safety through fast real-time control. In stochastic environments, set invariance-based methods that restrict the probability of risk events in infinitesimal time intervals may exhibit significant long-term risks due to cumulative uncertainties/risks. On the other hand, reachability-based approaches that account for the long-term future may require prohibitive computation in real-time decision making. To overcome this challenge involving stringent long-term safety vs. computation tradeoffs, we first introduce a novel technique termed 'probabilistic invariance'. This technique characterizes the invariance conditions of the probability of interest. When the target probability is defined using long-term trajectories, this technique can be used to design myopic conditions/controllers with assured long-term safe probability. Then, we integrate this technique into safe control and learning. The proposed control methods efficiently assure long-term safety using neural networks or model predictive controllers with short outlook horizons. The proposed learning methods can be used to guarantee long-term safety during and after training. Finally, we demonstrate the performance of the proposed techniques in numerical simulations. Copyright © 2024, The Authors. All rights reserved.

关键词： Stochastic systems

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