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Automation 5.0: The Key to Systems Intelligence and Industry 5.0

IEEE/CAA Journal of Automatica Sinica 2024年第8期11卷 1723-1727页

作者： Ljubo Vlacic Hailong Huang Mariagrazia Dotoli Yutong Wang Petros A.Ioannou Lili Fan Xingxia Wang Raffaele Carli Chen Lv Lingxi Li Xiaoxiang Na Qing-Long Han Fei-Yue Wang Institute of Intelligent and Integrated Systems and the School of Engineering and Built Environment Griffith UniversityNathanQLD 4111Australia Department of Aeronautical and Aviation Engineering The Hong Kong Polytechnic UniversityHong KongChina Department of Electrical and Information Engineering Polytechnic of Bari70126 BariItaly State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190and also with the Qingdao Academy of Intelligent IndustriesQingdao 266114China Department of Electrical Engineering-Systems University of Southern CaliforniaLos AngelesCA 90007 USA School of Automation Beijing Institute of TechnologyBeijing 100081China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190 School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049 Beijing Huairou Academy of Parallel Sensing Beijing 101499China School of Mechanical and Aerospace Engineering Nanyang Technological UniversitySingapore 639798Singapore Department of Electrical and Computer Engineering Purdue School of Engineering and TechnologyIndiana University-Purdue University IndianapolisIndianapolisIN 46202 USA Department of Engineering University of CambridgeCB21TN CambridgeU.K. School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourne VIC 3122Australia State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190 School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China Dazhou Artificial Intelligence Institute Dazhouand the Faculty of Innovation EngineeringMacao University of Science and TechnologyMacao 999078China

AUTOMATION has come a long way since the early days of mechanization,i.e.,the process of working exclusively by hand or using animals to work with *** rise of steam engines and water wheels represented the first generation of industry,which is now called Industry Citation:***,***,***,***,***,***,***,***,***,***,***,Q.-***,and F.-***,“Automation 5.0:The key to systems intelligence and Industry 5.0,”IEEE/CAA ***,vol.11,no.8,pp.1723-1727,Aug.2024.

关键词： Automation machinery intelligence

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Brain Sensing with Ultrasound Tomography and Deep Learning Algorithms 23

Brain Sensing with Ultrasound Tomography and Deep Learning A...

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29th Annual International Conference on Mobile Computing and Networking, MobiCom 2023

作者： Klosowski, Grzegorz Rymarczyk, Tomasz Soleimani, Manuchehr Faculty of Management Lublin University of Technology Lublin60011739 Poland Institute of Computer Science and Innovative Technologies Wsei University Lublin60274650 Poland Department of Electronics and Electrical Engineering University of Bath Bath60030480 United Kingdom

ISBN: (纸本)9781450399906

Ultrasound computer tomography (USCT) represents a medical imaging modality designed to visualize alterations in the speed of ultrasonic waves. The primary objective of the study presented was to devise a lightweight, portable, and cost-effective tomographic device capable of non-invasively capturing internal images of the human brain in real-time. To achieve this aim, a prototype ultrasonic tomograph was developed, comprising a lightweight head hoop integrated with ultrasonic transducers and a tomograph unit. Ultrasonic measurements were transformed into images using a heterogeneous convolutional neural network (CNN). The USCT system was engineered to facilitate wireless communication between the sensors embedded within the wearable head cap and the tomographic apparatus. © 2023 ACM.

关键词： Ultrasonic transducers

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Droop-Free Distributed Frequency Control of Hybrid PV-BES Microgrid with SOC Balancing and Active Power Sharing

Droop-Free Distributed Frequency Control of Hybrid PV-BES Mi...

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2022 IEEE Power and Energy Society General Meeting, PESGM 2022

作者： Mohiuddin, Sheik M. Yogarathnam, Amirthagunaraj Yue, Meng Qi, Junjian Stevens Institute of Technology Department of Electrical and Computer Engineering HobokenNJ United States Brookhaven National Laboratory Sustainable Energy Technologies Department UptonNY United States

ISBN: (数字)9781665408233

ISBN: (纸本)9781665408233

In this paper, we propose a droop-free distributed frequency control for the hybrid photovoltaic and battery energy storage (PV-BES) based microgrid. A distributed state of charge (SOC) balancing regulator achieves balanced SOC among the distributed generators (DGs) with BES utilizing a distributed average SOC estimator and the power sharing regulator ensures proportional power sharing among the PV-BES based DGs. These regulators generate two frequency correction terms which are then added to the microgrid rated frequency to generate references for the lower level controllers. The performance of the proposed distributed control is validated through real-time simulations in OPAL-RT, which demonstrates the effectiveness of the proposed control in achieving frequency regulation, SOC balancing, and active power sharing in the hybrid PV-BES units under both islanded and grid-connected operation modes. © 2022 IEEE.

关键词： Charging (batteries)

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Broadcast Channel Simulation

Broadcast Channel Simulation

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2023 IEEE International Symposium on Information Theory, ISIT 2023

作者： Cao, Michael X. Ramakrishnan, Navneeth Berta, Mario Tomamichel, Marco National University of Singapore Department of Electrical and Computer Engineering Singapore National University of Singapore Centre for Quantum Technologies Singapore Imperial College London Department of Computing London United Kingdom Institute for Quantum Information Rwth Aachen University Aachen Germany

ISBN: (纸本)9781665475549

We study the problem of random-assisted simulation of discrete broadcast channel in one-shot and i.i.d. setups. We derive one-shot inner and outer bounds of the set of attainable message-size pairs for simulating WYZ|X within some total variation distance (TVD) tolerance of ?. The inner bounds are based on the bipartite convex split lemma. Whereas the outer bounds are based on the properties of the multi-partite max information. Using these bounds, we establish a single-letter expression of the simulation region of a broadcast channel. © 2023 IEEE.

关键词： Codes (symbols)

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Evaluation of Optical-DAC based Transmitter for 1.6-Tbps Data-Centre Interconnection

Evaluation of Optical-DAC based Transmitter for 1.6-Tbps Dat...

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IEEE Photonics Conference (IPC)

作者： Stylianos Sygletos Konstantinos Moschopoulos Moshe Nazarathy Ioannis Tomkos Aston Institute of Photonic Technologies Aston University Birmingham UK Department of Electrical Engineering University of Patras Patras Greece Faculty of Electrical and Computer Engineering Technion Haifa Israel

We present a numerical evaluation study of a 2Serial-2Parallel optical digital-to-analog converter over a datacenter interconnection link. Our results reveal superior performance compared to standard transmitters and ... 详细信息

ISBN: (数字)9798350361957

ISBN: (纸本)9798350361964

关键词： Optical interconnections Digital-analog conversion Optical transmitters Optical modulation Standards Photonics

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Evaluation of the Improvement in Hierarchical Lossless Video Compression 9

Evaluation of the Improvement in Hierarchical Lossless Video...

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9th International Conference on Control, Decision and Information technologies, CoDIT 2023

作者： Ammous, Donia Kessentini, Amina Khlif, Naziha Kammoun, Fahmi Masmoudi, Nouri National Engineering School of Sfax Sfax University Laboratory of Electronics and Information Technologies Electrical Engineering Department Road Soukra Km 3.5 BP 3038 Sfax Tunisia Higher Institute of Computer and Multimedia of Gabes Gabes University Electronics and Telecommunications Department BP 122 Gabes Tunisia

ISBN: (纸本)9798350311402

The digital medical video is widely used in several healthcare institutions and medical centers. Therefore, a considerable storage space and a high transmission bandwidth are necessary. Thus, efficient compression tools are required to reduce the storage space and fit bandwidth capacities. Indeed, several lossy and lossless compression techniques are applied. Lossy techniques ensure a good performance. But they lead to erroneous medical data, which can negatively affect the doctor's decisions about the patient's health conditions. As a result, lossless compression guarantees an optimal solution for medical applications. Among available lossless techniques, H.264/AVC (Advanced Video Coding) presents high coding performance in lossless compression. H.264/AVC was chosen since it simplicity in terms of computational complexity. It is suitable for mobile devices that require high resolution. In this paper, several techniques used with the H.264/AVC are discussed. Moreover, a new approach is applied to enhance the lossless techniques. Its results show an important compression ratio compared to the previous works. The enhanced method results in an increase in compression ratio from 8,429 to 16,159. Besides, the gain performance reaches from 32,625 to 42.074 %. © 2023 IEEE.

关键词： Medical applications

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Smart Composting Solutions for Organic Waste Management and Soil Enrichment in Agriculture with IoT and Gradient Boosting 4

Smart Composting Solutions for Organic Waste Management and ...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Riazulhameed, Arshadh Ariff Mohamed Abuthahir Ranganathan, Chitra Sabapathy Pandey, Pramod Sakthisaravanan, B. Sasikala, K. Murugan, S. Blackstraw Technologies Pvt Ltd Tamil Nadu Chennai India Mphasis Corporation Tamil Nadu Chennai India Nagpur Campus Maharashtra Pune India Sri Venkateshwara College of Engineering Department of Computer Science and Engineering Karnataka Bengaluru India Department of Electrical and Electronics Engineering Tamil Nadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331540364

Modern agricultural operations have significant challenges due to organic waste management since conventional approaches often prove ineffective and unsustainable. To overcome these challenges, this research suggests a new method for smart composting that combines IoT with gradient-boosting algorithms. This system accurately manages and optimizes the composting process by employing Internet of Things (IoT) sensors to monitor important factors, including temperature, moisture, pH, and oxygen content. Real-time data analysis is then performed using Gradient Boosting algorithms. By combining the IoT with gradient boosting may preventatively measures to maximize decomposition and microbial activity, which speeds up the composting process and reduces resource waste. The produced compost has higher nutritional content and more diverse microbes, making it useful as a soil supplement in farming. In addition to improving organic waste management, this novel method helps with soil enrichment and sustainable agriculture, promoting efficiency and resource conservation. Accurate sensor readings, data integration, real-time tracking, and quickly achieving optimal composting conditions are all challenges. © 2024 IEEE.

关键词： Composting efficiency Data-driven optimization Microbial diversity Nutrient cycling Precision agriculture Sensor networks

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Bird Swarm Algorithm with Fuzzy Min-Max Neural Network for Financial Crisis Prediction

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computers, Materials & Continua 2022年第10期73卷 1541-1555页

作者： K.Pradeep Mohan Kumar S.Dhanasekaran I.S.Hephzi Punithavathi P.Duraipandy Ashit Kumar Dutta Irina V.Pustokhina Denis A.Pustokhin Department of Computing Technologies Associate ProfessorSRM Institute of Science and TechnologyKattankulathur603203India Department of Information Technology Kalasalingam Academy of Research and Education626126India Department of Computer Science and Engineering Sphoorthy Engineering CollegeHyderabadTelangana501510India Department of Electrical and Electronics Engineering J B Institute of Engineering and TechnologyHyderabadTelangana500075India Department of Computer Science and Information System College of Applied SciencesAlMaarefa UniversityRiyadh11597Kingdom of Saudi Arabia Department of Entrepreneurship and Logistics Plekhanov Russian University of Economics117997MoscowRussia Department of Logistics State University of Management109542MoscowRussia

Financial crisis prediction(FCP)models are used for predicting or forecasting the financial status of a company or financial *** is considered a challenging issue in the financial *** and machine learning(ML)models can be employed for the design of accurate FCP *** numerous works have existed in the literature,it is needed to design effective FCP models adaptable to different *** study designs a new bird swarm algorithm(BSA)with fuzzy min-max neural network(FMM-NN)model,named BSA-FMMNN for *** major intention of the BSA-FMMNN model is to determine the financial status of a firm or *** presented BSA-FMMNN model primarily undergoes minmax normalization to transform the data into uniformity ***,k-medoid clustering approach is employed for the outlier removal ***,the classification process is carried out using the FMMNN model,and the parameters involved in it are tuned by the use of *** utilization of proficient parameter selection process using BSA demonstrate the novelty of the *** experimental result analysis of the BSA-FMMNN model is validated using benchmark dataset and the comparative outcomes highlighted the supremacy of the BSA-FMMNN model over the recent approaches.

关键词： Financial crisis predictive model machine learning outlier removal clustering metaheuristics

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Reducing Barriers to Self-Supervised Learning: HuBERT Pre-training with Academic Compute

arXiv

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arXiv 2023年

作者： Chen, William Chang, Xuankai Peng, Yifan Ni, Zhaoheng Maiti, Soumi Watanabe, Shinji Language Technologies Institute Carnegie Mellon University United States Electrical and Computer Engineering Carnegie Mellon University United States Meta United States

Self-supervised learning (SSL) has led to great strides in speech processing. However, the resources needed to train these models has become prohibitively large as they continue to scale. Currently, only a few groups with substantial resources are capable of creating SSL models, which harms reproducibility. In this work, we optimize HuBERT SSL to fit in academic constraints. We reproduce HuBERT independently from the original implementation, with no performance loss. Our code and training optimizations make SSL feasible with only 8 GPUs, instead of the 32 used in the original work. We also explore a semi-supervised route, using an ASR model to skip the first pre-training iteration. Within one iteration of pre-training, our models improve over HuBERT on several tasks. Furthermore, our HuBERT Large variant requires only 8 GPUs, achieving similar performance to the original trained on 128. As our contribution to the community, all models, configurations, and code are made open-source in ESPnet. Copyright © 2023, The Authors. All rights reserved.

关键词： Supervised learning

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Transparent but Powerful: Explainability, Accuracy, and Generalizability in ADHD Detection from Social Media Data

arXiv

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arXiv 2024年

作者： Wiechmann, Daniel Kempa, Edward Kerz, Elma Qiao, Yu Institute for Logic Language & Computation University of Amsterdam Netherlands Department of Computer and Information Science and Engineering University of Florida United States Exaia Technologies GmbH Germany

MSC Codes 68T50Attention-deficit/hyperactivity disorder (ADHD) is a prevalent mental health condition affecting both children and adults, yet it remains severely underdiagnosed. Recent advances in artificial intelligence, particularly in Natural language Processing (NLP) and Machine Learning (ML), offer promising solutions for scalable and non-invasive ADHD screening methods using social media data. This paper presents a comprehensive study on ADHD detection, leveraging both shallow machine learning models and deep learning approaches, including BiLSTM and transformer-based models, to analyze linguistic patterns in ADHD-related social media text. Our results highlight the trade-offs between interpretability and performance across different models, with BiLSTM offering a balance of transparency and accuracy. Additionally, we assess the generalizability of these models using cross-platform data from Reddit and Twitter, uncovering key linguistic features associated with ADHD that could contribute to more effective digital screening tools. © 2024, CC BY-NC-ND.

关键词： Adversarial machine learning

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