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Industry 4.0: Architecture and Equipment Revolution

computers, Materials & Continua 2021年第2期66卷 1175-1194页

作者： Ahmed Bashar Fakhri Saleem Latteef Mohammed Imran Khan Ali Safaa Sadiq Basem Alkazemi Prashant Pillai Bong Jun Choi Department of Medical Instrumentation Techniques Engineering Electrical Engineering Technical CollegeMiddle Technical UniversityBaghdadIraq Department of Electrical Engineering University of Engineering and Technology PeshawarPeshawarPakistan School of Mathematics and Computer Science University of WolverhemptonWulfruna StreetWolverhemptonUK Center of Artificial Intelligence Research and Optimization Torrens UniversityBrisbaneAustralia Department of Computer Science College of Computer&Information SystemsUmm Al-Qura UniversityMeccaSaudi Arabia School of Computer Science and Engineering Soongsil UniversitySeoulSouth Korea

The development of science and technology has led to the era of Industry *** core concept is the combination of“material and informationization”.In the supply chain and manufacturing process,the“material”of the physical entity world is realized by data,identity,intelligence,and *** 4.0 is a disruptive transformation and upgrade of intelligent industrialization based on the Internet-of-Things and Big Data in traditional *** goal is“maximizing production efficiency,minimizing production costs,and maximizing the individual needs of human beings for products and services.”Achieving this goal will surely bring about a major leap in the history of the industry,which will lead to the“Fourth Industrial Revolution.”This paper presents a detailed discussion of industrial big data,strategic roles,architectures,characteristics,and four types of innovative business models that can generate profits for *** key revolutionary aspect of Industry 4.0 is explained,which is the equipment *** important attributes of equipment are explained under the Industry 4.0 perspective.

关键词： Industry 4.0 big data smart manufacturing business model equipment revolution

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Sea-Land Segmentation of Remote Sensing Images Based on SDW-UNet

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computer systems science & engineering 2023年第5期45卷 1033-1045页

作者： Tianyu Liu Pengyu Liu Xiaowei Jia Shanji Chen Ying Ma Qian Gao The Information Department Beijing University of TechnologyBeijing 100124China School of Physics and Electronic Information Engineering Qinghai Minzu UniversityXining810000China Advanced Information Network Beijing Laboratory Beijing100124China Computational Intelligence and Intelligent Systems Beijing key Laboratory Beijing100124China Department of Computer Science University of Pittsburgh15260USA

Image segmentation of sea-land remote sensing images is of great importance for downstream applications including shoreline extraction,the monitoring of near-shore marine environment,and near-shore target *** mitigate large number of parameters and improve the segmentation accuracy,we propose a new Squeeze-Depth-Wise UNet(SDW-UNet)deep learning model for sea-land remote sensing image *** proposed SDW-UNet model leverages the squeeze-excitation and depth-wise separable convolution to construct new convolution modules,which enhance the model capacity in combining multiple channels and reduces the model *** further explore the effect of position-encoded information in NLP(Natural Language Processing)domain on sea-land segmentation *** have conducted extensive experiments to compare the proposed network with the mainstream segmentation network in terms of accuracy,the number of parameters and the time cost for *** test results on remote sensing data sets of Guam,Okinawa,Taiwan China,San Diego,and Diego Garcia demonstrate the effectiveness of SDW-UNet in recognizing different types of sea-land areas with a smaller number of parameters,reduces prediction time cost and improves performance over other mainstream segmentation *** also show that the position encoding can further improve the accuracy of model segmentation.

关键词： Sea-land segmentation UNet depth-wise separable convolution squeeze-excitation position encoding

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SCALABLE HPC JOB SCHEDULING AND RESOURCE MANAGEMENT IN SST

arXiv

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arXiv 2025年

作者： Abdurahman, Abubeker Hossain, Abrar Brown, Kevin A. Yoshii, Kazutomo Ahmed, Kishwar Department of Electrical Engineering and Computer Science The University of Toledo ToledoOH United States Mathematics and Computer Science Division Argonne National Laboratory LemontIL United States

Efficient job scheduling and resource management contributes towards system throughput and efficiency maximization in high-performance computing (HPC) systems. In this paper, we introduce a scalable job scheduling and resource management component within the structural simulation toolkit (SST), a cycle-accurate and parallel discrete-event simulator. Our proposed simulator includes state-of-the-art job scheduling algorithms and resource management techniques. Additionally, it introduces a workflow management components that supports the simulation of task dependencies and resource allocations, crucial for workflows typical in scientific computing and data-intensive applications. We present validation and scalability results of our job scheduling simulator. Simulation shows that our simulator achieves good accuracy in various metrics (e.g., job wait times, number of nodes usage) and also achieves good parallel performance. © 2025, CC BY.

关键词： Job shop scheduling

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Position: scarce resource allocations that rely on machine learning should be randomized 24

Position: scarce resource allocations that rely on machine l...

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Proceedings of the 41st International Conference on Machine Learning

作者： Shomik Jain Kathleen Creel Ashia Wilson Institute for Data Systems and Society MIT Department of Philosophy & Religion and Khoury College of Computer Sciences Northeastern University Institute for Data Systems and Society and Department of Electrical Engineering and Computer Science MIT

Contrary to traditional deterministic notions of algorithmic fairness, this paper argues that fairly allocating scarce resources using machine learning often requires randomness. We address why, when, and how to randomize by proposing stochastic procedures that more adequately account for all of the claims that individuals have to allocations of social goods or opportunities.

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ChatBCI: A P300 Speller BCI Leveraging Large Language Models for Improved Sentence Composition in Realistic Scenarios

arXiv

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arXiv 2024年

作者： Hong, Jiazhen Wang, Weinan Najafizadeh, Laleh Integrated Systems and NeuroImaging Laboratory Department of Electrical and Computer Engineering Rutgers University PiscatawayNJ08854 United States

P300 speller brain computer interfaces (BCIs) allow users to compose sentences by selecting target keys on a graphical user interface (GUI) through the detection of P300 component in their electroencephalogram (EEG) signals following visual stimuli. Most existing P300 speller BCIs require users to spell all or the first few initial letters of the intended word, letter by letter. Consequently, a large number of keystrokes are required to write a sentence, which can be time consuming, increasing user’s cognitive load and fatigue. Therefore, there is a need for more efficient and user-friendly methods for faster, and practical sentence composition. In this work, we introduce ChatBCI, a P300 speller BCI that leverages the zero-shot learning capabilities of large language models (LLMs) to suggest words from user-spelled initial letters or predict the subsequent word(s), reducing keystrokes and accelerating sentence composition. ChatBCI retrieves word suggestions through remote queries to the GPT-3.5 API. A new GUI, displaying GPT-3.5 word suggestions as extra keys is designed. Stepwise linear discriminant analysis (SWLDA) is used for the P300 classification. Seven subjects completed two online spelling tasks: 1) copy-spelling a self-composed sentence using ChatBCI, and 2) improvising a sentence using ChatBCI’s word suggestions. Results demonstrate that in Task 1, on average, ChatBCI outperforms letter-by-letter BCI spellers, reducing time and keystrokes by 62.14% and 53.22%, respectively, and increasing information transfer rate by 198.96%. In Task 2, ChatBCI achieves 80.68% keystroke savings and a record 8.53 characters/min for typing speed. Overall, ChatBCI by employing remote LLM queries enhances sentence composition in realistic scenarios, significantly outperforming traditional spellers without requiring local model training or storage. ChatBCI’s (multi-) word predictions, combined with its new GUI, pave the way for developing next-generation speller BCIs that are e

关键词： Electroencephalography

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Identification of Medicinal Plants and Related Fungal Diseases Based on Deep Learning Model

Identification of Medicinal Plants and Related Fungal Diseas...

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Mobile Computing and Sustainable Informatics (ICMCSI), International Conference on

作者： Mathina Kani M Mahalakshmi R Shunmugavel V Abdul Kareem M Department of Computer Science and Engineering Sethu Institute of Technology Virudhunagar India Department of Computer Science and Business Systems Sethu Institute of Technology Virudhunagar India Department of Electrical and Electronics Engineering Quantanics TechServ pvt Ltd Madurai India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

In today's technological landscape, manual methods are increasingly being replaced by automated computer systems, streamlining tasks and enhancing efficiency. Within the realm of agricultural research, two critical areas plant identification and disease classification have shifted toward computerized solutions. With millions of plant species globally, medicinal plants hold particular significance due to their healing properties in traditional medicine. These readily accessible and culturally important remedies not only contribute to affordable healthcare but also sustain livelihoods in indigenous and rural communities. To address these challenges, our study evaluates the performance of two widely used pre-trained deep learning models U-Net and F-Net. By comparing their accuracy levels, we aim to enhance disease detection. Additionally, we present performance metrics such as accuracy and error rates, visualized through graphs. Our user interface (UI) leverages Kivy, an open-source multi-platform GUI development library for Python. Kivy enables cross-device app development, emphasizing code reusability and rapid prototyping, making it a valuable tool for interaction design across iOS, Android, Windows, OS X, and GNU/Linux platforms.

关键词： Medicinal plants Deep learning Measurement Visualization Accuracy Medical services Manuals Rapid prototyping Mobile computing Diseases

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Exploring the Use of Machine Learning and the Internet of Things in Intelligent Agriculture: Short Survey

Exploring the Use of Machine Learning and the Internet of Th...

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International Conference on Optimization and Applications (ICOA)

作者： Karima El Majdouby Mohamed El Mohadab Rachid Ait Daoud Karim Abouelmehdi Computer Science department ELITES laboratory Faculty of Sciences University of Chouaib Doukkali Eljadida Morocco Computer Science department Systems Engineering Laboratory Sultan Moulay Slimane University Beni Mellal Morocco

ISBN: (数字)9798350387353

ISBN: (纸本)9798350387360

Agriculture is evolving towards more sustainable practices thanks to the integration of the machine learning and Internet of Things, which addresses many of the issues related to agricultural production and leads to increased yields with reduced input and labor costs. Smart farming has developed solutions to many conventional agricultural problems to automatically manage and monitor farmland. These technologies reduce the risk of field management in the event of bad weather, and also provide benefits to farmers in the event of labor shortages. The Internet of Things ensures the connection of physical objects that are equipped with sensors to the internet, enabling them to collect and exchange data and communicate with each other and with computer systems to gather information, perform analysis and take actions in real time that require rapid data analysis, and this task can be accomplished through machine learning algorithms with greater efficiency and high-quality decision-making. In addition, we carry out an in-depth state-of-the-art survey to identify very recent work that has implemented the Internet of Things and machine learning in smart agriculture, analyzing the development of practical solutions and discussing recent research trends and future prospects.

关键词： Smart agriculture Surveys Machine learning algorithms Crops Machine learning Transforms Real-time systems Internet of Things Faces Water resources

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Big Data Architectures in a patient-driven healthcare system: Implications, Scrutiny and Analysis for future prospects 1

Big Data Architectures in a patient-driven healthcare system...

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1st International Conference on Innovative engineering sciences and Technological Research, ICIESTR 2024

作者： Memon, Kamran Ali Memon, Imran Kumbhar, Farooque Bala, Abu Bakar Iqbal, Naveed Qureshi, Khurram Karim King Fahd University of Petroleum and Minerals Center for Communication Systems and Sensing Dhahran31261 Saudi Arabia Shah Abdul Latif University Department of Computer Science Shahdadkot Campus Pakistan King Fahd University of Petroleum and Minerals Electrical Engineering Department Center for Communication Systems and Sensing Dhahran31261 Saudi Arabia

ISBN: (纸本)9798350348637

The term "big data"describes a significant amount of valuable information. Additionally, a sizable amount of big data related to public health comes from biological research. It is necessary to manage and analyse this data with care to get accurate information. There are several problems with every step of large data management that can only be fixed with sophisticated big data computing solutions. This is the reason health professionals need to be fully equipped with the tools necessary to efficiently collect and analyse big data to deliver appropriate solutions to promote public health. By creating new opportunities for contemporary health, proper big data management, analysis, and interpretation may completely transform the game. For this reason, several industries, including the health industry, are moving quickly to seize this chance to provide improved services and financial gains. Modern healthcare organizations have the potential to transform medical and personal medicine via the effective integration of big data and healthcare services. This article outlines many analytical paths that the patient-driven healthcare system might take, as seen from the viewpoints of different players. We have also examined several big data architectures in terms of their analytical influence, application fields, and underlying data sources and challenges in collecting the big data for healthcare systems. © 2024 IEEE.

关键词： Electronic health record

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***: A Benchmark for Evaluating Questioning Capability in Education for Large Language Models

arXiv

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arXiv 2024年

作者： Chen, Yuyan Wu, Chenwei Yan, Songzhou Liu, Panjun Zhou, Haoyu Xiao, Yanghua Shanghai Key Laboratory of Data Science School of Computer Science Fudan University China Electrical Engineering and Computer Science Department University of Michigan United States School of Computer Science Beijing Institute of Technology China

Teachers are important to imparting knowledge and guiding learners, and the role of large language models (LLMs) as potential educators is emerging as an important area of study. Recognizing LLMs’ capability to generate educational content can lead to advances in automated and personalized learning. While LLMs have been tested for their comprehension and problem-solving skills, their capability in teaching remains largely unexplored. In teaching, questioning is a key skill that guides students to analyze, evaluate, and synthesize core concepts and principles. Therefore, our research introduces a benchmark to evaluate the questioning capability in education as a teacher of LLMs through evaluating their generated educational questions, utilizing Anderson and Krathwohl’s taxonomy across general, monodisciplinary, and interdisciplinary domains. We shift the focus from LLMs as learners to LLMs as educators, assessing their teaching capability through guiding them to generate questions. We apply four metrics, including relevance, coverage, representativeness, and consistency, to evaluate the educational quality of LLMs’ outputs. Our results indicate that GPT-4 demonstrates significant potential in teaching general, humanities, and science courses;Claude2 appears more apt as an interdisciplinary teacher. Furthermore, the automatic scores align with human perspectives. © 2024, CC BY.

关键词： Problem solving

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Onboard Perception-Assisted High Fidelity Simulation Framework for Autonomous Planetary Soft-Landing

Onboard Perception-Assisted High Fidelity Simulation Framewo...

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AIAA science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Alibekov, Ulugbek Banerjee, Avijit Satpute, Sumeet Gajanan Nikolakopoulos, George ERASMUS MUNDUS JOINT MASTER in Intelligent Field Robotic Systems Faculty of Informatics Eötvös Loránd University Budapest Hungary Robotics and AI research group Department of Computer Science Electrical and Space Engineering Luleå University of Technology Luleå Sweden

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

This article presents an onboard perception-assisted high-fidelity simulation framework for autonomous planetary soft-landing, enabling visual information processing tightly integrated with advanced onboard guidance system in a realistic simulation environments. Utilizing the open source Unity game engine, a physics-based simulation toolbox integrated with the Robotic Operating System (ROS2), we effectively emulate the motion of spacecraft approaching planetary terrain. The simulation environment features a realistic 3D terrain model and a lander spacecraft equipped with onboard camera to effectively capture detailed local terrain images. The simulation engine accurately captures the spacecraft’s 6-degrees of freedom motion, seamlessly integrating space craft orientation with the associated camera feed while incorporating various illumination conditions using a light source representing the Sun. To demonstrate the efficacy of the framework, we implement a recently developed attitude-constrained minimum jerk guidance algorithm to emulate spacecraft motion. Concurrently, the visual feed from the onboard RGB camera is processed through a gradient extraction-based perception system, providing the visual odometer. This system identifies provably safe landing sites, effectively avoiding potential risks of landing on uneven terrain. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Planetary landers

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