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International Conference on computer Applications and Industrial Electronics (ICCAIE)

作者： Wandy Wandy Kusworo Adi Media Anugerah Ayu Doctoral Program of Information Systems School of Postgraduate Studies Diponegoro University Semarang Indonesia Department of Physics Faculty of Science and Mathematics Diponegoro University Semarang Indonesia Department of Computer Science Faculty of Engineering and Technology Sampoerna University South Jakarta Indonesia

Sports science is an interdisciplinary and multidisciplinary science that strives to increase athletic performance and endurance. Sport science recognizes and prevents injuries. Sensors and statistics formalize Sports science. Runners need coaches and teams to support them before, during, and after the run race. Coaches generate running training plans to boost performance. Running race performances may be impacted by air pollution exposure while training, so coaches should consider limiting air pollution exposure when training. One of the external factors is Particulate Matter (PM 2.5 and PM 10 ). Sensors connecting to the Internet can record external factors and produce csv data. The foundation of supervised machine learning is the labeling process. Labeling a set of data is one of the laborious and time-consuming phases in every machine-learning application because it requires verifying the accuracy of the labels and making any necessary revisions. This research aimed to find a solution to automatically label numerous air particulate matter raw data using a rule based on parameters to reduce manual work, human errors and faster processes. This labeled data will later be used for supervised machine learning classification to support the coach in generating training programs for the runners in a Sports information System. Based on Indonesia Air Quality index rule-based approach, labeled text data in csv has been generated and tested with PM 2.5 and PM 10 parameters in three scenarios with a 100% success rate. It was possible to automate the labeling process, and it explained how automation results in fast and accurate results.

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International Conference of computer and Informatics engineering (IC2IE)

ISBN: (数字)9798331505530

ISBN: (纸本)9798331505547

Full-marathon and Half-marathon distances are categorized as road running. Full-marathon running is becoming increasingly popular, and Half-marathon is increasing worldwide in both sexes and all age groups. Some aspects might relate to Full-marathon and Half-marathon running performance during training and races. Technology also plays an essential role in supporting runners and running races. Technology like artificial intelligence (AI) now supports the running athlete, not only predicting performance and results. It can also be used later to help the coach generate training programs for the athlete. This research aimed to find many aspects of marathons and performance and analyze them to see if artificial intelligence could later support them. It used secondary data and a systematic literature review proposed by Kitchenham. Out of the 58 articles, 21 of them (36.21%) received a score of 1 from Q1. Additionally, 19 articles (32.76%) received a score of 1 from both Q2 and Q3. Among the 58 articles, 9 (15.52%) received a total score of 3, with all three Q1, Q2, and Q3 scores being 1. This indicates that artificial intelligence will likely support the content of these nine articles. Several factors were also discovered to be connected to marathons and athletic performance. These findings suggested that additional investigation into marathons and performance, later backed by artificial intelligence, remained pertinent and essential.

关键词： Training Roads Artificial intelligence Informatics

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The Smart Analysis of Performing Scalable Inference for Big Data Analytics

The Smart Analysis of Performing Scalable Inference for Big ...

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International Conference on Power Energy, Environment and Intelligent Control (PEEIC)

作者： Vishakha D Bhandarkar Aruna Sri Rongali Mohamed Dawood Shamout Prof. Sachin Pund Gigih Forda Nama Shrishailappa Patil Applied Mathematics and Humanities Yeshwantrao Chavan College of Engineering India Graduate Doctoral Information Technology/Graduate (Information Technology Emphasis/Information Technology Ph.D.) University of The Cumberlands US Department of Management College of Business Administration University of Sharjah Sharjah United Arab Emirates Department of Industrial Engineering Shri Ramdeobaba College of Engineering and Management Nagpur Maharashtra India Department of Informatics Doctoral Program of Environtmental Science University of Lampung Bandar Lampung Computer engineering Vishwakarma Institute of Technology Pune

This paper examines the reproducibility of massive information analytics under particular factors. The paper proposes the “performing Scalable Inference” technique to cope with scalability troubles and to exploit current big statistics platforms for efficient computing and statistics garage of the statistics. In particular, the paper describes how to perform leak-free, parallelizable visible analytics over massive datasets using present extensive records analytics frameworks such as Apache Flink. This method presents an automated manner to execute analytics that preserves reproducibility and the ability to make adjustments without re-running the entire technique. The paper also demonstrates how these analytics may help several real-world use instances, explore affected person cohorts for studies, and develop stratified patient cohorts for hospital therapy. In the end, the paper observes how the proposed method may be exercised within the real world. Actively scalable inference for massive information analytics is pivotal in optimizing decision-making and allocation of assets. Typically, such inferences are made based on information accumulated from numerous sources, databases, unstructured data, and different digital sources. So one can ensure scalability, a complete cloud-primarily based platform has to be hired. This solution will permit the ***, deploying the essential records series and evaluation algorithms are prime here. It could permit the platform to recognize the styles inside the statistics and discover any ability correlations or traits. Additionally, predictive analytics and system mastering strategies may be incorporated to provide insights into the results of the information. In the long run, by leveraging those techniques, the platform can draw efficient inferences and appropriately compare situations in an agile and green way..

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Nanoscale 3D printing of glass photonic crystals with near-unity reflectance in the visible spectrum

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science Advances 2025年第21期11卷 eadv0267页

作者： Wang Zhang Hongtao Wang Hao Wang Son Tung Ha Lei Chen Xue Liang Li Cheng-Feng Pan Bochang Wu Md. Abdur Rahman Yujie Ke Qifeng Ruan Xiaolong Yang Thomas Christensen Joel K. W. Yang Engineering Product Development Singapore University of Technology and Design Singapore 487372 Singapore. Singapore-HUJ Alliance for Research and Enterprise (SHARE) The Smart Grippers for Soft Robotics (SGSR) Programme Campus for Research Excellence and Technological Enterprise (CREATE) Singapore 138602 Singapore. School of Instrumentation and Optoelectronic Engineering Beihang University Beijing 100191 China. Hangzhou International Innovation Institute Beihang University Hangzhou 311115 China. Institute of Materials Research and Engineering (IMRE) Agency for Science Technology and Research (A*STAR) 2 Fusionopolis Way Innovis #08-03 Singapore 138634 Singapore. College of Mechanical and Vehicle Engineering Hunan University Changsha 410082 China. Department of Electrical and Computer Engineering National University of Singapore (NUS) 4 Engineering Drive 3 Singapore 117576 Singapore. School of Interdisciplinary Studies Lingnan University Tuen Mun Hong Kong SAR China. Ministry of Industry and Information Technology Key Lab of Micro-Nano Optoelectronic Information System Harbin Institute of Technology (Shenzhen) Shenzhen 518055 China. College of Mechanical and Electrical Engineering Nanjing University of Aeronautics and Astronautics Nanjing 210016 China. Department of Electrical and Photonics Engineering Technical University of Denmark Kongens Lyngby 2800 Denmark. POLIMA—Center for Polariton-driven Light–Matter Interactions University of Southern Denmark Campusvej 55 DK-5230 Odense M Denmark.

Glass is widely used as an optical material due to its high transparency, thermal stability, and mechanical properties. The ability to fabricate and sculpt glass at the nanoscale would naturally expand its application domain in nanophotonics. Here, we report an approach to print glass in three dimensions with nanoscale resolutions. We developed Glass-Nano, an organic-inorganic hybrid resin containing silicon elements. Using this high-resolution resin, three-dimensional (3D) photonic crystals (PhCs) were printed with two-photon lithography. After printing, the structures were heated to high temperatures in air to remove organic components and convert the remaining material into silica glass. 3D glass PhCs with periodicities as small as 260 nanometers were obtained after sintering at 650°C. The 3D glass PhCs exhibit ~100% reflectance in the visible range, surpassing the typical reflectances observed from similar structures in low–refractive index materials. The quality of PhCs achieved is observed in both electron microscopy and the excellent agreement with band structure calculations of idealized structures.

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Q-Learning Energy Management System (Q-EMS) in Wireless Sensor Network

Q-Learning Energy Management System (Q-EMS) in Wireless Sens...

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Smart Computing, IoT and Machine Learning (SIML), International Conference on

作者： Deden Ardiansyah Mustafid Teddy Mantoro Doctoral Program of Information Systems School of Postgraduate Universitas Diponegoro Semarang Central Java Indonesia Computer Engineering Department Vocational School Universitas Pakuan Bogor West Java Indonesia Media-Tech-Lab Computer Science Department Sampoerna University DKI Jakarta Indonesia

ISBN: (数字)9798350364101

ISBN: (纸本)9798350364118

The energy control of a Wireless Sensor Network (WSN) often leads to an unbalanced state between the battery storage system, energy extraction through photovoltaic systems energy, and energy utilization in the WSN. These disparities result in suboptimal wireless sensor network performance. The reliance on batteries is a major factor contributing to this problem. The Q-Learning Energy Management System (Q-EMS) is designed to address these challenges and improve energy management strategies. The Q-EMS algorithm used a learning process resulting in optimal actions for sensor nodes in different situations. The rewards or punishments nodes receive determine their decisions, which are determined by the Q-EMS algorithm. The Q-learning model approach is aimed at reducing energy consumption and supply in WSNs. Energy supply is categorized into battery-based, transfer-based, and harvesting, while energy consumption can be classified into task-cycle, mobility-based, and data-driven. The development of the Q Learning algorithm model has three scenarios: determining energy needs for WSN, effective energy harvesting strategy, and effective energy transfer. As a result, effective energy demand, harvesting and transfer using the Q-learning algorithm are balanced. However, it needs to be studied in more depth using real data in the field. In future research, I will use real data and optimize the use of the Q-Learning algorithm.

关键词： Wireless communication Wireless sensor networks Energy consumption Q-learning Machine learning algorithms Energy resources Classification algorithms

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The research and design on hardware in the loop of bus-based automotive body control with rule description

The research and design on hardware in the loop of bus-based...

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Chinese Control Conference (CCC)

作者： Jiang-Hong Han Lei Liu Xing Wei Wei Xia School of Computer & Information Hefei University of Technology 230009. China Computer Science & Technology Post-doctoral Research Center Hefei University of Technology Hefei China Engineering Research Center of Safety Critical Industrial Measurement & Control Technology Ministry of Education Hefei China

Oriented the design procedures of bus-based automotive body control system, rules are used to describe the control intention of prototype model first. This paper represents the approach of modeling bus-based automotive body control system with rules description, and designed hardware-in-the-loop system based on distributed control, which merges virtual reality into physical simulation on vehicle. The HILS can verify the reliability and safety in the system modeling phase. We made HIL simulation using typical case, and the results shows that HIL approach could quantify the system time delay which affects the reliability, and improve the robustness of body control system.

关键词： Automotive engineering Control systems Solid modeling Delay Delay effects Reliability Safety

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An Integrated Model for Evaluation of Big Data Interactive Systems Under Uncertainty

Research Square

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Research Square 2021年

作者： Asemi, Adeleh Asemi, Asefeh Department of Software Engineering Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur50603 Malaysia Doctoral School of Economic Business & Informatics Corvinus University of Budapest Fovam ter 8. Budapest1093 Hungary

The study aimed to propose a judgment-based evaluation model for usability evaluating of big data interactive system s. Human judgment is associated with uncertainty and gray information. We used the fuzzy technique for integration, summarization, and distance calculation of quality value judgment. The proposed model is an integrated fuzzy Multi Factors Evaluation (MFE) model based on experts’ judgments in HCI, ISPD, and AMLMs. We provided a Fuzzy Inference System (FIS) for scoring usability evaluation metrics in different big data interactive system s. A multi-model big data interactive system is implemented for experimental testing of the model. The achieved results from the proposed model and experimental tests are compared using statistical correlation tests. The results show the ability of the proposed model for usability evaluation of big data interactive systems without the need for conducting empirical tests. It is concluded that applying a dataset in a neuro-FIS and training system cause to produce more than a hundred effective rules. The findings indicate that the proposed model can be applied for big data interactive system evaluation, informative evaluation, and complex empirical tests. Future studies may improve the FIS with the integration of artificial neural networks. © 2021, CC BY.

关键词： Fuzzy systems

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Social media image classification and retrieval method based on deep hash algorithm

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International Journal of Web Based Communities 2022年第3-4期18卷 276-287页

作者： Li, Zilong Zhou, Yong Wang, Hongdong School of Information Engineering Xu’zhou University of Technology Xuzhou221018 China School of Computer Science and Technology China University of Mining and Technology Xuzhou221116 China Post Doctoral Research Center Onnes Power Technology Co. Ltd. Xuzhou221003 China

In order to solve the problems of large classification error and low retrieval accuracy of traditional retrieval methods, a social media image classification retrieval method based on deep hash algorithm is proposed. With the help of CBOW model to extract semantic features and colour histogram to extract colour features of social media images, social media image classification is completed. Hash algorithm is used to deal with the unidirectional irreversibility of social media image, and the pixel feature points in the image are regarded as density function for one-to-one correspondence. The loss function is used to control the convergence of the hash algorithm to achieve social media image retrieval. Experimental results show that the error of social media image classification is only 2%, the retrieval accuracy is always higher than 90%, and the retrieval time is only 3.4 s, which has the advantage of high retrieval efficiency. © 2022 Inderscience Enterprises Ltd.

关键词： Clustering algorithms

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Developing Critical Thinking Practices Interwoven with Generative AI Usage in an Introductory Programming Course

Developing Critical Thinking Practices Interwoven with Gener...

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IEEE Education engineering (EDUCON)

作者： Arne Styve Outi T. Virkki Usman Naeem Department of ICT and Natural Sciences Norwegian University of Science and Technology (NTNU) Ålesund Norway Information Technology Degree Programme Haaga-Helia University of Applied Sciences Helsinki Finland School of Electronic Engineering and Computer Science Queen Mary University of London London UK

ISBN: (数字)9798350394023

ISBN: (纸本)9798350394030

Software development has evolved significantly. In the past, developers were required to have comprehensive understanding of programming languages, algorithms, and computer architecture. However, with the emergence of the Internet, software libraries, frameworks, and forums became widely available, which utilize reusable software components that can reduce development time and costs. The advent of Generative Artificial Intelligence (AI) tools, such as ChatGPT, GitHub Copilot, and Amazon CodeWhisperer, has further enhanced the developer's toolkit, as these tools can be used for a wide variety of tasks such as code generation, documentation, commenting and reviewing. As programming is often slow and requires trial and error, novice programmers can be tempted to apply the first solution found on the Internet or proposed by an AI tool without much critical reflection or notion of responsibility. Hence, the advances of AI have raised both excitement and concerns among information Technology (IT)/computer science (CS) students and educators. Yet, AI tools are here to stay, and students must learn to use them responsibly. The aim of this paper is to investigate how to design learning activities that introduce Generative AI tools (GitHub Copilot and ChatGPT) for programming while promoting critical thinking practices among students in an introductory programming course in the first semester. Students' opinions and customs were surveyed before and after the AI-based programming assignment. The results indicate that students' awareness of the possibilities and limitations of AI, as well as practices of critical thinking in programming increased. This is encouraging as critical thinking is an integral part of best programming practices.

关键词： Software libraries Generative AI Software algorithms Chatbots Software Reflection Internet

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GA-based kernel optimization for pattern recognition: theory for EHW application

GA-based kernel optimization for pattern recognition: theory...

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Congress on Evolutionary Computation

作者： M. Yasunaga T. Nakamura I. Yoshihara J.H. Kim Institute Information Science and Electrical University of Tsukuba Tsukuba Ibaraki Japan Doctoral Program of Engineering University of Tsukuba Tsukuba Ibaraki Japan Miyazaki University Miyazaki Japan The Center for Advanced Computer Studies University of Louisiana Lafayette LA USA

An extension of the kernel-based pattern recognition method using a genetic algorithm is proposed. The method is suited to evolvable pattern recognition hardware using FPGAs. In the conventional method one common kernel function is used in the superposition to make discrimination functions. In the extended method each region of the kernel function is optimized individually. For the kernel-region optimization we use a genetic algorithm to solve a large combinatorial problem almost impossible to solve using any brute-force search. A chromosome represents the kernel region in an n-dimensional pattern space, and each locus corresponds to one of the candidates (genes) for an edge length of the kernel region. We have applied the extended method to a sonar spectrum recognition problem and obtained a recognition accuracy of 83.9%, which is much higher than the 62.0% obtained using the conventional kernel-based method and is also better than 82.7% obtained using the nearest neighbor method and the 83.0% obtained using a neural network (backpropagation algorithm). We have analyzed the individually optimized kernel regions and shown that the GA process automatically extracts features in the patterns and embeds the features in the kernel regions.

关键词： Kernel Pattern recognition Genetic algorithms Hardware Field programmable gate arrays Optimization methods Biological cells Sonar applications Nearest neighbor searches Neural networks

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