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6th International Conference on Vocational Education and electrical engineering, ICVEE 2023

作者： Widayaka, Parama Diptya Puspitaningayu, Pradini Alamsyah, Sayyidul Aulia Muktiadji, Rifqi Firmansyah Widodo, Arif Abdillah, Rifqi Universitas Negeri Surabaya Dept. of Electrical Engineering Surabaya Indonesia King Abdulaziz University Dept. of Electrical and Computer Engineering Jeddah Saudi Arabia Okayama University Graduate School of Interdisciplinary Science and Engineering in Health Systems Okayamas Japan Institut Teknologi Sepuluh Nopember Surabaya Dept. of Informatics Surabaya Indonesia

ISBN: (纸本)9798350326642

Technological developments have impacts and challenges in human life. Technological advances have made human life easier. The impact of technological developments is the industrial manufacturing process getting faster with the help of robotics technology. Robots can shorten manufacturing process time and efficient. On the other hand, challenges with technological developments force us to keep learning and adaptive to every changes around us, especially in the education sector. In education sector, formal educational institutions or campuses must be able to facilitate students to be able to learn comprehensively in the field of technology, especially robotics, where we can make an innovative learning platform based on robotics technology. In this study, we discussed a 2-DOF arm manipulator robot as kinematics learning platform of industrial robot to increase student competencies in preparation for facing the industrial environment in century. Inverse kinematics equation has been applied in the robot by providing input coordinates on the x-Axis and y-Axis where the accuracy value on the x-Axis is 77% and the accuracy on the y-Axis is 83.1%. © 2023 IEEE.

关键词： Machine design

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Predicting Metabolic Reactions with a Molecular Transformer for Drug Design Optimization 21

Predicting Metabolic Reactions with a Molecular Transformer ...

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21st IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2024

作者： Multari, Silvia Özçelik, Riza Mazzolari, Angelica Nobile, Marco Salvatore Grisoni, Francesca Ca' Foscari University of Venice Dept. of Molecular Sciences and Nanosystems Venice Italy Eindhoven University of Technology Dept. of Biomedical Engineering Eindhoven Netherlands University of Milan Dept. of Pharmaceutical Sciences Milan Italy Ca' Foscari University of Venice Dept. of Environmental Sciences Informatics and Statistics Venice Italy

ISBN: (纸本)9798350356632

Metabolism prediction is a crucial step of drug development, as the biotransformations a drug candidate undergoes inside the human body can affect the clinical outcome. computer-aided drug design has been extensively employed to speed up the process and enhance its efficiency and effectiveness, but among the investigated areas, metabolism has received less attention. This project aimed at leveraging machine learning to analyze large metabolic datasets, make predictions, and recognize patterns, in order to fill this knowledge gap and enhance our understanding of metabolism and its impact on drug development. To achieve this goal, we developed a Deep Learning model for metabolism prediction using natural language processing techniques trained on molecular string representations, i.e., Simplified Molecular Input Line Entry Systems (SMILES) strings. To this end, we employ a Molecular Transformer, because of its ability to capture sequential and contextual information within strings (in this case, SMILES) enabling the learning of complex relationships. The transformer was trained using a high-quality dataset, MetaQSAR, from which we derived approximately 100000 instances of metabolic reactions. In this work, we investigate whether the Transformer architecture bears the potential to learn a mapping between the input molecular structures and their corresponding metabolites, in order to expedite drug discovery and improve patient safety. © 2024 IEEE.

关键词： Deep learning

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An Eye Tracking Based Evaluation Protocol and Method for In-Vehicle Infotainment Systems 28

An Eye Tracking Based Evaluation Protocol and Method for In-...

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28th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2023

作者： Crescenti, Riccardo Dondi, Piercarlo Porta, Marco Resta, Cristiano Rotondo, Gianfranco University of Pavia Dept. of Electrical Computer and Biomedical Engineering Via A. Ferrata 5 Pavia27100 Italy 27018 Italy

ISBN: (纸本)9798350339918

In the automotive field, the development of new vehicles is nowadays a challenge for manufacturers, which try to find a trade-off between "beauty of design"and driver safety. Modern cars often have touch displays and hardly any physical buttons. Touch interaction, however, may distract the driver from the road, which is a major safety issue. For the evaluation of the ergonomics of in-vehicle infotainment interfaces, this paper proposes a test protocol and an evaluation method based on eye tracking technology. To the best of our knowledge, there are currently no standard protocols or methodologies for objective assessment of infotainment systems in vehicles. Unlike what often happens, we were able to test our proposals not simply through simulators but implementing actual experiments with real cars, which we believe is an important aspect that characterizes our work. © 2023 IEEE.

关键词： Ergonomics

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Single-lead ECG Compression for Connected Healthcare Applications 5

Single-lead ECG Compression for Connected Healthcare Applica...

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5th IEEE International Symposium in Robotics and Manufacturing Automation, ROMA 2022

作者： Abdou, Abdelrahman Krishnan, Sridhar Toronto Metropolitan University Dept. of Electrical Computer and Biomedical Engineering Toronto Canada

ISBN: (数字)9781665459327

ISBN: (纸本)9781665459327

Preventive healthcare is achievable through physiological long-term remote monitoring. In connected healthcare, wearables that can collect physiological signals such as electrocardiograms (ECG) and electroencephalogram (EEG) can help improve health outcomes in society. For single-lead ECG devices, there are still limitations for this role that includes short time continuous operability and uncomfortable sensors worn by the user making it non-appealing for uninterrupted remote monitoring. However, with the current advances in microelectronics, embedded systems, sensors, and Internet of Medical Things (IoMT), long-term monitoring is realizable. A decrease to the overall power consumption of the wearable leads to an increase in device longevity while dry ECG electrodes can be used to increase user comfort. This work proposes a lossless LempelZiv Welch (LZW) compression algorithm used to compress and optimize the raw ECG data obtained from a 3D printed dry electrode based single-lead ECG device. This approach utilizes the ECG's inherent waveform characteristics. The single-lead ECG's R-peak and RR-intervals are used as one-bit information that are further compressed for shorter wireless transmission, leading to an increase in battery life and device operation. The algorithm showed a high compression ratio (CR) for 10 seconds, 30 seconds, 1-minute and 5-minute ECG signals where CR was 0.99, 0.91, 0.91, 0.92, respectively. For the 5-minute ECG signal, the size of data decreased from 225 Kbytes to 18.75 Kbytes while retaining R-peak and RR interval information for heart rate (HR) and heart rate variability (HRV) calculations. This work adds to the current progress in single-lead ECG in long-term continuous remote monitoring for connected healthcare applications. © 2022 IEEE.

关键词： Electrocardiograms

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Using the Scatter of Opinions to Predict Responses to Tweets 12

Using the Scatter of Opinions to Predict Responses to Tweets

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12th International Congress on Advanced Applied informatics, IIAI-AAI 2022

作者： Suzuki, Tokinori Deguchi, Shintaro Tomiura, Yoichi Kyushu University Dept. of Informatics Fukuoka Japan Kyushu University Dept. of Electrical Engineering and Computer Science Fukuoka Japan

ISBN: (数字)9781665497558

ISBN: (纸本)9781665497558

The importance of social media, such as Twitter and Instagram, has increased as a result of COVID-19: with social media, users can easily communicate with other people. Accordingly, social media have produced both new problems and benefits;these have occasionally occurred with upsurges in reactions owing to the huge numbers of social media followers. "Flaming"is a negative aspect of an upsurge: it is a rapid increase in blame, harassment, and insults directed at a person. Conversely, it is possible to use an upsurge to identify popular topics or trends, which is a positive aspect. Predicting the reactions to a social media post is currently very difficult;however, the aim is to prevent the above problems and provide support by forecasting upsurges at an early stage. We observed that such upsurges were affected by the scatter of opinions in the responses. If there are two groups of conflicting opinions, the original social media post tended to receive both sets of opinions. Toward predicting the reactions to a social media post, this study investigate the relationship between the upsurge and scatter of opinions. We quantify the scatter of opinions in the responses, which we term the scatter of replies. We enter that scatter into multiple regression analysis by means of clustering, and we evaluated our reaction prediction method. We found that for most settings, the correlation coefficient between the predicted and actual values exceeded 0.6, which is a moderate level of correlation. © 2022 IEEE.

关键词： COVID-19

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Analyzing Multimodal Sensory Signals Using Unsupervised Machine Learning 10

Analyzing Multimodal Sensory Signals Using Unsupervised Mach...

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10th International Conference on computer, Control, informatics and its Applications, IC3INA 2023

作者： Angdresey, Apriandy Ooi, Jing-Quan Universitas Katolik de la Salle Dept. of Informatics Engineering Manado Indonesia Universiti Tunku Abdul Rahman Dept. Electrical and Electronics Engineering Selangor Malaysia

ISBN: (纸本)9798350394870

The Internet of Things (IoT) relies on sensor technology to enable seamless communication and data gathering across diverse devices and systems. This study explores the functionality of flexible and multimodal sensors based on GNP/HEC nanocomposites. These sensors hold promise for a wide range of applications spanning from medical contexts to industrial settings. A central aspect of comprehending IoT data organization is clustering analysis. In our study, we contribute by integrating GNP/HEC sensors into an Arduino system and applying the k-means clustering algorithm to identify patterns and anomalies in environmental monitoring. Our evaluation demonstrates that optimized clustering with k = 6, resulting in a Silhouette Score of 0.6557769, has the potential to significantly enhance industrial safety applications, particularly in the monitoring of temperature and ammonia levels. © 2023 IEEE.

关键词： K-means clustering

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A mathematical formulation for constructing feasible solutions for the Post Enrollment Course Timetabling Problem 7

A mathematical formulation for constructing feasible solutio...

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7th South-East Europe Design Automation, computer engineering, computer Networks and Social Media Conference, SEEDA-CECNSM 2022

作者： Dimitsas, Angelos Nastos, Vasileios Valouxis, Christos Gogos, Christos University of Ioannina Dept. of Informatics and Telecommunications Arta Greece University of Patras Dept. of Electrical and Computer Engineering Rio Greece

ISBN: (数字)9798350398588

ISBN: (纸本)9798350398588

The scheduling community has long been interested in educational timetabling. Particularly in academia, since timetabling dictates the day to day operation of Universities, great effort has been exercised to produce high quality schedules. Typically, timetabling problems are NP-Hard and several approaches have been tried in order to generate schedules that satisfy all stakeholders. A number of timetabling competitions have been organized through the last two decades focusing on problems stemming from educational operations. In this paper we use data from two such competitions, ITC2002 and ITC2007 about the post enrollment course timetabling problem. We propose a mathematical model that captures the problem in its entirety and we use it in order to construct feasible solutions initially, and then explore the prospect of optimization. We employ a pre-process stage that attempts to reduce the size of the model and then use an open source solver, that produces solutions in reasonable time for most of the cases. We also propose a simple decomposition of the problem in a day by day basis that can improve the initial feasible solutions. © 2022 IEEE.

关键词： Scheduling

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RadioModRec-l: A Large Scale Radio Frequency Dataset for Automatic Modulation Recognition Research 4

RadioModRec-l: A Large Scale Radio Frequency Dataset for Aut...

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4th International Conference on Emerging Trends in Networks and computer Communications, ETNCC 2024

作者： Adetiba, Emmanuel Olasina, Jamiu R. Obiyemi, Obiseye Covenant University Covenant Applied Informatics and Communication Africa Center of Excellence Dept. of Electrical and Information Engineering Ota Nigeria HRA Institute for Systems Science Durban University of Technology Durban4001 South Africa Federal Polytechnic Ilaro Dept. of Computer Engineering Ilaro Nigeria Durban University of Technology Space Science Centre Dept. of Electrical Power Engineering Durban South Africa

ISBN: (纸本)9798350353266

Access to well-curated large datasets remains a significant bottleneck in AI-based research within wireless communication. Rapid advancements in neighbouring fields, such as computer vision and natural language processing, are largely due to the availability of extensive open-access datasets. However, similar progress has not been observed in wireless communication. To address this gap, we curated a comprehensive dataset for fifteen digital modulation schemes, including 4QAM, 16QAM, 64QAM, 256QAM, 8PSK, 16PSK, 32PSK, 64PSK, 128PSK, 256PSK, CPFSK, DBPSK, DQPSK, GFSK, and GMSK. Our dataset considers Rayleigh and Rician channel models under Additive White Gaussian Noise (A WGN) with SNRs ranging from -20dB to + 20dB in 5dB increments. The data samples were converted to constellation signal images and carefully pre-processed. Named RadioModRec, this dataset provides a valuable resource for researchers to train and evaluate AI models. It is freely accessible on Kaggle, promoting further innovation in the wireless communication domain. © 2024 IEEE.

关键词： Signal to noise ratio

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Chaotic Dynamics in the 2D System of Nonsmooth Ordinary Differential Equations

Iraqi Journal for Computer Science and Mathematics

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Iraqi Journal for computer Science and Mathematics 2022年第2期3卷 8-17页

作者： Rahman, Zain-Aldeen S.A. Jasim, Basil H. Al-Yasir, Yasir I.A. Dept. of Electrical Techniques Technical Institute/Qurna Southern Technical University Basra Iraq Dept. of Electrical Engineering College of Engineering University of Basrah Basra Iraq School of Electrical Engineering and Computer Science Faculty of Engineering and Informatics University of Bradford BradfordBD7 1DP United Kingdom

Over the last decade, the chaotic behaviors of dynamical systems have been extensively explored. Recently, discovering or developing a 2D system of ordinary differential equations (ODEs) capable of exhibiting chaotic dynamical behaviors is an attractive research topic. In this study, a chaotic system with a 2D system of nonsmooth ODEs has been developed. This system is can exhibit chaotic dynamical behaviors. Its main dynamical behaviors, including time-series trajectories, phase portraits of attractors, and equilibria and their stability, have been investigated. The developed system has been verified by an excessive variety of fascinating chaotic behaviors, such as chaotic attractor, symmetry, sensitivity to initial conditions (ICs), fractal dimension, autocorrelation, power spectrum, Lyapunov exponent, and bifurcation diagram. Analytical and numerical simulations are used to study the dynamical behaviors of such a system. The developed system has extreme sensitivity to ICs, a fractal dimension of more than 1.8 and less than 2.05, an autocorrelation fluctuating randomly about an average of zero, a broadband power spectrum, and one positive Lyapunov exponent. The obtained numerical simulation results have proven the capability of the developed 2D system for exciting chaotic dynamical behaviors. © 2022 Autoctonía. Revista de Ciencias Sociales e Historia. All rights reserved.

关键词： Fractal dimension

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Multi-Objective Algorithms for Learning Open-Ended Robotic Problems 10

Multi-Objective Algorithms for Learning Open-Ended Robotic P...

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10th IEEE World Forum on Internet of Things, WF-IoT 2024

作者： Robert, Martin Brodeur, Simon Ferland, Francois Universite De Sherbrooke Dept. of Math and Informatics Sherbrooke Canada Menya AI division Levio Sherbrooke Canada Universite De Sherbrooke Dept. of Electrical Eng. and Computer Eng Sherbrooke Canada

ISBN: (纸本)9798350373011

Quadrupedal locomotion is a complex, open-ended problem vital to expanding autonomous vehicle reach. Traditional reinforcement learning approaches often fall short due to training instability and sample inefficiency. We propose a novel method leveraging multi-objective evolutionary algorithms as an automatic curriculum learning mechanism, which we named Multi-Objective Learning (MOL). Our approach significantly enhances the learning process by projecting velocity commands into an objective space and optimizing for both performance and diversity. Tested within the MuJoCo physics simulator, our method demonstrates superior stability and adaptability compared to baseline approaches. As such, it achieved 19% and 44% fewer errors against our best baseline algorithm in difficult scenarios based on a uniform and tailored evaluation respectively. This work introduces a robust framework for training quadrupedal robots, promising significant advancements in robotic locomotion and open-ended robotic problems. © 2024 IEEE.

关键词： Contrastive Learning

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