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2022 research, Invention, and Innovation Congress: Innovative Electricals and Electronics, RI2C 2022

作者： Mekruksavanich, Sakorn Jantawong, Ponnipa Hnoohom, Narit Jitpattanakul, Anuchit University of Phayao School of Information and Communication Technology Department of Computer Engineering Phayao Thailand Mahidol University Image Information and Intelligence Laboratory Department of Computer Engineering Faculty of Engineering Nakhon Pathom Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science Bangkok Thailand

ISBN: (数字)9781665473248

ISBN: (纸本)9781665473248

People have been encouraged to wear masks and avoid touching their faces in public as part of the new measures to prevent the spread of coronavirus 2019 (COVID-19). During the COVID-19 epidemic, few research have examined the effect of everyday living on the frequency of facial touch activity. To develop a face touching avoidance system, deep learning algorithms have been proposed and have demonstrated their amazing performance. However, an important drawback of deep learning is its extensive dependence on hyperparameters. The results of deep learning algorithms may vary depending on hyperparameters, such as the size of the filters, the number of filters, the batch size, the number of epochs, and the training optimization technique used. In this paper, we present an effective approach for hyperparameter tuning of convolutional neural networks (CNNs) for efficiently recognized face touching activities based on accelerometer data. Two hyperparameter tuning methods (Grid search and Bayesian optimization) were evaluated in order to construct the CNN with high performance. The experiment results show that Bayesian optimization can provide suitable hyperparameters for CNNs for face touching recognition with the highest accuracy of 96.61%. © 2022 IEEE.

关键词： Convolutional neural networks

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Deep Learning Approaches for Unobtrusive Human Activity Recognition using Insole-based and Smartwatch Sensors 3

Deep Learning Approaches for Unobtrusive Human Activity Reco...

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3rd International Conference on Big Data Analytics and Practices, IBDAP 2022

作者： Hnoohom, Narit Maitrichit, Nagorn Mekruksavanich, Sakorn Jitpattanakul, Anuchit Mahidol University Image Information and Intelligence Laboratory Department of Computer Engineering Faculty of Engineering Nakorn Pathom Thailand University of Phayao School of Information and Communication Technology Department of Computer Engineering Phayao Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science Bangkok Thailand

ISBN: (数字)9781665469388

ISBN: (纸本)9781665469388

In this digital age, human activity recognition (HAR) has been playing an increasingly important role in almost all aspects of life. Healthcare systems for monitoring the activities of daily living (ADL), security environments for automatically detecting abnormal activity and notifying the pertinent authorities, and increasing human contact with computers are all examples of HAR applications. The data collecting tools used in HAR research can also be categorized (sensors or cameras). In sensor-based HAR, various wearable sensors have been investigated for their usefulness in effectively detecting both simple and complex human activities. In this work, we studied HAR using signal data acquired from insole-based and smartwatch sensors. To achieve our research goal, we proposed a ResNeXt inspired deep learning (DL) network called HARNeXt. The proposed model was evaluated on a public data set named 19NonSense data that collected activity signal data recorded from insole-based and smartwatch sensors. In addition, we compared the proposed model to baseline DL models. The results showed that the HARNeXt outperformed other DL models and achieved the highest F1-score of 97.03%. © 2022 IEEE.

关键词： Wearable sensors

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Badminton Activity Recognition and Player Assessment based on Motion Signals using Deep Residual Network 13

Badminton Activity Recognition and Player Assessment based o...

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13th IEEE International Conference on Software engineering and Service science, ICSESS 2022

作者： Mekruksavanich, Sakorn Jantawong, Ponnipa Hnoohom, Narit Jitpattanakul, Anuchit University of Phayao School of Information and Communication Technology Department of Computer Engineering Phayao Thailand Faculty of Engineering Mahidol University Image Information and Intelligence Laboratory Department of Computer Engineering Nakhon Pathom Thailand Faculty of Applied Science King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Bangkok Thailand

ISBN: (数字)9781665410328

ISBN: (纸本)9781665410311

With the fast expansion of digital technologies and sporting events, interpreting sports data has become an immensely complicated endeavor. Internet-sourced sports big data exhibit a significant development trend. Big data in sports offer a wealth of information on sportspeople, coaching, athletics, swimming, and badminton. Today, various sports data are freely accessible, and incredible data analysis tools based on wearable sensors have been established, allowing us to investigate the usefulness of these data thoroughly. In this research, we investigate the detection of badminton action and player evaluation based on movement data captured by wearable sensors. Movement data captured by an accelerometer, gyroscope, and magnetometer are utilized for training and validating a classification model for badminton actions. In addition, the movement signals are used to train a player evaluation model employing a deep residual network. To assess our suggested technique, we utilized a publicly available benchmark dataset consisting of inertial measurement unit (IMU) sensors attached to every investigator's dominant wrist, palm, and both legs. The experimental findings indicate that the proposed deep residual network obtained good performance with a maximum accuracy of 98.00% for identifying badminton activities and 98.56% for evaluating badminton players. © 2022 IEEE.

关键词： Wearable sensors

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Laser damage characteristics and nanosecond laser conditioning of multilayer dielectric gratings for picosecond-petawatt laser systems

Laser damage characteristics and nanosecond laser conditioni...

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Pacific-Rim Laser Damage 2023: Optical Materials for High-Power Lasers

作者： Shuai, Kun Liu, Xiaofeng Zhao, Yuan’an Qiu, Keqiang Li, Dawei Lin, Xiangkun Gong, He Sun, Jian Dai, Yaping Shao, Jianda Laboratory of Thin Film Optics Shanghai Institute of Optics and Fine Mechanics Shanghai201800 China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of Sciences Beijing100049 China Key Laboratory of Materials for High Power Laser Chinese Academy of Sciences Shanghai201800 China National Synchrotron Radiation Laboratory University of Science and Technology of China Anhui Hefei230029 China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China Research Center of Laser Fusion China Academy of Engineering Physics Sichuan Mianyang621900 China Hangzhou Institute for Advanced Study University of Chinese Academy of Sciences Hangzhou310024 China

ISBN: (纸本)9781510672826

Multilayer dielectric gratings (MLDGs) are the core optics of the picosecond-petawatt laser systems based on chirped pulse amplification (CPA). The MLDGs encounter nanosecond- and picosecond-laser irradiation during the broadening and recompression of the laser pulse in CPA. Therefore, a comprehensive evaluation of the laser-induced damage performance of MLDG in the nanosecond (ns) and picosecond (ps) regimes is required. Herein, we examined the laser damage characteristics of MLDGs induced by the 8-ns and 8.6-ps laser pulses. In the two tested laser pulse widths, the damage of the MLDG was dominated by the nano absorbing defects and nodular defects, but the damage mechanism has changed. When the laser pulse width is shortened from ns to ps, the damage sites caused by the absorbing defects transfer from the interface to the grating pillars, where there is the maximum electric field. And the nodular ejection pit changes from a complete eruption to a local damage corresponding to the electric field enhancement region. For the nodular defects, ns-laser conditioning was introduced for removing them and achieved a maximum enhancement of 40% in the ps laser-induced damage threshold. © 2023 SPIE.

关键词： Laser damage

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Deep Learning Networks for Eating and Drinking Recognition based on Smartwatch Sensors

Deep Learning Networks for Eating and Drinking Recognition b...

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2022 research, Invention, and Innovation Congress: Innovative Electricals and Electronics, RI2C 2022

ISBN: (数字)9781665473248

ISBN: (纸本)9781665473248

Smartwatches are becoming more popular for recognizing and monitoring human actions in everyday life. These wearable devices are equipped with various IMU sensors for ubiquitous data processing and recording of human physical activity data. Sensor-based human activity recognition (HAR) has risen to the top of the list of the most active research topic due to its widely real-life applications in various practical domains, such as healthcare monitoring, sports and exercise tracking, and misbehavior prevention. Many machine learning and deep learning approaches have been recently proposed to solve the problem of human activity recognition, focusing on activities of daily living. However, an exciting and challenging HAR topic deals with more complex human activities such as eating-related activities. This paper proposes a sensor-based HAR framework using data from eating-related activities recorded by a smartwatch sensor. In this framework, five d eep learning networks (CNN, LSTM, BiLSTM, Stacked LSTM, CNN-LSTM, and LSTM-CNN) are evaluated for their recognition of eating-related activities. To ensure the model's dependability, data from eating-related activities on the standard publicly available dataset WISDM-HARB are utilized to evaluate the proposed framework using state-of-the-art metrics: accuracy and confusion matrices. Experiment findings demonstrate that the S tacked LSTM model outperforms other deep learning models, achieving an accuracy of 97.37%. © 2022 IEEE.

关键词： Learning systems

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A Deep Residual-based Model on Multi-Branch Aggregation for Stress and Emotion Recognition through Biosignals 19

A Deep Residual-based Model on Multi-Branch Aggregation for ...

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19th International Conference on Electrical engineering/Electronics, computer, Telecommunications and Information Technology, ECTI-CON 2022

作者： Mekruksavanich, Sakorn Hnoohom, Narit Jitpattanakul, Anuchit University of Phayao School of Information and Communication Technology Department of Computer Engineering Phayao Thailand Mahidol University Image Information and Intelligence Laboratory Department of Computer Engineering Faculty of Engineering Nakhon Pathom Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science Bangkok Thailand

ISBN: (纸本)9781665485845

Stress and emotion recognition (SER) is a rapidly growing field of study that has applications in various areas, including psychological wellbeing, rehabilitative services, athletic training, and human-computer interaction. Biological information such as the electrocardiogram (ECG), electromyography (EMG), and electrodermal activity (EDA) has been frequently utilized for the SER for learning-based approaches. This study introduces a convolutional neural network motivated by ResNeXt to facilitate multimodal awareness. The proposed model, named StressNeXt, can extract high-level insights from raw bio-signal signals and classify emotional expressions effectively. We undertake a series of investigations using a publicly released standard dataset (WESAD) to determine the optimal implementation of the proposed solution for recognizing stress and emotion. After incorporating preliminary fusion events, we examined deep learning models using 5-fold cross-validation. Our study demonstrates that the suggested technique can comprehend robust multimodal representations with an accuracy of 87.73% utilizing EDA. Additionally, the identification was designed to provide better to 99.92% by fusing with accelerometer sensor data. © 2022 IEEE.

关键词： Emotion Recognition

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RETRACTED ARTICLE: A Systematic Survey on Implementation of Fuzzy Regression Models for Real Life Applications (Archives of Computational Methods in engineering, (2024), 31, 1, (291-311), 10.1007/s11831-023-09978-x)

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Archives of Computational Methods in engineering 2025年

作者： Khan, Mufala Kumar, Rakesh Aledaily, Arwa N. Kariri, Elham Viriyasitavat, Wattana Yadav, Kusum Dhiman, Gaurav Kaur, Amandeep Sharma, Ashutosh Vimal, S. Department of Mathematics Lovely Professional University Punjab Phagwara144411 India Department of Computer Science Government Bikram College of Commerce Punjab Patiala147001 India College of Computer Science and Engineering University of Ha’il Hail Saudi Arabia College of Computer Science and Engineering Prince Sattam Bin Abdulaziz University Al‑Kharj Saudi Arabia Business Information Technology Division Department of Statistics Faculty of Commerce and Accountancy Chulalongkorn University Bangkok Thailand Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon University Centre for Research and Development Department of Computer Science and Engineering Chandigarh University Mohali Gharuan140413 India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun248002 India Division of Research and Development Lovely Professional University Phagwara India Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India School of Computer Science University of Petroleum and Energy Studies Dehradun India Department of Artificial Intelligence and Data Science Ramco Institute of Technology Tamil Nadu Rajaplayam India

The Editors-in-Chief have retracted this article. After publication, concerns were raised about the originality of the study reported. An investigation conducted by the Publisher confirmed that the article contains material that substantially overlaps with a previous study by different authors [1]. In particular, the research methodology, research questions, numerous formulations, and discussion are almost identical with those in [1]. The Editors-in-Chief therefore consider this article redundant. None of the authors have responded to correspondence from the Publisher about this retraction. © The Author(s) under exclusive licence to International center for Numerical Methods in engineering (CIMNE) 2025.

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The Effect of Sensor Placement for Accurate Fall Detection based on Deep Learning Model

The Effect of Sensor Placement for Accurate Fall Detection b...

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2022 research, Invention, and Innovation Congress: Innovative Electricals and Electronics, RI2C 2022

ISBN: (数字)9781665473248

ISBN: (纸本)9781665473248

The development of inertial sensor technology and the growing utilization of wearable electronics (such as smartwatches, smart bands, and other intelligent gadgets) have facilitated the advancement of studies into automated Fall Detection Systems (FDSs). In the last decade, there has been significant scientific interest in maintaining FDSs. Focused on assessing the data acquired by wearable inertial sensors, machine learning (ML) techniques have demonstrated high efficacy in distinguishing falls from typical motions or activities of daily living (ADLs). In most research, unfortunately, the effectiveness of machine learning classifiers was constrained by feature extraction and selection processes that relied on human-made decisions. Recently, deep learning (DL) model findings s how their effectiveness for FDS. One of these effective DL models is the ResNeXt model, a deep neural network that operates based on convolutional layers with aggregated residual transformation. This study investigates the influence of sensor placement on various body locations for the fall detection issue. The ResNeXt model was assessed and compared to other baseline deep learning algorithms using the public UMAFall dataset for fall detection. Employing sensor data on waist location, the suggested model attained the most significant classification ac curacy of 97.275% when classifying falls. © 2022 IEEE.

关键词： Wearable sensors

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Overcoming common flaws in the evaluation of selective classification systems 24

Overcoming common flaws in the evaluation of selective class...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Jeremias Traub Till J. Bungert Carsten T. Lüth Michael Baumgartner Klaus H. Maier-Hein Lena Maier-Hein Paul F. Jäger German Cancer Research Center (DKFZ) Heidelberg Interactive Machine Learning Group Germany and Helmholtz Imaging DKFZ Heidelberg Germany German Cancer Research Center (DKFZ) Heidelberg Interactive Machine Learning Group Germany and Helmholtz Imaging DKFZ Heidelberg Germany and Faculty of Mathematics and Computer Science University of Heidelberg Germany Helmholtz Imaging DKFZ Heidelberg Germany and DKFZ Heidelberg Division of Medical Image Computing (MIC) Germany and Faculty of Mathematics and Computer Science University of Heidelberg Germany Helmholtz Imaging DKFZ Heidelberg Germany and DKFZ Heidelberg Division of Medical Image Computing (MIC) Germany and Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany and Faculty of Mathematics and Computer Science University of Heidelberg Germany and National Center for Tumor Diseases (NCT) Heidelberg Helmholtz Imaging DKFZ Heidelberg Germany and DKFZ Heidelberg Division of Intelligent Medical Systems (IMSY) Germany and Faculty of Mathematics and Computer Science University of Heidelberg Germany and National Center for Tumor Diseases (NCT) Heidelberg

ISBN: (纸本)9798331314385

Selective Classification, wherein models can reject low-confidence predictions, promises reliable translation of machine-learning based classification systems to real-world scenarios such as clinical diagnostics. While current evaluation of these systems typically assumes fixed working points based on pre-defined rejection thresholds, methodological progress requires benchmarking the general performance of systems akin to the AUROC in standard classification. In this work, we define 5 requirements for multi-threshold metrics in selective classification regarding task alignment, interpretability, and flexibility, and show how current approaches fail to meet them. We propose the Area under the Generalized Risk Coverage curve (AUGRC), which meets all requirements and can be directly interpreted as the average risk of undetected failures. We empirically demonstrate the relevance of AUGRC on a comprehensive benchmark spanning 6 data sets and 13 confidence scoring functions. We find that the proposed metric substantially changes metric rankings on 5 out of the 6 data sets.

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Anisotropic, multiband, and strong-coupling superconductivity of the Pb0.64Bi0.36 alloy

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Physical Review B 2024年第21期110卷 214510-214510页

作者： Sylwia Gutowska Karolina Górnicka Paweł Wójcik Tomasz Klimczuk Bartlomiej Wiendlocha University of Vienna Faculty of Physics and Center for Computational Materials Science Kolingasse 14-16 A-1090 Vienna Austria AGH University of Krakow Faculty of Physics and Applied Computer Science Aleja Mickiewicza 30 30-059 Krakow Poland Materials Science and Technology Division Oak Ridge National Laboratory Oak Ridge Tennessee 37831 USA Faculty of Applied Physics and Mathematics and Advanced Materials Centre Gdansk University of Technology ul. Narutowicza 11/12 80-233 Gdańsk Poland

This paper presents theoretical and experimental studies on the superconductivity of Pb0.64Bi0.36 alloy, which is a prototype of strongly coupled superconductors and exhibits one of the strongest coupling under ambient pressure among the materials studied so far. The critical temperature, the specific heat in the superconducting state, and the magnetic critical fields are experimentally determined. Deviations from the single-gap s-wave BCS-like behavior are observed. The electronic structure, phonons, and electron-phonon interactions are analyzed in relation to the metallic Pb, explaining why the Pb-Bi alloy exhibits such a large value of the electron-phonon coupling parameter λ≃2. Superconductivity is studied using the isotropic Eliashberg formalism as well as the anisotropic density functional theory for superconductors. We find that while Pb is a two-gap superconductor with well-defined separate superconducting gaps, in the Pb-Bi alloy an overlapped three-gap-like structure is formed with a strong anisotropy. Furthermore, the chemical disorder, inherent to this alloy, leads to strong electron scattering, which is found to reduce the critical temperature.

关键词： Anisotropy

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