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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Damin Ding Yagang Wang Houji Zhang Qiaoling Meng Hongliu Yu Yanzhao Lin Qiu Chen Rehabilitation Engineering and Technology Institute School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai China Rehabilitation Engineering and Technology Institute School of Optical-Electrical School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai China Guangzhou Foreign Language School Guangzhou China Rehabilitation Engineering and Technology Institute University of Shanghai for Science and Technology Shanghai China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai China Major of Electrical Engineering and Electronics Graduate School of Engineering Kogakuin University Tokyo Japan

In recent years, Chinese society has become increasingly ageing while the fertility rate continues to decline. This situation has led to a growing public demand for rehabilitation devices such as walking aids. A novel intelligent robotic walker named ReRobo Walker is proposed which can assist groups of elderly people with dysfunctional legs with rehabilitation training and indoor and outdoor walking. We design robust mechanical structures for robotic walker, install special 3D force sensor, 2D LIDAR and other sensors, and design new algorithms to enable intelligent functionality while guaranteeing the safety of robotic walker.(1) Real-time monitoring of the user’s physical status, such as falls, through laser range sensors and 3D force sensor; (2) Modelling of the scene through 2D LIDAR for path planning, obstacle avoidance and navigation functions; (3) Precise control through LADRC-based algorithm for uphill assistance, downhill control and prevention of sharp shifts of the robotic walker. Experiment results demonstrate the solid mechanical structure, stable reliability and the effectiveness of intelligent control algorithms of the intelligent robotic walker.

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Reinforcement Learning-Based Edge Caching for IoT in Compute-First Networks

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IEEE Transactions on Consumer Electronics 2025年

作者： Asmat, Hamid Din, Ikram Ud Almogren, Ahmad Rodrigues, Joel J. P. C. Department of Information Technology University of Haripur Haripur22620 Pakistan King Saud University College of Computer and Information Sciences Chair of Cyber Security Department of Computer Science Riyadh11633 Saudi Arabia Post-graduation Program in Electrical Engineering Teresina Brazil

6G networks require ultra-low latency and intelligent resource management in IoT environments. Compute-First Networking (CFN), integrating distributed computing and edge caching, addresses backhaul congestion. This paper proposes CaRL, a DRL-based caching framework using TD3 algorithm. CaRL optimizes cache placement at edge nodes by incorporating real-time network state, content popularity, and resource utilization. Simulations show CaRL improves cache hit ratio, reduces latency, and enhances bandwidth efficiency, and ensures secure caching decisions under dynamic network conditions. Unlike existing methods, CaRL adapts to dynamic network conditions by integrating latency, CPU, and memory metrics, demonstrating scalability and adaptability in CFN-driven 6G environments. © 1975-2011 IEEE.

关键词： Edge computing

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Algorithms of Sampling-Frequency-Independent Layers for Non-integer Strides

arXiv

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arXiv 2023年

作者： Imamura, Kanami Nakamura, Tomohiko Takamune, Norihiro Yatabe, Kohei Saruwatari, Hiroshi Graduate School of Information Science and Technology The University of Tokyo Tokyo Japan Department of Electrical Engineering and Computer Science Tokyo University of Agriculture and Technology Tokyo Japan

In this paper, we propose algorithms for handling non-integer strides in sampling-frequency-independent (SFI) convolutional and transposed convolutional layers. The SFI layers have been developed for handling various sampling frequencies (SFs) by a single neural network. They are replaceable with their non-SFI counterparts and can be introduced into various network architectures. However, they could not handle some specific configurations when combined with non-SFI layers. For example, an SFI extension of Conv-TasNet, a standard audio source separation model, cannot handle some pairs of trained and target SFs because the strides of the SFI layers become non-integers. This problem cannot be solved by simple rounding or signal resampling, resulting in the significant performance degradation. To overcome this problem, we propose algorithms for handling non-integer strides by using windowed sinc interpolation. The proposed algorithms realize the continuous-time representations of features using the interpolation and enable us to sample instants with the desired stride. Experimental results on music source separation showed that the proposed algorithms outperformed the rounding- and signal-resampling-based methods at SFs lower than the trained SF. © 2023, CC BY-SA.

关键词： Network architecture

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Comparison of Coherent and Incoherent Donut Beams for Deep Tissue STED Microscopy

Comparison of Coherent and Incoherent Donut Beams for Deep T...

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2023 Conference on Lasers and Electro-Optics, CLEO 2023

作者： Shanavas, Thariq McLeod, Robert R. Siemens, Mark E. Gopinath, Juliet T. Department of Physics University of Colorado BoulderCO80309 United States Department of Electrical Computer and Energy Engineering University of Colorado BoulderCO80309 United States Department of Physics University of Denver DenverCO80204 United States Materials Science Engineering Program University of Colorado BoulderCO80309 United States

We numerically compare the null quality for STED microscopy generated by Laguerre-Gaussian beams with orbital angular momentum and donut beams generated by incoherent addition of orthogonal Hermite Gaussian beams when... 详细信息

ISBN: (纸本)9781957171258

关键词： Tissue

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Ill-Condition Enhancement for Bc Speech Using Regularized Modified Covariance Method

SSRN

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SSRN 2024年

作者： Ohidujjaman Hasan, Mahmudul Zhang, Shiming Uddin, Mohammad Shorif Graduate School of Science and Engineering Saitama University 255 Shimo-Okubo Sakura-ku Saitama338-8570 Japan Department of Computer Science and Engineering Comilla University Cumilla3506 Bangladesh School of Electrical and Information Northeast Agricultural University Harbin150030 China Computer Science and Engineering Jahangirnagar University Savar1342 Bangladesh

This paper improves the ill-condition of bone-conducted (BC) speech signal by reducing the eigenvalue expansion. BC speech commonly contains a large spectral dynamic range that causes ill-condition for the classical linear prediction (LP) methods. In the field of numerical analysis, we often face the situation where an ill-conditioned case occurs in finding the solution. Principally, eigenvalue expansion causes ill-condition in numerical analysis. To mitigate this problem, the regularized least squares (RLS) technique is commonly used. Motivated by the RLS concept, we derive the regularized modified covariance (RMC) method for BC speech analysis in this study. The RMC method reduces eigenvalue expansion by compressing the spectral dynamic range of the speech signal. Thus, the RMC method resolves the ill-conditioned problem of LP. In experiments, we show that the RMC method provides compressed eigenvalue expansion than the conventional methods for BC speech where synthetic and real BC speeches are considered. The performance of the RMC method is affected by the setting of the regularization parameter. In this paper, the regularization parameter in practice is iteratively and rule-based derived. The RMC method with such a setting provides the best performance for BC speech analysis. © 2024, The Authors. All rights reserved.

关键词： Parameterization

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Deep Learning Model for Classifying Public Opinions on Energy Sector IHSG Stocks on the Twitter Social Media Platform

Deep Learning Model for Classifying Public Opinions on Energ...

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Artificial Intelligence Robotics, Signal and Image Processing (AIRoSIP), International Conference on

作者： Bambang Sulistio Yaya Heryadi Lukas Wayan Suparta Ilvico Sonata Computer Science Department Binus Graduate Program – Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Cognitive Engineering Research Group Universitas Katolik Indonesia Atma Jaya Jakarta Indonesia Department of Electrical Engineering Faculty of Industrial Technology Institut Teknologi Nasional Yogyakarta Yogyakarta Indonesia

ISBN: (数字)9798350327472

ISBN: (纸本)9798350327489

Capital market transactions provide an opportunity for investors to acquire ownership of company shares and capital gains, as well as dividends. However, alongside the benefits, there are risks of capital loss and liquidation, leading to stress and depression due to profit targets and decision-making errors. To mitigate the risk of decision-making errors in investment, data analysis is needed, including sentiment analysis, which influences stock prices. This study aims to develop a new deep learning model to classify Indonesian public opinion on JCI stocks, especially the Energy sector, obtained from the Twitter social media platform. The model will perform sentiment analysis and categorize opinions as negative, neutral, or positive. We created a dataset that was trained using Bidirectional Encoder Representations from Transformers (BERT) to summarize the analysis of public sentiment above so that it can assist investors in studying public sentiment as a reference for investing with a yield precision of 76%, Recall of 77%, and F1-score on 76%.

关键词： Deep learning Sentiment analysis Analytical models Social networking (online) Blogs Decision making Text categorization Bidirectional control Transformers Encoding

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An Improved Transfer Learning-Based Model with Data Augmentation for Brain Tumor Detection

An Improved Transfer Learning-Based Model with Data Augmenta...

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International Conference on Artificial Intelligence in Information and Communication (ICAIIC)

作者： Rizal Dwi Prayogo Nur Hamid Hidetaka Nambo Division of Electrical Engineering and Computer Science Graduate School of Natural Science and Technology Kanazawa University Kanazawa Japan Faculty of Transdisciplinary Sciences Institute of Philosophy in Interdisciplinary Sciences Kanazawa University Kanazawa Japan

Current advances in deep learning have brought various breakthroughs in processing medical data. However, dealing with a limited number of medical datasets remains a challenge in deep learning and often leads to overfitting. To solve this research gap, here we show a new approach to improve the performance of a transfer learning-based model for brain tumor detection from 253 brain magnetic resonance imaging (MRI) sample images. The concept of transfer learning has been applied using a pretrained Inception V3combined with data augmentation. Modified layers using dropout and regularization have been additionally utilized to deal with overfitting. The proposed method shows an increase in accuracy of 6.430%, a precision of 5.531 %, a recall of 10.545%, and an F1-score of 8.040% compared to the baseline method. We show that our proposed method has been able to effectively enhance performance and reduce overfitting, even with a small number of datasets. Moreover, our proposed method outperforms state-of-the-art brain tumor detection.

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Thermodynamic and Environmental Assessment of Low-GWP Alternative Refrigerants for Domestic Cooling

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Journal of The Institution of Engineers (India): Series C 2023年第2期104卷 377-383页

作者： Islam, Md. Amirul Rupam, Tahmid Hasan Hosan, Shahadat Saha, Bidyut Baran Institute for Materials Chemistry and Engineering Kyushu University Kasuga-Koen 6-1 Kasuga-Shi Fukuoka816-8580 Japan Department of Electrical and Electronics Engineering Bangabandhu Sheikh Mujibur Rahman Science and Technology University Gopalganj8100 Bangladesh Kyushu University 744 Motooka Nishi-Ku Fukuoka819-0395 Japan Kyushu University Program for Leading Graduate School Green Asia Education Center Kyushu University Fukuoka Japan Department of Mechanical Engineering Kyushu University 744 Motooka Nishi-Ku Fukuoka819-0395 Japan

Nearly two billion air-conditioning (AC) units are currently being used for space cooling worldwide. The majority of these ACs use R134a as the working fluid, a greenhouse gas (GHG) with a global warming potential (GWP) of 1300 kg-CO2-eq. According to the rules set by the world environment safety organizations, the GWP of domestic air-conditioning systems must not exceed 750 kg-CO2-eq. Hence, the R134a must be phased out by 2025, and an alternative refrigerant with similar thermodynamic properties yet low GWP is required. Therefore, the authors have investigated several potential binary/ternary refrigerant blends in this study. Among the studied refrigerants, R445A, R30A, and NPB2 (newly proposed blend: 8% R152a + 92% R1234yf) can reduce 90–99% global warming impact (more than 200 million tonnes of CO2 emission equivalent) caused by the domestic air-conditioning systems every year. © 2023, The Institution of Engineers (India).

关键词： Refrigerants

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MERNet: Multimodal Emotion Recognition in Conversation Network via Cross-Modal Knowledge Distillation and Contrastive Learning

MERNet: Multimodal Emotion Recognition in Conversation Netwo...

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International Conference on Big Data and Smart Computing (BIGCOMP)

作者： Sooyeon Hwang Gahgene Gweon Eunseong Kwon Interdisciplinary Program in Artificial Intelligence Seoul National University South Korea Graduate School of Convergence Science and Technology Seoul National University South Korea Dept. of Computer Science and Engineering POSTECH South Korea

ISBN: (数字)9798331529024

ISBN: (纸本)9798331529031

Multimodal Emotion Recognition in Conversation (ERC) is a task of predicting the emotion of each utterance in a conversation by utilizing both verbal and non-verbal modalities. However, existing approaches often struggle to bridge cross-modal gaps, resulting in misaligned features and frequent misclassification of minority emotions into semantically similar majority emotions. To address these challenges, we propose MERNet, a framework that employs cross-modal knowledge distillation and contrastive learning to align multimodal features and effectively distinguish subtle emotions in conversations. Our framework consists of two stages: 1) guiding non-verbal modalities with the text modality to transfer knowledge and align their features, and 2) applying contrastive learning with emotion labels as anchors to distinguish subtle differences between similar emotions and address the class imbalance problem. Experiments conducted on two benchmark datasets, IEMOCAP and MELD, demonstrate that our MERNet outperforms existing state-of-the-art models.

关键词： Knowledge engineering Bridges Emotion recognition Visualization Computational modeling Contrastive learning Benchmark testing Linguistics Big Data

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An Assessment of Patent Scoring Using Natural Language programming

An Assessment of Patent Scoring Using Natural Language Progr...

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2022 Portland International Conference on Management of engineering and Technology, PICMET 2022

作者： Khongamnuaisak, Lertchai Pentrakoon, Duanghathai Sinthupinyo, Sukree Suanpong, Kwanrat Chulalongkorn University Technopreneurship and Innovation Management Program Graduate School Bangkok Thailand Chulalongkorn University Faculty of Science Department of Materials Science Bangkok Thailand Faculty of Engineering Chulalongkorn University Department of Computer Engineering Bangkok Thailand Chulalongkorn Business School Commerce Department Bangkok Thailand

ISBN: (数字)9781890843410

ISBN: (纸本)9781890843410

Patent has been an increasingly important role in the world because it is not only significant to protect the invention of the company's business but also to generate revenue from the commercialization. WIPO (2018) reports that the repository has many patents so, to assess patent, it consumes time and requires various expertise. Generally, several models have been developed to address this matter, which to assess using a questionnaire tool for portfolios by human. Perhaps, there is not precise because it is assessed base on personal bias or previous experience of expertise. So that, occurs bias any limitation exists that our research will address by artificial intelligence. Thus, this research applies a Natural Language programming to assess for commercial of the patent. Then, the obtained data is used to assigned factors and calculate the model for assessing the commercialization of patent. This finding should deliver an alternative effective patent assessment, which is tool addresses, some current deficiency patent assessment for commercialization. © 2022 PICMET.

关键词： Patents and inventions

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