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15th International Conference on ICT, Society and Human Beings, ICT 2022, 19th International Conference on Web Based Communities and Social Media, WBC 2022 and 14th International Conference on e-Health, EH 2022 - Held at the 16th Multi Conference on Computer Science and Information Systems, MCCSIS 2022

作者： Yazdanpanah, Hamed Silva, Augusto C.M. Guedes, Murilo Morales, Hugo M.P. dos Coelho, Leandro S. Moro, Fernando G. Department of Research Robô Laura PR Curitiba Brazil Department of Computer Science University of São Paulo SP São Paulo Brazil Industrial and Systems Engineering Graduate Program Pontifical Catholic University of Parana PR Curitiba Brazil Electrical Engineering Graduate Program Federal University of Parana PR Curitiba Brazil

ISBN: (纸本)9789898704405

Early recognition of clinical deterioration (CD) has vital importance in patients’ survival from exacerbation or death. Electronic health records (EHRs) data have been widely employed in Early Warning Scores (EWS) to measure CD risk in hospitalized patients. Recently, EHRs data have been utilized in Machine Learning (ML) models to predict mortality and CD. The ML models have shown superior performance in CD prediction compared to EWS. Since EHRs data are structured and tabular, conventional ML models are generally applied to them, and less effort is put into evaluating the artificial neural network’s performance on EHRs data. Thus, in this article, an extremely boosted neural network (XBNet) is used to predict CD, and its performance is compared to eXtreme Gradient Boosting (XGBoost) and random forest (RF) models. For this purpose, 103,105 samples from thirteen Brazilian hospitals are used to generate the models. Moreover, the principal component analysis (PCA) is employed to verify whether it can improve the adopted models’ performance. The performance of ML models and Modified Early Warning Score (MEWS), an EWS candidate, are evaluated in CD prediction regarding the accuracy, precision, recall, F1-score, and geometric mean (G-mean) metrics in a 10-fold cross-validation approach. According to the experiments, the XGBoost model obtained the best results in predicting CD among Brazilian hospitals’ data. © 2022 15th International Conference on ICT, Society and Human Beings, ICT 2022, 19th International Conference on Web Based Communities and Social Media, WBC 2022 and 14th International Conference on e-Health, EH 2022 - Held at the 16th Multi Conference on Computer Science and Information Systems, MCCSIS 2022. All rights reserved.

关键词： Neural networks

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Comparative Analysis of SAAS Model and NPC Integration for Enhancing VR Shopping Experiences

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Applied Sciences (Switzerland) 2024年第15期14卷 6573页

作者： Doungtap, Surasachai Wang, Jenq-Haur Phanichraksaphong, Varinya International Graduate Program of Electrical Engineering and Computer Science National Taipei University of Technology Taipei10608 Taiwan Department of Computer Science and Information Engineering National Taipei University of Technology Taipei10608 Taiwan

This article examines the incorporation of the Shopping Assistance Automatic Suggestion (SAAS) model into Virtual Reality (VR) environments in order to improve the online shopping experience. The SAAS model employs sophisticated deep learning methods to offer customized product recommendations, which are conveyed by non-player characters (NPCs) via voice-based interactions. Our goal is to develop an interactive shopping experience that replicates real-life interactions by integrating AI-powered recommendations with immersive VR technology. We gather and standardize data from several open commerce databases, such as Amazon Product and Customer Reviews. The SAAS model, in conjunction with GPT-3, BERT, and T5, undergoes training and testing to evaluate its effectiveness across multiple criteria. The results demonstrate that the SAAS model surpasses other models in delivering contextually aware and pertinent recommendations. The integration process outlines the specific steps involved in capturing, processing, and transforming user interactions in virtual reality (VR) into vocal suggestions provided by non-player characters (NPCs). This strategy improves customization and utilizes the immersive features of virtual reality to effectively engage people. The results of our research establish a higher standard for e-commerce, with the goal of enhancing the user experience of online purchasing by making it more instinctive, engaging, and pleasurable. © 2024 by the authors.

关键词： Data mining

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ClusteredSHAP: Faster GradientExplainer based on K-means Clustering and Selections of Gradients in Explaining 12-Lead ECG Classification Model 23

ClusteredSHAP: Faster GradientExplainer based on K-means Clu...

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13th International Conference on Advances in Information Technology, IAIT 2023

作者： Mo, Bo-Yu Nuannimnoi, Sirapop Baskoro, Angger Khan, Azam Ariesta Dwi Pratiwi, Jasmine Huang, Ching-Yao Department of Electronics Engineering National Yang-Ming Chiao Tung University Hsinchu Taiwan Eecs International Graduate Program National Yang-Ming Chiao Tung University Hsinchu Taiwan International Health Program National Yang-Ming Chiao Tung University Hsinchu Taiwan

ISBN: (纸本)9798400708497

The vast majority of healthcare systems are operating at or near their full capacity. Providing an inaccurate diagnosis is a further prevalent issue. Although it is common knowledge that physicians get significant training, it is nevertheless possible for them to misdiagnose patients, overlook warning signs, or commit any number of other human blunders. Deep Learning (DL) has emerged as a helpful tool in medical diagnostics, particularly in ECG (electrocardiogram) signal classification, enabling efficient and precise detection of cardiac abnormalities. However, the inherent black-box nature of DL poses challenges for its direct implementation in real-world medical settings. To address this, Explainable AI (XAI) techniques such as SHAP, LIME, and CAM have been introduced, aiming to render the complex decisions of neural networks interpretable. Among these, SHAP is recognized for its robustness and comprehensive explanation capabilities. Nonetheless, the high computational demands of traditional SHAP methods hinder their real-time application, especially in urgent medical scenarios. In this paper, we propose an optimized SHAP approach leveraging K-Means clustering to group gradients by importance, namely ClusteredSHAP. Our methodology focuses on a select cluster of high-magnitude gradients, enhancing the efficiency of the GradientExplainer. Our evaluation encompasses both the computational efficiency and the usability of the resulting explanations, through a custom questionnaire and explanation usability scores inspired by established user experience metrics and medical tool usability standards. The results show that our proposed ClusteredSHAP not only provides significantly faster explanations, but also achieves a similar level of average explanation usability scores with the GradientExplainer. Through this work, we strive to bridge XAI with clinical practice, ensuring timely, transparent, and effective patient care. © 2023 ACM.

关键词： 12-Lead ECG Classification Explainable AI Heart Disease Diagnosis K-means Clustering SHapley Additive exPlanations

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Mathematical Analysis and Space Vector Modulation of the Three-phase Buck-Boost Rectifier with Reactive Power Capability

Mathematical Analysis and Space Vector Modulation of the Thr...

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IEEE Energy Conversion Congress and Exposition, ECCE

作者： Yan Pontes Figueiredo Montie Alves Vitorino Jens Friebe Graduate Program in Electrical Engineering (PPgEE-COPELE) Industrial Electronics and Machine Drive Laboratory (LEIAM) Federal University of Campina Grande (UFCG) Campina Grande Brazil Department of Power Electronics University of Kassel Kassel Germany

ISBN: (数字)9798350376067

ISBN: (纸本)9798350376074

Buck-boost current-source based converters are attractive for applications in electric vehicles (EVs) due to several inherent benefits regarding high-power charging. However, methods are still needed to increase the efficiency of these converters. This paper covers a mathematical analysis of the three-phase buck-boost rectifier with reactive power capability and shows its equation and operating conditions for a wide range of power factors. In addition, the control strategy based on space vector modulation (SVM) is presented in detail, as well as the passive elements design. Experimental results show that the converter modulated by SVM is more efficient in terms of switching losses compared to scalar modulation.

关键词： Support vector machines Reactive power Mathematical analysis Modulation Rectifiers Switching loss Energy conversion Aerospace electronics Electric vehicles Vectors

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Comparison of State-of-Charge and State-of-Health Balancing Schemes Through a Smart Battery Model with Online Degradation 8

Comparison of State-of-Charge and State-of-Health Balancing ...

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8th Southern Power electronics Conference and the 17th Brazilian Power electronics Conference, SPEC / COBEP 2023

作者： De Oliveira, Jonathan Silva Da Silveira Brito, Erick Matheus Pereira, Heverton Augusto Cupertino, Allan Fagner Centro Federal de Educação Tecnológica de Minas Gerais Graduate Program in Electrical Engineering Belo Horizonte Brazil Institute of Exact and Technological Sciences Universidade Federal de Viçosa Florestal Brazil Universidade Federal de Viçosa Department of Electrical Engineering Viçosa Brazil Universidade Federal de Juiz de Fora Department of Electrical Energy Juiz de Fora Brazil

ISBN: (纸本)9798350321128

This work evaluates the state-of-charge (SOC) and state-of-health (SOH) balancing techniques for energy storage systems with distributed power electronics architecture, i.e., batteries with integrated power converters. Two approaches are compared: a SOC balancing technique described in the literature and a cascaded SOH/SOC balancing technique proposed in this paper. In the proposed technique, the SOH balancing computes a SOC deviation, leading to different degradation rates in each battery of the pack. The techniques are compared through long-term simulations using: average models for the power converters, a battery-equivalent electric circuit, and an online degradation model. Comparative graphs of capacity and SOH are presented to compare the techniques. Based on simulation results, it was noticed that SOC balancing has fast dynamics equalization, but it leaves the SOH unbalanced if different capacity initial conditions are considered. On the other hand, the SOH balancing is responsible for imposing imbalance on SOC until the SOH reaches the balance. The results reveal that the proposed technique can balance SOH and leads to higher reliability when compared with the traditional SOC balancing. © 2023 IEEE.

关键词： Balancing Smart Battery State of charge State of health

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Magnetoelastic coupling driven magnon gap in a honeycomb antiferromagnet

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Physical Review B 2024年第10期110卷 104419页

作者： Jeongheon Choe Andrey Baydin Gaihua Ye Bowen Ma Cynthia Nnokwe Martin Rodriguez-Vega David Lujan Swati Chaudhary Fuyang Tay Jiaming He Hiroyuki Nojiri Jianshi Zhou Gregory A. Fiete Junichiro Kono Rui He Xiaoqin Li Department of Physics Center of Complex Quantum Systems Center for Dynamics and Control of Materials Department of Electrical and Computer Engineering Smalley–Curl Institute Department of Physics Applied Physics Graduate Program Smalley–Curl Institute Department of Mechanical Engineering Institute for Materials Research Department of Physics and Astronomy Department of Materials Science and NanoEngineering

Cobalt titanate, CoTiO3, is a honeycomb antiferromagnet recently confirmed experimentally to host Dirac magnons, topological spin-orbit excitons, and chiral phonons. Here, we investigate a magnon gap at the zone center which calls for a refined spin Hamiltonian. We propose a microscopic model for the magnon gap and attribute it to a lattice-distortion (phonon)-induced higher-order spin interaction. Strong magnetoelastic coupling in CoTiO3 is also evident in Raman spectra, in which the magnetic order exerts a stronger influence on phonons corresponding to in-plane ionic motions than those with out-of-plane motions. We further examine the evolution of the zone-center magnons in a high magnetic field up to 18.5 T via THz absorption spectroscopy measurements. Based on this field dependence, we propose a spin Hamiltonian that not only agrees with magnon dispersion measured by inelastic neutron scattering but also includes fewer exchange constants and a realistic anisotropy term. Our work highlights the broad implications of magnetoelastic coupling in the study of topologically protected bosonic excitations.

关键词： Magnetism Magnons Topological materials Raman spectroscopy Terahertz spectroscopy

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A Federated Dictionary Learning Method for Process Monitoring With Industrial Applications

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第5期4卷 1017-1028页

作者： Huang, Keke Liu, Xinyi Li, Fanbiao Yang, Chunhua Kaynak, Okyay Huang, Tingwen Central South University School of Automation Changsha410083 China Peng Cheng Laboratory Shenzhen518055 China Bogazici University Department of Electrical and Electronics Engineering Istanbul34342 Turkey University of Science and Technology Beijing Beijing100083 China Texas A&m University at Qatar Science Program Doha23874 Qatar

Edge computing is an indispensable technology in the Industry 4.0 era. The data are stored at the edge and transmission is prohibited due to concerns about data privacy. Thus, the traditional centralized data-driven modeling methods will face many difficulties and can hardly be used in practice. Federated learning aims to let local nodes learn a global model cooperatively while keeping their data localized. Motivated by the pioneering framework of federated learning, a federated dictionary learning method is proposed for process monitoring. In detail, local nodes train local data by the K-SVD method and learn local dictionaries. Then, local dictionaries, instead of local data, are transferred to the fusion center for calculation of the global dictionary. In order to guarantee an optimal global dictionary, a novel federated dictionary average strategy is introduced. Finally, the reconstruction errors of local data and the control limit are calculated based on the global dictionary for process monitoring. The performance of the proposed method is verified on a numerical simulation, the continuous stirred tank heater benchmark process, and the industrial aluminum electrolysis process. Process monitoring can help industrial enterprises monitor the operating status of the industrial process in real time based on the collected process data. However, industrial data are stored at the edge and enterprises pay more attention to the privacy of industrial data. The prohibition of external transmission of raw data leads to challenges for traditional centralized process monitoring methods. The federated dictionary learning method proposed in this article can have the good process monitoring performance under the premise of protecting data privacy, which is superior to some state-of-the-art methods. For example, in the aluminum electrolysis industrial process, the false alarm rate is only 1.91%, and the false discovery rate is 86.23%. The method has high data security and low mod

关键词： Process monitoring

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Natural Light Can Also be Dangerous: Traffic Sign Misinterpretation Under Adversarial Natural Light Attacks

Natural Light Can Also be Dangerous: Traffic Sign Misinterpr...

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IEEE Workshop on Applications of Computer Vision (WACV)

作者： Teng-Fang Hsiao Bo-Lun Huang Zi-Xiang Ni Yan-Ting Lin Hong-Han Shuai Yung-Hui Li Wen-Huang Cheng Department of Electronics and Electrical Engineering National Yang Ming Chiao Tung University Taiwan Hon Hai Research Institute Information Engineering Graduate Institute of Taiwan University

Common illumination sources like sunlight or artificial light may introduce hidden vulnerabilities to AI systems. Our paper delves into these potential threats, offering a novel approach to simulate varying light conditions, including sunlight, headlights, and flashlight illuminations. Moreover, unlike typical physical adversarial attacks requiring conspicuous alterations, our method utilizes a model-agnostic black-box attack integrated with the Zeroth Order Optimization (ZOO) algorithm to identify deceptive patterns in a physically-applicable space. Consequently, attackers can recreate these simulated conditions, deceiving machine learning models with seemingly natural light. Empirical results demonstrate the efficacy of our method, misleading models trained on the GTSRB and LISA datasets under natural-like physical environments with an attack success rate exceeding 70% across all digital datasets, and remaining effective against all evaluated real-world traffic signs. Importantly, after adversarial training using samples generated from our approach, models showcase enhanced robustness, underscoring the dual value of our work in both identifying and mitigating potential threats. 1

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Unveiling Multivariate EEG Features: A Novel Approach to Enhancing ADHD Diagnosis Through Visual and Auditory Attention Tests

Unveiling Multivariate EEG Features: A Novel Approach to Enh...

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2023 International Conference on Fuzzy Theory and Its Applications, iFUZZY 2023

作者： Fan, Zuo-Cian Lin, Ro-Wei Tsai, Ching-Shu Chou, Wen-Jiun Li, Chia-Jung Huang, Zih-Jun Shih, Bin-Yu Wang, Liang-Jen Ko, Li-Wei Department of Biological Science & Technology Hsinchu300 Taiwan National Yang Ming Chiao Tung University International Ph.D. Program in Interdisciplinary Neuroscience Hsinchu300 Taiwan Kaohsiung Chang Gung Memorial Hospit Department of Child and Adolescent Psychiatry National Yang Ming Chiao Tung University Institute of Electrical and Control Engineering Department of Electronics and Electrical Engineering Hsinchu300 Taiwan

ISBN: (纸本)9798350305791

We sought to revolutionize the classification of Attention-Deficit/Hyperactivity Disorder (ADHD) by pioneering an innovative approach that seamlessly integrated auditory and visual tests with electroencephalogram (EEG) coherence analysis, forming the foundation for our Support Vector Machine (SVM) models. Our primary objective was to achieve precise differentiation between individuals with ADHD and those without, aiming to reshape the landscape of ADHD diagnosis and early intervention. We constructed an SVM model based on CATA coherence, incorporating a carefully selected set of 180 features, and another grounded in CPT coherence with 70 features. Astonishingly, our exploration uncovered that a model incorporating CATA's coherence and Power Spectral Density (PSD) as features achieved an astounding accuracy rate of 97.7%, using only half the number of features. This revelation hints at untapped potential for multivariate advancements in the realm of ADHD classification. The landscape of ADHD diagnosis and intervention is perpetually evolving, most notably with the FDA's approval of game therapy for ADHD. Leveraging the transformative power of our findings, we envision the development of user-friendly game therapies for in-home use, empowering both clinicians and parents alike. This marks a paradigm shift in the realm of ADHD intervention, offering objective, data-driven tools for the early management of ADHD. Gamified therapy not only enhances accessibility but also fosters engagement and compliance, all of which are pivotal in the long-term care of individuals with ADHD. In summary, our study represents a significant advancement in our comprehension and classification of ADHD. By fusing auditory and visual tests with EEG coherence analysis, fortified by state-of-the-art machine learning techniques, we have achieved an unprecedented level of accuracy in identifying individuals with ADHD. Our findings, aligned with the FDA-approved game therapy, offer a glimpse into a

关键词： Electroencephalography

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Spectral DefocusCam: hyperspectral imaging using defocus and a spectral filter array

Spectral DefocusCam: hyperspectral imaging using defocus and...

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Computational Optical Sensing and Imaging, COSI 2022

作者： Foley, Christian Monakhova, Kristina Yanny, Kyrollos Waller, Laura Department of Electrical Engineering and Computer Sciences University of California Berkeley United States UCB/UCSF Joint Graduate Program in Bioengineering University of California Berkeley United States

We propose a compact hyperspectral camera based on a focus-tunable lens and spectral filter array. The camera captures then fuses multiple defocused measurements to obtain a high-resolution hyperspectral volume. ©... 详细信息

ISBN: (纸本)9781557528209

关键词： Hyperspectral imaging

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