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26th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, SYNASC 2024

作者： Pan, Victor Y. Lehman College of the City University of New York Department of Computer Science BronxNY10468 United States The Graduate Center of the City University of New York Ph.D. Programs in Mathematics and Computer Science New YorkNY10016 United States

ISBN: (纸本)9798331532833

Our root-finder at ACM-SIAM SOdA 2024 accelerates classical subdivision iterations for approximation of all zeros of a polynomial as well as all its zeros lying in a fixed Region of Interest in the complex plane. The root-finder enhances the power of subdivision classical iterations and promises to compete for user's choice. At SOdA 2024 we proved that that root-finder is near-optimal in some detail, except for its stage of near-maximal compression of a complex disc without losing its roots. Here we complete the proof and also propose to simplify that stage empirically by means of incorporation of Schröder's iterations. © 2024 IEEE.

关键词： Polynomial approximation

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Cuisine Image Recognition and Recommender System using Convolutional Neural Network 5

Cuisine Image Recognition and Recommender System using Convo...

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5th IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2023

作者： Chang, Chin-Chih Wei, Chi-Hung Wu, Kuan-Yun Kao, Hao-Xiang Hsiao, Sean Chung Hua University Department of Computer Science and Information Engineering Hsinchu Taiwan Chung Hua University Ph.D. Program in Engineering Science Hsinchu Taiwan National Kaohsiung University of Science and Technology Department of Computer Science and Information Engineering Kaohsiung Taiwan Chung Hua University Program of Applied Foreign Language and Smart Commerce Hsinchu Taiwan

ISBN: (纸本)9798350314694

The fusion of technology and culinary exploration has allowed for the emergence of advanced online customer service systems. We developed a novel approach to enhance the dining experience. We used a Cuisine Image Recognition and Recommender System (CIRRS) powered by Convolutional Neural Network (CNN) to identify and suggest diverse cuisines based on visual inputs. CIRRS swiftly identified diverse cuisines from user-captured images, offering information on origin, ingredients, and variations by crawling websites. The system enhanced dining experiences by suggesting personalized menus based on individual preferences and past selections. Extensively tested across various culinary genres, CIRRS consistently demonstrated accuracy and adaptability. User feedback validated its potential to simplify dining choices and elevate satisfaction. This innovative system enriched dining experiences as a valuable tool for culinary enthusiasts, chefs, and restaurateurs, bridging the gap between technology and gastronomy, It also offers a unique way to explore Taiwanese cuisines. © 2023 IEEE.

关键词： convolutional neural network (CNN) deep learning image recognition recommender system web crawler

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Real-Time Control and Monitoring of photovoltaic Arrays Using RTdS and BeagleBoard Technology

arXiv

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arXiv 2024年

作者： Rafy, Md Fazley LCSEE WVU doing his Ph.D. in Computer Science United States

Increasing integration of alternate electricity generation due to declining fossil fuels is becoming an essential option for future generations. As photovoltaic technology brings forth enormous benefits to the alternate solution for future power grid systems, this paper presents a comprehensive real-time system designed to model, control, and monitor a PV array subjected to dynamic loads using the Real-Time digital Simulator (RTdS) environment. Integration with the Generic Transducer Network (GTNET)Socket(SKT) module allows for the simulation of various environmental conditions, such as changes in insolation and temperature, and their direct impact on the PV array’s performance. Utilizing BeagleBoard technology, the system demonstrates the capability to modify these conditions through real-time data input, subsequently observing the effects on current and voltage output curves. The real-time simulation results are visualized as a SCAdA system within the Real-time Simulation for Automated Controller design (RSCAd) runtime environment, providing insights into the effective management of solar power *** Codes 94C99 © 2024, CC BY-NC-Nd.

关键词： Microcontrollers

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A Fitness Movement Evaluation System Using deep Learning 15

A Fitness Movement Evaluation System Using Deep Learning

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15th International Conference on Knowledge and Systems Engineering, KSE 2023

作者： Chang, Chin-Chih Wei, Chi-Hung Wu, Hao-Wei Hsiao, Sean Chung Hua University Department of Computer Science and Information Engineering Hsinchu City Taiwan Ph.D. Program in Engineering Science Chung Hua University Hsinchu City Taiwan Bachelor Program in Applied Foreign Language and Smart Commerce Chung Hua University Hsinchu City Taiwan

ISBN: (纸本)9798350329742

This paper proposes a fitness movement evaluation system using deep learning. The system uses a deep convolutional neural network (CNN) to extract features from pictures of fitness movements. The features are then used to classify the movements into different categories. The system is evaluated on a dataset of pictures of fitness movements. The results show that the system can accurately classify the movements into different categories. The system is designed to provide feedback to users on their fitness movements. The proposed system is a valuable tool for fitness enthusiasts. The main contribution of this paper is to propose a way to give users a score for their fitness movement. It can help users improve their fitness and track their progress over time. The system is also a valuable tool for fitness professionals. It can help professionals develop new fitness programs and provide feedback to their clients. © 2023 IEEE.

关键词： Convolutional neural networks

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An Application-Aware Clustering Strategy in Vehicular Ad Hoc Network 6

An Application-Aware Clustering Strategy in Vehicular Ad Hoc...

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6th International Conference on Computing, Electronics and Communications Engineering, iCCECE 2023

作者： Wei, Chi-Hung Chang, Chin-Chih Chu, Chien-Hao Hsiao, Sean Chung Hua University Ph.D. Program in Engineering Science Hsinchu City Taiwan Chung Hua University Department of Computer Science and Information Engineering Hsinchu City Taiwan Chung Hua University Bachelor Program in Applied Foreign Language and Smart Commerce Hsinchu City Taiwan

ISBN: (纸本)9798350340921

VANET (Vehicular Ad Hoc Network) was developed to address the unique challenges of communication in a mobile vehicular environment. This technology can enhance driving safety and traffic control. Moreover, similar principles can be applied to unmanned aerial vehicles and robotics. When transmitting data in moving vehicles, it is challenging as access points and base stations keep changing, requiring handovers between different access points and base stations. Clustering is a popular approach used to address this challenge. Common clustering strategies are based on the proximity, communication range, the density, the application requirements of the moving vehicles, or hybrid of them. In this paper, we propose a clustering strategy based on the application sublayer in the vehicle communication protocol. If two computing devices on the move run the same application, they can be grouped together. The contribution of this research is to develop a clustering mechanism based on the application with very low overhead. We also validate the proposed method through simulations. The simulation results indicate that the proposed method is feasible. © 2023 IEEE.

关键词： Vehicular ad hoc networks

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Machine-learning-based sampling inspection under OQC capacity for real-time quality monitoring in the TFT-LCd industry

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International Journal of Production Research 2025年第6期63卷 2090-2113页

作者： Shih, Ming-Sung Chen, James C. Chen, Tzu-Li Chiang, Chih-Hsiung Hsu, Ching-Lan International Intercollegiate Ph.D. Program National Tsing Hua University Hsinchu Taiwan Department of Industrial Engineering and Engineering Management National Tsing Hua University Hsinchu Taiwan Graduate Institute of Intelligent Manufacturing Technology National Taiwan University of Science and Technology Taipei Taiwan Division of Quality Control AUO Corporation Taichung Taiwan

In the thin film transistor-liquid crystal display (TFT-LCd) industry, the competition is fierce, and the external failure cost is high. To improve the quality of released products, sampling inspection under outgoing quality control (OQC) is conducted before products are released. Owing to the variety and complexity of inspection items, the task must be carried out manually while realising automation is difficult. Traditional random sampling inspection is limited by sampling quantity, manpower, and material resources. To effectively improve acceptable quality levels, this paper proposes a quality predictive monitoring framework tailored to the production and inspection characteristics of the TFT-LCd industry. This framework includes real-time automatic quality monitoring based on an optimal machine learning prediction model with two-phase feature creation, and it also addresses OQC inspection capacity constraints. Experimental results show the prediction performance based on proposed model is better than the traditional random sampling process. In practice, superior average outgoing quality improves by more than 70% at the same sampling level. In addition, the features of the process can be further controlled and managed based on the prediction model to monitor the key parameters in real time and respond to abnormalities quickly, thereby further improving the quality of released products. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Liquid crystal displays

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Engineering H2O2 Self-Supplying Platform for Xdynamic Therapies via Ru-Cu Peroxide Nanocarrier: Tumor Microenvironment-Mediated Synergistic Therapy

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ACS Applied Materials and Interfaces 2024年第19期16卷 24172-24190页

作者： dirersa, Worku Batu Kan, Tzu-Chun Chang, Jungshan Getachew, Girum Ochirbat, Sonjid Kizhepat, Shamsa Wibrianto, Aswandi Rasal, Akash Chen, Hung-An Ghule, Anil Vithal Chou, Tzung-Han Chang, Jia-Yaw Department of Chemical Engineering National Taiwan University of Science and Technology Taipei106335 Taiwan Graduate Institute of Medical Sciences College of Medicine Taipei Medical University Taipei11031 Taiwan International Master/Ph.D. Program in Medicine College of Medicine Taipei Medical University Taipei11031 Taiwan International Ph.D. Program for Cell Therapy and Regeneration Medicine College of Medicine Taipei Medical University Taipei11031 Taiwan Green Nanotechnology Laboratory Department of Chemistry Shivaji University Kolhapur416004 India Department of Chemical and Materials Engineering National Yunlin University of Science and Technology Yunlin64002 Taiwan

Of the most common, hypoxia, overexpressed glutathione (GSH), and insufficient H2O2 concentration in the tumor microenvironment (TME) are the main barriers to the advancment of reactive oxygen species (ROS) mediated Xdynamic therapies (X = photo, chemodynamic, chemo). Maximizing Fenton catalytic efficiency is crucial in chemodynamic therapy (CdT), yet endogenous H2O2 levels are not sufficient to attain better anticancer efficacy. Specifically, there is a need to amplify Fenton reactivity within tumors, leveraging the unique attributes of the TME. Herein, for the first time, we design RuxCu1-xO2-Ce6/CPT (RCpCCPT) anticancer nanoagent for TME-mediated synergistic therapy based on heterogeneous Ru-Cu peroxide nanodots (RuxCu1-xO2 Nds) and chlorine e6 (Ce6), loaded with ROS-responsive thioketal (TK) linked-camptothecin (CPT). The Ru-Cu peroxide Nds (RCp Nds, x = 0.50) possess the highest oxygen vacancy (OV) density, which grants them the potential to form massive Lewis’s acid sites for peroxide adsorption, while the dispersibility and targetability of the Nds were improved via surface modification using hyaluronic acid (HA). In TME, RCpCCPT degrades, releasing H2O2, Ru2+/3+, and Cu+/2+ ions, which cooperatively facilitate hydroxyl radical (•OH) formation and deactivate antioxidant GSH enzymes through a cocatalytic loop, resulting in excellent tumor therapeutic efficacy. Furthermore, when combined with laser treatment, RCpCCPT produces singlet oxygen (1O2) for PdT, which induces cell apoptosis at tumor sites. Following ROS generation, the TK linkage is disrupted, releasing up to 92% of the CPT within 48 h. In vitro investigations showed that laser-treated RCpCCPT caused 81.5% cell death from PdT/CdT and chemotherapy (CT). RCpCCPT in cancer cells produces red-blue emission in images of cells taking them in, which allows for fluorescence image-guided Xdynamic treatment. The overall results show that RCp Nds and RCpCCPT are more biocompatible and have excellent Xdynamic the

关键词： Oxidation

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Integrating physical Wheelchairs and Virtual Basketball for Enhanced Access and Enjoyment in Wheelchair Basketball 6

Integrating Physical Wheelchairs and Virtual Basketball for ...

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6th IEEE International Conference on Artificial Intelligence and eXtended and Virtual Reality, AIxVR 2024

作者： Hsu, Yu-Hung Fang, Iuan-Kai Wang, Chun Chen, Jui-Chien Hu, Min-Chun National Tsing Hua University Department of Computer Science Hsinchu Taiwan National Tsing Hua University International Intercollegiate Ph.D. Program Hsinchu Taiwan National Taiwan University Department of Electrical Engineering Taipei Taiwan University of Texas at Austin School of Information United States

ISBN: (纸本)9798350372021

Wheelchair basketball for people with disabilities is a niche market with limited attention and resources due to its smaller scale. We utilize the characteristics of VR technology, combining wearable devices with physical wheelchair seating, to create a virtual and real interactive game of wheelchair basketball. This enables people with disabilities to exercise at home and allows the general public to experience the challenges of disability sports, fostering empathy and emphasizing the importance of social justice. © 2024 IEEE.

关键词： Virtual reality

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deep-Learning Approach for An Analysis of Real-estate Prices and Transactions

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IEEE Access 2025年 13卷 89248-89265页

作者： Yang, Cheng-Hong Lee, Borcy Lin, Yu-da Tainan University of Technology Department of Information Management Tainan Taiwan National Kaohsiung University of Science and Technology Department of Electronic Engineering Kaohsiung Taiwan Kaohsiung Medical University Ph.D. Program in Biomedical Engineering Kaohsiung Taiwan Kaohsiung Medical University School of Dentistry Kaohsiung Taiwan Kaohsiung Medical University Drug Development and Value Creation Research Center Kaohsiung Taiwan National Penghu University of Science and Technology Department of Computer Science and Information Engineering Penghu Taiwan

Research on the study of houses, condominiums and buildings in Taiwan’s metropolitan areas continues to be an important area of research. In real estate forecasting and analysis, methods such as statistical analysis and questionnaire data collection are widely used. However, when multidimensional data is considered, these methods are time-consuming and inadequate. This study aimed to build a real estate forecasting model that can adapt to a changing environment. data were collected from public government databases, the collected data were standardized for accurate clustering, an appropriate data clustering algorithm was applied to the standardized data, and cross-statistical analysis was performed to verify the adopted algorithm. We used a deep learning based on autoencoder algorithm to increase the accuracy of the clustering analysis. A double-bottom map particle swarm optimization (dBM-PSO) clustering algorithm was then used to determine the optimal clustering solution. Cluster analysis and deep learning were conducted on data collected from public websites to understand the factors that led to the sustained increase in housing prices in Taiwan over the past decade. The results of this study indicate that three key factors—the number of real estate transactions, the average unit price of real estate transactions, and the building material and construction index—significantly affected real estate prices in Taiwan. Our results could help researchers and governments to focus on specific aspects of real estate development without being influenced by other related factors. In addition, the relationships between real estate trends and the aforementioned three key factors were determined to obtain valuable information that can enable the Taiwanese government to regulate the property market and prevent excessive growth. The framework proposed in this paper allows researchers and governments to focus on specific aspects of real estate development without being influenced b

关键词： Houses

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Overcoming the Blood-Brain Tumor Barrier with docetaxel-Loaded Mesoporous Silica Nanoparticles for Treatment of Temozolomide-Resistant Glioblastoma

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ACS Applied Materials and Interfaces 2024年第17期16卷 21722-21735页

作者： Hsu, Tsung-I Chen, Yi-Ping Zhang, Rong-Lin Chen, Zih-An Wu, Cheng-Hsun Chang, Wen-Chang Mou, Chung-Yuan Chan, Hardy Wai-Hong Wu, Si-Han Ph.D. Program in Medical Neuroscience College of Medical Science and Technology Taipei Medical University and National Health Research Institutes Taipei110 Taiwan International Master Program in Medical Neuroscience College of Medical Science and Technology Taipei Medical University Taipei110 Taiwan Graduate Institute of Nanomedicine and Medical Engineering Taipei Medical University Taipei110 Taiwan International Ph.D. Program in Biomedical Engineering College of Biomedical Engineering Taipei Medical University Taipei110 Taiwan Nano Targeting & Therapy Biopharma Inc. Taipei110 Taiwan Graduate Institute of Medical Sciences College of Medicine Taipei Medical University Taipei110 Taiwan Department of Chemistry National Taiwan University Taipei106 Taiwan

While temozolomide (TMZ) has been a cornerstone in the treatment of newly diagnosed glioblastoma (GBM), a significant challenge has been the emergence of resistance to TMZ, which compromises its clinical benefits. Additionally, the nonspecificity of TMZ can lead to detrimental side effects. Although TMZ is capable of penetrating the blood-brain barrier (BBB), our research addresses the need for targeted therapy to circumvent resistance mechanisms and reduce off-target effects. This study introduces the use of PEGylated mesoporous silica nanoparticles (MSN) with octyl group modifications (C8-MSN) as a nanocarrier system for the delivery of docetaxel (dTX), providing a novel approach for treating TMZ-resistant GBM. Our findings reveal that C8-MSN is biocompatible in vitro, and dTX@C8-MSN shows no hemolytic activity at therapeutic concentrations, maintaining efficacy against GBM cells. Crucially, in vivo imaging demonstrates preferential accumulation of C8-MSN within the tumor region, suggesting enhanced permeability across the blood-brain tumor barrier (BBTB). When administered to orthotopic glioma mouse models, dTX@C8-MSN notably prolongs survival by over 50%, significantly reduces tumor volume, and decreases side effects compared to free dTX, indicating a targeted and effective approach to treatment. The apoptotic pathways activated by dTX@C8-MSN, evidenced by the increased levels of cleaved caspase-3 and PARP, point to a potent therapeutic mechanism. Collectively, the results advocate dTX@C8-MSN as a promising candidate for targeted therapy in TMZ-resistant GBM, optimizing drug delivery and bioavailability to overcome current therapeutic limitations. © 2024 The Authors. Published by American Chemical Society.

关键词： Tumors

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