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Wireless Power Week (WPW)

作者： Xiaochen Yu Ya-Xun Lin Ivona Z. Mitrovic Steve Hall der-Sheng Chao Jenq-Horng Liang Yi Huang Ta-Jen Yen Jiafeng Zhou International Intercollegiate Ph.D. Program National Tsing Hua University Hsinchu Taiwan Department of Electrical Engineering and Electronics University of Liverpool Liverpool United Kingdom Nuclear Science and Technology Development Center National Tsing Hua University Hsinchu Taiwan Department of Engineering and System Science National Tsing Hua University Hsinchu Taiwan Department of Materials Science and Engineering National Tsing Hua University Hsinchu Taiwan

ISBN: (数字)9798350349139

ISBN: (纸本)9798350349146

This paper presents a 20 W high-power RF-to-dC rectifier that can be applied to wireless power transfer (WPT) systems. To address the power limitations of conventional diodes, a rectifier is designed based on a gallium nitride (GaN) integrated circuit (IC) quasi-vertical Schottky barrier diode (SBd). Leveraging the advantages of high mobility, wide bandgap, and saturation velocity of the GaN, significant benefits of the diode have been derived, with an ultra-low turn-on voltage of 0.23 ± 0.005 V (at 1 A/cm 2 ) and a high breakdown voltage of 180 V, to improve the rectification performance. Using this diode, a high-power rectifier with a wide input power range and high efficiency is developed. The proposed rectifier structure consists of a single-shunt self-developed diode and a topology with class-F harmonics control networks. It achieves a peak power conversion efficiency of 72.9% with an input power of 43 dBm at 900 MHz.

关键词： Wireless communication Integrated circuits Schottky diodes Wireless sensor networks Breakdown voltage Rectifiers Wireless power transfer

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An Application-Aware Clustering Strategy in Vehicular Ad Hoc Network

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

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International Conference on Computing, Electronics & Communications engineering (iCCECE)

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

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.

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A Fitness Movement Evaluation System Using deep Learning

A Fitness Movement Evaluation System Using Deep Learning

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International Conference on Knowledge and Systems engineering (KSE)

作者： Chin-Chih Chang Chi-Hung Wei Hao-Wei Wu Sean Hsiao Department of Computer Science and Information Engineering Chung Hua University 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

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.

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Advances in liposomal nanotechnology: from concept to clinics

RSC Pharmaceutics

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RSC pharmaceutics 2024年第5期1卷 928-948页

作者： Reem M. Senjab Nour AlSawaftah Waad H. Abuwatfa Ghaleb A. Husseini Biomedical Engineering Program College of Engineering American University of Sharjah Sharjah P.O. Box 26666 United Arab Emirates ghusseini@aus.edu Materials Science and Engineering Ph.D. Program College of Arts and Sciences American University of Sharjah Sharjah P.O. Box 26666 United Arab Emirates Department of Chemical and Biological Engineering College of Engineering American University of Sharjah Sharjah P.O. Box 26666 United Arab Emirates Biosciences and Bioengineering PhD Program College of Engineering American University of Sharjah Sharjah P.O. Box 26666 United Arab Emirates

Liposomes, spherical phospholipid vesicles with a unique morphology mimicking that of body cells, have emerged as versatile nanoparticles for drug delivery. Their biocompatibility, low cytotoxicity, targeted delivery, and hydrophobic and hydrophilic characteristics make them stand out over traditional drug delivery systems. Liposomes can be tailored in size, composition, lamellarity, and surface charge, offering a unique level of customization for various applications. Extensive research in liposome technology has led to the development of a wide range of liposomal formulations with enhanced functionalities, such as PEGylated liposomes, ligand-targeted liposomes, and stimuli-responsive liposomes. Beyond their crucial role in cancer treatment, liposomes play a significant role in influenza, COVId-19, cancer, and hepatitis A vaccines. They are also utilized in pain management, fungal treatment, brain targeting, and topical and ocular drug delivery. This review offers insight into the types of liposomes, their composition, preparation methods, characterization methods, and clinical applications. Additionally, it discusses challenges and highlights potential future directions in liposome-based drug delivery.

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Selectivity Toward H2S Against Various Gaseous disease Markers in Exhaled Breath of Flame-Produced CuOx-doped SnO2 Nanosensors

SSRN

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SSRN 2024年

作者： Liewhiran, Chaikarn Puninsang, Matawee Inyawilert, Kanittha Siriwalai, Mameaseng Wisitsoraat, Anurat Department of Physics and Materials Science Faculty of Science Chiang Mai University Chiang Mai50202 Thailand Ph.D. Program in Nanoscience and Nanotechnology International Program/Interdisciplinary Faculty of Science Chiang Mai University Chiang Mai50200 Thailand Office of Research Administration Chiang Mai University Chiang Mai50200 Thailand Pathum Thani Klong Luang12120 Thailand

In this research, the gas-sensing behaviors particularly selectivity toward H2S against various gaseous disease markers in exhaled breath of flame-produced SnO2 nanoparticles augmented with 0–3 wt% Cu were extensively investigated. Structural and chemical characterization results revealed 0.3–0.8 nm CuOx sub-nanoparticles with Cu+ and Cu2+ states embedded on 5–20 nm cassiterite SnO2 nanoparticles. From gas-sensing measurements, the best Cu amount of 2 wt% provided an outstanding response of ∼1.56 × 105 with a decent response time of 8 s to 10 ppm H2S at the most suitable sensing temperature of 200 °C. Also, the most effective CuOx-doped SnO2 sensor offered a very low limit of H2S detection (LOds) of 0.012 ppm (12 ppb) and high H2S selectivity against several gaseous disease markers, including H2, CH4, C2H6, C5H8, CO, NO, NH3, CH3OH, C2H5OH, C3H7OH, C3H6O, (CH3)2S, CH3SH, CH3COOH, C2H5COOH, and C3H7COOH. Moreover, the CuOx-doped SnO2 sensors showed high response and excellent long-term stability. Furthermore, the CuOx-doped SnO2 sensors exhibited modestly low H2S response decline at 80%RH and 200°C. Therefore, the flame-produced 2 wt% CuOx-doped SnO2 nanomaterial would be highly attractive for ultrasensitive and selective H2S detections in biomedical applications especially exhaled breath analysis of gastrointestinal diseases. © 2024, The Authors. All rights reserved.

关键词： Spray pyrolysis

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A Shoe Shopping System based on Convolutional Neural Network Image Recognition

A Shoe Shopping System based on Convolutional Neural Network...

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IEEE Eurasia Conference on IOT, Communication and engineering (ECICE)

作者： Chin-Chih Chang Chi-Hung Wei Chen-Sheng Chen Jian-An Chen Shih-Hsien Yeh Sean Hsiao Department of Computer Science and Information Engineering Chung Hua University Hsinchu Taiwan Ph.D. Program in Engineering Science Chung Hua University Hsinchu Taiwan Department of Computer Science and Information Engineering Chang Gung University Taoyuan Taiwan Bachelor Program in Applied Foreign Language and Smart Commerce Chung Hua University Hsinchu Taiwan

In this article, we present an innovative approach to enhance the online shoe shopping experience. The convolutional neural network (CNN) image recognition technology was used to enhance shoe classification and recommendations. By training the CNN model on an extensive dataset, unique shoe features and styles were learned. Integrated into a user-friendly online platform, the system offers real-time image recognition, allowing users to snap a photo of a desired shoe for instant identification, including brand, price, and availability details. Moreover, the CNN-based recommendation engine provides personalized suggestions based on style, color, and customer preferences, enriching the shopping experience. Evaluation results confirmed the system's feasibility, and user feedback highlighted its effectiveness in simplifying the shopping process and enhancing satisfaction. This innovative system presents a significant leap in merging AI and e-commerce and shows the potential of image recognition to transform online marketplaces, benefiting consumers, offering valuable insights for retailers, and ultimately reshaping the future of online shoe shopping.

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Robust Feature Learning Against Noisy Labels

Robust Feature Learning Against Noisy Labels

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IEEE International Conference on Image Processing

作者： Tsung-Ming Tai Yun-Jie Jhang Wen-Jyi Hwang Free University of Bozen-Bolzano NVIDIA AI Technology Center International Intercollegiate Ph.D. Program National Tsing Hua University Department of Computer Science and Information Engineering National Taiwan Normal University

Supervised learning of deep neural networks heavily relies on large-scale datasets annotated by high-quality labels. In contrast, mislabeled samples can significantly degrade the generalization of models and result in memorizing samples, further learning erroneous associations of data contents to incorrect annotations. To this end, this paper proposes an efficient approach to tackle noisy labels by learning robust feature representation based on unsupervised augmentation restoration and cluster regularization. In addition, progressive self-bootstrapping is introduced to minimize the negative impact of supervision from noisy labels. Our proposed design is generic and flexible in applying to existing classification architectures with minimal overheads. Experimental results show that our proposed method can efficiently and effectively enhance model robustness under severely noisy labels.

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Cuisine Image Recognition and Recommender System using Convolutional Neural Network

Cuisine Image Recognition and Recommender System using Convo...

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IEEE Eurasia Conference on IOT, Communication and engineering (ECICE)

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

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.

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discrete null field equation methods for solving Laplace's equation: Boundary layer computations

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Numerical Methods for Partial differential Equations 2024年第4期40卷 e23092-e23092页

作者： Zhang, Li-Ping Li, Zi-Cai Lee, Ming-Gong Huang, Hung-Tsai School of Mathematical Sciences Qufu Normal University Shandong Qufu China Department of Applied Mathematics National Sun Yat-sen University Kaohsiung Taiwan Department of Tourism and Leisure/Ph.D. Program in Engineering Science Chung Hua University Hsinchu Taiwan Department of Data Science and Analytics I-Shou University Kaohsiung Taiwan

Consider dirichlet problems of Laplace's equation in a bounded simply-connected domain (Formula presented.), and use the null field equation (NFE) of Green's representation formulation, where the source nodes (Formula presented.) are located on a pseudo-boundary (Formula presented.) outside (Formula presented.) but not close to its boundary (Formula presented.). Simple algorithms are proposed in this article by using the central rule for the NFE, and the normal derivatives (Formula presented.) of the solutions on the boundary (Formula presented.) can be easily obtained. These algorithms are called the discrete null field equation method (dNFEM) because the collocation equations are, indeed, the direct discrete form of the NFE. The bounds of the condition number are like those by the method of fundamental solutions (MFS) yielding the exponential growth as the number of unknowns increases. One trouble of the dNFEM is the near singularity of integrations for the solutions in boundary layers in Green's representation formulation. The traditional BEM also suffers from the same trouble. To deal with the near singularity, quadrature by expansions and the sinh transformation are often used. To handle this trouble, however, we develop two kinds of new techniques: (I) the interpolation techniques by Taylor's formulas with piecewise (Formula presented.) -degree polynomials and the Fourier series, and (II) the mini-rules of integrals, such as the mini-Simpson's and the mini-Gaussian rules. Error analysis is made for technique I to achieve optimal convergence rates. Numerical experiments are carried out for disk domains to support the theoretical analysis made. The numerical performance of the dNFEM is excellent for disk domains to compete with the MFS. The errors with (Formula presented.) can be obtained by combination algorithms, which are satisfactory for most engineering problems. In summary, the new simple dNFEM is based on the NFE, which is different from the boundary elem

关键词： Fourier series

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Acid-catalyzed hydrothermal treatment of sewage sludge: effects of reaction temperature and acid concentration on the production of hydrolysis by-products

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Biomass Conversion and Biorefinery 2023年第9期13卷 7533-7546页

作者： pham, Van Toi Guan, Chung-Yu Han, Po-Chun Matsagar, Babasaheb M. Wu, Kevin C. W. Ahamad, Tansir Chang, Ching-Yuan Yu, Chang-Ping Graduate Institute of Environmental Engineering National Taiwan University Taipei106 Taiwan Faculty of Environmental Engineering National University of Civil Engineering 55 Giai Phong Hai Ba Trung Hanoi100000 Viet Nam Department of Environmental Engineering National Ilan University Yilan260 Taiwan Ph.D. Program of Green Materials and Precision Devices National Taiwan University Taipei10617 Taiwan Department of Chemical Engineering National Taiwan University No. 1 Sec. 4 Roosevelt Road Taipei10617 Taiwan Department of Chemistry College of Science King Saud University P.O. Box 2455 Riyadh11451 Saudi Arabia Water Innovation Low Carbon and Environmental Sustainability Research Center National Taiwan University Taipei106 Taiwan

Sewage sludge (SS) is an organic biomass from wastewater treatment plants, but the use of SS to produce bio-based chemicals under acid-catalyzed hydrolysis (ACH) processes is not well investigated. In this study, ACH processes were performed at 120, 150, and 180 °C using sulfuric acid (H2SO4) with concentrations of 0–0.5 M within a reaction time of 90–180 min to produce bio-based chemicals from SS. The results showed that the reaction temperature, reaction time, and concentrations of H2SO4 affected the yields of sugars, levulinic acid (LA), and 5-hydroxymethylfurfural (HMF). The highest xylose and glucose yields were 7.69 mol% and 5.22 mol% at 120 °C with 0.5 M H2SO4 during the 180 min of reaction time, respectively. Besides, under the ACH process at 180 °C and 180 min, the yield of LA reached a maximum value of 0.48 mol% at 0.5 M H2SO4, and the highest yield of HMF was 1.66 mol% at 0.1 M H2SO4. The data obtained also indicated that the ACH process led to an increase in soluble chemical oxygen demand, dissolved organic carbon, and total dissolved nitrogen. Fluorescence excitation-emission matrix of dissolved organic matter and thermogravimetric analysis of the treated SS further supported the enhanced solubilization of SS after ACH process, especially under higher reaction temperature and concentrations of H2SO4. Although the lower yields of bio-based chemicals might restrict the downstream recovery of the target products, the largely improved SS solubilization by ACH suggests that acid-catalyzed hydrothermal hydrolysis process has potential as an alternative pretreatment method of SS. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Sewage sludge

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