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International Conference on Imaging, Signal Processing and Communication (ICISPC)

作者： James Wang Fang-Biau Ueng Yun-Hsuan Sung Hsuan-Fu Wang Dept. Electrical Engineering National Chung Hsing University Taichung Taiwan Bachelor’s Program in Intelligent Robot National Yunlin University of Science and Technology Yunlin Taiwan

ISBN: (纸本)9781665454810

In order to achieve high throughput requirements of next-generation communication systems, Multiple-Input Multiple-Output (MIMO) technology should be employed. Generalized Frequency Division Multiplexing (GFDM) signal has lower Out-Of-Band (OOB) emission and peak-to-average power ratio and can be used for next-generation communications. But the pulse shaping filter used by GFDM will cause Inter Symbol Interference (ISI) and Inter Carrier Interference (ICI). In this paper, we propose low-complexity joint receivers to suppress ISI, ICI and multi-user interference (MUI) for multi-user MIMO-GFDM uplink communication. The traditional solution is to design the equalizer and the GFDM demodulation separately, but this paper deals with the two parts together to reduce the complexity initially. The complexity of the preliminary joint receiver is still not low enough, so we derive two closed-form expressions for the low-complexity joint receivers, which not only greatly reduces the complexity, but also achieves the optimal error rate performance. Some simulation examples are given to demonstrate the effectiveness of the proposed low-complexity multi-user MIMO-GFDM receivers.

关键词： Wireless communication Symbols Receivers Interference Throughput Complexity theory Uplink

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Thickness, Annealing, and Surface Roughness Effect on Magnetic and Significant Properties of Co40Fe40B10Dy10 Thin Films

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Materials 2023年第17期16卷 5995页

作者： Liu, Wen-Jen Chang, Yung-Huang Chiang, Chia-Chin Chen, Yuan-Tsung Wang, Yu-Zhi Wu, Chueh-Lin Lin, Shih-Hung Ou, Sin-Liang Department of Materials Science and Engineering I-Shou University Kaohsiung84001 Taiwan Bachelor Program in Industrial Technology National Yunlin University of Science and Technology 123 University Road Section 3 Douliou64002 Taiwan Department of Mechanical Engineering National Kaohsiung University of Science and Technology Kaohsiung80778 Taiwan Graduate School of Materials Science National Yunlin University of Science and Technology 123 University Road Section 3 Douliou64002 Taiwan Department of Electronic Engineering National Yunlin University of Science and Technology 123 University Road Section 3 Douliou64002 Taiwan Bachelor Program for Design and Materials for Medical Equipment and Devices Da-Yeh University Changhua51591 Taiwan

In this study, Co40Fe40B10Dy10 thin films were deposited using a direct current (DC) magnetron sputtering technique. The films were deposited on glass substrates with thicknesses of 10, 20, 30, 40, and 50 nm, and heat-treated in a vacuum annealing furnace at 100, 200, and 300 °C. Various instruments were used to examine and analyze the effects of roughness on the magnetic, adhesive, and mechanical properties. From the low frequency alternating current magnetic susceptibility (χac) results, the optimum resonance frequency is 50 Hz, and the maximum χac value tends to increase with the increase in the thicknesses and annealing temperatures. The maximum χac value is 0.18 at a film thickness of 50 nm and an annealing temperature of 300 °C. From the four-point probe, it is found that the resistivity and sheet resistance values decrease with the increase in film deposition thicknesses and higher annealing temperatures. From the magnetic force microscopy (MFM), the stripe-like magnetic domain distribution is more obvious with the increase in annealing temperature. According to the contact angle data, at the same annealing temperature, the contact angle decreases as the thickness increases due to changes in surface morphology. The maximal surface energy value at 300 °C is 34.71 mJ/mm2. The transmittance decreases with increasing film thickness, while the absorption intensity is inversely proportional to the transmittance, implying that the thickness effect suppresses the photon signal. Smoother roughness has less domain pinning, more carrier conductivity, and less light scattering, resulting in superior magnetic, electrical, adhesive, and optical performance. © 2023 by the authors.

关键词： Thin films

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Pipeline System for Versatile Medical Image Processing

Pipeline System for Versatile Medical Image Processing

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Data Science and engineering in Healthcare, Medicine and Biology, IEEE EMBS Special Topic Conference on

作者： A. B. Rodríguez-Carpio E. Bojorges-Valdez O. Yáñez-Suarez bachelor student of Biomedical Engineering program at Universidad Iberoamericana Ciudad de México CDMX 01219 MX Universidad Iberoamericana Ciudad de México CDMX 01219 MX Universidad Autónoma Metropolitana Iztapalapa CDMX 09340 MX

Machine learning (ML) algorithms are valuable for various clinical imaging tasks, including organ and lesion segmentation and diagnostic support. This paper suggests a versatile system architecture that follows a standard pipeline of capture, processing, and display and a manager that evaluates input image and performs user-selected tasks over it. This architecture can perform three distinct tasks: detecting specific images in a scene, segmenting based on imaging modality, and classifying images related to pathology. The system was evaluated using Dice index (DI) in segmentation tasks such as lung from CT and transmission radiography (DI: 0.956 ± 0.015, DI: 0.8013 ± 0.277 respectively), heart from CT (DI: 0.8396 ± 0.2188), and optic disc from slit lamps (DI: 0.9134 ± 0.079). In diagnostic tasks, accuracy was evaluated for five-class fundus pathology classification (0.854 ± 0.015) and six class thoracic radiography classification (0.972 ± 0.0069). The real time operation of the system depends on the active mode: detection and segmentation are performed at 23 frames per second (fps), detection and diagnostic 3 fps, all modes at once 2 fps. These values show the viability of the architecture presented here as proof of *** Relevance: The presented project aims to build a versatile visualization assistance system for medical staff, that allows three operation modes by an easy change of internal ML model.

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Inventory policy design to minimize overstock and stockout of coal using periodic review power approximation method

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AIP Conference Proceedings 2023年第1期2482卷

作者： Triafini Nugroho Muhammad Nashir Ardiansyah Prafajar Suksessano Muttaqin 1Industrial Engineering Bachelor Degree Program Telkom University Bandung Indonesia 2Logistic Engineering Bachelor Degree Program Telkom University Bandung Indonesia

Coal is one of the raw materials used as a source of heat energy in several industries and Indonesia is one of the largest coal producing countries in the world and coal is spread in several areas in Indonesia. Tanjung Enim is located in South Sumatra is one of the coal-producing areas and there are many companies engaged in coal mining, one of which is FGH Ltd. FGH Ltd is one of the state-owned enterprises in Indonesia which the main activity is producing coal. However, there are several problems with coal inventory especially in 2020, where due to unpredictable coal demand, amount of coal inventory provided by FGH Ltd is “as much as possible” so it caused overstock, but there is also a stockout condition because FGH Ltd does not have a safety stock policy when that condition happened. Based on these problems, this research aims to minimize overstock and stockout of coal inventories using the Periodic Review (R, s, S) Power Approximation method then the results are Interval Review (R)=1,2 days, minimum inventory level (s)=167,675.22 tons, Maximum inventory level (S)=170,246.01 tons, and safety stock (ss)=100,306.73 tons. Then the author tried to simulate the proposed inventory policy with simple simulation in excel then the result are total inventory cost is decrease 39% from the actual condition where the proposed total inventory cost consist of total holding cost is decrease 20%, total shortage cost is decrease 100%, total procurement cost is increase 27%.

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Mean-variance and Safety-first Portfolio Selection Strategy for High-volume/Highvalue Traded Stocks in Philippine Stock Exchange (PSE)

Mean-variance and Safety-first Portfolio Selection Strategy ...

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IEEE International Conference on Industrial engineering and engineering Management

作者： T. C. De Guzman M. N. Young Y. T. Prasetyo S. F. Persada R. Nadilfatin School of Graduate Studies Mapua University Manila Philippines School of Industrial Engineering and Engineering Management Mapua University Manila Philippines Department of Industrial Engineering and Management Yuan Ze University Taoyuan City Taiwan International Bachelor Program in Engineering Yuan Ze University Taoyuan City Taiwan Entrepreneurship Business Creation Business School Binus University West Jakarta Indonesia Department of Information Systems Institut Teknologi Sepuluh Nopember Surabaya Indonesia

ISBN: (数字)9798350386097

ISBN: (纸本)9798350386103

This research evaluates the effectiveness of Mean-Variance (MV) and Safety-First (SF) portfolio selection strategies for high-volume/high-value traded stocks on the Philippine Stock Exchange (PSE). Analyzing data from 288 stocks over 7,670 trading days (30 years), we estimated stock returns, assigned probability weights, and conducted optimization models. The results show that the Mean-Variance (MV0.5) strategy achieved the best balance between risk and return, boasting the highest mean and cumulative returns with moderate volatility. The Safety-First (SF) strategy, however, yielded negative cumulative returns, indicating potential issues. The Market strategy had the lowest mean return but also the least risk, appealing to risk-averse investors. The Portfolio Mean-Variance (PMV) strategy provided a reasonable balance of returns and risk. Statistical analysis revealed that only the MV0.5 strategy outperformed market returns. These findings offer crucial insights into risk assessment and portfolio management for investors evaluating these strategies against market performance.

关键词： Statistical analysis Optimization models Oils Safety Risk management Stock markets Portfolios Standards Monitoring Investment

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A DEA-CCR Model Application in Clustered Stocks Portfolio with Technical Investment Strategies and Mean-Variance Model

A DEA-CCR Model Application in Clustered Stocks Portfolio wi...

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IEEE International Conference on Industrial engineering and engineering Management

作者： M.M. Navarro M. N. Young Y.T. Prasetyo J.L. Camino B.B. Navarro V.T. Ramos School of Industrial Engineering and Engineering Management Mapúa University Manila Philippines School of Graduate Studies Mapúa University Manila Philippines Department of Industrial Engineering Technological Institute of the Philippines Quezon City Philippines International Bachelor Program in Engineering Yuan Ze University Taiwan Department of Civil Engineering Technological Institute of the Philippines Quezon City Philippines Department of Electrical Engineering Technological Institute of the Philippines Quezon City Philippines

The stock market is one of the investment options that offer the best potential returns. However, investors are aware of the risk associated with their investments, which is why stock portfolios are established to diversify investments or distribute investments. Managing a portfolio may be challenging because there are so many different stocks. One of the challenges is deciding how much money to invest and how to allocate it for the best results. This paper used the K-means Algorithm for cluster analysis in identifying Decision Making Unit (DMU), the stocks will be grouped according to their characteristics considering their risk and return. Asset allocation per clustered portfolio was performed and the Mean-Variance Model was used to optimize clustered portfolios. This paper aims to use Data Envelopment Analysis (DEA) to evaluate the efficiency of these clustered portfolios/DMUs. In addition, this paper introduces subjective choice in decision-making using the criterion's level of importance to help investors decide which among these efficient DMUs is a better option. The results show 4 Efficient DMUs for 2019 Data, and 3 Efficient DMUs in 2020 Data. All simulations were carried out using the MATLAB environment platform.

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The Effects of Growth Environments on Ws2 in Electrocatalytic Hydrogen Evolution Reaction

SSRN

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SSRN 2023年

作者： Chang, Yung-Huang Huang, Chien-Sheng Chen, Yuan-Tsung Bachelor Program in Industrial Technology National Yunlin University of Science and Technology Yunlin Taiwan Department of Electronic Engineering National Yunlin University of Science and Technology Yunlin Taiwan Graduate School of Materials Science National Yunlin University of Science and Technology Yunlin Taiwan

Transition metal dichalcogenides (TMDs) recently have attracted much attention because of their outstanding catalytic activity, low cost, and earth abundance in the hydrogen evolution reaction (HER). Among these TMDs, MoS2 and WS2 are recognized as promising HER electrocatalysts and have been explored to be one of the candidates for replacing precious metals. In this work, the electrocatalytic activity of WS2 grown in Ar-only, Ar + H2, and ambient environments were studied, where the WS2 was synthesized by thermolysis from (NH4)2WS4 precursor. Different surface morphologies are shown under the different growth atmospheres. The structures of pure WS2, WS2 and WO3 mixed, and almost WO3 grow under the growth atmospheres of Ar-only, Ar + H2, and ambient environment, respectively. All the materials prepared in these different environments exhibit high crystalline characteristic. Especially, the pure WS2 grown in an Ar-only environment presents the preferred (103) lattice plane. The pure WS2 performs the best H2 evolution efficiency, indicating that high crystallinity and preferred (103) lattice plane are relative to the electrocatalytic activity. The studies provide fundamental insight for further design and preparation of electrocatalysts, useful in the development of clean energy. © 2023, The Authors. All rights reserved.

关键词： Electrocatalysts

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Stock Price Prediction and Recommendation Approach Based on Machine Learning

Stock Price Prediction and Recommendation Approach Based on ...

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

作者： Chin-Chih Chang Kei-Wen Luo Sean Hsiao Bachelor’s Program in Applied Foreign Language and Smart Commerce Chung Hua University Hsinchu Taiwan Department of Computer Science and Information Engineering Chung Hua University Hsinchu Taiwan

ISBN: (纸本)9781665482097

Investment is a lifetime work. Investing in stocks is a popular measure. However, investing in stocks is not easy, and losing money is not uncommon. We investigate if there is a systematic way to find buyable stocks and more importantly when to buy and sell them. Nowadays, with the progress of hardwarel, artificial intelligence (AI) has become a feasible solution to many issues. Machine learning and deep learning are two widely adopted methods in AI. In this study, we show how to apply machine learning to stock prediction and recommendation. After trials, we found that LightGBM shows its potential to predict and recommend stocks. We used the data of the Taiwan stock market to validate the method. We compared the prediction and recommendation with the wellknown Taiwan ETFs 0050 and 0056. The results show that the method is feasible, effective, efficient, and has better performance than Taiwan ETFs 0050 and 0056.

关键词： Deep learning Systematics Stock markets Investment

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Customer Service Chatbot Enhanced with Conversational Language Understanding and Knowledge Base

Customer Service Chatbot Enhanced with Conversational Langua...

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

作者： Chin-Chih Chang Wen-Shing Cheng Sean Hsiao Bachelor’s Program in Applied Foreign Language and Smart Commerce Chung Hua University Hsinchu Taiwan Department of Computer Science and Information Engineering Chung Hua University Hsinchu Taiwan

ISBN: (纸本)9781665482097

A customer service chatbot enhanced with conversational language understanding and knowledge base is developed. Here, we explore LUIS and QnA Maker which are unified as Azure cognitive service for language. LUIS is a cloudbased conversational AI service that responds a user intelligence. QnA maker is a knowledge base for custom question answering. This question-and-answer knowledge base is especially useful for customer dialogs. Hence, by combining the services, we provide a smart response and provide a knowledge base to understand and improve the service. We implement this chatbot on the LINE Bot platform. Users easily access it by simply adding the representative of this chatbot on LINE. In addition, we put marketing information through this chatbot. The experiments are conducted, and the results show the chatbot is feasible and has a high user acceptance.

关键词： Customer services Knowledge based systems Chatbots Question answering (information retrieval)

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Transfer Learning for Keypoint Detection in Low-Resolution Thermal TUG Test Images

arXiv

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arXiv 2025年

作者： Chen, Wei-Lun Hsieh, Chia-Yeh Kao, Yu-Hsiang Liu, Kai-Chun Peng, Sheng-Yu Tsao, Yu Research Center for Information Technology Innovation Academic Sinica Taiwan Graduate Institute of Electrical Engineering National Taiwan University Taiwan Bachelor’s Program in Medical Informatics and Innovative Applications Fu Jen Catholic University Taiwan Department of Electrical Engineering National Taiwan University Taiwan College of Information and Computer Sciences University of Massachusetts AmherstMA01003 United States Department of Electrical Engineering National Taiwan University of Science of Technology Taiwan

This study presents a novel approach to human keypoint detection in low-resolution thermal images using transfer learning techniques. We introduce the first application of the Timed Up and Go (TUG) test in thermal image computer vision, establishing a new paradigm for mobility assessment. Our method leverages a MobileNetV3-Small encoder and a ViTPose decoder, trained using a composite loss function that balances latent representation alignment and heatmap accuracy. The model was evaluated using the Object Keypoint Similarity (OKS) metric from the COCO Keypoint Detection Challenge. The proposed model achieves better performance with AP, AP50, and AP75 scores of 0.861, 0.942, and 0.887 respectively, outperforming traditional supervised learning approaches like Mask R-CNN and ViTPose-Base. Moreover, our model demonstrates superior computational efficiency in terms of parameter count and FLOPS. This research lays a solid foundation for future clinical applications of thermal imaging in mobility assessment and rehabilitation monitoring. Copyright © 2025, The Authors. All rights reserved.

关键词： Thermography (imaging)

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