Chun‐Yu Chen

Michigan United

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Chien‐Ming Wu

National Taiwan University

Chun-Ming Huang

National Applied Research Laboratories

Kai H. Chang

National Taiwan University

Chih-Chyau Yang

National Applied Research Laboratories

Siyi Hou

Sichuan University

Wen‐Hsiung Wu

Kaohsiung Medical University

Yu Shao

Henan University of Technology

Chih-Ting Kuo

Taiwan Semiconductor Manufacturing Company (Taiwan)

Yu-Tsang Chang

National Applied Research Laboratories

Shih-Sung Liao

Auburn University

Cooperative Institutions

National Taiwan University

National Applied Research Laboratories

National Tsing Hua University

National Cheng Kung University

Chinese Academy of Sciences

Taiwan Semiconductor Manufacturing Company (Taiwan)

National Yang Ming Chiao Tung University

National Central University

Beijing Institute of Technology

Northeastern University

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3D Face Reconstruction Using A Spectral-Based Graph Convolution Encoder

arXiv (Cornell University) (2024)

Haoxin Xu Zezheng Zhao Yuxin Cao Chun‐Yu Chen Hao Ge

Monocular 3D face reconstruction plays a crucial role in avatar generation, with significant demand in web-related applications such as generating virtual financial advisors in FinTech. Current reconstruction methods predominantly rely on deep learning techniques and employ 2D self-supervision as a means to guide model learning. However, these methods encounter challenges in capturing the comprehensive 3D structural information of the face due to the utilization of 2D images for model training purposes. To overcome this limitation and enhance the reconstruction of 3D structural features, we propose an innovative approach that integrates existing 2D features with 3D features to guide the model learning process. Specifically, we introduce the 3D-ID Loss, which leverages the high-dimensional structure features extracted from a Spectral-Based Graph Convolution Encoder applied to the facial mesh. This approach surpasses the sole reliance on the 3D information provided by the facial mesh vertices coordinates. Our model is trained using 2D-3D data pairs from a combination of datasets and achieves state-of-the-art performance on the NoW benchmark.

Convolution (computer science)

10.48550/arxiv.2403.05218

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CIC signal processing embedded system a modulizable platform for multi-domain signal processing

Annual International Conference of the IEEE Engineering in Medicine and Biology Society (2012)

Chih-Ting Kuo Chun‐Yu Chen Yu-Tsang Chang Chun-Pin Lin Chien‐Ming Wu

IT (Information Technology) industry is well developed in IC (Integrated Circuit) design, SoC (System-on-a-Chip), embedded, and etc. in Taiwan. Most of the commonly-used platforms are not configurable or modulizable at the moment. The biomedical academia in Taiwan is eager for a flexible biomedical signal sensing platform. This paper presents a novel multi-domain nano-sensor signal processing embedded system. The platform can be tailored for different demands of biomedical signal sensing.

SIGNAL (programming language)

10.1109/embc.2012.6346557

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A modular wireless sensor platform and its applications

2022 IEEE International Symposium on Circuits and Systems (ISCAS) (2017)

Chun-Ming Huang Yi-Jie Hsieh Wei-Lin Lai Shuaicheng Liu Chun-Ying Juan

In this paper, we propose a modular wireless sensor platform that consists of sensor modules. Each sensor module is a part of sensor system and in charge of one job in the system, such as computation, communication, output or sensing. Users can stack multiple modules together to build a unique sensor platform. Since users are able to easily replace one module with others, the proposed platform is highly extendable and reusable. Besides, we also design different kinds of mounts, so that sensors can be mounted on objects. This is especially helpful for some moving sensing applications such as attitude monitoring. To demonstrate the proposed platform, we show an alcohol detection application in the paper. The results show that the proposed platform is suitable for academic researches and industrial prototype verification.

10.1109/iscas.2017.8050611

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Context-Aware Anomaly Detection Using Vehicle Dynamics

Chun‐Yu Chen Kang G. Shin Soodeh Dadras

Dynamics

Anomaly (physics)

10.1145/3678890.3678895

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一個以TCP連線行為基礎來防禦後門程式的機制; A Novel Behavior-Based Solution to Backdoors

陳俊佑 Chun‐Yu Chen

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Design of intelligent Japanese teaching system based on web

Chun‐Yu Chen

The rapid economic growth has promoted the development of intelligent technology, which has been widely used in various fields, including education, and many intelligent teaching systems with good performance and sound functions have been developed. Through the application of this system, not only the teaching effect has been improved, but also the students have more knowledge, and the workload of teachers has been reduced, so that teachers have more time and energy to prepare for teaching. Therefore, this paper takes Japanese teaching as the research object and web technology as the core, and designs an intelligent Japanese teaching system to provide support for further improving Japanese teaching effect.

Research Object

Computer aided instruction

10.1117/12.2655675

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Distributed exponentially tracking control of multiple wheeled mobile robots

American Control Conference (2014)

Chun‐Yu Chen Felipe De La Torre Wenjie Dong

In this paper a consensus problem is considered for leader-following of multiple wheeled mobile robots. Laplacian matrix is introduced to characterize the communication topology. Since there are parameter uncertainties for each mechanical system, adaptive control method is applied for controller design of the dynamical systems. Distributed adaptive state feedback control laws are presented with the aid of the agent's neighboring information. Simulation results are presented to validate the proposed control laws.

Tracking (education)

10.1109/acc.2014.6858886

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綠建築規範應用於既有建築物改善及申請補助之探討 - 以國立中央大學總圖書館為例 ; Green Building Assessment on the renovation & Sponsored Project for Existing Buildings - a Case Study on the Library of National Central University

陳俊佑 Chun‐Yu Chen

Green Building

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基于动态调整简化粒子群优化的组合测试用例生成方法 (Combinatorial Test Case Generation Method Based on Simplified Particle Swarm Optimizationwith Dynamic Adjustment).

计算机科学 (2018)

Xiaoan Bao Chao Bao Yuting Jin Chun‐Yu Chen Junyan Qian

Dynamic testing

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Security monitoring method for ICS based on information gain ratio and maximum entropy model

Shenggang Li Wenli Shang Chun‐Yu Chen Yan Lu Zhiwei Dong

In today's information age, in order to meet the requirement of high efficiency outlier detection in the production process of industrial control system, the intrusion characteristics are analyzed and warned in time to prevent from happening. An intrusion detection scheme (IGR-MEM) based on information gain ratio feature selection and maximum entropy model is proposed. The collected industrial control data are normalized and a new information gain ratio feature selection method considering feature correlation and redundancy is proposed to choose the best feature subset from the network connection data. According to the extracted training sample feature subset, the maximum entropy model is devoted to construct the classifier, and finally the trained classifier is applied for intrusion detection. The test results express that the IGR-MEM scheme is able to select the best feature collection, improve the detection efficiency, and improve the accuracy of ICS security monitoring and reduce the false alarm rate compared with other algorithms.

Information gain ratio

Information gain

10.1109/cac51589.2020.9327281

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Citations (1)