Yuchen Sun

BC Platforms (Finland)

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i-10 index

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Co-Authors

Liangtian Wan

Dalian University of Technology

Yu‐Wen Huang

National Taiwan University

Shawmin Lei

Jiangsu University

Baozhen Ge

Tianjin University

Lu Sun

Dalian Maritime University

Shan Liu

Marine Biomedical Research Institute of Qingdao

Yi-Wen Chen

Guilin University of Electronic Technology

Kejun Huang

University of Florida

Feng Xia

RMIT University

T. J. Chuang

National Taipei University of Technology

Cooperative Institutions

Dalian University of Technology

Chinese Academy of Sciences

MediaTek (Taiwan)

National Yang Ming Chiao Tung University

Dalian University

University of Electronic Science and Technology of China

MediaTek (China)

University of Chinese Academy of Sciences

Qualcomm (United States)

Tianjin University

Author Statistics

Papers

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Research Field

Rate-distortion optimized mode selection method for multiple description video coding

Multimedia Tools and Applications (2013)

Yuchen Sun Wen-Jiin Tsai

Mode (computer interface)

Rate distortion

Rate–distortion optimization

10.1007/s11042-013-1434-8

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Citations (3)

Low-Complexity Video PSNR Measurement in Real-Time Communication Products

Yuchen Sun Jie Dong Ahmed Fouad Jian Zhou Roger Zhou

10.1109/icmew63481.2024.10645398

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Citations (0)

Improvements of HEVC SCC Palette Mode and Intra Block Copy

IEEE Journal on Emerging and Selected Topics in Circuits and Systems (2016)

Yuchen Sun Shih-Ta Hsiang Jungsun Kim Yi-Wen Chen Xiaozhong Xu

Palette mode and intra block copy are two new coding tools that have been adopted in the screen content coding extensions of high efficiency video Ccoding (HEVC SCC). Palette mode can represent color clusters for screen content efficiently. Intra block copy (IBC) facilitates block prediction within the same frame. Both tools have demonstrated promising benefits for screen content coding. In addition to HEVC SCC, this paper introduces five new coding techniques to enhance palette mode or IBC: transition copy, prediction across coding unit boundary, advanced motion vector coding, modified temporal merge candidate derivation, and new default IBC merge candidates. Experimental results show that the proposed techniques can achieve 5.4%, 5.2%, and 4.9% BD-rate savings compared with HEVC SCC for “YUV, text & graphics with motion, 1080p & 720p sequences” under all intra, random access, and low-delay B common test conditions, respectively.

Merge (version control)

Motion vector

Algorithmic efficiency

10.1109/jetcas.2016.2598193

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Citations (2)

Performance analysis of quantum convolutional layers for image classification

Physica Scripta (2024)

Qiuyu Xiang Dongfen Li Yuchen Sun Zhikang Hu Yuhang Yuan

Abstract In recent years, with the rapid development of quantum computing technology, the fusion of quantum computing and machine learning techniques is becoming a research hotspot in the field of machine learning. This article aims to explore the impact of the depth and width of quantum convolutional layers on image classification tasks in Quantum−Classical Hybrid Convolutional Neural Networks. To this end, a model combining parameterized quantum circuits and classical neural networks is designed, and a series of experiments are conducted on the MNIST dataset to assess the specific effects of different configurations of quantum convolutional layers on model performance. The research results indicate that simply increasing the depth or width of quantum convolutional layers does not guarantee performance improvement and sometimes may even lead to performance degradation. Therefore, when designing quantum convolutional layers, we should make reasonable choices based on the actual needs of the application scenarios. Finally, based on these findings, a multidimensional optimization strategy is proposed to enhance the overall performance of the model. The achievements of this research not only provide important guidance for the design and optimization of Quantum−Classical Hybrid Convolutional Neural Networks but also offer new research perspectives for researchers in the field of quantum machine learning.

10.1088/1402-4896/ad8d17

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Transport of Zinc from the Soil to the Grain: Nonlinear Process Simulation Based on Artificial Neural Network and Big Data

Kai-Ning Lv Yong Huang Guo‐Li Yuan Yuchen Sun Jun Li

Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL Copy DOI

10.2139/ssrn.4777309

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Applications of augmented reality-based natural interactive learning in magnetic field instruction

Interactive Learning Environments (2016)

Su Cai Feng‐Kuang Chiang Yuchen Sun Chenglong Lin Joey J. Lee

Educators must address several challenges inherent to the instruction of scientific disciplines such as physics -- expensive or insufficient laboratory equipment, equipment error, difficulty in simulating certain experimental conditions. Augmented reality (AR) can be a promising approach to address these challenges. In this paper, we discuss the design and implementation of an AR and motion-sensing learning technology that teaches magnetic fields in a junior high school physics course. The purpose of this study is to explore the effects of using natural interaction on students’ physics learning and deep understanding compared to traditional learning tools. The 38 eighth graders who participated in this study were assigned to either an experimental group or a control group. Analysis of the results shows that the AR-based motion-sensing software can improve students’ learning attitude and learning outcome. This study provides a case for the application of AR technology in secondary physics education.

10.1080/10494820.2016.1181094

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Citations (214)

Error-resilient video coding using multiple reference frames

Wen-Jiin Tsai Yuchen Sun

An error-resilient scheme based on multiple reference frames is proposed, which employs error-resilient frames (ER-frame) as part of reference frames and adopts a rate distortion optimized (RDO) technique to choose the blocks referring to ER-frames. To reduce the computational cost, two techniques are further proposed. Experimental results show that adopting ER-frames enhances error-resilient performance; using RDO makes the scheme adaptive to various packet loss rates and video sequences; and applying time reduction techniques reduces computational cost significantly with neglectable performance loss.

10.1109/icip.2013.6738386

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Citations (39)

Volume-Regularized Nonnegative Tucker Decomposition with Identifiability Guarantees

ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (2023)

Yuchen Sun Kejun Huang

It is well-known that the Tucker decomposition of a multi-dimensional tensor is not unique, because its factors are subject to rotation ambiguities similar to matrix factorization models. Inspired by the recent success in the identifiability of nonnegative matrix factorization, the goal of this work is to achieve similar results for nonnegative Tucker decomposition (NTD). We propose to add a matrix volume regularization as the identifiability criterion, and show that NTD is indeed identifiable if all of the Tucker factors satisfy the sufficiently scattered condition. We then derive an algorithm to solve the modified formulation of NTD that minimizes the generalized Kullback-Leibler divergence of the approximation plus the proposed matrix volume regularization. Numerical experiments show the effectiveness of the proposed method.

Identifiability

Tucker Decomposition

Regularization

Non-negative Matrix Factorization

Matrix (chemical analysis)

Divergence (linguistics)

10.1109/icassp49357.2023.10096076

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Citations (4)

Robust Video Coding Based on Hybrid Hierarchical B Pictures

IEEE Transactions on Circuits and Systems for Video Technology (2013)

Wen-Jiin Tsai Yuchen Sun Po-Jui Chiu

Compressed video streams transmitted over error-prone environments are usually corrupted by transmission errors. Error-concealment techniques can be used to recover the lost information. In this paper, a hybrid model is proposed to improve the error-concealment performance. The model combines two hierarchical B-picture coding structures such that key-frames, reference B frames, or even nonreference B frames have buddy frames to serve as their data recovery frames when they are lost. With buddy frames, the distance between a lost frame and its recovering frame can be substantially reduced. In addition, an improved estimation method is also proposed to further increase the accuracy of recovering motion. Error-concealment performance can thus be significantly improved with little bit-rate redundancy. We have conducted experiments to compare its performance with other methods, and the results show that the proposed hybrid model outperforms these competed methods. The advantages of the proposed hybrid model are demonstrated in error-free and packet-loss environments.

Error concealment

Robustness

10.1109/tcsvt.2013.2291280

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Citations (1)

Soft Measuring Method of Pcocess Parameter Based on Multiple Feature Subsets

Zhuo Liu Jian Tang Gang Yu Yuchen Sun

Difficulty-to-measure process parameter relative to production quality and efficient of complex industrial process is obtained normally by off-line analysis or expert estimation. One of the main reason is that the soft measuring model between multi-source input features and such process parameter is difficulty to be constructed. Aim at the above issue, a new soft measuring method is proposed in this study. At first, linear and nonlinear feature sub-sets are selected by using correlation coefficient and mutual information method. Then, four types of linear and nonlinear candidate sub-models are constructed based on the above feature subsets. At last, optimization and weighting algorithms are used to select and combine the selected ensemble sub-models. Thus, the final selective ensemble learning-based soft measuring model is obtained. The modeling results based on high dimensional mechanical vibration frequency spectrum validate effectiveness of this approach.

Feature (linguistics)

Soft sensor

Line (geometry)

10.1109/cac48633.2019.8996942

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Citations (0)