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769 results on '"Zhao, Shiwei"'

2. Unveiling microstructural damage for leakage current degradation in SiC Schottky diode after heavy ions irradiation under 200 V

Author

Yan, Xiaoyu, Zhai, Pengfei, Yang, Chen, Zhao, Shiwei, Nan, Shuai, Hu, Peipei, Zhang, Teng, Chen, Qiyu, Xu, Lijun, Li, Zongzhen, and Liu, Jie

Subjects
Condensed Matter - Materials Science, Physics - Applied Physics
Abstract

Single-event burnout and single-event leakage current (SELC) in SiC power devices induced by heavy ions severely limit their space application, and the underlying mechanism is still unclear. One fundamental problem is lack of high-resolution characterization of radiation damage in the irradiated SiC power devices, which is a crucial indicator of the related mechanism. In this letter, high-resolution transmission electron microscopy (TEM) was used to characterize the radiation damage in the 1437.6 MeV 181Ta-irradiated SiC junction barrier Schottky diode under 200 V. The amorphous radiation damage with about 52 nm in diameter and 121 nm in length at the Schottky metal (Ti)-semiconductor (SiC) interface was observed. More importantly, in the damage site the atomic mixing of Ti, Si, and C was identified by electron energy loss spectroscopy and high-angle annular dark-field scanning TEM. It indicates that the melting of the Ti-SiC interface induced by localized Joule heating is responsible for the amorphization and the formation of titanium silicide, titanium carbide, or ternary phases. These modifications at nanoscale in turn cause the localized degradation of the Schottky contact, resulting in the permanent increase in leakage current. This experimental study provides very valuable clues to thorough understanding of the SELC mechanism in SiC diode., Comment: 4 pages,4 figures

Published
2023
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3. Examining the Effect of Pre-training on Time Series Classification

Author

Pu, Jiashu, Zhao, Shiwei, Cheng, Ling, Chang, Yongzhu, Wu, Runze, Lv, Tangjie, and Zhang, Rongsheng

Subjects
Computer Science - Machine Learning
Abstract

Although the pre-training followed by fine-tuning paradigm is used extensively in many fields, there is still some controversy surrounding the impact of pre-training on the fine-tuning process. Currently, experimental findings based on text and image data lack consensus. To delve deeper into the unsupervised pre-training followed by fine-tuning paradigm, we have extended previous research to a new modality: time series. In this study, we conducted a thorough examination of 150 classification datasets derived from the Univariate Time Series (UTS) and Multivariate Time Series (MTS) benchmarks. Our analysis reveals several key conclusions. (i) Pre-training can only help improve the optimization process for models that fit the data poorly, rather than those that fit the data well. (ii) Pre-training does not exhibit the effect of regularization when given sufficient training time. (iii) Pre-training can only speed up convergence if the model has sufficient ability to fit the data. (iv) Adding more pre-training data does not improve generalization, but it can strengthen the advantage of pre-training on the original data volume, such as faster convergence. (v) While both the pre-training task and the model structure determine the effectiveness of the paradigm on a given dataset, the model structure plays a more significant role.

Published
2023

10. Omni-frequency Channel-selection Representations for Unsupervised Anomaly Detection

Author

Liang, Yufei, Zhang, Jiangning, Zhao, Shiwei, Wu, Runze, Liu, Yong, and Pan, Shuwen

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Density-based and classification-based methods have ruled unsupervised anomaly detection in recent years, while reconstruction-based methods are rarely mentioned for the poor reconstruction ability and low performance. However, the latter requires no costly extra training samples for the unsupervised training that is more practical, so this paper focuses on improving this kind of method and proposes a novel Omni-frequency Channel-selection Reconstruction (OCR-GAN) network to handle anomaly detection task in a perspective of frequency. Concretely, we propose a Frequency Decoupling (FD) module to decouple the input image into different frequency components and model the reconstruction process as a combination of parallel omni-frequency image restorations, as we observe a significant difference in the frequency distribution of normal and abnormal images. Given the correlation among multiple frequencies, we further propose a Channel Selection (CS) module that performs frequency interaction among different encoders by adaptively selecting different channels. Abundant experiments demonstrate the effectiveness and superiority of our approach over different kinds of methods, e.g., achieving a new state-of-the-art 98.3 detection AUC on the MVTec AD dataset without extra training data that markedly surpasses the reconstruction-based baseline by +38.1 and the current SOTA method by +0.3. Source code is available at https://github.com/zhangzjn/OCR-GAN.

Published
2022
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38. Three-dimensional, multifunctional neural interfaces for cortical spheroids and engineered assembloids.

Author

Park, Yoonseok, Franz, Colin, Ryu, Hanjun, Luan, Haiwen, Cotton, Kristen, Kim, Jong, Chung, Ted, Zhao, Shiwei, Vazquez-Guardado, Abraham, Yang, Da, Li, Kan, Avila, Raudel, Phillips, Jack, Quezada, Maria, Jang, Hokyung, Kwak, Sung, Won, Sang, Kwon, Kyeongha, Jeong, Hyoyoung, Bandodkar, Amay, Han, Mengdi, Zhao, Hangbo, Osher, Gabrielle, Wang, Heling, Lee, KunHyuck, Zhang, Yihui, Huang, Yonggang, Finan, John, and Rogers, John

Subjects
Nervous System, Neurons
Abstract

Three-dimensional (3D), submillimeter-scale constructs of neural cells, known as cortical spheroids, are of rapidly growing importance in biological research because these systems reproduce complex features of the brain in vitro. Despite their great potential for studies of neurodevelopment and neurological disease modeling, 3D living objects cannot be studied easily using conventional approaches to neuromodulation, sensing, and manipulation. Here, we introduce classes of microfabricated 3D frameworks as compliant, multifunctional neural interfaces to spheroids and to assembloids. Electrical, optical, chemical, and thermal interfaces to cortical spheroids demonstrate some of the capabilities. Complex architectures and high-resolution features highlight the design versatility. Detailed studies of the spreading of coordinated bursting events across the surface of an isolated cortical spheroid and of the cascade of processes associated with formation and regrowth of bridging tissues across a pair of such spheroids represent two of the many opportunities in basic neuroscience research enabled by these platforms.

Published
2021

47. Trajectory-Based Mobile Game Bots Detection with Gaussian Mixture Model

Author

Su, Yueyang, Yao, Di, Li, Jingwei, Wang, Baoli, Bi, Jingping, Zhao, Shiwei, Wu, Runze, Tao, Jianrong, Deng, Hao, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Pimenidis, Elias, editor, Angelov, Plamen, editor, Jayne, Chrisina, editor, Papaleonidas, Antonios, editor, and Aydin, Mehmet, editor

Published
2022
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48. A GNN-Enhanced Game Bot Detection Model for MMORPGs

Author

Qi, Xianyang, Pu, Jiashu, Zhao, Shiwei, Wu, Runze, Tao, Jianrong, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Gama, João, editor, Li, Tianrui, editor, Yu, Yang, editor, Chen, Enhong, editor, Zheng, Yu, editor, and Teng, Fei, editor

Published
2022
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