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101 results on '"Zhao, Shuaijie"'

12. The Variation Characteristic of EMG Signal Under Varying Active Torque: A Preliminary Study

Author

Zheng, Boxuan, Guan, Xiaorong, Li, Zhong, Zhao, Shuaijie, Wang, Zheng, Li, Hengfei, 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, Liu, Honghai, editor, Yin, Zhouping, editor, Liu, Lianqing, editor, Jiang, Li, editor, Gu, Guoying, editor, Wu, Xinyu, editor, and Ren, Weihong, editor

Published
2022
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18. Copper diffusion into epoxy under high temperature

Author

Zhao, Shuaijie, primary, Chen, Chuantong, additional, Nishijima, Masahiko, additional, Haga, Motoharu, additional, Ueshima, Minoru, additional, Suzuki, Hirose, additional, Takenaka, Hiroto, additional, and Suganuma, Katsuaki, additional

Published
2024
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31. A New Denoising Method VSCE for High-Temperature Shear Wave Electromagnetic Ultrasonic Defect Detection at a Low Excitation Voltage

Author

Zhou, Jinjie, Zhao, Shuaijie, Zheng, Yang, Liu, Yao, and Ge, Zhizeng

Abstract

Low excitation voltage (LEV) electromagnetic ultrasonic detection is needed for the safety of high-temperature boilers, reactors, pipelines, and other equipment. However, the low signal intensity and high noise due to the LEV and the high-temperature environment result in a low signal-to-noise ratio (SNR) in the defect detection process. This study proposes a novel noise reduction method, called VMD-singular value decomposition (SVD)-CEEMD (VSCE), which utilizes variational mode decomposition (VMD), SVD, and complementary ensemble empirical mode decomposition (CEEMD) to extract defect information and suppress pulsating noise, instrument noise, and high-temperature structural noise. The shear wave generated by an electromagnetic acoustic transducer was used to detect two different materials at 25 V LEV, with a temperature range of 25 °C–700 °C. The VSCE method was found to improve the SNR of received signals by two to three times compared to conventional denoising methods, such as wavelet threshold denoising (WTD), VMD, and empirical mode decomposition (EMD). The positioning error of the defect location is less than 2.06%, which has proven the great application prospects and value of the VSCE methods in the LEV detection of high-temperature equipment.

Published
2024
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41. Dual‐band filter based on air‐filled SIW cavity for 5G application

Author

Fei Liang, Zhao Shuaijie, Liu Leping, and Fu Qiuyun

Subjects
Materials science, business.industry, Optoelectronics, Multi-band device, Electrical and Electronic Engineering, Condensed Matter Physics, business, Dual band filter, Atomic and Molecular Physics, and Optics, Electronic, Optical and Magnetic Materials
Published
2019

42. Interface-Mechanical and Thermal Characteristics of Ag Sinter Joining on Bare DBA Substrate During Aging, Thermal Shock and 1200 W/cm 2 Power Cycling Tests.

Author

Chen, Chuantong, Kim, Dongjin, Zhang, Zheng, Wakasugi, Naoki, Liu, Yang, Hsieh, Ming-Chun, Zhao, Shuaijie, Suetake, Aiji, and Suganuma, Katsuaki

Subjects
THERMAL shock, TRAVERTINE, THERMAL engineering, POWER density, SHEAR strength, THERMAL resistance
Abstract

In this article, an SiC die was directly attached on a bare DBA (Al/AlN/Al) substrate via micron-sized Ag sintering at 250 °C without pressure. The micron-sized structure of the Ag–Al joint revealed robust bonding (33.6 MPa), which was attributed to the excellent sinterability of the Ag paste. A high-temperature storage test was conducted for 1000 h at 250 °C, and the thermal shock test was conducted from −50 to 250 °C for 2000 cycles. The shear strength was >30 MPa even after 1000 h of high-temperature storage. Furthermore, the online thermal resistance (Rth) of the direct bonding of SiC–DBA was measured during the power cycling test employing an n-doped 4 H-SiC thermal engineering group (TEG) chip. The SiC–TEG chip exhibited ultra-high power density (1200 W/cm2) and ensured that the junction temperature reached 200 °C. The total Rth increased from 0.58 to 0.7 K/W after 10 000 power cycles at a swing temperature, ΔTj, of 175 °C, indicating that the increase in the Rth was 20.7% at such a large power density. The results revealed that the SiC–DBA power module structure exhibited very good bonding and thermal-resistance reliability, which can be employed as a new structure for next-generation SiC power modules. [ABSTRACT FROM AUTHOR]

Published
2022
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43. 金属樹脂直接成形接合の接合メカニズムに関する研究

Author

Zhao, Shuaijie

Abstract

学位の種別: 課程博士, 審査委員会委員 : （主査）東京大学准教授 梶原 優介, 東京大学教授 吉江 尚子, 東京大学教授 新野 俊樹, 東京大学教授 金 範埈, 東京大学准教授 土屋 健介

Published
2020

45. WiFi location method based on TSNE-KNN

Author

Yanru Zhong, Xie Qingbo, Zhao Shuaijie, and Luo Xiaonan

Subjects
Set (abstract data type), Fourth floor, Fingerprint database, Signal strength, Computer science, Dimensionality reduction, Fingerprint (computing), Embedding, Data dimension, Data mining, computer.software_genre, computer
Abstract

Aiming at the problems of low positioning accuracy and high data dimension of traditional WIFI fingerprint locating method, propose the WIFI fingerprint indoor locating method based on TSNE-KNN method to solution the problem. In the offline stage, the WIFI fingerprint database is dimensionalized by using the TSNE (t-distributed embedding), and the TSNE parameters are adjusted to obtain the 2d(two-dimensional) WIFI fingerprint database with high differentiation. In the online phase: firstly, the real-time WIFI signal strength collected together with the original WIFI fingerprint database is used as the input of TSNE. The 2d WIFI fingerprint database obtained in the offline phase is used as the initial solution, and a set of arbitrary data is added as the initial solution. The TSNE parameters obtained in the offline phase are used to calculate the dimensionality reduction data. Then use KNN (k-nearestneighbor) algorithm to achieve WIFI location; Finally, the fingerprint database on the fourth floor of EE building of XJTLU north campus is used as input in the experiment. Experiments show that the TSNE-KNN can effectively display the characteristics of high-dimensional datas with low-dimensional datas, and improve the location accuracy also.

Published
2019

46. An Improved Method for Indoor Positioning of Wifi Based on Location Fingerprint

Author

Yanru Zhong, Luo Xiaonan, Zeng Congwen, Yuan Zhixiang, and Zhao Shuaijie

Subjects
Computer science, business.industry, Received signal strength indication, Posterior probability, Fingerprint (computing), Computer vision, Noise (video), Artificial intelligence, Filter (signal processing), business, Wireless sensor network, Multipath propagation, Positioning technology
Abstract

Indoor positioning technology is one of the key technologies in the field of wireless sensor network (WSN) research. Received Signal Strength Indication (RSSI) has become the focus of indoor positioning technology research. In order to improve the accuracy of indoor positioning, this paper proposes a WIFI indoor positioning method based on location fingerprint. First, Gauss filtering preprocessing is performed on the RSSI in the fingerprint database to filter out redundant points caused by multipath effects and noise factors, which may cause errors in the construction of the location fingerprint database. Then, according to the working principle of the Bayes algorithm, the posterior probability function of the fingerprint data vector collected by the mobile terminal and the corresponding position of each fingerprint reference point is obtained. Finally, the posterior probability value is used as the weight coefficient on each fingerprint reference point in the target location area, and the location of the mobile terminal is accurately calculated. The experimental results show that the method can effectively improve the indoor positioning accuracy.

Published
2018

47. A Wifi Positioning Method Based on Stack Auto Encoder

Author

Yuan Zhixiang, Yanru Zhong, Zhao Shuaijie, and Luo Xiaonan

Subjects
Artificial neural network, Computer science, business.industry, Feature extraction, Feature selection, Autoencoder, law.invention, Bluetooth, Stack (abstract data type), law, Wireless, Computer vision, Artificial intelligence, business, Curse of dimensionality
Abstract

Based on the traditional Wifi positioning algorithm, the fingerprint database has large redundant information, which is easy to cause dimensionality disaster. In view of this situation, this paper proposes a Wifi positioning method based on ReliefF-SAE. Firstly, the feature selection of the constructed Wifi fingerprint database is carried out, which can remove the redundant information existing in the fingerprint database. Then the reduced fingerprint database is established. The deep neural network is constructed by stacked auto encoder, and pre-training is carried out to obtain a more accurate Wifi indoor positioning model. The positioning experiment is carried out by using the standard database UJIIndoor Loc. The experimental results show that the algorithm can effectively remove the redundant information in the fingerprint database, and at the same time, through the deep neural network learning, better positioning accuracy can be obtained.

Published
2018

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