Institution
University of Electronic Science and Technology of China
Education•Chengdu, China•
About: University of Electronic Science and Technology of China is a education organization based out in Chengdu, China. It is known for research contribution in the topics: Computer science & Antenna (radio). The organization has 50594 authors who have published 58502 publications receiving 711188 citations. The organization is also known as: UESTC.
Papers published on a yearly basis
Papers
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TL;DR: Partially-Connected Differentiable Architecture Search (PC-DARTS) as mentioned in this paper performs operation search in a subset of channels while bypassing the held out part in a shortcut, which alleviates the undesired inconsistency on selecting the edges of super-net caused by sampling different channels.
Abstract: Differentiable architecture search (DARTS) provided a fast solution in finding effective network architectures, but suffered from large memory and computing overheads in jointly training a super-network and searching for an optimal architecture. In this paper, we present a novel approach, namely, Partially-Connected DARTS, by sampling a small part of super-network to reduce the redundancy in exploring the network space, thereby performing a more efficient search without comprising the performance. In particular, we perform operation search in a subset of channels while bypassing the held out part in a shortcut. This strategy may suffer from an undesired inconsistency on selecting the edges of super-net caused by sampling different channels. We alleviate it using edge normalization, which adds a new set of edge-level parameters to reduce uncertainty in search. Thanks to the reduced memory cost, PC-DARTS can be trained with a larger batch size and, consequently, enjoys both faster speed and higher training stability. Experimental results demonstrate the effectiveness of the proposed method. Specifically, we achieve an error rate of 2.57% on CIFAR10 with merely 0.1 GPU-days for architecture search, and a state-of-the-art top-1 error rate of 24.2% on ImageNet (under the mobile setting) using 3.8 GPU-days for search. Our code has been made available at: this https URL.
306 citations
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TL;DR: This paper proposes a privacy-preserved data sharing framework for IIoTs, where multiple competing data consumers exist in different stages of the system, and provides for both algorithms a comprehensive consideration on privacy, data utility, bandwidth efficiency, payment, and rationality for data sharing.
Abstract: The effective physical data sharing has been facilitating the functionality of Industrial IoTs, which is believed to be one primary basis for Industry 4.0. These physical data, while providing pivotal information for multiple components of a production system, also bring in severe privacy issues for both workers and manufacturers, thus aggravating the challenges for data sharing. Current designs tend to simplify the behaviors of participants for better theoretical analysis, and they cannot properly handle the challenges in IIoTs where the behaviors are more complicated and correlated. Therefore, this paper proposes a privacy-preserved data sharing framework for IIoTs, where multiple competing data consumers exist in different stages of the system. The framework allows data contributors to share their contents upon requests. The uploaded contents will be perturbed to preserve the sensitive status of contributors. The differential privacy is adopted in the perturbation to guarantee the privacy preservation. Then the data collector will process and relay contents with subsequent data consumers. This data collector will gain both its own data utility and extra profits in data relay. Two algorithms are proposed for data sharing in different scenarios, based on whether the service provider will further process the contents to retain its exclusive utility. This work also provides for both algorithms a comprehensive consideration on privacy, data utility, bandwidth efficiency, payment, and rationality for data sharing. Finally, the evaluation on real-world datasets demonstrates the effectiveness of proposed methods, together with clues for data sharing towards Industry 4.0.
306 citations
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01 Jun 2016
TL;DR: In this paper, adaptive neural network tracking control of a robotic manipulator with input deadzone and output constraint is presented, where adaptive neural networks are used to approximate the deadzone function and the unknown model of the robotic manipulators.
Abstract: In this paper, we present adaptive neural network tracking control of a robotic manipulator with input deadzone and output constraint A barrier Lyapunov function is employed to deal with the output constraints Adaptive neural networks are used to approximate the deadzone function and the unknown model of the robotic manipulator Both full state feedback control and output feedback control are considered in this paper For the output feedback control, the high gain observer is used to estimate unmeasurable states With the proposed control, the output constraints are not violated, and all the signals of the closed loop system are semi-globally uniformly bounded The performance of the proposed control is illustrated through simulations
306 citations
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TL;DR: In this article, a test-time augmentation-based aleatoric uncertainty was proposed to analyze the effect of different transformations of the input image on the segmentation output, and the results showed that the proposed test augmentation provides a better uncertainty estimation than calculating the testtime dropout-based model uncertainty alone and helps to reduce overconfident incorrect predictions.
305 citations
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TL;DR: In this article, an all-fiber hybrid piezoelectric-enhanced triboelectoric nanogenerator fabricated by electrospinning silk fibroin and poly(vinylidene fluoride) (PVDF) nanofibers on conductive fabrics is presented.
305 citations
Authors
Showing all 51090 results
Name | H-index | Papers | Citations |
---|---|---|---|
Gang Chen | 167 | 3372 | 149819 |
Frede Blaabjerg | 147 | 2161 | 112017 |
Kuo-Chen Chou | 143 | 487 | 57711 |
Yi Yang | 143 | 2456 | 92268 |
Guanrong Chen | 141 | 1652 | 92218 |
Shuit-Tong Lee | 138 | 1121 | 77112 |
Lei Zhang | 135 | 2240 | 99365 |
Rajkumar Buyya | 133 | 1066 | 95164 |
Lei Zhang | 130 | 2312 | 86950 |
Bin Wang | 126 | 2226 | 74364 |
Haiyan Wang | 119 | 1674 | 86091 |
Bo Wang | 119 | 2905 | 84863 |
Yi Zhang | 116 | 436 | 73227 |
Qiang Yang | 112 | 1117 | 71540 |
Chun-Sing Lee | 109 | 977 | 47957 |