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Volume 24 No. 06
05 December 2019

Feng Wang, Huaping Liu, Fuchun Sun, Haihong Pan

2019, 24(06): 645-653.   doi:10.26599/TST.2018.9010095
Abstract ( 134 HTML ( 2   PDF(1827KB) ( 248 )   Save

In this work, we use a deep learning method to tackle the Zero-Shot Learning (ZSL) problem in tactile material recognition by incorporating the advanced semantic information into a training model. Our main technical contribution is our proposal of an end-to-end deep learning framework for solving the tactile ZSL problem. In this framework, we use a Convolutional Neural Network (CNN) to extract the spatial features and Long Short-Term Memory (LSTM) to extract the temporal features in dynamic t...

Bin Fang, Xiang Wei, Fuchun Sun, Haiming Huang, Yuanlong Yu, Huaping Liu

2019, 24(06): 654-662.   doi:10.26599/TST.2018.9010096
Abstract ( 64 HTML ( 0   PDF(9559KB) ( 88 )   Save

With the accelerated aging of the global population and escalating labor costs, more service robots are needed to help people perform complex tasks. As such, human-robot interaction is a particularly important research topic. To effectively transfer human behavior skills to a robot, in this study, we conveyed skill-learning functions via our proposed wearable device. The robotic teleoperation system utilizes interactive demonstration via the wearable device by directly controlling the speed o...

Qi Dang, Jianqin Yin, Bin Wang, Wenqing Zheng

2019, 24(06): 663-676.   doi:10.26599/TST.2018.9010100
Abstract ( 69 HTML ( 0   PDF(7163KB) ( 129 )   Save

Human pose estimation has received significant attention recently due to its various applications in the real world. As the performance of the state-of-the-art human pose estimation methods can be improved by deep learning, this paper presents a comprehensive survey of deep learning based human pose estimation methods and analyzes the methodologies employed. We summarize and discuss recent works with a methodology-based taxonomy. Single-person and multi-person pipelines are first reviewed sep...

Kaiming Nan, Sicong Liu, Junzhao Du, Hui Liu

2019, 24(06): 677-693.   doi:10.26599/TST.2018.9010103
Abstract ( 14 HTML ( 0   PDF(8106KB) ( 9 )   Save

Despite the rapid development of mobile and embedded hardware, directly executing computation-expensive and storage-intensive deep learning algorithms on these devices’ local side remains constrained for sensory data analysis. In this paper, we first summarize the layer compression techniques for the state-of-the-art deep learning model from three categories: weight factorization and pruning, convolution decomposition, and special layer architecture designing. For each category of layer compr...

Jiawei Tao, Tao Zhang, Yongfang Nie

2019, 24(06): 694-705.   doi:10.26599/TST.2018.9010094
Abstract ( 12 HTML ( 0   PDF(71407KB) ( 3 )   Save

In this paper, a flexible spacecraft attitude control scheme that guarantees vibration suppression and prescribed performance on transient-state behavior is proposed. Here, parametric uncertainty, external disturbance, unmeasured elastic vibration, actuator saturation, and even configuration misalignment are considered. To guarantee prescribed performance bounds on the transient- and steady-state control errors, a performance constrained control law is formulated with an error transformed fun...

Yanhua Yu, Jie Li

2019, 24(06): 706-715.   doi:10.26599/TST.2018.9010092
Abstract ( 105 HTML ( 1   PDF(850KB) ( 98 )   Save

In this paper, we propose a novel Residuals-Based Deep Least Squares Support Vector Machine (RBD-LSSVM). In the RBD-LSSVM, multiple LSSVMs are sequentially connected. The second LSSVM uses the fitting residuals of the first LSSVM as input time series, and the third LSSVM trains the residuals of the second, and so on. The original time series is the input of the first LSSVM. Additionally, to obtain the best hyper-parameters for the RBD-LSSVM, we propose a model validation method based on redun...

Yunlei Zhang, Bin Wu, Yu Liu, Jinna Lv

2019, 24(06): 716-727.   doi:10.26599/TST.2018.9010106
Abstract ( 156 HTML ( 0   PDF(1576KB) ( 192 )   Save

Local community detection aims to find a cluster of nodes by exploring a small region of the network. Local community detection methods are faster than traditional global community detection methods because their runtime does not depend on the size of the entire network. However, most existing methods do not take the higher-order connectivity patterns crucial to the network into consideration. In this paper, we develop a new Local Community Detection method based on network Motif (LCD-Motif) ...

Qin Shi, Sihao Zhao, Xiaowei Cui, Mingquan Lu, Mengdi Jia

2019, 24(06): 728-737.   doi:10.26599/TST.2018.9010102
Abstract ( 8 HTML ( 0   PDF(4294KB) ( 6 )   Save

Localization systems utilizing Ultra-WideBand (UWB) have been widely used in dense urban and indoor environments. A moving UWB tag can be located by ranging to fixed UWB anchors whose positions are surveyed in advance. However, manually surveying the anchors is typically a dull and time-consuming process and prone to artificial errors. In this paper, we present an accurate and easy-to-use method for UWB anchor self-localization, using the UWB ranging measurements and readings from a low-cost ...

Yuzhao Wu, Yongqiang Lyu, Yuanchun Shi

2019, 24(06): 738-749.   doi:10.26599/TST.2018.9010127
Abstract ( 10 HTML ( 0   PDF(1314KB) ( 3 )   Save

With ever greater amounts of data stored in cloud servers, data security and privacy issues have become increasingly important. Public cloud storage providers are semi-trustworthy because they may not have adequate security mechanisms to protect user data from being stolen or misused. Therefore, it is crucial for cloud users to evaluate the security of cloud storage providers. However, existing security assessment methods mainly focus on external security risks without considering the trustwo...

Yuling Tian, Xiangyu Liu

2019, 24(06): 750-762.   doi:10.26599/TST.2018.9010144
Abstract ( 15 HTML ( 0   PDF(1331KB) ( 13 )   Save

The extraction of rolling bearing fault features using traditional diagnostic methods is not sufficiently comprehensive and the features are often chosen subjectively and depend on human experience. In this paper, an improved deep convolutional process is used to extract a set of features adaptively. The hidden multi-layer feature of deep convolutional neural networks is also exploited to improve the extraction features. A deterministic detection of low-confidence samples is performed to ensu...