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161 results on '"Zhang, Chengkun"'

1. Design and Control of a Low-cost Non-backdrivable End-effector Upper Limb Rehabilitation Device

Author

Li, Fulan, Guo, Yunfei, Xu, Wenda, Zhang, Weide, Zhao, Fangyun, Wang, Baiyu, Du, Huaguang, and Zhang, Chengkun

Subjects
Computer Science - Robotics, Electrical Engineering and Systems Science - Systems and Control
Abstract

This paper presents the development of an upper limb end-effector based rehabilitation device for stroke patients, offering assistance or resistance along any 2-dimensional trajectory during physical therapy. It employs a non-backdrivable ball-screw-driven mechanism for enhanced control accuracy. The control system features three novel algorithms: First, the Implicit Euler velocity control algorithm (IEVC) highlighted for its state-of-the-art accuracy, stability, efficiency and generalizability in motion restriction control. Second, an Admittance Virtual Dynamics simulation algorithm that achieves a smooth and natural human interaction with the non-backdrivable end-effector. Third, a generalized impedance force calculation algorithm allowing efficient impedance control on any trajectory or area boundary. Experimental validation demonstrated the system's effectiveness in accurate end-effector position control across various trajectories and configurations. The proposed upper limb end-effector-based rehabilitation device, with its high performance and adaptability, holds significant promise for extensive clinical application, potentially improving rehabilitation outcomes for stroke patients., Comment: 12 pages, 15 figures

Published
2024

2. Wide-mode-area slow light waveguides in valley photonic crystal heterostructures

Author

Zhang, Chengkun, Ota, Yasutomo, and Iwamoto, Satoshi

Subjects
Physics - Optics
Abstract

We designed slow-light waveguides with a wide mode area based on slab-type valley photonic crystal (VPhC) heterostructures which are composed of a graphene-like PhC sandwiched by two topologically distinct VPhCs. The group velocity of the topological guided mode hosted in a VPhC heterostructure can be slowed down by shifting the VPhC lattice toward the graphene-like PhC at the domain interfaces. Simultaneously, the mode width of the slow-light topological guided mode can be widened by increasing the size of the graphene-like PhC domain. We found that employing the graphene-like structure at the center domain is crucial for realizing a topological single-guided mode in such heterostructures. Furthermore, the impact of random fluctuations in air-hole size in the graphene-like domain was numerically investigated. Our simulation results demonstrate that the transmittance for the slow-light states can be kept high as far as the size fluctuation is small although it drops faster than that for fast-light states when the disorder level increases. The designed wide-mode-area slow-light waveguides are based on hole-based PhCs, offering novel on-chip applications of topological waveguides.

Published
2024

20. Deep Eutectic Solvent‐Based Solid Polymer Electrolytes for High‐Voltage and High‐Safety Lithium Metal Batteries.

Author

Zhang, Chengkun, Zheng, Hongfei, Lin, Liang, Wen, Jiansen, Zhang, Shiyu, Hu, Xinchao, Zhou, Dongwei, Sa, Baisheng, Wang, Laisen, Lin, Jie, Xie, Qingshui, Peng, Dong‐Liang, and Lu, Jun

Subjects
*POLYELECTROLYTES, *SUPERIONIC conductors, *SOLID electrolytes, *LITHIUM cells, *IONIC conductivity, *FLAMMABLE liquids, *ELECTROLYTES
Abstract

Solid‐state electrolytes (SSE) exhibit great promise in enhancing the safety of Li metal batteries by replacing flammable liquid electrolytes. However, the practical application of SSE is hampered mainly due to the poor electrode–electrolyte interface, low ion conductivity, and inferior electrochemical stability. Herein, superior nonflammable solid polymer electrolytes are elaborately designed by in situ encapsulating succinonitrile (SN)‐based deep eutectic solvent (DES) into the ethoxylated trimethylolpropane triacrylate (ETPTA) matrix (DES‐ETPTA). Benefiting from strong polarity and high anti‐oxidation capability, as‐prepared DES‐ETPTA electrolyte shows high ionic conductivity (9.55 × 10−4 S cm−1 at 30 °C), high Li+ transference number (0.68), and good electrochemical stability. As a result, the assembled LiFePO4 || Li full cells based on the designed DES‐ETPTA electrolyte deliver a high reversible capacity and capacity retention at −10 °C and room temperature. Furthermore, considering the compatibility with high‐voltage layered oxide cathode, the electrochemical stability of the ETPTA is further improved through the decoration of cyanoacrylate (CA) with strong electron‐withdrawing characteristic of C≡N. Consequently, the constructed 4.5 V LiCoO2 || Li full cells using DES‐ETPTA‐CA electrolyte deliver a high reversible capacity of 144 mAh g−1 and a superior retention rate of 93% after 200 cycles at 0.5 C. This work paves a new pathway to design high‐safety and high‐voltage solid polymer electrolytes for lithium metal batteries. [ABSTRACT FROM AUTHOR]

Published
2024
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28. Regulating Lewis Acid–Base Interaction in Poly (ethylene oxide)‐Based Electrolyte to Enhance the Cycling Stability of Solid‐State Lithium Metal Batteries.

Author

Zhang, Chengkun, Zhang, Shiyu, Zhang, Yinggan, Wu, Xiaoyu, Lin, Liang, Hu, Xinchao, Wang, Laisen, Lin, Jie, Sa, Baisheng, Wei, Guoying, Peng, Dong-Liang, and Xie, Qingshui

Subjects
*SOLID state batteries, *ETHYLENE oxide, *LITHIUM cells, *IONIC conductivity, *POLYELECTROLYTES, *SOLID electrolytes
Abstract

Solid polymer electrolytes (SPE) offer an outstanding choice because of their lightweight, flexibility, and excellent thin‐film forming ability. However, the low ionic conductivity and poor lithium ion transfer number (tLi+) restrict its application in all‐solid‐state lithium batteries (ASSLBs). Herein, UIO66‐X metal‐organic frameworks with controllable Lewis basicity, acidity, or neutrality functional groups are synthesized successfully and then incorporated into the poly (ethylene oxide) (PEO) matrix to fabricate SPE. The influence of different organic ligands on the interface interaction between PEO and LiTFSI is investigated by solid‐state nuclear magnetic resonance, Fourier transform infrared spectoscopy and Raman tests, as well as density functional theory calculations. The Lewis acidity group plays a key role in enhancing the ionic conductivity and tLi+. As a result, the constructed Li–Li symmetrical cells retain stable cycling for 2300 h and the LiFePO4‐based ASSLBs deliver outstanding electrochemical properties with 147 mAh g−1 of reversible capacity after 500 cycles at 1C and 60 °C, 131 mAh g−1 after 150 cycles at 0.1C and 30 °C. The fabricated SPEs are self‐standing and flexible with good mechanical stability, demonstrating the great potential for practical application. The results can guide choosing the inorganic filler to prepare high‐performance SPE. [ABSTRACT FROM AUTHOR]

Published
2024
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31. Spectral-spatial Classification of Hyperspectral Image Based on Locality Preserving Discriminant Analysis

Author

Han, Min, Zhang, Chengkun, Wang, Jun, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Cheng, Long, editor, Liu, Qingshan, editor, and Ronzhin, Andrey, editor

Published
2016
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32. RaSRNet: An End-to-End Relation-Aware Semantic Reasoning Network for Change Detection in Optical Remote Sensing Images

Author

Liang, Yi, Zhang, Chengkun, and Han, Min

Abstract

Optical remote sensing images (RSIs) are used in surface observation, and one of the most interesting research topics is change detection (CD). The internal problem of RSIs, including multiscales changed objects and cluttered background, still deserves attention. Existing methods make great efforts to solve this problem but inevitably miss detection, which affects the model performance. To address this dilemma, this article proposes a relation-aware semantic reasoning network (RaSRNet) in an end-to-end manner to pop-out change objects in RSIs, where the key point is to perceive contextual semantic information. The relation-aware (Ra) module in RaSRNet combats the lack of contextual information caused by the limited receptive field (RF) of the general convolutional layer, which facilitates all-around changed object detection. The multilevel semantic reasoning encoder–decoder (ED) backbone in RaSRNet extracts and reconstructs pixel semantic information, alleviates the interference of background noise, and improves the integrity recognition of changed objects. In addition, the decoder backend undertakes two semantic segmentation branches and introduces a semantic reasoning loss between the two branches to infer pixel semantic categories, which provides more accurate semantic features for the CD. Extensive experiments are conducted on the three public RSI CD datasets, and the results demonstrate that the proposed RaSRNet can accurately locate changed objects, which consistently outperforms the state-of-the-art CD competitors.

Published
2024
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33. Cross-view Action Recognition via Dual-Codebook and Hierarchical Transfer Framework

Author

Zhang, Chengkun, Zheng, Huicheng, Lai, Jianhuang, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Cremers, Daniel, editor, Reid, Ian, editor, Saito, Hideo, editor, and Yang, Ming-Hsuan, editor

Published
2015
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38. Robots Under COVID-19 Pandemic: A Comprehensive Survey

Author

Shen, Yang, Guo, Dejun, Long, Fei, Mateos, Luis A., Ding, Houzhu, Xiu, Zhen, Hellman, Randall B., King, Adam, Chen, Shixun, Zhang, Chengkun, and Tan, Huan

Subjects
General Computer Science, Coronavirus disease 2019 (COVID-19), Computer science, review, sensors, Intelligent Transportation Systems, 03 medical and health sciences, drones, 0302 clinical medicine, autonomous systems, Order (exchange), Pandemic, survey, General Materials Science, 030212 general & internal medicine, learning, SARS-CoV-2, business.industry, public health, General Engineering, COVID-19, Robotics, TK1-9971, Systems, Man, and Cybernetics, Risk analysis (engineering), 030220 oncology & carcinogenesis, robots, Robot, Electrical engineering. Electronics. Nuclear engineering, Artificial intelligence, Robotics and Automation, business, Industry Applications
Abstract

As a result of the difficulties brought by COVID-19 and its associated lockdowns, many individuals and companies have turned to robots in order to overcome the challenges of the pandemic. Compared with traditional human labor, robotic and autonomous systems have advantages such as an intrinsic immunity to the virus and an inability for human-robot-human spread of any disease-causing pathogens, though there are still many technical hurdles for the robotics industry to overcome. This survey comprehensively reviews over 200 reports covering robotic systems which have emerged or have been repurposed during the past several months, to provide insights to both academia and industry. In each chapter, we cover both the advantages and the challenges for each robot, finding that robotics systems are overall apt solutions for dealing with many of the problems brought on by COVID-19, including: diagnosis, screening, disinfection, surgery, telehealth, care, logistics, manufacturing and broader interpersonal problems unique to the lockdowns of the pandemic. By discussing the potential new robot capabilities and fields they applied to, we expect the robotics industry to take a leap forward due to this unexpected pandemic.

Published
2021

40. Clustering and Geometric Methods in Recommender Systems

Author

Zhang, Chengkun

Subjects
Collaborative Filtering, Recommender Systems, Data Mining
Abstract

Recommender systems refer to information filtering systems that seek to understand user preferences and item characteristics for making accurate recommendations. In the past decades, along with the tremendous development of the internet and online services, immense research has been devoted to this field. In terms of different types of data, recommender systems can generally be divided into several streams, including the most common ones as collaborative filtering and content-based filtering. Collaborative filtering provides automatic predictions by analyzing historical user-item interactions, such as rating matrices. With regard to various types of collaborative filtering methods, model-based approaches have achieved significant success and matrix factorization has been the backbone of various state-of-the-art algorithms. On the other hand, content-based filtering makes recommendations by directly utilizing user/item descriptions and profiles. In particular, along with the rapid evolution of natural language processing techniques, textual analysis has greatly empowered content-based methods by extending the model for cross-domain information. This thesis investigates the state-of-the-art recommendation methods and proposes new techniques to extend related research into new directions. In particular, we focus on utilizing clustering and geometric methods (graphs and hyperbolic space) to strengthen traditional recommender systems. We conclude three major contributions within this thesis. First, we propose group-specific 1-bit matrix completion that utilizes cluster information to boost the conventional factorization model, and cluster developing 1-bit matrix completion that learns clusters automatically during the completion process. The second contribution of this thesis is that we introduce geometric inductive matrix completion. Based on the insight of the first work, we further consolidate group-specific latent factors for the inductive matrix completion task. We also introduce edge embedding within graph neural networks for subgraph feature extraction and hyperbolic vertex interactions for regression. Last but not least, we further explore hyperbolic space within the context of content-based recommender systems and propose hyperbolic hierarchical attention network for learning semantic embeddings. We apply our proposed methods for comprehensive benchmark comparison and ablation analysis. Our methods show effectiveness regarding model performance and provide insights for modern recommender systems research.

Published
2022

47. Long short-term memory network model to estimate PM2.5 concentrations with missing-filled satellite data in Beijing.

Author

Jia, Shuqin, Han, Min, and Zhang, Chengkun

Subjects
ESTIMATION theory, PARTICULATE matter, EARTH stations, AEROSOLS, INTERPOLATION
Abstract

Fine particulate matter (PM2.5) concentrations pollution is one of serious environmental issues. It is necessary for PM2.5 concentrations estimation because the existing PM2.5 ground monitoring stations are relatively sparse and cannot obtain continuous PM2.5 concentrations over a large area. Several studies have been applied aerosol optical depth (AOD) to PM2.5 concentrations estimation. However, the missing of the AOD data does not improve the accuracy of PM2.5 estimations. Therefore, we need a filling technique to fill the AOD data. The purpose of this study is to deal with the missing AOD data using machine learning interpolation techniques and to estimate the PM2.5 concentrations at 1 km resolution by adding auxiliary factors to fit the relationship between AOD data and PM2.5 data. We used a long short-term memory network (LSTM), model to fit the filled AOD data. We estimated PM2.5 values based on the meteorological conditions and the AOD data at the station, and we also established the temporal and spatial relationships of PM2.5. Overall, the method is suitable for PM2.5 estimations with R2 = 0.75. We conducted experiments at existing stations in Beijing. The results of the study demonstrate the validity of gap-filling AOD data for PM2.5 estimations, with PM2.5 distributions being higher in the south and lower in the north. [ABSTRACT FROM AUTHOR]

Published
2022
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