[email protected]
Academic Editor
Author
Contributions by role
Author 1
Reviewer 12
Editor 34
Membership
ORCID
0000-0001-6234-1001
Jinchao Chen
School of Computer Science, Northwestern Polytechnical University, Xi’an 710072, China
Summary
Dr. Jinchao Chen is an Associate Professor in School of Computer Science at Northwestern Polytechnical University, Xi’an, China. He has received his Ph.D. degree in Computer Science from the same institution in 2016. He focuses on the multi-processor scheduling, embedded and real-time systems, simulation and verification, decision-making and intelligent control of unmanned aerial vehicles, human-computer interaction systems. He has over 50 papers and 4 ESI highly-cited papers published in international conferences and journals (e.g., IEEE Transactions on Intelligent Transportation Systems, IEEE Transactions on Industrial Electronics, IEEE Transactions on Cybernetics, IEEE Real-Time Systems Symposium). He is the Editor-in-Chief of ASP Transactions on Computers and ASP Transactions on Computers, and the Academic Editor of International Journal of Aerospace Engineering. He is a TCP member of many conferences and regular reviewer of IEEE Transactions on Industrial Informatics, IEEE Transactions on Intelligent Transportation Systems, IEEE Transactions on Transportation Electrification, IEEE Transactions on Vehicular Technology, and Concurrency and Computation Practice and Experience. He is a member of IEEE and CCF.
Edited Journals
ICCK Transactions on Internet of Things
ICCK Transactions on Intelligent Unmanned Systems
ICCK Transactions on Computer Science
ICCK Contributions
Articles
1
Edited
34
Free Access | Research Article | 19 April 2024 | Cited: 1
Design of an Intelligent Rehabilitation Medical System for Elderly Care Apartments
by
Qinghan Yang
,
Xinke Pan
,
Yuli Guo
,
Hongyue Qu
ICCK Transactions on Internet of Things | Volume 2, Issue 2: 44-49, 2024 | DOI: 10.62762/TIOT.2024.256200
Abstract
This paper aims to design a rehabilitation medical product system tailored to the needs of future intelligent elderly care environments, with the goal of enhancing the efficiency of rehabilitation training for older adults. Through an in-depth analysis of the anticipated medical needs in elderly care apartments, we propose a comprehensive design concept for a rehabilitation medical product system. The design is approached from three key perspectives: products, services, and systems, ensuring that it aligns with the healthcare requirements specific to elderly care settings. The proposed solutions focus on optimizing rehabilitation training by integrating intelligent technologies and user-cent... More >
Graphical Abstract
Design of an Intelligent Rehabilitation Medical System for Elderly Care Apartments
Free Access | Research Article | 07 April 2024
Detection of Arctic Sea Ice Using 89 GHz Microwave Radiometer Channels
by
Yue Zhao
,
Xingdong Wang
,
Changfeng Luo
ICCK Transactions on Internet of Things | Volume 2, Issue 2: 36-43, 2024 | DOI: 10.62762/TIOT.2024.528361
Abstract
Sea ice is a crucial component of the cryosphere, and extensive research has been conducted on sea ice using microwave remote sensing due to its robustness against cloud cover and illumination variations. This paper focuses on classifying Arctic sea ice based on microwave remote sensing data. Leveraging the high stability of microwave radiometers, we analyze the characteristics of different sea ice types across the Arctic region in January 2017 using high-resolution AMSR-E/AMSR2 data at the 89 GHz frequency band. Data at this frequency are less susceptible to cloud and water vapor interference, while lower frequency bands have traditionally been more commonly used in similar studies. However... More >
Graphical Abstract
Detection of Arctic Sea Ice Using 89 GHz Microwave Radiometer Channels
Free Access | Research Article | 12 March 2024
Advancements in Multi-Year Ice Concentration Estimation from SSM/I 91.6GHz Observations
by
Yue Zhao
,
Xingdong Wang
,
Zifan Zhang
ICCK Transactions on Internet of Things | Volume 2, Issue 1: 26-35, 2024 | DOI: 10.62762/TIOT.2024.682080
Abstract
To enhance the LOMAX algorithm for sea ice concentration analysis in the polar regions, SSM/I 91.6GHz data was utilized, addressing the underuse of higher frequency data. The refinement process involved redefining PCT values for one-year and multi-year ice regions through both interpolation and least squares methods. Moreover, band operations were conducted to facilitate Arctic multi-year ice concentration retrieval. Comparative analyses with the NT algorithm indicated that the Arctic sea ice extents determined by both algorithms were similar, affirming the credibility of the modified LOMAX algorithm. When examining the results for March and September, the updated LOMAX algorithm demonstrate... More >
Graphical Abstract
Advancements in Multi-Year Ice Concentration Estimation from SSM/I 91.6GHz Observations
Free Access | Research Article | 12 February 2024
Application of Dimension Reduction Methods to High-Dimensional Single-Cell 3D Genomic Contact Data
by
Zilin Wang
,
Ping Zhang
,
Weicheng Sun
,
Dongxu Li
ICCK Transactions on Internet of Things | Volume 2, Issue 1: 20-25, 2024 | DOI: 10.62762/TIOT.2024.186430
Abstract
The volume and complexity of data in various fields, particularly in biology, are increasing exponentially, posing a challenge to existing analytical methods, which often struggle with high-dimensional data such as single-cell Hi-C data. To address this issue, we employ unsupervised methods, specifically Principal Component Analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE), to reduce data dimensions for visualization. Furthermore, we assess the information retention of the decomposed components using a Linear Discriminant Analysis (LDA) classifier model. Our findings indicate that these dimensionality reduction techniques effectively capture and present information not r... More >
Graphical Abstract
Application of Dimension Reduction Methods to High-Dimensional Single-Cell 3D Genomic Contact Data
Free Access | Research Article | 14 January 2024
3D Convolutional Neural Network-Based Multi-Parameter Video Quality Assessment Model on Cloud Platforms
by
Xue Li
,
Jiali Qiu
ICCK Transactions on Internet of Things | Volume 2, Issue 1: 8-19, 2024 | DOI: 10.62762/TIOT.2024.369369
Abstract
In light of the rapid advancements in big data and artificial intelligence technologies, the trend of uploading local files to cloud servers to mitigate local storage limitations is growing. However, the surge of duplicate files, especially images and videos, results in significant network bandwidth wastage and complicates server management. To tackle these issues, we have developed a multi-parameter video quality assessment model utilizing a 3D convolutional neural network within a video deduplication framework. Our method, inspired by the analytic hierarchy process, thoroughly evaluates the effects of packet loss rate, codec, frame rate, bit rate, and resolution on video quality. The model... More >
Graphical Abstract
3D Convolutional Neural Network-Based Multi-Parameter Video Quality Assessment Model on Cloud Platforms
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