[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
IECE Transactions on Internet of Things
IECE Transactions on Intelligent Unmanned Systems
IECE Transactions on Computer Science
IECE Contributions
Articles
1
Edited
34
Free Access | Research Article | 08 December 2024
Optimized CNNs for Rapid 3D Point Cloud Object Recognition
by
Tianyi Lyu
,
Dian Gu
,
Peiyuan Chen
,
Yaoting Jiang
,
Zhenhong Zhang
,
Huadong Pang
,
Li Zhou
,
Yiping Dong
IECE Transactions on Internet of Things | Volume 2, Issue 4: 83-94, 2024 | DOI: 10.62762/TIOT.2024.758153
Abstract
This study introduces a method for efficiently detecting objects within 3D point clouds using convolutional neural networks (CNNs). Our approach adopts a unique feature-centric voting mechanism to construct convolutional layers that capitalize on the typical sparsity observed in input data. We explore the trade-off between accuracy and speed across diverse network architectures and advocate for integrating an L1 penalty on filter activations to augment sparsity within intermediate layers. This research pioneers the proposal of sparse convolutional layers combined with L1 regularization to effectively handle large-scale 3D data processing. Our method’s efficacy is demonstrated on the MVTec... More >
Graphical Abstract
Optimized CNNs for Rapid 3D Point Cloud Object Recognition
Free Access | Review Article | 07 December 2024
An Overview of Data Persistence Approaches for Enterprise Web Applications
by
Zhiwei Xu
,
Ming Lei
IECE Transactions on Computer Science | Volume 2, Issue 1: 10-17, 2024 | DOI: 10.62762/TCS.2024.529749
Abstract
In the era of digital transformation, enterprise web applications have become indispensable tools for business operations, necessitating the efficient and reliable management of vast amounts of data. Data persistence is critical to ensure consistency, security, and scalability, especially in complex environments involving high concurrency and sensitive information. This paper reviews the key requirements for data persistence in enterprise-level web applications, such as reliability, security, scalability, and high availability, while addressing the challenges posed by modern business needs. Various persistence solutions, including relational databases, NoSQL databases, and distributed storag... More >
Graphical Abstract
An Overview of Data Persistence Approaches for Enterprise Web Applications
Free Access | Research Article | 07 December 2024
Predictive Analysis for Road Safety Enhancement in Chicago County
by
Reshma Shaik
,
Kislay Raj
,
Aditya Singh
,
Teerath Kumar
IECE Transactions on Computer Science | Volume 2, Issue 1: 1-9, 2024 | DOI: 10.62762/TCS.2024.766854
Abstract
With the increasing incidents of fatal road injuries, there is an urgent need for developing effective road safety management systems. The study aims to develop predictive models based on machine learning to forecast the likelihood of road collisions depending on factors like weather, road condition, time, and driver behaviour in Chicago, USA. A machine learning approach has been applied to the crash dataset to evaluate the factors affecting the prevalence of road accidents. Python programming and the Jupyter Notebook platform have been used for performing descriptive statistics, correlation and three classification algorithms (Random Forest, KNN, Decision Tree and MLP Classification). Obtai... More >
Graphical Abstract
Predictive Analysis for Road Safety Enhancement in Chicago County
Free Access | Research Article | 23 November 2024
Reforming Curriculum Ideology and Political Construction: A Case Study of the ’Information Security’ Course
by
Pengyi Zheng
,
Xijuan Wang
,
Juanjuan Li
IECE Transactions on Computer Science | Volume 1, Issue 1: 26-31, 2024 | DOI: 10.62762/TCS.2024.210691
Abstract
In alignment with the objective of integrating ideological and political education within curricula, this study uses the "Information Security" course as a model to illustrate the approach of embedding core ideologies pertinent to the forefront of international scientific application, national key needs, and the primary battlegrounds of the economy. By weaving frontier science and technology along with national security regulations into the educational practice, this research delves into teaching cases within the information security domain to construct a curriculum system that seamlessly integrates ideological, political, and academic content. The system aims to foster the cultivation and p... More >
Graphical Abstract
Reforming Curriculum Ideology and Political Construction: A Case Study of the ’Information Security’ Course
Free Access | Research Article | 17 November 2024
Innovating Internet+ Education for College Student Members of the Communist Party of China in the Context of Major Public Health Emergency
by
Hong Xiao
IECE Transactions on Computer Science | Volume 1, Issue 1: 21-25, 2024 | DOI: 10.62762/TCS.2024.251125
Abstract
In the post-COVID-19 era, the education of Communist Party of China members among college students continues to face evolving challenges and opportunities. This paper examines the sustained integration of the Internet with Party member education in colleges as a means to maintain flexibility, enrich educational content, and enhance engagement. The "Internet + education" model, which gained prominence during the pandemic, remains a vital tool for overcoming spatial and temporal constraints in education. However, as the pandemic has subsided, new challenges have emerged, including the need to build high-quality online resources, ensure alignment between educational supply and student demand, a... More >
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