XG

Xiaozhong Geng

Changchun Institute of Technology

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Trends

Author Order

Document Type

Co-Authors

Dezhi Li

National University of Defense Technology

Gaochao Xu

Jilin University

Xi Zhang

University of Hong Kong

Xintong Zhang

Jilin University of Finance and Economics

Jie Bao

Changchun Institute of Technology

Linen Wang

Jilin University of Chemical Technology

Ping Yu

Changchun Institute of Technology

Mengzhe Yue

Changchun Institute of Technology

Ping Yu

Changchun Institute of Technology

Hanlin Chen

Tianjin University of Technology

Cooperative Institutions

Changchun Institute of Technology

Jilin University

Jilin Province Science and Technology Department

Chinese Academy of Sciences

Suqian University

Zhejiang University

Changchun University of Science and Technology

National University of Defense Technology

Harbin Institute of Technology

Xidian University

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Field

Design of a dynamic load balancing model for multiprocessor systems

Wentao Wang Xiaozhong Geng Qing Wang

Dynamic load balancing is an important factor affecting the parallel computing performance in a multiprocessor system. On the basis of an introduction to the basic principle of dynamic load balancing algorithm, this paper first finds through analysis that the basic reason for overhead occurring in load balancing is the load migration, and then qualitatively gives the granularity formula of moving load each time, proposes a new dynamic load balancing algorithm, defines the four possible states of the node, and discusses the implementation rule of the algorithm. Finally, a comparison is made between the operating results of the algorithm and those of other load balancing algorithms and no load balancing through experiment, showing that the load balancing algorithm is better than other algorithms in case of the node with no-load, different loads, and different data scale.

Granularity

Dynamic load testing

Load management

Round-robin DNS

10.1109/iccsn.2011.6014808

Cite

Citations (4)

Research on the Construction of E-Commerce Credit System Based on Blockchain

2022 14th International Conference on Measuring Technology and Mechatronics Automation (ICMTMA) (2020)

Jie Bao Xiaozhong Geng Ping Yu

The rapid development of e-commerce has greatly promoted the prosperity and development of social economy. However, due to information asymmetry and other factors, the multi-party credit problem is more and more obvious, which is not conducive to the healthy ecological development of e-commerce. This paper points out that the construction of e-commerce credit system based on blockchain technology has obvious advantages, and constructs the framework of e-commerce credit system based on blockchain. Then, this article analyzes the application scenarios of traceability, payment, SCM and so on in the area of blockchain e-commerce. Finally, the development of blockchain e-commerce and the problems that need further study are summarized.

Traceability

Prosperity

10.1109/icmtma50254.2020.00170

Cite

Citations (4)

Fusion of Pattern Recognition and Motion for Multi-Channel Motion Imagery EEG Based on Deep Spatiotemporal Feature

Advances in transdisciplinary engineering (2024)

Yingjie Zhang Xiaozhong Geng Hui Yan Huijie Jiang Guangqiao Zhang

Changes in the membrane potential of neurons in the cerebral cortex produce measurable electrical brain activity. Electroencephalograms (EEGs) measure and record these electrical activity signals through the montage of the brain electrical activity signal channels formed by non-invasive sensing electrode arrays attached to the cortex. As a non-invasive detection method, EEG is widely used in the research fields of neuroscience and cognitive psychology. With the rapid development of electronic and computer technology, the development of Brain Computer Interface (BCI) technology has been promoted. This paper proposed a self-adaptive coupled motion control strategy based on the current working pose and micro-torque feedback. Compared with the conventional control way, no matter the coherence or the stability, this self-adaptive coupled motion control strategy manifests the better performance than conventional non-feedback mechanism.

Feature (linguistics)

10.3233/atde231185

Cite

Citations (0)

The Research on Strategies of China's Green Finance Development

Ying Shi Xiaozhong Geng

Green finance (ecological finance) is an important tendency to develop modern financial industry.The development of green finance has significant role to protect the ecological environment, accelerate economy and develop the financial industry.Green Finance is also the necessary way to balance the relationship among economy, society and environment, and to satisfy the needs for sustainable development.Currently, with the rapid development of economy, the whole financial concentration level is improved.However, many problems should be designed to take into account.How to develop Chinese financial industry becomes an important issue that needs to be re-considered in post-crisis era.To that end, it is an exploring field to do deep investment in the economic development model of green finance in order to achieve the future development of China's financial industry, finally promote financial sustainable development and Social Harmonious Development.

Geography of finance

Investment

Green Economy

Green development

10.2991/citcs.2012.140

Cite

Citations (3)

Research on classification of Motor Imagery EEG signal based on CNN architecture

Yingjie Zhang Xiaozhong Geng Hui Yan

BCI based on machine learning could makes use of the EEG signals to communicate to output under the condition of without the participation of peripheral nerves and muscles. Extracting the essential features of the EEG signals in the presence of artifacts, training the classification algorithms and optimizing the performance of classifier is critical procedure for BCI system. In the realization of BCI, the most important step is the feature extraction and classification of EEG signals. Due to the obvious individual difference and low signal-to-noise ratio of EEG signals, the current feature extraction and classification algorithms have low accuracy. The emergence of deep learning has attracted much attention in many fields. At present, some researchers try to apply deep learning algorithm to the recognition of EEG signals, and obtain good results. Based on convolutional Neural Networks (CNN), this paper studies the application of deep learning in motor imagery task classification by end-to-end deep learning.

Motor Imagery

10.1109/icvris51417.2020.00112

Cite

Citations (2)

An artifact removing method fusing FastICA and CNN for EEG signal

Mengzhe Yue Xiaozhong Geng Linen Wang Xi Zhang

Electroencephalogram (EEG) is susceptible to interference from various noises during the acquisition process. In order to remove artifacts and improve classification accuracy, an artifact removal algorithm that combines FastICA and convolutional neural networks is proposes. First, the Convolutional Neural Network (CNN) is used to detect artifacts by calculating the voltage amplitude of the EEG signal, and then the EEG signal containing artifacts is decomposed into independent components by the FastICA algorithm based on negative entropy; Then the threshold method is used to automatically identify the artifact components, and the identified artifact components are removed by FastICA; finally, a pure EEG signal is obtained through reconstruction. Experimental results prove that the fusion method can effectively remove artifacts while retaining useful EEG signals, and ultimately improve the classification accuracy.

FastICA

Artifact (error)

SIGNAL (programming language)

10.1109/icitbs55627.2022.00014

Cite

Citations (0)

Design of Security Protection Based on Industrial Internet of Things Technology

2022 14th International Conference on Measuring Technology and Mechatronics Automation (ICMTMA) (2022)

Ping Yu Yunxin Long Hui Yan Hanlin Chen Xiaozhong Geng

In order to overcome new business changes that bring new security threats and challenges to many Industrial Internet of Things (IIoT) fields such as smart grids, smart factories, and smart transportation, the paper proposed the architecture of the industrial Internet of Things system, and analyzed the security threats of the industrial Internet of Things system. Combining various attack methods, targeted security protection strategies for the perception layer, network layer, platform layer and application layer are designed. The results show that the security protection strategy can effectively meet the security protection requirements of IIoT systems.

Industrial Internet

Security association

Application layer

10.1109/icmtma54903.2022.00109

Cite

Citations (3)

MBDP-BCT: Multi-bank data promotion with bank coherence technique for NUCA on CMP

IEEE Conference Anthology (2013)

Dan Wang Gaochao Xu Yuhang Sun Xiaozhong Geng

Non-Uniform Cache Architecture (NUCA) has almost been the trend of cache architecture. Data promotion technology is used to reduce the CPU's access latency in Dynamic NUCA (D-NUCA). However, due to the limitation of the replacement strategy, more useful data in the target bank may be pushed further from the request CPU in the promotion process, which restricts the performance of technology. Moreover, multiple processors using the shared data may result in data competition. Considering all the above questions, this paper proposes multi-bank data promotion technology with bank coherence technique (MBDP-BCT) in CMP NUCA, keeping the coherence of the shared data on CMP and determines the destination bank according to the data state of the cache line in banks. Experiment results verifies that the promotion technology proposed in this paper improves the access performance of CMP overall.

MESI protocol

Promotion (chess)

Cache coherence

10.1109/anthology.2013.6784800

Cite

Citations (0)

A Task Scheduling Algorithm for Multi-Core-Cluster Systems

Journal of Computers (2012)

Xiaozhong Geng Gaochao Xu Xiaodong Fu Yuan Zhang

The quantity of cores on one chip increases rapidly with the development of multi-core technology, which has led to more complex structure of cluster system and greatly increasing number of tasks. In order to schedule tasks in multi-core-cluster systems efficiently, a task schedule model based on the directed acyclic graph(DAG) is built, and then a algorithm based on task duplication is proposed. The algorithm is composed of two steps of operations, in which the processes are assigned to processor nodes in the first step and the threads in processes are assigned to core nodes in the second step respectively. The time complexity of this algorithm is less than similar algorithms. For the algorithm, minimization scheduling length is the primary objective, and keeping load balancing between processing nodes is secondary objectives. It can be seen through comparison with correlative work that the algorithm has advantages in scheduling length; furthermore, while the ratio of total communication cost and total computation cost in the task schedule model becomes larger, the advantage of this algorithm is more obvious.

Minification

Multi-core processor

10.4304/jcp.7.11.2797-2804

Cite

Citations (8)

An Efficient Division Method of Traffic Cell Based on Improved K-means Clustering Algorithm for the Location of Infrastructure in Vehicular Networks

IEEE Transactions on Vehicular Technology (2024)

Hui Yan Yaqi Shi Yunxin Long Ping Yu Xiaozhong Geng

With the rapid development of urban construction, the number of motor vehicles has increased sharply. Reasonable division of traffic districts is the key to ensure traffic flexibility and network connectivity. Aiming at the problems of unreasonable number of clusters and random selection of initial clustering centres in existing clustering algorithms for dividing traffic cells. This paper propose a systematic clustering combined with Genetic Algorithm-K-means algorithm for traffic cell delineation method. Our proposed algorithm is validated through clustering experiments on real UCI datasets, and its clustering advantages are demonstrated by combining clustering evaluation indicators. This paper takes Shenzhen, China as an example and divides traffic districts based on Global Positioning System (GPS) data from 127979 taxis, buses and trucks. By analyzing the group trajectories of different types of vehicles, reference is provided for the location of infrastructure in vehicular networks and the establishment of regions of interest in swarm-aware networks.

10.1109/tvt.2024.3370777

Cite

Citations (1)