scholarly journals A case study of distributed network fault detection technology in distance education

2013 ◽  
Author(s):  
Qianjun Tang ◽  
Yan Zhang ◽  
Yongju Li
Keyword(s):  
Distance Education ◽  
Fault Detection ◽  
Distributed Network ◽  
Detection Technology ◽  
Network Fault

scholarly journals A Remote Educational Video Dynamic Image Sharp Processing Algorithm

2013 ◽  
Vol 760-762 ◽  
pp. 1562-1566
Author(s):  
Qian Jun Tang ◽  
Yan Zhang
Keyword(s):  
Distance Education ◽  
Fault Detection ◽  
Transmission Efficiency ◽  
Video Image ◽  
Transmission Network ◽  
Characteristic Analysis ◽  
Remote Education ◽  
Transmission Distance ◽  
Education Network ◽  
Network Fault

In distance education network transmission process, because transmission distance is too long, transmission network will be affected by complicated external environment factors, which leads to network failure and failure in remote education video image formation, and finally causes unsmooth transmission. This paper puts forward a distributed network fault detection technology to perform fault detection for remote education transmission network nodes and characteristic analysis of the use of network fault by using genetic neural network, accurately locate fault node area so as to realize the remote education networks fault detection. Experiments show that this method can avoid distance education network fault resulted from long transmission distance and improve the transmission efficiency of remote education video image.

A Case Study of Distributed Network Fault Detection Technology in Distance Education

2013 ◽  
Vol 760-762 ◽  
pp. 1282-1287
Author(s):  
Qian Jun Tang ◽  
Yan Zhang ◽  
Yong Ju Li
Keyword(s):  
Intrusion Detection ◽  
Pattern Matching ◽  
High Efficiency ◽  
Detection System ◽  
Attribute Reduction ◽  
Security Technology ◽  
Whole Process ◽  
Detection Technology ◽  
Network Fault

The intrusion detection under the environment of IPv6 is an important security technology along with firewall in system security defense system, which can be used for real-time detection and monitoring of the system in the whole process of system invasion. This paper puts forward an intrusion detection system under IPv6 platform based on intrusion detection feature attribute reduction by using pattern matching, so as to expand the range of application and user group of the security products. By the analysis and comparison of various pattern matching algorithms, the new algorithm realizes the intrusion feature module matching under IPv6, and make detection system be of high efficiency. Later experiments have proved this view.

PNN and BP Neural Network Fault Diagnosis Research on Electronic Accelerator Pedal Detection

2020 ◽  
Vol 14 (2) ◽  
pp. 205-220
Author(s):  
Yuxiu Jiang ◽  
Xiaohuan Zhao
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Fault Detection ◽  
Lower Limit ◽  
Bp Neural Network ◽  
Accelerator Pedal ◽  
Electronic Throttle ◽  
Measured Voltage ◽  
Upper Limit ◽  
Network Fault

Background: The working state of electronic accelerator pedal directly affects the safety of vehicles and drivers. Effective fault detection and judgment for the working state of the accelerator pedal can prevent accidents. Methods: Aiming at different working conditions of electronic accelerator pedal, this paper used PNN and BP diagnosis model to detect the state of electronic accelerator pedal according to the principle and characteristics of PNN and BP neural network. The fault diagnosis test experiment of electronic accelerator pedal was carried out to get the data acquisition. Results: After the patents for electronic accelerator pedals are queried and used, the first measured voltage, the upper limit of first voltage, the first voltage lower limit, the second measured voltage, the upper limit of second voltage and the second voltage lower limit are tested to build up the data samples. Then the PNN and BP fault diagnosis models of electronic accelerator pedal are established. Six fault samples are defined through the design of electronic accelerator pedal fault classifier and the fault diagnosis processes are executed to test. Conclusion: The fault diagnosis results were analyzed and the comparisons between the PNN and the BP research results show that BP neural network is an effective method for fault detection of electronic throttle pedal, which is obviously superior to PNN neural network based on the experiment data.

scholarly journals Improved Process Monitoring Scheme Using Multi-Scale Independent Component Analysis

2021 ◽  
Author(s):  
K Ramakrishna Kini ◽  
Muddu Madakyaru
Keyword(s):  
Fault Detection ◽  
Independent Component Analysis ◽  
Component Analysis ◽  
Independent Component ◽  
Noisy Environment ◽  
Process Data ◽  
Multi Scale ◽  
Efficient Information ◽  
Non Gaussian

AbstractThe task of fault detection is crucial in modern chemical industries for improved product quality and process safety. In this regard, data-driven fault detection (FD) strategy based on independent component analysis (ICA) has gained attention since it improves monitoring by capturing non-gaussian features in the process data. However, presence of measurement noise in the process data degrades performance of the FD strategy since the noise masks important information. To enhance the monitoring under noisy environment, wavelet-based multi-scale filtering is integrated with the ICA model to yield a novel multi-scale Independent component analysis (MSICA) FD strategy. One of the challenges in multi-scale ICA modeling is to choose the optimum decomposition depth. A novel scheme based on ICA model parameter estimation at each depth is proposed in this paper to achieve this. The effectiveness of the proposed MSICA-based FD strategy is illustrated through three case studies, namely: dynamic multi-variate process, quadruple tank process and distillation column process. In each case study, the performance of the MSICA FD strategy is assessed for different noise levels by comparing it with the conventional FD strategies. The results indicate that the proposed MSICA FD strategy can enhance performance for higher levels of noise in the data since multi-scale wavelet-based filtering is able to de-noise and capture efficient information from noisy process data.

scholarly journals Investigating the Concerns of First-Time Distance Education Instructors

2019 ◽  
Vol 20 (5) ◽  
Author(s):  
Halil Kayaduman ◽  
Turgay Demirel
Keyword(s):  
Distance Education ◽  
Content Knowledge ◽  
Stages Of Concern ◽  
Concerns Based Adoption Model ◽  
Descriptive Case Study ◽  
Time Distance ◽  
Adaptation Processes ◽  
First Time ◽  
Ongoing Concern

The purpose of the study is to investigate the concern developments of first-time distance education instructors using the concerns-based adoption model (CBAM). This study used stages of concern (SoC), a component of CBAM, as its theoretical framework. A descriptive case study was implemented, which focused on the adaptation processes of nine instructors lecturing for the first time via distance education. The instructors attended a two-day training, which was designed based on their initial concerns. Then instructors implemented their courses for four weeks via distance education. While the informational and personal stages (self-concerns) decreased compared to the initial findings, the consequence stage increased in intensity. However, self-concerns remained predominant in the process despite the reduction in self-concerns and increase in the consequence stage. Based on the findings, the implications for distance education and recommendations for addressing the instructors’ concerns are discussed. Recommendations for alleviating the concerns of first-time distance education instructors include: the provision of ongoing concern-based interventions that incorporate technological, pedagogical, and content knowledge; providing working examples related to distance education from which instructors can learn vicariously; and encouraging collaboration among instructors.

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