Analysis of Radiation Effects for Monitoring Circuit Based on Deep Belief Network and Support Vector Method
| Title | Analysis of Radiation Effects for Monitoring Circuit Based on Deep Belief Network and Support Vector Method |
| Publication Type | Conference Paper |
| Year of Publication | 2018 |
| Authors | Xing, Z., Liu, L., Li, S., Liu, Y. |
| Conference Name | 2018 Prognostics and System Health Management Conference (PHM-Chongqing) |
| Date Published | oct |
| Keywords | belief networks, circuit analysis computing, circuit degradation trend, circuit reliability, circuit signal, Collaboration, composability, DBN-SVM, deep belief network, deep belief network model, feature extraction, Human Behavior, integrated circuit reliability, intelligent analysis method, Metrics, Monitoring, monitoring circuit, policy-based governance, pubcrawl, radiation effects, regression analysis, remaining life assessment, remaining useful life, resilience, Resiliency, Scalability, support vector machine, Support vector machines, support vector regression, total ionizing dose, Training |
| Abstract | The monitoring circuit is widely applied in radiation environment and it is of significance to study the circuit reliability with the radiation effects. In this paper, an intelligent analysis method based on Deep Belief Network (DBN) and Support Vector Method is proposed according to the radiation experiments analysis of the monitoring circuit. The Total Ionizing Dose (TID) of the monitoring circuit is used to identify the circuit degradation trend. Firstly, the output waveforms of the monitoring circuit are obtained by radiating with the different TID. Subsequently, the Deep Belief Network Model is trained to extract the features of the circuit signal. Finally, the Support Vector Machine (SVM) and Support Vector Regression (SVR) are applied to classify and predict the remaining useful life (RUL) of the monitoring circuit. According to the experimental results, the performance of DBN-SVM exceeds DBN method for feature extraction and classification, and SVR is effective for predicting the degradation. |
| URL | https://ieeexplore.ieee.org/document/8603401 |
| DOI | 10.1109/PHM-Chongqing.2018.00093 |
| Citation Key | xing_analysis_2018 |
- Monitoring
- Training
- total ionizing dose
- support vector regression
- Support vector machines
- support vector machine
- Scalability
- Resiliency
- resilience
- remaining useful life
- remaining life assessment
- regression analysis
- radiation effects
- pubcrawl
- policy-based governance
- monitoring circuit
- belief networks
- Metrics
- intelligent analysis method
- integrated circuit reliability
- Human behavior
- feature extraction
- deep belief network model
- Deep Belief Network
- DBN-SVM
- composability
- collaboration
- circuit signal
- circuit reliability
- circuit degradation trend
- circuit analysis computing