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RISS 인기검색어
- 검색키워드
- 저자 : Shao Xiaorui (검색결과 4 건)
- 무료
- 기관 내 무료
- 유료
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Fault Detection of Electric Vehicle Charging Pile on Basis of CNN-LSTM
Xiaorui Shao,Chang-Soo Kim 한국디지털융합학회 2018 IJICTDC Vol.3 No.2
This paper presented a fault detection method based on deep learning Convolutional Neural Networks(CNN) and Long Short-Term Memory. Using CNN we get more abstract features representation in the higher level to find the distributed characteristics of the data. After obtaining the features, use LSTM to further mining useful information in the time dimension. First, we presented a CNN model which has 9 layers to extract more abstract features. By comparing three different CNN models, we realized that the shape of the original data set is much important. 16×16 shape of data set has high accuracy, it is 95%. Also comparing with traditional fault detection model, it is much better than random forest and Deep Neutral network(DNN). And the results show that the proposed CNN model can extract the features automatically for fault detection intelligently. However, data has a complex time correlation with each other. How to get the most information in the data for fault detection? We presented LSTM to extract more useful information in the time dimension. The proposed CNN-LSTM method has the highest accuracy which up to 96.13%. The proposed CNN-LSTM exhibits the best performance in the electric vehicle charging pile diagnosis.
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Self-Supervised Long-Short Term Memory Network for Solving Complex Job Shop Scheduling Problem
( Xiaorui Shao ),( Chang Soo Kim ) 한국인터넷정보학회 2021 KSII Transactions on Internet and Information Syst Vol.15 No.8
The job shop scheduling problem (JSSP) plays a critical role in smart manufacturing, an effective JSSP scheduler could save time cost and increase productivity. Conventional methods are very time-consumption and cannot deal with complicated JSSP instances as it uses one optimal algorithm to solve JSSP. This paper proposes an effective scheduler based on deep learning technology named self-supervised long-short term memory (SS-LSTM) to handle complex JSSP accurately. First, using the optimal method to generate sufficient training samples in small-scale JSSP. SS-LSTM is then applied to extract rich feature representations from generated training samples and decide the next action. In the proposed SS-LSTM, two channels are employed to reflect the full production statues. Specifically, the detailed-level channel records 18 detailed product information while the system-level channel reflects the type of whole system states identified by the k-means algorithm. Moreover, adopting a self-supervised mechanism with LSTM autoencoder to keep high feature extraction capacity simultaneously ensuring the reliable feature representative ability. The authors implemented, trained, and compared the proposed method with the other leading learning-based methods on some complicated JSSP instances. The experimental results have confirmed the effectiveness and priority of the proposed method for solving complex JSSP instances in terms of make-span.
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Fault Diagnosis of Bearing Based on Convolutional Neural Network Using Multi-Domain Features
( Xiaorui Shao ),( Lijiang Wang ),( Chang Soo Kim ),( Ilkyeun Ra ) 한국인터넷정보학회 2021 KSII Transactions on Internet and Information Syst Vol.15 No.5
Failures frequently occurred in manufacturing machines due to complex and changeable manufacturing environments, increasing the downtime and maintenance costs. This manuscript develops a novel deep learning-based method named Multi-Domain Convolutional Neural Network (MDCNN) to deal with this challenging task with vibration signals. The proposed MDCNN consists of time-domain, frequency-domain, and statistical-domain feature channels. The Time-domain channel is to model the hidden patterns of signals in the time domain. The frequency-domain channel uses Discrete Wavelet Transformation (DWT) to obtain the rich feature representations of signals in the frequency domain. The statistic-domain channel contains six statistical variables, which is to reflect the signals’ macro statistical-domain features, respectively. Firstly, in the proposed MDCNN, time-domain and frequency-domain channels are processed by CNN individually with various filters. Secondly, the CNN extracted features from time, and frequency domains are merged as time-frequency features. Lastly, time-frequency domain features are fused with six statistical variables as the comprehensive features for identifying the fault. Thereby, the proposed method could make full use of those three domain-features for fault diagnosis while keeping high distinguishability due to CNN's utilization. The authors designed massive experiments with 10-folder cross-validation technology to validate the proposed method's effectiveness on the CWRU bearing data set. The experimental results are calculated by ten-time averaged accuracy. They have confirmed that the proposed MDCNN could intelligently, accurately, and timely detect the fault under the complex manufacturing environments, whose accuracy is nearly 100%.
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서버통신 기술을 활용한 실내 화분 자동 급수 시스템 개발
이성주,Shao Xiaorui,김창수 한국재난정보학회 2021 한국재난정보학회 학술대회 Vol.2021 No.11
본 논문에서는 아두이노를 이용하여 다양한 조건에 대응하는 자동급수시스템을 개발한다. 여행, 출장 등으로 가정이나 사무 실을 비울 경우, 많은 사람들은 화분의 급수 때문에 여러 가지 해결방법을 고민하고 있으나, 현재 시중에 판매되는 대부분의 아두이노 기반 제품들 또는 IoT 장치들을 이용한 제품들로는 쉽게 해결되지 못하고 있다. 또한 다양한 조건들을 고려한 맞춤형 자동급수시스템의 경우 가격이 고가이며 맞춤제작하는것도 쉽지않다. 따라서 온습도 센서, 조도센서, 수분량 센서, CO2센서, 서보모터를 통해 스마트팜 하드웨어를 구축하였고, 아두이노를 통해 스마트 팜 자동제어가 되도록 하였으며 서버 통신을 접목 한 앱 개발을 통해서 사용자가 원격 조절도 할 수 있도록 구현 하였다.
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