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- 저자 : Shuaitang Zhang (검색결과 3 건)
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Cao, Hongyi,Ren, Qiaomu,Zou, Xiuguo,Zhang, Shuaitang,Qian, Yan Korea Information Processing Society 2019 Journal of information processing systems Vol.15 No.5
In recent years, the problem of data drifted of the smart grid due to manual operation has been widely studied by researchers in the related domain areas. It has become an important research topic to effectively and reliably find the reasonable data needed in the Supervisory Control and Data Acquisition (SCADA) system has become an important research topic. This paper analyzes the data composition of the smart grid, and explains the power model in two smart grid applications, followed by an analysis on the application of each parameter in density-based spatial clustering of applications with noise (DBSCAN) algorithm. Then a comparison is carried out for the processing effects of the boxplot method, probability weight analysis method and DBSCAN clustering algorithm on the big data driven power grid. According to the comparison results, the performance of the DBSCAN algorithm outperforming other methods in processing effect. The experimental verification shows that the DBSCAN clustering algorithm can effectively screen the power grid data, thereby significantly improving the accuracy and reliability of the calculation result of the main grid's theoretical line loss.
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Hongyi Cao,Qiaomu Ren,Xiuguo Zou,Shuaitang Zhang,Yan Qian 한국정보처리학회 2019 Journal of information processing systems Vol.15 No.5
In recent years, the problem of data drifted of the smart grid due to manual operation has been widely studiedby researchers in the related domain areas. It has become an important research topic to effectively and reliablyfind the reasonable data needed in the Supervisory Control and Data Acquisition (SCADA) system has becomean important research topic. This paper analyzes the data composition of the smart grid, and explains the powermodel in two smart grid applications, followed by an analysis on the application of each parameter in densitybasedspatial clustering of applications with noise (DBSCAN) algorithm. Then a comparison is carried out forthe processing effects of the boxplot method, probability weight analysis method and DBSCAN clusteringalgorithm on the big data driven power grid. According to the comparison results, the performance of theDBSCAN algorithm outperforming other methods in processing effect. The experimental verification showsthat the DBSCAN clustering algorithm can effectively screen the power grid data, thereby significantlyimproving the accuracy and reliability of the calculation result of the main grid’s theoretical line loss.
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Identification of Tea Diseases Based on Spectral Reflectance and Machine Learning
Xiuguo Zou,Qiaomu Ren,Hongyi Cao,Yan Qian,Shuaitang Zhang 한국정보처리학회 2020 Journal of information processing systems Vol.16 No.2
With the ability to learn rules from training data, the machine learning model can classify unknown objects. Atthe same time, the dimension of hyperspectral data is usually large, which may cause an overfitting problem. In this research, an identification methodology of tea diseases was proposed based on spectral reflectance andmachine learning, including the feature selector based on the decision tree and the tea disease recognizer basedon random forest. The proposed identification methodology was evaluated through experiments. Theexperimental results showed that the recall rate and the F1 score were significantly improved by the proposedmethodology in the identification accuracy of tea disease, with average values of 15%, 7%, and 11%, respectively. Therefore, the proposed identification methodology could make relatively better feature selection and learnfrom high dimensional data so as to achieve the nondestructive and efficient identification of different teadiseases. This research provides a new idea for the feature selection of high dimensional data and the nondestructiveidentification of crop diseases.
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