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- 저자 : Hongyi Cao (검색결과 5 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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Interference Cancellation for Relay-Assisted D2D Communication
( Hongyi Zhao ),( Yang Cao ),( Yingzhuang Liu ) 한국인터넷정보학회 2015 KSII Transactions on Internet and Information Syst Vol.9 No.9
Relay-assisted D2D communication extends the communication range of the D2D pairs and helps users to form D2D pairs effectively. However, due to the introduction of the multi-hop relaying, the D2D communication has to occupy extra transmission time, which may decrease the efficiency of the communication system. In this paper, we propose a scheme to make node receive D2D signal and BS signal at overlapping time to improve the spectrum efficiency according to ZigZag decoding and successive-interference-cancellation (SIC). In this way, more data can be delivered during the same duration, thus the network throughput can be further improved. Numerical results verify the performance improvement of the proposed scheme when compared with a baseline scheme. Moreover, we expand the proposed scheme from one-hop relay scenario to multi-hop relay scenario.
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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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Yaxu Wang,Ruirui Wang,Jiwen Wang,Ningbo Li,Hongyi Cao 대한토목학회 2023 KSCE Journal of Civil Engineering Vol.27 No.7
Tunnel Boring Machines (TBMs) have been the main equipment for tunneling and underground construction due to their high safety performance and tunneling efficiency. However, the unknown and changing geological conditions during construction pose a challenge to TBM construction. As one of the essential parameters of rock properties, accurate acquisition of uniaxial compressive strength (UCS) is crucial for TBMs to adapt to changing ground conditionsin a timely manner. Therefore, this study proposes a Catboost intelligent model based on Bayesian Optimization to predict UCS. Rock mass are velocity information and key TBM operational parameters are used as model input variables. The Gaussian data augmentation method is used to compensate for the difficulty of obtaining field data in large quantities. The Zhujiang Delta Water Resources Allocation Engineering field data are used in the model, and the obtained evaluation indicators MAPE, RMSE, VAF and a20-index are obtained as 9.91%, 499.38 MPa, 90.7% and 0.95, respectively. In addition, another project is selected to verify the applicability of the model. The validation results also confirm that the model is valid and reliable when applied to practical engineering.
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