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- 저자 : Guoqing Gui (검색결과 3 건)
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Guoqing Gui,Hong Pan,Zhibin Lin,Yonghua Li,Zhijun Yuan 대한토목학회 2017 KSCE JOURNAL OF CIVIL ENGINEERING Vol.21 No.2
Rapid detecting damages/defeats in the large-scale civil engineering structures, assessing their conditions and timely decision making are crucial to ensure their health and ultimately enhance the level of public safety. Advanced sensor network techniques recently allow collecting large amounts of data for structural health monitoring and damage detection, while how to effectively interpret these complex sensor data to technical information posts many challenges. This paper presents three optimization-algorithm based support vector machines for damage detection. The optimization algorithms, including grid-search, partial swarm optimization and genetic algorithm, are used to optimize the penalty parameters and Gaussian kernel function parameters. Two types of feature extraction methods in terms of time-series data are selected to capture effective damage characteristics. A benchmark experimental data with the 17 different scenarios in the literature were used for verifying the proposed data-driven methods. Numerical results revealed that all three optimized machine learning methods exhibited significantly improvement in sensitivity, accuracy and effectiveness over conventional methods. The genetic algorithm based SVM had a better prediction than other methods. Two different feature methods used in this study also demonstrated the appropriate features are crucial to improve the sensitivity in detecting damage and assessing structural health conditions. The findings of this study are expected to help engineers to process big data and effectively detect the damage/defects, and thus enable them to make timely decision for supporting civil infrastructure management practices.
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Deep BBN Learning for Health Assessment toward Decision-Making on Structures under Uncertainties
Hong Pan,Guoqing Gui,Zhibin Lin,Changhui Yan 대한토목학회 2018 KSCE Journal of Civil Engineering Vol.22 No.3
Structural systems are often exposed to harsh environment, while these environmental factors in turn could degrade the system over time. Their health state and structural conditions are key for structural safety control and decision-making management. Although great efforts have been paid on this field, the high level of variability due to noise and other interferences, and the uncertainties associated with data collection, structural performance and in-service operational environments post great challenges in finding information to assist decision making. The machine learning techniques in recent years have been gaining increasing attentions due to their merits capturing information from statistical representation of events and thus enabling making decision. In this study, the deep Bayesian Belief Network Learning (DBBN) was used to extract structural information and probabilistically determine structural conditions. Different to conventional shallow learning that highly relies on the quality of the hand-crafted features, the deep learning is an end-to-end method to encode the information and interpret vast amount of data with minimizing or no features. A case study was conducted to address the methods for structure under viabilities and uncertainties due to operation, damage and noise interferences. Numerical results revealed that the deep learning exhibits considerably enhanced accuracy for structural diagnostics, as compared to the supervised shallow learning. With predetermined training set, the DBBN could accurately determine the structural health state in terms of damage level, which could dramatically help decision making for further structural retrofit or not. Note that the noise interference could contaminate the data representation and in turn increase the difficulty of the data mining, though the deep learning could reduce the impacts, as compared to conventional shallow learning techniques.
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Wenwei Tang,Xinping Zeng,Yusheng Zhao,Guoqing Ye,Wenchi Gui,Yaming Ni 한국식품과학회 2011 Food Science and Biotechnology Vol.20 No.4
A study was carried out on the disinfection efficiency of electrolyzed oxidizing water (EOW) on spores of Bacillus subtilis var. niger. The results showed a remarkable fungicidal rate of 100% after 20 min duration of 191mg/L active available chlorine (ACC). The disinfection effect was improved with increased ACC or prolonged disinfection time, while organic interferents exerted a strong concentration-dependent inhibition against the disinfection. The disinfection mechanism was also investigated at bio-molecular level. EOW decreased dehydrogenase activity, intensified membrane permeability,elevated suspension conductivity, and caused leakage of intracellular K+, proteins, and DNA, indicating a damage of cell walls and membranes. Effects of EOW on microbiological ultra-structures were also verified by transmission electronic microscopy (TEM) images,showing that EOW destroyed protective barriers of the microbe and imposed some damages upon the nucleus area.
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