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RISS 인기검색어
- 검색키워드
- 저자 : Da-Shuai Xie (검색결과 3 건)
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Xie, Da-Shuai,Peng, Wei,Chen, Jun-Cheng,Li, Liang,Zhao, Chong-Bo,Yang, Shi-Long,Xu, Min,Wu, Chun-Jie,Ai, Li 한국식품과학회 2016 Food Science and Biotechnology Vol.25 No.6
Hawthorn (CFS) has commonly been applied as an important traditional Chinese medicine and food for thousands of years. The raw material of CFS is commonly processed by stir-frying to obtain yellow (CFY), dark brown (CFD), and carbon dark (CFC) colored products, which are used for different clinical uses. In this study, an intelligent sensory system (ISS) was used to obtain the color, gas, and flavor samples data, which were further employed to develop a novel and accurate method for the identification of CFS and its processed products using principal component analysis. Moreover, this research developed a model of an artificial neural network, which could be used to predict the total organic acid, total flavonoids, citric acid, hyperin, and 5-hydroxymethyl furfural via determination of the color, odor, and taste of a sample. In conclusion, the ISS and the artificial neural network are useful tools for rapid, accurate, and effective discrimination of CFS and its processed products.
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Da-Shuai Xie,Wei Peng,Jun-Cheng Chen,Liang Li,Chong-Bo Zhao,Shi-Long Yang,Min Xu,Chun-Jie Wu,Li Ai 한국식품과학회 2016 Food Science and Biotechnology Vol.25 No.6
Hawthorn (CFS) has commonly been applied as an important traditional Chinese medicine and food for thousands of years. The raw material of CFS is commonly processed by stir-frying to obtain yellow (CFY), dark brown (CFD), and carbon dark (CFC) colored products, which are used for different clinical uses. In this study, an intelligent sensory system (ISS) was used to obtain the color, gas, and flavor samples data, which were further employed to develop a novel and accurate method for the identification of CFS and its processed products using principal component analysis. Moreover, this research developed a model of an artificial neural network, which could be used to predict the total organic acid, total flavonoids, citric acid, hyperin, and 5-hydroxymethyl furfural via determination of the color, odor, and taste of a sample. In conclusion, the ISS and the artificial neural network are useful tools for rapid, accurate, and effective discrimination of CFS and its processed products.
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Operational performance evaluation of bridges using autoencoder neural network and clustering
Chunfeng Wan,Songtao Xue,Huachen Jiang,Liyu Xie,Da Fang,Shuai Gao,Kang Yang,YouLiang Ding 국제구조공학회 2024 Smart Structures and Systems, An International Jou Vol.33 No.3
To properly extract the strain components under varying operational conditions is very important in bridge health monitoring. The abnormal sensor readings can be correctly identified and the expected operational performance of the bridge can be better understood if each strain components can be accurately quantified. In this study, strain components under varying load conditions, i.e., temperature variation and live-load variation are evaluated based on field strain measurements collected from a real concrete box-girder bridge. Temperature-induced strain is mainly regarded as the trend variation along with the ambient temperature, thus a smoothing technique based on the wavelet packet decomposition method is proposed to estimate the temperature-induced strain. However, how to effectively extract the vehicle-induced strain is always troublesome because conventional threshold setting-based methods cease to function: if the threshold is set too large, the minor response will be ignored, and if too small, noise will be introduced. Therefore, an autoencoder framework is proposed to evaluate the vehicleinduced strain. After the elimination of temperature and vehicle-induced strain, the left of which, defined as the model error, is used to assess the operational performance of the bridge. As empirical techniques fail to detect the degraded state of the structure, a clustering technique based on Gaussian Mixture Model is employed to identify the damage occurrence and the validity is verified in a simulation study.
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