In fish,sex determination(SD) system shows high variation. The SD mechanisms include environmental and genetic regulation. The research on SD system and related genes in intensively studied fish species was reviewed. ...In fish,sex determination(SD) system shows high variation. The SD mechanisms include environmental and genetic regulation. The research on SD system and related genes in intensively studied fish species was reviewed. Although some genes have been described as sex-related,only DMRT1bY can be considered as a master sex determination gene and none of them has been utilized in aquaculture. The variation of fish SD system,the importance of sex-related genes in evolution research and the relations between environmental factors and sex-related genes were also discussed. The fish sex determination mechanism remains largely unknown. Further research needs to be done considering the significance of fish SD studies in basic and applied aspects.展开更多
Monitoring the operational state of sensors promptly and the accurate diagnosis of faults are essential. This paper proposes an improved fault diagnosis scheme for sensors, which includes both fault detection and faul...Monitoring the operational state of sensors promptly and the accurate diagnosis of faults are essential. This paper proposes an improved fault diagnosis scheme for sensors, which includes both fault detection and fault identification. Firstly, trend analysis combined with least squares support vector machine（TA-LSSVM） method is proposed and implemented to detect faults. Secondly, an improved error correcting output coding-support vector machine（ECOC-SVM） based fault identification method is proposed to distinguish different sensor failure modes. To demonstrate the effectiveness of the proposed scheme, experiments are conducted with an MTi-series sensor, and some comparisons are made with other fault identification methods. The experimental results demonstrate that the proposed fault diagnosis scheme offers an essential improvement with detection real-time property and better identification accuracy.展开更多
A local discriminant regularized soft k-means （LDRSKM） method with Bayesian inference is proposed for multimode process monitoring. LDRSKM extends the regularized soft k-means algorithm by exploiting the local and n...A local discriminant regularized soft k-means （LDRSKM） method with Bayesian inference is proposed for multimode process monitoring. LDRSKM extends the regularized soft k-means algorithm by exploiting the local and non-local geometric information of the data and generalized linear discriminant analysis to provide a better and more meaningful data partition. LDRSKM can perform clustering and subspace selection simultaneously, enhancing the separability of data residing in different clusters. With the data partition obtained, kernel support vector data description （KSVDD） is used to establish the monitoring statistics and control limits. Two Bayesian inference based global fault detection indicators are then developed using the local monitoring results associated with principal and residual subspaces. Based on clustering analysis, Bayesian inference and manifold learning methods, the within and cross-mode correlations, and local geometric information can be exploited to enhance monitoring performances for nonlinear and non-Gaussian processes. The effectiveness and efficiency of the proposed method are evaluated using the Tennessee Eastman benchmark process.展开更多
文摘In fish,sex determination(SD) system shows high variation. The SD mechanisms include environmental and genetic regulation. The research on SD system and related genes in intensively studied fish species was reviewed. Although some genes have been described as sex-related,only DMRT1bY can be considered as a master sex determination gene and none of them has been utilized in aquaculture. The variation of fish SD system,the importance of sex-related genes in evolution research and the relations between environmental factors and sex-related genes were also discussed. The fish sex determination mechanism remains largely unknown. Further research needs to be done considering the significance of fish SD studies in basic and applied aspects.
基金supported in part by the National Natural Science Foundation of China under Grant Nos.61304254,61321002 and 61120106010the Key Exploration Project under Grant No.7131253
文摘Monitoring the operational state of sensors promptly and the accurate diagnosis of faults are essential. This paper proposes an improved fault diagnosis scheme for sensors, which includes both fault detection and fault identification. Firstly, trend analysis combined with least squares support vector machine（TA-LSSVM） method is proposed and implemented to detect faults. Secondly, an improved error correcting output coding-support vector machine（ECOC-SVM） based fault identification method is proposed to distinguish different sensor failure modes. To demonstrate the effectiveness of the proposed scheme, experiments are conducted with an MTi-series sensor, and some comparisons are made with other fault identification methods. The experimental results demonstrate that the proposed fault diagnosis scheme offers an essential improvement with detection real-time property and better identification accuracy.
基金supported by the National Natural Science Foundation of China(No.61272297)
文摘A local discriminant regularized soft k-means （LDRSKM） method with Bayesian inference is proposed for multimode process monitoring. LDRSKM extends the regularized soft k-means algorithm by exploiting the local and non-local geometric information of the data and generalized linear discriminant analysis to provide a better and more meaningful data partition. LDRSKM can perform clustering and subspace selection simultaneously, enhancing the separability of data residing in different clusters. With the data partition obtained, kernel support vector data description （KSVDD） is used to establish the monitoring statistics and control limits. Two Bayesian inference based global fault detection indicators are then developed using the local monitoring results associated with principal and residual subspaces. Based on clustering analysis, Bayesian inference and manifold learning methods, the within and cross-mode correlations, and local geometric information can be exploited to enhance monitoring performances for nonlinear and non-Gaussian processes. The effectiveness and efficiency of the proposed method are evaluated using the Tennessee Eastman benchmark process.
