Effective fault detection techniques can help flotation plant reduce reagents consumption,increase mineral recovery,and reduce labor intensity.Traditional,online fault detection methods during flotation processes have...Effective fault detection techniques can help flotation plant reduce reagents consumption,increase mineral recovery,and reduce labor intensity.Traditional,online fault detection methods during flotation processes have concentrated on extracting a specific froth feature for segmentation,like color,shape,size and texture,always leading to undesirable accuracy and efficiency since the same segmentation algorithm could not be applied to every case.In this work,a new integrated method based on convolution neural network(CNN)combined with transfer learning approach and support vector machine(SVM)is proposed to automatically recognize the flotation condition.To be more specific,CNN function as a trainable feature extractor to process the froth images and SVM is used as a recognizer to implement fault detection.As compared with the existed recognition methods,it turns out that the CNN-SVM model can automatically retrieve features from the raw froth images and perform fault detection with high accuracy.Hence,a CNN-SVM based,real-time flotation monitoring system is proposed for application in an antimony flotation plant in China.展开更多
A 58-year-old male patient, complaining of dysuresia, which increased over a period of 2 months, had a history of urine retention that did not respond to treatment administered in an outpatient clinic. Upon admission ...A 58-year-old male patient, complaining of dysuresia, which increased over a period of 2 months, had a history of urine retention that did not respond to treatment administered in an outpatient clinic. Upon admission to the hospital on August 2, 2005, examination showed that his prostate was midrange size by rectal palpation, and without pain or prostate nodus. An ultrasound examination indicated the prostate size was 6.1 cm×4.7 cm×3.6 cm, without an occupying lesion in the prostate.展开更多
基金Projects(61621062,61563015)supported by the National Natural Science Foundation of ChinaProject(2016zzts056)supported by the Central South University Graduate Independent Exploration Innovation Program,China
文摘Effective fault detection techniques can help flotation plant reduce reagents consumption,increase mineral recovery,and reduce labor intensity.Traditional,online fault detection methods during flotation processes have concentrated on extracting a specific froth feature for segmentation,like color,shape,size and texture,always leading to undesirable accuracy and efficiency since the same segmentation algorithm could not be applied to every case.In this work,a new integrated method based on convolution neural network(CNN)combined with transfer learning approach and support vector machine(SVM)is proposed to automatically recognize the flotation condition.To be more specific,CNN function as a trainable feature extractor to process the froth images and SVM is used as a recognizer to implement fault detection.As compared with the existed recognition methods,it turns out that the CNN-SVM model can automatically retrieve features from the raw froth images and perform fault detection with high accuracy.Hence,a CNN-SVM based,real-time flotation monitoring system is proposed for application in an antimony flotation plant in China.
文摘A 58-year-old male patient, complaining of dysuresia, which increased over a period of 2 months, had a history of urine retention that did not respond to treatment administered in an outpatient clinic. Upon admission to the hospital on August 2, 2005, examination showed that his prostate was midrange size by rectal palpation, and without pain or prostate nodus. An ultrasound examination indicated the prostate size was 6.1 cm×4.7 cm×3.6 cm, without an occupying lesion in the prostate.
