For real-time classification of rock-masses in hard-rock tunnels,quick determination of the rock lithology on the tunnel face during construction is essential.Motivated by current breakthroughs in artificial intellige...For real-time classification of rock-masses in hard-rock tunnels,quick determination of the rock lithology on the tunnel face during construction is essential.Motivated by current breakthroughs in artificial intelligence technology in machine vision,a new automatic detection approach for classifying tunnel lithology based on tunnel face images was developed.The method benefits from residual learning for training a deep convolutional neural network(DCNN),and a multi-scale dilated convolutional attention block is proposed.The block with different dilation rates can provide various receptive fields,and thus it can extract multi-scale features.Moreover,the attention mechanism is utilized to select the salient features adaptively and further improve the performance of the model.In this study,an initial image data set made up of photographs of tunnel faces consisting of basalt,granite,siltstone,and tuff was first collected.After classifying and enhancing the training,validation,and testing data sets,a new image data set was generated.A comparison of the experimental findings demonstrated that the suggested approach outperforms previous classifiers in terms of various indicators,including accuracy,precision,recall,F1-score,and computing time.Finally,a visualization analysis was performed to explain the process of the network in the classification of tunnel lithology through feature extraction.Overall,this study demonstrates the potential of using artificial intelligence methods for in situ rock lithology classification utilizing geological images of the tunnel face.展开更多
Fluid film bearings are widely used as support elements of rotating shaft for HDD （hard disk drive） spindle motors. Recently, the opportunity for the HDD spindle motors exposed to external vibration has been increas...Fluid film bearings are widely used as support elements of rotating shaft for HDD （hard disk drive） spindle motors. Recently, the opportunity for the HDD spindle motors exposed to external vibration has been increasing because the HDDs are used for various information related equipments such as mobile PCs, car navigation systems. Hence, the rotating shaft has a possibility to come in contact with the bearing and it causes wear or seizure to the bearing surface. In order to avoid the problems, it is extremely important to enhance the dynamic characteristics of the fluid film bearings for spindles. However, verification from both theory and experiment of dynamic characteristics such as spring coefficients and damping coefficients is rare and few. In this paper, the bearing vibration characteristics when the HDD spindle is oscillated are investigated theoretically and experimentally. And then the identification method ofoil film coefficients of fluid film bearing spindles is described.展开更多
基金the National Natural Science Foundation of China under Grant No.60310213(国家自然科学基金重大国际(地区)合作研究项目)the National Natural Science Foundation of China under Grant No.60325206(国家自然科学基金杰出青年基金项目)
基金funded by the National Natural Science Foundation of China(Grant No.51978460)the Open Fund of State Key Laboratory of Shield Machine and Boring Technology(No.SKLST-2019-K08).
文摘For real-time classification of rock-masses in hard-rock tunnels,quick determination of the rock lithology on the tunnel face during construction is essential.Motivated by current breakthroughs in artificial intelligence technology in machine vision,a new automatic detection approach for classifying tunnel lithology based on tunnel face images was developed.The method benefits from residual learning for training a deep convolutional neural network(DCNN),and a multi-scale dilated convolutional attention block is proposed.The block with different dilation rates can provide various receptive fields,and thus it can extract multi-scale features.Moreover,the attention mechanism is utilized to select the salient features adaptively and further improve the performance of the model.In this study,an initial image data set made up of photographs of tunnel faces consisting of basalt,granite,siltstone,and tuff was first collected.After classifying and enhancing the training,validation,and testing data sets,a new image data set was generated.A comparison of the experimental findings demonstrated that the suggested approach outperforms previous classifiers in terms of various indicators,including accuracy,precision,recall,F1-score,and computing time.Finally,a visualization analysis was performed to explain the process of the network in the classification of tunnel lithology through feature extraction.Overall,this study demonstrates the potential of using artificial intelligence methods for in situ rock lithology classification utilizing geological images of the tunnel face.
文摘Fluid film bearings are widely used as support elements of rotating shaft for HDD （hard disk drive） spindle motors. Recently, the opportunity for the HDD spindle motors exposed to external vibration has been increasing because the HDDs are used for various information related equipments such as mobile PCs, car navigation systems. Hence, the rotating shaft has a possibility to come in contact with the bearing and it causes wear or seizure to the bearing surface. In order to avoid the problems, it is extremely important to enhance the dynamic characteristics of the fluid film bearings for spindles. However, verification from both theory and experiment of dynamic characteristics such as spring coefficients and damping coefficients is rare and few. In this paper, the bearing vibration characteristics when the HDD spindle is oscillated are investigated theoretically and experimentally. And then the identification method ofoil film coefficients of fluid film bearing spindles is described.
