260 research outputs found
Supplemental Materials - Moving towards darkness: The personality-environment association between the Dark Triad and residential mobility
No full textSupplemental Materials for Moving towards darkness: The personality-environment association between the Dark Triad and residential mobility by Shijiang Zuo, Xueli Zhu, Fang Wang, Niwen Huang, and Pan Cai in European Journal of Personality.</p
Supplemental Materials - Moving towards darkness: The personality-environment association between the Dark Triad and residential mobility
No full textSupplemental Materials for Moving towards darkness: The personality-environment association between the Dark Triad and residential mobility by Shijiang Zuo, Xueli Zhu, Fang Wang, Niwen Huang, and Pan Cai in European Journal of Personality.</p
Supplemental Material - Prevalence and Associated Factors of Atherosclerotic Plaque and Stenosis in Renal Arteries: A Community-Based Study
No full textSupplemental Material for Prevalence and Associated Factors of Atherosclerotic Plaque and Stenosis in Renal Arteries: A Community-Based Study by Dongxue Wang, Yuesong Pan, Xueli Cai, Jing Jing, Hongyi Yan, Suying Wang, Xia Meng, Lerong Mei, Yanli Zhang, Shan Li, Tiemin Wei, Yilun Zhou and Yongjun Wang in Angiology</p
Methods for Improving Time and Accuracy in Deep Learning and Its Applications
Get PDFDeep learning has achieved great performance in various areas, such as computer vision, natural language processing, and speech recognition. In this research, we design methods to improve the prediction performance and decrease training time of deep learning models. We first propose an efficient evolutionary algorithm (EA) to automatically tune hyperparameters in a deep learning model in Chapter 2. We use a variable length genetic algorithm (GA) to systematically and automatically tune the hyperparameters of a Convolutional Neural Network (CNN) to improve its performance. Experiment results show that our algorithm can find good CNN model hyperparameters efficiently. In Chapter 3, we propose a method to intelligently freeze layers during the training process to decrease training time. Our method involves designing a formula to calculate normalized gradient differences for all layers with weights in the model and then use the calculated values to decide how many layers should be frozen. We implemented our method on top of stochastic gradient descent (SGD) and performed experiments on standard image classification dataset CIFAR-10. Results show that our method can accelerate training on VGG nets, ResNets, and DenseNets while having similar test accuracy. Next, in Chapter 4, we propose to incorporate prior knowledge into the training process to improve classification accuracy. We incorporate class similarity knowledge into CNN models using a graph convolution layer. We evaluate our method on two benchmark image datasets: MNIST and CIFAR-10 and analyze the results on different data and model sizes. Experimental results show that our model can improve classification accuracy, especially when the amount of available data is small. In Chapter 5, we map Electronic Health Records (EHRs) to images and feed them to CNNs for feature relationship learning. The relationships between EHR features are quantitatively measured before mapping to images. We add this relationship-learning part as a boosting module on the original machine learning model. Experimental results show that our proposed models have better performance compared with the baseline models. In summary, this research proposes various methods to improve the training time and performance of deep learning models.Doctor of Philosophy (PhD)Computer Scienc
Vibration Adaptive Anomaly Detection of Hydropower Unit in Variable Condition Based on Moving Least Square Response Surface
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Bearing fault diagnosis of a wind turbine based on variational mode decomposition and permutation entropy
No full text Variational mode decomposition is a new signal decomposition method, which can process non-linear and non-stationary signals. It can overcome the problems of mode mixing and compensate for the shortcomings in empirical mode decomposition. Permutation entropy is a method which can detect the randomness and kinetic mutation behavior of a time series. It can be considered for use in fault diagnosis. The complexity of wind power generation systems means that the randomness and kinetic mutation behavior of their vibration signals are displayed at different scales. Multi-scale permutation entropy analysis is therefore needed for such vibration signals. This research investigated a method based on variational mode decomposition and permutation entropy for the fault diagnosis of a wind turbine roller bearing. Variational mode decomposition was adopted to decompose the bearing vibration signal into its constituent components. The components containing key fault information were selected for the extraction of their permutation entropy. This entropy was used as a bearing fault characteristic value. The nearest neighbor algorithm was employed as a classifier to identify faults in a roller bearing. The experimental data showed that the proposed method can be applied to wind turbine roller bearing fault diagnosis. </jats:p
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