Accurate fault-cause identification is highly important to the fault analysis of overhead transmission lines (OTLs). In order to improve the efficiency and accuracy of fault identification, this study proposes a fault...
详细信息
Accurate fault-cause identification is highly important to the fault analysis of overhead transmission lines (OTLs). In order to improve the efficiency and accuracy of fault identification, this study proposes a fault identification method based on the ADBN (adaptive deep belief network) model and the time-frequency characteristics of a travelling wave. According to the mechanisms of different OTL faults, the appropriate time-frequency characteristic parameters of the fault current travelling wave were selected as the input of the ADBN model, and the fault-type labels were selected as the output. The ADBN model introduces the idea of adaptive learning rate into CD (contrastivedivergence) algorithm and improves its performance with self-adjusting learning rate. The parameters of the ADBN model were pre-trained with the improved CD algorithm and adjusted by back propagation algorithm with the labels of the samples. The performance of the ADBN model was verified by field data, and the accuracy of fault identification was analysed under different model parameters, characteristic parameters, and sample sizes. The results showed that the model helps to characterise the inherent relationship between characteristic parameters and fault causes, and the proposed method can effectively identify different fault causes in OTLs.
To have the sparsity of deep neural networks is crucial, which can improve the learning ability of them, especially for application to high-dimensional data with small sample size. Commonly used regularization terms f...
详细信息
To have the sparsity of deep neural networks is crucial, which can improve the learning ability of them, especially for application to high-dimensional data with small sample size. Commonly used regularization terms for keeping the sparsity of deep neural networks are based on L-1-norm or L-2-norm;however, they are not the most reasonable substitutes of L-0-norm. In this paper, based on the fact that the minimization of a log-sum function is one effective approximation to that of L-0-norm, the sparse penalty term on the connection weights with the log-sum function is introduced. By embedding the corresponding iterative re-weighted-L-1 minimization algorithm with k-step contrastivedivergence, the connections of deep belief networks can be updated in a way of sparse self-adaption. Experiments on two kinds of biomedical datasets which are two typical small sample size datasets with a large number of variables, i.e., brain functional magnetic resonance imaging data and single nucleotide polymorphism data, show that the proposed deep belief networks with self-adaptive sparsity can learn the layer-wise sparse features effectively. And results demonstrate better performances including the identification accuracy and sparsity capability than several typical learning machines.
Implicit feedback such as video watch time is commonly seen in many internet products. Though recommender systems with explicit feedback have been abundantly researched, there are not many methods proposed for buildin...
详细信息
ISBN:
(纸本)9781538650356
Implicit feedback such as video watch time is commonly seen in many internet products. Though recommender systems with explicit feedback have been abundantly researched, there are not many methods proposed for building recommender systems with implicit feedback. Restricted Boltzmann Machine (RBM), which uses a two-layer graphical model to describe response variables through hidden units, is a promising approach. An RBM model for collaborative filtering was proposed in the literature, but it only deals with categorized ratings, which is explicit feedback. We propose a novel extension of the RBM method for collaborative filtering with implicit feedback. The model parameters can be learned efficiently through the contrastive divergence algorithm. Compared to other methods, the proposed RBM method can directly predict preferences for a new user given feedback on a few items. Also, it protects privacy by keeping user data locally and only sends updated parameters to a central server. The results on real data with several million records show it works superiorly compared to other prevalent methods.
This article presents a probabilistic model based on a bipartite convolutional architecture for unsupervised change detection. We aim to develop a robust change detection method that can adapt to different types of da...
详细信息
This article presents a probabilistic model based on a bipartite convolutional architecture for unsupervised change detection. We aim to develop a robust change detection method that can adapt to different types of data and scenarios for multitemporal coregistered remote sensing images of the same spatial resolution. On the premise of coregistration, unsupervised change detection usually suffers from the distinct appearances (different intensities or data structures) of the same object in multitemporal images, such as images obtained in different climatic conditions (season, illumination, and so on), and by different and even heterogeneous sensors. Since change detection in heterogeneous images can also adapt to other scenarios, many methods have been proposed recently focusing on such data, but most of them are limited by the need for labeled data or by specific assumptions. With the excellent and flexible feature learning capability of neural networks, we model the change detection into a Gibbs probabilistic model based on a bipartite neural network. The model is driven by an energy function defined as the squared feature distance, which is the core of change detection. Via optimizing the model, the difference degree of each pixel is automatically obtained for further identification. The probabilistic model learns to capture the distribution in an unsupervised way. Therefore, the proposed method can adapt to various scenarios without being trained by labeled data. Experiments on different types of data and scenarios demonstrate the superiority of the proposed method.
To enhance the prediction performance for building energy consumption, this paper presents a modified deep belief network (DBN) based hybrid model. The proposed hybrid model combines the outputs from the DBN model wit...
详细信息
To enhance the prediction performance for building energy consumption, this paper presents a modified deep belief network (DBN) based hybrid model. The proposed hybrid model combines the outputs from the DBN model with the energy-consuming pattern to yield the final prediction results. The energy-consuming pattern in this study represents the periodicity property of building energy consumption and can be extracted from the observed historical energy consumption data. The residual data generated by removing the energy-consuming pattern from the original data are utilized to train the modified DBN model. The training of the modified DBN includes two steps, the first one of which adopts the contrastivedivergence (CD) algorithm to optimize the hidden parameters in a pre-train way, while the second one determines the output weighting vector by the least squares method. The proposed hybrid model is applied to two kinds of building energy consumption data sets that have different energy-consuming patterns (daily-periodicity and weekly-periodicity). In order to examine the advantages of the proposed model, four popular artificial intelligence methodsthe backward propagation neural network (BPNN), the generalized radial basis function neural network (GRBFNN), the extreme learning machine (ELM), and the support vector regressor (SVR) are chosen as the comparative approaches. Experimental results demonstrate that the proposed DBN based hybrid model has the best performance compared with the comparative techniques. Another thing to be mentioned is that all the predictors constructed by utilizing the energy-consuming patterns perform better than those designed only by the original data. This verifies the usefulness of the incorporation of the energy-consuming patterns. The proposed approach can also be extended and applied to some other similar prediction problems that have periodicity patterns, e.g., the traffic flow forecasting and the electricity consumption prediction.
暂无评论