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This paper aims to develop a framework for vehicle type classification using convolutional neural network based on vehicle rear view images. Compared with the extraction of the appearance features from vehicle side view and frontal view images, there has been relatively little research on vehicle type classification by using vehicle rear view images' information. The vehicle rear view images are detected...
In order to improve the accuracy and stability of industrial fault detection and diagnosis, this paper introduces the deep learning theory and proposes an improved Deep Belief Networks (DBNs). In the first, this paper introduces the “centering trick” in the pre-training process of network. This method is done by subtracting offset values from visible and hidden variables. Then, in the process of network...
Manual wafer-level die inking is a common procedure for excluding die locations that are likely to be defective. Although this is a more cost-effective process, as compared to the expensive burn-in tests, it remains a labor-intensive step during IC testing. For each manufactured wafer, test engineers have to visually inspect every failure map in order to identify any regions where additional die need...
Convolutional neural networks (CNNs) provide the current state of the art in visual object classification, but they are far less accurate when classifying partially occluded objects. A straightforward way to improve classification under occlusion conditions is to train the classifier using partially occluded object examples. However, training the network on many combinations of object instances and...
Sensor based algorithms need to extract features from raw sensor data. However, different devices have different sensor data distributions. This distribution differences lead to a problem that model trained on device A may be invalid when applied to device B. However, it is labor-consuming to collect data and label them on device B from scratch. To solve the problem, a solution is proposed to learn...
In real-world applications, the assumption of independent and identical distribution is no longer consistent. To alleviate the significant mismatch between source and target domains, importance weighting import vector machine, which is an adaptive classifier, is proposed. This adaptive probabilistic classification method, which is sparse and computationally efficient, can be used for unsupervised...
The evolution of convolutional neural networks (CNNs) into more complex forms of organization, with additional layers, larger convolutions and increasing connections, established the state-of-the-art in terms of accuracy errors for detection and classification challenges in images. Moreover, as they evolved to a point where Gigabytes of memory are required for their operation, we have reached a stage...
The current focus of our research is to detect and classify the plant disease in agricultural domain, by implementing image processing techniques. We aim to propose an innovative set of statistical texture features for classification of plant diseases images of leaves. The input images are taken by various mobile cameras. The Scale-invariant feature transform (SIFT) features used as texture feature...
VGG 16 and Inception-v3 networks were trained using a texture dataset of muddied and clean cows. A new dataset with 600 images that is similar to the actual texture dataset was introduced and used to train the networks. The method used to train the networks was transfer learning. ImageNet weights were trained using the similar dataset, then the newly trained weights were trained again using the actual...
The death of the patients is an important event in the intensive care unit (ICU), mortality risk prediction thus offers much information for clinical decision making. However, Patient ICU mortality prediction faces challenges in many aspects, such as high dimensionality, imbalance distribution. In this paper, we modified the cost-sensitive principal component analysis (CSPCA), which is denoted by...
Sensors in industrial systems fault frequently leading to serious consequences regarding cost and safety. The authors propose support vector machine-based classifier with diverse time- and frequency-domain feature models to detect and classify these faults. Three different kernels, i.e., linear, polynomial, and radial-basis function, are employed separately to examine classifier's performance in each...
One-class support vector machines (OCSVM) have been recently applied to detect anomalies in wireless sensor networks (WSNs). Typically, OCSVM is kernelized by radial bais functions (RBF, or Gausian kernel) whereas selecting Gaussian kernel hyperparameter is based upon availability of anomalies, which is rarely applicable in practice. This article investigates the application of OCSVM to detect anomalies...
The present status of heart sound recognition is introduced in the paper. In order to improve the performance of heart sound recognition, a new model based on SVM is proposed. Firstly, the wavelet transform is used to reduce the noise of the heart sound, and then MFCC feature is extracted from heart sound. On this basis, the Support Vector Machine is used to build the classification model. In the...
At present, shallow characteristics are usually utilized to represent the distributed features of text for Chinese spam classification, causing the problem of inexact text vector representation and low classification performance. A novel Chinese spam classification method based on weighted distributed feature is proposed by combining the features of TF-IDF weighted algorithm with the distributed text-based...
In this paper, because of infinite-dimensional feature and complex nonlinearities of the distributed parameter systems, a new data-driven modeling method has been proposed. The temporal-spatial output of the system is measured at a finite number of spatial locations. At the same time it is assumed that the input of the system is a temporal variable. Firstly, Karhunen-Loève(KL) decomposition is used...
We propose associative domain adaptation, a novel technique for end-to-end domain adaptation with neural networks, the task of inferring class labels for an unlabeled target domain based on the statistical properties of a labeled source domain. Our training scheme follows the paradigm that in order to effectively derive class labels for the target domain, a network should produce statistically domain...
Considering the problems of low recognition rate and poor robustness in traditional recognition algorithms, we propose a license plate character recognition algorithm based on convolution neural network. In this paper, we adopt a coarse-to-fine strategy for designing the network architecture. Through the convolutional layers and pooling layers, features of input images will be extracted and then sent...
A Brain-Computer Interface (BCI) speller system based on the Steady-State Visually Evoked Potentials (SSVEP) paradigm is presented. The potentials are elicited through the gaze fixation at one out of the four checkerboards shown on screen, which are flickering at 5, 12, 15 and 20 Hz. After the feature extraction, two dimensionality reduction algorithms, Principal Components Analysis (PCA) and Linear...
This paper deals with the problem of audio source separation. To handle the complex and ill-posed nature of the problems of audio source separation, the current state-of-the-art approaches employ deep neural networks to obtain instrumental spectra from a mixture. In this study, we propose a novel network architecture that extends the recently developed densely connected convolutional network (DenseNet),...
Recently, the combination of classification systems with semi-supervised learning has attracted researchers in several fields. Usually, for tasks with high complexity such as handwriting based age prediction, individual systems, using one classifier associated with specific data features, cannot provide satisfactory performance. In this paper, we investigate the contribution of the Co-training approach,...
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