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In this paper, we address the problem of fault detection (FD) of chemical processes using improved generalized likelihood ratio test. The improved GLRT is the method that combines the advantages of the exponentially weighted moving average (EWMA) filter with those of the GLRT method. The idea behind the developed EWMA-GLRT is to compute a new GLRT statistic that integrates current and previous data...
Car Recognition is a part of Intelligent Transportation System. This research proposes the manufacture of ITS-based system to identify car model from Its frontal image using Binary Robust Invariant Scalable method. The BRISK method is used to detect image keypoint, and it uses Hamming Distance for keypoint matching. As for matching error, this research depends on RANSAC. BRISK method excellence lies...
Latent Dirichlet Allocation (LDA), is heavily cited in the machine learning literature, but its feasibility and effectiveness in information retrieval is mostly unknown. Learning to rank is useful for document retrieval, it uses feature vector to rank, but there is no feature about document topic. Our paper combines LDA and learning to rank, adds a topic feature into the feature vector of learning...
Object detection is a challenging task in the field of pattern recognition. The objective of object detection is to locate the target objects in the testing images. In this paper, we use SVM trained active basis model as a sparse coding model for representing objects. The sparse coding model represents each image as the linear superposition of a small number of Gabor wavelets selected from an over-complete...
This work presents results of effectiveness analyses of using deep neural networks models for syntactic parsing of SynTagRus dataset. A set of modular neural network topologies based on composition of Stack long short-term memory layers, multilayer perceptron has been compared with widely used algorithms of classification on base of Gradient boosting trees and Support vector machine. Results allow...
A Regionlet model explored here provides a new object representation strategy for generic object detection, which integrates local deformation handling into object classifier learning and feature extraction. Generic object detection deals with different degrees of variations in discrete object classes with tractable computations and hence faces problems. This generates a need for representational...
As of today, diagnosis of ADHD is highly dependent on subjective observations, which has motivated researchers to investigate quantitative methods for the discrimination of ADHD and Non-ADHD subjects using EEG data. The goal of the effort reported here is to classify subjects with high accuracy, as well as to do so based on a select few channels. By making use of AR model features, several classifiers...
This paper presents a robust machine learning based computational solution for human detection. The proposed mechanism is specifically applicable for pose-variant situations in video frames. In order to address the pose variance problem, features are extracted using an improved variant of Histograms of Gradients (HoG) and local Binary Pattern features (LBP). The two feature sets are combined to form...
Analog circuits contribute to as much test cost as digital circuits. Traditional model-based testing (e.g. catastrophic, range-based, regression models) had limited success due to diversity of circuits and contributing metrics. We propose a model-free analog circuit testing using a combination of advanced signal processing and machine learning techniques with low computation and memory requirements...
Product Families are gaining interest because ofthe increasing demand for customizable products. However, testing a Product Family is a difficult task, in special, fordependable products, in which the exception handling mustalso be well tested. Model-based testing (MBT) can be usefulfor testing Product Families, in which a behavior model can beobtained from the requirements, this model being used...
Facial age estimation is an important and challenging problem in computer vision and pattern recognition. Linear canonical correlation analysis (CCA) has been widely applied owing to low complexity, small and fixed amount of model parameters and good scalability. However, linear CCA based regression gets lower accuracy than its kernel version on the age estimation problem. The inexactness of metric...
Numerous classification algorithms have been developed for a variety of data types. However, it is nearly impossible for one classifier to perform the best in all kinds of datasets. Therefore, ensemble learning models which aim to take advantages of different classifiers have received a lot of attentions recently. In this paper, a scalable classifier ensemble framework assisted by a set of judgers...
A novel motion retrieval scheme is proposed. Based on semantic analysis and graph model, this scheme involves system learning in the first stage. In system learning, a Motion Semantic Dictionary (MSD) is derived by clustering. A Dynamic Bayesian Network (DBN) graph model is constructed based on the MSD and learning parameters. MSD and DBN are combined to derive motion information as features. Motion...
Current state of the art crowd density estimation methods are based on computationally expensive Gaussian process regression or Ridge regression models which can only handle a small number of features. In many computer vision applications, it has been empirically shown that a richer set of image features can lead to enhanced performances. In this paper, we reason that using more image features could...
Social networking on mobile devices has become a commonplace of everyday life. In addition, photo capturing process has become trivial due to the advances in mobile imaging. Hence people capture a lot of photos everyday and they want them to be visually-attractive. This has given rise to automated, one-touch enhancement tools. However, the inability of those tools to provide personalized and content-adaptive...
Apparel classification encompasses the identification of an outfit in an image. The area has its applications in social media advertising, e-commerce and criminal law. In our work, we introduce a new method for shopping apparels online. This paper describes our approach to classify images using Convolutional Neural Networks. We concentrate mainly on two aspects of apparel classification: (1) Multiclass...
In large population speaker identification (SI) system, likelihood computations during testing stage can be time-consuming. In such a case, clustering method is applied to this situation. But the traditional clustering algorithm based on K-means is sensitive to the randomly chosen initial cluster centers. To address this issue, the paper proposes an improved clustering algorithm which uses an initial...
Saliency detection is one of the most active research area in computer vision. Since L. Itti et al. [1] suggested computational model of visual attention, numerous detection algorithms have been proposed. However, most of modern saliency detection methods are based on superpixels which make detection results have abrupt edges inside the salient part. In this paper, we propose pixel-wise detection...
This work presents an implementation of a speaker-dependent speech recognition system used to control a gripper. The application was made using MATLAB and the gripper was assembled using the Lego Mindstorm NXT robotic kit. Four commands are implemented for controlling the gripper: Open, close, rotate left and rotate right. The development was divided into two stages. In training stage, we use Mel...
System Testing is a key technology of CVIS (Cooperative Vehicle Infrastructure System). A shortest test sequence generating approach in CVIS is proposed in this paper. The paper firstly analyzes the spatial-temporal state of vehicle and complexity of infrastructure in network. Then paper designs system features and test cases, and introduces the concept of support index of the test case to system...
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