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This paper describes a latent space understandable network: Self- excited Generative Adversarial Network (Self- ExGAN), a novel self- excited structure based on adversarial learning. Compared with the conventional generative adversarial networks, SelfExGAN consists of three components, which are en- coder (E), generator (G), and discriminator (D). Different from other papers which directly apply reconstruction...
EAST Articulated Maintenance Arm is an articulated serial robot arm for inspection and maintenance in an experimental advanced superconductor tokamak. This paper implements soft computing algorithm for software calibration and compensation of pitch joint movement. An adaptive neurofuzzy inference system is applied to forecast the disclosed hysteresis loop compensation data. Joint position accuracy...
Generative models are widely used for unsupervised learning with various applications, including data compression and signal restoration. Training methods for such systems focus on the generality of the network given limited amount of training data. A less researched type of techniques concerns generation of only a single type of input. This is useful for applications such as constraint handling,...
A variety of applications (App) installed on mobile systems such as smartphones enrich our lives, but make it more difficult to the system management. For example, finding the specific Apps becomes more inconvenient due to more Apps installed on smartphones, and App response time could become longer because of the gap between more, larger Apps and limited memory capacity. Recent work has proposed...
Color is one of the attributes that play a role in identifying specific objects, color processing including the extraction of information about the spectral properties of the object's surface and look for the best similarity of a set of descriptions which have been known to do an introduction. Therefore, the classification is needed right fuji apples to obtain good quality fruit. Fuzzy model is one...
A class of recent approaches for generating images, called Generative Adversarial Networks (GAN), have been used to generate impressively realistic images of objects, bedrooms, handwritten digits and a variety of other image modalities. However, typical GAN-based approaches require large amounts of training data to capture the diversity across the image modality. In this paper, we propose DeLiGAN...
We propose to jointly learn a Discriminative Bayesian dictionary along a linear classifier using coupled Beta-Bernoulli Processes. Our representation model uses separate base measures for the dictionary and the classifier, but associates them to the class-specific training data using the same Bernoulli distributions. The Bernoulli distributions control the frequency with which the factors (e.g. dictionary...
Ship category recognition is one of the remote sensing applications that requires designing accurate image representation and classification models. Training these models is usually a data hungry process, that requires a lot of labeled data which are usually scarce and expensive. As unlabeled data are more abundant and relatively cheaper, transductive methods exploiting these data are highly preferred...
Numerical simulation methods, like the finite element method, lead to large systems of equations. Well-known and highly optimized methods are applied to solve equation systems. Their performance varies depending on the considered simulation (discretization and physics) and the available hardware. Choosing a suitable method includes the selection of a well performing solver and preconditioner which...
As a fundamental task in automated video surveillance, person re-identification, which has received increasing attention in recent years, aims to match people across non-overlapping camera views in a multi-camera surveillance system. It has been reported that KISS metric learning has been followed by most of the previous supervised work because of its state of the art performance for person re-identification...
Attributes are defined as mid-level image characteristics shared among different categories. These characteristics are suitable in order to handle classification problems especially when training data are scarce. In this paper, we design discriminative real-valued attributes by learning nonlinear inductive maps. Our method is based on solving a constrained optimization problem that mixes three criteria;...
Training kernel SVM on large datasets suffers from high computational complexity and requires a large amount of memory. However, a desirable property of SVM is that its decision function is solely determined by the support vectors, a subset of training examples with non-vanishing weights. This motivates a novel efficient algorithm for training kernel SVM via support vector identification. The efficient...
Supervised methods for inferring gene regulatory networks (GRNs) perform well with good training data. However, when training data is absent, these methods are not applicable. Unsupervised methods do not need training data but their accuracy is low. In this paper, we combine supervised and unsupervised methods to infer GRNs using time-series gene expression data. Specifically, we use results obtained...
Numerical simulation methods like the finite element method lead to large systems of linear equations solved with well-known methods. Their performance varies depending on the considered simulation (discretization and physics) and the available hardware. To predict a suitable method including the solver and a well performing preconditioner, a feed-forward neural network is used. It computes performance...
Forecasting electricity price allows market participants to make informed and sound decisions. Selecting the best training variables is often involved in forecasting in order to obtain optimal prediction. Support Vector Regression (SVR) provides an effective method to fit data and find minimal risk slack variables around a fit line. The best fit depends on the selected input feature set and the tuning...
Model Predictive Control (MPC) is an effective control method for nonlinear control systems including quantized control systems; however, the optimization process requires huge computation for such cases and is therefore hard to realize. In this study, a controller design method based on a machine learning technique, in particular a neural network with denoising autoencoder (DAE), is proposed. The...
Document Categorization is an area of important research over the last couple of decades. The basic task in document categorization is classifying a given document in some predefined classes. Bengali is among the top ten most spoken languages in the world and is spoken by more than 200 million people, but the candid truth is, it still lacks significant research efforts in the area of Bengali Document...
In this paper, a multiple scaling factor based Semi-Blind watermarking scheme for grayscale image watermarking using Online Sequential Extreme Learning Machine (OS-ELM) is proposed. Four-level DWT is applied on three standard test images of size 512 × 512. LL4 sub-band coefficients are chosen for watermark embedding. OS-ELM is initially tuned with a fixed number of training data used in its initial...
We develop fast anomaly detection algorithms using extreme learning machines (ELM) to discover operationally significant anomalies in large aviation data sets. Anomaly detection (aka one-class classification or outlier detection) is an active area of research to identify safety risks in aviation. Aviation data is characterized by high dimensionality, heterogeneity (continuous and categorical variables),...
Machine learning has become a powerful tool in real applications such as decision making, sentiment prediction and ontology engineering. In the form of learning strategies, machine learning can be specialized into two types: supervised learning and unsupervised learning. Classification is a special type of supervised learning task, which can also be referred to as categorical prediction. In other...
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