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Free-head 3D gaze tracking outputs both the eye location and the gaze vector in 3D space, and it has wide applications in scenarios such as driver monitoring, advertisement analysis and surveillance. A reliable and low-cost monocular solution is critical for pervasive usage in these areas. Noticing that a gaze vector is a composition of head pose and eyeball movement in a geometrically deterministic...
The ability to predict and therefore to anticipate the future is an important attribute of intelligence. It is also of utmost importance in real-time systems, e.g. in robotics or autonomous driving, which depend on visual scene understanding for decision making. While prediction of the raw RGB pixel values in future video frames has been studied in previous work, here we introduce the novel task of...
We propose a novel approach for unsupervised zero-shot learning (ZSL) of classes based on their names. Most existing unsupervised ZSL methods aim to learn a model for directly comparing image features and class names. However, this proves to be a difficult task due to dominance of non-visual semantics in underlying vector-space embeddings of class names. To address this issue, we discriminatively...
As a state-of-the-art ensemble method, random forest which exhibits a good ability to predict and generalize on various dataset is often composed of a large number of trees. Redundancy of ensemble and connotative decision rules result in expensive operational costs as well as difficulties in comprehension. In this paper, novel leaf node-level pruning methods for random forest are proposed. Each leaf...
Short-term load forecasting (STLF) models are very important for electric industry in the trade of energy. These models have many applications in the day-to-day operations of electric utilities such as energy generation planning, load switching, energy purchasing, infrastructure maintenance, and contract evaluation. A large variety of STLF models have been developed that trade off between forecast...
In this empirical study we develop forecasting models for electricity demand using publicly available data and three models based on machine learning algorithms. It compares accuracy of these models using different evaluation metrics. The data consist of several measurements and observations related to the electricity market in Turkey from 2011 to 2016. It is available in different time granularities...
Neural network technique has been recently preferred in textile sector for the prediction task because the traditional mathematical and statistical methods can be inadequate to derive complex relations within textile datasets. Meanwhile ensemble learning has become a popular machine learning approach in recent years due to the high prediction performance it provides. Therefore, this study proposes...
This work investigates an approach to combining accurate lithium-ion battery (LIB) dynamic modeling and effective state-of-charge (SOC) prediction at various operating conditions using a structured recurrent neural network (RNN). The RNN model is trained with drive cycle data so that model parameters do not have to be determined with characterization tests, as is typically necessary for an equivalent...
ESN load forecasting model has high stability, and is able to learn fast and not easy to fall into local optimum, compared with standard recurrent neural network. In the process of constructing the typical ESN model, the choice of parameters is always empirical or random. The forecasting performance of ESN was analyzed on the basis of its key parameters. While the dynamic reserve pool has black box...
For processing purposes of silver colloidal suspensions in view of specific applications, this study evaluates the suitability of using alginate/lignosulfonate mixtures as an efficient dispersion matrix for the silver nanoparticles. The rheological behavior of the in situ obtained silver nanoparticle suspensions was investigated by rotational measurements performed using cone-plate geometry, considering...
Artificial neural networks (ANN) are among the nonlinear prediction techniques popular in the last two decades. Recent studies show that ANN can be modeled with different training techniques. ANN is usually trained by the backpropagation method (BP). In this study, ANN structures were trained by using artificial bee colony algorithm (ABC) and, weight and bias values were tried to be determined. ABC...
For airborne electronic equipment, temperature is the most important factor affecting its performance, so it is very important to invest its dynamic temperature response process in a flight environment. By analyzing the heat exchange relationship between different devices in an electronic wing pod cabin, a temperature prediction method for electronic pod cabin based on the Random Vector Functional...
Accurate prediction of the future locations of the host vehicle as well as that of the surrounding objects is one of the key challenges in improving road traffic safety. The traditional approach for this task has been using physics-based motion models such as kinematic and dynamic models, the result of which is not reliable for long-term prediction. In this paper, we present simulation results demonstrating...
This paper presents a support vector machine (SVM) based model predictive control (MPC) strategy to manage the engine speed to the set-point of idle speed. The predictive model is trained by SVM due to its accuracy of learning nonlinear process, simple training program and no over-fitting nature. To reduce the computational burden of controller and retain the dynamic information of system, the instantaneous...
Video scene parsing is challenging due to the following two reasons: firstly, it is non-trivial to learn meaningful video representations for producing the temporally consistent labeling map; secondly, such a learning process becomes more difficult with insufficient labeled video training data. In this work, we propose a unified framework to address the above two problems, which is to our knowledge...
Understanding the visual relationship between two objects involves identifying the subject, the object, and a predicate relating them. We leverage the strong correlations between the predicate and the hsubj; obji pair (both semantically and spatially) to predict predicates conditioned on the subjects and the objects. Modeling the three entities jointly more accurately reflects their relationships...
Many early stage lung cancer patients have resectable tumors, however, their cardiopulmonary function needs to be properly evaluated before they are deemed operative candidates. Such patients are typically asked to undergo standard pulmonary function tests, including cardiopulmonary exercise tests (CPET) or stair climbs. The standard tests are conducted only at selected healthcare provider locations,...
Short-term forecasting tool for wind power generation (WPG) can effectively enhance the scheduling of dispatchable generation and avoid wind power (WP) curtailment. This paper presents a Group Method of Data Handling (GMDH)-Neural Network (NN) approach for forecasting wind speed (Ws) and wind power output (WPO) in the short-term. Wavelet denoising is used to filter the high frequency outliers in the...
In order to solve the problem of lacking shear wave velocity information in oil and gas field, based on conventional logging data, a support vector machine(SVM) model is used to map the relationship between shear wave velocity and natural gamma, acoustic time difference and resistivity of shale, and then a machine learning method for shear wave velocity prediction is proposed. The model was trained...
Collaborative filtering is a well-known technique used for designing recommender systems when advertising services and products offered to the Internet users. In this paper, we employ the Restricted Boltzmann Machine (RBM) for collaborative filtering and propose the neighborhood-conditional RBM (N-CRBM) model based on joint distributions of similarity and popularity scores. The model is trained and...
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