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Modeling the spatial variation of resources is necessary because it gives an estimate of what to expect during their exploration and exploitation. We focus on the spatial modeling of polymetallic nodules found in the deep sea regions of the Clarion-Clipperton zone in the Pacific. The data from this region available in the open domain is sparse, which warrants modeling techniques that can efficiently...
In the literature, a number of methods have been proposed for semi-supervised learning. Recently, graph-based methods of semi-supervised learning have become popular because of their capability of handling large amounts of unlabeled data. However, the existing graph based semi-supervised learning algorithms do not optimize the process of selecting better labeled data. We have developed a new selective...
Reliable uncertainty estimation for time series prediction is critical in many fields, including physics, biology, and manufacturing. At Uber, probabilistic time series forecasting is used for robust prediction of number of trips during special events, driver incentive allocation, as well as real-time anomaly detection across millions of metrics. Classical time series models are often used in conjunction...
In conditions of a mass character of higher education, the problems of student identification are relevant because of the differences in the contingent of students in terms of level of training, personal and cognitive characteristics. The learning process is characterized by the presence of uncertainty factors, which requires modeling and control of the application process of methods and tools of...
This paper presents a self-memory prediction model to mitigate the effects of image based visual servoing (IBVS) system under uncertainty. The performance of IBVS system is easily influenced by different tasks, diverse environments and uncertain disturbances. Through building a self-memory prediction model to keep previous movement tendency in the every current movement, the framework of a self-memory...
Degradation reliability prediction under stochastic failure threshold is studied. The explicit expression of reliability is derived, by charactering the uncertainty of failure threshold with probability distribution. Then, a possibilistic approach for reliability modeling and prediction for degrading components is presented, by use of possibility distribution.
The ability to conduct fast and reliable simulations of dynamic systems is of special interest to many fields of operations. Such simulations can be very complex and, to be thorough, involve millions of variables, making it prohibitive in CPU time to run repeatedly for many different configurations. Reduced-Order Modeling (ROM) provides a concrete way to handle such complex simulations using a realistic...
The interaction between a human driver and an automated driving system may improve when the automation is designed in such a way that it behaves in a human-like manner. This paper introduces a human-like steering model, in which the driver adapts to the risk due to uncertainty in the environment. Current steering models take a risk-neutral approach, while the fields of economics and sensorimotor control...
The article considers the stages of constructing the Bayesian network of trust for modeling complex natural processes with the unpredictability effect, as well as its structure, training and simulation results.
This paper proposes a control method that compensates for a system with time delay by predicting the future states of the plant. The proposed method contains a state predictor that predicts the future values of a system. Furthermore, the proposed method also considers the uncertainty of the model in terms of the modeling error, which might cause the predicted value to degrade the performance of the...
Deep convolutional neural networks (CNNs) have delivered superior performance in many computer vision tasks. In this paper, we propose a novel deep fully convolutional network model for accurate salient object detection. The key contribution of this work is to learn deep uncertain convolutional features (UCF), which encourage the robustness and accuracy of saliency detection. We achieve this via introducing...
Many prediction tasks contain uncertainty. In some cases, uncertainty is inherent in the task itself. In future prediction, for example, many distinct outcomes are equally valid. In other cases, uncertainty arises from the way data is labeled. For example, in object detection, many objects of interest often go unlabeled, and in human pose estimation, occluded joints are often labeled with ambiguous...
This paper presents a model predictive control (MPC) approach to economic scheduling for a building microgrid at California State University, Long Beach. We first propose a peak demand cost model to extend MPC-based microgrid energy scheduling. The corresponding objective function is then formulated as a mixed-integer linear programming (MILP) problem. The MPC framework is implemented into MILP optimization...
Nowadays, with the unprecedented penetration of renewable distributed energy resources (DERs), the necessity of an efficient energy forecasting model is more demanding than before. Generally, forecasting models are trained using observed weather data while the trained models are applied for energy forecasting using forecasted weather data. In this study, the performance of several commonly used forecasting...
The paper discusses a novel probabilistic approach for online parameter estimation of the predictor model used in an MPC (Model Predictive Control) setting in the presence of model uncertainties and external disturbances. Model uncertainty makes it hard to compute an optimal control in general case, because it is needed to take into account all possible values of model parameters. Therefore, it is...
Cloud service recommendation has become an important technique that helps users decide whether a service satisfies their requirements or not. However, the few existing recommendation systems are not suitable for real world environments and only deal with services hosted in a single cloud, which is simply unrealistic. In addition, a same service may be hosted on more than one cloud and, hence, may...
Model-based predictive control is an effective method for control the large scale systems. Method is based on on-lin solution of control task over the control horizon using current and past measurements as well as the system model. Because model and measurement uncertainty, predicted and plant outputs might be different and plant output may exceed plant output constraints. Generated control is not...
Model inaccuracies or parameter uncertainties are unavoidable in the practical control systems, while the uncertain properties could be modeled and estimated by the grey system. Among many grey models, fractional grey model is recently proposed and popularly used in many model analysis and prediction problems. In this paper, the structure uncertainties and external disturbances are considered using...
The aim of this article is to design a moment transformation for Student-t distributed random variables, which is able to account for the error in the numerically computed mean. We employ Student-t process quadrature, an instance of Bayesian quadrature, which allows us to treat the integral itself as a random variable whose variance provides information about the incurred integration error. Advantage...
This paper presents a detailed study on the performance, generality and robustness of proportional method. A probabilistic model was proposed to predict the number of iterations needed for air balancing under different sensor uncertainties. This model was later validated by simulations in a duct system with 12 nodes and 20 branches. And the correlation between the efficiency of proportional method...
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