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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...
Estimating model parameters is a crucial step to understand the behavior of biological systems. To perform parameter estimation, a commonly used formulation is the least square method that minimizes the mean squared error. This method finds the model parameters that minimize the sum of the squared error between experimental data and model predictions. However, such a formulation can misguide parameter...
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...
We develop Bayesian algorithms to perform realtime positioning and uncertainty quantification on Global Positioning System (GPS) data. We test the algorithms on GPS data from several global locations and score their predictions using the log-score. The best algorithm is a Kalman filter that assumes an Ornstein-Uhlenbeck (OU) noise model. The OU model accounts for the observed autocorrelated process-noise...
Bayesian Optimization or Efficient Global Optimization (EGO) is a global search strategy that is designed for expensive black-box functions. In this algorithm, a statistical model (usually the Gaussian process model) is constructed on some initial data samples. The global optimum is approached by iteratively maximizing a so-called acquisition function, that balances the exploration and exploitation...
Pervasive mobile games utilise contextual data about players and their environment to explore new means of interaction and enhance the gaming experience. However, the inherent imperfection of contextual data acquisition poses a challenge for developers and designers of pervasive games. In these games, both sensor inaccuracies and uncertainties need to be identified and properly handled to prevent...
The success of Big Data relies fundamentally on the ability of a person (the data scientist) to make sense and generate insights from this wealth of data. The process of generating actionable insights, called data exploration, is a difficult and time-consuming task. Data exploration of a big dataset usually requires first generating a small and representative data sample that can be easily plotted...
The problem of spatial sensor location under parametric uncertainty of the repetitive distributed-parameter process is discussed. The idea is to reduce the uncertainty of the model used for the design of the iterative learning control, thus increasing the system performance. Particularly, an iterative scheme for estimation of the system parameter distributions is proposed based on the sequential experimental...
In this paper, we propose an optimization model for planning a robust path against changes in traffic volume. Robustness is based on the form of the travel time function. The proposed model can be applied not only when traffic volume increases but also when it decreases. In addition, the proposed model can set the ratio of consideration by a parameter depending on whether the traffic volume is increasing...
The article considers methods of processing uncertainties in solving dynamic planning problems. Various types of uncertainties are considered, such as stochastic uncertainties, uncertainties in the parameters and structure of models, the uncertainty of the amplitude type and the probabilistic type. Methods for processing data for reducing uncertainties are proposed.
Large-scale inverse problems and uncertainty quantification (UQ), i.e., quantifying uncertainties in complex mathematical models and their large-scale computational implementations, is one of the outstanding challenges in computational science and will be a driver for the acquisition of future supercomputers. These methods generate significant amounts of simulation data that is used by other parts...
Objectives. The article proposes a modification of the method for quantitative risk assessment (the Monte Carlo simulation method) by taking into account the multifactorial relationship between the key parameters and the risk factors of the investment project, which makes it possible to obtain a more relevant and efficient sample of simulations. Methods. Investigations described in the article are...
In this paper, we introduce the main concepts of a new maximum livelihood evidential reasoning (MAKER) framework for data-driven inferential modelling and decision making under different types of uncertainty. It consists of two types of model: state space model (SSM) and evidence space model (ESM), driven by the data that reflects the relationships between system inputs and output. SSM is constructed...
In practice of the time series analysis there is a specific problem on how to estimate tendencies of nonlinear dynamics evolution when several periodical processes alternate (so-called intermittency phenomena). Such alternations relate to bifurcations, but particular regularities and mechanisms of intermittency phenomena remain insufficiently understandable due to the height sensitivity of these phenomena...
This paper investigates curve-fitting methods to explore uncertainty quantification approaches for demand fluctuations in a power grid. Specifically, we use data sets from the Pennsylvania-New Jersey-Maryland (PJM) Interconnection, where we employ two stages of fitting. In stage one, a trigonometric based fit for historical data sets is carried out. In stage two, load forecasting for day-ahead market...
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...
As a Minkowski summation of several linear segments in the real space, zonotopes have been widely used in set-membership identification (SMI) for uncertain system because of its advantages, such as higher accuracy, compactness of representation and less complexity. A new SMI approach is proposed in this paper to develop a control oriented model based on zonotope, with which complex mathematical calculation...
In this paper, we develop a data-driven model of a model-sized aircraft using system identification (SysID) techniques. The emphasis is placed on multiple short data records that are used in obtaining an initial model of the system. The records are “short” with respect to the length of a “typical” identification experiment and are necessary because of the unstable nature of the open-loop system. Owing...
We present a method to model and classify trajectory data that come from surveillance videos. Observations of the locations of moving entities are used to estimate their expected velocity in the scene. Such estimation is performed by a Gaussian process regression that enables to approximate probabilistically the expected velocity of entities given some observed evidence in the scene. Subsequently,...
Most systems in the real life are complex systems. In the study of complex systems, we tend to get highly uncertain information due to the dynamic and complexity of the cases and our knowledge limitations. Moreover, complex systems are compounded of many parts, and the methods of data collection and storage vary considerably. Hence, the data processed by complex systems are associated with multi-type...
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