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In cyber-physical systems, state estimation errors can be caused not just by process noise or measurement noise in sensors, but also by errors in the communication network. Jitter in packet delivery over a communication network carrying sensor measurements can result in timing errors which result in large outlier type state estimation errors, such as, for example, velocity estimation errors in vehicular...
Through this paper we introduce an exclusive library sysid for system identification in the R® platform. This open-source library, the first of its kind on this platform, is designed primarily for classroom training and academic purposes. The library contains routines for input design, simulation and standard estimation methods for understanding the subject of and developing data-driven models for...
This document aims to solve the parametric estimation problem of nonlinear time-varying systems modeled by Wiener-Hammerstein models (W-H) with unknown time-varying parameters. A recursive instrumental variable estimation method is developed in order to estimate the parameters of the considered blocks-oriented models despite the existence of a correlated noise with observations. The estimation method...
The purpose of this study is to develop a model to predict the number of vehicles owned in consideration of recent circumstances of emerging countries. In the previous studies, prediction models have been developed from different viewpoints such as energy issue. However, recent growth of the number of vehicles owned in the emerging countries has been more rapid than the predictions. On the other hand,...
In software project management, software development effort estimation (SDEE) is one of the critical activities. Analogy-Based Estimation (ABE) is most popular estimation technique suggested in SDEE literature [1, 7, 22]. Researchers have proposed various methods to improve the accuracy of ABE by adjusting the retrieved solution. The research suggests all published calibration methods depend on linear...
The identification of slowly-varying noise parameters in non-linear state-space models constitutes a long-standing problem. The present paper addresses this task using the Bayesian framework and sequential Monte Carlo (SMC) methodology. The proposed approach utilizes an algebraic structure of the model so that the Rao-Blackwellization of the parameters can be performed, thus involving a finite-dimensional...
In data stream analyses, detecting the concept drift accurately is important to maintain the classification performance. Most drift detection methods assume that the class labels become available immediately after a data sample arrives. However, this assumption is overly optimistic, as labeling costs are high and much time is needed to obtain the label of data samples. Therefore, it is un-realistic...
Query expansion technique is commonly used for the vocabulary mismatch problem in Information Retrieval. External sources can provide useful information and result in improving query expansion effectiveness. Since each source has different characteristics, selective query expansion aims to determine the most effectiveness expansion method for each query. In this paper, we propose the Simple-phrase...
With the growing number of location-based SNS (Social Networking Service) users, the utilization of SNS data is getting more and more important. In this paper, we focus on the prediction of users' locations from location-based SNS data. The location-based SNS data consists of sequence of checkins which are too sparse to predict the users' locations. In our previous research we generated users' probability...
In multi-player games with imperfect information, e.g., Poker and Mahjong, they have imperfect information differing from Shogi and Reversi. Therefore, it is difficult to decide optimal movements. In Mahjong, fold is very important, and it is necessary to check predominance between a player's hand and other players' hands. To this end, it is required to estimate the rate arriving at a winning hand...
In this paper, we propose a novel regression-based method for employing privileged information to estimate the height using human metrology. The actual values of the anthropometric measurements are difficult to estimate accurately using state-of-the-art computer vision algorithms. Hence, we use ratios of anthropometric measurements as features. Since many anthropometric measurements are not available...
The state of energy (SOE) of Li-ion batteries is a key indicator for the energy management and optimization of energy storage systems (ESSs) in smart grids and electric vehicles (EVs). In order to improve the SOE estimation accuracy, an electrical Li-ion battery model is presented in this study against the dynamic loads and the rate energy effects of the battery. Firstly, in order to take into thorough...
Modelling rare or extreme events is critical in many domains, including financial risk, computer security breach, network outage, corrosion and fouling, manufacturing quality and environmental extremes such as floods, snowfalls, heat-waves, seismic hazards and meteorological-oceanographic events like extra-tropical storms, hurricanes and typhoons. Statistical modelling enables us to understand extremes...
Inference systems basically aim to provide and present the knowledge (outputs) that decision-makers can take advantage of in their decision-making process. Nowadays one of the most commonly used inference systems for time series prediction is the computational inference system based on artificial neural networks. Although they have the ability of handling uncertainties and are capable of solving real...
Missing Data (MD) is a widespread problem that can affect the ability to use data to construct effective software development effort prediction systems. This paper investigates the use of missing data (MD) techniques with Fuzzy Analogy. More specifically, this study analyze the predictive performance of this analogy-based technique when using toleration, deletion or k-nearest neighbors (KNN) imputation...
Simulation is widely used to predict the performance of complex systems. The main drawback of simulation is that it is slow in execution and the related compute experiments can be very expensive. On the other hand, analytical methods are used to rapidly provide performance estimates, but they are often approximate because of their restrictive assumptions. Recently, Extended Kernel Regression (EKR)...
In this paper, we tackle the problem of no-reference image quality assessment. This paper proposes a non-distortion-specific image quality evaluator, i.e., deep learning based blind image quality index DL-BIQI, which trained several deep models to estimate the visual quality. Since different distortion types lead to the different influence on images, each model is designed for a specific distortion...
In this paper, we study the effects of using smoothed variance estimates in place of the sample variances on the performance of stochastic kriging (SK). Different variance estimation methods are investigated and it is shown through numerical examples that such a replacement leads to improved predictive performance of SK. An SK-based dual metamodeling approach is further proposed to obtain an efficient...
This paper proposes a system to convert neutral speech to emotional with controlled intensity of emotions. Most of previous researches considering synthesis of emotional voices used statistical or concatenative methods that can synthesize emotions in categorical emotional states such as joy, angry, sad, etc. While humans sometimes enhance or relieve emotional states and intensity during daily life,...
When the model of the system under observation is not precisely known, state estimation and the subsequent prediction of system trajectories from this state estimate is subject to uncertainty. A useful method state estimation and prediction is to provide bounds within which the estimates are guaranteed to lie. When a model set is available, predictions can be made for every element of this set, with...
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