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Model-based software estimation uses algorithms and past project data to make predictions for new projects. This paper presents a comparative assessment of four modeling approaches, including the original COCOMO, COCOMO calibration, k-Nearest Neighbors, and a combination of COCOMO calibration and k-Nearest Neighbors. Our results indicate that using kNN to select the nearest projects and calibrating...
This paper proposes a Model predictive control (MPC) for linear induction motor drive. Model Predictive Control (MPC) is a strong method for controlling drives of the linear induction motor. In comparison with the Direct Force Control (DFC), MPC is more precise and more effective in the selection of the voltage vector. The results show that this control method has a better performance in comparison...
Aiming at the integrated navigation system with model errors, the model errors is assumed as a process noise for Gaussian white noise to process in Kalman filtering, thus causing larger state estimation error of nonlinear filtering system and even divergent. This paper presents a nonlinear model predictive particle filtering method considers the model error of real-time estimation, and then corrects...
Total number of failures of a software system can help practitioners to have a better understanding of the software quality. In this paper, we propose a model to predict the total number of software failures in a software system by analyzing the failure data from testing using models based on Zipf's law together with the information on code coverage. Failure data and code coverage are combined in...
Flapping Wings MAV (FWMAV) is the result of UAV technology development with the mechanism of flapping wings and with the dimensions of the object as small as possible. Modeling of FWMAV dynamics is needed to predict future conditions based on information from current conditions and designed control actions. Modeling of the FWMAV system is done by system identification, based on measurable data from...
It is shown that the excessive inhalation of PM (Particulate Matter) 2.5 will seriously affect the health of human. Many countries have deployed various detectors for air pollution in order to report concentration of PM2.5 to show how much seriousness of air pollution is. But, what more important is how much PM 2.5 has been inhaled by people anytime and anywhere. Therefore, in this paper, we propose...
With the increase of state dimension, the calculation of UKF algorithm increases rapidly, and UKF is more sensitive to model error, and it is not suitable for the system model with noise as non-Gaussian distribution. Aiming at this problem, this paper proposes a robust model predictive Unscented Kalman filter based on the study of robust estimation, model predictive filtering and UKF. The algorithm...
The paper proposed prediction model to study dengue occurrence in Malaysia, focusing on a region of Petaling district, in the state of Selangor. A number of different linear regression models were compared using model orders of lag time, and best model is selected using Akaike Information Criterion (AIC) value. First, dengue estimation models were built for Petaling district using weather variables...
Many prediction studies using real life measurements such as wind speed, power, electricity load and rainfall utilize linear autoregressive moving average (ARMA) based models due to their simplicity and general character. However, most of the real life applications exhibit nonlinear character and modelling them with linear time series may become problematic. Among nonlinear ARMA models, polynomial...
When local identification of a nonstationary ARX system is carried out, two important decisions must be taken. First, one should decide upon the number of estimated parameters, i.e., on the model order. Second, one should choose the appropriate estimation bandwidth, related to the (effective) number of input-output data samples that will be used for identification/tracking purposes. Failure to make...
Vehicle dynamics have very complex characteristic and nonlinear behaviour. Vehicle dynamics are decomposed of many internal and external components which influence vehicle stability. External components come from environment such as wind forces, surface coarse of road, lane bend or sudden maneuver, which will change the value of vehicle stability parameters, i.e. yaw rate and sideslip. Both are influenced...
A robust estimation of road course and traffic lanes is an essential part of environment perception for next generations of Advanced Driver Assistance Systems and development of self-driving vehicles. In this paper, a flexible method for modeling multiple lanes in a vehicle in real time is presented. Information about traffic lanes, derived by cameras and other environmental sensors, that is represented...
The paper deals with the task of estimating methanol concentration in the process of methyl-tert-butyl ether production by soft-sensor. A comparative analysis of the methods for determining of the main informative input sets for the soft-sensor model is presented. The dynamic soft sensor modeling based on the use of convolution sums in order to take into account the time of measurement delay is used...
To determine investment and cost estimation scientifically and simplify the investment estimating preparation, an improved BP neural network estimation model with GA optimization is proposed, based on the learning process of standard BP neural network. Our scheme set initial weight and whitening positioning coefficient as genetic population. The coefficients are optimized according to the principle...
The paper considers the problem of decision support systems constructing for solving the problems of modeling and estimating selected types of risks with the possibility for application of alternative data processing techniques, modeling and estimation of parameters and states for the processes under study. The system proposed has a modular architecture that provides a possibility for easy extension...
A problem of geopolitical actor strategy quality estimation (socio-economic, military-political etc.) with a view to increasing or decreasing the assets market value is formalized and solved in the paper. Aiming to solve this problem the random process of assets market value changes is introduced as random function X(t). The geopolitical actor assets market value changes are represented as a random...
In this paper, we develop an autocovariance-based method for estimating plant-model mismatch in unconstrained model predictive control systems using discrete-time, linear time-invariant state space models. We rely on knowledge of the process noise model, together with other reasonable assumptions, to derive an explicit expression for the autocovariance matrix of the closed-loop outputs. Then, we prove...
This paper describes a quality model for HTTP Adaptive Streaming. It integrates existing audio and video quality scores to a final quality estimation, factoring in quality variations over time, the recency effect, as well as location and length of buffering events at the player side. We built the model based on data gathered from more than 17 subjective quality tests. It was submitted to the ITU-T...
Estimation of precipitation is necessary for optimum utilization of water resources and their appropriate management. The economy of India being heavily dependent on agriculture becomes vulnerable due to lack of adequate irrigation facilities. In this paper, a multiple linear regression model has been developed to reckon annual precipitation over Cuttack district, Odisha, India. The model forecasts...
It is well recognized that effort estimation is an essential part of successful software management. Among many estimation models, the Case-Base Effort Estimation (CBEE) has been intensively used among researchers and practitioners as a promising model for better and accurate effort prediction. The common challenges with this model are: (1) finding the nearest cases to the new case, (2) selecting...
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