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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...
Given some (but not all) monthly totals of people with measles (or counts of product-units sold, or counts of retweets), how can we recover the weekly counts? Requiring smoothness between successive weeks is reasonable - but can we do better, if we have some domain knowledge? For example, we know that measles (flu, count-of-retweets, etc) follow a specific cascade model, like the so-called 'SIS'....
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...
In this paper, we consider an optimal parameter and state estimation problem arising in an one-dimensional (1D) magnetohydrodynamic (MHD) flow system, whose dynamics can be modeled by a coupled partial differential equations (PDEs). In this model, the coefficients of the Reynolds number and initial conditions as well as state variables are supposed to be unknown and need to be estimated. An adjoint-based...
The development of Information and Communication Technologies (ICT) and the widespread growth of Internet has revolutionized the way of performing commercial operations. Due to this the organizations must reconsider the use of systemic thinking and aim their business models towards more globalized tendencies. All this has helped many companies to see the electronic commerce as a new way of on-line...
In order to describe the inherent hysteresis nonlinearity of the piezo-actuated stages, a Krasnosel'skii-Pokrovskii (KP) model is presented in the paper. The parameters of the KP model are identified by a modified bat optimization algorithm based on Levy fights trajectory. The modified bat optimization algorithm uses the Levy fights trajectory to improve the global searching ability and break away...
Modelling of a database performance depending on numerous factors is the first step towards its optimization. The linear regression model with optional parameters was created. Regression equation coefficients are optimized with the Flower Pollination metaheuristic algorithm. The algorithm is executed with numerous possible execution parameter combinations and results are discussed. Potential obstacles...
The efficient and timely distribution of freight goods is critical for supporting the demands of modern urban areas. Optimum freight ensures the survival and development of urban areas. In the contemporary logistic there are two main distribution strategies: direct distribution and multi-echelon distribution. In the direct distribution, means of transport, starting from the main distribution center,...
A fractional-order circuit model is explored to represent the leakage/self-discharge behaviour of commercially available supercapacitors. This fractional order-model is composed of two elements, a fractional-order capacitor with impedance Z = 1/CαSα and a parallel resistance Rp, which set the discharge based on the time constant τ = (RpC)1/α and order α. Self-discharging data was collected from a...
We estimate the moment magnitudes of microseismic events by fitting theoretical models to the amplitude spectra of the corresponding recorded signals. To this end, we combine the available information by stacking the seismic traces that contain the event and use Very Fast Simulated Annealing (VFSA) to solve the optimization problem. We test the procedure on pseudo-synthetic and real data considering...
Anugrah Citra Boga is a food processing industry that produces meatballs as their main product. One of the problems in the company is the distribution of the products. Moreover, the distribution cost was never examined scientifically to obtain the optimal value. By simulating the distribution route to minimize the distribution time, then lower cost of the distribution will be expected. Therefore,...
This study presents an identification-based construction of the inverse generalized Prandtl-Ishlinskii (P-I) model to facilitate inverse model-based feedforward compensation of asymmetric hysteresis nonlinearities. Compared with the derivation of the inverse model analytically from a generalized P-I model, this direct modeling approach has the following advantages. First, direct inverse model identification...
The photovoltaic (PV) array is one of the main components of the PV system, and the accuracy of the PV array model is directly related to the validity of the simulation results. The parameters of the PV array may change with the operation conditions. Therefore, it is important to identify the parameters of the PV array model according to the measured data. In this paper, the conventional four-parameter...
Cognitive Radio (CR) is one of the most promising techniques for optimizing the spectrum usage. However, the large amount of data of spectral information that must be processed to identify and assign spectral resources increases the channel assignment times, therefore worsening the quality of service for the devices using the spectrum. Compressive Sensing (CS) is a digital processing technique that...
Memristive systems are nonlinear resistors with memory. Most of them are realized as resistive switching devices in nanotechnology. One example, with appropriate properties especially in neuromorphic applications, is the double barrier memristive device (DBMD). A continuous resistance range makes the DBMD suitable for replacing the synapses in neuromorphic circuits. Structural and functional descriptions...
This paper presents an economic optimization case study for a medium-scale hybrid PV-battery system. PV energy yield and battery operation models based on hourly satellite insolation and daily temperature data form the basis of an underlying objective function aiming to maximize the net present value of potential energy cost savings. Forecasted system prices and energy tariffs over a nine-year period...
Gaussian Processes (GPs) are state-of-the-art tools for regression. Inference of GP hyperparameters is typically done by maximizing the marginal log-likelihood (ML). If the data truly follows the GP model, using the ML approach is optimal and computationally efficient. Unfortunately very often this is not case and suboptimal results are obtained in terms of prediction error. Alternative procedures...
Spectral clustering based subspace clustering methods have emerged recently. When the inputs are 2-dimensional (2D) data, most existing clustering methods convert such data to vectors as preprocessing, which severely damages spatial information of the data. In this paper, we propose a novel subspace clustering method for 2D data with enhanced capability of retaining spatial information for clustering...
Dissolved oxygen directly affects the growth status of fishes in intensive aquaculture, thus we set up a prediction model to determine the future changing trend of dissolved oxygen. The dissolved oxygen prediction model we proposed was based on the least squares support vector regression (LSSVR) model with fruit fly optimization algorithm (FOA) to find optimal parameters (γ and σ) of LSSVR. Because...
Health analysis often involves prediction of multiple outcomes of mixed type. The existing work is restrictive to either a limited number or specific outcome types. We propose a framework for mixed-type multioutcome prediction. Our proposed framework proposes a cumulative loss function composed of a specific loss function for each outcome type–as an example, least square (continuous outcome), hinge...
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