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When microbiology laboratory technicians examine the specimen on the microscope for recognition of bacteria, firstly they provide optimum focusing by adjusting of the microscope stage on the Z axis. Following the achievement of the optimal focus, the movement of the microscope stage on 3D axis (X-Y-Z) are performed without losing focusing in order to scan whole view of the specimen. Therefore, microscope...
This paper focuses on addressing the decentralized data fusion (DDF) problem in dynamic sensor networks based on Chernoff rule. Generally, the Chernoff rule is challenging to implement since the fused probability density functions (pdfs) that cannot be obtained in closed form. Besides, the existing works for implementing Chernoff rule are mostly confined to iterative fusion of two sensors. To address...
In this paper we present a method to calculate estimated values for diagnostic coverage and false alarm rates of two channel redundant sensor systems. Avoiding time-consuming methodologies going into detail of system-sub-block failure rates and detailed fault cases we show a worst-case approach based on statistical methods. Diagnostic coverage and false alarm rates are important to develop a safety...
In a natural population, extreme individuals are very important for survival of the population. When the main population is destroyed by catastrophes, the few extreme individuals gain significance and will insure the survival of the population. Differential Evolution (DE) has been successfully applied to various optimization problems. However, DE sometimes trapped into some local solutions, which...
The Evolutionary algorithm (EA) for researching parameters of nonlinear system is a rapidly growing field of identification. This can owe to the importance of EA for both the theoretical field and the engineering community. However, the identification of the nonlinear system is still a knotty problem, especially when heavy-tailed noises exists. Compared to classical identification methods, EA has...
Photovoltaics (PV) are considered as one of the most promising renewable energy source for Singapore. This paper proposes an optimization strategy for a distribution grid that includes PV. The objective is to provide optimal grid operation for seamless integration of distributed generation (DG). A novel approach for grid reconfiguration considering a probabilistic statistical model for the solar irradiance...
Cognitive radio (CR) has been identified as an enabling technology toward meeting the high spectrum utilization efficiency demand in future internet-of-things (IoT) systems. Development of new spectrum sensing schemes better suited to CR-based IoT networks, which are typically heterogeneous with perfect network-wide synchronization difficult to achieve, is thus rather crucial. Motivated by the low-complexity...
In this paper, an improved estimation of distribution algorithm (EDA) is proposed and applied to the identification of ARMA model parameters. The system parameter identification problem is transformed into the optimization problem in high dimensional parameter space. Based on the traditional EDA algorithm, the parameters of preliminary estimation and data selection are added to improve the speed of...
Considering the uncertain and stochastic of intermittent distributed generations (DGS) in active distribution network (ADN), a scenario method using Wasserstein distance metric and K-means cluster scenes reduction technology to generate optimal scene is proposed in this paper. So the stochastic problem is transformed into a deterministic problem. The multi-scenario tree models of wind-photovoltaic-load...
We consider the problem of parameter estimation under a sequential framework. Specifically we assume that an i.i.d. random process is observed sequentially with its common pdf having a random parameter that must be estimated. We are interested in designing a stopping time that will decide when is the best moment to stop sampling the process and an estimator that will use the acquired samples in order...
This paper proposes a new approach to solve Chance Constrained Optimization Problems (CCOPs). The stochastic objective and constraint values in CCOP are evaluated efficiently by using an approximation of Cumulative Distribution Function (CDF) instead of the primitive Monte Carlo simulation. In order to approximate CDF from samples, a technique of the computational statistics called Empirical CDF (ECDF)...
In the new era of electrical power industry with more emphasis on green energy resources and active customer participation, the distribution utilities (DISCOMs) are being challenged. Being an important link between wholesale and retail electricity markets, these DISCOMs are exposed to risks on both sides. Under such circumstances, they are looking for new analytics to optimize operations and maximize...
The stability proofs of Model Predictive Control without terminal constraints and/or cost are tightly based upon the principle of optimality, which does not hold in most currently employed approaches to Stochastic MPC. In this paper, we first provide a stability proof for Stochastic Model Predictive Control without terminal cost or constraints under the assumption of optimization over feedback laws...
A reconfigurable over-the-air chamber represents a reverberation chamber whose walls are lined with antennas that are terminated in reconfigurable impedances, allowing synthesis of a wide range of channel conditions for over-the-air testing of mobile wireless devices. While these chambers have potential for practical device testing, finding the right impedances to achieve the desired channel characteristics...
Conventional yield optimization approaches rely on accurate yield estimation for given design parameters, which would be computational intensive. In this paper, a novel Bayesian yield optimization approach is proposed for analog and SRAM circuits. An equivalent problem is formulated via applying Bayes' theorem on the augmented yield problem. The yield optimization problem is converted to identifying...
Large integration of renewable distributed generation (RDG) and energy storage (ES) in distribution networks provides an opportunity for energy loss minimization. This paper proposes a method for joint optimum allocation of RDG and distributed ES for energy loss minimization. The main contribution of the paper is formulation of probabilistic generation model and ES model to perform a combined optimization...
To acquire the optimal attribute reduction, a novel method was proposed on the basis of cross-entropy algorithm. The method divides the samples generated by the cross entropy into the elite samples and the common samples, and generates a good sample set for the variation of the elite samples and the update of the common samples. The model of attribute reduction of rough set is solved by using the...
In this paper, a novel solution to the active fault diagnosis problem for stochastic linear Markovian switching systems on the infinite-time horizon is proposed. The imperfect state information problem of designing an active fault detector that minimizes a general detection cost criterion is reformulated as the perfect state information problem using sufficient statistics. The reformulation decreases...
Filtering is an effective method of alarm management family that can reduce false and missed alarm rates significantly. Simple and effective techniques of fault diagnosis methods are popular in industry. So, deriving a simple analytic filter design approach is important. This study proposes a simple analytic linear filter design based on a probabilistic model of the system. At last, the effectiveness...
This paper describes a variant of the Incremental Ant Colony Optimization algorithm for continuous optimization (IACOℝ). The original IACOℝ approach estimates the Probability Density Function (PDF) using Gaussians constructed around candidate solutions to generate new solutions. We use Support Vector Regression (SVR) to fit a regressor to the candidate solutions. The minima of the fitted regressor...
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