Mixed Biogeography-Based Optimization for GENCOs’ Maintenance Scheduling in Restructured Power Systems

Power industry restructuring has brought new challenges to the generation unit maintenance scheduling problem. Maintenance scheduling establishes the outage time scheduling of units in a particular time horizon. In the restructured power systems, the decision-making process is decentralized where each generating company (GENCO) tries to maximize its own benefit. Therefore, the principle to draw up the unit maintenance scheduling is different from the traditional centralized power systems. The objective function for GENCOs is to minimize his maintenance investment loss. Therefore, he hopes to put its maintenance on the weeks when the market-clearing price is lowest so that maintenance investment loss descends. This paper addresses the unit maintenance scheduling problem of GENCOs in restructured power systems. The problem is formulated as a mixed integer programming problem, and it is solved by using an optimization method known as biogeography-based optimization (BBO). BBO is simple to implement in practice and requires a reasonably small amount of computing time and a small amount of data communication. BBO has been tested by applying it to a GENCO with three generating units. This model consists of an objective function and related constraints, e.g., maintenance window, generation capacity, load and network flow. The simulation result of this method is compared with a classic method. The outcome is very encouraging and proves that BBO is powerful for minimizing GENCOs’ objective function.     

From 2D to 3D geodesic-based garment matching

Multimedia Tools and Applications - A new approach for 2D to 3D garment retexturing is proposed based on Gaussian mixture models and thin plate splines (TPS). An automatically segmented garment of...     

Incremental one-class classifier based on convex–concave hull

Pattern Analysis and Applications - One subject that has been considered less is a binary classification on data streams with concept drifting in which only information of one class (target class)...     

Application of ANFIS for modeling of layer thickness of chromium carbonitride coating

In the present work, layer thickness of duplex coating made from thermo-reactive deposition and diffusion has been predicted by Adaptive network-based fuzzy inference systems (ANFIS). A duplex surface treatment on five steels has been developed involving nitrocarburizing and followed by chromium thermo-reactive deposition (TRD) techniques. The TRD process was performed in molten salt bath at 550, 625 and 700 °C for 1–30 h. The process formed a thickness up to 9.5 μm of chromium carbonitride coatings on a hardened diffusion zone. A model based on ANFIS for predicting the layer thickness of duplex coating of the specimens has been presented. To build the model, training and testing using experimental results from 84 specimens were conducted. The data used as inputs in ANFIS models are arranged in a format of twelve parameters that cover the chemical composition (C, Mn, Si, Cr, Mo, V, W), the pre-nitriding time, ferro-chromium particle size, ferro-chromium weight percent, salt bath temperature and coating time. According to these input parameters, in the Adaptive network-based fuzzy inference system models, the layer thickness of duplex coating of each specimen was predicted. The training and testing results in ANFIS models have shown a strong potential for predicting the layer thickness of duplex coating.     

Computer user training and attitudes: a study of business undergraduates

For many users, the first real encounter with a computer occurs when taking an introductory course to computers at a college. To the extent that these training courses impact user understanding and motivation, they are important determinants of the user attitudes towards computers and merit serious assessment. Using 327 business undergraduates at two universities in the US, this paper reports on the reactions of students to computers and computer-related tasks before and after an introductory course to computers. Responses to a 20-item scale were analysed to examine the pattern of attitude change experienced by students in their training course. Factor analysis revealed five constructs for describing patterns of computer user attitude: negative reaction to computers; positive reaction to computers; reaction to computers for children education; reaction to computer-mediated services; and reaction to computer games. Four factors show significant change in mean scores after the training courses. The attitudes changed for males more than females, indicating improvement in attitudes. The respondents' attitude to computer-mediated services remained unchanged. While the directions of changes indicate an overall improvement in respondents' reactions, many attitudes did not change significantly after having taken the training courses. This may be due to the content or the format of these courses.     

Semi-supervised model adaptation for statistical machine translation

Statistical machine translation systems are usually trained on large amounts of bilingual text (used to learn a translation model), and also large amounts of monolingual text in the target language (used to train a language model). In this article we explore the use of semi-supervised model adaptation methods for the effective use of monolingual data from the source language in order to improve translation quality. We propose several algorithms with this aim, and present the strengths and weaknesses of each one. We present detailed experimental evaluations on the French–English EuroParl data set and on data from the NIST Chinese–English large-data track. We show a significant improvement in translation quality on both tasks.     

A decoupled source current reconstruction method for noisy and reactive near‐field to far‐field transformation

In this article, a decoupled source current reconstruction method (SRM) for noisy and reactive near‐field (NF) to far‐field (FF) transformation is introduced. It is shown that the traditional SRM for NF/FF transformation shows instability in the regions that the amounts of noise or reactive radiations are noticeable. Therefore, in these regions, equivalent currents should be determined from a Tikhonov SRM equation. However, this equation increases the computational cost of the SRM. To simplify the Tikhonov SRM equation, a Tikhonov radial field retrieval algorithm is also proposed. In this algorithm, the Tikhonov integral equation is decoupled by considering and retrieving the radial components of the electric field. Results of far‐field calculation with both the proposed Tikhonov SRM equation and Tikhonov radial field retrieval algorithm with three different antennas are presented and compared with those of the full‐wave simulation and measurements. The results show more accurate field transformation with the proposed algorithms.     

Evaluation of MODIS data for improved monitoring of the Caspian Sea

Moderate-Resolution Imaging Spectroradiometer (MODIS) optical and infrared data are used to monitor changes in the Caspian Sea coastline. The information extracted from MODIS images is converted into total water volume and mean lake level by combining a digital elevation model (DEM) with remote-sensing data. The elevation estimates were enhanced by reprocessing the MODIS data at the sub-pixel scale. The water volume variations estimated from MODIS data along with DEM are compared to other estimations derived from altimetry data sets, and show fair agreement.     

Abstraction and approximation in fuzzy temporal logics and models

Recently, by defining suitable fuzzy temporal logics, temporal properties of dynamic systems are specified during model checking process, yet a few numbers of fuzzy temporal logics along with capable corresponding models are developed and used in system design phase, moreover in case of having a suitable model, it suffers from the lack of a capable model checking approach. Having to deal with uncertainty in model checking paradigm, this paper introduces a fuzzy Kripke model (FzKripke) and then provides a verification approach using a novel logic called Fuzzy Computation Tree Logic* (FzCTL*). Not only state space explosion is handled using well-known concepts like abstraction and bisimulation, but an approximation method is also devised as a novel technique to deal with this problem. Fuzzy program graph, a generalization of program graph and FzKripke, is also introduced in this paper in consideration of higher level abstraction in model construction. Eventually modeling, and verification of a multi-valued flip-flop is studied in order to demonstrate capabilities of the proposed models.     

Coupled particle swarm optimization method with genetic algorithm for the static–dynamic performance of the magneto-electro-elastic nanosystem

As a first attempt, Fourier series expansion (FSE), particle swarm optimization (PSO), and genetic algorithm (GA) methods are coupled for analysis of the static–dynamic performance and propagated waves in the magneto-electro-elastic (MEE) nanoplate. The FSE method is presented for solving the motion equations of the MEE nanoplate. For increasing the performance of genetic algorithms for solving the problem, the particle swarm optimization technique is added as an operator of the GA. Accuracy, convergence, and applicability of the proposed mixed approach are shown in the results section. Also, we prove that for obtaining the convergence results of the PSO and GA, we should consider more than 16 iterations. Finally, it is shown that if designers consider the presented algorithm in their model, the results of phase velocity of the nanosystem will be increased by 27%. A useful suggestion is that there is a region the same as a trapezium in which there are no effects from magnetic and electric potential of the MEE face sheet on the phase velocity of the smart nanoplate, and the region will be bigger by increasing the wavenumber.