Train-train transshipment terminals are used to transship containers among trains. Scheduling container movements per crane (SCMC) is one of the sub-problems in train-train transshipment. The objective is to determine the sequence of container movements for each crane such that all containers are positioned on the appropriate train or on the yard, while minimizing the make-span. This study analyzes the sequence of container transshipment per crane in modern train-train transshipment terminals. We propose a simulated annealing (SA) based heuristic for solving the SCMC. The proposed SASCMC heuristic is tested on four sets of instances and the results are presented. The computational results show that the proposed algorithm improves the solution more than 15%.

Skin detection is one of the most important and primary stages in some of image processing applications such as face detection and human tracking. So far, many approaches are proposed to done this case. Near all of these methods have tried to find best match intensity distribution with skin pixels based on popular color spaces such as RGB, CMYK or YCbCr. Results show these methods cannot provide an accurate approach for every kinds of skin. In this paper, an approach is proposed to solve this problem using statistical features technique. This approach is including two stages. In the first one, from pure skin statistical features were extracted and at the second stage, the skin pixels are detected using HSV and YCbCr color spaces. In the result part, the proposed approach is applied on FEI database and the accuracy rate reached 99.25 ± 0.2. Further proposed method is applied on complex background database and accuracy rate obtained 95.40±0.31%. The proposed approach can be used for all kinds of skin using train stage which is the main advantages of it. Low noise sensitivity and low computational complexity are some of other advantages.

This paper presents an algorithm for solving the multi-objective reactive power dispatch problem in a power system. Modal analysis of the system is used for static voltage stability assessment. Loss minimization and maximization of voltage stability margin are taken as the objectives. Generator terminal voltages, reactive power generation of the capacitor banks and tap changing transformer setting are taken as the optimization variables. League championship algorithm (LCA) inspired from the competition of sport teams in a sport league, an algorithm is presented for solving constrained optimization problems. A number of individuals (solutions) as sport teams compete in an artificial league for several weeks (iterations). Based on the league schedule in each week, teams play in pairs and their game outcome is determined in terms of win or loss, given known the playing strength (fitness value) along with the teams’ intended formations. Modeling an artificial match analysis, each team devises a new formation/ playing strategy (a new solution) for the next week contest and this process is repeated for a number of seasons (stopping condition). In order to evaluate the proposed algorithm, it has been tested on IEEE 30 bus system and compared to other algorithms reported those before in literature. Results show that HS is more efficient than others for solution of single-objective ORPD problem.

The importance of employing classifiers with high accuracy in many applications in real life is important. Developmental process to construct the high accuracy classification rules by using the features associated with noisy information and duplication is avoided. Our algorithm is compared with 7 known classification algorithms in 9 different areas of application. Experimental results using several non-parametric statistical tests that are used in classification are investigated. The results show that our algorithm achieves the best competitive accuracy and that’s why it can be recognized as an accurate classifier.

Segmentation of color images is one of the basic steps in most of image processing applications and quality of its result has great influence on future processes. A novel segmentation method using artificial neural network (ANN) for color image segmentation is proposed in this paper. For increasing color differences, images are represented in a modified L*u*v* color space. Self-organizing neural network with unsupervised learning is used for image segmentation based on color reduction. In color reduction, image colors are projected into a small set of prototypes using Self-Organizing Map (SOM) network. Results of this method can be considered as a near optimal segmentation with a low computation cost. Experimental results show that this system has a desirable ability for color image segmentation in computer vision applications.

Persian handwritten numerals recognition has been a frontier area of research for the last few decades under pattern recognition. Recognition of handwritten numerals is a difficult task owing to various writing styles of individuals. A robust and efficient method for Persian/Arabic handwritten numerals recognition based on K Nearest Neighbors (K-NN) classifier is presented in this paper. The system first prepares a contour form of the handwritten numerals, then the transit, angle and distance features information about the character is extracted and finally K-NN classifier is used to character recognition. Angle, transit and distance features of a character have been computed based on distribution of points on the bitmap image of character. In K-NN method, the Euclidean distance between testing point and reference points is calculated in order to find the k-nearest neighbors. We evaluated our method on 20,000 handwritten samples of Persian numerals. Using 15,000 samples for training, we tested our method on other 5,000 samples and obtained 99.82% correct recognition rate. Further, we obtained 89.90% accuracy using four-fold cross validation technique on 20,000 dataset.

— Due to the increasing growth in wireless networks in different areas, reinforcing and developing faults management in wireless sensor networks will have an important role in effective use of them, so we must identify and put aside faulty sensors so that they won’t affect the entire network performance. these faults may be transient, permanent or intermittent .The proposed method covers any defects in the sensor nodes and each sensor in a self-constructive, distributed and local manner in such a fashion that the value received from the environment periodically sends data to all its neighbors, and all sensors make decisions about this point as if the sensor is faulty based on information received from the neighbors or not. This algorithm optimizes sensor energy sources, enhances detection accuracy and reduces the error percentage in detecting faulty wireless sensor networks.