International Journal of Smart Electrical Engineering
Volume:2 Issue: 3, Summer 2013

In this paper, a novel Patch Geodesic Derivative Pattern (PGDP) describing the texture map of a face through its shape data is proposed. Geodesic adjusted textures are encoded into derivative patterns for similarity measurement between two 3D images with different pose and expression variations. An extensive experimental investigation is conducted using the publicly available Bosphorus and BU-3DFE databases covering face recognition under pose and expression changes. The performance of the proposed method is compared with the performance of the state-of-the-art benchmark approaches. The encouraging experimental results demonstrate that the proposed method is a new solution for 3D face recognition in single model databases.

In this paper, a novel multi-objective bus stop location model is proposed, which considers not only the coverage of demand and minimization of access time but also the necessities of suitable stops for transit network design phase. Three objective functions are considered including minimizing (I) sum of the total access distance (time), (II) the weighted combination of stops, and (III) the number of stops. A sum-weighted method is used to solve the proposed multi-objective model considering the different scenarios of weights. A detailed analysis is carried out Tehran CBD to generate sensible stops results.

Permanent magnet synchronous generators (PMSGs) are novel generators which can be used in high-performance wind farms. High efficiency and flexibility in producing electricity from variable rotation make them good candidate for wind power applications. Furthermore, because these kinds of generators have no excitation winding, there is no copper loss on rotor; hence, they can operate at high power factor. Besides, performance characteristics of such generators could be further improved by design optimization. This paper presents design optimization of PMSGs used in small wind turbines using novel and efficient optimization algorithm i.e. Artificial Bee Colony (ABC) Algorithm. Then, a well-known optimization algorithm i.e. Genetic Algorithm (GA) is used to show the validity and efficiency of the before-mentioned algorithm. For this purpose, the necessary equations are provided. Objective function of this study is to reduce the total volume of motor. Case study of this study is a 5 kW, 220 V, 50 Hz, 100 rpm generator. Finally, results obtained by optimization are verified with Maxwell software which is based on finite element method (FEM). Comparison shows that the results of optimization approach are in good agreement with that of FEM ones.

In the software development process, testing is one of the most human intensive steps. Many researchers try to automate test case generation to reduce the manual labor of this step. Negative selection is a famous algorithm in the field of Artificial Immune System (AIS) and many different applications has been developed using its idea. In this paper we have designed a new algorithm based on negative selection for breeding test cases. Our approach, belongs to the category of black-box software testing. Moreover, this algorithm can be implemented in a distributed model. Two well-known case studies from software testing benchmarks is selected and results show the efficiency of this algorithm

The biggest challenges faced in big cities are greenhouse gas emission and growing energy needs. Efficient utilization of existing infrastructures has a prominent role in response to the challenges. Energy hub approach embraces performance of different energy networks. Energy hub is defined as a super node in electrical system receiving distinctive energy carriers such as gas and electricity in its input, and based on minimum cost decides when and how much of which energy carrier should supply the hub requirements. In this paper, we examine impact of renewable energy resources (wind and solar) and energy storages (electrical and thermal storages) on short term scheduling of energy hub. Effect of the technologies is also investigated on total operation costs of the energy hub in hot and cold climates. Mixed Integer Linear Programming (MILP) model is used for modeling proposed energy hub. CPLEX solver of GAMS software is employed to solve the problem. The results reveal that when and how much of which energy carrier should be exploited to satisfy hub required demands

Search Pointers organize the main part of the application on the Internet. However, because of Information management hardware, high volume of data and word similarities in different fields the most answers to the user s’ questions aren`t correct. So the web graph clustering and cluster placement in corresponding answers helps user to achieve his or her intended results. Community (web communities) can be used to generate automated directory services. In this paper the act of clustering has been done by finding the complete bipartite sub- graphs. The sub- graphs form the core of a community or clustering and by extending the core we can attain to the whole clustering .The whole set of graphs in England are 18 million pages and 300 million links

Wireless sensor networks, due to the characteristics of sensors such as wireless communication channels, the lack of infrastructure and targeted threats, are very vulnerable to the various attacks. Routing attacks on the networks, where a malicious node from sending data to the base station is perceived. In this article, a method that can be used to transfer the data securely to prevent attacks is suggested. The selection based on optimal path by routing using genetic algorithm uses. The proposed optimal paths to transmit data perceived to have chosen and ensures reliable data transmission.

In this paper, an Adaptive-Network-based Fuzzy Inference System (ANFIS) is used for forecasting of natural gas consumption. It is clear that natural gas consumption prediction for future, surly can help Statesmen to decide more certain. There are many variables which effect on gas consumption but two variables that named Gross Domestic Product (GDP) and population, are selected as two input variables. The input variables data and output variable (gas consumption) data are collected in years 1993 till 2012. Pre-process is done on the primary data to obtain better results then finally our outputs are post-processed. In this paper, many fuzzy models are applied and the results and error of every model are investigated. All ANFIS outputs are compared with real output by considering Mean Absolute Percentage Error (MAPE). The best model, which has the lowest MAPE, is chosen for forecasting gas consumption. In this paper, the values of gas consumption are forecasted from 2013 to 2020