This work presents the 70V/18V non-isolated bidirectional DC-DC converter for PV applications. The open loop simulation analysis of the converter with step up and step down mode is presented using MATLAB/SIMULINK software. Finally, the prototype model of the converter is developed and tested in the laboratory. This converter has the advantages of high voltage gain, high step up/step down voltage conversion ratio, maximum conversion efficiency, having less number of switches and Low voltage stress across switches.

The job-shop scheduling issues (JSSPs) has been solving by many algorithms in the last few decades, including traditional type techniques.in this paper, a hybrid framework which is based on a combination of Grey Wolf-Chicken Swarm Optimization is proposed in order to reduce maximum completion time. The GWO algorithm is modified and hybridized in the implementation to optimize the search procedure and to solve the multi-objective flexible job shop scheduling issues (FJSSP). To establish the effectiveness of GWO and efficiency of the hybrid algorithm, some problems of different scales are used. The results show that proposed hybrid Grey Wolf-Chicken Swarm Optimization algorithm can accomplish good optimization results in terms various iteration levels of both optimization accuracy and robustness also easily applied in real industrial conditions and for large size problems. proposed method consumes 3.86s for 1000 iteration compared with GA (28.72s) and ACO (26.06s), proposed method was very less time period and cost for 6 machines to complete 16 jobs.

This paper presents a new solution approach to economic dispatch (ED) problems with practical operating constraints using a Cauchy mutated memetic particle swarm optimization (CMMPSO) algorithm. The practical operating constraints such as valve point effect, multiple fuel options, prohibited operating zones, ramp rates of generators and environmental constraints are considered in this paper. ED problems have non-smooth cost functions with equality and inequality constraints that make the problem of finding the global optimum difficult using any traditional optimization approaches. The main objective of this paper is to develop a simple, reliable and efficient algorithm for solving the Economic Dispatch problem with multiple practical operating constraints. The proposed CMMPSO algorithm is used to test ED problems with different practical operating constraints. The results of the CMMPSO are compared with the results of Particle Swarm Optimization (PSO) and differential evolutionary algorithm.

Facial expression recognition is an innovative research area in human-machine interaction. Identification of facial emotion using facial expression is a challenging task. Principal Component Analysis along with the statistical analysis is used to extract the different features of the seven facial expressions. Feed Forward Back Propagation Neural Network (FFBPNN) is trained with seven features and it is used as a classifier to recognize different facial expressions such as happy, neutral, fear, anger, disgust, sad and surprise. Three different kinds of data bases such as JAFEE, CK+ and real time image are used for experimental studies. Results of the proposed feature extraction and classification is compared with the existing method and show the good performance. The recognition rate of JAFFE database and CK+ database is as follows 100% and 98.16% shows the better performance of the proposed method.

This research tries to explain the factors influencing the students from Indonesia in taking the decision of lectures at the Zhejiang University of Technology Hangzhou. Data obtained from sample 49 respondents with census method. The research type is explanatory research with quantitative approach.\nThere are four findings. First, world-class education has a significant effect on the decision to choose a campus, with coefficient marked positive 0,478 with t statistic value 3,031 where the value is bigger than 2.0. Secondly, career opportunity has a significant effect on the decision of choosing a campus, with coefficient marked positive equal to 0,050 with t statistic value 2,565 where the value is bigger than 2.0. The three cost of living has a significant effect on the decision of choosing campus, the result of this research is seen from the coefficient of positively marked equal to 0,131 with t statistic value 2,306 where the value is bigger than 2.0 and fourth typical experience influential, not significant with positive direction to decision choosing campus, it is seen from the coefficient of positive marked 0.353 with a statistical value of 1.737 t where the value is smaller than 2.0.

Greenhouse gas emission estimates in Togo\