جستجوی مقالات مرتبط با کلیدواژه « رفتار » در نشریات گروه « برق »

Optimization is a very important process in engineering. Engineers can create better production only if they make use of optimization tools in reduction of its costs including consumption time. Many of the engineering real-word problems are of course non-solvable mathematically (by mathematical programming solvers). Therefore, meta-heuristic optimization algorithms are needed to solve these problems. Based on this assumption, many new meta-heuristic optimization algorithms have been proposed inspired by natural phenomena, such as IWO [58], BBO [59], WWO [61], and so on. Inspired by domino toppling theory, we proposed an optimization algorithm. Using domino pieces, we can create countless complex structures. To simulate the domino movement in the search space of a problem, we consider the particles in the search space as the domino pieces and, by creating an optimal path, we will try to direct the dominoes to the optimal path. The optimal paths will be updated in each iteration. After initializing the dominoes randomly at the beginning of each evaluation, the picking piece or the first moving piece will be identified and then the particles will be selected by the optimal path. Applying a motion equation to each domino will move the dominoes forward in that direction. At first, a predefined dominoes will be randomly distributed in the problem space. Choosing the optimal path will accelerate the convergence of the domino particles towards the target. After choosing the path in current iteration, we now have to do the domino movement. The particles will move to a new location by applying the new location equation. By applying this equation, each domino piece will sit on the track ahead of itself. The front piece will also move to a new location by applying an equation separate from the rest. After moving the dominoes to the new location, the worst iteration of the previous iteration will be removed from the problem space. In the new iteration, the optimal domino path, the new locations of domino pieces and the global optimum will be updated. At the end of the algorithm, the global optimum will be determined as the optimal solution. This method is implemented in a simulator environment. To evaluate the performance of the Domino Optimization algorithm, we use a complete benchmark including 30 objective functions called CEC 2014 [67] that are single-objective numerical functions. In all cases, we set the population size to 50, the dimension size to 30, and the number of fitness function evaluation to 150,000. We compare the proposed Domino Optimization algorithm (DO) with the algorithms LOA [57], ICS [62], NPSO [63], MOHS [64], BCSO [65] and FFFA [66]. The results obtained from the 3 unimodal functions show that the proposed method is able to achieve a better solution than any of the state of the art algorithms at the equal resources. Results in the multimodal functions show that the proposed method has the best performance in finding the optimal solution in all of the available 13 functions in this section. In all of 6 functions in the hybrid section, the quality of the proposed method is better than all of the state of the art algorithms at the equal resources. The standard deviation values ​​of the proposed method, which are often small numbers, indicate algorithm convergence around the optimal solution. Also among the available methods, two algorithms, named NPSO and LOA, have good results after the proposed method. In the convergence analysis of dominoes, the diversity of objective functions in 100 distinct iterations shows a big value at the beginning of the algorithm, and a low value at the end of the algorithm.

Optimization is an important and determinant task in structural design. Better designs will be achieved if designers be able to reduce design time and cost using optimization methods. Many optimization problems in engineering are naturally more complicated and difficult to be solved by conventional optimization methods such as mathematical programming. Nature is a basis of many optimizations algorithms, so researchers focus on behavioral patterns of organisms and events in nature by considering a structure toward a target. In this study, a new optimization algorithm is proposed based on the behavioral pattern of wild mice. Studying targeted and beneficial behaviors of wild mice in colony motivates these kinds of behaviors could be a pattern for solving an uncertain complex problem. In this research, based on the experimental results on this animal, the behavior of the mice in the production phases of the population, mating, struggle for survival has been implemented. The mice are organized in several colonies that will fight for survival based on the command of an colony head that is elite. Also, the motor pattern of the mice was defined based on the colony-head location and the average colony members that were effective in an optimal search in the problem space. The behavioral pattern of this living organism was implemented in the simulation environment and results show that the proposed algorithm is a suitable pattern to find an optimal solution for complicated problems.

Any weakness in the export of oil and gas engineering services، not only robs the opportunity of Iranian national development but also threatens it in the long term since if like today we rely on energy export and we don’t use that knowledge in order to develop engineering service outside the boarders، Iran will be the importer of energy rather than exporter. Development of such services not only provides the opportunity for energy maintenance but also enhance the countries capability to provide employment، technology development and technical science، enhancing the political relationships، and civil and cultural export. However، there are lots of efforts but still there are some obstacles. In this article according to semi-structured interviews and biblical studies from experts in a thematic analysis، a conceptual model has been devised that investigates the development of oil and gas engineering services industries. Also one of the accomplishments of this study is identification of obstacles in the three dimensions of theme، behavior and structure.

In this paper, the dynamic Bayesian network for modeling behavior has been used for door-crossing behavior. This network easily trains and it supports dependencies between of data’s sensors and the desired behavior of robot on ideal environment and it detects error on data’ sensor by using data based method in real environment. Elimination of undesired sensor performs with structure changing base on interpretation of sensor’s data. Evaluation is based on robots with artificial error in the readings of the sensors. Proposed model for the "door-crossing", which is based on dynamic Bayesian networks, has been able to resist much higher than the model based on Bayesian networks (fixed structure), in the face of adverse and multiple interpretations of the constant error, drift, also cross-talk, random movement and slight changes in the environment map. This model is much resistant by adding maneuver part to it on dynamic environments. The performance of the proposed model on the simulation environment has been evaluated.

In this paper a new structure based on Bayesian networks is presented to improve mobile robot behavior, in which there exist faulty robot sensors. If a robot likes to follow certain behavior in the environment to reach its goal, it must be capable of making inference and mapping based on prior knowledge and also should be capable of understanding its reactions on the environment over time. Old learning models for knowledge learning, especially on dynamic environment, are quite complex and have uncertainty in sensors. In this paper a new structure based on Bayesian network is presented for knowledge learning on robot behavior when the malfunction sensors exist. In this paper successful door crossing behavior is explained. In this issue the belt of ultrasonic sensors is used to receive environment information. Simulation results show that using the Bayesian network is very effective in robot behavior with faulty sensors.