Buckling optimization of laminated composite plates using genetic algorithm and generalized pattern search algorithm

In this paper, genetic algorithm and generalized pattern search algorithm are used for optimal stacking sequence of a composite panel, which is simply supported on four sides and is subject to biaxial in-plane compressive loads. The problem has several global optimum configurations in the vicinity of local optima. The composite plate under consideration is 64-ply laminate made of graphite/epoxy. The laminate is taken to be symmetric and balanced, comprised of two-ply stacks with discrete fiber angles of 0₂, ± 45, 90₂ in the laminate sequence. The critical buckling loads are maximized for several combinations of load case and plate aspect ratio, and are compared with published results. Performance of both algorithms is compared in terms of capability of identifying global optima. It is found that genetic algorithm is efficient for problems with global optima.     

Noise generated by turbulent flow over forward facing steps

In this study, aeroacoustical characteristics of forward facing steps (FFSs) subject to turbulent freestream are assessed by means of linearized Euler equations (LEE). The investigated parameters are the velocity scaling of SPL and the different construction of sharp edge of FFSs. In addition to numerical simulation, an analytical vortex injection method and flow measurement are also utilized for validation purposes. It is found that the sound signal obtained from the simulation and from the theoretical approach at prescribed positions are of same phases and have comparable amplitudes. Moreover, it is reported that sound intensity scales with 5.8th power of freestream velocity. This result agrees well with the theoretical result of Curle et al. Sound radiation pattern is found to have dipole character lying horizontally. Finally, it is shown that one gains reduction in SPL by replacing sharp edged FFS with the half round edged one. Furthermore, round edged FFS, i.e. r=h, is more silent than half round edged FFS at the same frequency level.     

Effects of an online problem based learning course on content knowledge acquisition and critical thinking skills

This study investigated how the online problem based learning (PBL) approach employed in an online learning environment influenced undergraduate students’ critical thinking skills (CTS) and content knowledge acquisition. The pretest–posttest control group design was used in the study. The subjects included the students who were enrolled at the Department of Primary School Mathematics Teaching in Anadolu University Education Faculty. Subjects attended to Computer II course in 2008 spring. Experiment group attended the online PBL course whereas the control group attended the online instructor-led course. Each group consisted of 20 students. Data collection tools consisted of a multiple choice content knowledge acquisition scale and the Watson–Glaser critical thinking skills test. The results of two-way mixed design ANOVA indicated that learning in the online PBL group did not have a significant effect on the content knowledge acquisition scores. It was also revealed that learning in the online PBL group had a significant effect on increasing the critical thinking skills.     

Behavioral task processing for cognitive robots using artificial emotions

This paper presents an artificial emotional-cognitive system-based autonomous robot control architecture for a four-wheel driven and four-wheel steered mobile robot. Discrete stochastic state-space mathematical model is considered for behavioral and emotional transition processes of the autonomous mobile robot in the dynamic realistic environment. The term of cognitive mechanism system which is composed from rule base and reinforcement self-learning algorithm explain all of the deliberative events such as learning, reasoning and memory (rule spaces) of the autonomous mobile robot. The artificial cognitive model of autonomous robot control architecture has a dynamic associative memory including behavioral transition rules which are able to be learned for achieving multi-objective robot tasks. Motivation module of architecture has been considered as behavioral gain effect generator for achieving multi-objective robot tasks. According to emotional and behavioral state transition probabilities, artificial emotions determine sequences of behaviors for long-term action planning. Also reinforcement self-learning and reasoning ability of artificial cognitive model and motivational gain effects of proposed architecture can be observed on the executing behavioral sequences during simulation. The posture and speed of the robot and the configurations, speeds and torques of the wheels and all deliberative and cognitive events can be observed from the simulation plant and virtual reality viewer. This study constitutes basis for the multi-goal robot tasks and artificial emotions and cognitive mechanism-based behavior generation experiments on a real mobile robot.     

Prediction of flashover voltage of insulators using least squares support vector machines

The importance of the research on insulator pollution has been increased considerably with the rise of the voltage of transmission lines. In order to determine the flashover behavior of polluted high voltage insulators and to identify to physical mechanisms that govern this phenomenon, the researchers have been brought to establish a modeling. In this paper, a dynamic model of AC flashover voltages of the polluted insulators is constructed using the least square support vector machine (LS-SVM) regression method. For this purpose, a training set is generated by using a numerical method based on Finite Element Method (FEM) for several of common insulators with different geometries. To improve the resulting model’s generalization ability, an efficient optimization algorithm known as the grid search are adopted to tune parameters in LS-SVM design.In addition, two different testing set, which are not introduced to the LS-SVM during the training procedures, is used to evaluate the effectiveness and feasibility of the proposed method. Then, optimum LS-SVM model is firstly obtained and the performance of the proposed system with other intelligence method based on ANN is compared. It can be concluded that the performance of LS-SVM model outperforms those of ANN, for the data set available, which indicates that the LS-SVM model has better generalization ability.     

A new hybrid approach based on SARIMA and partial high order bivariate fuzzy time series forecasting model

In the literature, there have been many studies using fuzzy time series for the purpose of forecasting. The most studied model is the first order fuzzy time series model. In this model, an observation of fuzzy time series is obtained by using the previous observation. In other words, only the first lagged variable is used when constructing the first order fuzzy time series model. Therefore, this model can not be sufficient for some time series such as seasonal time series which is an important class in time series models. Besides, the time series encountered in real life have not only autoregressive (AR) structure but also moving average (MA) structure. The fuzzy time series models available in the literature are AR structured and are not appropriate for MA structured time series. In this paper, a hybrid approach is proposed in order to analyze seasonal fuzzy time series. The proposed hybrid approach is based on partial high order bivariate fuzzy time series forecasting model which is first introduced in this paper. The order of this model is determined by utilizing Box-Jenkins method. In order to show the efficiency of the proposed hybrid method, real time series are analyzed with this method. The results obtained from the proposed method are compared with the other methods. As a result, it is observed that more accurate results are obtained from the proposed hybrid method.     

Modeling realistic hybrid flexible flowshop scheduling problems

This paper aims to contribute to the recent research efforts to bridge the gap between the theory and the practice of scheduling by modelizing a realistic manufacturing environment and analyzing the effect of the inclusion of several characteristics in the problem formulation. There are several constraints and characteristics that affect the scheduling operations at companies. While these constraints are many times tackled in the literature, they are seldom considered together inside the same problem formulation. We propose a formulation along with a mixed integer modelization and some heuristics for the problem of scheduling n jobs on m stages where at each stage we have a known number of unrelated machines. The jobs might skip stages and, therefore, we have what we call a hybrid flexible flowshop problem. We also consider per machine sequence-dependent setup times which can be anticipatory and non-anticipatory along with machine lags, release dates for machines, machine eligibility and precedence relationships among jobs. Manufacturing environments like this appear in sectors like food processing, ceramic tile manufacturing and several others. The optimization criterion considered is the minimization of the makespan. The MIP model and the heuristics proposed are tested against a comprehensive benchmark and the results evaluated by advanced statistical tools that make use of decision trees and experimental designs. The results allow us to identify the constraints that increase the difficulty.     

A sensorimotor approach to sound localization

Sound localization is known to be a complex phenomenon, combining multisensory information processing, experience-dependent plasticity, and movement. Here we present a sensorimotor model that addresses the question of how an organism could learn to localize sound sources without any a priori neural representation of its head-related transfer function or prior experience with auditory spatial information. We demonstrate quantitatively that the experience of the sensory consequences of its voluntary motor actions allows an organism to learn the spatial location of any sound source. Using examples from humans and echolocating bats, our model shows that a naive organism can learn the auditory space based solely on acoustic inputs and their relation to motor states.