Robust model predictive control of biped robots with adaptive on-line gait generation

International Journal of Control, Automation and Systems - In this paper, an on-line gait control scheme is proposed for the biped robots for walking up and down the stairs. In the proposed...     

Multi-objective optimization of laminated composite shells for minimum mass/cost and maximum buckling pressure with failure criteria under external hydrostatic pressure

In the present study, Multi-objective optimization of composite cylindrical shell under external hydrostatic pressure was investigated. Parameters of mass, cost and buckling pressure as fitness functions and failure criteria as optimization criterion were considered. The objective function of buckling has been used by performing the analytical energy equations and Tsai-Wu and Hashin failure criteria have been considered. Multi-objective optimization was performed by improving the evolutionary algorithm of NSGA-II. Also the kind of material, quantity of layers and fiber orientations have been considered as design variables. After optimizing, Pareto front and corresponding points to Pareto front are presented. Trade of points which have optimized mass and cost were selected by determining the specified pressure as design criteria. Finally, an optimized model of composite cylindrical shell with the optimum pattern of fiber orientations having appropriate cost and mass is presented which can tolerate the maximum external hydrostatic pressure.

     

Classifying the Geometric Dilution of Precision of GPS satellites utilizing Bayesian decision theory

The errors resulting from satellite configuration geometry can be determined by Geometric Dilution of Precision (GDOP). Considering optimal satellite subset selection, lower GDOP value usually causes better accuracy in GPS positioning. However, GDOP computation based on complicated transformation and inversion of measurement matrices is a time consuming procedure. This paper deals with classification of GPS GDOP utilizing Parzen estimation based Bayesian decision theory. The conditional probability of each class is estimated by Parzen algorithm. Then based on Bayesian decision theory, the class with maximum posterior probability is selected. The experiments on measured dataset demonstrate that the proposed algorithm lead, in mean classification improvement, to 4.08% in comparison with Support Vector Machine (SVM) and 9.83% in comparison with K-Nearest Neighbour (KNN) classifier. Extra work on feature extraction has been performed based on Principle Component Analysis (PCA). The results demonstrate that the feature extraction approach has best performance respect to all classifiers.     

A new fuzzy decision-making procedure applied to emergency electric power distribution scheduling

In this paper a new fuzzy decision-making procedure is developed. Two levels of weightings, called upper and lower weights, are proposed to calculate the fuzzy weightings of different criteria. The preference table is introduced to calculate the upper and lower weights. Also a new method is developed to determine the expected preference values of different alternatives. These values are used for generating the priority list of alternatives. The procedure is applied for providing an emergency electric power distribution, scheduling time table. The IEEE 14-bus standard system and a 14-bus system with four power plants are considered as case studies. Using fuzzy decision-making procedure, the regions with more priorities are more connected to the distribution net, while the maximum consumption criterion is also reasonably respected.     

Fuzzy hypersemigroups

We introduce the notions of fuzzy hypersemigroup, fuzzy hypergroup, fuzzy hyperideal, homomorphism, hyper congruence, fuzzy homomorphism, fuzzy hypercongruence. The purpose of this note is the study of some characterization of fuzzy hypersemigroup, fuzzy hyperideal of a fuzzy hypersemigroup and homomorphism and hypercongruence on a hypersemigroup.     

A step forward in studying the compact genetic algorithm

The compact Genetic Algorithm (cGA) is an Estimation of Distribution Algorithm that generates offspring population according to the estimated probabilistic model of the parent population instead of using traditional recombination and mutation operators. The cGA only needs a small amount of memory; therefore, it may be quite useful in memory-constrained applications. This paper introduces a theoretical framework for studying the cGA from the convergence point of view in which, we model the cGA by a Markov process and approximate its behavior using an Ordinary Differential Equation (ODE). Then, we prove that the corresponding ODE converges to local optima and stays there. Consequently, we conclude that the cGA will converge to the local optima of the function to be optimized.     

An Immune-based Genetic Algorithm with Reduced Search Space Coding for Multiprocessor Task Scheduling Problem

Multiprocessor task scheduling is an important problem in parallel applications and distributed systems. In this way, solving the multiprocessor task scheduling problem (MTSP) by heuristic, meta-heuristic, and hybrid algorithms have been proposed in literature. Although the problem has been addressed by many researchers, challenges to improve the convergence speed and the reliability of methods for solving the problem are still continued especially in the case that the communication cost is added to the problem frame work. In this paper, an Immune-based Genetic algorithm (IGA), a meta-heuristic approach, with a new coding scheme is proposed to solve MTSP. It is shown that the proposed coding reduces the search space of MTSP in many practical problems, which effectively influences the convergence speed of the optimization process. In addition to the reduced search space offered by the proposed coding that eventuate in exploring better solutions at a shorter time frame, it guarantees the validity of solutions by using any crossover and mutation operators. Furthermore, to overcome the regeneration phenomena in the proposed GA (generating similar chromosomes) which leads to premature convergence, an affinity based approach inspired from Artificial Immune system is employed which results in better exploration in the searching process. Experimental results showed that the proposed IGA surpasses related works in terms of found makespan (20% improvement in average) while it needs less iterations to find the solutions (90% improvement in average) when it is applied to standard test benches.     

Predictability and forecasting automotive price based on a hybrid train algorithm of MLP neural network

In this paper, using the Hurst exponent value H, we first show that the automotive price in Iran Khodro Company (IRAN) is predictable and therefore a good forecasting can be done using neural networks. We then introduce a new global and fast hybrid multilayer perceptron neural network (MLP-NN) in order to forecast the automotive price. In our new framework, we hybridize the genetic algorithm (GA) and least square (LS) method in order to train the connected weights of the network, which leads us to have a global and fast network. To do so, the connected weights between input and hidden layers are trained by GA and the connected weights between the hidden and output layers are trained by LS method. We finally apply our new MLP-NN to forecast the automotive price in Iran Khodro Company, which is the biggest automotive manufacturing in IRAN. The results are well promising compared with the cases when we apply the GA and LS individually. We also compare the results with the case when we employ the gradient-based optimization techniques such as Levenberg–Marquardt method as well as some heuristic algorithms such as extended tabu search algorithm instead of LS method and hybridization of MLP-LM with GA.     

Genetic application in a facility location problem with random demand within queuing framework

In many service and industrial applications of the facility location problem, the number of required facilities along with allocation of the customers to the facilities are the two major questions that need to be answered. In this paper, a facility location problem with stochastic customer demand and immobile servers is studied. Two objectives considered in this problem are: (1) minimizing the average customer waiting time and (2) minimizing the average facility idle-time percentage. We formulate this problem using queuing theory and solve the model by a genetic algorithm within the desirability function framework. Several examples are presented to demonstrate the applications of the proposed methodology.     

CCGA packages for space applications

Commercial-off-the-shelf (COTS) area array packaging technologies in high reliability versions are now being considered for applications, including use in a number of NASA electronic systems being utilized for both the Space Shuttle and Mars Rover missions. Indeed, recently a ceramic package version specifically tailored for high reliability applications was used to provide the processing power required for the Spirit and Opportunity Mars Rovers built by NASA-JPL. Both Rovers successfully completed their 3-months mission requirements and continued exploring the Martian surface for many more moths, providing amazing new information on previous environmental conditions of Mars and strong evidence that water exists on Mars.Understanding process, reliability, and quality assurance (QA) indicators for reliability are important for low risk insertion of these newly available packages in high reliability applications. In a previous investigation, thermal cycle test results for a non-functional daisy-chained peripheral ceramic column grid array (CCGA) and its plastic ball grid array (PBGA) version, both having 560 I/Os, were gathered and are presented here. Test results included environmental data for three different thermal cycle regimes (−55/125 °C, −55/100 °C, and −50/75 °C). Detailed information on these—especially failure type for assemblies with high and low solder volumes—are presented. The thermal cycle test procedure followed those recommended by IPC-9701 for tin–lead solder joint assemblies. Its revision A covers guideline thermal cycle requirements for Pb-free solder joints. Key points on this specification are also discussed.In a recent investigation a fully populated CCGA with 717 I/Os was considered for assembly reliability evaluation. The functional package is a field-programmable gate array that has much higher processing power than its previous version. This new package is smaller in dimension, has no interposer, and has a thinner column wrapped with copper for reliability improvement. This paper will also present thermal cycle test results for assemblies of this and its plastic package version with 728 I/Os, both of which were exposed to four different cycle regimes. Two of these cycle profiles are specified by IPC-9701A for tin–lead, namely, −55 to 100 °C and −55 to 125 °C. One is a cycle profile specified by Mil-Std-883, namely, −65/150 °C, generally used for ceramic hybrid packages screening and qualification. The last cycle is in the range of −120 to 85 °C, a representative of electronic systems directly exposed to the Martian environment without use in a thermal control enclosure. Per IPC-9701A, test vehicles were built using daisy chain packages and were continuously monitored and/or manually checked for opens at intervals. The effects of many process and assembly variables—including corner staking commonly used for improving resistance to mechanical loading such as drop and vibration loads—were also considered as part of the test matrix. Optical photomicrographs were taken at various thermal cycle intervals to document damage progress and behavior. Representative samples of these are presented along with cross-sectional photomicrographs at higher magnification taken by scanning electron microscopy (SEM) to determine crack propagation and failure analyses for packages.