Current Issue : October - December Volume : 2015 Issue Number : 4 Articles : 6 Articles
Reducing building energy demand is a crucial part of the global response to climate change, and evolutionary\nalgorithms (EAs) coupled to building performance simulation (BPS) are an increasingly popular tool\nfor this task. Further uptake of EAs in this industry is hindered by BPS being computationally intensive:\noptimisation runs taking days or longer are impractical in a time-competitive environment. Surrogate\nfitness models are a possible solution to this problem, but few approaches have been demonstrated\nfor multi-objective, constrained or discrete problems, typical of the optimisation problems in building\ndesign. This paper presents a modified version of a surrogate based on radial basis function networks,\ncombined with a deterministic scheme to deal with approximation error in the constraints by allowing\nsome infeasible solutions in the population. Different combinations of these are integrated with Non-\nDominated Sorting Genetic Algorithm II (NSGA-II) and applied to three instances of a typical building\noptimisation problem. The comparisons show that the surrogate and constraint handling combined offer\nimproved run-time and final solution quality. The paper concludes with detailed investigations of the\nconstraint handling and fitness landscape to explain differences in performance....
How well does a given pitch fit into a tonal scale\nor tonal key, let it be a major or minor key?Asimilar question\ncan be asked regarding chords and tonal regions. Structural\nand probabilistic approaches in computational music theory\nhave tried to give systematic answers to the problem of\ntonal attraction.We will discuss two previous models of tonal\nattraction, one based on tonal hierarchies and the other based\non interval cycles. To overcome the shortcomings of these\nmodels, both methodologically and empirically, I propose a\nnew kind of models relying on insights of the new research\nfield of quantum cognition. I will argue that the quantum\napproach integrates the insights from both group theory\nand quantum probability theory. In this way, it achieves a\ndeeper understanding of the cognitive nature of tonal music,\nespecially concerning the nature of musical expectations\n(Leonhard Meyer) and a better understanding of the affective\nmeaning of music....
Class imbalance problem is important issue in design of classifier. This problem results in classifier that gives more errors for minor class. Cost Sensitive approach is efficient to solve this problem in case of neural network. This paper studies effect of cost sensitive learning on Multilayer Perceptron classifier (MLP). The results show that, due to this approach minor class also acquires importance in learning resulting in accuracy of minor class also. Levenberg Marquardt (LM) using new computation (LM) is efficient method for weight updation. But this algorithm can’t be used with complex networks due to large memory requirement. This problem is solved by using new computation technique. Analysis shows that memory requirement has been reduced significantly....
Grid environment is being a service oriented infrastructure in which many heterogeneous resources participate for providing the high performance computation facility to address big problems. Various resources of grid environment exposed as services. So to utilize grid facilities, service discovery becomes an important issue. Some of the services will not be discovered even if they are in same service category due to presence of imprecision and uncertainty in advertised service and requested services in grid environment. We have implemented a rough set algorithm to deal with imprecision and uncertainty. Various services are registered with service registry. This service registry is stored and maintained by Resource Manager Server (RM). Users interact with RM server to find out required service or resource for his job. As these requests may grow large in number, user may need to wait for longer time for response. To deal with such a situation we have implemented a system which creates multiple RM servers that are serving large number of requests in parallel fashion. The evaluation of result shows that implemented system is more efficient than existing one....
In this paper, we explore the application of Opt-AiNet, an immune network approach for search and\noptimisation problems, to learning qualitative models in the form of qualitative differential equations.\nThe Opt-AiNet algorithm is adapted to qualitative model learning problems, resulting in the proposed\nsystem QML-AiNet. The potential of QML-AiNet to address the scalability and multimodal search space\nissues of qualitative model learning has been investigated. More importantly, to further improve the\nefficiency of QML-AiNet, we also modify the mutation operator according to the features of discrete\nqualitative model space. Experimental results show that the performance of QML-AiNet is comparable\nto QML-CLONALG, a QML system using the clonal selection algorithm (CLONALG). More importantly,\nQML-AiNet with the modified mutation operator can significantly improve the scalability of QML and is\nmuch more efficient than QML-CLONALG....
Thermal errors can have significant effects on CNC machine tool accuracy. The errors come from thermal\ndeformations of the machine elements caused by heat sources within the machine structure or from\nambient temperature change. The effect of temperature can be reduced by error avoidance or numerical\ncompensation. The performance of a thermal error compensation system essentially depends upon the\naccuracy and robustness of the thermal error model and its input measurements. This paper first reviews\ndifferent methods of designing thermal error models, before concentrating on employing an adaptive\nneuro fuzzy inference system (ANFIS) to design two thermal prediction models: ANFIS by dividing the data\nspace into rectangular sub-spaces (ANFIS-Grid model) and ANFIS by using the fuzzy c-means clustering\nmethod (ANFIS-FCM model). Grey system theory is used to obtain the influence ranking of all possible\ntemperature sensors on the thermal response of the machine structure. All the influence weightings of\nthe thermal sensors are clustered into groups using the fuzzy c-means (FCM) clustering method, the\ngroups then being further reduced by correlation analysis.\nA study of a small CNC milling machine is used to provide training data for the proposed models and\nthen to provide independent testing data sets. The results of the study show that the ANFIS-FCM model\nis superior in terms of the accuracy of its predictive ability with the benefit of fewer rules. The residual\nvalue of the proposed model is smaller than\n�±4 m. This combined methodology can provide improved\naccuracy and robustness of a thermal error compensation system....
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