Papers by kajornsak kantapanit
A Fuzzy c-Means Clustering designed FLC (fuzzy logic controller) is presented in this paper and a... more A Fuzzy c-Means Clustering designed FLC (fuzzy logic controller) is presented in this paper and applied to air-conditioning system. In the design procedure, the auto-tuning PID controller was used to operate the plant of air-conditioning system and the plant data are collected. The fuzzy c-partition of the data are then analyzed by Fuzzy c-Means Clustering to use in the design to achieve optimum fuzzy sets of the FLC for temperature control in the air conditioning system. The results from the experiments show that when compared to the conventional FLC, the proposed FLC gives better temperature characteristics and can save the energy by about 11.5 percent.
In this paper, the use of Fuzzy c-means clustering algorithm in the design of membership function... more In this paper, the use of Fuzzy c-means clustering algorithm in the design of membership functions and fuzzy rules of a fuzry logic controller.are described. In the design procedure, an autotuning PID controller was used to operate an example plant which is a model of the air-conditioning system, and the plant operating data were collected.The fuzry c-partition of the data was then analyzed by Fuzzy c-means clustering to achieve optimum fuzzy sets and fuzzy rules of the FLC. The FLC was then implemented and simulated in controlling the plant. The results from simulation show that when compared to conventionally designed FLC, the proposed FLC gives better temperature characteristics.
In the routing process to select the data paths for Hierarchically Aggregation/Disaggregation and... more In the routing process to select the data paths for Hierarchically Aggregation/Disaggregation and Composition/Decomposition,(HAD) networks, a fast algorithm for finding optimum paths for dataflow is needed. In this research we propose an algorithm called the Reverse Shortest Path algorithm to improve the speed in the calculating procedure for finding the shortest paths. This algorithm performs the reversed calculation in stead of the forward calculation used in conventional algorithms. The demand in each original destination pair (OD pair) has been distributed to the sub OD pairs in each relevant subnetwork r (u,v) = r (u,l) = ... = r (l,k) = r (k,v) with l and k, the gateways and ancestors in the active path. For each different commodities, the parallel processing is carried out with the shared shortest path processing time of O(log(n)) which less than O(m log(n)) of HAD algorithm[1] where, n is the number of nodes in the networks, M is the number of commodities in each cluster and m is a positive integer which is less than M . The proposed algorithms have been developed and tested on a simulated network of 200 nodes clustered into 20 groups. Each group uses a personal computer as the processor for the group. Ten data Monte Carlo simulation patterns were generated for the test. The first five patterns represent typical normal dataflows which largely consist of short distance communications. The other five patterns represent the worst case data communication scenario. Test results on the proposed Reverse Shortest Path algorithm show that, for the tested network, the algorithm has improves the speed in finding the shortest paths by 20% as compared to the conventional shortest path algorithm.
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Papers by kajornsak kantapanit