Nakhon Thai, Thailand
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Thammawichai M.,Royal Thai Air Force Academy | Kerrigan E.C.,Imperial College London
2016 IEEE 55th Conference on Decision and Control, CDC 2016 | Year: 2016

Real-time scheduling algorithms proposed in the literature are often based on worst-case estimates of task parameters and the performance of an open-loop scheme can therefore be poor. To improve on such a situation, one can instead apply a closed-loop scheme, where feedback is exploited to dynamically adjust the system parameters at run-time. We propose an optimal control framework that takes advantage of feeding back information of finished tasks to solve a real-time multiprocessor scheduling problem with uncertainty in task execution times, with the objective of minimizing the total energy consumption. Specifically, we propose a linear programming-based algorithm to solve a workload partitioning problem and adopt McNaughton's wrap around algorithm to find the task execution order. Simulation results for a PowerPC 405LP and an XScale processor illustrate that our feedback scheduling algorithm can result in an energy saving of approximately 40% compared to an open-loop method. © 2016 IEEE.

Homsup N.,Kasetsart University | Jariyanorawiss T.,Kasetsart University | Homsup W.,Royal Thai Air Force Academy
Conference Proceedings - IEEE SOUTHEASTCON | Year: 2010

In this paper, the Finite-Different Time-Domain (FDTD) schemes were used to find antenna parameters of the simulated dipole antenna. The dipole model has a feeding gap, and there are four FDTD cells around the feeding point. For flexibility in the simulation, the width of the feeding gap can be varied, and results were compared with results from simulations using the Moment Method (MOM). For standard FDTD scheme , there are four equations represent field variations at the center-fed cell. However, this paper proposes a field formulations that reduce from four equations to two equations. In this paper, input impedances and the return losse are ploted as a function of the ratio of dipole's length and a wavelength (L/?). Simulations show that FDTD schemes give results agree well with the results from MoM. However, FDTD simulation time is much less than the one used in the MOM simulation. ©2010 IEEE.

Homsup N.,Kasetsart University | Jariyanorawiss T.,Kasetsart University | Homsup W.,Royal Thai Air Force Academy
Lecture Notes in Engineering and Computer Science | Year: 2014

This paper presents results of placing a one metal cell closed to a mobile phone. The one metal cell is the Yee's cell that has a metal characteristic, with high conductivity and low permittivity. In general, the mobile phone was modeled by a dipole antenna. The one metal cell's characteristic can be model as one Yee's cell (1-3). This simulation uses Finite Difference Time Domain (FDTD) and its domain is divided into two parts: the physical domain and the artificial domain. First, the physical domain consists of a dipole antenna located at 1 cm from a human head model and a one metal cell varied distance (Al) from the dipole. In addition, the dipole antenna operated at 900 MHz and 1800 MHz was used in the simulation. Second, the artificial domain is a Perfectly Matched Layer (PML). The PML acts as an electromagnetic field absorbing layer and was backed by a Perfect Electric Conductor (PEC). The Specific Absorption Rate (SAR) was computed and averaged on a tissue mass of one gram and ten grams, SAR 1-g and SAR 10-g, respectively. Also, the average power (Pivg) absorbed in various human tissues is computed with a distance between the dipole antenna and the one metal cell as a varying parameter (Al). There are three reference SAR values: the standard SAR 1-g (FCC, Federal Communications Commission), the simulation in an open area and the simulation with the metal wall. Results from the simulation show that the computed SAR 1-g and SAR 10-g values are not exceed the limitation values established by various standard institutes (1.6 Watt/kg), however, for Al = 0-5 cm, both of the SAR and the average power absorb are higher than the simulation with the metal wall and the simulation in an open area.

Iam-On N.,Mae Fah Luang University | Boongoen T.,Royal Thai Air Force Academy
Proceedings of the International Joint Conference on Neural Networks | Year: 2012

Cluster ensembles have been shown to be better than any standard clustering algorithm at improving accuracy. This meta-learning formalism helps users to overcome the dilemma of selecting an appropriate technique and the parameters for that technique, given a set of data. It has proven effective for many problem domains, especially microarray data analysis. Among different state-of-the-art methods, the link-based approach (LCE) recently introduced by [22], [23] provides a highly accurate clustering. This paper presents the improvement of LCE with a new link-based similarity measure being developed and engaged. Additional information that is already available in a network is included in the similarity assessment. As such, this refinement can increase the quality of the measures, hence the resulting cluster decision. The performance of this improved LCE is evaluated on synthetic and UCI benchmark datasets, in comparison with the original and several well-known cluster ensemble techniques. The findings suggest that the new model can improve the accuracy of LCE and performs better than the others investigated in the empirical study. © 2012 IEEE.

Iam-On N.,Mae Fah Luang University | Boongoen T.,Royal Thai Air Force Academy
Proceedings - IEEE-EMBS International Conference on Biomedical and Health Informatics: Global Grand Challenge of Health Informatics, BHI 2012 | Year: 2012

Subspace clustering is increasingly recognized as a useful and accurate alternative to conventional techniques. While a large number of hard subspace approaches have been introduced, only a handful of soft counterparts are developed with the common goal of obtaining the optimal cluster-specific dimension weights. These existing methods similarly extend k-means and rely on the iteratively modified cluster centers for the weight determination. As the quality of discovered centers are uncertain, the accuracy of weights may not always be maintained. Intuitively, by reducing such a dependency, the weight modification can be more effective, thus improving the goodness of data clustering. This paper presents a new soft subspace clustering method that implements the above-mentioned idea and demonstrates outstanding performance on real gene expression data, as compared to several existing algorithms found in the literature. © 2012 IEEE.

Iam-On N.,Mae Fah Luang University | Boongoen T.,Royal Thai Air Force Academy | Garrett S.,Aispire Consulting Ltd. | Price C.,Aberystwyth University
IEEE Transactions on Pattern Analysis and Machine Intelligence | Year: 2011

Cluster ensembles have recently emerged as a powerful alternative to standard cluster analysis, aggregating several input data clusterings to generate a single output clustering, with improved robustness and stability. From the early work, these techniques held great promise; however, most of them generate the final solution based on incomplete information of a cluster ensemble. The underlying ensemble-information matrix reflects only cluster-data point relations, while those among clusters are generally overlooked. This paper presents a new link-based approach to improve the conventional matrix. It achieves this using the similarity between clusters that are estimated from a link network model of the ensemble. In particular, three new link-based algorithms are proposed for the underlying similarity assessment. The final clustering result is generated from the refined matrix using two different consensus functions of feature-based and graph-based partitioning. This approach is the first to address and explicitly employ the relationship between input partitions, which has not been emphasized by recent studies of matrix refinement. The effectiveness of the link-based approach is empirically demonstrated over 10 data sets (synthetic and real) and three benchmark evaluation measures. The results suggest the new approach is able to efficiently extract information embedded in the input clusterings, and regularly illustrate higher clustering quality in comparison to several state-of-the-art techniques. © 2011 IEEE.

Chusilp P.,Defence Technology Institute | Charubhun W.,Defence Technology Institute | Nilubol O.,Royal Thai Air Force Academy
Proceedings of the 2014 7th IEEE Symposium on Computational Intelligence for Security and Defense Applications, CISDA 2014 | Year: 2015

Several shapes of cookie cutter function have been used in weapon effectiveness analyses. Although it is perceived that different cookie cutter shapes would lead to different analysis results, little proof has been developed. This paper investigates whether different cookie cutter shapes produce different results of Monte Carlo simulations to determine the probability of damage on area targets. Four cookie cutter shapes, which are circle, ellipse, rectangle, and the actual shape of weapon lethal zone, were employed in a case study on multiple shots of artillery weapons against a uniform value target. The Monte Carlo simulations were carried out on different number of shots, targets, and weapon lethality data. Statistical analyses were performed and it was suggested that the difference between the results determined by different shapes of cookie cutter function was statistically significant but the effect of the shapes of cookie cutter function on the results was small. © 2014 IEEE.

Iam-On N.,Mae Fah Luang University | Boongeon T.,Royal Thai Air Force Academy | Garrett S.,Aispire Consulting Ltd. | Price C.,Aberystwyth University
IEEE Transactions on Knowledge and Data Engineering | Year: 2012

Although attempts have been made to solve the problem of clustering categorical data via cluster ensembles, with the results being competitive to conventional algorithms, it is observed that these techniques unfortunately generate a final data partition based on incomplete information. The underlying ensemble-information matrix presents only cluster-data point relations, with many entries being left unknown. The paper presents an analysis that suggests this problem degrades the quality of the clustering result, and it presents a new link-based approach, which improves the conventional matrix by discovering unknown entries through similarity between clusters in an ensemble. In particular, an efficient link-based algorithm is proposed for the underlying similarity assessment. Afterward, to obtain the final clustering result, a graph partitioning technique is applied to a weighted bipartite graph that is formulated from the refined matrix. Experimental results on multiple real data sets suggest that the proposed link-based method almost always outperforms both conventional clustering algorithms for categorical data and well-known cluster ensemble techniques. © 2006 IEEE.

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