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A novel transmission line's atc assessment method by considering the Generation Dispatch limit (GDL) and renewable generation capacity with power flow sensitivity analysis
(2019-01-01)
Wannoi, Chaisit
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Wannoi, Narumon
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Boonkhun, Churirut
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Temiyasathit, Chivalai
;
Chompoo-Inwai, Chai
This paper presents a novel transmission line's Availability Transfer Capability (ATC) assessment method by considering the Generation Dispatch Limit (GDL) and renewable generation capacity. This method has calculated the power remaining from the Total Transfer Capability (TTC). The TTC in this study is the total generation available declaration of all maximum Generation Shift Factor (GSF) generations to transfer power on transmission line depended on the Power Purchase Agreement of each power generation and of each renewable generation integrated in the system. The maximum GSF generations are defined by using the power sensitivity analysis. Results of this novel method are compared with the traditional transmission line's thermal limit method. Main findings showed that newly proposed method for ATC calculation can be used for the ATC assessment of transmission line and can be a good predictive index to protect power system's voltage stability issue. Moreover, it can be applied to the congestion management scheme with the optimal primary respond generation to control the stability of power transfer on the transmission line.
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Regularizing multi-bands Common Spatial Patterns (RMCSP): A data processing method for brain-computer interface
(2015-05-13)
Thang, Le Quoc
;
Temiyasathit, Chivalai
In this paper, we propose a novel approach which is called the Regularizing Multi-bands Common Spatial Patterns (RMCSP) that particularly used for processing motor-imagery based Electroencephalography (EEG) data in Brain-computer Interface (BCI). The usage of BCI is severely limited due to the inconvenience of large number of channels used in recording devices. Moreover, Common Spatial Patterns (CSP) is a very well-known algorithm for its efficiency, but it just can extract the spatial information of the brain signals. To address these issues, we introduce the RMCSP method that exploits data in spectral, temporal and spatial domains in order to increase the classification accuracy in BCI. In addition, RMCSP is designed to handle EEG with small number of channels. To verify the efficacy of our approach, we rigorously tested the performances of the method in 17 subjects, from BCI competition datasets, in both two-class and four-class problems. Results show that RMCSP approach can outperform normal CSP method by nearly 10% in terms of median classification accuracy. It also enables us to significantly reduce the number of channels used in the datasets without decreasing the performances of the subjects.
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Increase performance of four-class classification for motor-imagery based brain-computer interface
(2014-01-01)
Thang, Le Quoc
;
Temiyasathit, Chivalai
Brain computer interface (BCI) is a system that provide a direct communication between human brain and external devices. BCIs which based on mental tasks of users are widely used for disabled or paralyzed patients, in order to help their mobility. Preprocessing techniques have been extensively developed to increase the signal-to-noise ratio and spatial distribution of the signals. Common Spatial Pattern (CSP) has shown to be a robust and effective method for processing Electroencephalogram (EEG) data. However, the results of CSP filter are still far from being completely explored. CSP was originally designed for two-class problem despite the fact that a practical application of Motor-imagery (MI) based BCI contains numbers of activities. It is necessary to design the classification algorithm which applicable to more than two-class problem. In this paper we investigate the performance of CSP by selecting optimal time slice and components for training CSP filters in four-class BCI by separating the four-class problem into multiple binary classifications. Our method is verified in the testing phase with four different types of classification approaches which are linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), linear support vector machines (LSVM), and support vector machines with radial basis function kernel (RBF-SVM). The result showed that, under the optimal time slice and components, the classification accuracy reach 78.82% for the best untrained subject in this dataset. © 2014 IEEE.
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Investigation of regularization theory for four-class classification in brain-computer interface
(2014-01-01)
Thang, Le Quoc
;
Temiyasathit, Chivalai
Common spatial patterns (CSP) is one of the most prevalent feature extraction approaches that has been used in Brain-computer interfaces (BCI) due to its simplicity and efficiency. Nevertheless, CSP suffers from the problems of sensitivity to noise and overfitting. To overcome these issues, the regularized CSP (RCSP) has been proposed recently. In addition, CSP was originally designed for two-class classification. However, a practical BCI usually needs four-class commands to be able to operate. Thus, there is a high demand for increasing the performance of multi-class BCI. In this paper, we provide a complete study of classification accuracy in multi-class BCI using regularization theory, and compare it with the standard CSP to determine the suitable method for feature extraction in BCI learning. Besides CSP, linear discriminant analysis (LDA) has shown its robust and widespread use for machine learning in BCI. LDA estimates covariance matrices from extracted features. But for high-dimensional features with only a small amount of training data given, the estimation may become imprecise. In the attempt of clarifying the regularizing effects in BCI, this paper also provides the classification results of the regularized LDA (RLDA). The performance evaluation of this work was taken on data from 9 subjects, from BCI competition datasets. Results show that the combination of standard CSP and LDA has a slightly better accuracy than the regularizing methods.
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An investigation of Lean implementation status in thai manufacturing
(2012-01-01)
Pradabwong, Jiraporn
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Sriariyawat, Nantawut
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Temiyasathit, Chivalai
Lean Manufacturing has been widely adopted in various business and industries worldwide. To determine the current stage of Lean implementation in Thailand and the barrier Lean implementation, the qualitative research involving interview with 10 industrial companies practicing Lean manufacturing was conducted. The result from this study shows that most Lean practitioners understand the principal of Lean manufacturing. Different organization functions adopt different types of Lean tools and techniques as well as the performance measures depending on their business characteristics. However, there are three performance measures, which are manufacturing cost per unit, total sales and part per million (PPM) in defective products shipped to customers, that were adopted by all 10 companies. The most important barrier in Lean implementation is the cultural change since it requires the entire company participation. Finally, the companies participated in this study confirmed that they are satisfy with the result of Lean manufacturing though some companies have not completely adopted the Lean approach into their manufacturing process. To excellent the Lean manufacturing, companies are required to satisfy customer needs, improve the manufacturing process, and increase their flexibility. Furthermore, it is necessary for the top management to provide a clear policies as well as plan and direction. If Lean implementation is to be successful, the communication and human resource department are also the main keys. © (2012) Trans Tech Publications, Switzerland.
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Classification of high-resolution NMR spectra based on complex wavelet domain feature selection and kernel-induced random forest
(2010-12-01)
Fan, Guangzhe
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Wang, Zhou
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Kim, Seoung Bum
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Temiyasathit, Chivalai
High-resolution nuclear magnetic resonance (NMR) spectra contain important biomarkers that have potentials for early diagnosis of disease and subsequent monitoring of its progression. Traditional features extraction and analysis methods have been carried out in the original frequency spectrum domain. In this study, we conduct feature selection based on a complex wavelet transform by making use of its energy shift-insensitive property in a multi-resolution signal decomposition. A false discovery rate based multiple testing procedure is employed to identify important metabolite features. Furthermore, a novel kernel-induced random forest algorithm is used for the classification of NMR spectra based on the selected features. Our experiments with real NMR spectra showed that the proposed method leads to significant reduction in misclassification rate. © Springer-Verlag Berlin Heidelberg 2010.
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An effective classification procedure for diagnosis of prostate cancer in near infrared spectra
(2010-05-01)
Kim, Seoung Bum
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Temiyasathit, Chivalai
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Bensalah, Karim
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Tuncel, Altug
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Cadeddu, Jeffrey
The main purpose of this study is to develop an effective classification procedure that discriminates between normal spectra and cancerous spectra in near infrared (NIR) spectroscopic data in which the classes are highly imbalanced and overlapped. Our proposed procedure consists of several steps. First, to ensure the comparability between spectra, normalization was done by dividing each spectral point by the area of the total intensity of the spectrum. Second, clustering analysis was performed with these normalized spectra to separate the spectra that represent the normal pattern from a mixed group that contains both normal and tumor spectra. Third, we conducted two-stage classification, the first being an effort to construct a classification model with the labels obtained from the preceding clustering analysis and the second being a classification to focus on the mixed group classified from the first classification model. To increase the accuracy, the second classification model was constructed based on the selected features that capture important characteristics of the spectral data. Our proposed procedure was evaluated by its classification ability in testing samples using a leave-one-out cross validation technique, yielding acceptable classification accuracy. © 2009 Elsevier Ltd. All rights reserved.
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Spatial prediction of ozone concentration profiles
(2009-09-01)
Temiyasathit, Chivalai
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Kim, Seoung Bum
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Park, Sun Kyoung
Ground level ozone is one of the major air pollutants in many urban areas. Ozone formation affects ecosystems and is known to be associated with many adverse health issues in humans. Effective modeling of ozone is a necessary step to develop a system to warn residents of high ozone levels. In the present study we propose a statistical procedure that uses multiscale and functional data analysis to improve the spatial prediction of ozone concentration profiles in the Dallas Fort Worth (DFW) area of Texas. This study uses daily eight-hour ozone concentrations and meteorological predictors during a period between 2003 and 2006 at 14 monitoring sites in the DFW area. Wavelet transformation was used as a means of multiscale data analysis, followed by functional modeling to reduce model complexity. Kriging was then used for spatial prediction. The experimental results with real data demonstrated that the proposed procedures achieved acceptable accuracy of spatial prediction. © 2009 Elsevier B.V. All rights reserved.

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