Limpiti, Tulaya

Preferred name

Limpiti, Tulaya

Alternative Name

Limpiti, T.

Main Affiliation

School of Engineering

tulaya.li@kmitl.ac.th

Now showing 1 - 10 of 16

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Estimating an optimal setpoint to lessen errors in filling weighing system based on Kalman filtering
(2014-01-01)
Sinchai, Sakkarin
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Saechia, Sukkharak
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Limpiti, Tulaya
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Koseeyaporn, Jeerasuda
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Wardkein, Paramote
A weighing system in which a sensor is not mounted to a discharger especially in vertical filling gives rise to an excess of weight added to the given target of weight. In addition, the excess is not constant on account of some factors, such as vibration of the machine, flow of the substance, and cycle time of the system. These factors cause the surplus to oscillate. To overcome this problem, Kalman filtering is performed to predict the optimal setpoint to meet the defined target. To illustrate the performance of the proposed technique, the resulting outcome is compared with that of using the conventional statistical method. The results have shown that the proposed approach has significantly increased the speed and lowered the error. It is pointed out that the proposed algorithm may be preferable to the traditional statistical technique due to its effectiveness and its simple implementation. © 2014 IEEE.
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A spatiotemporal framework for MEG/EEG evoked response amplitude and latency variability estimation
(2010-03-01)
Limpiti, Tulaya
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Van Veen, Barry D.
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Wakai, Ronald T.
This paper presents a spatiotemporal framework for estimating single-trial response latencies and amplitudes from evoked response magnetoencephalographic/ electroencephalographic data. Spatial and temporal bases are employed to capture the aspects of the evoked response that are consistent across trials. Trial amplitudes are assumed independent but have the same underlying normal distribution with unknown mean and variance. The trial latency is assumed to be deterministic but unknown. We assume that the noise is spatially correlated with unknown covariance matrix. We introduce a generalized expectationmaximization algorithm called Trial Variability in Amplitude and Latency ( TriViAL) that computes the maximum likelihood (ML) estimates of the amplitudes, latencies, basis coefficients, and noise covariance matrix. The proposed approach also performs ML source localization by scanning the TriViAL algorithm over spatial bases corresponding to different locations on the cortical surface. Source locations are identified as the locations corresponding to large likelihood values. The effectiveness of the TriViAL algorithm is demonstrated using simulated data and human evoked response experiments. The localization performance is validated using tactile stimulation of the finger. The efficacy of the algorithm in estimating latency variability is shown using the known dependence of the M100 auditory response latency to stimulus tone frequency. We also demonstrate that estimation of response amplitude is improved when latency is included in the signal model. © 2006 IEEE.
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Improved iterative pruning principal component analysis with graph-theoretic hierarchical clustering
(2012-10-02)
Amornbunchornvej, C.
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Limpiti, T.
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Assawamakin, A.
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Intarapanich, A.
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Tongsima, S.
Various unsupervised clustering algorithms have been used to infer population structure in genetic data. The goals are to separate individuals of similar genetic characteristics into clusters and to estimate the number of clusters within each dataset. Among them, a framework called iterative pruning principal component analysis (ipPCA) have been developed. It performs PCA iteratively on subsets of data samples and clusters them using fuzzy c-mean. We believe that the choice of model-based clustering method affects the individual assignments and cluster quality, as well as the estimated number of clusters. Thus, in this paper we introduce a hierarchical tree clustering concept from graph theory, whose performance is independent of cluster shapes, into the ipPCA framework. We also add a PCA-based feature selection technique as a data pre-processing step to reduce data dimension and increase computational efficiency. The resulting algorithm is called HiClust-ipPCA. We illustrate the improved clustering results of the HiClust-ipPCA algorithm using 47-breed bovine and 28-breed sheep datasets. © 2012 IEEE.
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Wireless intelligent fall detection and movement classification using fuzzy logic
(2012-12-01)
Putchana, Wuttichai
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Chivapreecha, Sorawat
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Limpiti, Tulaya
Global population aging leads to increased interests in preventive healthcare technology. As falls are the most common cause of injury or death in old persons, fall detection and movement classification is one of the key topics in this research area. In this paper we propose a simple wireless intelligent system prototype for fall detection and movement classification for real-time monitoring of the elderly. The portable sensor unit acquires data from a triaxial accelerometer and sends the data wirelessly to a computer using Zigbee technology. Alternative to classic methods, the movement data is analyzed using a fuzzy inference system. The system is designed to distinguish between four movement types: standing, sitting, forward fall, and backward fall. Its classification accuracy is investigated using experimental data. It is observed that the system performs well with high sensitivity and excellent specificity. Additionally, the system is applicable for monitoring rehabilitative patients and is extendable to a larger class of movements and postures. ©2012 IEEE.
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Time-frequency analysis for cancer detection using proteomic MS-spectra
(2011-12-01)
Limpiti, T.
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Assawamakin, A.
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Intarapanich, A.
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Tongsima, S.
Mass spectrum data is proven useful in cancer detection and biomarker discovery. Nevertheless, existing methods which analyze peaks of mass spectrum data still have some limitations, including variation in peak locations among individual samples, noisy data, irreproducibility of peak profiles, and computational burden. We introduce a simple algorithm in this paper which alleviate these drawbacks. Our approach is to analyze the mass spectrum data using time-frequency analysis. The data is transformed to features in the time-frequency domain. Informative features are then selected and subsequently used for detection or classification. To assess the efficacy of the proposed algorithm, we apply our algorithm to cancer detection problem. The performance of the algorithm is evaluated on real ovarian and prostate cancer datasets. The promising detection results with high sensitivity and specificity confirm the potential of our method in cancer detection. The algorithm is also applicable to multi-class classification and biomarker identification problems. © 2011 IEEE.
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A handheld moisture content sensor using coupled-dipole antennas
(2010-07-01)
Mearnchu, J.
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Limpiti, T.
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Torrungrueng, D.
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Akkaraekthalin, P.
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Krairiksh, M.
This paper presents an analysis and design of a handheld moisture content sensor. A dielectric property determination technique was used to measure the magnitudes of the reflection and the coupling coefficients of coupled-dipole antennas. These coefficients were plotted and their intersection determined to obtain the values of ε' <inf>r</inf> and ε" <inf>r</inf> (ε' <inf>r</inf> and ε" <inf>r</inf>, respectively, are the real and imaginary parts of the relative complex permittivity of the dielectric of interest). Moisture content measurements for paddy at various moisture content levels are shown. Comparison of measurements made by this technique with those made by conventional transmission measurement technique yielded a compensation scheme for error reduction. This sensor is useful for controlling the quality of paddy dried in a continuous microwave drying system process.
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PCA-based informative SNP selection for analyzing population structure
(2016-12-19)
Limpiti, Tulaya
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Intarapanich, Apichart
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Tongsima, Sissades
Phenotypic differences among individuals of the same species are the result of a set of genetic variations which can be observed in the DNA sequence. To conduct a population genetic study, a high throughput genotyping platform such as Single Nucleotide Polymorphism (SNP) array is popularly used to obtain a large set of SNPs for each individual. However, analyzing today's genotypic data can be computationally expensive due to its large size and complexity. Faulty substructure may also be detected if the data is noisy from redundant or non-informative SNPs. Considerable efforts have been done to extract a smaller informative SNP subset that still represents the same intrinsic structure of populations within a data set as the full panel of SNPs. This work describes a foundation of a PCA-based informative marker selection technique. The proposed technique is simple and efficient. It improves upon another spectral analysis technique called PCA-correlated SNPs. A new informativeness score based on a basis function expansion of the SNP variation patterns across individuals is introduced. Such score is computed for each SNP to select a subset of SNPs with the best scores. Using a bovine data set, we demonstrate that our technique is superior to the PCAcorrelated SNPs method, which requires accurate rank estimation to perform well. In contrast, our method is robust to the assumed rank of the data. High data representation accuracy is also achieved after a significant reduction of the number of SNPs while retaining information about the underlying population structure from the original data.
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Study of large and highly stratified population datasets by combining iterative pruning principal component analysis and structure
(2011-06-23)
Limpiti, Tulaya
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Intarapanich, Apichart
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Assawamakin, Anunchai
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Shaw, Philip J.
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Wangkumhang, Pongsakorn
Background: The ever increasing sizes of population genetic datasets pose great challenges for population structure analysis. The Tracy-Widom (TW) statistical test is widely used for detecting structure. However, it has not been adequately investigated whether the TW statistic is susceptible to type I error, especially in large, complex datasets. Non-parametric, Principal Component Analysis (PCA) based methods for resolving structure have been developed which rely on the TW test. Although PCA-based methods can resolve structure, they cannot infer ancestry. Model-based methods are still needed for ancestry analysis, but they are not suitable for large datasets. We propose a new structure analysis framework for large datasets. This includes a new heuristic for detecting structure and incorporation of the structure patterns inferred by a PCA method to complement STRUCTURE analysis.Results: A new heuristic called EigenDev for detecting population structure is presented. When tested on simulated data, this heuristic is robust to sample size. In contrast, the TW statistic was found to be susceptible to type I error, especially for large population samples. EigenDev is thus better-suited for analysis of large datasets containing many individuals, in which spurious patterns are likely to exist and could be incorrectly interpreted as population stratification. EigenDev was applied to the iterative pruning PCA (ipPCA) method, which resolves the underlying subpopulations. This subpopulation information was used to supervise STRUCTURE analysis to infer patterns of ancestry at an unprecedented level of resolution. To validate the new approach, a bovine and a large human genetic dataset (3945 individuals) were analyzed. We found new ancestry patterns consistent with the subpopulations resolved by ipPCA.Conclusions: The EigenDev heuristic is robust to sampling and is thus superior for detecting structure in large datasets. The application of EigenDev to the ipPCA algorithm improves the estimation of the number of subpopulations and the individual assignment accuracy, especially for very large and complex datasets. Furthermore, we have demonstrated that the structure resolved by this approach complements parametric analysis, allowing a much more comprehensive account of population structure. The new version of the ipPCA software with EigenDev incorporated can be downloaded from http://www4a.biotec.or.th/GI/tools/ippca. © 2011 Limpiti et al; licensee BioMed Central Ltd.
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Iterative Neighbor-Joining tree clustering algorithm for genotypic data
(2012-01-01)
Amornbunchornvej, C.
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Limpiti, T.
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Assawamakin, A.
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Intarapanich, A.
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Tongsima, S.
Issues to explore in genotypic datasets include the number and characteristic patterns of subpopulations and, possibly, relationships among them. Model-based clustering methods have been adopted to find a number of clusters and the individual assignments. However, they cannot infer genetic relationships among subpopulations the way phylogenetic trees, e.g., the widely-used Neighbor-Joining (NJ) tree, can. In this paper we propose an unsupervised, iterative clustering framework called iNJclust. It performs clustering on an NJ tree with a graph-based partitioning technique. The iterative process enhances the zooming ability and corrects the topology of the final NJ trees. Inference on genetic similarities between subpopulations is also possible. As final outputs, the iNJclust algorithm provides an estimate of the number of clusters, individual assignments, a population tree, as well as sub-trees of the terminal nodes. We illustrate the superior clustering performance of the proposed algorithm using Human 27 populations, bovine 47 breeds, and sheep 28 breeds datasets. © 2012 ICPR Org Committee.
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A short-term rain-induced attenuation model for satellite link quality prediction
(2014-01-01)
Poolsawut, Jirutchaya
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Limpiti, Tulaya
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Puttarak, Nattakan
This paper proposes a novel but simple satellite link quality prediction based on the relationship between rainfall rate and satellite signal attenuation. After quantifying their relationship statistically, we describe their contemporaneous variations using a linear dynamical system response model. The expectation-maximization (EM) algorithm is used to obtain the maximum likelihood estimates of the model parameters. Subsequently, the communication link quality is predicted by thresholding the estimated short-term signal attenuation level obtained from the Expectation step of the EM algorithm. The prediction performance of the proposed method is compared against the measured beacon signals from Thaicom-4 (IPSTAR) satellite during rainfalls. Using solely the observed rainfall rates from a tipping bucket rain gauge, the algorithm is able to decently predict the satellite link quality. We believe the proposed model can be modified as a predictive tool for real-time link unavailability monitoring of satellite communication system. © 2014 IEEE.

Limpiti, Tulaya

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