Chitsobhuk, Orachat

Preferred name

Chitsobhuk, Orachat

Main Affiliation

School of Engineering

orachat.ch@kmitl.ac.th

Now showing 1 - 10 of 18

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Storm eye identification using fuzzy inference system
(2016-08-01)
Warunsin, Kulwarun
;
Chitsobhuk, Orachat
In this paper, a study of the novel technique based on Fuzzy Inference System (FIS) for storm eye identification has been presented. The ocean wind vectors are provided by the NASA QuikSCAT satellite to predict the significance of tropical cyclogenesis. This database is slightly noisy, incomplete and indirect. For this reason, the cloud satellite image can be an alternative option. However, the cloud shape may be ambiguous, which can introduce a long search time. As a result, utilizing combined information from both resources can lead to a reduction in resource deficiency. The FIS is used to describe the uncertain behavior of the complex system consisting of several factors. It provides ability to model the dynamic behavior of the storm and designates the best candidate eye position in the region of interest. Then, the spiral cloud model is adopted to enhance the search results in order to achieve the accurate eye position. The experimental results are conducted based on six reference storms. The proposed system offers higher flexibility in analyzing the storm eye position with the minimum average distance error of 92.8 km and approximately 16.25% less average distance error compared to the reference. This demonstrates the significant performance improvement in detecting the eye location of the storm.
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Variance training data in image enhancement
(2019-07-01)
Ngernplubpla, Jaturon
;
Chitsobhuk, Orachat
This paper presents a study of neuro-fuzzy behavior in clustering gradient profile spectral characteristics. Various types of image scene are chosen to evaluate neuro-fuzzy performance. The combinations of training data subsets are learned by ANFIS model to generate gradient profile priors, which are used as optimum weight selection criteria for image enhancement. The experimental results illustrate quantitative performance improvement and perceptual improvement in recovery of the high-resolution details in various images.
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Cross domain sentiment classification of Thai reviews using co-train model
(2019-01-01)
Boonpetch, Warakorn
;
Chitsobhuk, Orachat
Online reviews are significant sources of information, which is useful for supporting customer and entrepreneur decision in terms of product and service satisfaction analysis. Online reviews containing feedback from various domains makes it difficult to analyze and classify all comments at once. The proposed technique analyses the cross-domain Thai review data using a co-train machine learning model. The co-train model consists of multiple single domain specific models followed by refinement analysis for the final sentiment classification. This allows for full flexibility in training of each individual domain, which can lessen the limitation on training complexity due to simple training on single domain. The experiments have been conducted on Wongnai restaurant domain and IMDB movie domain data. Our co-train model can achieve the highest average accuracy of 86.10 percent for cross-domain sentiment classification with approximately 38 seconds processing time.
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Multidestination Indoor Navigation Using Path Planning and WiFi Fingerprint Localization
(2018-09-11)
Chitsobhuk, Orachat
;
Warunsin, Kulwarun
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Udomthanapong, Sornchai
This paper presents an indoor navigation system based on multi-destination path planning and WiFi fingerprint localization. A user is allowed to specify multiple destinations and can detour the route at any time. Path planning will automatically update path using 2-opt and A∗ algorithms. The revised route will be analyzed according to user's current position supplied from the WiFi RSS fingerprint positioning. Naïve Bayes classification is adopted to learn from the RSS fingerprint priors stored in the database. Extensive experiments are conducted and performance comparison is analyzed and demonstrates significant performance improvement and higher noise tolerance with integration of the probabilistic priors. It can be seen that the proposed system enables user experience for indoor navigation service with support for automatic route updating and navigation refinement according to localization.
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Image enhancement based on edge boosting algorithm
(2015-01-01)
Ngernplubpla, Jaturon
;
Chitsobhuk, Orachat
In this paper, a technique for image enhancement based on proposed edge boosting algorithm to reconstruct high quality image from a single low resolution image is described. The difficulty in single-image super-resolution is that the generic image priors resided in the low resolution input image may not be sufficient to generate the effective solutions. In order to achieve a success in super-resolution reconstruction, efficient prior knowledge should be estimated. The statistics of gradient priors in terms of priority map based on separable gradient estimation, maximum likelihood edge estimation, and local variance are introduced. The proposed edge boosting algorithm takes advantages of these gradient statistics to select the appropriate enhancement weights. The larger weights are applied to the higher frequency details while the low frequency details are smoothed. From the experimental results, the significant performance improvement quantitatively and perceptually is illustrated. It can be seen that the proposed edge boosting algorithm demonstrates high quality results with fewer artifacts, sharper edges, superior texture areas, and finer detail with low noise.
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Fast MQ-coder
(2013-07-12)
Noikaew, Nopphol
;
Chitsobhuk, Orachat
The key algorithm in JPEG2000 image compression system is embedded block coding with optimized truncation (EBCOT). The EBCOT scheme consists of a bit-plane coder coupled with a MQ arithmetic coder. Recently, the bit-plane coding can generate more than one symbol per clock cycle. Consequently, the coding speed is limited and bottlenecked at the interface between the output of the bit-plane coding and the input of the MQ arithmetic coder. Moreover, an efficient designed architecture for MQ arithmetic coder should compromise between processing speed and hardware cost. Therefore, a single symbol processor for arithmetic coder architecture implemented on FPGA is proposed in this paper, since it offers high throughput but requires low hardware cost. The proposed architecture is separated into 2 pipelined stages to break down the whole task into smaller subtasks, which leads to great reduction in the critical path. Consequently, it demonstrates no stall, high clock speed and high throughput in the encoding process. These benefits are achieved with the suitable hardware design, pipelining technique, pre-calculation, and prediction process. As a result, the coding speed can be at least 188.99 MHz with the throughput of 188.99 MCxD/S.
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Plane alignment algorithm for torn document reconstruction
(2015-08-17)
Wattanacheep, Bhattarabhorn
;
Chitsobhuk, Orachat
Snippet alignment is a process to arrange pieces of the torn document to the original positions according to the direction of the alphabet line. It is a prerequisite to assure the effective reconstruction. The higher of the performance of the snippet alignment, the greater the opportunity for successful reconstruction. Therefore, this paper presents a plane alignment algorithm for torn document reconstruction. The proposed technique analyzes the contents inside the snippet such as the direction of the character alignment based on the histogram of the accumulated radius of the fitted ellipses. The direction result is then used to revert the snippet to its original position. Hough transform based local descriptor is extracted as shape feature. These parameters are helpful for accurate reconstruction. The proposed technique can achieve approximately 5.07 decrease in relative orientation error thus increase 24.11 percent in reverting precision. This can demonstrate the significant performance improvement of the proposed algorithm.
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Vehicle tracking using fuzzy-based vehicle detection window with adaptive parameters
(2018-01-01)
Chitsobhuk, Orachat
;
Kasemsiri, Watjanapong
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Glomglome, Sorayut
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Lapamonpinyo, Pipatphon
In this paper, fuzzy-based vehicle tracking system is proposed. The proposed system consists of two main processes: vehicle detection and vehicle tracking. In the first process, the Gradient-based Adaptive Threshold Estimation (GATE) algorithm is adopted to provide the suitable threshold value for the sobel edge detection. The estimated threshold can be adapted to the changes of diverse illumination conditions throughout the day. This leads to greater vehicle detection performance compared to a fixed user's defined threshold. In the second process, this paper proposes the novel vehicle tracking algorithms namely Fuzzy-based Vehicle Analysis (FBA) in order to reduce the false estimation of the vehicle tracking caused by uneven edges of the large vehicles and vehicle changing lanes. The proposed FBA algorithm employs the average edge density and the Horizontal Moving Edge Detection (HMED) algorithm to alleviate those problems by adopting fuzzy rule-based algorithms to rectify the vehicle tracking. The experimental results demonstrate that the proposed system provides the high accuracy of vehicle detection about 98.22%. In addition, it also offers the low false detection rates about 3.92%.
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Neuro-fuzzy profile clustering in image enhancement
(2019-03-01)
Ngernplubpla, Jaturon
;
Chitsobhuk, Orachat
This paper proposes a technique for clustering features into profile groups to obtain optimum enhancement weights for reconstructing high resolution images. Neuro-fuzzy model, which combines the fuzzy reasoning behavior with the adaptive learning capability and connectionist structure of neural networks, is adopted to analyze and learn with gradient data and statistics and to generate gradient profile priors. In enhancement process, the optimum weights are appropriately chosen according to the gradient profile priors. From the experimental results, the proposed algorithm demonstrates quantitative performance improvement in classifying data and perceptual improvement in recovery of the high-resolution image.
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Image super resolution with adaptive edge enhancement algorithm
(2014-02-24)
Ngernplubpla, Jaturon
;
Chitsobhuk, Orachat
In this paper, an adaptive edge enhancement algorithm is proposed to reconstruct a super resolution image from a single low resolution one. In order to improve the results of the high resolution reconstruction, edge statistics is learned from the scenes using a statistical analysis of the maximum likelihood estimation to approximate edge boosting weight that helps to significantly enhance edge information in the high frequency area. The edge sketch image will be adaptively combined with the results of wiener filter according to the values of the local variance. The experimental results on several test images show the success in reconstructing the super resolution both quantitatively and perceptually. © 2014 Copyright SPIE.

Chitsobhuk, Orachat

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