Sooraksa, Pitikhate

Preferred name

Sooraksa, Pitikhate

Alternative Name

Sooraksa, P.

Main Affiliation

School of Engineering

pitikhate.so@kmitl.ac.th

Now showing 1 - 10 of 98

Some of the metrics are blocked by your
consent settings
Micro Defect Detection on Air-Bearing Surface
(2015-03-27)
Kunakornvong, Pichate
;
Sooraksa, Pitikhate
This chapter presents texture analysis methods for detection of contaminations (micro particle, stain and metal) on the air-bearing surface (ABS). A complete system is developed composed of an image acquisition module, a feature extraction module and a decision-making module. The input ABS image is first analyzed by the texture unit and the co-occurrence matrix to obtain texture features which are then transformed by the principle component analysis (PCA) for effective classification of the defective samples. The chapter detects the contamination particle based on the theory of light scattering technologies: laser, detector and optic. Most light scattering techniques are used for counting particles. J. L. Blesener studied the non-imaging laser particle counter (LPC) for detection of a single particle. B. Bhushan utilized LPC instruments and sampling techniques for detecting and determining the size of particle contamination in rigid disk drives. S. Kochevar proposed the next generation of contamination monitoring using nanotechnology.
Some of the metrics are blocked by your
consent settings
Chen System as a Controlled Weather Model - Physical Principle, Engineering Design and Real Applications
(2018-04-01)
Sooraksa, Pitikhate
;
Chen, Guanrong
This paper presents the Chen system as a controlled weather model. Mathematically, the Chen system is dual to the Lorenz system via time reversal. Physically, the Chen system can be viewed as a controlled weather model from the anti-control perspective. This paper illustrates the physical principle of this controlled weather model, and develops an engineering design of the model for real indoor climate (temperature-humidity) regulation, with a perspective on outdoor weather control application.
Some of the metrics are blocked by your
consent settings
Physics-informed Platform for Flight Dynamics Simulation
(2025-01-01)
Atayagul, Pattiwat
;
Sooraksa, Pitikhate
In this study, we present the state-of-the-art development of the flight dynamics model of a rigid body from the theoretical aspect of flight dynamics, design the software architecture, and validate the proposed architecture by comparing the simulation results with the check-cases for the verification of six-degree-of-freedom flight vehicle simulations document issued by the National Aeronautics and Space Administration (NASA). We also design the computational workflow between the ordinary differential equations of the system and other axillary components by weaving those relations in the object-oriented programing style and powered with Python scientific libraries. The simulation outcomes in all cases are well matched with the majority of NASA baseline datasets under the same flight simulation conditions, which reflect the accuracy of the model with considerable confidence.
Some of the metrics are blocked by your
consent settings
Naphtha's price forecasting using neuro-fuzzy system
(2008-12-01)
Visetsripong, Porntip
;
Sooraksa, Pitikhate
;
Luenam, Pramote
;
Chaimongkol, Watchareeporn
Naphtha's price forecasting using Neuro-fuzzy system is a forecasting technique that applied information technology with statistics. 1950 daily prices were collected as a time-series data with trend component. The research found that Neuro-Fuzzy system is more accurate and more reliable than a statistical method; it also works well with continuous data and performs better with more training data. Neuro-Fuzzy system might be used with different data type, but, it might come across with other factors, e.g. seasonal or irregular event. The research also illustrates the multidisciplinary nature in today's world of works in the era of merging among many disciplines. © 2008 SICE.
Some of the metrics are blocked by your
consent settings
Combined chaotic attractor mobile robots
(2006-12-01)
Chanvech, Channawat
;
Klomkarn, Kitdakorn
;
Sooraksa, Pitikhate
The application of chaos in engineering field has been attracted much attention due to many potential applications include fan heaters, air-conditions, and dish washing machines. Even though the applied chaos in robot guidance is also not new, the problem of navigation of autonomous mobile robot in a totally unknown environment can be accomplished by using chaotic function. For implementation, most of researchers use CPUs with high performance to generate the chaotic trajectory. Unlike the others, in this paper, we use a chaotic circuit, which is low cost and easy to construct for trajectory generators. The Chua's circuit and a complex butterfly attractor are combined together providing pattern diversity and area coverage. A lab-scale two-wheel mobile robot is implemented and tested in our laboratory. The experimental results confirm that the proposed scheme of combined chaotic attractors is effective for path guiding for chaotic mobile robots. © 2006 ICASE.
Some of the metrics are blocked by your
consent settings
Deep Generative Model-based RSSI Synthesis for Indoor Localization
(2022-01-01)
Suroso, Dwi Joko
;
Cherntanomwong, Panarat
;
Sooraksa, Pitikhate
Indoor localization via deep learning (DL) is attracting researchers' attention. DL is mainly used for fingerprinting-based indoor localization as it generally employs a vast offline database to ensure its reliability. However, the long effort and high cost of constructing this database are the disadvantages of this technique. This paper implements variational autoencoders (VAE), one of the popular deep generative models, to alleviate the drawbacks of offline database issues. Our proposal works using the received signal strength indicator (RSSI); unfortunately, it is known for its fluctuation and instability. Thus, instead of using RSSI directly as a localization parameter, we learn its distribution via VAE to generate the synthetic RSSI values. We utilized the RSSI from an actual measurement campaign. The VAE implementation results show that we can obtain the RSSI synthesis by exploring the latent distribution learned from the input distribution. Thus, the offline database density grids can be enhanced. We validated the results by varying epochs to map the learned latent distribution. However, we still have relatively low accuracy in the synthetic RSSI values, especially when applying a small number of epochs, i.e., 10 and 100. When we applied epoch number 1000, the error was relatively low (-3dBm average error) in the sampled position. Our preliminary assumption is that the dataset is small for VAE learning, and probably the 3-by-3 RSSI-to-image size assumption could still be inadequate.
Some of the metrics are blocked by your
consent settings
A practical low-cost machine vision sensor system for defect classification on air bearing surfaces
(2017-01-01)
Kunakornvong, Pichate
;
Sooraksa, Pitikhate
In this paper, we present a newly adapted machine vision method and a practical low-cost machine vision sensor for defect classification of the air bearing surfaces (ABSs) of a hard disk drive, which controls the flying height of the recording heads moving above a disk in operation. A defective ABS can cause poor reading and writing performance; hence, it is necessary to verify its integrity before assembling it into the final product. The proposed sensor system was designed and implemented to detect defects by an effective combination of image segmentation and block matrix techniques as well as classifying them using an expert system under dark- and bright-field conditions. Our system processes subregions of interest and sub-blocks in parallel so that they can take advantage of multiple processor cores. From the trial runs, the small fractional error and low average processing time suggested that our proposed system is effective and can be used in an industrial assembly line.
Some of the metrics are blocked by your
consent settings
Location fingerprint technique using Fuzzy C-Means clustering algorithm for indoor localization
(2011-12-01)
Suroso, Dwi Joko
;
Cherntanomwong, Panarat
;
Sooraksa, Pitikhate
;
Takada, Jun Ichi
The recent researches in localization technique have been supported by the emerging of wireless sensor network (WSN) technology. The issues of power and time consumption have become the main research topics in WSN-based localization technique. ZigBee as IEEE 802.15.4 is commonly used as supporting device because of its advantages for low-power, small and smart sensor nodes. This paper proposes the new technique in radio frequency (RF) fingerprint technique-based localization using Fuzzy C-Means (FCM) clustering algorithm. This technique provides an efficient localization system that gives benefit in the time-efficient and low power consumption. In this paper, received signal strength indicator (RSSI) is used as the fingerprint information which indicates the location of sensor nodes. The different amount of the reference nodes is applied. The effectiveness of this method is verified by an indoor experiment. The estimated location results from different sets of reference nodes are compared. The time consumption in experiment is compared with those using the common fingerprint technique. © 2011 IEEE.
Some of the metrics are blocked by your
consent settings
Design of a Cyber-Physical Demonstration Using STEAM: Superconducting Chaotic Robots
(2018-08-21)
Tangsuknirundorn, Pirapat
;
Sooraksa, Paramat
;
Sooraksa, Pitikhate
In this paper, STEAM approach to create superconducting chaotic robots as a cyber-physical system is demonstrated. In science and technology viewpoints (ST), Meissner's effect is a fascinating phenomenon leading students to be motivated and inspired to learn more about this macroscopic quantum effect. In engineering and artistic design (EA), we utilize augmented reality to create a virtual octopus over a real environment. The virtual octopus is appeared whenever a superconducting phenomenon is detected. In other words, once a Meissner's effect is identified by a computer program on smartphones, then the octopus will chaotically floating over the superconductor disk in the real space. The octopus's trajectory can be mathematically (M) generated by any chaotic equation. Subjectively, the proposed CPS demonstration was impressed by computer engineering students in our classes.
Some of the metrics are blocked by your
consent settings
A deep neural network-correlation phase sensitive mask based estimation to improve speech intelligibility
(2023-09-01)
Sivapatham, Shoba
;
Kar, Asutosh
;
Bodile, Roshan
;
Mladenovic, Vladimir
;
Sooraksa, Pitikhate
General masking-based speech enhancement using a deep learning architecture (DNN) approach focuses on the spectral values of the speech in order to show improvement in intelligibility. But, the residual noise present in the phase spectrum and inter-channel correlation dependency between noise and noisy speech can impact the results of intelligibility in speech enhancement. This research work proposes a correlation phase-sensitive novel mask which contains phase, magnitude spectral and inter-channel correlation for a tangible improvement in speech intelligibility. The correlation parameter finds the dependency between the signals and phase spectrum factor eliminates the residual noise. In addition, selecting the prior features from the feature combination also helps in reducing the dimensionality and increases the accuracy of the enhanced speech. This work also aims to decrease the complexity of the DNN by analysing the network with different parameters. The performance of the mask is evaluated with various intelligibility factors. The proposed mask has been compared with different mask estimators. The proposed mask has generated estimated speech with an increase in the intelligibility of 0.02-0.001 over six different noises and four different signal-to-noise (SNR) levels.

Sooraksa, Pitikhate

Filters

Settings

Sort By

Results per page