Sooraksa, Pitikhate
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Sooraksa, Pitikhate
Alternative Name
Sooraksa, P.
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pitikhate.so@kmitl.ac.th
63 results
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Item type:Publication, Naphtha's price forecasting using neuro-fuzzy system(2008-12-01); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Combined chaotic attractor mobile robots(2006-12-01) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Deep Generative Model-based RSSI Synthesis for Indoor Localization(2022-01-01); 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 yourconsent settings
Item type:Publication, Location fingerprint technique using Fuzzy C-Means clustering algorithm for indoor localization(2011-12-01); ; Takada, Jun IchiThe 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 yourconsent settings
Item type:Publication, Design of a Cyber-Physical Demonstration Using STEAM: Superconducting Chaotic Robots(2018-08-21) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Improvement of state estimation for systems with chaotic noise(2008-12-01); Jandaeng, PrakobTo estimate the state of the system, one needs the covariance matrices as the inputs. The accuracy of the new prediction of the estimation is based recursively on the previous ones. To search for the optimal solution, researchers try to obtain best closed-state approximation for the covariance inputs using the Kalman filtering technique. Many variations of the technique have been proposed for many years. However, in this chapter, our version presents a new improvement of state estimation of the systems with various chaotic noises. Introducing an updated scaling factor to the covariance matrices is a simple modification yet provides a highly effective way to estimate the state of the system in the presence of chaotic noises. Performance comparison among the original Kalman filter, an adaptive version, and our enhanced one is carried out. Computer simulation shows remarkable improvement of the proposed method for estimation of the state of the systems with chaotic noises. © 2008 Springer Science+Business Media, LLC. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, 3D range-based indoor localization by using only two beacons(2023-05-22); Indoor localization has been active research in the past decade. The lack of a general model and the demand for high accuracy push the research forward. Some researchers have proposed unique variations and combinations to answer the challenges from the technologies to techniques. Some publications considered the low-cost equipment to the simple algorithm or model to the advanced, robust, and sophisticated system. Almost all of them use the two basics technique; range-based and range-free techniques. Each technique has advantages and disadvantages, including how the technique can efficiently handle the indoor multipath propagation effects. Most of the papers published are also considered the same room or single room indoor environment as the indoor localization system measurement campaign. This paper proposes a three-dimensional (3D) indoor localization for multi-story buildings using simple technology and technique and using only two beacons. We utilized the received signal strength indicator (RSSI) from ESP8266 based on the Wireless-Fidelity (Wi-Fi) standard as the localization parameter. We employ the range-based technique of min-max and least-square. We also compare both techniques to observe which one is suitable for the multi-story building implementation. We also emphasized the challenge of using only two beacons as our contribution. Our system performance results show that the mean error accuracy for min-max is 1.93m, while the least-square yields the mean error of 5.48m. The minimum error of min-max and least-square are 0.33m and 0.77m, respectively. The maximum error of least-square can reach more than 10m, while the min-max gives the maximum error of 4.38m. The results prove that RSSI-based min-max can be applied in a particular condition using only two beacons in the multi-story building. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, On comparison of attractors for chaotic mobile robots(2004-12-01); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Chaotic control for the reactor of a continuous microwave biomass carbonization process(2015-04-23); ; - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A discovery of sequential attack patterns of malware in botnets(2010-12-01); Terada, MasatoMore than 90 independent honeypots have observed malware traffic at the Japanese tier-1 backbone. Typical attacks were made by multiple servers, coordinating to send many kinds of malware. T his paper aims to discover some frequent new sequential attack patterns of malware. It is not easy to identify particular patterns logs of one year because the volume of dataset is too large to investigate one by one. To overcome the problem, this paper proposes data mining algorithm, the PrejixSpan method. We implement the PrejixSpan algorithm to analyze the malware footprints and show the experimental result. The result of analysis shows that the attacks are performed by multiple sequential attack patterns within a short amount of time. ©2010 IEEE.