Woraratpanya, Kuntpong

Preferred name

Alternative Name

Weraratpanya, Kuntpong

Main Affiliation

School of Information Technology

kuntpong.wo@kmitl.ac.th

Now showing 1 - 10 of 55

Some of the metrics are blocked by your
consent settings
Distributed compressed video sensing with a pre-learned consensus convolutional dictionary
(2026-02-27)
Muta, Ibuki
;
Weraratpanya, Kuntpong
;
Kuroki, Yoshimitsu
Distributed Compressed Video Sensing (DCVS) is a video coding framework well suited to low-power, low-complexity encoding environments. In conventional DCVS with Convolutional Sparse Representation (CSR), convolutional dictionary filters are learned from a key frame in each group of pictures (GOP), and the remaining non-key frames are reconstructed by solving a Convolutional Sparse Coding (CSC) problem using that dictionary. In this work, we investigate a CSR-based DCVS framework that instead employs a pre-learned convolutional dictionary trained offline on multiple video datasets via a consensus-based dictionary learning framework. Using this fixed dictionary, every frame in a sequence is reconstructed independently as if it were a key frame, i.e., without referencing other frames in the same sequence. We evaluate the proposed pre-learned-dictionary DCVS on the Foreman, Akiyo, and Coastguard sequences under two configurations that differ in the choice of data-fidelity term (L1 or L2) with symmetric boundary handling. Experimental results show that all test sequences can be successfully reconstructed using the pre-learned dictionary, indicating that sequence-specific key-frame-based dictionary learning at the decoder is not necessary. Moreover, the L1 data-fidelity term consistently yields better reconstruction quality than the L2 term in terms of PSNR and SSIM.
Some of the metrics are blocked by your
consent settings
A hybrid of fractal code descriptor and harmonic pattern generator for improving speech recognition of different sampling rates
(2018-01-01)
Hokking, Rattaphon
;
Woraratpanya, Kuntpong
Currently, the different sampling rate for speech recognition is a grand challenge due to supporting applications of divergent platform devices, such as mobile device interaction, interactive voice response system, voice search, voice dictation and voice identification. Furthermore, such applications require efficient speech features to represent input signals. However, the different sampling rates of speech signals lead to the different features. This phenomenon comes from speech harmonic signal lost. It becomes a key factor that decreases the speech recognition rate. Therefore, this paper proposes a hybrid of fractal code descriptor and harmonic pattern generator to convert all different sampling rate signals to standardized signals. In this method, an independent resolution property of fractal code descriptor is applied to training and testing speech signals. Then, the pitches of such signals are used to recover harmonic pattern of lost signals. This method can effectively reconstruct speech signals at any sampling rates. When its performance is evaluated with AN4 corpus of CMU Sphinx speech recognition engine, the experimental results show that the proposed method can significantly improve the speech recognition rate, even if the sampling rate of testing speeches differs from that of training speeches.
Some of the metrics are blocked by your
consent settings
Integrating Recurrent Neural Networks and Deep Q-Networks for Precision Irrigation Control
(2025-01-01)
Jogo, Fransiksus Serfian
;
Wahyunggoro, Oyas
;
Mustika, I. Wayan
;
Woraratpanya, Kuntpong
Agricultural irrigation accounts for a substantial portion of global water consumption, necessitating intelligent irrigation strategies. Conventional systems, such as fixed irrigation or evapotranspiration (ET)-based control, often exhibit inefficiencies due to reliance on static measurements and empirical farmer knowledge, lacking adaptive decision-making capabilities. To address this challenge, this paper presents an IoT-based framework integrating Recurrent Neural Networks (RNNs) for weather prediction and Deep Q-Networks (DQNs) for adaptive irrigation decisions. The RNN achieves high-precision forecasts for solar radiation, rainfall, and reference evapotranspiration (ETo) with determination coefficients (R<sup>2</sup>) of 0.819,0.915, and 0.892, respectively, surpassing the other two neural network models in our tests. Simultaneously, the DQN agent learns irrigation policies that reduce water consumption by 16.6-25.3% compared to conventional methods while maintaining post-irrigation soil moisture above the critical threshold (V<inf>mad</inf>=0.50) in testbed experiments. The proposed RNN-DQN architecture demonstrates significant improvements in water-use efficiency and plant health maintenance, offering a robust solution for smart agriculture in water-limited, data-scarce arid regions.
Some of the metrics are blocked by your
consent settings
Exploring LSTM and CNN Architectures for Sign Language Translation
(2023-01-01)
Boondamnoen, Mongkol
;
Thongsri, Kamolwich
;
Sahabantoegnsin, Thanapat
;
Woraratpanya, Kuntpong
Our study explores the application of deep learning models, specifically LSTM (Long Short-Term Memory) and CNN (Convolutional Neural Network), in the realm of sign language translation to address communication barriers faced by individuals with hearing disabilities. Using a dedicated dataset comprising ten frequently used American Sign Language words, we rigorously compare the performance of LSTM and CNN models, measuring precision and recall metrics. The LSTM model achieves a perfect accuracy score of 1, while the CNN model demonstrates a commendable accuracy of 0.9826. These results highlight the potential of these deep learning architectures to facilitate more inclusive and accessible communication avenues in sign language, bridging the communication divide.
Some of the metrics are blocked by your
consent settings
Modified differential box-counting method using weighted triangle-box partition
(2015-01-01)
Nunsong, Walairach
;
Woraratpanya, Kuntpong
Differential box-counting (DBC) is one of the commonly used methods to estimate fractal dimension (FD) for gray scale images. It has been successfully applied in many applications such as image segmentation, pattern recognition, texture analysis and medical signal analysis. However, the accuracy improvement of FD estimation is still a grand challenge. This paper proposes a modified differential box-counting method using weighted triangle-box partition (MDBC) to reduce the estimation error caused by an undercounting problem. The proposed method is derived from two assumptions: (i) increasing the precision of box-counts by using unequally triangle box partition, and (ii) weighting the box-counts in proportion to the size of triangle-box partition. Based on these assumptions, on each grid a square box is divided into four asymmetric triangle-box patterns. Each pattern is calculated the box-counts by a weighted box-counting technique. The maximum number of box-counts represents the better estimation. In this way, the experimental results show that MDBC outperforms the baseline methods in terms of fitting error. Furthermore, the proposed method applies to finger-knuckle-print recognition in order to test its efficiency. The results illustrate that it significantly enhances the recognition rate when compared with the conventional differential box-counting (DBC) and improved triangle box-counting in combination with DBC (ITBC-DBC) methods.
Some of the metrics are blocked by your
consent settings
Evaluation of deep learning algorithms for semantic segmentation of car parts
(2022-10-01)
Pasupa, Kitsuchart
;
Kittiworapanya, Phongsathorn
;
Hongngern, Napasin
;
Woraratpanya, Kuntpong
Evaluation of car damages from an accident is one of the most important processes in the car insurance business. Currently, it still needs a manual examination of every basic part. It is expected that a smart device will be able to do this evaluation more efficiently in the future. In this study, we evaluated and compared five deep learning algorithms for semantic segmentation of car parts. The baseline reference algorithm was Mask R-CNN, and the other algorithms were HTC, CBNet, PANet, and GCNet. Runs of instance segmentation were conducted with those five algorithms. HTC with ResNet-50 was the best algorithm for instance segmentation on various kinds of cars such as sedans, trucks, and SUVs. It achieved a mean average precision at 55.2 on our original data set, that assigned different labels to the left and right sides and 59.1 when a single label was assigned to both sides. In addition, the models from every algorithm were tested for robustness, by running them on images of parts, in a real environment with various weather conditions, including snow, frost, fog and various lighting conditions. GCNet was the most robust; it achieved a mean performance under corruption, mPC = 35.2, and a relative degradation of performance on corrupted data, compared to clean data (rPC), of 64.4%, when left and right sides were assigned different labels, and mPC = 38.1 and rPC = 69.6 % when left- and right-side parts were considered the same part. The findings from this study may directly benefit developers of automated car damage evaluation system in their quest for the best design.
Some of the metrics are blocked by your
consent settings
3DVAE-LSTM for Extremely Rare Anomaly Signal Generation
(2022-01-01)
Kaewkiriya, Thongchai
;
Woraratpanya, Kuntpong
To overcome the uncontrolled quality output problem of data augmentation, many data generation frameworks have been proposed recently. The main concept of the data generation is for ensuring the quality of the output samples which maintain the original characteristics and highly provide the diversity of data. The benefit of this concept is improving the performance of deep learning tasks that suffer from the lack of available training samples, such as anomaly classification. Recently, 3D variational autoencoder for extremely rare case signal generation (3DVAE-ERSG) was introduced. This framework achieves the best synthesis samples for multi-class classification deep learning training. However, it is not so well applicable to sequential data. Therefore, this paper proposed a 3DVAE-LSTM framework. The new framework was replaced a VAE's feed-forward neural network with a long short-term memory (LSTM) neural network that works well with time-series signals. The experimental results show that the classification models trained with data generated by 3DVAE-LSTM have better performance than 3DVAE-ERSG in every aspect.
Some of the metrics are blocked by your
consent settings
Improved triangle box-counting method for fractal dimension estimation
(2015-01-01)
Kaewaramsri, Yothin
;
Woraratpanya, Kuntpong
A fractal dimension (FD) is an effective feature, which characterizes roughness and self-similarity of complex objects. However, the FD in nature scene requires the effective method for estimation. The existing methods focus on the improvement of selecting the suitable height of box-counts. This cannot overcome the overcounting problem, which is a key factor to have an impact on the accuracy of the FD estimation. This paper proposes a more accurate FD estimation, an improved triangle box-counting method, to increase the precision of box-counts associated with box sizes. The triangle-box-partition technique provides the double precision for box-counts, thus it can solve the overcounting issue and enhance the accuracy of the FD estimation. The proposed method is evaluated its performance in terms of fitting error. The experimental results show that the proposed method outperforms the existing methods, including differential box-counting (DBC), improved DBC (IDBC), and box-counting with adaptable box height (ADBC) methods.
Some of the metrics are blocked by your
consent settings
A data generation framework for extremely rare case signals
(2021-08-01)
Chalongvorachai, Thasorn
;
Woraratpanya, Kuntpong
Unlike data augmentation, data generation for extremely rare cases is an approach that can spawn a significant number of high-quality samples based on very few original data. This could be useful in anomaly detection and classification tasks that have the limitation of publicly available datasets for research purposes. Though some other approaches have attempted to solve this problem, such as data augmentation techniques, there was nothing to ensure the characteristics of synthesized samples. Previously, we initiated a framework, called Data Augmentation and Generation for Anomalous Time-series Signals (DAGAT), that was in cooperation with important components: Data Augmentation, Variational Autoencoder (VAE), Data Picker (DP), Signal Fragment Assembler (SFA), and Quality Classifier (QC). And then, an upgraded framework, called An Advanced Data Generation for Anomalous Signals (ADGAS), was introduced to eliminate the limitations of DAGAT; those are uncontrollable outputs and the possibility of bad data included in a training set. By reforming DAGAT architecture, ADGAS achieves a better outcome of generated samples. Nonetheless, ADGAS could be improved through better SFA, DP, and QC. Hence, this paper proposed a Data Generation Framework for Extremely Rare Case Signals. The proposed framework is achievable in generating reliable data for various objectives. We challenged this framework by using the 1D-CNN to serve as the performance evaluator in multi-class anomalous classifications and using the water treatment and water distribution testbed (SWaT and WADI) as the real-world anomaly datasets. The result shows that it surpasses other baseline methods of anomaly data augmentation and data generation techniques.
Some of the metrics are blocked by your
consent settings
Enhancement of Anime Imaging Enlargement using Modified Super-Resolution CNN
(2021-01-01)
Intaniyom, Tanakit
;
Thananporn, Warinthorn
;
Woraratpanya, Kuntpong
Anime is a storytelling medium similar to movies and books. Anime images are a kind of artworks, which are almost entirely drawn by hand. Hence, reproducing existing Anime with larger sizes and higher quality images is expensive. Therefore, we proposed a model based on convolutional neural networks to extract outstanding features of images, enlarge those images, and enhance the quality of Anime images. We trained the model with a training set of 160 images and a validation set of 20 images. We tested the trained model with a testing set of 20 images. The experimental results indicated that our model successfully enhanced the image quality with a larger image-size when compared with the common existing image enlargement and the original SRCNN method.

Woraratpanya, Kuntpong

Filters

Settings

Sort By

Results per page