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AI-Enhanced Driver Fatigue Detection Through Multi-Parameter Analysis: Integration of Radar-Based Heart Rate Monitoring and Deep Learning Techniques
(2025-01-01)
Pairoch, Sathit
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Pavitpok, Siwagorn
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Wanluk, Nutthanan
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Phasukkit, Pattarapong
This research presents an innovative transportation safety system integrating millimeter-wave radar sensing with artificial intelligence. Our approach employs the MR60BHA1 radar sensor mounted behind the driver's seat to non-intrusively monitor vital signs, combined with deep learning algorithms that analyze correlations between heart rate variability, respiratory patterns, body movements, and environmental factors. Trained on Thailand's first comprehensive driver fatigue database, our custom neural network architecture achieved 97.5% detection accuracy, significantly outperforming traditional single parameter monitoring approaches. The research contributes three key innovations: (1) a novel deep learning framework optimized for Thai driving conditions that correlates multiple radar-derived physiological parameters; (2) an adaptive feature extraction algorithm that identifies and weights the most significant fatigue indicators based on individual characteristics and local contexts; and (3) a comprehensive multiparameter database incorporating synchronized measurements calibrated for various Thai driving environments. This culturally adaptive monitoring technology establishes a new paradigm for intelligent transportation systems particularly suited for tropical regions.
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Crack Localization Detection in Monolithic Zirconia Dental Crowns via 1D-Convolutional Neural Networks Algorithm-Based Acoustic Emission Analysis
(2024-01-01)
Tuntiwong, Kuson
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Wangman, Rangsinee
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Sritart, Hiranya
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Kanchanatawewat, Kanchana
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Tungjitkusolmun, Supan
The feasibility of utilizing the acoustic emission (AE) technique for the detection and classification of cracks within monolithic dental crown is assessed in this study, owing to its non-destructive nature which enables passive monitoring of structures. The AE signals captured are subjected to analysis to extract pertinent information regarding the source and location of the cracks. A novel approach is proposed, employing deep learning 1D convolutional neural networks (1D-CNNs) for the recognition and classification of the recorded cracked signals. The AE signals, obtained through a handmade AE data acquisition unit, are converted into .csv format and subjected to denoising using Bayesian methods to eliminate background noise. The signals are collected through the breakage of pencil lead (Vallen systeme) Hsu-Nielsen-Source 0.5 (ASTM E976) applied to each surface of the dental crown. Subsequently, the data signals are divided into training and testing groups following an 85 / 15 split. The performance of the deep learning 1D-CNNs is evaluated based on Precision, Recall and total accuracy metrics. The applicated of automated deep learning in this study demonstrated significantly high overall accuracy (98.67%). The integration of handmade data acquisition with 1D-CNN crack detection proves to be an effective method for early screening. The novel method harnesses acoustic emission signals in 1D-CNNs, thereby enhancing the accuracy of clinical dental restorative crack identification and determining the onset time.
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3D Kinect Camera Scheme with Time-Series Deep-Learning Algorithms for Classification and Prediction of Lung Tumor Motility
(2022-04-01)
Puangragsa, Utumporn
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Setakornnukul, Jiraporn
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Dankulchai, Pittaya
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Phasukkit, Pattarapong
This paper proposes a time-series deep-learning 3D Kinect camera scheme to classify the respiratory phases with a lung tumor and predict the lung tumor displacement. Specifically, the proposed scheme is driven by two time-series deep-learning algorithmic models: The respiratory-phase classification model and the regression-based prediction model. To assess the performance of the proposed scheme, the classification and prediction models were tested with four categories of datasets: Patient-based datasets with regular and irregular breathing patterns; and pseudopatientbased datasets with regular and irregular breathing patterns. In this study, 'pseudopatients' refer to a dynamic thorax phantom with a lung tumor programmed with varying breathing patterns and breaths per minute. The total accuracy of the respiratory-phase classification model was 100%, 100%, 100%, and 92.44% for the four dataset categories, with a corresponding mean squared error (MSE), mean absolute error (MAE), and coefficient of determination (R<sup>2</sup>) of 1.2-1.6%, 0.65-0.8%, and 0.97-0.98, respectively. The results demonstrate that the time-series deep-learning classification and regression-based prediction models can classify the respiratory phases and predict the lung tumor displacement with high accuracy. Essentially, the novelty of this research lies in the use of a low-cost 3D Kinect camera with time-series deep-learning algorithms in the medical field to efficiently classify the respiratory phase and predict the lung tumor displacement.
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Non-Ionic Deep Learning-Driven IR-UWB Multiantenna Scheme for Breast Tumor Localization
(2022-01-01)
Phasukkit, Pattarapong
This research proposes a deep learning-driven impulse radio ultra-wideband (IR-UWB) multiantenna scheme for non-ionic breast tumor localization. The structure of the multiantenna scheme consists of one side slot Vivaldi transmitting (Tx) and nine side slot Vivaldi receiving (Rx1-Rx9) antennas. To mitigate the attenuation and improve the diagnostic accuracy, the multiantenna scheme is rotated clockwise in 90° increments around the breast, with the angular position of the Tx antenna of 0°, 90°, 180°, and 270°. The deep learning algorithm is utilized to detect and localize the breast tumor, with 17 classification outputs, consisting of classifications 1-16 which correspond to 16 vertically discretized segments of the breast and classification 17 for cancer-free. Experiments were carried out using heterogenous breast replicas with a tumor of 1 cm in diameter, and the breast replicas possess the dielectric property and Hounsfield units (HU) similar to those of human breasts. The experimental results were compared with the computed tomography (CT) scan images. The results reveal that the multiantenna scheme could efficiently detect and accurately localize the breast tumor for nearly all classifications, with the total accuracy (average of F1 scores) of 99.11 %. Specifically, the novelty of this research lies in the use of deep learning with the IR-UWB technology to effectively localize breast tumors.
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Coronavirus infected lung CT scan image segmentation using Deep Learning
(2021-05-19)
Asipong, Kanatat
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Gabbualoy, Somprasonk
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Phasukkit, Pattarapong
This research presents an application of lung segmentation of an internal organ from computed tomography scan images using an artificial intelligence development approach. The deep convolutional neural network technique was used to perform semantic segmentation of an internal organ by training our framework through different computed tomography scan image slices with a medical dataset that has abnormal physiology. Coronavirus disease infected on the lung datasets were used as a study case in this research with adjusted deep fully convolutional neural network compared with an architecture of U-Net model that was used to implement the experimental process. However, the this research will aim to develop a deep learning model as an image processing to use with medical image data to reduce the time in a part of treatment planning. The result of this experiment has shown that an adjusted model of 1 layer U-Net shape can achieve a lung segmentation with 92.46% accuracy and less model training time compared to the original U-Net up to 84.76%.
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Deep learning-based acoustic emission scheme for nondestructive localization of cracks in train rails under a load
(2021-01-01)
Suwansin, Wara
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Phasukkit, Pattarapong
This research proposes a nondestructive single-sensor acoustic emission (AE) scheme for the detection and localization of cracks in steel rail under loads. In the operation, AE signals were captured by the AE sensor and converted into digital signal data by AE data acquisition module. The digital data were denoised to remove ambient and wheel/rail contact noises, and the denoised data were processed and classified to localize cracks in the steel rail using a deep learning algorithmic model. The AE signals of pencil lead break at the head, web, and foot of steel rail were used to train and test the algorithmic model. In training and testing the algorithm, the AE signals were divided into two groupings (150 and 300 AE signals) and the classification accuracy compared. The deep learning-based AE scheme was also implemented onsite to detect cracks in the steel rail. The total accuracy (average F1 score) under the first and second groupings were 86.6% and 96.6%, and that of the onsite experiment was 77.33%. The novelty of this research lies in the use of a single AE sensor and AE signal-based deep learning algorithm to efficiently detect and localize cracks in the steel rail, unlike existing AE crack-localization technology that relies on two or more sensors and human interpretation.
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Performance Comparison of Deep Learning Approach for Automatic CT Image Segmentation by Using Window Leveling
(2021-01-01)
Apivanichkul, Kamonchat
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Phasukkit, Pattarapong
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Dankulchai, Pittaya
In tumor radiotherapy process, radiologist need to make multipleorgans contouring on medical images such as CT scans for computing appropriate dose and making a suitable treatment plan for patients. This is a necessary step before treatment. This paper was written to be one of automatic image segmentation research by using deep learning. The experiment compared performance between preprocessing input datasets with custom window leveling normalization and following by organ types. We chose the bladder, the rectum and the femur as target organs in this paper. Datasets are directly obtained from Siriraj Hospital that contoured by radiologists. There are 10 datasets of each organs. We used U-Net as main structure to extract features on image then evaluated by dice similarity coefficient (DSC) and intersection over union (IoU). The experiment resulted that training with custom window leveling normalization is better performance. The bladder got DSC and IoU of 78.34% and 70.46%, femur were 39.71% and 28.03%, and rectum were 19.19% and 12.20%, respectively.
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Air turbulence forecasting of airbus type A320 in southeast asia using deep learning method
(2020-11-04)
Laon, Popphon
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Phasukkit, Pattarapong
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Pradabpet, Chusit
This paper is present the Air Turbulence Forecasting of Airbus type A320 in Southeast Asia using the Deep Learning Method. It will collect flight data of aircraft in the region of Southeast Asia (Thailand and Vietnam). In which data was collected for 40 flights to forecast the occurrence of air turbulence in 3 status consist of non-turbulence, the rapid decrease and increase the altitude is out of pilot control. In this research using deep learning with supervise learning and create the mathematical model for air turbulence forecasting which could help to reduce the wastage that may affect to the passenger. The results, using 5 layers of deep learning (1 input layer, 3 hidden layers, and 1 output layer). The most suitable model consists of 9 features such as vertical speed, calibrated altitude, wind speed, wind direction (wind angle), temperature, latitude, longitude, true airspeed, and indicated airspeed. The output layer consists of 3 classes (class1=non-turbulence, class2=increase altitude, and class3=decrease altitude) and optimization the weight with gradient descent. The epoch number is 1500 and the learning rate is 0.1, which will get accuracy 88% for the train set and 86 % for the test set.
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Investigation of deep learning optimizer for water pipe leaking detection
(2019-07-01)
Arunsuriyasak, Peerachai
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Boonme, Phattraporn
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Phasukkit, Pattarapong
Nowadays, Deep learning plays an important role in complex problems. Thus, one of important algorithm part is an optimizer. This paper aims to improve algorithm using optimizers. Adam optimizer, a powerful and effective optimizer, was used to adjust parameters in Deep Neural Networks model. Which, object datasets consist leaking water pipe, non-leaking water pipe are used to classify 2 object labels. Nevertheless, RMSprop and Adadelta are alternative optimizers that can be used in Deep Neural Network. Other than that, this experiment has been shown Adam gave an accuracy at 98.973% for leaking water pipe and 97.466% for non-leaking water pipe. While, Adadelta gave 76.755% and 70.448%. And RMSprop gave 98.973% and 97.466%.

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