• Imaging Science in Dentistry
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• v.51 no.2
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• pp.107-115
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• 2021

Purpose: This study aimed to demonstrate the presence of dental caries through a photoacoustic imaging system with visible and near-infrared wavelengths, highlighting the differences between the 2 spectral regions. The depth at which carious tissue could be detected was also verified. Materials and Methods: Fifteen permanent molars were selected and classified as being sound or having incipient or advanced caries by visual inspection, radiography, and optical coherence tomography analysis prior to photoacoustic scanning. A photoacoustic imaging system operating with a nanosecond pulsed laser as the light excitation source at either 532 nm or 1064 nm and an acoustic transducer at 5 MHz was developed, characterized, and used. En-face and lateral(depth) photoacoustic signals were detected. Results: The results confirmed the potential of the photoacoustic method to detect caries. At both wavelengths, photoacoustic imaging effectively detected incipient and advanced caries. The reconstructed photoacoustic images confirmed that a higher intensity of the photoacoustic signal could be observed in regions with lesions, while sound surfaces showed much less photoacoustic signal. Photoacoustic signals at depths up to 4 mm at both 532 nm and 1064 nm were measured. Conclusion: The results presented here are promising and corroborate that photoacoustic imaging can be applied as a diagnostic tool in caries research. New studies should focus on developing a clinical model of photoacoustic imaging applications in dentistry, including soft tissues. The use of inexpensive light-emitting diodes together with a miniaturized detector will make photoacoustic imaging systems more flexible, user-friendly, and technologically viable.

• Journal of Preventive Medicine and Public Health
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• v.54 no.1
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• pp.17-21
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• 2021

In 2020, the coronavirus disease 2019 (COVID-19) pandemic has caused unprecedented disruptions to global health systems. The Korea has taken full-fledged actions against this novel infectious disease, swiftly implementing a testing-tracing-treatment strategy. New obligations have therefore been given to the Health Insurance Review and Assessment Service (HIRA) to devote the utmost effort towards tackling this global health crisis. Thanks to the universal national health insurance and state-of-the-art information communications technology (ICT) of the Korea, HIRA has conducted far-reaching countermeasures to detect and treat cases early, prevent the spread of COVID-19, respond quickly to surging demand for the healthcare services, and translate evidence into policy. Three main factors have enabled HIRA to undertake pandemic control preemptively and systematically: nationwide data aggregated from all healthcare providers and patients, pre-existing ICT network systems, and real-time data exchanges. HIRA has maximized the use of data and pre-existing network systems to conduct rapid and responsive measures in a centralized way, both of which have been the most critical tactics and strategies used by the Korean healthcare system. In the face of new obligations, our promise is to strive for a more responsive and resilient health system during this prolonged crisis.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.6
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• pp.1053-1065
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• 2020

With the recent development of hardware performance and artificial intelligence technology, sophisticated fake videos that are difficult to distinguish with the human's eye are increasing. Face synthesis technology using artificial intelligence is called Deepfake, and anyone with a little programming skill and deep learning knowledge can produce sophisticated fake videos using Deepfake. A number of indiscriminate fake videos has been increased significantly, which may lead to problems such as privacy violations, fake news and fraud. Therefore, it is necessary to detect fake video clips that cannot be discriminated by a human eyes. Thus, in this paper, we propose a deep-fake detection model applied with Bidirectional Convolution LSTM and Attention Module. Unlike LSTM, which considers only the forward sequential procedure, the model proposed in this paper uses the reverse order procedure. The Attention Module is used with a Convolutional neural network model to use the characteristics of each frame for extraction. Experiments have shown that the model proposed has 93.5% accuracy and AUC is up to 50% higher than the results of pre-existing studies.

• Smart Media Journal
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• v.12 no.7
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• pp.59-67
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• 2023

Recently, research on behavior analysis tracking human posture and movement has been actively conducted. In particular, OpenPose, an open-source software developed by CMU in 2017, is a representative method for estimating human appearance and behavior. OpenPose can detect and estimate various body parts of a person, such as height, face, and hands in real-time, making it applicable to various fields such as smart healthcare, exercise training, security systems, and medical fields. In this paper, we propose a method for classifying four exercise movements - Squat, Walk, Wave, and Fall-down - which are most commonly performed by users in the gym, using OpenPose-based deep learning models, DNN and CNN. The training data is collected by capturing the user's movements through recorded videos and real-time camera captures. The collected dataset undergoes preprocessing using OpenPose. The preprocessed dataset is then used to train the proposed DNN and CNN models for exercise movement classification. The performance errors of the proposed models are evaluated using MSE, RMSE, and MAE. The performance evaluation results showed that the proposed DNN model outperformed the proposed CNN model.

• Convergence Security Journal
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• v.23 no.2
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• pp.85-94
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• 2023

In the face of the serious security situation with the increasing threat from North Korea, Korean Army is pursuing a reduction in troops through the performance improvement project of the GOP science-based border security system, which utilizes advanced technology. In order for the GOP science-based border security system to be an effective alternative to the decrease in military resources due to the population decline, it must guarantee a high detection and identification rate and minimize troop intervention by dramatically improving the false detection rate. Recently introduced in Korean Army, the GOP science-based border security system is known to ensure a relatively high detection and identification rate in good weather conditions, but its performance in harsh weather conditions such as rain and fog is somewhat lacking. As an alternative to overcoming this, a radar-based border security system that can detect objects even in bad weather has been proposed. This paper proves the effectiveness of the AI-based scientific border security system based on radar that is being currently tested at the 00th Division through the 2021 Rapid Acquisition Program, and suggests the direction of development for the GOP scientific border security system.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.2
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• pp.217-228
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• 2024

LiDAR(Light Detection And Ranging) sensors, which play a pivotal role among cameras, RADAR(RAdio Detection And Ranging), and ultrasonic sensors for the safe operation of autonomous vehicles, can recognize and detect objects in 360 degrees. However, since LiDAR sensors use lasers to measure distance, they are vulnerable to attackers and face various security threats. In this paper, we examine several security threats against LiDAR sensors: relay, spoofing, and replay attacks, analyze the possibility and impact of physical jamming attacks, and analyze the risk these attacks pose to the reliability of autonomous driving systems. Through experiments, we show that jamming attacks can cause errors in the ranging ability of LiDAR sensors. With vehicle-to-vehicle (V2V) communication, multi-sensor fusion under development and LiDAR anomaly data detection, this work aims to provide a basic direction for countermeasures against these threats enhancing the security of autonomous vehicles, and verify the practical applicability and effectiveness of the proposed countermeasures in future research.

• International Journal of Computer Science & Network Security
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• v.24 no.5
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• pp.1-10
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• 2024

Individuals with visual impairments face numerous challenges in their daily lives, with navigating streets and public spaces being particularly daunting. The inability to identify safe crossing locations and assess the feasibility of crossing significantly restricts their mobility and independence. Globally, an estimated 285 million people suffer from visual impairment, with 39 million categorized as blind and 246 million as visually impaired, according to the World Health Organization. In Saudi Arabia alone, there are approximately 159 thousand blind individuals, as per unofficial statistics. The profound impact of visual impairments on daily activities underscores the urgent need for solutions to improve mobility and enhance safety. This study aims to address this pressing issue by leveraging computer vision and deep learning techniques to enhance object detection capabilities. Two models were trained to detect objects: one focused on street crossing obstacles, and the other aimed to search for objects. The first model was trained on a dataset comprising 5283 images of road obstacles and traffic signals, annotated to create a labeled dataset. Subsequently, it was trained using the YOLOv8 and YOLOv5 models, with YOLOv5 achieving a satisfactory accuracy of 84%. The second model was trained on the COCO dataset using YOLOv5, yielding an impressive accuracy of 94%. By improving object detection capabilities through advanced technology, this research seeks to empower individuals with visual impairments, enhancing their mobility, independence, and overall quality of life.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.46 no.6
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• pp.70-78
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• 2009

In this paper, we propose a novel method for eye detection using two texture filters considering textural and structural characteristics of eye regions. The human eyes have two characteristics: 1) the eyes are horizontally long and 2) the pupas are of circular shapes. By considering these two characteristics of human eyes, two texture filters are utilized for the eye detection. One is Gabor filter for detecting eye shapes in horizontal direction. The other is ART descriptor for detecting pupils of circular shape. In order to effectively detect eye regions, the proposed method consists of four steps. The first step is to extract facial regions using AdaBoost method. The second step is to normalize the illumination by considering local information. The third step is to estimate candidate regions for eyes, by merging the results from two texture filters. The final step is to locate exact eye regions by using geometric information of the face. As experimental results, the performance of the proposed method has been improved by 2.9~4.4%, compared to the existing methods.

• Journal of Korea Multimedia Society
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• v.15 no.10
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• pp.1196-1204
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• 2012

In this paper, a series of algorithms are proposed for controlling different kinds of multimedia contents and realizing interact between human and computer by using single input device. Human gesture recognition based on NUI is presented firstly in my paper. Since the image information we get it from camera is not sensitive for further processing, we transform it to YCbCr color space, and then morphological processing algorithm is used to delete unuseful noise. Boundary Energy and depth information is extracted for hand detection. After we receive the image of hand detection, PCA algorithm is used to recognize hand posture, difference image and moment method are used to detect hand centroid and extract trajectory of hand movement. 8 direction codes are defined for quantifying gesture trajectory, so the symbol value will be affirmed. Furthermore, HMM algorithm is used for hand gesture recognition based on the symbol value. According to series of methods we presented, we can control multimedia contents by using human gesture recognition. Through large numbers of experiments, the algorithms we presented have satisfying performance, hand detection rate is up to 94.25%, gesture recognition rate exceed 92.6%, hand posture recognition rate can achieve 85.86%, and face detection rate is up to 89.58%. According to these experiment results, we can control many kinds of multimedia contents on computer effectively, such as video player, MP3, e-book and so on.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.43 no.4 s.310
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• pp.58-66
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• 2006

In this paper, we apply DCM(Dilation of Color and Motion information) mask and Active Contour Models(Snakes) to extract facial outline in moving pictures with complex background. First, we propose DCM mask which is made by applying morphology dilation and AND operation to combine facial color and motion information, and use this mask to detect facial region without complex background and to remove noise in image energy. Also, initial curves are automatically set according to rotational degree estimated with geometric ratio of facial elements to overcome the demerit of Active Contour Models which is sensitive to initial curves. And edge intensity and brightness are both used as image energy of snakes to extract contour at parts with weak edges. For experiments, we acquired total 480 frames with various head-poses of sixteen persons with both eyes shown by taking pictures in inner space and also by capturing broadcasting images. As a result, it showed that more elaborate facial contour is extracted at average processing time of 0.28 seconds when using interpolated initial curves according to facial rotation degree and using combined image energy of edge intensity and brightness.