• Korean Journal of Audiology
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• v.24 no.3
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• pp.133-139
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• 2020

Background and Objectives: The Gaps-In-Noise (GIN) test is a clinically effective measure of the integrity of the central auditory nervous system. The GIN procedure can be applied to a pediatric population above 7 years of age. The present study conducted the GIN test to compare the abilities of auditory temporal resolution among typically developing children, children with speech sound disorder (SSD), and children with cognitive difficulty (CD). Subjects and Methods: Children aged 8 to 11 years-(total n=30) participated in this study. There were 10 children in each of the following three groups: typically developing children, children with SSD, and children with CD. The Urimal Test of Articulation and Phonology was conducted as a clinical assessment of the children's articulation and phonology. The Korean version of the Wechsler Intelligence Scale for Children-III (K-WISC-III) was administered as a screening test for general cognitive function. According to the procedure of Musiek, the pre-recorded stimuli of the GIN test were presented at 50 dB SL. The results were scored by the approximated threshold and the overall percent correct score (%). Results: All the typically developing children had normal auditory temporal resolution based on the clinical cutoff criteria of the GIN test. The children with SSD or CD had significantly reduced gap detection performance compared to age-matched typically developing children. The children's intelligence score measured by the K-WISC-III test explained 37% of the variance in the percent-correct score. Conclusions: Children with SSD or CD exhibited poorer ability to resolve rapid temporal acoustic cues over time compared to the age-matched typically developing children. The ability to detect a brief temporal gap embedded in a stimulus may be related to the general cognitive ability or phonological processing.

• Journal of the Korea Society of Computer and Information
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• v.27 no.2
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• pp.25-32
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• 2022

In this paper, we propose a pet monitoring system based on deep learning using an activity recognition device. The system consists of a pet's activity recognition device, a pet owner's smart device, and a server. Accelerometer and gyroscope data were collected from an Arduino-based activity recognition device, and the number of steps was calculated. The collected data is pre-processed and the amount of activity is measured by recognizing the activity in five types (sitting, standing, lying, walking, running) through a deep learning model that hybridizes CNN and LSTM. Finally, monitoring of changes in the activity, such as daily and weekly briefing charts, is provided on the pet owner's smart device. As a result of the performance evaluation, it was confirmed that specific activity recognition and activity measurement of pets were possible. Abnormal behavior detection of pets and expansion of health care services can be expected through data accumulation in the future.

• Smart Structures and Systems
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• v.29 no.1
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• pp.41-51
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• 2022

Cables are critical components of cable-stayed bridges. A structural health monitoring system provides real-time cable tension recording for cable health monitoring. However, the measurement data involve multiple sources of variability, i.e., varying environmental and operational factors, which increase the complexity of cable condition monitoring. In this study, a one-class classification method is developed for cable condition assessment using Bayesian factor analysis (FA). The single-peaked vehicle-induced cable tension is assumed to be relevant to vehicle positions and weights. The Bayesian FA is adopted to establish the correlation model between cable tensions and vehicles. Vehicle weights are assumed to be latent variables and the influences of different transverse positions are quantified by coefficient parameters. The Bayesian theorem is employed to estimate the parameters and variables automatically, and the damage index is defined on the basis of the well-trained model. The proposed method is applied to one cable-stayed bridge for cable damage detection. Significant deviations of the damage indices of Cable SJS11 were observed, indicating a damaged condition in 2011. This study develops a novel method to evaluate the health condition of individual cable using the FA in the Bayesian framework. Only vehicle-induced cable tensions are used and there is no need to monitor the vehicles. The entire process, including the data pre-processing, model training and damage index calculation of one cable, takes only 35 s, which is highly efficient.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.4
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• pp.723-736
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• 2022

Steganography is being used as a means of secret communication for crimes that threaten national security such as terrorism and espionage. With the development of computers, steganography technologies develop and criminals produce and use their own programs. However, the research for steganography is not active because detailed information on national security cases is not disclosed. The development of investigation technologies and the responses of criminal law are insufficient. Therefore, in this paper, the detection and decoding process was examined for steganography investigation, and the method was analyzed for 'the spy case of Pastor Kim', who was convicted by the Supreme Court. Multiple security devices were prepared using symmetric steganography using the pre-promised stego key. Furthermore, the three criminal legal issues: (1) the relevance issue, (2) the right to participate, and (3) the public trial issue a countermeasure were considered in national security cases. Through this paper, we hope that the investigative agency will develop analysis techniques for steganography.

• The Korean Journal of Nuclear Medicine
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• v.13 no.1_2
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• pp.7-14
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• 1979

Radionuclide cardiac angiography has distinct advantages in safety, patient comfort, cost and ease of performance. This method offers diagnostic accuracy equivalent to that of cardiac catheterization. By this method the qualitative and quantitative diagnosis of the cardiac shunts are available. Also for it is repeatable with ease and more physiologic, it has application in following pre- and post-operative shunt patients. We performed the radionuclide cardiac angiographies in 147 cases of heart diseases and 26 cases of normal group. 1. The detection of left-to-right shunt was possible in 22 of 24 patients, and 2 patients were not diagnosed due to small shunt amount. (Qp/Qs<1.3) In 21 patients of right-to-left shunt, all were diagnosed by radionuclide cardiac angiography. 2. With the pulmonary time-activity curve, $C_2/C_1$ ratio was calculated. In normal control group, a range of $C_2/C_1$ ratios of $21{\sim}38%$ was established with a mean value of $28.6{\pm}4.6%$. In patients with left-to-right shunts determined by catheterization data, the range of $C_2/C_1$ ratio was $33{\sim}90%$, with a mean value of $67.8{\pm}12.2%$. 3. In 8 cases of left-to-right shunt, $Q_p/Q_s$ ratios determined by radionuclide cardiac angiography were compaired with those of cardiac catheterization. The correlation coefficient was 0.907. (P<0.001) 4. Postoperative radionuclide cardiac angiographies were done in 21 cases. 3 of 13 patients with left-to-right shunts were found to have residual shunts. 8 patients with right-to-left shunts were confirmed to have no residual shunt.

• Journal of Biomedical Engineering Research
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• v.44 no.2
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• pp.147-156
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• 2023

Efforts to employ smart home sensors to monitor the indoor activities of elderly single residents have been made to assess the feasibility of a safe and healthy lifestyle. However, the bathroom remains an area of blind spot. In this study, we have developed and evaluated a new edge computer device that can automatically detect water usage activities in the bathroom and record the activity log on a cloud server. Three kinds of sound as flushing, showering, and washing using wash basin generated during water usage were recorded and cut into 1-second scenes. These sound clips were then converted into a 2-dimensional image using MEL-spectrogram. Sound data augmentation techniques were adopted to obtain better learning effect from smaller number of data sets. These techniques, some of which are applied in time domain and others in frequency domain, increased the number of training data set by 30 times. A deep learning model, called CRNN, combining Convolutional Neural Network and Recurrent Neural Network was employed. The edge device was implemented using Raspberry Pi 4 and was equipped with a condenser microphone and amplifier to run the pre-trained model in real-time. The detected activities were recorded as text-based activity logs on a Firebase server. Performance was evaluated in two bathrooms for the three water usage activities, resulting in an accuracy of 96.1% and 88.2%, and F1 Score of 96.1% and 87.8%, respectively. Most of the classification errors were observed in the water sound from washing. In conclusion, this system demonstrates the potential for use in recording the activities as a lifelog of elderly single residents to a cloud server over the long-term.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.4
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• pp.601-608
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• 2023

Mobile robots are widely used in industries because mobile robots perform tasks in various environments. In order to carry out tasks, determining the precise location of the robot in real-time is important due to the need for path generation and obstacle detection. In particular, when mobile robots autonomously navigate in indoor environments and carry out assigned tasks within pre-determined areas, highly precise positioning performance is required. However, mobile robots frequently experience data missing in wireless communication environments. The robots need to rely on predictive techniques to autonomously determine the mobile robot positions and continue performing mobile robot tasks. In this paper, we propose an extended Kalman filter-based algorithm to enhance the accuracy of mobile robot localization and address the issue of data missing. Trilateration algorithm relies on measurements taken at that moment, resulting in inaccurate localization performance. In contrast, the proposed algorithm uses residual values of predicted measurements in data missing environments, making precise mobile robot position estimation. We conducted simulations in terms of data missing to verify the superior performance of the proposed algorithm.

• Journal of the Korea Society of Computer and Information
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• v.14 no.6
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• pp.51-57
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• 2009

Wireless sensor networks consist of numerous sensor nodes that have inexpensive and limited resources. Generally, most of the sensors are assigned to the hazardous or uncontrollable environments. If the sensor nodes are randomly assigned to the wide target area, it is very hard to see the accurate locations of sensor nodes. Therefore, this study provides an efficient key distribution scheme to solve these problems. Based on the provided scheme, the study enabled the closely neighboring nodes to exchange information with each other after securing safe links by using the pre-distributed keys. At the same time, the provided scheme could increase the probability of multiparty key detection among nodes by using the location information of sensor node. Lastly, the study intended to show the superiority of the limitation method through a performance test.

• The Bulletin of The Korean Astronomical Society
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• v.46 no.2
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• pp.65.2-66
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• 2021

Observational studies of supernova (SN) feedback are limited. In our galaxy, most supernova remnants (SNRs) are located in the Galactic plane, so there is contamination from foreground/background sources. SNRs located in other galaxies are too far, so we cannot study them in detail. The Large Magellanic Cloud (LMC) is a unique place to study the SN feedback due to their proximity, which makes possible to study the structure of individual SNRs in some detail together with their environment. Recently, we carried out a systematic study of 13 LMC SNRs using [P II] (1.189 ㎛) and [Fe II] (1.257 ㎛) narrowband imaging with SIRIUS/IRSF, four SNRs (SN 1987A, N158A, N157B and N206), show [P II]/[Fe II] ratio much higher than the cosmic abundance. While the high ratio of SN 1987A could be due to enhanced abundance in SN ejecta, we do not have a clear explanation for the other cases. We investigate the [P II] knots found in SNRs N206, N157B and N158A, using optical spectra obtained last November with GMOS-S mounted on Gemini-South telescope. We detected several emission lines (e.g., H I, [O I], He I, [O III], [N II] and [S II]) that are present in all three SNRs, among other lines that are only found in some of them (e.g., [Ne III], [Fe III] and [Fe II]). Various line ratios are measured from the three SNRs, which indicate that the ratios of N157B tend to differ from those of other two SNRs. We will use the abundances of He and N (from the detection of [N II] and He I emission lines), together with velocity measurements to tell whether the origin of the [P II] knots are SN ejecta or CSM/ISM. For this purpose we have built a family of radiative shock with self-consistent pre-ionization using MAPPINGS 5.1.18, with shock velocities in the range of 100 to 475 km/s. We will compare the observed and modeled line fluxes for different depletion factors.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.2
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• pp.72-81
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• 2023

This research proposes a novel approach to tackle the challenge of categorizing unstructured customer complaints in the automotive industry. The goal is to identify potential vehicle defects based on the findings of our algorithm, which can assist automakers in mitigating significant losses and reputational damage caused by mass claims. To achieve this goal, our model uses the Word2Vec method to analyze large volumes of unstructured customer complaint data from the National Highway Traffic Safety Administration (NHTSA). By developing a score dictionary for eight pre-selected criteria, our algorithm can efficiently categorize complaints and detect potential vehicle defects. By calculating the score of each complaint, our algorithm can identify patterns and correlations that can indicate potential defects in the vehicle. One of the key benefits of this approach is its ability to handle a large volume of unstructured data, which can be challenging for traditional methods. By using machine learning techniques, we can extract meaningful insights from customer complaints, which can help automakers prioritize and address potential defects before they become widespread issues. In conclusion, this research provides a promising approach to categorize unstructured customer complaints in the automotive industry and identify potential vehicle defects. By leveraging the power of machine learning, we can help automakers improve the quality of their products and enhance customer satisfaction. Further studies can build upon this approach to explore other potential applications and expand its scope to other industries.