• Journal of Korea Water Resources Association
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• v.53 no.4
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• pp.313-322
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• 2020

There is always a risk of water disasters due to sudden storms in mountainous regions in Korea, which is more than 70% of the country's land. In this study, a radar-based risk prediction technique for sudden downpour is applied in the mountainous region and is evaluated for its applicability using Mt. Biseul rain radar. Eight local heavy rain events in mountain regions are selected and the information was calculated such as early detection of cumulonimbus convective cells, automatic detection of convective cells, and risk index of detected convective cells using the three-dimensional radar reflectivity, rainfall intensity, and doppler wind speed. As a result, it was possible to confirm the initial detection timing and location of convective cells that may develop as a localized heavy rain, and the magnitude and location of the risk determined according to whether or not vortices were generated. In particular, it was confirmed that the ground rain gauge network has limitations in detecting heavy rains that develop locally in a narrow area. Besides, it is possible to secure a time of at least 10 minutes to a maximum of 65 minutes until the maximum rainfall intensity occurs at the time of obtaining the risk information. Therefore, it would be useful as information to prevent flash flooding disaster and marooned accidents caused by heavy rain in the mountainous area using this technique.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.46 no.5
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• pp.8-14
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• 2009

In the research field of speech recognition, pinpointing the endpoints of speech utterance even with the presence of background noise is of great importance. These noise present during recording introduce disturbances which complicates matters since what we just want is to get the stationary parameters corresponding to each speech section. One major cause of error in automatic recognition of isolated words is the inaccurate detection of the beginning and end boundaries of the test and reference templates, thus the necessity to find an effective method in removing the unnecessary regions of a speech signal. The conventional methods for speech endpoint detection are based on two linear time-domain measurements: the short-time energy, and short-time zero-crossing rate. They perform well for clean speech but their precision is not guaranteed if there is noise present, since the high energy and zero-crossing rate of the noise is mistaken as a part of the speech uttered. This paper proposes a novel approach in finding an apparent threshold between noise and speech based on Lyapunov Exponents (LEs). This proposed method adopts the nonlinear features to analyze the chaos characteristics of the speech signal instead of depending on the unreliable factor-energy. The excellent performance of this approach compared with the conventional methods lies in the fact that it detects the endpoints as a nonlinearity of speech signal, which we believe is an important characteristic and has been neglected by the conventional methods. The proposed method extracts the features based only on the time-domain waveform of the speech signal illustrating its low complexity. Simulations done showed the effective performance of the Proposed method in a noisy environment with an average recognition rate of up 92.85% for unspecified person.

• Analytical Science and Technology
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• v.31 no.1
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• pp.23-30
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• 2018

Twenty-two allergy-induced aromatics in children were analyzed using a gas chromatography flame ionization detector (GC-FID) and gas chromatography mass spectrometer (GC-MSD). Analytes were extracted using an automatic Soxhlet extractor and centrifuged for 10 min in a fast freezing centrifuge, and the supernatant was transferred into a 2 mL vial and injected in split mode. Under the established conditions, the calibration curve showed linearity with a correlation coefficient of 0.996 or more. A wide range of sensitivity of 6.7 to 1,859,839 depending on the device characteristics and detector used was shown. The detection limit of the device was 0.0032 to $0.0335{\mu}g/mL$, and the maximum detection limit was less than $0.1{\mu}g/mL$. The detection limit of the method ranged from 0.0033 to $0.1161{\mu}g/mL$. In addition, the limit of quantification ranged from 0.0100 to $0.5422{\mu}g/mL$, with a level of precision ranging from 0.21 % to 4.89 % and a degree of accuracy ranging from 89 % to 111 %. The analytical method developed in this study was applied to commercial products.

• Journal of the Korean Association of Geographic Information Studies
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• v.15 no.1
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• pp.87-98
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• 2012

A river boundary is defined as the intersection between a main stream of a river and the land. Mapping of the river boundary is important for the protection of the properties in river areas, the prevention of flooding and the monitoring of the topographic changes in river areas. However, the utilization of the ground surveying technologies is not efficient for the mapping of the river boundary due to the irregular surfaces of river zones and the dynamic changes of water level of a river stream. Recently, the spatial information data sets such as the airborne LiDAR and aerial images are widely used for coastal mapping due to the acquisition of the topographic information without human accessibility. Due to these advantages, this research proposes a semi-automatic method for mapping of the river boundary using the spatial information data set such as the airborne LiDAR and the aerial photographs. Multiple image processing technologies such as the image segmentation algorithm and the edge detection algorithm are applied for the generation of the 3D river boundary using the aerial photographs and airborne topographic LiDAR data. Check points determined by the experienced expert are used for the measurement of the horizontal and vertical accuracy of the generated 3D river boundary. Statistical results show that the generated river boundary has a high accuracy in horizontal and vertical direction.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.45 no.1
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• pp.47-54
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• 2008

In this paper, we propose an auto-detection algorithm of basal cell carcinoma(BCC) from the protophorphyrin IX(PpIX) fluorescence image induced by appling the methyl 5-aminolaevulinate(MAL) ointment-induced protophorphyrin IX(PpIX) to the skin tumour area and then shining the wood lamp on the area. The proposed algorithm first generates 3 mask areas-tumor area, suspected tumor area and tumor free area and then applies local watershed algorithm to the turner and the suspected tumor areas to make small watershed regions that include similar luminance value pixels. Next, small watershed regions are merged by hierarchical queue based fast region merging that uses the difference between the average luminance values of adjacent watershed regions as a region merging criterion and finally BCC regions are detected. 50 tissue samples are acquired from the tumour regions of 10 patients with BCC that are extracted by using the proposed algorithm and are performed pathological examination by expert dermatologist. Experiment result shows the rate of tumor detection from BCC lesion using presurgical in vivo of MAL-indeuced PpIX fluorescence has high sensitivity 94.1% and relatively high specificity 82.6%.

• Geophysics and Geophysical Exploration
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• v.18 no.4
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• pp.181-196
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• 2015

A 3.5 kHz or chirp sub-bottom profiling survey is widely used in the marine geological and engineering purpose exploration. However, swells in the sea degrade the quality of the survey data. The horizontal continuity of profiler data can be enhanced and the quality can be improved by correcting the influence of the swell. Accurate detection of sea bottom location is important in correcting the swell effect. In this study, we tried to pick sea bottom locations by finding the position of crossing a threshold of the maximum value for the raw data and transformed data of envelope or energy ratio. However, in case of the low-quality data where the sea bottom signals are not clear due to sea wave noise, automatic sea bottom detection at the individual traces was not successful. We corrected the mispicks for the low quality data and obtained satisfactory results by picking a sea bottom within a range considering the previous average of sea bottom, and excluding unreliable big-difference picks. In case of trace by trace picking, fewest mispicks were found when using energy ratio data. In case of picking considering the previous average, the correction result was relatively satisfactory when using raw data.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.4
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• pp.244-254
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• 2009

This paper describes a remote system to monitor the circadian behavioral patterns of elders who live alone. The proposed system was designed and implemented to provide more conveniently and reliably the required functionalities of a remote monitoring system for elders based on the development of first phase prototype[2]. The developed system is composed of an in-house sensing system and a server system. The in-house sensing system is a set of wireless sensor nodes which have pyroelectric infrared (PIR) sensor to detect a motion of elder. Each sensing node sends its detection signal to a home gateway via wireless link. The home gateway stores the received signals into a remote database. The server system is composed of a database server and a web server, which provides web-based monitoring system to caregivers (friends, family and social workers) for more cost effective intelligent care service. The improved second phase system can provide 'automatic diagnosis', 'going out detection', and enhanced user interface functionalities. We have evaluated the first and second phase monitoring systems from real field experiments of 3/4 months continuous operation with installation of 9/15 elders' houses, respectively. The experimental results show the promising possibilities to estimate the behavioral patterns and the current status of elder even though the simplicity of sensing capability.

• Korean Journal of Radiology
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• v.19 no.6
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• pp.1179-1186
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• 2018

Objective: The purposes of this study were to evaluate size-specific dose estimate (SSDE) of low-dose CT (LDCT) in the Korean Lung Cancer Screening (K-LUCAS) project and to determine whether CT protocols from Western countries are appropriate for lung cancer screening in Korea. Materials and Methods: For participants (n = 256, four institutions) of K-LUCAS pilot study, volume CT dose index ($CTDI_{vol}$) using a 32-cm diameter reference phantom was compared with SSDE, which was recalculated from $CTDI_{vol}$ using size-dependent conversion factor (f-size) based on the body size, as described in the American Association of Physicists in Medicine Report 204. This comparison was subsequently assessed by body mass index (BMI) levels (underweight/normal vs. overweight/obese), and automatic exposure control (AEC) adaptation (yes/no). Results: Size-specific dose estimate was higher than $CTDI_{vol}$ ($2.22{\pm}0.75mGy$ vs. $1.67{\pm}0.60mGy$, p < 0.001), since the f-size was larger than 1.0 for all participants. The ratio of SSDE to $CTDI_{vol}$ was higher in lower BMI groups; 1.26, 1.37, 1.43, and 1.53 in the obese (n = 103), overweight (n = 70), normal (n = 75), and underweight (n = 4), respectively. The ratio of SSDE to $CTDI_{vol}$ was greater in standard-sized participants than in large-sized participants independent of AEC adaptation; with AEC, SSDE/$CTDI_{vol}$ in large- vs. standard-sized participants: $1.30{\pm}0.08$ vs. $1.44{\pm}0.08$ (p < 0.001) and without AEC, $1.32{\pm}0.08$ vs. $1.42{\pm}0.06$ (p < 0.001). Conclusion: Volume CT dose index based on a reference phantom underestimates radiation exposure of LDCT in standard-sized Korean participants. The optimal radiation dose limit needs to be verified for standard-sized Korean participants.

• Journal of the Korea Convergence Society
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• v.10 no.7
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• pp.15-21
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• 2019

Research in detecting fake information gained a lot of interest after the US presidential election in 2016. Information from unknown sources are produced in the shape of news, and its rapid spread is fueled by the interest of public drawn to stimulating and interesting issues. In addition, the wide use of mass communication platforms such as social network services makes this phenomenon worse. Poynter Institute created the International Fact Checking Network (IFCN) to provide guidelines for judging the facts of skilled professionals and releasing "Code of Ethics" for fact check agencies. However, this type of approach is costly because of the large number of experts required to test authenticity of each article. Therefore, research in automated fake news detection technology that can efficiently identify it is gaining more attention. In this paper, we investigate fake news detection systems and researches that are rapidly developing, mainly thanks to recent advances in deep learning technology. In addition, we also organize shared tasks and training corpus that are released in various forms, so that researchers can easily participate in this field, which deserves a lot of research effort.

• Smart Media Journal
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• v.9 no.4
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• pp.102-108
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• 2020

In this paper, we propose a real-time method of detecting hand motions and extracting the signal frame induced by EF(Electric Field) sensors. The signal induced by hand motion includes not only noises caused by various environmental sources as well as sensor's physical placement, but also different initial off-set conditions. Thus, it has been considered as a challenging problem to detect the motion signal and extract the motion frame automatically in real-time. In this study, we remove the PLN(Power Line Noise) using LPF with 10Hz cut-off and successively apply MA(Moving Average) filter to obtain clean and smooth input motion signals. To sense a hand motion, we use two thresholds(positive and negative thresholds) with offset value to detect a starting as well as an ending moment of the motion. Using this approach, we can achieve the correct motion detection rate over 98%. Once the final motion frame is determined, the motion signals are normalized to be used in next process of classification or recognition stage such as LSTN deep neural networks. Our experiment and analysis show that our proposed methods produce better than 98% performance in correct motion detection rate as well as in frame-matching rate.