• Journal of the Korea institute for structural maintenance and inspection
• /
• v.25 no.4
• /
• pp.56-64
• /
• 2021

Leakage due to deterioration and damage is one of the major causes of volume change by freezing and thawing, and it leads micro-cracking and surface scaling in concrete structures. The deterioration of damaged concrete accelerates with the chloride attack. Thus, in the detailed guidelines for facility performance evaluation (2020), the quality of cover concrete and the freeze-thaw (FT) repetition cycle were newly suggested for concrete durability assessment. The quality of cover concrete should be evaluated by the rebound hammer test and the FT repetition cycle should be also considered in the deterioration environmental assessment. This study suggested the application of fast dynamic based nonlinear ultrasound method to monitor initial micro-scale damage under freezing and thawing environment. Concrete specimens were fabricated with different water-cement ratios (40%, 60%) and air contents (1.5% and 3.0%). The compressive strength, rebound number, relative dynamic modulus, and nonlinear ultrasound were measured with different FT cycles. The scanning electron microscopy was also performed to investigate the micro-scale FT damage. As a result, both the rebound number and the relative dynamic modulus had difficulty to detect early damage but the proposed method showed a potential to detect initial micro-scale damage and predict the FT resistance performance of concrete.

• Journal of Intelligence and Information Systems
• /
• v.29 no.3
• /
• pp.249-265
• /
• 2023

Recently, the satellite industry has been paying attention to the private-led 'New Space' paradigm, which is a departure from the traditional government-led industry. The space industry, which is considered to be the next food industry, is still receiving relatively little attention in Korea compared to the global market. Therefore, the purpose of this study is to explore future signals that can help determine the market entry strategies of private companies in the domestic satellite industry. To this end, this study utilizes the theoretical background of future signal theory and the Keyword Portfolio Map method to analyze keyword potential in patent document data based on keyword growth rate and keyword occurrence frequency. In addition, news data was collected to categorize future signals into first symptom and early information, respectively. This is utilized as an interpretive indicator of how the keywords reveal their actual potential outside of patent documents. This study describes the process of data collection and analysis to explore future signals and traces the evolution of each keyword in the collected documents from a weak signal to a strong signal by specifically visualizing how it can be used through the visualization of keyword maps. The process of this research can contribute to the methodological contribution and expansion of the scope of existing research on future signals, and the results can contribute to the establishment of new industry planning and research directions in the satellite industry.

• Geophysics and Geophysical Exploration
• /
• v.26 no.3
• /
• pp.126-137
• /
• 2023

In this study, a recurrent neural network (RNN) was employed as a methodological approach to classify dolphin click signals derived from ocean monitoring data. To improve the accuracy of click signal classification, the single time series data were transformed into fractional domains using fractional Fourier transform to expand its features. Transformed data were used as input for three RNN models: long short-term memory (LSTM), gated recurrent unit (GRU), and bidirectional LSTM (BiLSTM), which were compared to determine the optimal network for the classification of signals. Because the fractional Fourier transform displayed different characteristics depending on the chosen angle parameter, the optimal angle range for each RNN was first determined. To evaluate network performance, metrics such as accuracy, precision, recall, and F1-score were employed. Numerical experiments demonstrated that all three networks performed well, however, the BiLSTM network outperformed LSTM and GRU in terms of learning results. Furthermore, the BiLSTM network provided lower misclassification than the other networks and was deemed the most practically appliable to field data.

• Journal of Internet of Things and Convergence
• /
• v.10 no.2
• /
• pp.1-15
• /
• 2024

The construction of smart communities is a new method and important measure to ensure the security of residential areas. In order to solve the problem of low accuracy in face recognition caused by distorting facial features due to monitoring camera angles and other external factors, this paper proposes the following optimization strategies in designing a face recognition network: firstly, a global graph convolution module is designed to encode facial features as graph nodes, and a multi-scale feature enhancement residual module is designed to extract facial keypoint features in conjunction with the global graph convolution module. Secondly, after obtaining facial keypoints, they are constructed as a directed graph structure, and graph attention mechanisms are used to enhance the representation power of graph features. Finally, tensor computations are performed on the graph features of two faces, and the aggregated features are extracted and discriminated by a fully connected layer to determine whether the individuals' identities are the same. Through various experimental tests, the network designed in this paper achieves an AUC index of 85.65% for facial keypoint localization on the 300W public dataset and 88.92% on a self-built dataset. In terms of face recognition accuracy, the proposed network achieves an accuracy of 83.41% on the IBUG public dataset and 96.74% on a self-built dataset. Experimental results demonstrate that the network designed in this paper exhibits high detection and recognition accuracy for faces in surveillance videos.

• Journal of the korean academy of Pediatric Dentistry
• /
• v.27 no.1
• /
• pp.169-179
• /
• 2000

The purpose of this study is to develop the system which convert the optical difference of teeth texture between intact enamel and incipient caries lesion into shade difference by laser fluorescence and to develop new and simple caries activity test using laser fluorescence. The experimental design of this study consists of three parts. In first part, a new method for the in vitro assessment of changes in initial enamel caries lesion of Bovine teeth using laser fluorescence is tested. In second part, in vivo assessment undertaken. Number of teeth which showed incipient carious lesion on buccal surface examined by laser fluorescence was compared with the caries activity test of $Cariescreen^{(R)}$ test and other oral environmental test of dDfFtT. In third part, new caries activity test measured by laser fluorescence was developed on the basis of above results and evaluated the sensitivity, specificity, and diagnostic power. Optical density measured by laser fluorescence was increased as increasing the depth of incipient carious lesion and showed high correlation$(\gamma=0.7015)$ with lesion depth. Optical density showed direct proportion to lesion depth. Linear equation was obtained between the optical density and the lesion depth by regression analysis. The result of caries activity test with laser fluorescence showed high correlation with those of $Cariescreen^{(R)}$ test and dDfFtT examination. Caries activity test with laser fluorescence showed 48% of sensitivity, 52% of specificity, and 45% of diagnostic power on the basis of dDfFtT examination, and also showed 48% of sensitivity, 51% of specificity, and 36% of diagnostic power on the basis of $Cariescreen^{(R)}$ test. In regard above result, caries activity test with laser fluorescence considered to be reliable for caries activity test compared with other oral environmental test. and it was also considered to be practical because it would be simple, inexpensive, and time saving method.

• Sleep Medicine and Psychophysiology
• /
• v.10 no.1
• /
• pp.52-60
• /
• 2003

Objectives: Stage 1 sleep provides important information regarding interpretation of nocturnal polysomnography, particularly sleep onset. It is a short transition period from wakeful consciousness to sleep. The lack of prominent sleep events characterizing stage 1 sleep is a major obstacle in automatic sleep stage scoring. In this study, utilization of simultaneous EEG and EOG processing and analyses to detect stage 1 sleep automatically were attempted. Methods: Relative powers of the alpha waves and the theta waves were calculated from spectral estimation. A relative power of alpha waves less than 50% or relative power of theta waves more than 23% was regarded as stage 1 sleep. SEM(slow eye movement) was defined as the duration of both-eye movement ranging from 1.5 to 4 seconds, and was also regarded as stage 1 sleep. If one of these three criteria was met, the epoch was regarded as stage 1 sleep. Results were compared to the manual rating results done by two polysomnography experts. Results: A total of 169 epochs were analyzed. The agreement rate for stage 1 sleep between automatic detection and manual scoring was 79.3% and Cohen’s Kappa was 0.586 (p<0.01). A significant portion (32%) of automatically detected stage 1 sleep included SEM. Conclusion: Generally, digitally-scored sleep staging shows accuracy up to 70%. Considering potential difficulty in stage 1 sleep scoring, accuracy of 79.3% in this study seems to be strong enough. Simultaneous analysis of EOG differentiates this study from previous ones which mainly depended on EEG analysis. The issue of close relationship between SEM and stage 1 sleep raised by Kinnari remains a valid one in this study.

• Journal of Biomedical Engineering Research
• /
• v.25 no.1
• /
• pp.11-19
• /
• 2004

Stage 1 sleep provides important information regarding interpretation of nocturnal polysomnography, particularly sleep onset. It is a short transition period from wakeful consciousness to sleep. Lack of prominent sleep events characterizing stage 1 sleep is a major obstacle in automatic sleep stage scoring. In this study, we attempted to utilize simultaneous EEC and EOG processing and analyses to detect stage 1 sleep automatically. Relative powers of the alpha waves and the theta waves were calculated from spectral estimation. Either the relative power of alpha waves less than 50％ or the relative power of theta waves more than 23％ was regarded as stage 1 sleep. SEM (slow eye movement) was defined as the duration of both eye movement ranging from 1.5 to 4 seconds and regarded also as stage 1 sleep. If one of these three criteria was met, the epoch was regarded as stage 1 sleep. Results f ere compared to the manual rating results done by two polysomnography experts. Total of 169 epochs was analyzed. Agreement rate for stage 1 sleep between automatic detection and manual scoring was 79.3％ and Cohen's Kappa was 0.586 (p＜0.01). A significant portion (32％) of automatically detected stage 1 sleep included SEM. Generally, digitally-scored sleep s1aging shows the accuracy up to 70％. Considering potential difficulties in stage 1 sleep scoring, the accuracy of 79.3％ in this study seems to be robust enough. Simultaneous analysis of EOG provides differential value to the present study from previous oneswhich mainly depended on EEG analysis. The issue of close relationship between SEM and stage 1 sleep raised by Kinnariet at. remains to be a valid one in this study.

• Korean Journal of Remote Sensing
• /
• v.37 no.6_1
• /
• pp.1757-1766
• /
• 2021

The green coverage ratio is the ratio of the land area to green coverage area, and it is used as a practical urban greening index. The green coverage ratio is calculated based on the land cover map, but low spatial resolution and inconsistent production cycle of land cover map make it difficult to calculate the correct green coverage area and analyze the precise green coverage. Therefore, this study proposes a new method to calculate green coverage area using aerial images and deep neural networks. Green coverage ratio can be quickly calculated using manned aerial images acquired by local governments, but precise analysis is difficult because components of image such as acquisition date, resolution, and sensors cannot be selected and modified. This limitation can be supplemented by using an unmanned aerial vehicle that can mount various sensors and acquire high-resolution images due to low-altitude flight. In this study, we proposed a method to calculate green coverage ratio from manned or unmanned aerial images, and experimentally verified the proposed method. Aerial images enable precise analysis by high resolution and relatively constant cycles, and deep learning can automatically detect green coverage area in aerial images. Local governments acquire manned aerial images for various purposes every year and we can utilize them to calculate green coverage ratio quickly. However, acquired manned aerial images may be difficult to accurately analyze because details such as acquisition date, resolution, and sensors cannot be selected. These limitations can be supplemented by using unmanned aerial vehicles that can mount various sensors and acquire high-resolution images due to low-altitude flight. Accordingly, the green coverage ratio was calculated from the two aerial images, and as a result, it could be calculated with high accuracy from all green types. However, the green coverage ratio calculated from manned aerial images had limitations in complex environments. The unmanned aerial images used to compensate for this were able to calculate a high accuracy of green coverage ratio even in complex environments, and more precise green area detection was possible through additional band images. In the future, it is expected that the rust rate can be calculated effectively by using the newly acquired unmanned aerial imagery supplementary to the existing manned aerial imagery.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
• /
• v.21 no.2
• /
• pp.49-57
• /
• 2016

Even if an external forcing that will drive a climate change is given uniformly over the globe, the corresponding climate change and the feedbacks by the climate system differ by region. Thus the detection of global warming signal has been made on a regional scale as well as on a global average against the internal variabilities and other noises involved in the climate change. The purpose of this study is to estimate a timing of unprecedented climate due to global warming and to analyze the regional differences in the estimated results. For this purpose, unlike previous studies that used climate simulation data, we used an observational dataset to estimate a magnitude of internal variability and a future temperature change. We calculated a linear trend in surface temperature using a historical temperature record from 1880 to 2014 and a magnitude of internal variability as the largest temperature displacement from the linear trend. A timing of unprecedented climate was defined as the first year when a predicted minimum temperature exceeds the maximum temperature record in a historical data and remains as such since then. Presumed that the linear trend and the maximum displacement will be maintained in the future, an unprecedented climate over the land would come within 200 years from now in the western area of Africa, the low latitudes including India and the southern part of Arabian Peninsula in Eurasia, the high latitudes including Greenland and the mid-western part of Canada in North America, the low latitudes including Amazon in South America, the areas surrounding the Ross Sea in Antarctica, and parts of East Asia including Korean Peninsula. On the other hand, an unprecedented climate would come later after 400 years in the high latitudes of Eurasia including the northern Europe, the middle and southern parts of North America including the U.S.A. and Mexico. For the ocean, an unprecedented climate would come within 200 years over the Indian Ocean, the middle latitudes of the North Atlantic and the South Atlantic, parts of the Southern Ocean, the Antarctic Ross Sea, and parts of the Arctic Sea. In the meantime, an unprecedented climate would come even after thousands of years over some other regions of ocean including the eastern tropical Pacific and the North Pacific middle latitudes where an internal variability is large. In summary, spatial pattern in timing of unprecedented climate are different for each continent. For the ocean, it is highly affected by large internal variability except for the high-latitude regions with a significant warming trend. As such, a timing of an unprecedented climate would not be uniform over the globe but considerably different by region. Our results suggest that it is necessary to consider an internal variability as well as a regional warming rate when planning a climate change mitigation and adaption policy.

• Korean Journal of Remote Sensing
• /
• v.34 no.6_4
• /
• pp.1503-1517
• /
• 2018

Mt. Baekdu is a stratovolcano located at the border between China and North Korea and is known to have formed through its differentiation stage after the Oligocene epoch in the Cenozoic era. There has been a growing interest in the magma re-activity of Mt. Baekdu volcano since 2010. Several research projects have been conducted by government such as Korea Meteorological Administration and Korea Institute of Geoscience and Mineral Resources. Because, however, the Mt. Baekdu volcano is located far from South Korea, it is quite difficult to collect in-situ observations by terrestrial equipment. Remote sensing is a science to analyze and interpret information without direct physical contact with a target object. Various types of platform such as automobile, unmanned aerial vehicle, aircraft and satellite can be used for carrying a payload. In the past several decades, numerous volcanic studies have been conducted by remotely sensed observations using wide spectrum of wavelength channels in electromagnetic waves. In particular, radar remote sensing has been widely used for volcano monitoring in that microwave channel can gather surface's information without less limitation like day and night or weather condition. Radar interferometric technique which utilized phase information of radar signal enables to estimate surface displacement such as volcano, earthquake, ground subsidence or glacial movement, etc. In 2018, long-term research project for collaborative observation for Mt. Baekdu volcano between Korea and China were selected by Korea government. A volcanic specialized research center has been established by the selected project. The purpose of this paper is to introduce about remote sensing techniques for volcano monitoring and to review selected studies with remote sensing techniques to monitor Mt. Baekdu volcano. The acquisition status of the archived observations of six synthetic aperture radar satellites which are in orbit now was investigated for application of radar interferometry to monitor Mt. Baekdu volcano. We will conduct a time-series analysis using collected synthetic aperture radar images.