• Journal of Navigation and Port Research
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• v.34 no.5
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• pp.349-354
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• 2010

Cheonan, Republic of Korea Navy patrol ship sank had happened by an unknown incident in the vicinity of Baekryeongdo southwest 1.6km(1 mile) sea at 21:45 on March 26, 2010. In terms of coastal researcher's point of view, it is meaningful to provide the sea condition of basic data necessary for search and rescue, more detailed predictions and inference data through the numerical simulations. Thus, in this study, we investigated the weather, wave, tide, tidal current, bottom soil conditions, and suspended sediment are investigated at the coast of Baekryeong-Daechung islands. And based on these data, the characteristics of sea conditions were analyzed. The tidal period at the time of incident corresponds between neap tide to mean tide. Until April 3-4 after March 26, the date of incident, the strongest velocity was progressed towards the spring tide. Thus, it was considered to be difficult to search and rescue operations. Also, because the ebb tide was in progress during 21:00 to 22:00, mass transport seems to be prevailed to the southeast. In particular, as the sudden turbulence due to the irregular topography existed was anticipated, we had carried out particle tracking experiment. From this experiment, depending on the situation of flow, the initial movement of the particles were directed to the southeast but it turned out moving towards the offshore based on the long term prediction. Through this result, it is considered that the scope of the search operation should be expanded towards the open sea.

• The Journal of the Korea Contents Association
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• v.21 no.1
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• pp.552-560
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• 2021

Currently, the domestic aquaculture industry is pursuing smartization, but it is still proceeding with human subjective judgment in many processes in the aquaculture stage. The prerequisite for the smart aquaculture industry is to effectively grasp the condition of fish in the farm. If real-time monitoring is possible by identifying the number of fish populations, size, pathways, and speed of movement, various forms of automation such as automatic feed supply and disease determination can be carried out. In this study, we proposed an algorithm to identify the state of fish in real time using underwater video data. The fish detection performance was compared and evaluated by applying the latest deep learning-based object detection models, and an algorithm was proposed to measure fish object identification, path tracking, and moving speed in continuous image frames in the video using the fish detection results. The proposed algorithm showed 92% object detection performance (based on F1-score), and it was confirmed that it effectively tracks a large number of fish objects in real time on the actual test video. It is expected that the algorithm proposed in this paper can be effectively used in various smart farming technologies such as automatic feed feeding and fish disease prediction in the future.

• Journal of the Korea Society for Simulation
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• v.32 no.3
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• pp.43-53
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• 2023

In this detailed and comprehensive study, our primary focus has been placed on accurately gauging the number of visitors and their real-time locations in commercial spaces. Particularly, in a real cafe, using security cameras, we have developed a system that can offer live updates on available seating and predict future congestion levels. By employing YOLO, a real-time object detection and tracking algorithm, the number of visitors and their respective locations in real-time are also monitored. This information is then used to update a cafe's indoor map, thereby enabling users to easily identify available seating. Moreover, we developed a model that predicts the congestion of a cafe in real time. The sophisticated model, designed to learn visitor count and movement patterns over diverse time intervals, is based on Long Short Term Memory (LSTM) to address the vanishing gradient problem and Sequence-to-Sequence (Seq2Seq) for processing data with temporal relationships. This innovative system has the potential to significantly improve cafe management efficiency and customer satisfaction by delivering reliable predictions of cafe congestion to all users. Our groundbreaking research not only demonstrates the effectiveness and utility of indoor location tracking technology implemented through security cameras but also proposes potential applications in other commercial spaces.

• Journal of the Korean Geotechnical Society
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• v.39 no.1
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• pp.15-25
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• 2023

Recent weather changes have led to an increase in heavy rainfall resulting in frequent large-scale slope failures. To minimize damage to life and property, a measurement system is used in slope failure warning systems. However, understanding the slope failure behavior is difficult as the measurement system only measures a specific point. Therefore, numerical analysis must be p erformed with the measurement system. The soil water characteristic curve (SWCC) drying curve and boundary conditions that consider evapotranspiration and precipitation have been applied to numerical analysis, but the hysteresis of SWCC affects the numerical analysis results. To address this, a new evapotranspiration calculation method is proposed and applied to boundary conditions, and the measurement data are compared with the results of the numerical analysis. This method takes into account the different infiltration behaviors on evapotranspiration according to the drying and wetting curves of the SWCC, and allows for a more rational prediction of water movement on unsaturated slopes.

• Journal of Korean Society of Disaster and Security
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• v.17 no.1
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• pp.1-8
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• 2024

In Korea, mountainous areas cover 60% of the land, leading to increased factors such as concentrated heavy rainfall and typhoons, which can result in debris flow and landslide. Despite the high risk of disasters like landslides and debris flow, there has been a tendency in most regions to focus more on post-damage recovery rather than preventing damage. Therefore, in this study, precise topographic data was constructed by conducting on-site surveys and drone measurements in areas where debris flow actually occurred, to analyze the risk zones for such events. The numerical analysis program RAMMS model was utilized to perform debris flow analysis on the areas prone to debris flow, and the actual distribution of debris flow was compared and analyzed to evaluate the applicability of the model. As a result, the debris flow generation area calculated by the RAMMS model was found to be 18% larger than the actual area, and the travel distance was estimated to be 10% smaller. However, the simulated shape of debris flow generation and the path of movement calculated by the model closely resembled the actual data. In the future, we aim to conduct additional research, including model verification suitable for domestic conditions and the selection of areas for damage prediction through debris flow analysis in unmeasured watersheds.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.131-145
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• 2020

In line with the trend of industrial innovation, IoT technology utilized in a variety of fields is emerging as a key element in creation of new business models and the provision of user-friendly services through the combination of big data. The accumulated data from devices with the Internet-of-Things (IoT) is being used in many ways to build a convenience-based smart system as it can provide customized intelligent systems through user environment and pattern analysis. Recently, it has been applied to innovation in the public domain and has been using it for smart city and smart transportation, such as solving traffic and crime problems using CCTV. In particular, it is necessary to comprehensively consider the easiness of securing real-time service data and the stability of security when planning underground services or establishing movement amount control information system to enhance citizens' or commuters' convenience in circumstances with the congestion of public transportation such as subways, urban railways, etc. However, previous studies that utilize image data have limitations in reducing the performance of object detection under private issue and abnormal conditions. The IoT device-based sensor data used in this study is free from private issue because it does not require identification for individuals, and can be effectively utilized to build intelligent public services for unspecified people. Especially, sensor data stored by the IoT device need not be identified to an individual, and can be effectively utilized for constructing intelligent public services for many and unspecified people as data free form private issue. We utilize the IoT-based infrared sensor devices for an intelligent pedestrian tracking system in metro service which many people use on a daily basis and temperature data measured by sensors are therein transmitted in real time. The experimental environment for collecting data detected in real time from sensors was established for the equally-spaced midpoints of 4×4 upper parts in the ceiling of subway entrances where the actual movement amount of passengers is high, and it measured the temperature change for objects entering and leaving the detection spots. The measured data have gone through a preprocessing in which the reference values for 16 different areas are set and the difference values between the temperatures in 16 distinct areas and their reference values per unit of time are calculated. This corresponds to the methodology that maximizes movement within the detection area. In addition, the size of the data was increased by 10 times in order to more sensitively reflect the difference in temperature by area. For example, if the temperature data collected from the sensor at a given time were 28.5℃, the data analysis was conducted by changing the value to 285. As above, the data collected from sensors have the characteristics of time series data and image data with 4×4 resolution. Reflecting the characteristics of the measured, preprocessed data, we finally propose a hybrid algorithm that combines CNN in superior performance for image classification and LSTM, especially suitable for analyzing time series data, as referred to CNN-LSTM (Convolutional Neural Network-Long Short Term Memory). In the study, the CNN-LSTM algorithm is used to predict the number of passing persons in one of 4×4 detection areas. We verified the validation of the proposed model by taking performance comparison with other artificial intelligence algorithms such as Multi-Layer Perceptron (MLP), Long Short Term Memory (LSTM) and RNN-LSTM (Recurrent Neural Network-Long Short Term Memory). As a result of the experiment, proposed CNN-LSTM hybrid model compared to MLP, LSTM and RNN-LSTM has the best predictive performance. By utilizing the proposed devices and models, it is expected various metro services will be provided with no illegal issue about the personal information such as real-time monitoring of public transport facilities and emergency situation response services on the basis of congestion. However, the data have been collected by selecting one side of the entrances as the subject of analysis, and the data collected for a short period of time have been applied to the prediction. There exists the limitation that the verification of application in other environments needs to be carried out. In the future, it is expected that more reliability will be provided for the proposed model if experimental data is sufficiently collected in various environments or if learning data is further configured by measuring data in other sensors.

• The korean journal of orthodontics
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• v.30 no.5 s.82
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• pp.657-668
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• 2000

The purpose of this study was to evaluate the amount and interrelationship of the soft tissue of nose and maxillary changes and to identify the nasal morphologic features that indicate susceptibility to nasal deflection in such a manner that they would be useful in presurgical prediction of nasal changes after maxillary advancement surgery in skeletal Class III malocclusion. The sample consisted of 25 adult patients (13 males and 12 females) who had severe anteroposterior skeletal discrepancy. The patients had received presurgical orthodontic treatment. They underwent a Le Fort I advancement osteotomy, rigid internal fixation, alar cinch suture and V-Y advancement lip closure. The presurgical and postsurgical lateral cephalograms and lateral and frontal facial photographs were evaluated. The computerized statistical analysis was carried out. Soft tissue of nose change to h point change ratios were calculated by regression equations. The results were as follows 1. The correlation of maxillary hard tissue horizontal changes and nasal soft tissue vortical changes were high and the ${\beta}_0$ for soft tissue to ADV were 0.228 at ANt, 0.257 at SNt. 2. The correlation of maxillary hard tissue and nasal soft tissue horizontal changes were high and the ${\beta}_0$ for soft tissue to ADV were 0.484 at ANt, 0.431 at SNt, 0.806 at Sn. 3. The correlation of maxillary hard tissue horizontal changes and width changes of ala of nose were high and the ${\beta}_0$ lot alar base width ratio to ADV were 0.002. 4. The DRI, Prominence of nose, Pre-Op CA is not a quantitative measure that can be used clinically to improve the predictability of vertical and horizontal nasal tip deflection. In this study, increases in nasal tip projection and anterosuperior rotation occur when there is an anterior vector of maxillary movement. These nasal changes were Quantitatively correlated to magnitude of maxillary(A point) movement.

• Korean Journal of Environment and Ecology
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• v.37 no.6
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• pp.429-438
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• 2023

The forest in the lower Tumen River serves as an important ecosystem spanning the territories of North Korea, Russia, and China, and it provides habitat and movement corridors for diverse mammals, including the endangered Amur tiger (Panthera tigris) and Amur leopard (Panthera pardus). This study focuses on the Mijiang area, situated as a potential ecological corridor connecting North Korea and China in the lower Tumen River, playing a crucial role in conserving and restoring the biodiversity of the Korean Peninsula. This study aimed to identify mammal species and estimate their relative abundance, occupancy, and distribution based on the 48 camera traps installed in the Mijiang area from May 2019 to May 2021. The results confirmed the presence of 18 mammal species in the Mijiang area, including large carnivores like tigers and leopards. Among the dominant mammals, four species of ungulates showed high occupancy and detection rates, particularly the Roe deer (Capreolus pygargus) and Wild boar (Sus scrofa). The roe deer was distributed across all areas with a predicted high occupancy rate of 0.97, influenced by altitude, urban residential areas, and patch density. Wild boars showed a predicted occupancy rate of 0.73 and were distributed throughout the entire area, with factors such as wetland ratio, grazing intensity, and spatial heterogeneity in aspects of the landscape influencing their occupancy and detection rates. Sika deer (Cervus nippon) exhibited a predicted occupancy rate of 0.48, confined to specific areas, influenced by slope, habitat fragmentation diversity affecting detection rates, and the ratio of open forests impacting occupancy. Water deer (Hydropotes inermis) displayed a very low occupancy rate of 0.06 along the Tumen River Basin, with higher occupancy in lower altitude areas and increased detection in locations with high spatial heterogeneity in aspects. This study confirmed that the Mijiang area serves as a habitat supporting diverse mammals in the lower Tumen River while also playing a crucial role in facilitating animal movement and habitat connectivity. Additionally, the occupancy prediction model developed in this study is expected to contribute to predicting mammal distribution within the disrupted Tumen River basin due to human interference and identifying and protecting potential ecological corridors in this transboundary region.

• Korean Journal of Cognitive Science
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• v.29 no.2
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• pp.121-135
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• 2018

Dancers' motions are perceived by observers through visual processes with visual information forming the basis for the observers' appreciation and evaluation of the dancers' motions. There have been many discussions as to whether or not observers' personal athletic capabilities form an essential basis for accurate assessment of the motions of others but, so far, no valid conclusions have been reached. The purpose of this study is to investigate how the ability to predict motions of others varies depending on the athletic expertise of the observers. Participants of this research were ballet dancers of varying athletic expertise. Twenty seven participants were divided into three groups with nine in each: beginners, intermediate experts and experts. The participants watched the same dance video and then evaluated whether the motion would be successful or not. The movement related visual information required to evaluate the success of the motion was systematically adjusted by controlling the length of the video. Using the temporal occlusion method, this study measured the response accuracy of the participants by category of expertise. Under the circumstance with insufficient visual information to utilize, the experts showed higher rates of correct response than the intermediate experts and the beginners. The beginners showed higher rates of wrong response than the experts and the intermediate experts. These results showed that the ability to predict success or failure of a dance motion varied depending on motion expertise of the observers, although they had similar level of expertise in perception. Participants considered to have high athletic expertise showed high prediction ability on the result of the motion. In addition, high expertise in perception reduced the likelihood that participants would make hasty responses under the circumstance with insufficient information and helped to reduce wrong response rate. In conclusion, this study showed that motor expertise and perceptual expertise contribute to prediction accuracy of observed motions.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.7
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• pp.802-811
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• 2023

Marine spatial planning (MSP) is a crucial element for rational allocation and sustainable use of marine areas. Particularly, Fishing Activity Protected Areas constitute essential zones accounting for 45.6% designated for sustainable fishing activities. However, the current assessment of these zones does not adequately consider future demands and potential values, necessitating appropriate evaluation methods and predictive tools for long-term planning. In this study, we selected key fish species (Scomber japonicus, Trichiurus lepturus, Engraulis japonicus, and Larimichthys polyactis) within the Fishing Activity Protected Area to predict their distribution and compare it with the current designated zones for evaluating the ability of the prediction tool. Employing the Intergovernmental Panel on Climate Change (IPCC) 6th Assessment Report scenarios (SSP1-2.6 and SSP5-8.5), we used species distribution models (such as MaxEnt) to assess the movement and distribution changes of these species owing to future variations. The results indicated a 30-50% increase in the distribution area of S. japonicus, T. lepturus, and L. polyactis, whereas the distribution area of E. japonicus decreased by approximately 6-11%. Based on these results, a species richness map for the four key species was created. Within the marine spatial planning boundaries, the overlap between areas rated "high" in species richness and the Fishing Activity Protected Area was approximately 15%, increasing to 21% under the RCP 2.6 scenario and 34% under the RCP 8.5 scenario. These findings can serve as scientific evidence for future evaluations of use zones or changes in reserve areas. The current and predicted distributions of species owing to climate change can address the limitations of current use zone evaluations and contribute to the development of plans for sustainable and beneficial use of marine resources.