• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.9
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• pp.2209-2215
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• 2014

This paper presents, a deinterlacing method by improving the Direction-Oriented Interpolation (DOI) technique. The technique is considered to be a very strong tool for intrafield-based deinterlacing. However, DOI has some problems such as long processing time, wrong edge detection in periodic pattern. To remedy this problem, we replace the full search in DOI by a two-step search to reduce processing time and introduces two additional processes to improve image quality. In the proposed method, the spatial direction vectors (SDVs) misread data are reconsidered to prevent them utilizing in the next interpolation step, resulting in an accurate deinterlacing method. We conduct experiments with ISO experimental images to compare the proposed method with the existing methods including line evarage (LA), edge-based line averaging (ELA), DOI, selective deinterlacing algorithm (SDA). Experimental results show the proposed method gives better performance in objective and subjective quality than existing deinterlacing methods.

• Journal of The Geomorphological Association of Korea
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• v.24 no.3
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• pp.105-118
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• 2017

The debris slope landforms which are existent in Korean mountains is generally on the steep slopes and mostly covered by vegetation, it is difficult to investigate the landform. Therefore a scientific method is required to come up with an effective field investigation plan. For this purpose, the use of Remote Sensing and GIS technologies for a spatial analysis is essential. This study has extracted the potential area of debrisslope landform formation using Fuzzy set and Bayesian Predictive Discriminate Model as mathematical data integration methods. The first step was to obtain information about debris locations and their related factors. This information was verified through field investigation and then used to build a database. In the second step, the map that zoning the study area based on the degree of debris formation possibility was generated using two modeling methods, and then cross validation technique was applied. In order to quantitatively analyze the accuracy of two modeling methods, the calculated potential rate of debrisformation within the study area was evaluated by plotting SRC(Success Rate Curve) and calculating AUC(Area Under the Curve). As a result, the prediction accuracy of Fuzzy set model wes 83.1% and Bayesian Predictive Discriminate Model wes 84.9%. It showed that two models are accurate and reliable and can contribute to efficient field investigation and debris landform management.

• Journal of Cadastre & Land InformatiX
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• v.49 no.1
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• pp.5-15
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• 2019

This study is to review methodological limitations of previous studies, propose Tobit Model as an analysis model. We model used annual rent-free as dependent variable and considering to contract characteristics and building characteristics as independent variable. We model was consisted to the three model as follow: Basic model, Time control model, Quadratic model. As a result of the analysis, existing variables revealed through previous studies such as contract period, contract area, total floor area, and building age were all statistically significant. These results were robust when considering time effects. Also, floor area and annual rent-free was quadratic relationship by inverse U shape. This result provide to methodological contribution for related research. Also, provide to more accurate information for participants in seoul office market.

• Journal of information and communication convergence engineering
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• v.17 no.1
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• pp.74-83
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• 2019

This study designs a squat posture recognition system that can provide correct squat posture guidelines. This system comprises two modules: a Kinect camera for monitoring users' body movements and a Wii Balance Board(WBB) for measuring balanced postures with legs. Squat posture recognition involves two states: "Stand" and "Squat." Further, each state is divided into two postures: correct and incorrect. The incorrect postures of the Stand and Squat states were classified into three and two different types of postures, respectively. The factors that determine whether a posture is incorrect or correct include the difference between shoulder width and ankle width, knee angle, and coordinate of center of pressure(CoP). An expert and 10 participants participated in experiments, and the three factors used to determine the posture were measured using both Kinect and WBB. The acquired data from each device show that the expert's posture is more stable than that of the subjects. This data was classified using a support vector machine (SVM) and $na{\ddot{i}}ve$ Bayes classifier. The classification results showed that the accuracy achieved using the SVM and $na{\ddot{i}}ve$ Bayes classifier was 95.61% and 81.82%, respectively. Therefore, the developed system that used Kinect and WBB could classify correct and incorrect postures with high accuracy. Unlike in other studies, we obtained the spatial coordinates using Kinect and measured the length of the body. The balance of the body was measured using CoP coordinates obtained from the WBB, and meaningful results were obtained from the measured values. Finally, the developed system can help people analyze the squat posture easily and conveniently anywhere and can help present correct squat posture guidelines. By using this system, users can easily analyze the squat posture in daily life and suggest safe and accurate postures.

• Journal of the Korea Society of Computer and Information
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• v.28 no.5
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• pp.9-15
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• 2023

In this paper, we propose a method predicting the speed of each lane from the link speed using a neural network. We took three measures for configuring learning data to increase prediction accuracy. The first one is to expand the spatial range of the data source by including 14 links connected to the beginning and end points of the link. We also increased the time interval from 07:00 to 22:00 and included the data generation time in the feature data. Finally, we marked weekdays and holidays. Results of experiments showed that the speed error was reduced by 21.9% from 6.4 km/h to 5.0 km/h for straight lane, by 12.9% from 8.5 km/h to 7.4 km/h for right turns, and by 5.7% from 8.7 km/h to 8.2 km/h for left-turns. As a secondary result, we confirmed that the prediction accuracy of each lane was high for city roads when the traffic flow was congested. The feature of the proposed method is that it predicts traffic conditions for each lane improving the accuracy of prediction.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.1
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• pp.216-238
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• 2023

In intelligent transportation systems, traffic management is an important task. The accurate forecasting of traffic characteristics like flow, congestion, and density is still active research because of the non-linear nature and uncertainty of the spatiotemporal data. Inclement weather, such as rain and snow, and other special events such as holidays, accidents, and road closures have a significant impact on driving and the average speed of vehicles on the road, which lowers traffic capacity and causes congestion in a widespread manner. This work designs a model for multivariate short-term traffic congestion prediction using SLSTM_AE-BiLSTM. The proposed design consists of a Bidirectional Long Short Term Memory(BiLSTM) network to predict traffic flow value and a Convolutional Neural network (CNN) model for detecting the congestion status. This model uses spatial static temporal dynamic data. The stacked Long Short Term Memory Autoencoder (SLSTM AE) is used to encode the weather features into a reduced and more informative feature space. BiLSTM model is used to capture the features from the past and present traffic data simultaneously and also to identify the long-term dependencies. It uses the traffic data and encoded weather data to perform the traffic flow prediction. The CNN model is used to predict the recurring congestion status based on the predicted traffic flow value at a particular urban traffic network. In this work, a publicly available Caltrans PEMS dataset with traffic parameters is used. The proposed model generates the congestion prediction with an accuracy rate of 92.74% which is slightly better when compared with other deep learning models for congestion prediction.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.297-299
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• 2022

Malignant glioma has a poor prognosis with the reported median survival of between 6 months to 14 months. Thus, it is crucial to predict the accurate survival of patients with malignant glioma. In this paper, we propose a convolutional neural network to predict the overall survival and age of the patients. A total of four MRI modalities, T1, T1-contrast enhanced, T2, and fluid-attenuated inversion recovery, which effectively capture spatial characteristics of malignant glioma, were used as input images. Age is an important factor impacting the overall survival, thus incorporating it into the model will thereby improve the performance of the proposed model. Our model successfully predicted overall survival and age of the patients with pearson correlation coefficients of 0.1748 and 0.3056, respectively.

• Journal of The Korean Society of Agricultural Engineers
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• v.65 no.6
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• pp.65-78
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• 2023

In recent times, the frequency and intensity of natural disasters, such as heavy rains and typhoons, have been increasing due to the impacts of climate change. This has led to a rise in social and economic damages. Rural areas, in particular, possess limited disaster response capabilities due to their underdeveloped infrastructure and are highly vulnerable to flooding. Therefore, it is crucial to establish preventative and responsive measures. In this study, an Inventory-Based Flood Loss Estimation (IB-FLE) method utilizing high-resolution spatial information was developed for estimating flood-related losses in rural areas. Additionally, the developed approach was applied to a study area and compared with the Multidimensional Flood Damage Analysis (MD-FDA) method. Compared to the MD-FDA, the IB-FLE enables faster and more accurate estimation of flood damages and allows for the assessment of individual building and agricultural land losses using up-to-date information. The findings of this study are expected to contribute to the rational allocation of budgets for rural flood damage prevention and recovery, as well as enhancing disaster response capabilities.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.3
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• pp.551-562
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• 2024

This study relates to a method and device for extracting and tracking moving objects. In particular, objects are extracted using different images between adjacent images, and the location information of the extracted object is continuously transmitted to provide accurate location information of at least one moving object. It relates to a method and device for extracting and tracking moving objects based on tracking moving objects. People tracking, which started as an expression of the interaction between people and computers, is used in many application fields such as robot learning, object counting, and surveillance systems. In particular, in the field of security systems, cameras are used to recognize and track people to automatically detect illegal activities. The importance of developing a surveillance system, that can detect, is increasing day by day.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.17 no.3
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• pp.181-188
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• 2012

Despite the ecological importance of seagrass beds, their distributional information in Korean coastal waters is insufficient. Therefore, we used hydroacoustic system to collect accurate bathymetry and classification of seagrass, and Kompsat-2 (4 m spatial resolution) image for detection of seagrass beds at Deukryang Bay, Korea. The accuracy of Kompsat-2 image classification was evaluated using hydracoustic survey result using error matrix and Kappa value. The total area of seagrass beds from satellite image classification was underestimated compared to the hydroacoustic survey, estimated 3.9 and $4.5km^2$ from satellite image and hydroacoustic data, respectively. Nonetheless, the accuracy of Kompsat-2 image classification over hydroacoustic-based method showing 90% (Kappa=0.85) for the three class maps (seagrass, unvegetated seawater and aquaculture). The agreement between the satellite image classification and the hydroacoustic result was 77.1% (the seagrass presence/absence map). From our result of satellite image classification, Kompsat-2 image is suitable for mapping seagrass beds with high accuracy and non-destructive method. For more accurate information, more researches with a variety of high-resolution satellite image will be preceded.