• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.11
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• pp.1661-1670
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• 2016

In this paper, we present a risk prediction system and customized evacuation pathfinding algorithm in fire scenarios. For the risk prediction, we apply a multi-level clustering mechanism using collected temperature at sensor nodes throughout the network in order to predict the temperature at the time that users actually evacuate. Based on the predicted temperature and its reliability, we suggest an evacuation pathfinding algorithm that finds a suitable evacuation path from a user's current location to the safest exit. Simulation results based on FDS(Fire Dynamics Simulator) of NIST for a wireless sensor network consisting of 47 stationary nodes for 1436.41 seconds show that our proposed prediction system achieves a higher accuracy by a factor of 1.48. Particularly for nodes in the most reliable group, it improves the accuracy by a factor of up to 4.21. Also, the customized evacuation pathfinding based on our prediction algorithm performs closely with that of the ground-truth temperature in terms of the ratio of safe nodes on the selected path, while outperforming the shortest-path evacuation with a factor of up to 12% in terms of a safety measure.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.31 no.2
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• pp.111-119
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• 2013

It is essential and fundamental to extract boundary information of target object via massive three-dimensional point data acquired from laser scanner. Especially extracting boundary information of manmade features such as buildings is quite important because building is one of the major components consisting complex contemporary urban area, and has artificially defined shape. In this research, extended ${\chi}$-algorithm using geometry information of point data was proposed to extract boundary information of building from three-dimensional point data consisting building. The proposed algorithm begins with composing Delaunay triangulation process for given points and removes edges satisfying specific conditions process. Additionally, to make whole boundary extraction process efficient, we used Sweep-hull algorithm for constructing Delaunay triangulation. To verify the performance of the proposed extended ${\chi}$-algorithm, we compared the proposed algorithm with Encasing Polygon Generating Algorithm and ${\alpha}$-Shape Algorithm, which had been researched in the area of feature extraction. Further, the extracted boundary information from the proposed algorithm was analysed against manually digitized building boundary in order to test accuracy of the result of extracting boundary. The experimental results showed that extended ${\chi}$-algorithm proposed in this research proved to improve the speed of extracting boundary information compared to the existing algorithm with a higher accuracy for detecting boundary information.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.10 no.3
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• pp.249-255
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• 2017

The finger vein recognition technology is a method to acquire a finger vein image by illuminating infrared light to the finger and to authenticate a person through processes such as feature extraction and matching. In order to recognize a finger vein, a 2D mask-based two-dimensional convolution method can be used to detect a finger edge but it takes too much computation time when it is applied to a low cost micro-processor or micro-controller. To solve this problem and improve the recognition rate, this study proposed an extraction method for the region of interest based on virtual core points and moving average filtering based on the threshold and absolute value of difference between pixels without using 2D convolution and 2D masks. To evaluate the performance of the proposed method, 600 finger vein images were used to compare the edge extraction speed and accuracy of ROI extraction between the proposed method and existing methods. The comparison result showed that a processing speed of the proposed method was at least twice faster than those of the existing methods and the accuracy of ROI extraction was 6% higher than those of the existing methods. From the results, the proposed method is expected to have high processing speed and high recognition rate when it is applied to inexpensive microprocessors.

• Structural Engineering and Mechanics
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• v.75 no.4
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• pp.507-518
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• 2020

With the rapid development of rail transit, rail transit noise needs to be paid more and more attention. In order to accurately and effectively analyze the characteristics of low-frequency noise, a prediction model of vibration of box girder was established based on the hybrid FE-SEA method. When the train speed is 140 km/h, 200 km/h and 250 km/h, the vibration and noise of the box girder induced by the vertical wheel-rail interaction in the frequency range of 20-500 Hz are analyzed. Detailed analysis of the energy level, sound pressure contribution, modal analysis and vibration loss power of each slab at the operating speed of 140 km /h. The results show that: (1) When the train runs at a speed of 140km/h, the roof contributes more to the sound pressure at the far sound field point. Analyzing the frequency range from 20 to 500 Hz: The top plate plays a very important role in controlling sound pressure, contributing up to 70% of the sound pressure at peak frequencies. (2) When the train is traveling at various speeds, the maximum amplitude of structural vibration and noise generated by the viaduct occurs at 50 Hz. The vibration acceleration of the box beam at the far field point and near field point is mainly concentrated in the frequency range of 31.5-100 Hz, which is consistent with the dominant frequency band of wheel-rail force. Therefore, the main frequency of reducing the vibration and noise of the box beam is 31.5-100 Hz. (3) The vibration energy level and sound pressure level of the box bridge at different speeds are basically the same. The laws of vibration energy and sound pressure follow the rules below: web

• Journal of Korea Water Resources Association
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• v.42 no.10
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• pp.891-896
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• 2009

The puff models have been developed to simulate the advection-diffusion processes of dredging suspended solids, either alone or in combination with Eulerian models. Computational efficiency and accuracy are of prime importance in designing these hybrid approaches to simulate a pollutant discharge, and we characterize two relatively simple Lagrangian techniques in this regard: forward Gaussian puff tracking (FGPT), and backward Gaussian puff tracking (BGPT). FGPT and BGPT offer dramatic savings in computational expense, but their applicability is limited by accuracy concerns in the presence of spatially variable flow or diffusivity fields or complex no-flux or open boundary conditions. For long simulations, particle and/or puff methods can transition to an Eulerian model if appropriate, since the relative computational expense of Lagrangian methods increases with time for continuous sources. Although we focus on simple Lagrangian models that are not suitable to all environmental applications, many of the implementation and computational efficiency concerns outlined herein would also be relevant to using higher order particle and puff methods to extend the near field.

• Journal of the Korean Dietetic Association
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• v.10 no.4
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• pp.390-400
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• 2004

Food and nutrition sites are the major portion of the health information sites. For the point of public health it is very important to secure validity and reliability of information on those web sites. Therefore, in this study we would like to identify problems when acquiring recipes in web sites by analyzing and reviewing recipes in web sites. To investigate Korean food recipes provided in web sites, domestic search engines such as Simmani, Naver, Hanmir, and Empas and foreign search engines such as Yahoo Korea, Lycos and Altabista Korea were used. Searchs were done using 'recipe' and 'Joribeob (cooking method)' from March 20, 2002 to June 20, 2002. Informations in each sites were reviewed and analyzed Results are as follow; When classifying 46sites searched with 'Joribeob' by the information provider, 24sites were individual, 16sites were corporate and 6sites were others. When searching 'recipe', total 12,654recipes were returned. Out of them, individual provided 2,581sites(20.4%), corporate provided 7,249sites(57.3%), and others provided 2,824sites(22.3%). 9,979(78.9%) recipes out of 12,654recipes were proved to be appropriate as Korean food. Classifying recipes by dish group, vegetables 11.7%, soups and hot soups 9.7%, stew and casseroles 8.2%, pan cakes 8.0%, stir fried foods and skewers 7.8%, rice 7.2%, hard boiled food 7.1%, steam 6.4%, noodles and mandu 5.3%, Kimchi 4.5%, fried 4.1%, and porridge 3.7% in order. 21.1% of recipes were not appropriate as Korean food but provided as Korean Food. The proportion of individual as the information provider were higher than that of enterprises. Recipes from enterprises were based on food and nutrient information and more reliable. However, there were some cases that they provided the same amount of ingredients with different calories or provided the same calories with different ingredients. Additionally, depending on sites, they provided different calories even for the same recipe. There were some cases that the calories provided on the site were too high or too low, for the suggested amount of ingredients and serving size. Recipes those provide amount of calories were evaluated using the nutrient analysis program. Calculated calories and provided calories on the Web were compared together. There are difference between two valus. With these results, it may lead misuse of recipe by those who need accuracy in diet such as patients or who are interested in recipe information for academic purposes. These results could be used as basic materials to improve quantity and quality of recipes in the future. Also, to improve the accuracy of recipies for Korean foods in the web sites, there should be some systems to monitor and let internet users know monitoring results.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.11
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• pp.527-536
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• 2017

With the recent development of smart grid industry, the necessity for efficient EMS(Energy Management System) has been increased. In particular, in order to reduce electric load and energy cost, sophisticated electric load forecasting and efficient smart grid operation strategy are required. In this paper, for more accurate electric load forecasting, we extend the data collected at demand time into high time resolution and construct an artificial neural network-based forecasting model appropriate for the high time resolution data. Furthermore, to improve the accuracy of electric load forecasting, time series data of sequence form are transformed into continuous data of two-dimensional space to solve that problem that machine learning methods cannot reflect the periodicity of time series data. In addition, to consider external factors such as temperature and humidity in accordance with the time resolution, we estimate their value at the time resolution using linear interpolation method. Finally, we apply the PCA(Principal Component Analysis) algorithm to the feature vector composed of external factors to remove data which have little correlation with the power data. Finally, we perform the evaluation of our model through 5-fold cross-validation. The results show that forecasting based on higher time resolution improve the accuracy and the best error rate of 3.71% was achieved at the 3-min resolution.

• Korean Journal of Remote Sensing
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• v.27 no.4
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• pp.435-444
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• 2011

The land cover map derived from spectral features of high resolution optical images has low spectral resolution and heterogeneity in the same land cover class. For this reason, despite the same land cover class, the land cover can be classified into various land cover classes especially in vegetation area. In order to overcome these problems, detailed vegetation classification is applied to optical satellite image and SAR(Synthetic Aperture Radar) integrated data in vegetation area which is the result of pre-classification from optical image. The pre-classification and vegetation classification were performed with MLC(Maximum Likelihood Classification) method. The hierarchical land cover classification was proposed from fusion of detailed vegetation classes and non-vegetation classes of pre-classification. We can verify the facts that the proposed method has higher accuracy than not only general SAR data and GLCM(Gray Level Co-occurrence Matrix) texture integrated methods but also hierarchical GLCM integrated method. Especially the proposed method has high accuracy with respect to both vegetation and non-vegetation classification.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.22 no.4
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• pp.383-390
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• 2004

IGS provides so accute a final precise ephmerides which is offered in the 13rd, and it also offers a rapid precise ephmerides for more prompt application and an ultra-rapid precise ephmerides for real-time application. The purpose of this study is to analyze the accuracy of a rapid precise ephemerides and an ultra-rapid precise ephemerides based on a final precise ephmerides and determine the degree of the Lagrange Interpolation which needs to decide the location of a satellite. As the result of this study, the root mean square error of x,y,z coordinates of a rapid precise ephemerides was $\pm$0.0l6m or so, and the root mean square error of an observed ultra-rapid precise ephemerides was approximately $\pm$0.024m. The root mean square error of an ultra-rapid precise ephemerides predicted for 24 hours was $\pm$0.07m or so and the one of an ultra-rapid precise ephemerides predicted for 6 hours was $\pm$0.04m or so. Therefore, I could figure out that it had higher accuracy than a broadcast ephemerides. Also, in case that the location of a satellite was calculated with the method of the Lagrange Interpolation, it was confirmed that using the 9th order polynomial was efficient.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.1
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• pp.23-33
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• 2020

Recently, high-resolution images can be easily acquired using UAV (Unmanned Aerial Vehicle), so that it is possible to produce small area observation and spatial information at low cost. In particular, research on the generation of cover maps in crop production areas is being actively conducted for monitoring the agricultural environment. As a result of comparing classification performance by applying RF(Random Forest), SVM(Support Vector Machine) and CNN(Convolutional Neural Network), deep learning classification method has many advantages in image classification. In particular, land cover classification using satellite images has the advantage of accuracy and time of classification using satellite image data set and pre-trained parameters. However, UAV images have different characteristics such as satellite images and spatial resolution, which makes it difficult to apply them. In order to solve this problem, we conducted a study on the application of deep learning algorithms that can be used for analyzing agricultural lands where UAV data sets and small-scale composite cover exist in Korea. In this study, we applied DeepLab V3 +, FC-DenseNet (Fully Convolutional DenseNets) and FRRN-B (Full-Resolution Residual Networks), the semantic image classification of the state-of-art algorithm, to UAV data set. As a result, DeepLab V3 + and FC-DenseNet have an overall accuracy of 97% and a Kappa coefficient of 0.92, which is higher than the conventional classification. The applicability of the cover classification using UAV images of small areas is shown.