• Journal of Astronomy and Space Sciences
• /
• v.26 no.4
• /
• pp.677-692
• /
• 2009

In recent years, lots of studies on the planetary rover systems have been performed around space advanced agencies such as NASA, ESA, JAXA, etc. Among the various technologies for the planetary rover system, the mobility system, navigation algorithm, and scientific payload have been focused particularly. In this paper, the conceptual design for a ground-based model of planetary rover's mobility system to evaluate mobility and moving stability on ground is presented. The status of overseas research and development of the planetary rover systems is also addressed in terms of technical issues. And then, the requirements of the planetary rover's mobility system are derived by means of considering mobility and stability. The designed rover's mobility system has an active suspension with 6 legs that controls 6 joints on the each leg in order to achieve high stability and mobility. This kind of mobility system has already applied to the ATHELE of NASA for various purposes such as transportation and habitation for human lunar exploration activities in the near future (i.e., Constellation program). However, the proposed system has been designed by focusing on the small-sized unmanned explorations, which may be applied for the future Korea Lunar exploration missions. Therefore, we expect that this study will be an useful reference and experience in order to develop the planetary exploration rover system in Korea.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.51 no.6
• /
• pp.209-220
• /
• 2014

Compressive Sensing (CS) is a new signal acquisition paradigm which enables sampling under Nyquist rate for a special kind of signal called sparse signal. There are plenty of CS recovery methods but their performance are still challenging, especially at a low sub-rate. For CS recovery of natural images, regularizations exploiting some prior information can be used in order to enhance CS performance. In this context, this paper addresses improving quality of reconstructed natural images based on Dantzig selector and smooth filters (i.e., Gaussian filter and nonlocal means filter) to generate a new regularization called smooth residual error regularization. Moreover, total variation has been proved for its success in preserving edge objects and boundary of reconstructed images. Therefore, effectiveness of the proposed regularization is verified by experimenting it using augmented Lagrangian total variation minimization. This framework is considered as a new CS recovery seeking smoothness in residual images. Experimental results demonstrate significant improvement of the proposed framework over some other CS recoveries both in subjective and objective qualities. In the best case, our algorithm gains up to 9.14 dB compared with the CS recovery using Bayesian framework.

• Journal of Advanced Navigation Technology
• /
• v.22 no.5
• /
• pp.384-390
• /
• 2018

This paper introduces a MATLAB graphical user interface (GUI) based software for analysis of korea augmentation satellite system (KASS) availability performance. This software uses minimum variance (MV) estimator and Kriging algorithm to generate integrity information such as user differential range error (UDRE) and grid ionospheric vertical error (GIVE). The information is offered to ground and aviation users in Korean region. The software also gives accuracy data, protection level data and availability map about each user position by using the integrity information. In particular the software calculates the protection level along a path of aircraft. We verified the result of protection level of aviation user by comparing them with the results of SBASimulator#2, which is a simulation tool of european geostationary navigation overlay service (EGNOS). As a result, the protection level error between the result of our software and the SBASimulator#2 was about 2% which means that the result of our software is accurate.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.40 no.1
• /
• pp.25-33
• /
• 2020

A drought event is characterized by duration, severity and affected area. In general, after calculating a drought index using hydro-meteorological time series at a station, a drought event is defined based on the run theory to identify the beginning and end time. However, this one-dimensional analysis has limitations for analyzing the spatio-temporal occurrence characteristics and movement paths of drought. Therefore, this study is to define a three-dimensional drought event using a simple clustering algorithm and to develop a drought map that can be used to understand the drought severity according to the spatio-temporal expansion of drought. As a result, compared with the two-dimensional monitoring information to show spatial distribution of drought index, a proposed drought map is able to show three-dimensional drought characteristics inclusing drought duration, spatial cumulative severity, and centroid of drought. The analysis of drought map indicated that there was a drought event which had the affected area less than 10 % while on occations while there were 11 drought events (44 %) which had the affected area more a than 90 % of the total area. This means that it is important to understand the relationship between spatial variation of drought affected area and severity corresponding to various drought durations. The development of drought map based on three-dimensional drought analysis is useful to analyze the spatio-temporal occurrence characteristics and propagation patterns of regional drought which can be utilized in developing mitigation measures for future extreme droughts.

• Korean Journal of Food Science and Technology
• /
• v.21 no.5
• /
• pp.676-685
• /
• 1989

Optimal operating conditions of on-farm red pepper dryer were searched by using the simulation-optimization algorithm combining the drying and quality deterioration models of red pepper with Box's complex method. Determination of control variables such as air temperature, air recycle ratio and air flow rate was based on a criterion of minimizing energy consumption under the constrainst conditions that satisfied the specified color retention of carotenoids. As quality constraint was stricter, energy consumption increased and total drying time decreased with lower recycle ratio and higher air flow rate Product mixing during drying was found to be able to improve the energy efficiency and product quality. Currently used air flow rate was assessed to be increased for the optimal operation. Two stage drying at the fixed optimal air flow rate was proven to be useful means for further saying of energy consumption. In the optimal bistaged drying, the second stage began at about one third of the total drying time and low air temperature in the first stage Increased to a high value and air recycle ratio increased slightly in the second stage. Optimal control variable scheme could be explained by the dryer performance and the carotenoids destruction kinetics in red pepper drying.

• Journal of Intelligence and Information Systems
• /
• v.24 no.2
• /
• pp.1-19
• /
• 2018

Since stock movements forecasting is an important issue both academically and practically, studies related to stock price prediction have been actively conducted. The stock price forecasting research is classified into structured data and unstructured data, and it is divided into technical analysis, fundamental analysis and media effect analysis in detail. In the big data era, research on stock price prediction combining big data is actively underway. Based on a large number of data, stock prediction research mainly focuses on machine learning techniques. Especially, research methods that combine the effects of media are attracting attention recently, among which researches that analyze online news and utilize online news to forecast stock prices are becoming main. Previous studies predicting stock prices through online news are mostly sentiment analysis of news, making different corpus for each company, and making a dictionary that predicts stock prices by recording responses according to the past stock price. Therefore, existing studies have examined the impact of online news on individual companies. For example, stock movements of Samsung Electronics are predicted with only online news of Samsung Electronics. In addition, a method of considering influences among highly relevant companies has also been studied recently. For example, stock movements of Samsung Electronics are predicted with news of Samsung Electronics and a highly related company like LG Electronics.These previous studies examine the effects of news of industrial sector with homogeneity on the individual company. In the previous studies, homogeneous industries are classified according to the Global Industrial Classification Standard. In other words, the existing studies were analyzed under the assumption that industries divided into Global Industrial Classification Standard have homogeneity. However, existing studies have limitations in that they do not take into account influential companies with high relevance or reflect the existence of heterogeneity within the same Global Industrial Classification Standard sectors. As a result of our examining the various sectors, it can be seen that there are sectors that show the industrial sectors are not a homogeneous group. To overcome these limitations of existing studies that do not reflect heterogeneity, our study suggests a methodology that reflects the heterogeneous effects of the industrial sector that affect the stock price by applying k-means clustering. Multiple Kernel Learning is mainly used to integrate data with various characteristics. Multiple Kernel Learning has several kernels, each of which receives and predicts different data. To incorporate effects of target firm and its relevant firms simultaneously, we used Multiple Kernel Learning. Each kernel was assigned to predict stock prices with variables of financial news of the industrial group divided by the target firm, K-means cluster analysis. In order to prove that the suggested methodology is appropriate, experiments were conducted through three years of online news and stock prices. The results of this study are as follows. (1) We confirmed that the information of the industrial sectors related to target company also contains meaningful information to predict stock movements of target company and confirmed that machine learning algorithm has better predictive power when considering the news of the relevant companies and target company's news together. (2) It is important to predict stock movements with varying number of clusters according to the level of homogeneity in the industrial sector. In other words, when stock prices are homogeneous in industrial sectors, it is important to use relational effect at the level of industry group without analyzing clusters or to use it in small number of clusters. When the stock price is heterogeneous in industry group, it is important to cluster them into groups. This study has a contribution that we testified firms classified as Global Industrial Classification Standard have heterogeneity and suggested it is necessary to define the relevance through machine learning and statistical analysis methodology rather than simply defining it in the Global Industrial Classification Standard. It has also contribution that we proved the efficiency of the prediction model reflecting heterogeneity.

• Korean Journal of Remote Sensing
• /
• v.31 no.5
• /
• pp.449-459
• /
• 2015

The conventional National Forest Inventory(NFI)-based forest carbon stock estimation method is suitable for national-scale estimation, but is not for regional-scale estimation due to the lack of NFI plots. In this study, for the purpose of regional-scale carbon stock estimation, we created grid-based forest carbon stock maps using spatial ancillary data and two types of up-scaling methods. Chungnam province was chosen to represent the study area and for which the $5^{th}$ NFI (2006~2009) data was collected. The first method (method 1) selects forest type map as ancillary data and uses regression model for forest carbon stock estimation, whereas the second method (method 2) uses satellite imagery and k-Nearest Neighbor(k-NN) algorithm. Additionally, in order to consider uncertainty effects, the final AGB carbon stock maps were generated by performing 200 iterative processes with Monte Carlo simulation. As a result, compared to the NFI-based estimation(21,136,911 tonC), the total carbon stock was over-estimated by method 1(22,948,151 tonC), but was under-estimated by method 2(19,750,315 tonC). In the paired T-test with 186 independent data, the average carbon stock estimation by the NFI-based method was statistically different from method2(p<0.01), but was not different from method1(p>0.01). In particular, by means of Monte Carlo simulation, it was found that the smoothing effect of k-NN algorithm and mis-registration error between NFI plots and satellite image can lead to large uncertainty in carbon stock estimation. Although method 1 was found suitable for carbon stock estimation of forest stands that feature heterogeneous trees in Korea, satellite-based method is still in demand to provide periodic estimates of un-investigated, large forest area. In these respects, future work will focus on spatial and temporal extent of study area and robust carbon stock estimation with various satellite images and estimation methods.

• Journal of the Korea Concrete Institute
• /
• v.16 no.4 s.82
• /
• pp.493-500
• /
• 2004

FREP(Fiber Reinforced Epoxy Panel) are used for strengthening the damaged RC beams due to its good tensile strength, low weight, resistance to corrosion, and easy applicability. This study sets up structure equation for FREP bending reinforcement before and during the usage of RC beam. It finds the difference and finds the mechanical characteristics of rip-off failure that is caused by stress concentration in reinforcement material cutting part to estimate the performance of bending reinforcement. The result of this research can be summarized as two main consequences. The main failure of FREP reinforced concrete beam is rip-off failure and it evaluated rip-off failure of RC reinforcing bean based on the test and analytical conditions of this study. It found that stress was concentrated due to rapid change of bending rigidity in reinforced cutting part as a result of excessive reinforcement thickness of FREP. It resulted in rip-off failure. It means that it should evaluate the rip-off failure when designing reinforcement. It analyzed the reinforcement effect according to reinforced period for FREP. It found that reinforcement effect of P-Type that was reinforced during the usage decreased compared to I-Type that was reinforced before the usage. So when reinforcing a existing structure that is being used, it should consider the stress that is produced due to the fixed load.

• The Journal of Engineering Geology
• /
• v.20 no.3
• /
• pp.231-242
• /
• 2010

In order to assess applicability of debris flow simulation on natural terrain in Korea, this study introduced the DEBRIS-2D program which had been developed by Liu and Huang (2006). For simulation of large debris flows composed of fine and coarse materials, DEBRIS-2D was developed using the constitutive relation proposed by Julien and Lan (1991). Based on the theory of DEBRIS-2D, this study selected a valley where a large debris flow was occurred on July 16th, 2006 at Deoksanri, Inje county, Korea. The simulation results show that all mass were already flowed into the stream at 10 minutes after starting. In 10minutes, the debris flow reached the first geological turn and an open area, resulting in slow velocity and changing its flow direction. After that, debris flow started accelerating again and it reached the village after 40 minutes. The maximum velocity is rather low between 1 m/sec and 2 m/sec. This is the reason why debris flow took 50 minutes to reach the village. The depth change of debris flow shows enormous effect of the valley shape. The simulated result is very similar to what happened in the field. It means that DEBRIS-2D program can be applied to the geologic and topographic conditions in Korea without large modification of analysis algorithm. However, it is necessary to determine optimal reference values of Korean geologic and topographic properties for more reliable simulation of debris flows.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.19 no.1
• /
• pp.94-106
• /
• 2020

Recently, the radio navigation method utilizing the GPS(Global Positioning System) satellite information is widely used as the method to measure the position of objects. As GPS applications become wider and fields based on various positioning information emerge, new methods for achieving higher accuracy are required. In the case of autonomous vehicles, the INS(Inertial Navigation System) using the IMU(Inertial Measurement Unit), and the DR(Dead Reckoning) algorithm using the in-vehicle sensor, are used for the purpose of preventing degradation of accuracy of the GPS and to measure the position in the shadow area. However, these positioning methods have many elements of problems due not only to the existence of various shaded areas such as building areas that are continually enlarged, tunnels, underground parking lots and but also to the limitations of accumulation-based location estimation methods that increase in error over time. In this paper, an efficient positioning method in a large underground parking space using Fingerprint method is proposed by placing the AP(Access Points) and directional antennas in the form of four anchors using WLAN, a popular means of wireless communication, for positioning the vehicle in the GPS shadow area. The proposed method is proved to be able to produce unchanged positioning results even in an environment where parked vehicles are moved as time passes.