• Proceedings of the IEEK Conference
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• 2002.06b
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• pp.61-64
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• 2002

We have optimally designed and implemented by a monolithic microwave integrated circuit(MMIC) the low noise amplifier(LNA) of 5.8GHz band composed of receiver front-end(RFE) in a on-board equipment system for dedicated short range communication using a depletion-mode GaAs MESFET. The LNA is provided with two active devices, matching circuits, and two drain bias circuits. Operating at a single supply of 3V and a consumption current of 18㎃, The gain at center frequency 5.8GHz is 13.4dB, Noise figure(NF) is 1.94dB, Input 3rd order intercept point(lIPS) is 3dBm, and Input return loss(5$_{11}$) and Output return loss(S$_{22}$) is -l8dB and -13.3dB, respectively. The circuit size is 1.2$\times$O.7$\textrm{mm}^2$.EX>.>.

• The Journal of the Korea Contents Association
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• v.7 no.6
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• pp.185-194
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• 2007

Today, there is in the world-wide cognition about RFID technology due to established standard, EPC(Electronic Product Code). RFID technology will have wide influence over various fields. Especially, RFID technology's advantages will have positive influence on logistics field. We can solve limited various problems presented in SCM by applying RFIB technology. We can have higher cantrol system, better benefits and actual decision information. Many organizations, however, still have hesitated now because RFID technology has not been validated and RFID technology is in early stage. Other problems are employee resistance against new technology and lack of academic empirical research on assimilation RFID technology for organization. Above reasons I mentioned make me study on analysis on assimilation RFID technology decision in SCM field. This study attempts to analyze the assimilation RFID technology factor from previous researches by using AHP. This outcomes of study will be helpful when we assimilate RFID technology in SCM fields.

• Journal of Korean Society of Rural Planning
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• v.28 no.1
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• pp.57-69
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• 2022

Crop classification is very important for estimating crop yield and figuring out accurate cultivation area. The purpose of this study is to classify crops harvested in fall in Idam-ri, Goesan-gun, Chungcheongbuk-do by using unmanned aerial vehicle (UAV) images and support vector machine (SVM) model. The study proceeded in the order of image acquisition, variable extraction, model building, and evaluation. First, RGB and multispectral image were acquired on September 13, 2021. Independent variables which were applied to Farm-Map, consisted gray level co-occurrence matrix (GLCM)-based texture characteristics by using RGB images, and multispectral reflectance data. The crop classification model was built using texture characteristics and reflectance data, and finally, accuracy evaluation was performed using the error matrix. As a result of the study, the classification model consisted of four types to compare the classification accuracy according to the combination of independent variables. The result of four types of model analysis, recursive feature elimination (RFE) model showed the highest accuracy with an overall accuracy (OA) of 88.64%, Kappa coefficient of 0.84. UAV-based RGB and multispectral images effectively classified cabbage, rice and soybean when the SVM model was applied. The results of this study provided capacity usefully in classifying crops using single-period images. These technologies are expected to improve the accuracy and efficiency of crop cultivation area surveys by supplementing additional data learning, and to provide basic data for estimating crop yields.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.173-198
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• 2020

For a long time, many studies have been conducted on predicting the success of campaigns for customers in academia, and prediction models applying various techniques are still being studied. Recently, as campaign channels have been expanded in various ways due to the rapid revitalization of online, various types of campaigns are being carried out by companies at a level that cannot be compared to the past. However, customers tend to perceive it as spam as the fatigue of campaigns due to duplicate exposure increases. Also, from a corporate standpoint, there is a problem that the effectiveness of the campaign itself is decreasing, such as increasing the cost of investing in the campaign, which leads to the low actual campaign success rate. Accordingly, various studies are ongoing to improve the effectiveness of the campaign in practice. This campaign system has the ultimate purpose to increase the success rate of various campaigns by collecting and analyzing various data related to customers and using them for campaigns. In particular, recent attempts to make various predictions related to the response of campaigns using machine learning have been made. It is very important to select appropriate features due to the various features of campaign data. If all of the input data are used in the process of classifying a large amount of data, it takes a lot of learning time as the classification class expands, so the minimum input data set must be extracted and used from the entire data. In addition, when a trained model is generated by using too many features, prediction accuracy may be degraded due to overfitting or correlation between features. Therefore, in order to improve accuracy, a feature selection technique that removes features close to noise should be applied, and feature selection is a necessary process in order to analyze a high-dimensional data set. Among the greedy algorithms, SFS (Sequential Forward Selection), SBS (Sequential Backward Selection), SFFS (Sequential Floating Forward Selection), etc. are widely used as traditional feature selection techniques. It is also true that if there are many risks and many features, there is a limitation in that the performance for classification prediction is poor and it takes a lot of learning time. Therefore, in this study, we propose an improved feature selection algorithm to enhance the effectiveness of the existing campaign. The purpose of this study is to improve the existing SFFS sequential method in the process of searching for feature subsets that are the basis for improving machine learning model performance using statistical characteristics of the data to be processed in the campaign system. Through this, features that have a lot of influence on performance are first derived, features that have a negative effect are removed, and then the sequential method is applied to increase the efficiency for search performance and to apply an improved algorithm to enable generalized prediction. Through this, it was confirmed that the proposed model showed better search and prediction performance than the traditional greed algorithm. Compared with the original data set, greed algorithm, genetic algorithm (GA), and recursive feature elimination (RFE), the campaign success prediction was higher. In addition, when performing campaign success prediction, the improved feature selection algorithm was found to be helpful in analyzing and interpreting the prediction results by providing the importance of the derived features. This is important features such as age, customer rating, and sales, which were previously known statistically. Unlike the previous campaign planners, features such as the combined product name, average 3-month data consumption rate, and the last 3-month wireless data usage were unexpectedly selected as important features for the campaign response, which they rarely used to select campaign targets. It was confirmed that base attributes can also be very important features depending on the type of campaign. Through this, it is possible to analyze and understand the important characteristics of each campaign type.

• Journal of KIISE:Databases
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• v.35 no.5
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• pp.432-446
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• 2008

Queries for tracing tag locations are among the most challenging requirements in RFID based applications, including automated manufacturing, inventory tracking and supply chain management. For efficient query processing, a previous study proposed the index scheme for storing tag objects, based on the moving object index, in 3-dimensional domain with the axes being the tag identifier, the reader identifier, and the time. In a different way of a moving object index, the ranges of coordinates for each domain are quite different so that the distribution of query regions is skewed to the reader identifier domain. Previous indexes for tags, however, do not consider the skewed distribution for query regions. This results in producing many overlaps between index nodes and query regions and then causes the problem of traversing many index nodes. To solve this problem, we propose a new disproportional insertion and split policy of the index for RFID tags which is based on the R*-tree. For efficient insertion of tag data, our method derives the weighted margin for each node by using weights of each axis and margin of nodes. Based the weighted margin, we can choose the subtree and the split method in order to insert tag data with the minimum cost. Proposed insertion method also reduces the cost of region query by reducing overlapped area of query region and MBRs. Our experiments show that the index based on the proposed insertion and split method considerably improves the performance of queries than the index based on the previous methods.

• Journal of the Korean Magnetics Society
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• v.17 no.1
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• pp.10-13
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• 2007

Single crystalline, $LuFe_2O_4$, was grown by the floating zone method. The crystal structure was a two-dimensional layered-type rhombohedral($R\={3}mh$) structure, with an $a_0=3.440(2)\;{\AA}\;and\;a\;c_0=25.263(2)\;{\AA}$. The magnetic $N\'{e}el$ temperature($T_N$) was determined to be 250 K. The $M\"{o}ssbauer$ spectrum at 12 K was fitted with four sextet sets which was resulted from the crystal structure. The spectrm at room temperature consisted of three singlets and a doublet with the electric quadrupole splitting. The isomer shift($\delta$) value of the singlet was $0.20{\pm}0.01mm/s$ relative to the Fe metal indicating the $Fe^{3+}$ valence state, and the value of the doublet was $0.70{\pm}0.01mm/s$ indicating $Fe^{2+}$. The $M\"{o}ssbauer$ absorption area ratio between $Fe^{3+}$ and $Fe^{2+}$ at room temperature was 1:1. The doublet phase of spectra gradually disappears by up to 360 K. At 360 K, the spectrum shows the singlet phase. We suggested that the spontaneous polarization effect of $LuFe_2O_4$ was caused by the change of iron behavior.

• Korean Journal of Food Science and Technology
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• v.38 no.1
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• pp.99-103
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• 2006

Hepatoprotective effects of Bulro Kugi (Lycium chinense Mill) fruit extracts on $CCl_4-administered$ rats were investigated in vivo. Administration of $CCl_4$ increased plasma glutamic oxalacetic transaminase (GOT), glutamic pyruvic transaminase (GPT), and lactate dehydrogenase (LDH) activities, induced lipid peroxidation as measured by malondialdehyde (MDA) content of rat liver, and significantly increased liver weight. Feeding of B. Kugi (Lycium chinense Mill) slightly increased body weight gain, although not significantly different from normal group. B. Kugi (Lycium chinense Mill) fruit extracts reduced blood cholesterol level and inhibited $CCl_4-induced$ increases of plasma GPT, GOT, and LDH activities, whereas increased contents of MDA and cytochrome P-450, and GST activity in liver tissue of $CCl_4-administered$ rats. Roasted B. Kugi (Lycium chinense Mill) fruit extract showed highest hepatoprotective effect among samples tested. These results suggest water extracts of B. Kugi (Lycium chinense Mill) fruit possess promising hepatoprotective activity against $CCl_4-induced$ hepatic damage in rats.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.413-413
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• 2019

하천과 호수에서 남조류의 이상 과잉증식 문제(이하 녹조문제)는 담수생태계의 생물다양성을 감소시키며, 음용수의 이취미 원인물질을 발생시켜 물 이용에 장해가 된다. 또한 독소를 생산하는 유해남조류가 대량 증식할 경우에는 가축이나 인간의 건강에 치명적 해를 끼치기도 한다. 그 동안 국내에서 녹조문제는 댐 저수지와 하구호와 같은 정체수역에서 간헐적으로 문제를 일으켰으나, 4대강사업(2010-2011)으로 16개의 보가 설치된 이후 낙동강, 금강, 영산강 등 대하천에서도 광범위하게 발생되고 있어 중요한 사회적 환경적 이슈로 대두되었다. 한편, 대하천에 설치된 보 구간에서 빈번히 발생하는 녹조현상의 원인에 대해서는 전 지구적 기온상승에 따른 기후변화의 영향이라는 주장과 유역으로부터 영양염류의 과도한 유입, 가뭄에 따른 유량감소, 보 설치에 따른 체류시간 증가 등 다양한 의견이 제시되고 있으나, 대상 유역과 수체의 특성에 따라 녹조 발생의 원인이 상이하거나 또는 다양한 요인이 복합적으로 작용하기 때문에 보편적 해석(universal interpretation)이 어려운 것이 현실이다. 따라서 각 수계별, 보별 녹조현상에 대한 정확한 원인분석과 효과적인 대책 마련을 위해서는 집중된 실험자료와 데이터마이닝 기법에 근거로 한 보다 과학적이고 객관적인 접근이 이루어져야 한다. 본 연구에서는 2012년 보 설치 이후 남조류에 의한 녹조현상이 빈번히 발생하고 있는 낙동강 4개보(강정고령보, 달성보, 합천창녕보, 창녕함안보)를 대상으로 집중적인 현장조사와 실험분석을 수행하고, 수집된 기상, 수문, 수질, 조류 자료에 대해 통계분석과 다양한 데이터모델링 기법을 적용하여 보별 남조류 우점 환경조건과 이를 제어하기 위한 주요 조절변수를 규명하는데 있다. 연구대상 보 별 수질과 식물플랑크톤의 정성 및 정량 실험은 2017년 5월부터 2018년 11월까지 2년에 걸쳐 실시하였으며, 남조류 세포수 밀도와 환경요인과의 상관성 분석을 실시하고, 단계적 다중회귀모델(Step-wise Multiple Linear Regressions, SMLR), 랜덤포레스트(Random Forests, RF) 모델과 재귀적 변수 제거 기법(Recursive Feature Elimination using Random Forest, RFE-RF)을 이용한 변수중요도 평가, 의사결정나무(Decision Tree, DT), 주성분분석(Principal Component Analysis, PCA) 기법 등 다양한 모수적 및 비모수적 데이터마이닝 결과를 바탕으로 각 보별 남 조류 우점 환경요인을 종합적으로 해석하였다.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.3
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• pp.321-335
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• 2023

Monitoring sediment loads in natural rivers is the key process in river engineering, but it is costly and dangerous. In practice, suspended loads are directly measured, and total loads, which is a summation of suspended loads and bed loads, are estimated. This study proposes a real-time sediment discharge monitoring system using the horizontal acoustic Doppler current profiler (H-ADCP) and support vector regression (SVR). The proposed system is comprised of the SVR model for suspended sediment concentration (SVR-SSC) and for total loads (SVR-Q_TL), respectively. SVR-SSC estimates SSC and SVR-Q_TL mimics the modified Einstein procedure. The grid search with K-fold cross validation (Grid-CV) and the recursive feature elimination (RFE) were employed to determine SVR's hyperparameters and input variables. The two SVR models showed reasonable cross-validation scores (R²) with 0.885 (SVR-SSC) and 0.860 (SVR-Q_TL). During the time-series sediment load monitoring period, we successfully detected various sediment transport phenomena in natural streams, such as hysteresis loops and sensitive sediment fluctuations. The newly proposed sediment monitoring system depends only on the gauged features by H-ADCP without additional assumptions in hydraulic variables (e.g., friction slope and suspended sediment size distribution). This method can be applied to any ADCP-installed discharge monitoring station economically and is expected to enhance temporal resolution in sediment monitoring.

• Journal of the Korean Magnetics Society
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• v.16 no.1
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• pp.6-10
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• 2006

The sulphur spinel $FeCr_{2-x}M_xS_4$(M=Ga, In) have been studied with Mossbauer spectroscopy, x-ray diffraction (XRD), and vibrating sample magnetometer. The XRB patterns for samples $FeCr_{2-x}M_xS_4$(M=Ga, In: x=0.1, 0.3) reveal a single phase, which the Ga and In ions are partially occupied to the tetrahedral (A) site. The Neel temperature for the Ga substituted samples increases from 180 to 188 K, with increase from x=0.1 to 0.3. While, it decreases from 173 to 160 K, for the In substituted samples of the x=0.1 and 0.3, respectively. The Mossbauer spectra were collected from 4.2 K to room temperature. We have analyzed the Mossbauer spectra using eight Lorentzian lines fitting method for the $FeCr_{2-x}In_xS_4$(x=0.1) at 4.2 K, yielding the 1311owing results; $H_{hf}=146.0kOe,\;{\Delta}E_Q=1.88mm/s,\;\theta=36^{\circ},\;\phi=0^{\circ},\;\eta=0.6$, and R=1.9. The Ga ions enter into the both sites octahedral (B) and tetrahedral (A), simultaneously the same amounts of Fe ions migrate from the A to the B site, this result is an agreement with XRD results, too. The ${\Delta}E_Q$ of the A and B site in Mossbauer spectra of the samples $FeCr_{2-x}Ga_xS_4$(x=0.3) are 0.83 and 2.94mm/s, respectively. While they are 0.56 and 2.36mm/s for the $FeCr_{2-x}In_xS_4$(x=0.3). It is noticeable that the ${\Delta}E_Q$ for the Ga doped samples are larger than that of the corresponding In doped samples, in spite of the larger ionic radius for In ions. The bond lengths of Cr-S, for the Ga and In doped samples (x=0.3) are found to be 2.41 and $2.43\;{\AA}$, respectively. We interpret that the larger covalence effect from the smaller bond length induces a large asymmetric charge distribution. Finally, it gives a large quadrupole interaction.