• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.4
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• pp.316-328
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• 2021

In this study, the leaf Na content prediction model for spring potato was established using 400-1000 nm hyperspectral sensor to develop the multispectral sensor for the salinity monitoring in reclaimed land. The irrigation conditions were standard, drought, and salinity (2, 4, 8 dS/m), and the irrigation amount was calculated based on the amount of evaporation. The leaves' Na contents were measured 1st and 2nd weeks after starting irrigation in the vegetative, tuber formative, and tuber growing periods, respectively. The reflectance of the leaves was converted from 5 nm to 10 nm, 25 nm, and 50 nm of FWHM (full width at half maximum) based on the 10 nm wavelength intervals. Using the variance importance in projections of partial least square regression(PLSR-VIP), ten band ratios were selected as the variables to predict salinity damage levels with Na content of spring potato leaves. The MLR(Multiple linear regression) models were estimated by removing the band ratios one by one in the order of the lowest weight among the ten band ratios. The performance of models was compared by not only R2, MAPE but also the number of band ratios, optimal FWHM to develop the compact multispectral sensor. It was an advantage to use 25 nm of FWHM to predict the amount of Na in leaves for spring potatoes during the 1st and 2nd weeks vegetative and tuber formative periods and 2 weeks tuber growing periods. The selected bandpass filters were 15 bands and mainly in red and red-edge regions such as 430/440, 490/500, 500/510, 550/560, 570/580, 590/600, 640/650, 650/660, 670/680, 680/690, 690/700, 700/710, 710/720, 720/730, 730/740 nm.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.123-138
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• 2017

Since the stock market is driven by the expectation of traders, studies have been conducted to predict stock price movements through analysis of various sources of text data. In order to predict stock price movements, research has been conducted not only on the relationship between text data and fluctuations in stock prices, but also on the trading stocks based on news articles and social media responses. Studies that predict the movements of stock prices have also applied classification algorithms with constructing term-document matrix in the same way as other text mining approaches. Because the document contains a lot of words, it is better to select words that contribute more for building a term-document matrix. Based on the frequency of words, words that show too little frequency or importance are removed. It also selects words according to their contribution by measuring the degree to which a word contributes to correctly classifying a document. The basic idea of constructing a term-document matrix was to collect all the documents to be analyzed and to select and use the words that have an influence on the classification. In this study, we analyze the documents for each individual item and select the words that are irrelevant for all categories as neutral words. We extract the words around the selected neutral word and use it to generate the term-document matrix. The neutral word itself starts with the idea that the stock movement is less related to the existence of the neutral words, and that the surrounding words of the neutral word are more likely to affect the stock price movements. And apply it to the algorithm that classifies the stock price fluctuations with the generated term-document matrix. In this study, we firstly removed stop words and selected neutral words for each stock. And we used a method to exclude words that are included in news articles for other stocks among the selected words. Through the online news portal, we collected four months of news articles on the top 10 market cap stocks. We split the news articles into 3 month news data as training data and apply the remaining one month news articles to the model to predict the stock price movements of the next day. We used SVM, Boosting and Random Forest for building models and predicting the movements of stock prices. The stock market opened for four months (2016/02/01 ~ 2016/05/31) for a total of 80 days, using the initial 60 days as a training set and the remaining 20 days as a test set. The proposed word - based algorithm in this study showed better classification performance than the word selection method based on sparsity. This study predicted stock price volatility by collecting and analyzing news articles of the top 10 stocks in market cap. We used the term - document matrix based classification model to estimate the stock price fluctuations and compared the performance of the existing sparse - based word extraction method and the suggested method of removing words from the term - document matrix. The suggested method differs from the word extraction method in that it uses not only the news articles for the corresponding stock but also other news items to determine the words to extract. In other words, it removed not only the words that appeared in all the increase and decrease but also the words that appeared common in the news for other stocks. When the prediction accuracy was compared, the suggested method showed higher accuracy. The limitation of this study is that the stock price prediction was set up to classify the rise and fall, and the experiment was conducted only for the top ten stocks. The 10 stocks used in the experiment do not represent the entire stock market. In addition, it is difficult to show the investment performance because stock price fluctuation and profit rate may be different. Therefore, it is necessary to study the research using more stocks and the yield prediction through trading simulation.

• Journal of Korea Spatial Information System Society
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• v.6 no.1 s.11
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• pp.73-85
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• 2004

Recently, as the growth of the wireless Internet, PDA and HPC, the focus of research and development related with GIS(Geographic Information System) has been changed to the Real-Time Mobile GIS to service LBS. To offer LBS efficiently, there must be the Real-Time GIS platform that can deal with dynamic status of moving objects and a location index which can deal with the characteristics of location data. Location data can use the same data type(e.g., point) of GIS, but the management of location data is very different. Therefore, in this paper, we studied the Real-Time Mobile GIS using the HBR-tree to manage mass of location data efficiently. The Real-Time Mobile GIS which is developed in this paper consists of the HBR-tree and the Real-Time GIS Platform HBR-tree. we proposed in this paper, is a combined index type of the R-tree and the spatial hash Although location data are updated frequently, update operations are done within the same hash table in the HBR-tree, so it costs less than other tree-based indexes Since the HBR-tree uses the same search mechanism of the R-tree, it is possible to search location data quickly. The Real-Time GIS platform consists of a Real-Time GIS engine that is extended from a main memory database system. a middleware which can transfer spatial, aspatial data to clients and receive location data from clients, and a mobile client which operates on the mobile devices. Especially, this paper described the performance evaluation conducted with practical tests if the HBR-tree and the Real-Time GIS engine respectively.

• Knowledge Management Research
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• v.21 no.3
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• pp.45-65
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• 2020

It is desirable to invest in wine that increases its value, but wine investment itself is unfamiliar in Korea. Also, the process itself is unreasonable, and information is often forged, because pricing in the wine market is done by a small number of people. With the right solution, however, the wine market can be a desirable investment destination in that the longer one invests, the higher one can expect. Also, it is expected that the domestic wine consumption market will expand through the steady increase in domestic wine imports. This study presents the consortium block chain framework for revitalizing the wine market and enhancing transparency as the "right solution" of the nation's wine investment market. Blockchain governance can compensate for the shortcomings of the wine market because it guarantees desirable decision-making rights and accountability. Because the data stored in the block chain can be checked by consumers, it reduces the likelihood of counterfeit wine appearing and complements the process of unreasonably priced. In addition, digitization of assets resolves low cash liquidity and saves money and time throughout the supply chain through smart contracts, lowering entry barriers to wine investment. In particular, if the governance of the block chain is composed of 'chateau-distributor-investor' through consortium blockchains, it can create a desirable wine market. The production process is stored in the block chain to secure production costs, set a reasonable launch price, and efficiently operate the distribution system by storing the distribution process in the block chain, and forecast the amount of orders for futures trading. Finally, investors make rational decisions by viewing all of these data. The study presented a new perspective on alternative investment in that ownership can be treated like a share. We also look forward to the simplification of food import procedures and the formation of trust within the wine industry by presenting a framework for wine-owned sales. In future studies, we would like to expand the framework to study the areas to be applied.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.65-82
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• 2021

Stock market investors are generally split into foreign investors, institutional investors, and individual investors. Compared to individual investor groups, professional investor groups such as foreign investors have an advantage in information and financial power and, as a result, foreign investors are known to show good investment performance among market participants. The purpose of this study is to propose an investment strategy that combines investor-specific transaction information and machine learning, and to analyze the portfolio investment performance of the proposed model using actual stock price and investor-specific transaction data. The Korea Exchange offers daily information on the volume of purchase and sale of each investor to securities firms. We developed a data collection program in C# programming language using an API provided by Daishin Securities Cybosplus, and collected 151 out of 200 KOSPI stocks with daily opening price, closing price and investor-specific net purchase data from January 2, 2007 to July 31, 2017. The self-organizing map model is an artificial neural network that performs clustering by unsupervised learning and has been introduced by Teuvo Kohonen since 1984. We implement competition among intra-surface artificial neurons, and all connections are non-recursive artificial neural networks that go from bottom to top. It can also be expanded to multiple layers, although many fault layers are commonly used. Linear functions are used by active functions of artificial nerve cells, and learning rules use Instar rules as well as general competitive learning. The core of the backpropagation model is the model that performs classification by supervised learning as an artificial neural network. We grouped and transformed investor-specific transaction volume data to learn backpropagation models through the self-organizing map model of artificial neural networks. As a result of the estimation of verification data through training, the portfolios were rebalanced monthly. For performance analysis, a passive portfolio was designated and the KOSPI 200 and KOSPI index returns for proxies on market returns were also obtained. Performance analysis was conducted using the equally-weighted portfolio return, compound interest rate, annual return, Maximum Draw Down, standard deviation, and Sharpe Ratio. Buy and hold returns of the top 10 market capitalization stocks are designated as a benchmark. Buy and hold strategy is the best strategy under the efficient market hypothesis. The prediction rate of learning data using backpropagation model was significantly high at 96.61%, while the prediction rate of verification data was also relatively high in the results of the 57.1% verification data. The performance evaluation of self-organizing map grouping can be determined as a result of a backpropagation model. This is because if the grouping results of the self-organizing map model had been poor, the learning results of the backpropagation model would have been poor. In this way, the performance assessment of machine learning is judged to be better learned than previous studies. Our portfolio doubled the return on the benchmark and performed better than the market returns on the KOSPI and KOSPI 200 indexes. In contrast to the benchmark, the MDD and standard deviation for portfolio risk indicators also showed better results. The Sharpe Ratio performed higher than benchmarks and stock market indexes. Through this, we presented the direction of portfolio composition program using machine learning and investor-specific transaction information and showed that it can be used to develop programs for real stock investment. The return is the result of monthly portfolio composition and asset rebalancing to the same proportion. Better outcomes are predicted when forming a monthly portfolio if the system is enforced by rebalancing the suggested stocks continuously without selling and re-buying it. Therefore, real transactions appear to be relevant.

• Journal of Intelligence and Information Systems
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• v.17 no.3
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• pp.187-201
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• 2011

KOSPI200 index is the Korean stock price index consisting of actively traded 200 stocks in the Korean stock market. Its base value of 100 was set on January 3, 1990. The Korea Exchange (KRX) developed derivatives markets on the KOSPI200 index. KOSPI200 index futures market, introduced in 1996, has become one of the most actively traded indexes markets in the world. Traders can make profit by entering a long position on the KOSPI200 index futures contract if the KOSPI200 index will rise in the future. Likewise, they can make profit by entering a short position if the KOSPI200 index will decline in the future. Basically, KOSPI200 index futures trading is a short-term zero-sum game and therefore most futures traders are using technical indicators. Advanced traders make stable profits by using system trading technique, also known as algorithm trading. Algorithm trading uses computer programs for receiving real-time stock market data, analyzing stock price movements with various technical indicators and automatically entering trading orders such as timing, price or quantity of the order without any human intervention. Recent studies have shown the usefulness of artificial intelligent systems in forecasting stock prices or investment risk. KOSPI200 index data is numerical time-series data which is a sequence of data points measured at successive uniform time intervals such as minute, day, week or month. KOSPI200 index futures traders use technical analysis to find out some patterns on the time-series chart. Although there are many technical indicators, their results indicate the market states among bull, bear and flat. Most strategies based on technical analysis are divided into trend following strategy and non-trend following strategy. Both strategies decide the market states based on the patterns of the KOSPI200 index time-series data. This goes well with Markov model (MM). Everybody knows that the next price is upper or lower than the last price or similar to the last price, and knows that the next price is influenced by the last price. However, nobody knows the exact status of the next price whether it goes up or down or flat. So, hidden Markov model (HMM) is better fitted than MM. HMM is divided into discrete HMM (DHMM) and continuous HMM (CHMM). The only difference between DHMM and CHMM is in their representation of state probabilities. DHMM uses discrete probability density function and CHMM uses continuous probability density function such as Gaussian Mixture Model. KOSPI200 index values are real number and these follow a continuous probability density function, so CHMM is proper than DHMM for the KOSPI200 index. In this paper, we present an artificial intelligent trading system based on CHMM for the KOSPI200 index futures system traders. Traders have experienced on technical trading for the KOSPI200 index futures market ever since the introduction of the KOSPI200 index futures market. They have applied many strategies to make profit in trading the KOSPI200 index futures. Some strategies are based on technical indicators such as moving averages or stochastics, and others are based on candlestick patterns such as three outside up, three outside down, harami or doji star. We show a trading system of moving average cross strategy based on CHMM, and we compare it to a traditional algorithmic trading system. We set the parameter values of moving averages at common values used by market practitioners. Empirical results are presented to compare the simulation performance with the traditional algorithmic trading system using long-term daily KOSPI200 index data of more than 20 years. Our suggested trading system shows higher trading performance than naive system trading.

• Journal of Korean Society for Geospatial Information Science
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• v.8 no.1 s.15
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• pp.51-63
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• 2000

Dynamic traffic assignment(DTA) has been a topic of substantial research during the past decade. While DTA is gradually maturing, many aspects of DTA still need improvement, especially regarding its formulation and solution algerian Recently, with its promise for In(Intelligent Transportation System) and GIS(Geographic Information System) applications, DTA have received increasing attention. This potential also implies higher requirement for DTA modeling, especially regarding its solution efficiency for real-time implementation. But DTA have many mathematical difficulties in searching process due to the complexity of spatial and temporal variables. Although many solution algorithms have been studied, conventional methods cannot iud the solution in case that objective function or constraints is not convex. In this paper, the genetic algorithm to find the solution of DTA is applied and the Merchant-Nemhauser model is used as DTA model because it has a nonconvex constraint set. To handle the nonconvex constraint set the GENOCOP III system which is a kind of the genetic algorithm is used in this study. Results for the sample network have been compared with the results of conventional method.

• Proceedings of the Korea Water Resources Association Conference
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• 2006.05a
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• pp.483-487
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• 2006

본 연구는 비점오염원에 의한 수질오염현상을 억제하기 위한 수변지역의 관리기법 중 하나인 식생여과대를 효과적으로 설치하기 위하여 식생에 의한 오염물질의 저감능력을 파악하고 효율적 설치방안을 모색하기 위하여 수행되었다. 연구결과 T-N과 T-P의 깊이별 오염물질 저감효과는 나대지에서 표면유출이 각각 17.6%, -23.9%, 단면유출이 각각 51.7%, 91.0%, 하부유출이 각각 38.4%, 89.3%인 것으로 나타나 표면층을 통한 유출보다는 토양층에 침투될수록 오염물질의 저감효과가 높아지는 것으로 나타났다. 잔디로 표면층을 식재한 실험에서는 표면, 단면, 하부유출에서의 T-N이 각각 16.0%, 57.1%, 38.4%의 오염물질 저감효과를 보였고, T-P의 저감효과는 각각 -9.7%, 83.6%, 88.8%인 것으로 조사되었다. 또한 돌미나리로 표면층을 식재한 실험에서는 표면, 단면, 하부유출에서의 T-N 처리효율이 각각 -2.6%, 62.1%, 71.2%로 나타났으며, T-P는 각각 -7.2%, 94.5%, 84.5%인 것으로 조사되었다. 결론적으로 식생을 한 경우 전체적으로 오염물질의 저감효율이 비교적 안정되게 유지되는 것으로 나타났으나, T-P의 표면층을 제외하면 전체적인 저감효과에 크게 영향을 미치지 않은 것으로 판단된다. 그러나 일반적으로 알려져 있는 식생에 따른 오염물 저감효과에 대한 순기능 즉 토양입자의 침식방지, 토양용액의 저류작용 및 공극확대에 따른 침투능력 증대, 토성의 개량 등과 같은 기능을 무시할 수 없으므로 오염저감효과를 극대화하기 위해서는 처리대상이 되는 하천유역의 자생적 식생을 보다 효과적으로 활용할 수 있는 방안마련이 비용적 측면이나 생태 보존적 측면에서 유리할 것으로 판단된다.one)을 설치하는 대책이 필요하다. 저수지 관리를 효과적으로 수행하기 위해서는 저수지 내부의 탁도 거동을 정확히 예측할 수 있어야 한다. 따라서 추후 동수역학 및 열역학에 기초한 3차원 수치모형 연구와 성층흐름에 정밀한 밀도류 실험연구 및 이에 대한 적용이 필요할 것으로 판단된다.함으로써 정보의 질적보장과 정보전환의 표준화방안을 제시하는 정보분석시스템이다.이용, 수자원의 지속적 확보기술의 특성에 따른 4개의 평가기준과 26개의 평가속성으로 이루어진 2단계 기술가치평가 모형을 구축하였으며 2개의 개별기술에 대한 시범적용을 실행하였다.하는 것으로 추정되었다.면으로의 월류량을 산정하고 유입된 지표유량에 대해서 배수시스템에서의 흐름해석을 수행하였다. 그리고, 침수해석을 위해서는 2차원 침수해석을 위한 DEM기반 침수해석모형을 개발하였고, 건물의 영향을 고려할 수 있도록 구성하였다. 본 연구결과 지표류 유출 해석의 물리적 특성을 잘 반영하며, 도시지역의 복잡한 배수시스템 해석모형과 지표범람 모형을 통합한 모형 개발로 인해 더욱 정교한 도시지역에서의 홍수 범람 해석을 실시할 수 있을 것으로 판단된다. 본 모형의 개발로 침수상황의 시간별 진행과정을 분석함으로써 도시홍수에 대한 침수위험 지점 파악 및 주민대피지도 구축 등에 활용될 수 있을 것으로 판단된다. 있을 것으로 판단되었다.4일간의 기상변화가 자발성 기흉 발생에 영향을 미친다고 추론할 수 있었다. 향후 본 연구에서 추론된 기상변화와 기흉 발생과의 인과관계를 확인하고 좀 더 구체화하기 위한 연구가 필요할 것이다.게 이루어질 수 있을 것으로 기대된다.는 초과수익률이 상승하지만, 이후로는 감소하므로, 반전거래전략을 활용하는 경우 주식투자기간은 24개월이하의 중단기가 적합함을 발견하였

• Proceedings of the Korea Water Resources Association Conference
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• 2006.05a
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• pp.66-71
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• 2006

최근에 하도계획의 주된 과제는 하천환경의 보전과 창조를 위한 비용의 최소화이며, 침식과 세굴에 대한 하천관리 시설의 안정성을 확보하기 위한 질적 안전도의 최대화에 있다. 이를 위해 하도의 변화를 전제로 이동상 현상을 고려하고 저수로의 특성을 파악하여 하도계획에 반영하는 것이 필요하다. 이러한 하도계획과 관리를 위한 주요한 도구로서 하도특성조사가 제시되고 있다. 우리나라에서는 수변 생태환경의 기능회복으로의 하천환경에 관심을 기울이기 시작한 단계에 머물러 있다. 국내에서도 일본 등 선진 외국과 같이 홍수시 재해를 크게 경감하고, 자연환경을 실질적이고 체계적으로 배려하면서 경제적 하도계획 및 관리에 역점을 두어야 할 시점에 이르렀다. 이를 위해서는 우선적으로 하천이 갖고 있는 고유 특성인 하도특성에 대한 조사가 선행되어야 한다. 본 연구에서는 대상하천을 선정하고 측량, 하상재료 채취 및 분석, 유사량 채취 등의 기초조사를 수행하였으며, 이에 따른 하도특성량을 분석하였다. 대상하천은 금강유역의 지천 하류부의 약 2km 구간을 선정하였으며, 2003년${\sim}$2004년에 3회에 걸쳐 측량을 실시하고, 구간내 구룡수위표에서 유량 몇 유사량을 측정하였다. 또한 대상구간내 14개 지점에서 하상토를 채취하여 입도분석을 실시하였으며, 구룡수위표 자료를 이용하여 수문자료를 구축하였다. 수집된 기초자료를 이용하여 대상구간의 강턱유량, 특정재현기간유량, 유효유량을 산정하여 지배유량을 결정하였다. 산정된 지배유량을 이용하여 하상경사, 대표입경, 저수로내 평균수심, 에너지 경사, 마찰속도, 무차원소류력, 하폭-수심비, 수심-입경비 등의 하도특성량을 산출하여 하천환경정비를 위한 기초자료로서 활용 될 수 있도록 하였다.구에 맞는 작물 생산 및 농촌관광단지 조성을 통해 부가가치증대 및 소득증대를 꾀함으로 농촌문제 해결에 도움이 될 것으로 기대된다. 본 연구를 통해 GIS 와 RS의 기술이 농촌분야에 더 효율적으로 적용될 것으로 기대되며, 농업기술센터를 통한 정보제공을 함으로써 대농민 서비스 및 농업기관의 위상이 제고 될 것으로 기대된다.여 전자파의 공간적인 가시화를 수행할 수 있었다. 본 전자파 시뮬레이션 기법이 실무에 이용될 경우, 일반인이 전자파의 분포에 대한 전문지식을 습득할 필요 없이, 검색하고자 하는 지역과 송전선, 전철 등 각종 전자파의 발생 공간 객체를 선택하여 실생활과 관련된 전자파 정보에 예측할 수 있어, 대민 환경정보 서비스 질의 개선측면에서 획기적인 계기를 마련할 것으로 사료된다.acid$(C_{18:3})$가 대부분을 차지하였다. 야생 돌복숭아 과육 중의 지방산 조성은 포화지방산이 16.74%, 단불포화지방산 17.51% 및 다불포화지방산이 65.73%의 함유 비율을 보였는데, 이 중 다불포화지방산인 n-6계 linoleic acid$(C_{18:2})$와 n-3계 linolenic acid$(C_{18:3})$가 지질 구성 총 지방산의 대부분을 차지하는 함유 비율을 나타내었다.했다. 하강하는 약 4일간의 기상변화가 자발성 기흉 발생에 영향을 미친다고 추론할 수 있었다. 향후 본 연구에서 추론된 기상변화와 기흉 발생과의 인과관계를 확인하고 좀 더 구체화하기 위한 연구가 필요할 것이다.게 이루어질 수 있을 것으로 기대된다.는 초과수익률이 상승하지만, 이후로는 감소하므로, 반전거래전략을 활용하는 경우 주식투자기간은 24개월이하의 중단기가 적합함을 발견하였다. 이상의 행

• Applied Chemistry for Engineering
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• v.8 no.2
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• pp.179-190
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• 1997

To study the effects of chemical composition on the fusion temperatures of coal ashes, the chemical composition, mineral matter, and fusion temperature were studied with 54 kinds of coal ash samples including Korean anthracite coals. CaO, MgO and $Fe_2O_3$ were observed to be major fluxing elements in reducing and oxidizing atmosphere. The fluxing effect of $Fe_2O_3$ was increased more in reducing atmosphere. In a base/acid ratio, the fusion temperature decreased with increasing amounts of basic components. Nevertheless, the correlation between a fusion temperature and base/acid ratio was not shown well in a higher ratio of $Fe_2O_3/CaO$. The differences of fusion temperatures between oxidizing and reducing atmosphere showed close relationship with $SiO_2/Al_2O_3$ ratio rather than with $Fe_2O_3$ contents. Multiple regression was used to predict the fusion temperature of coal ashes, and it was established that the major predictors in oxidizing atmosphere were Base/Acid, $Fe_2O_3/CaO$, $SiO_2/Al_2O_3$, and $(SiO_2/A1_2O_3){\cdot}(Base/Acid)$ and Base/Acid, $Fe_2O_3/CaO$, $SiO_2$, and $TiO_2$ were major ones in reducing atmosphere.