• Journal of Practical Agriculture & Fisheries Research
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• v.23 no.1
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• pp.89-104
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• 2021

Based on the TF-IDF weighted value that evaluates the importance of words that play a key role, the semantic network analysis(SNA) was conducted on the self-introduction letter of freshman at Korea National College of Agriculture and Fisheries(KNCAF) in 2020. The top three words calculated by TF-IDF weights were agriculture, mathematics, study (Q. 1), clubs, plants, friends (Q. 2), friends, clubs, opinions, (Q. 3), mushrooms, insects, and fathers (Q. 4). In the relationship between words, the words with high betweenness centrality are reason, high school, attending (Q. 1), garbage, high school, school (Q. 2), importance, misunderstanding, completion (Q.3), processing, feed, and farmhouse (Q. 4). The words with high degree centrality are high school, inquiry, grades (Q. 1), garbage, cleanup, class time (Q. 2), opinion, meetings, volunteer activities (Q.3), processing, space, and practice (Q. 4). The combination of words with high frequency of simultaneous appearances, that is, high correlation, appeared as 'certification - acquisition', 'problem - solution', 'science - life', and 'misunderstanding - concession'. In cluster analysis, the number of clusters obtained by the height of cluster dendrogram was 2(Q.1), 4(Q.2, 4) and 5(Q. 3). At this time, the cohesion in Cluster was high and the heterogeneity between Clusters was clearly shown.

• Maritime Security
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• v.7 no.1
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• pp.31-60
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• 2023

This study proposes a new convergence weapon system that combines the covert placement and detection abilities of a self-propelled mobile mine with the rapid tracking and attack abilities of supercavitating rocket torpedoes. This innovative system has been designed to counter North Korea's new underwater weapon, 'Haeil'. The concept behind this convergence weapon system is to maximize the strengths and minimize the weaknesses of each weapon type. Self-propelled mobile mines, typically placed discreetly on the seabed or in the water, are designed to explode when a vessel or submarine passes near them. They are generally used to defend or control specific areas, like traditional sea mines, and can effectively limit enemy movement and guide them in a desired direction. The advantage that self-propelled mines have over traditional sea mines is their ability to move independently, ensuring the survivability of the platform responsible for placing the sea mines. This allows the mines to be discreetly placed even deeper into enemy lines, significantly reducing the time and cost of mine placement while ensuring the safety of the deployed platforms. However, to cause substantial damage to a target, the mine needs to detonate when the target is very close - typically within a few yards. This makes the timing of the explosion crucial. On the other hand, supercavitating rocket torpedoes are capable of traveling at groundbreaking speeds, many times faster than conventional torpedoes. This rapid movement leaves little room for the target to evade, a significant advantage. However, this comes with notable drawbacks - short range, high noise levels, and guidance issues. The high noise levels and short range is a serious disadvantage that can expose the platform that launched the torpedo. This research proposes the use of a convergence weapon system that leverages the strengths of both weapons while compensating for their weaknesses. This strategy can overcome the limitations of traditional underwater kill-chains, offering swift and precise responses. By adapting the weapon acquisition criteria from the Defense force development Service Order, the effectiveness of the proposed system was independently analyzed and proven in terms of underwater defense sustainability, survivability, and cost-efficiency. Furthermore, the utility of this system was demonstrated through simulated scenarios, revealing its potential to play a critical role in future underwater kill-chain scenarios. However, realizing this system presents significant technical challenges and requires further research.

• Journal of the Korean Institute of Landscape Architecture
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• v.40 no.5
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• pp.100-108
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• 2012

The purpose of the study was to evaluate the influence of shading and ventilation on Mean Radiant Temperature(MRT) of the outdoor space at a summer outdoor. The Wind Speed(WS), Air Temperature(AT) and Globe Temperature(GT) were recorded every minute from $1^{st}$ of May to the $30^{th}$ of September 2011 at a height of 1.2m above in four experimental plots with different shading and ventilating conditions, with a measuring system consisting of a vane type anemometer(Barini Design's BDTH), Resistance Temperature Detector(RTD, Pt-100), standard black globe(${\O}$ 150mm) and data acquisition systems(National Instrument's Labview and Compfile Techs' Moacon). To implement four different ventilating and shading conditions, three hexahedral steel frames, and one natural plot were established in the open grass field. Two of the steel frames had a dimension of $3m(W){\times}3m(L){\times}1.5m(H)$ and every vertical side covered with transparent polyethylene film to prevent lateral ventilation(Ventilation Blocking Plot: VP), and an additional shading curtain was applied on the top side of a frame(Shading and Ventilation Blocking Plot: SVP). The third was $1.5m(W){\times}1.5m(L){\times}1.5m(H)$, only the top side of which was covered by the shading curtain without the lateral film(Shading Plot: SP). The last plot was natural condition without any kind of shading and wind blocking material(Natural Open Plot: NP). Based on the 13,262 records of 44 sunny days, the time serial difference of AT and GT for 24 hour were analyzed and compared, and statistical analysis was done based on the 7,172 records of daytime period from 7 A.M. to 8 P.M., while the relation between the MRT and solar radiation and wind speed was analyzed based on the records of the hottest period from 11 A.M. to 4 P.M.. The major findings were as follows: 1. The peak AT was $40.8^{\circ}C$ at VP and $35.6^{\circ}C$ at SP showing the difference about $5^{\circ}C$, but the difference of average AT was very small within${\pm}1^{\circ}C$. 2. The difference of the peak GT was $12^{\circ}C$ showing $52.5^{\circ}C$ at VP and $40.6^{\circ}C$ at SP, while the gap of average GT between the two plots was $6^{\circ}C$. Comparing all four plots including NP and SVP, it can be said that the shading decrease $6^{\circ}C$ GT while the wind blocking increase $3^{\circ}C$ GT. 3. According to the calculated MRT, the shading has a cooling effect in reducing a maximum of $13^{\circ}C$ and average $9^{\circ}C$ MRT, while the wind blocking has heating effect of increasing average $3^{\circ}C$ MRT. In other words, the MRT of the shaded area with natural ventilation could be cooler than the wind blocking the sunny site to about $16^{\circ}C$ MRT maximum. 4. The regression and correlation tests showed that the shading is more important than the ventilation in reducing the MRT, while both of them do an important role in improving the outdoor thermal comfort. In summary, the results of this study showed that the shade is the first and the ventilation is the second important factor in terms of improving outdoor thermal comfort in summer daylight hours. Therefore, it can be apparently said that the more shade by the forest, shading trees etc., the more effective in conditioning the microclimate of an outdoor space reducing the useless or even harmful heat energy for human activities. Furthermore, the delicately designed wind corridor or outdoor ventilation system can improve even the thermal environment of urban area.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.147-168
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• 2017

There have been many studies on accurate stock market forecasting in academia for a long time, and now there are also various forecasting models using various techniques. Recently, many attempts have been made to predict the stock index using various machine learning methods including Deep Learning. Although the fundamental analysis and the technical analysis method are used for the analysis of the traditional stock investment transaction, the technical analysis method is more useful for the application of the short-term transaction prediction or statistical and mathematical techniques. Most of the studies that have been conducted using these technical indicators have studied the model of predicting stock prices by binary classification - rising or falling - of stock market fluctuations in the future market (usually next trading day). However, it is also true that this binary classification has many unfavorable aspects in predicting trends, identifying trading signals, or signaling portfolio rebalancing. In this study, we try to predict the stock index by expanding the stock index trend (upward trend, boxed, downward trend) to the multiple classification system in the existing binary index method. In order to solve this multi-classification problem, a technique such as Multinomial Logistic Regression Analysis (MLOGIT), Multiple Discriminant Analysis (MDA) or Artificial Neural Networks (ANN) we propose an optimization model using Genetic Algorithm as a wrapper for improving the performance of this model using Multi-classification Support Vector Machines (MSVM), which has proved to be superior in prediction performance. In particular, the proposed model named GA-MSVM is designed to maximize model performance by optimizing not only the kernel function parameters of MSVM, but also the optimal selection of input variables (feature selection) as well as instance selection. In order to verify the performance of the proposed model, we applied the proposed method to the real data. The results show that the proposed method is more effective than the conventional multivariate SVM, which has been known to show the best prediction performance up to now, as well as existing artificial intelligence / data mining techniques such as MDA, MLOGIT, CBR, and it is confirmed that the prediction performance is better than this. Especially, it has been confirmed that the 'instance selection' plays a very important role in predicting the stock index trend, and it is confirmed that the improvement effect of the model is more important than other factors. To verify the usefulness of GA-MSVM, we applied it to Korea's real KOSPI200 stock index trend forecast. Our research is primarily aimed at predicting trend segments to capture signal acquisition or short-term trend transition points. The experimental data set includes technical indicators such as the price and volatility index (2004 ~ 2017) and macroeconomic data (interest rate, exchange rate, S&P 500, etc.) of KOSPI200 stock index in Korea. Using a variety of statistical methods including one-way ANOVA and stepwise MDA, 15 indicators were selected as candidate independent variables. The dependent variable, trend classification, was classified into three states: 1 (upward trend), 0 (boxed), and -1 (downward trend). 70% of the total data for each class was used for training and the remaining 30% was used for verifying. To verify the performance of the proposed model, several comparative model experiments such as MDA, MLOGIT, CBR, ANN and MSVM were conducted. MSVM has adopted the One-Against-One (OAO) approach, which is known as the most accurate approach among the various MSVM approaches. Although there are some limitations, the final experimental results demonstrate that the proposed model, GA-MSVM, performs at a significantly higher level than all comparative models.