• Journal of Intelligence and Information Systems
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• v.26 no.3
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• pp.71-90
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• 2020

Recently, the global insurance industry is rapidly developing digital transformation through the use of artificial intelligence technologies such as machine learning, natural language processing, and deep learning. As a result, more and more foreign insurers have achieved the success of artificial intelligence technology-based InsurTech and platform business, and Ping An Insurance Group Ltd., China's largest private company, is leading China's global fourth industrial revolution with remarkable achievements in InsurTech and Digital Platform as a result of its constant innovation, using 'finance and technology' and 'finance and ecosystem' as keywords for companies. In response, this study analyzed the InsurTech and platform business activities of Ping An Insurance Group Ltd. through the ser-M analysis model to provide strategic implications for revitalizing AI technology-based businesses of domestic insurers. The ser-M analysis model has been studied so that the vision and leadership of the CEO, the historical environment of the enterprise, the utilization of various resources, and the unique mechanism relationships can be interpreted in an integrated manner as a frame that can be interpreted in terms of the subject, environment, resource and mechanism. As a result of the case analysis, Ping An Insurance Group Ltd. has achieved cost reduction and customer service development by digitally innovating its entire business area such as sales, underwriting, claims, and loan service by utilizing core artificial intelligence technologies such as facial, voice, and facial expression recognition. In addition, "online data in China" and "the vast offline data and insights accumulated by the company" were combined with new technologies such as artificial intelligence and big data analysis to build a digital platform that integrates financial services and digital service businesses. Ping An Insurance Group Ltd. challenged constant innovation, and as of 2019, sales reached $155 billion, ranking seventh among all companies in the Global 2000 rankings selected by Forbes Magazine. Analyzing the background of the success of Ping An Insurance Group Ltd. from the perspective of ser-M, founder Mammingz quickly captured the development of digital technology, market competition and changes in population structure in the era of the fourth industrial revolution, and established a new vision and displayed an agile leadership of digital technology-focused. Based on the strong leadership led by the founder in response to environmental changes, the company has successfully led InsurTech and Platform Business through innovation of internal resources such as investment in artificial intelligence technology, securing excellent professionals, and strengthening big data capabilities, combining external absorption capabilities, and strategic alliances among various industries. Through this success story analysis of Ping An Insurance Group Ltd., the following implications can be given to domestic insurance companies that are preparing for digital transformation. First, CEOs of domestic companies also need to recognize the paradigm shift in industry due to the change in digital technology and quickly arm themselves with digital technology-oriented leadership to spearhead the digital transformation of enterprises. Second, the Korean government should urgently overhaul related laws and systems to further promote the use of data between different industries and provide drastic support such as deregulation, tax benefits and platform provision to help the domestic insurance industry secure global competitiveness. Third, Korean companies also need to make bolder investments in the development of artificial intelligence technology so that systematic securing of internal and external data, training of technical personnel, and patent applications can be expanded, and digital platforms should be quickly established so that diverse customer experiences can be integrated through learned artificial intelligence technology. Finally, since there may be limitations to generalization through a single case of an overseas insurance company, I hope that in the future, more extensive research will be conducted on various management strategies related to artificial intelligence technology by analyzing cases of multiple industries or multiple companies or conducting empirical research.

• Journal of Intelligence and Information Systems
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• v.16 no.3
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• pp.77-97
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• 2010

Market timing is an investment strategy which is used for obtaining excessive return from financial market. In general, detection of market timing means determining when to buy and sell to get excess return from trading. In many market timing systems, trading rules have been used as an engine to generate signals for trade. On the other hand, some researchers proposed the rough set analysis as a proper tool for market timing because it does not generate a signal for trade when the pattern of the market is uncertain by using the control function. The data for the rough set analysis should be discretized of numeric value because the rough set only accepts categorical data for analysis. Discretization searches for proper "cuts" for numeric data that determine intervals. All values that lie within each interval are transformed into same value. In general, there are four methods for data discretization in rough set analysis including equal frequency scaling, expert's knowledge-based discretization, minimum entropy scaling, and na$\ddot{i}$ve and Boolean reasoning-based discretization. Equal frequency scaling fixes a number of intervals and examines the histogram of each variable, then determines cuts so that approximately the same number of samples fall into each of the intervals. Expert's knowledge-based discretization determines cuts according to knowledge of domain experts through literature review or interview with experts. Minimum entropy scaling implements the algorithm based on recursively partitioning the value set of each variable so that a local measure of entropy is optimized. Na$\ddot{i}$ve and Booleanreasoning-based discretization searches categorical values by using Na$\ddot{i}$ve scaling the data, then finds the optimized dicretization thresholds through Boolean reasoning. Although the rough set analysis is promising for market timing, there is little research on the impact of the various data discretization methods on performance from trading using the rough set analysis. In this study, we compare stock market timing models using rough set analysis with various data discretization methods. The research data used in this study are the KOSPI 200 from May 1996 to October 1998. KOSPI 200 is the underlying index of the KOSPI 200 futures which is the first derivative instrument in the Korean stock market. The KOSPI 200 is a market value weighted index which consists of 200 stocks selected by criteria on liquidity and their status in corresponding industry including manufacturing, construction, communication, electricity and gas, distribution and services, and financing. The total number of samples is 660 trading days. In addition, this study uses popular technical indicators as independent variables. The experimental results show that the most profitable method for the training sample is the na$\ddot{i}$ve and Boolean reasoning but the expert's knowledge-based discretization is the most profitable method for the validation sample. In addition, the expert's knowledge-based discretization produced robust performance for both of training and validation sample. We also compared rough set analysis and decision tree. This study experimented C4.5 for the comparison purpose. The results show that rough set analysis with expert's knowledge-based discretization produced more profitable rules than C4.5.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.63-83
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• 2019

Investors prefer to look for trading points based on the graph shown in the chart rather than complex analysis, such as corporate intrinsic value analysis and technical auxiliary index analysis. However, the pattern analysis technique is difficult and computerized less than the needs of users. In recent years, there have been many cases of studying stock price patterns using various machine learning techniques including neural networks in the field of artificial intelligence(AI). In particular, the development of IT technology has made it easier to analyze a huge number of chart data to find patterns that can predict stock prices. Although short-term forecasting power of prices has increased in terms of performance so far, long-term forecasting power is limited and is used in short-term trading rather than long-term investment. Other studies have focused on mechanically and accurately identifying patterns that were not recognized by past technology, but it can be vulnerable in practical areas because it is a separate matter whether the patterns found are suitable for trading. When they find a meaningful pattern, they find a point that matches the pattern. They then measure their performance after n days, assuming that they have bought at that point in time. Since this approach is to calculate virtual revenues, there can be many disparities with reality. The existing research method tries to find a pattern with stock price prediction power, but this study proposes to define the patterns first and to trade when the pattern with high success probability appears. The M & W wave pattern published by Merrill(1980) is simple because we can distinguish it by five turning points. Despite the report that some patterns have price predictability, there were no performance reports used in the actual market. The simplicity of a pattern consisting of five turning points has the advantage of reducing the cost of increasing pattern recognition accuracy. In this study, 16 patterns of up conversion and 16 patterns of down conversion are reclassified into ten groups so that they can be easily implemented by the system. Only one pattern with high success rate per group is selected for trading. Patterns that had a high probability of success in the past are likely to succeed in the future. So we trade when such a pattern occurs. It is a real situation because it is measured assuming that both the buy and sell have been executed. We tested three ways to calculate the turning point. The first method, the minimum change rate zig-zag method, removes price movements below a certain percentage and calculates the vertex. In the second method, high-low line zig-zag, the high price that meets the n-day high price line is calculated at the peak price, and the low price that meets the n-day low price line is calculated at the valley price. In the third method, the swing wave method, the high price in the center higher than n high prices on the left and right is calculated as the peak price. If the central low price is lower than the n low price on the left and right, it is calculated as valley price. The swing wave method was superior to the other methods in the test results. It is interpreted that the transaction after checking the completion of the pattern is more effective than the transaction in the unfinished state of the pattern. Genetic algorithms(GA) were the most suitable solution, although it was virtually impossible to find patterns with high success rates because the number of cases was too large in this simulation. We also performed the simulation using the Walk-forward Analysis(WFA) method, which tests the test section and the application section separately. So we were able to respond appropriately to market changes. In this study, we optimize the stock portfolio because there is a risk of over-optimized if we implement the variable optimality for each individual stock. Therefore, we selected the number of constituent stocks as 20 to increase the effect of diversified investment while avoiding optimization. We tested the KOSPI market by dividing it into six categories. In the results, the portfolio of small cap stock was the most successful and the high vol stock portfolio was the second best. This shows that patterns need to have some price volatility in order for patterns to be shaped, but volatility is not the best.

• 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.

• Proceedings of the KOR-BRONCHOESO Conference
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• 1972.03a
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• pp.3.4-5
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• 1972

생활수준이 낮은 단계에 있어서는 우선 식량에 대한 수요가 강하다. 인간의 욕구가 만족스럽게 먹는다는 것에 대하여 제일 강하게 발동하는 것이다 그러나 점차 과학기술과 산업과 경제가 발전하여 성장과정에 오르게 되고 소득수준도 향상하게 되면 시장기구를 통해서 구입 할 수 있는 개인의 물적 소비재에 대해서는 점차 충족하게 되며 식량이외에도 의복, 전기기구 및 일용생활용품, 자동차 등에 이르기까지 더욱 고차원의 소비재가 보급하게 되는 것이다. 이렇게 되며는 사람의 욕구는 사적 재물이나 물적 수요에서 점진적으로 공공재나 또는 질적 수요(주택, 생활환경 등)의 방향으로 움직이게 되는 것으로써 여기에 환경오염 또는 공해문제에 대하여 의식하게 된다. 그러나 여기에서 더욱이 문제점이 되는 것은 소득 수준의 향상 과정이란 그 자체가 환경오염의 커다란 요인이라는 점이며 자동차의 급격한 보급과 생활의 편의성을 구하여 집중되는 도시인구의 집적, 높은 소득을 보장하기 위한 생산성 높은 중화학공업의 발전 등등은 그 자체가 환경권이란 사람이 요구하는 고차원의 권리를 침해하는 직접적인 요인이 된다는 것이다. 이와 같은 환경오염이나 공해문제에 대한 세계적인 논의는 이미 시작된 지 오래이지만 현재는 우리의 건강보호를 위해서나 생활환경의 보전을 위해서라는 점에서는 그치는 것이 아니고, 더욱 넓혀서 자연의 보호, 자원의 보호라는 견지로 확대되고 있다. 이와 같은 세계적인 확대된 이해와 이에 대한 대책강구의 제안은 1968년 국제연합의 경제사회이사회에서 스웨덴 정부대표에 의하여 제시되었으며 1969년의 우- 탄트 사무총장의 인간환경에 관한 보고서, 1970년 Nixon 미대통령의 연두일반교서 그리고 1972년 5월 6일 스웨덴의 스톡홀롬에서 개최되는 인간환경회의의 주제 등을 통해서 알 수 있고, 종래의 공해나 생활환경의 오염문제라는 좁은 개념에서가 아니고 인간환경전체의 문제로 다루고 있는 것이다. 즉 환경개발(도시, 산업, 지역개발에 수반된 문제), 환경오염(인위적 행위에 의하여 환경의 대인간조건이 악화하는 문제) 자연ㆍ자원의 보호관리(지하, 해양자원, 동식물, 풍경경치의 문제)란 3개 측면에서 다루고 있는 것이다. 환경오염이란 문제를 중 심하여 보면 환경을 구성하는 기본적인 요소로서 대기, 물, 토지 또는 지각. 그리고 공간의 사대요소로 집약하여 생각할 수 있음으로 이 4요소의 오염이 문제가 되는 것이다. 대기의 오염은 환경의 오염중 가장 널리 알려진, 또 가장 오랜 역사를 가진 오염의 문제로써 이에 속하는 오염인자는 분진, 매연, 유해가스(유황산화물, 불화수소, 염화수소, 질소산화물, 일산 화염소 등) 등 대기의 1차 오염과 1차 존재한 물질이 자외선의 작용으로 변화발생 하는 오존, PAN등 광화학물질이 형성되는 2차적인 오염을 들 수 있다. 기외 카도미움, 연등 유해중금속이나 방사선물질이 대기로부터 토지를 오염시켜서 토지에 서식하는 생물의 오염을 야기케 한다는 점등이 명백하여지고 있으며 대기의 오염은 이런 오염물질이 대기중에서 이동하여 강우에 의한 침강물질의 변화를 일으키게 되며 소위 광역오염문제를 발생케하며 동시에 토지의 토질저하등을 가져오게 한다. 물의 오염은 크게 내육수의 오염과 해양의 오염의 양면으로 나누어 볼 수 있다. 하천의 오염을 방지하고 하천을 보호하기 위한 움직임 역시 환경오염의 역사상 오래된 문제이며 시초에는 인분뇨와의 연결에서 오는 세균에 의한 오염이나 양수 기타 일반하수와의 연결에서 오는 오염에 대비하는 것부터 시작하였지만 근래에는 산업공장폐수에 의한 각종 화학적유해물질과 염료 그리고 석유화학의 발달에 의한 폐유등으로 인한 수질오탁문제가 점차 크게 대두되고 있다. 이것은 측 오염이란 시초에 우리에게 주는 불쾌감이 크므로 이것을 피하자는 것부터 시작하여 인간의 건강을 지키고 각종 사용수를 보존하자는 용수보존으로 그리고 이제는 건강과 용수보존뿐만 아니라 이것이 농림 수산물에 대한 큰 피해를 주게됨으로써 오는 자연환경의 생태계보전의 문제로 확대전환하고 있는 것이다. ？간 특히 해양오염에 대한 문제는 국지적인 것에만 끝이는 것이 아니고 전세계의 해양에 곧 연결되는 것이므로 세계각국의 공통관심사로 등장케 되었으며 이것은 특히 폐유가 유류수송 도중에 해양에 투기되는 유류에 의한 해양의 유막성형에서 오는 기상의 변화와 물피해등이 막심함으로 심각화 되고 있다. 각국이 자국의 해안과 해양을 보호하기 위하여 조치를 서두르고 있는 현시점에서 볼 때에는 이는 국제문제화하고 있으며 세계적인 국제적 협력과 협조의 필요성이 강조되는 좋은 예라 하겠다. 토양의 오염에 있어서는 대기나 수질의 오염이 구국적으로 토양과 관련되고 토양으로 환원되는 것이지만 근래에 많이 보급사용되는 농약과 화학비료의 문제는 토양자체의 오염에만 그치는 것이 아니고 농작물을 식품으로 하여 섭취함으로써 발생되는 인체나 기타생물체의 피해를 고려할 때 더욱 중요한 것이며, 또 토질의 저하를 가져오게 하여 농림생산에 미치는 영향이 적지 않을 것이다. 지반강하는 지각 에 주는 인공적 영향의 대표적인 것으로써 지하수나 지하 천연가스를 채취이용하기 위하여 파들어 감으로써 지반이 침하 하는 것이며 건축물에 대한 영향 특히 풍수해시의 재해를 크게 할 우려가 있는 것이다. 공간에 있어서의 환경오염에는 소음, 진동, 광선, 악취 등이 있다. 이들은 특수한 작업환경의 경우를 제외하고는 건강에 직접적인 큰 피해를 준다고 생각할 수 없으나 소음, 진동, 관선, 악취 등은 일반 일상시민생활에 불쾌나 불안을 줌으로써 안정된 생활을 방해하는 요인이 되는 것이다. 공간의 오염물로써 새로운 주목을 끌게된 것은 도시산업폐기물로써 이들은 대기나 물 또는 토지를 오염시킬 뿐만 아니라 공간을 점령함으로써 도시의 미관이나 기능을 손상케 하는 것이다. 즉 노배폐차의 잔해, 냉장고등고형폐기물등의 재생불가능한 것이나 비니루등 합성물질로 된 용기나 포장 등으로 연소분해 되지 않은 내구소비재가 이에 해당하는 것으로 이는 maker의 양식에 호소하여 그 책임 하에 해결되어야 할 문제로 본다. 이렇듯 환경오염은 각양각색으로 그 오염물질의 주요 발생원인 산업장이나 기타 기관에서의 발생요인을 살펴보며는 다음과 같은 것으로 요약할 수 있다. A. 제도적 요인 1. 관리체재의 미비 2. 관리법규의 미비 3. 책임소재의 불명확 B. 자재적 요인 1. 사용자재의 선택부적 2. 개량대책급 연구의 미흡 C. 기술적 요인 1. 시설의 설계불량, 공정의 결함 2. 시설의 점검, 보전의 불충분 3. 도출물의 취급에 대한 검사부족 4. 발생방지 시설의 미설치, 결함 D. 교육적 요인 1. 오염물질 방제지식의 결여 2. 법규의 오해, 미숙지 E. 경제적 요인 1. 자금부족 2. 융자상의 문제 3. 경제성의 문제 F. 정신적 요인 1. 사회적 도의심의 결여(이기주의) 2. 태만 3. 무지, 무관심 등이다. 따라서 환경오염의 방지란 상기한 문제의 해결에 기대하지 않을 수 없으나 이를 해결하기 위하여는 국내적 국제적 상호협조에 의한 사회각층의 총력적 대책이 시급한 것이다. 이와 같은 환경오염이 단속된다 하며는 미구에 인류의 건강은 물론 그 존립마저 기대하기 어려울 것이며, 현재는 점진적으로 급성피해에 대하여는 그 흥미가 집중되어 그 대비책도 많이 논의되고 있지만 미량의 단속접촉에 의한 만성축적에 관한 문제나 이와 같은 환경오염이 앞으로 태어날 신생률에 대한 영향이나 유전정보에 관한 연구는 장차에 대비하는 문제로써 중요한 것이라 생각된다. 기외에 우려되는 점은 오염방지책을 적극 추진함으로써 올 수 있는 파생적인 문제이다. 즉 오염을 방지하기 위하여 생산기업체가 투자를 하게 되며는 그만큼 생산원가가 상승할 것이며 소비가격도 오를 것이다. 반면 이런 시책에 뒤떨어진 후진국의 값싼 생산국은 자연 수입이 억제 당할 것이며, 이렇게되면 후진국은 무역경쟁에서 큰 상처를 입게될 것이고 뿐만 아니라 선진국에 필요한 오염물질의 발생이 높은 생산기기를 자연후진국에 양도하게 될 것임으로 후진국의 환경오염은 배가할 우려가 있는 것이다. 또 해양오염을 방지할 목적에서와 같이 자국의 해안보호를 위하여 마련된 법의 규제는 타국의 선박운항에 많은 제약을 가하게 될 것이며 이것 역시 시설이 미약한 약소후진국의 선박에 크게 영향을 미치게 될 것임으로 교통, 해운, 무역등을 통한 약소후진국의 경제성장에 제동을 거는 것이 될 것이다. 이렇듯 환경오염의 문제는 환경자체에 대해서만 아니라 부산물적으로 특히 후진국에는 의외 문제를 던져주게 되는 것임으로 환경오염에 대해서는 물론, 전술한 바와 같이 인간환경전체의 문제로써 Nixon 대통령이 말한 결의와 창의와 그리고 자금을 가지고 과감하게 대처해 나가야 할 것이다.

• The Korean Journal of Air & Space Law and Policy
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• v.18
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• pp.135-183
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• 2003

Aerospace force is a determining factor in a modem war. The combat field is expanding to space. Thus, the legitimacy of establishing aerospace force is no longer an debating issue, but "how should we establish aerospace force" has become an issue to the military. The standard limiting on the military use of space should be non-aggressive use as asserted by the U.S., rather than non-military use as asserted by the former Soviet Union. The former Soviet Union's argument is not even strongly supported by the current Russia government, and realistically is hard to be applied. Thus, the multi-purpose satellite used for military surveillance or a commercial satellite employed for military communication are allowed under the U.S. principle of peaceful use of space. In this regard, Air Force may be free to develop a military surveillance satellite and a communication satellite with civilian research institute. Although MTCR, entered into with the U.S., restricts the development of space-launching vehicle for the export purpose, the development of space-launching vehicle by the Korea Air Force or Korea Aerospace Research Institute is beyond the scope of application of MTCR, and Air Force may just operate a satellite in the orbit for the military purpose. The primary task for multi-purpose satellite is a remote sensing; SAR sensor with high resolution is mainly employed for military use. Therefore, a system that enables Air Force, the Korea Aerospace Research Institute, and Agency for Defense Development to conduct joint-research and development should be instituted. U.S. Air Force has dismantled its own space-launching vehicle step by step, and, instead, has increased using private space launching vehicle. In addition, Military communication has been operated separately from civil communication services or broadcasting services due to the special circumstances unique to the military setting. However, joint-operation of communication facility by the military and civil users is preferred because this reduces financial burden resulting from separate operation of military satellite. During the Gulf War, U.S. armed forces employed commercial satellites for its military communication. Korea's participation in space technology research is a little bit behind in time, considering its economic scale. In terms of budget, Korea is to spend 5 trillion won for 15 years for the space activities. However, Japan has 2 trillion won annul budget for the same activities. Because the development of space industry during initial fostering period does not apply to profit-making business, government supports are inevitable. All space development programs of other foreign countries are entirely supported by each government, and, only recently, private industry started participating in limited area such as a communication satellite and broadcasting satellite, Particularly, Korea's space industry is in an infant stage, which largely demands government supports. Government support should be in the form of investment or financial contribution, rather than in the form of loan or borrowing. Compared to other advanced countries in space industry, Korea needs more budget and professional research staff. Naturally, for the efficient and systemic space development and for the prevention of overlapping and distraction of power, it is necessary to enact space-related statutes, which would provide dear vision for the Korea space development. Furthermore, the fact that a variety of departments are running their own space development program requires a centralized and single space-industry development system. Prior to discussing how to coordinate or integrate space programs between Agency for Defense Development and the Korea Aerospace Research Institute, it is a prerequisite to establish, namely, "Space Operations Center"in the Air Force, which would determine policy and strategy in operating space forces. For the establishment of "Space Operations Center," policy determinations by the Ministry of National Defense and the Joint Chief of Staff are required. Especially, space surveillance system through using a military surveillance satellite and communication satellite, which would lay foundation for independent defense, shall be established with reference to Japan's space force plan. In order to resolve issues related to MTCR, Air Force would use space-launching vehicle of the Korea Aerospace Research Institute. Moreover, defense budge should be appropriated for using multi-purpose satellite and communication satellite. The Ministry of National Defense needs to appropriate 2.5 trillion won budget for space operations, which amounts to Japan's surveillance satellite operating budges.