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RISS 인기검색어
- 검색키워드
- 저자 : 유승선 (검색결과 7 건)
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유승선 韓國外國語大學校 經營大學院 2007 국내석사
Abstract A Study on development Between National and Private Health Insurance A private insurance companies are going to be on the market a type of actual loss of produces of insurance , in response to medical expenses and demands, to make up for national insurance's defects. Especially There are too much discussion about things, which are the coverage of insurance, the responsibility of private health insurance, the role of national health insurance and private health insurance, information share and industrialized medical care etc. Accordingly What are the best solutions? It focus on points of which the best ways and plays a role for consumers. National Health Insurance(NHI) and Private Health Insurance(PHI) need to be improved and advanced the system, complying with consumer's needs about services of the medical. Therefore, this study examines policies of NHI and PHI to develop them after reviewing their existing studies, theoretical models.
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企業負債의 擧示經濟的 役割에 關한 硏究 : 企業負債의 投資 變動性의 擴大效果 를 中心으로
본 연구에서는 기업의 부채부담이 거시적으로 경제의 변동성을 높이는 작용을 하고 있음을 밝히고자 하였다. 이를 위하여 부채부담의 지표로 부채비율을 선택하여 부채비율에 따라 기업의 투자행위에 차이가 있음을 실증분석 하였다. 실증분석 결과 부채비율이 높을수록 부채가 투자를 위축시키는 경향이 컸으며, 투자가 현금흐름의 변동에 더욱 민감해지는 것으로 나타났다. 또한 매출변동에 대한 투자의 조정속도가 빠른 것으로 나타났다. 경기하락 충격이 발생하는 경우에는 부채비율이 높은 기업일수록 투자에 미치는 충격의 강도가 큰 것으로 나타났다. 이러한 결과들은 부채비율이 높은 기업일수록 투자의 변동성이 높다는 것을 의미한다. 이로부터 우리나라 경제가 미국 등 선진국에 비해 높은 변동성을 나타내는 원인의 하나가 기업의 높은 부채비율임을 알 수 있었다. 이처럼 부채비율이 높은 기업에서 투자변동성이 높아지는 것은 이들 기업들은 자금차입 시 내부자금보다 높은 외부자금비용을 감수하게 되어 이자지출이 확대될 뿐만 아니라 높은 파산위험을 의식하여 투자자들이 해당기업에 대해 자금공급 축소 혹은 등 자금운용을 엄격히 함으로써 기업들이 자금조달에도 어려움이 커지기 때문이다. 특히 우리나라의 독립기업들은 투자자들로부터 부채비율 상승시 신뢰성 하락에 따른 자금차입 곤란에 직면할 위험이 높은 것으로 분석되며, 독립기업들의 현금흐름수준이 높은 것은 신뢰성을 유지하여 그 같은 위험을 피하기 위한 것으로 보인다. 본 연구에서는 10년 이상 성장된 기업을 대상으로 분석하였다. 그래서 이들 기업들은 상대적으로 금융사장에 기업내용이 자세히 알려져 있을 것이다. 만약 비상장기업들을 대상으로 동일한 분석을 한다면 이들의 기업내용이 금융시장에 잘 알려지지 않았을 것이므로 부채비율 상승으로 인한 금융제약은 더욱 클 것으로 예상된다.
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신경망을 이용한 공조 시스템의 실시간 고장예지 및 진단시스템 설계 및 구현
1. Result of research Computer-based automatic systems are being adopted for optimal operation of complex facility systems, and these systems are monitored by adequate operating management programs. However, the current technology of observation control software cannot be helpful at detecting system malfunction and fault, and since fault diagnosis is accomplished by operators, quick recovery and optimal operation cannot be expected under facility system malfunction. Hence, it is necessary to develop automatic fault detection and diagnosis processing systems that can provide building management optimization, default detection and maintenance diagnosis determination to operators. Through this research, we developed a system that could automatically predict and analyze faults in real time using neural network, while only computer analyzation had been used to make and analyze fault diagnosis model around the world, and proved that its performance through computer analyzation. And when the research was applied to a actual building system, the system was able to analyze 99.8% of faults. The results of this research can be summarized into below sub-categories. 1) Fault detection and analysis techniques We have analyzed fault detection and analysis techniques that have been used by IEA, most of them being based on computer analysis. We also analyzed fault detection methods that use forecast method and state variable and that use pattern recognition methods such as neuron network, and we analyzed operation mechanism and application process of fault detection methods based on expert system 2) Computer analysis program development We have developed a computer simulation program , for fault interpretation and operation optimization technique development. Unlike existing programs that can only interpret temperature control, it interpreted performance degrade and faults in a condition same as real environment, since it can perform the interpretation of pressure and ventilation quantity. Consequently, it confirmed that we can save from as little as 2% to as much as 40% of energy if we can detect malfunctioning in early stage by calculating the energy waste ratio from malfunctioning. 3) Development of real-time automatic fault detection system development that uses neuron network For the fault detection of HVAC systems, we used pattern recognition method of neuron network. In this research, we created artificial fault signals in an actual system, composed the data of 9 symptoms of fault into input pattern of 2-layer neuron network, and used them as a learning data for the neuron network. Through the tests, we confirmed that it can accurately detect 11 artificially generated faults. In addition, after learning with input patterns of (0,±0.5), (0,±0.6), (0,±0.7), (0,±1), we found out that it detects faults optimally when the input pattern is higher than k0.7 By adopting 2-layer neuron network into 11 defaults for the first time, the system was able to detect faults successfully when applied to an actual system. Through the result of this research, we assert a technology been established that can detect and analyze performance degrade and faults of energy facilities. 2. Application areas Fault detection and analysis technology for the improvement of stability and reliability in a system has been studied' in developed countries for some areas where stability is highly necessary, such as aerospace. However, the technology is being extended to general industry process, generator, automobile, heat equipment, etc. This conforms to the trend that systems are getting complex and automatized to cut-down labor costs, and the technology can be applied to all areas where complex automatic control is necessary. The results of this research should be applied to below areas. 1) Integrated management system of HVAC facilities that most affect power consumption during the summer. 2) Operating and monitoring system of power plants, chemical processes and heat equipments by establishing automatic fault detection and process technology. 3) Operating and monitoring system of high-tech building with energy-saving in mind. 4) Possible application as a basis technology for industry automation that minimize the operation and mending costs by developing automatic fault prediction, analysis and treatment technology with reliability and stability in mind. 5) Possible application as a fault detection and process technology for various automation equipments and sensors. 6) Possible application as a disaster prediction and protection technology. 3. Further study - Complete fault detection requires various fault patterns, related data and learning. - It is necessary to research not only fault detection but also the recovery feature of sensors and actuators themselves.
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