32 research outputs found
A Basic Study on the Pump Characteristics Experiments Operating in Air-Water Two-Phase Flow
In a general centrifugal pump, if it is operated for a two-phase flow the activity of the impeller usually degrades and occasionally losses its function.
However, the effect of break down of centrifugal pump due to entrained air has not been clarified yet. Thus, air-water two-phase flow experimental apparatus was installed to acquire basic data. This paper tries to analyze the single-phase flow and air-water two-phase flow characteristics through this air-water two-phase flow experimental apparatus. The pump rpm and the shaft torque are measured by rpm sensor and torque sensor. The casing is made up with transparency acrylic for confirmation the flow patterns. According to void fraction, mass and shape of impeller, the pump characteristics under a single-phase flow were measured. The test pump's maximum rpm, head, kW are 1,750, 15m and 1.5kW, respectively. The performance results of a single-stage closed-type centrifugal pump satisfied reappearance and reliance. Also, in a single-phase flow, compare to semi-open type impeller, closed-type impeller has superior and efficiency. But in air-water two-phase flow, the rate of decrease of efficiency and head decreases.์ 1 ์ฅ ์๋ก = 1
1.1 ์ฐ๊ตฌ ๋ฐฐ๊ฒฝ = 1
1.2 ์ฐ๊ตฌ ๋ชฉ์ = 3
์ 2 ์ฅ ๊ธฐ์ก์ด์๋ฅ ์คํ์ฅ์น์ ๊ตฌ์ถ = 8
2.1 ๊ธฐ์ก์ด์๋ฅ ์คํ์ฅ์น์ ๊ตฌ์ฑ = 8
2.1.1 ์คํ์ฅ์น์ ๊ฐ์ = 8
2.1.2 ์ํ ๋ฌ์ ์ผ์ด์ฑ = 8
2.1.3 ์ธก์ ์์คํ
= 13
2.2 ๊ธฐ์ก์ด์๋ฅ ์คํ์ฅ์น์ ์ด์ = 15
2.2.1 ์คํ์ฅ์น์ ๊ธฐ๋ = 15
2.2.2 ํ๋ก๊ทธ๋จ ์กฐ์๋ฐฉ๋ฒ = 15
์ 3 ์ฅ ๊ธฐ์ก์ด์๋ฅ ์คํ์ฅ์น์ ์ฌํ์ฑ ๋ฐ ์ ๋ขฐ์ฑ ๊ฒ์ฆ = 21
3.1 ์คํ ํํ = 21
3.2 ์ ๋๊ณ์ ๊ฒ์ฆ = 23
3.2.1 ์ง๊ฐ 3๊ฐ ์์ด๋ก ์ธก์ ํ๋ ๋ฐฉ๋ฒ = 23
3.2.2 ์ ์์ ๋๊ณ์ ์๋ฆฌ = 24
3.2.3 ์ง๊ฐ3๊ฐ ์์ด์คํ๊ณผ ์ ์์ ๋๊ณ ์คํ๊ฒฐ๊ณผ์ ๋น๊ต = 24
3.3 ์ฑ๋ฅ์คํ ๊ฒฐ๊ณผ์ ์ฌํ์ฑ ๋ฐ ์ ๋ขฐ์ฑ ๊ฒ์ฆ = 30
์ 4 ์ฅ ๋จ์๋ฅ์์ ์์ฌํํ ํน์ฑ = 34
4.1 ์ํ ๋ฌ ์ง๋์ ๋ณํ์ ๋ฐ๋ฅธ ํน์ฑ = 34
4.2 ์ํ ๋ฌ ํ์์ ๋ฐ๋ฅธ ํน์ฑ = 37
์ 5 ์ฅ ๊ธฐ์ก์ด์๋ฅ์์ ์์ฌํํ์ ํน์ฑ = 39
5.1 ๊ณต๊ธฐ๊ณต๊ธ์ฅ์น์ ๊ตฌ์ฑ = 39
5.2 ์ํ ๋ฌ ํ์์ ๋ฐ๋ฅธ ๊ธฐ์ก์ด์๋ฅ ํน์ฑ = 41
5.3 ๊ธฐ์ก์ด์๋ฅ์์ ์ ๋ํจํด = 46
5.3.1 ๊ฐ์ํ ์คํ์ฅ์น์ ๊ตฌ์ฑ ๋ฐ ์คํ๋ฐฉ๋ฒ = 46
5.3.2 ์ ๋กํ์์์ ์ ๋ํน์ฑ = 46
์ 6 ์ฅ ๊ฒฐ๋ก = 52
์ฐธ๊ณ ๋ฌธํ = 5
Injection Characteristics with Valve Geometries for a Diesel Engine
Injection technology is one of the important technologies in a diesel engine. Many studies have done on the injection system. In this study, the fuel chamber geometry, the orifice ratio and the needle lift of the injection valve of a diesel engine for generating electricity are varied, tested and simulated. In the test, the injection pressure, duration and spray shapes are obtained with pressure transducer, needle lift sensor and high speed camera. The result shows that the nozzle hole size has influence on the rail pressure and injection duration sensuously. In the simulation using average 55,000 grids, static and dynamic pressure on outlet surface and velocity vector are obtained. Decrease of the static pressure at the place O(between needles) and increase of the dynamic pressure on the outlet surface are occurred owing to the rise of the nozzle hole diameter, high increase rate of the dynamic pressure with changing nozzle diameter exposed at needle lift 0.4mm. In the consequence, the data of the experimental test and simulation were identical and the highest dynamic pressure of the outlet was occurred at needle lift 0.4mm and nozzle hole diameter 0.328mm.ABSTRACT
๊ทธ ๋ฆผ ๋ชฉ ์ฐจ
โ
. ์๋ก 1
1.1 ์ฐ๊ตฌ์ ๋ฐฐ๊ฒฝ ๋ฐ ๋ชฉ์ 1
1.2 ์ฐ๊ตฌ์ ๋ด์ฉ 4
โ
ก. ์คํ์ ์ํ ๋ถ์ฌํน์ฑ
2.1 ์คํ์ฅ์น ๋ฐ ์คํ์กฐ๊ฑด 5
2.1.1 ์คํ์ฅ์น 5
2.1.2 ์คํ์กฐ๊ฑด 6
2.2 ์คํ๊ฒฐ๊ณผ ๋ฐ ๊ณ ์ฐฐ 8
2.2.1 ์ฐ๋ฃ์ค ํ์ ๋ณํ 8
2.2.2 ๋๋ค ์์ ๋ณํ 11
2.2.3 ๋
ธ์ฆ ์ง๊ฒฝ ๋ณํ 14
โ
ข. ๊ณ์ฐ์ ์ํ ๋ถ์ฌํน์ฑ
3.1 ์ํ์ ๋ชจ๋ธ ๋ฐ ๊ณ์ฐ์กฐ๊ฑด 17
3.2 ๊ณ์ฐ ๊ฒฐ๊ณผ ๋ฐ ๊ณ ์ฐฐ 21
3.2.1 ์ฐ๋ฃ์ค ํ์๋ณํ 21
3.2.2 ๋๋ค ์์ ๋ณํ 26
3.2.2.1 ๋
ธ์ฆ ํ ์ง๊ฒฝ์ด 0.288mm์ผ ๋ 26
3.2.2.2 ๋
ธ์ฆ ํ ์ง๊ฒฝ์ด 0.308mm์ผ ๋ 33
3.2.2.3 ๋
ธ์ฆ ํ ์ง๊ฒฝ์ด 0.328์ผ ๋ 40
3.2.3 ๋
ธ์ฆ ํ ์ง๊ฒฝ๋ณํ 47
3.2.3.1 ๋๋ค ์์ ์ด 0.4mm์ผ ๋ 47
3.2.3.2 ๋๋ค ์์ ์ด 0.5mm์ผ ๋ 54
3.2.3.3 ๋๋ค ์์ ์ด 0.6mm์ผ ๋ 61
3.3 ๊ฒฐ๊ณผ ์์ฝ 68
โ
ฃ. ๊ฒฐ ๋ก 69
์ฐธ๊ณ ๋ฌธํ 70
๋ถ ๋ก 7
๋ ธ๋์ดํ๋ฆฌ์ ๋ถ์์ฑ์ ๊ณผ ์ฌ์ถ๊ด์์์ ์กฐ์งํน์ด์ ์ธ ์ ์ ์ DC0(Drosophila Protein kinase A catalytic subunit gene) ๋ฐํ๊ณผ ๊ทธ ๊ธฐ๋ฅ์ ๋ํ ์ฐ๊ตฌ
ํ์๋
ผ๋ฌธ(์์ฌ)--์์ธ๋ํ๊ต ๋ํ์ :์๋ฌผํ๊ณผ,1997.Maste
Speed Improvement for a Dual-Stream H.264 Encoder
ํ์๋
ผ๋ฌธ(์์ฌ) --์์ธ๋ํ๊ต ๋ํ์ :์ ๊ธฐ. ์ปดํจํฐ๊ณตํ๋ถ,2010.2.Maste
An Improved Feature Extraction and Description Method For Flame Alarm Systems
ํ์๋
ผ๋ฌธ (์์ฌ)-- ์์ธ๋ํ๊ต ๋ํ์ : ์ ๊ธฐยท์ ๋ณด๊ณตํ๋ถ, 2017. 2. ๊นํ์ .Recently, while the machine learning based approach has been growing rapidly, the automated visual surveillance camera systems has also become one of the sought-after
area in computer vision. Fire detection is one of the things which can be applicable to the visual surveillance system. Thus, there have been several researches for vision based
fire detection, though, they still have a high false positive ratio as many fire-like colored moving objects exist in the world. For this reason, An Improved Feature Extraction
and Description Method For Flame Alarm Systems is proposed. In this algorithm, fire candidates are detected by the defined chroma-intensity map based on the color property of the flame. Next, the Brownian correlation descriptors are extracted from the candidates through multiple adjacent frames in fire and non-fire video. After
extracting them, the Brownian motion classifier is trained and tested by the Support Vector Machine to discriminate the dynamic property between the fire and fire-like objects. The proposed method is evaluated by comparing with 2D classical correlation method and one of the recent algorithm using multiple positive and negative videos.1 INTRODUCTION 1
2 Proposed Method 5
2.1 The Overall Framework For Fire Detection 5
2.2 Fire Candidates Extraction 6
2.3 Brownian Correlation Descriptor 10
2.3.1 Distance Covariance 2 10
2.3.2 Brownian Correlation Descriptor 11
2.4 Brownian Motion Classifier 14
2.5 Fire Alarm Rule 14
3 Experiment 15
4 Evaluation 17
5 Conclusion 20
Abstract in Korean 23Maste
์์ฏํ์ด๋จธํ ์น๋งค ํ์์์์ ํด๋ง์ฒด ์ฒด์ ๊ณผ ์์ฑ์ ์๊ธฐ๊ณต๋ช ๋ถ๊ด์๊ฒฌ์ ์๊ด๊ด๊ณ์ ๋ํ ์ฐ๊ตฌ
Thesis (doctoral)--์์ธ๋ํ๊ต ๋ํ์ :์ํ๊ณผ ์ ์ ๊ณผํ ์ ๊ณต,2003.Docto
Machining dynamics of ball end milling in sculptured surface machining
ํ์๋
ผ๋ฌธ(๋ฐ์ฌ)--์์ธ๋ํ๊ต ๋ํ์ :๊ธฐ๊ณ์ค๊ณํ๊ณผ,1999.Docto
A Study on the Effects of Irregular Employment Change on the Production Efficiency in the Water Transportation Service Industries
In this study, we try to look at the impact of employment change on efficiency of Koreaโs water transportation companies in the time that their competitive advantages become very important factors in the current Korean economy. In addition, we would like to look at how management efficiency has been affected by the employment practices of non-regular workers in our countryโs water transportation industry. The following conclusions may be summarized through the subsequent regression analysis and the derivation of sales efficiency and value-added efficiency models in the water transport industries.
First, The technology efficiency average of the sales model is measured at 0.619. DMU1 is measured in highest. DMU4 is measured lowest. The pure technology efficiency average is measured at 0.563. DMU3 is measured in highest. DMU2 is measured lowest. The scale efficiency is measured at 0.712 on average. DMU1 is measured in highest. DMU4 is measured lowest.
Second, the technology efficiency average of the value-added efficiency model is measured low at 0.269. DMU4 is measured highest. DMU2 is measured lowest. Average pure technology efficiency is measured at 0.563. DMU4 is measured highest. DMU2 is measured lowest at 0.343. Returns to scale result show that DMU4 is decreasing, while the rest of DMU is increasing.
Third, returns to scale in sales model show that "DMU 1" and "DMU 6" are declining. "DMU 5" shows decreasing trends early period and increasing trends in the later period. In the value-added model, "DMU 4" shows a trend of decreasing returns to scale and then has changed to increasing since 2018. The rest of the industries show increasing trends in terms of returns to scale.
Fourth, in analyzing the impact factors of a sales regression model using technology efficiency as a dependent variable, per capita assets increase by 1% caused 0.323 percent decrease of efficiency. The ratio of non-regular workers increases by 1% caused 0.120% pure technical efficiency decrease. An 1% increase of per capita assets resulted in a 0.068% decrease in the efficiency index. The efficiency of scale model as a dependent variable showed increases per capita assets by 1% caused 0.254 percent decreasing efficiency.
Fifth, in the value added regression model using technology efficiency as a dependent variable the one percentage increase of non-regular workers caused decreasing 0.040% efficiency level. Per capita assets increase by 1 percent caused decreasing efficiency level by 0.439%. Pure technology efficiency model using sales as a dependent variable showed that per capita assets increase by 1 percent caused 0.64% efficiency decrease, and the number of workers per company increase by 1 percent caused 0.658% decrease of efficiency. In the model using scale efficiency as a dependent variable, per capita assets increase by 1 percent caused 0.202% decrease of the efficiency index, and the number of workers per company increase by 1 percent caused 0.552 percent increase of the efficiency index.
Sixth. in the water transport industriesโ DEA model compared with the value-added model. we can see that sales model efficiency is higher. It means that the number of DRS cases is more than IRS cases in the sales model compared with value-added model.
Seventh, pure technical efficiency regression in sales models showed that the increase in non-regular workers in the water transport industry caused the decrease of efficiency. Per capita assetsโ coefficient is -0.068. An increase of 1% per capita assets reduces the efficiency index by 0.068%. For unemployment, this coefficient is โ0.215. An increase in unemployment rate of 1 percent caused 0.215 percent decrease of the efficiency index.
Eighth, in value added regression model of technology efficiency, the non-regular worker coefficient is โ0.040. It means that 1% increase in non-regular workers caused 0.040% decrease of the efficiency index. Per capita asset coefficient is โ0.439. An 1% increase in per capita assets leads to a decrease in the efficiency index of 0.439 points.
Based on these fact-finding results, we can derive the following policy implications.
First, it can be interpreted as the implication that increased employment of non-regular workers can be a factor that reduces the efficiency of water transport industries. In the case of non-regular workers, they receive about half of their wages, even if they are equal to or higher than regular workersโ wages. Increasing the employment of irregular workers does not increase the efficiency of management. On the contrary, we can expect that it could be diminished. Therefore, in order to enhance the competitiveness of Korean water transport industries, full-time employment must be increased. The government should provide various incentives to reduce irregular workers.
second, The consequences of the increase in assets due to inefficiency can be interpreted as follows: In the case of the transport industry, facing a crisis after the 2008 financial crisis. it seems that management efficiency was very low following rapid asset growth. The effects of these economic business cycles appear to have caused less contributions to the efficiency, following asset growth. Therefore considering the overall transportation industry, the increase in assets is believed to have put a burden on the reduction in management efficiency.
Third, Increasing unemployment has also been identified as having a negative impact on management efficiency. The shipping industry is very sensitive to economic fluctuations. If the economy shrinks, it affects the shipping industry faster than the general economy. Management efficiency is also reflected faster. Maintaining ships is a very expensive industry because the water transport industry is mainly run by ships. Therefore In the case of our country the shipping industry is often slack when the economy is good. We have experienced that if the shipping industry worsens, it will become unaffordable. Hanjin Shipping's bankruptcy was largely due to the company's own factors, but partly to the government policy, Considering the characteristics of the shipping industry's response, it is pointed out that if the economy improves, the government can withdraw government-backed bailout funds. It suggests that it will be a national burden depending on economic fluctuations, but it can be a good opportunity to understand the characteristics of the water transportation industry and enhance the national competitiveness in good economy.
Fourth, it is necessary to increase the asset efficiency of water transport industry. If the economy worsens, we will face the reality that ship assets are a heavy burden on business management. In recession, ship assets are reduced or sold. Where a preemptive response is made by rapidly increasing ship assets during the good times, it can be suggested that the impact on asset management efficiency will be positive. Due to the nature of the ship company, we would like to point out that the operational capability of ship assets is paramount because they require a lot of funds. In the case of the water transportation industry, the government should encourage the companies to efficiently manage ship assets by utilizing research institutesโ forecasting ability to predict changes in the shipping industry. We need a system to support private companies. Considering the fact that it is lagging behind the priority of supporting domestic research institutes, active attention at the government level is needed. Along with the task of securing the excellence of shipping research personnel, the government needs to pay attention to training shipping-related personnel.
Fifth, In value-added efficiency model, there is a lot of decreasing returns to scale, but in the sales model there is a lot of increasing returns to scale. This is interpreted as a phenomenon that occurs when there are many companies with small added value. If value added is very low compared with sales, value added efficiency can be higher than sales efficiency in terms of using inputs. In the case of sales, if the size is very large compared to added value; It suggests that increasing sales can reduce returns to scale. Due to the nature of the shipping industry, restructuring seems to be delayed in the event of a recession. The shipping industry's characteristic is that ship sales are not easy and employment is not greatly reduced. But in our country, we have experienced the shipping industry restructuring many times. Therefore, it is believed that various measures should be sought to increase returns to scale. For this purpose, categorized by enterprise and by industry, the government and business companies should obtain the data on returns to scale and make decisions based on those data. Based on reliable data on returns to scale, the company should improve its ability to respond to economic changes.๋ณธ ์ฐ๊ตฌ์์๋ ์ฐ๋ฆฌ๋๋ผ ์์์ด์ก๊ธฐ์
์ ๊ฒฝ์๋ ฅ ํ๋ณด๊ฐ ๋งค์ฐ ์ค์ํด์ง๋ ํ์ค ์์์ ์ด๋ค ๊ธฐ์
๋ค์ ํจ์จ์ฑ์ ์ค์ํ ์ํฅ์ ๋ฏธ์น๋ ๊ฒ์ผ๋ก ์๋ ค์ง ๊ณ ์ฉ๊ตฌ์กฐ๋ณํ์ ์ํฅ์ ์ดํด๋ณด๊ณ ์ ํ๋ค. ์์ธ๋ฌ ์ฐ๋ฆฌ๋๋ผ ์์์ด์ก ์ฐ์
์์ ๊ฒฝ์ํจ์จ์ฑ์ด ๋น์ ๊ท์ง์ ๊ณ ์ฉ๊ดํ์ ์ํด ์ด๋ค ์ํฅ์ ๋ฐ์๋์ง๋ฅผ ์ดํด๋ณด๊ณ ์ ํ๋ค. ์์์ด์ก์
์ ๋งค์ถ์ก ํจ์จ์ฑ๊ณผ ๋ถ๊ฐ๊ฐ์น์ก ํจ์จ์ฑ ๋ชจ๋ธ์ ๋์ถํ๊ณ ์ดํ ํ๊ท๋ถ์์ ํตํ ์ถ์ ๊ฒฐ๊ณผ๋ฅผ ์ ๋ฆฌํ๋ฉด ๋ค์๊ณผ ๊ฐ๋ค.
์ฒซ์งธ, ๋งค์ถ์ก ํจ์จ์ฑ ๋ชจ๋ธ์ ๊ธฐ์ ํจ์จ์ฑ ํ๊ท ์ 0.619๋ก, DMU1์ด ๊ฐ์ฅ ๋๊ฒ, DMU4๊ฐ ๊ฐ์ฅ ๋ฎ๊ฒ ์ธก์ ๋์๋ค. ์๊ธฐ์ ํจ์จ์ฑ ํ๊ท ์ 0.563์ผ๋ก, DMU3์ด ๊ฐ์ฅ ๋๊ฒ, DMU2๊ฐ ๊ฐ์ฅ ๋ฎ๊ฒ ์ธก์ ๋์๋ค. ๊ท๋ชจ์ ํจ์จ์ฑ ํ๊ท ์ 0.712๋ก ์ธก์ ๋๊ณ , DMU1์ด ๊ฐ์ฅ ๋๊ฒ, DMU4๊ฐ ๊ฐ์ฅ ๋ฎ๊ฒ ์ธก์ ๋์๋ค.
๋์งธ, ๋ถ๊ฐ๊ฐ์น์ก ํจ์จ์ฑ ๋ชจ๋ธ์ ๊ธฐ์ ํจ์จ์ฑ ํ๊ท ์ 0.269๋ก ์๋์ ์ผ๋ก ๋ฎ์๊ณ , DMU4๊ฐ ๊ฐ์ฅ ๋๊ฒ, DMU2๊ฐ ๊ฐ์ฅ ๋ฎ๊ฒ ์ธก์ ๋์๋ค. ์๊ธฐ์ ํจ์จ์ฑ ํ๊ท ์ 0.563์ผ๋ก, DMU4๊ฐ ๊ฐ์ฅ ๋๊ฒ, DMU2๊ฐ ๊ฐ์ฅ ๋ฎ๊ฒ ์ธก์ ๋์๋ค. ๊ท๋ชจ์ ํจ์จ์ฑ ํ๊ท ์ 0.343์ผ๋ก ๋ฎ์๊ณ , DMU4๊ฐ ๊ฐ์ฅ ๋๊ฒ, DMU2๊ฐ ๊ฐ์ฅ ๋ฎ๊ฒ ์ธก์ ๋์๋ค. ๊ท๋ชจ์์ต์ DMU4๊ฐ ๊ท๋ชจ์ ๋ํ ์์ต์ด ๊ฐ์ํ๋ ๊ฒ์ผ๋ก, ๋๋จธ์ง DMU๋ ๊ท๋ชจ์ ๋ํ ์์ต ์ฆ๊ฐ๋ก ๋ํ๋๋ค.
์
์งธ, ๊ท๋ชจ์์ต์ ๋งค์ถ์ก ๋ชจํ์์๋ DMU 1์ DMU 6์ ๊ท๋ชจ์ ๋ํ ์์ต์ด ๊ฐ์ํ๋ ์ถ์ธ๋ฅผ ๋ณด์ด๊ณ ์์ผ๋ฉฐ, DMU 5๋ ๊ฐ์ํ๋ค ์ดํ ์ฆ๊ฐํ๋ ์ถ์ธ๋ก ๋ฐ๋์๋ค. ๋๋จธ์ง ์ฐ์
๋ค์ ๊ท๋ชจ์ ๋ํ ์์ต์ด ์ฆ๊ฐํ๋ ์ถ์ธ๋ก ๋ํ๋ฌ๋ค. ๋ถ๊ฐ๊ฐ์น๋ชจํ์์๋ DMU 4๋ ๊ท๋ชจ์์ต์ ๋ํ ์์ต์ด ๊ฐ์ํ๋ ์ถ์ธ๋ฅผ ๋ณด์ด๋ค๊ฐ 2018๋
์ดํ ๊ท๋ชจ์์ต์ ์ฆ๊ฐํ๋ ์ถ์ธ๋ก ๋ฐ๋์๋ค. ๋๋จธ์ง์ ์ฐ์
๋ค์ ๊ท๋ชจ์์ต์ ๋ํ ์ฆ๊ฐํ๋ ์ถ์ธ๋ก ๋ํ๋ฌ๋ค.
๋ท์งธ, ์ํฅ์์ธ ๋ถ์์์๋ ๋งค์ถ์ก ํ๊ท๋ถ์ ๋ชจํ์์ ๊ธฐ์ ํจ์จ์ฑ์ ์ข
์๋ณ์๋ก ํ์ฌ ์ธก์ ํ ๊ฒฐ๊ณผ 1์ธ๋น ์์ฐ์ด 1% ์ฆ๊ฐ ํ ๋, ํจ์จ์ฑ์ง์๊ฐ 0.323%๋งํผ ๊ฐ์ํ๋ ๊ฒ์ผ๋ก ๋ํ๋ฌ๋ค. ์๊ธฐ์ ํจ์จ์ฑ์ ์ข
์๋ณ์๋ก ํ์ฌ ์ธก์ ํ ๊ฒฐ๊ณผ ๋น์ ๊ท์ง๋น์จ์ด 1% ์ฆ๊ฐ ํ ๋, ํจ์จ์ฑ์ง์๊ฐ 0.120%๋งํผ ๊ฐ์ํ๊ณ , 1์ธ๋น ์์ฐ์ด 1% ์ฆ๊ฐํ ๋, ํจ์จ์ฑ์ง์๊ฐ 0.068%๋งํผ ๊ฐ์ํ์๋ค. ๊ท๋ชจ์ ํจ์จ์ฑ์ ์ข
์๋ณ์๋ก ํ์ฌ ์ธก์ ํ ๊ฒฐ๊ณผ 1์ธ๋น ์์ฐ์ด 1% ์ฆ๊ฐ ํ ๋, ํจ์จ์ฑ์ง์๊ฐ 0.254%๋งํผ ๊ฐ์ํ๋ ๊ฒ์ผ๋ก ์ธก์ ๋์๋ค.
๋ค์ฏ์งธ, ๋ถ๊ฐ๊ฐ์น ํ๊ท๋ถ์ ๋ชจํ์์ ๊ธฐ์ ํจ์จ์ฑ์ ์ข
์๋ณ์๋ก ํ์ฌ ์ธก์ ํ ๊ฒฐ๊ณผ ๋น์ ๊ท์ง๋น์จ์ด 1% ์ฆ๊ฐ ํ ๋, ํจ์จ์ฑ์ง์๋ 0.040%๋งํผ ๊ฐ์ํ๊ณ , 1์ธ๋น์์ฐ์ด 1% ์ฆ๊ฐ ํ ๋, ํจ์จ์ฑ ์ง์๋ 0.439%๋งํผ ๊ฐ์ํ๋ค. ์๊ธฐ์ ํจ์จ์ฑ์ ์ข
์๋ณ์๋ก ํ์ฌ ์ธก์ ํ ๊ฒฐ๊ณผ 1์ธ๋น ์์ฐ์ด 1% ์ฆ๊ฐ ํ ๋, ํจ์จ์ฑ์ง์๋ 0.64% ๋งํผ ๊ฐ์ํ๊ณ , ๊ธฐ์
๋น ๊ทผ๋ก์์๊ฐ 1% ์ฆ๊ฐ ํ ๋, ํจ์จ์ฑ์ง์๋ 0.658%๋งํผ ๊ฐ์ํ๋ ๊ฒ์ผ๋ก ์ธก์ ๋์๋ค. ๊ท๋ชจ์ ํจ์จ์ฑ์ ์ข
์๋ณ์๋ก ํ์ฌ ์ธก์ ํ ๊ฒฐ๊ณผ 1์ธ๋น ์์ฐ์ด 1% ์ฆ๊ฐ ํ ๋, ํจ์จ์ฑ์ง์๋ 0.202%๋งํผ ๊ฐ์ํ๊ณ , ๊ธฐ์
๋น ๊ทผ๋ก์์๊ฐ 1%์ฆ๊ฐํ ๋, ํจ์จ์ฑ์ง์๋ 0.552%๋งํผ ์ฆ๊ฐํ์๋ค.
์ฌ์ฏ์งธ, ์์์ด์ก์
DEA๋ชจํ์์ ๋ถ๊ฐ๊ฐ์น ๋ชจํ๋ณด๋ค ๋งค์ถ์ก ๋ชจํ์ ํจ์จ์ฑ์ด ๋๊ฒ ๋์ค๊ณ , ๊ท๋ชจ์ด์ต์ด ๊ฐ์ํ๋ ๊ธฐ์
๋ณด๋ค ์ฆ๊ฐํ๋ ๊ธฐ์
์ด ๋ง์์ ์ ์ ์๋ค.
์ผ๊ณฑ์งธ, ๋งค์ถ์ก ๋ชจํ ๊ธฐ๋ฐ ์๊ธฐ์ ํจ์จ์ฑ์ ํ๊ท๋ถ์์์ ์ฐ๋ฆฌ๋๋ผ ์์์ด์ก์
์์ ๋น์ ๊ท์ง์ ์ฆ๊ฐ๋ ๋งค์ถ์ก ์๊ธฐ์ ํจ์จ์ฑ์ ์ ํ์ํจ๋ค๋ ๊ฒฐ๊ณผ๋ฅผ ๋ณด์ฌ์ฃผ๊ณ ์๋ค. 1์ธ๋น ์์ฐ์ ๊ฒฝ์ฐ ๊ณ์๊ฐ โ0.068๋ก ๋ํ๋๊ณ ์์ด 1์ธ๋น ์์ฐ์ 1% ์ฆ๊ฐ๋ ํจ์จ์ฑ์ง์๋ฅผ 0.068% ํ๋ฝ์ํค๋ ๊ฒ์ ์ ์ ์๋ค. ์ค์
์จ์ ๊ฒฝ์ฐ ๊ณ์๊ฐ โ0.215๋ก ๋ํ๋๊ณ ์์ด, ์ค์
์จ์ 1% ์ฆ๊ฐ๋ ํจ์จ์ฑ์ง์๋ฅผ 0.215% ํ๋ฝ์ํค๋ ๊ฒ์ผ๋ก ๋ํ๋๊ณ ์๋ค.
์ฌ๋์งธ, ๋ถ๊ฐ๊ฐ์น๋ชจํ ๊ธฐ์ ํจ์จ์ฑ ํ๊ท๋ถ์์์ ๋น์ ๊ท์ง ๊ณ์๋ โ0.040์ด ๋ํ๋๊ณ ์์ด ๋น์ ๊ท์ง์ด 1% ์ฆ๊ฐํ ๋ ํจ์จ์ฑ์ง์๋ 0.040% ํ๋ฝ์ํค๋ ๊ฒ์ผ๋ก ๋ํ๋๊ณ ์๊ณ 1์ธ๋น ์์ฐ์ ๊ณ์์ ๊ฒฝ์ฐ โ0.439์ผ๋ก ๋ํ๋๊ณ ์์ด 1์ธ๋น ์์ฐ์ด 1%์ ์ฆ๊ฐ๋ ํจ์จ์ฑ์ง์ 0.439% ํ๋ฝ์ํค๋ ๊ฒ์ผ๋ก ๋ํ๋๊ณ ์์๋ค.
์ด์ ๊ฐ์ ์ธก์ ๊ฒฐ๊ณผ์ ์ถ์ ๊ฒฐ๊ณผ๋ฅผ ๋ฐํ์ผ๋ก ๋ค์๊ณผ ๊ฐ์ ์ ์ฑ
์ ํจ์๋ฅผ ๋์ถํ ์ ์๋ค.
์ฒซ์งธ, ๋น์ ๊ท์ง์ ๊ณ ์ฉ์ฆ๊ฐ๋ ์์์ด์ก๊ธฐ์
๋ค์ ํจ์จ์ฑ์ ์ ํ์ํค๋ ์์ธ์ด ๋ ์ ์๋ค. ์ฆ ๋น์ ๊ท์ง์ ๊ฒฝ์ฐ ์ ๊ท์ง์ ๋นํด ์
๋ฌด๋์ ๋์ผํ๊ฑฐ๋ ๋ง์๋ฐ๋ ๋ถ๊ตฌํ๊ณ ์๊ธ์ ์ ๋ฐ์ ๋๋ฅผ ๋ฐ๋๋ค๊ณ ํ ๋ ๋น์ ๊ท์ง์ ๊ณ ์ฉ์ฆ๊ฐ๊ฐ ๊ธฐ์
์ ๊ฒฝ์ํจ์จ์ฑ์ ์ฆ๋์ํค์ง ๋ชปํ๊ณ ์คํ๋ ค ์ ํ์ํฌ ์ ์๋ค๋ ๊ฒ์ด๋ค. ๋ฐ๋ผ์ ์ฐ๋ฆฌ๋๋ผ ์์์ด์ก๊ธฐ์
์ ๊ฒฝ์๋ ฅ์ ๋์ด๊ธฐ ์ํด์๋ ์ ๊ท์ง๊ณ ์ฉ์ด ๋์ด๋๋๋ก ํด์ผ ํ ๊ฒ์ด๊ณ ๋น์ ๊ท์ง์ ์ค์ผ ์ ์๋๋ก ์ ๋ถ์์๋ ๋ค์ํ ์ธ์ผํฐ๋ธ๋ฅผ ์ ๊ณตํ ํ์๊ฐ ์๋ค.
๋์งธ, ์์ฐ์ฆ๊ฐ๋ ํจ์จ์ฑ์ ํ๋ฅผ ์ด๋ํ๋ค. ์ด๊ฒ์ ํ๊ตญ ์์์ด์ก์ฐ์
์ด 2008๋
๊ธ์ต์๊ธฐ ์ดํ ์๊ธฐ๋ฅผ ๋ง์ดํ๋ฉด์ ์์ฐ์ฆ๊ฐ์ ๋นํด ๊ฒฝ์ํจ์จ์ฑ์ด ๋งค์ฐ ๋ฎ์๋ ๊ฒ์ ๊ธฐ์ธํ ๊ฒ์ผ๋ก ํด์๋๋ค. ์ด๋ฌํ ๊ธ๊ฒฉํ ๊ฒฝ๊ธฐ์นจ์ฒด์ ์ํฅ์ผ๋ก ์์ฐ์ ํจ์จ์ฑ์ ๋ํ ๊ธฐ์ฌ๋๋ ๋ฎ์์ ธ ์ ์ฒด ์์์ด์ก์ฐ์
์ ๋ํ ๊ฒฝ์ํจ์จ์ฑ์ ํ์ ์๋ฐ์์ธ์ผ๋ก ์์ฉํ์ ๊ฒ์ผ๋ก ํ๋จ๋๋ค.
์
์งธ, ์ค์
์จ์ ์ฆ๊ฐ๊ฐ ๊ฒฝ์ํจ์จ์ฑ์ ์(-)์ ์ํฅ์ ์ฃผ๋ ๊ฒ์ผ๋ก ๋ํ๋ฌ๋ค. ํด์ด์ฐ์
์ ๊ฒฝ๊ธฐ๋ณ๋์ ๋งค์ฐ ๋ฏผ๊ฐํ ์ฐ์
์ด๋ค. ๊ฒฝ๊ธฐ๊ฐ ์์ถ๋๋ฉด ๊ทธ ์ํฅ์ด ๋ค๋ฅธ ์ฐ์
๋ณด๋ค ํด์์ด์ก์ฐ์
๊ณผ ๊ทธ์ ๊ฒฝ์ํจ์จ์ฑ์ ๋ ๋น ๋ฅด๊ฒ ์ํฅ์ ๋ฏธ์น๋ค. ์์์ด์ก์ฐ์
์ ์๋ณธ์ฌ์ ๋ฐ์ ์ค์ฌ์ผ๋ก ๊ฒฝ์์ ํ๋ฏ๋ก ์ ๋ฐ์ ์ ์งํ๋ ๊ณ ์ ๋น์ฉ์ด ๋งค์ฐ ํฐ ์ฐ์
์ด๋ค. ๋ฐ๋ผ์ ์ฐ๋ฆฌ๋๋ผ์ ๊ฒฝ์ฐ ํด์ด์ฐ์
์ด ๊ฒฝ๊ธฐ๊ฐ ์ข์ ๋๋ ๋ฐฉ๋งํ๊ฒ ์ด์๋๋ ๊ฒฝ์ฐ๊ฐ ๋ง์ด ๋ํ๋๊ณ ๊ฒฝ๊ธฐ๊ฐ ๋๋น ์ง๋ฉด ๊ฐ๋นํ ์ ์๋ ์ํ์ ์ด๋ฅด๊ฒ ๋๋ ๊ฒ์ ๋ชฉ๊ฒฉํ ๋ฐ ์๋ค. ํ์งํด์ด์ ํ์ฐ์ ๊ธฐ์
์์ฒด์ ์์ธ์ด ํฌ๊ฒ ์์ฉํ์ง๋ง ์ ๋ถ์ฐจ์์์ ๋์์ธก๋ฉด์์ ํด์ด์ฐ์
์ ํน์ฑ์ ๊ณ ๋ คํ๋ค๋ฉด ๊ฒฝ๊ธฐ๊ฐ ์ข์์ง ๊ฒฝ์ฐ ์ ๋ถ๊ฐ ์ง์ํ ๊ตฌ์ ๊ธ์ต์ ์๊ธ์ ํ์ํ ์ ์๋ค๋ ์ธก๋ฉด์ด ๊ณ ๋ ค๋์ด์ผํ๋ค๋ ์ ์ ๋ณธ ์ฐ๊ตฌ์์๋ ์ง์ ํ๊ณ ์ ํ๋ค. ์์์ด์ก์ฐ์
์ ํน์ฑ์ ์ ์ดํดํ๊ณ ๊ฒฝ๊ธฐ๋ณ๋์ ๋ฐ๋ผ ๊ตญ๊ฐ์ ๋ถ๋ด์ด ๋๋ ์ฐ์
์ด ๋ ์๋ ์์ง๋ง ํธ๊ฒฝ๊ธฐ์๋ ๊ตญ๊ฐ๊ฒฝ์๋ ฅ์ ๋์ผ ์ ์๋ ์ ํธ์ ๊ธฐํ๊ฐ ๋ ์ ์๋ค๋ ์์ฌ์ ์ ๋์ ธ์ฃผ๋ ๊ฒ์ด๋ค.
๋ท์งธ, ์์์ด์ก๊ธฐ์
์ ์์ฐํจ์จ์ฑ์ ์ฆ๋ํ ํ์๊ฐ ์๋ค๋ ๊ฒ์ด๋ค. ๊ฒฝ๊ธฐ๊ฐ ๋๋น ์ง ๊ฒฝ์ฐ ์ ๋ฐ์์ฐ์ด ๊ธฐ์
๊ฒฝ์์ ํฐ ๋ถ๋ด์ผ๋ก ์์ฉํ๋ ํ์ค์ ์ง์ํ๊ณ ๋ถ๊ฒฝ๊ธฐ์๋ ์ ๋ฐ์์ฐ๊ตฌ์
์ ์ค์ด๊ฑฐ๋ ๋งค๊ฐํ๊ณ ํธ๊ฒฝ๊ธฐ์๋ ์ ๋ฐ์์ฐ์ ๋น ๋ฅด๊ฒ ๋๋ ค ์ ์ ์ ์ผ๋ก ๋์ํ ๊ฒฝ์ฐ ์์ฐ์ ๊ฒฝ์ํจ์จ์ฑ์ ๋ํ ์ํฅ์ ๊ธ์ ์ ์ผ๋ก ๋ํ๋ ๊ฒ์ด๋ผ๋ ์์ฌ์ ์ ์ป์ ์ ์๋ค. ํด์ด๊ธฐ์
์ ํน์ฑ์ ์ ๋ฐ์์ฐ์ด ๋งค์ฐ ํฐ ์๊ธ์ด ์์๋๋ฏ๋ก ์ ๋ฐ์์ฐ์ ์ด์ฉ๋ฅ๋ ฅ์ด ๋ฌด์๋ณด๋ค๋ ์ค์ํ๋ค๋ ๊ฒ์ ์ง์ ํ๊ณ ์ ํ๋ค. ์ฐ๋ฆฌ๋๋ผ ์์์ด์ก์ฐ์
์ ๊ฒฝ์ฐ ์ ๋ถ๊ฐ ์ฐ๊ตฌ๊ธฐ๊ด์ ํ์ฉํด ํด์ด์ฐ์
์ ๊ฒฝ๊ธฐ๋ณ๋์ ๋ํด ์์ธก๋ ฅ์ ๋์ฌ ์ ๋ฐ์์ฐ์ ํจ์จ์ ์ผ๋ก ๊ด๋ฆฌํ ์ ์๋๋ก ๋ฏผ๊ฐ ๊ธฐ์
๋ค์ ์ง์ํ๋ ์์คํ
์ด ํ์ํ๋ค. ์์ธ๋ฌ ์์์ด์ก๊ณผ ๊ด๋ จ๋ ์ฐ๊ตฌ๊ธฐ๊ด์ด ์ฐ๋ฆฌ๋๋ผ ์ฐ๊ตฌ๊ธฐ๊ด์ง์์ ์ฐ์ ์์์์ ๋ค๋ก ๋ฐ๋ฆฌ๋ ํ์ค์ ๊ฐ์ํ ๋ ์ ๋ถ์ฐจ์์์ ํด์ด์ฐ๊ตฌ๊ธฐ๊ด์ ๋ํ ๋ณด๋ค ์ ๊ทน์ ์ธ ๊ด์ฌ์ด ํ์ํ๋ค๋ ๊ฒ์ด๋ค. ํด์ด ์ฐ๊ตฌ ์ธ๋ ฅ์ ์ฐ์์ฑ์ ํ๋ณดํ๋ ๊ณผ์ ๊ฐ ์๊ณ ๋๊ตฌ๋ ํด์ด๊ด๋ จ ์ธ๋ ฅ์์ฑ์ ์ ๋ถ์ฐจ์์ ๋ณด๋ค ํญ๋์ ๊ด์ฌ์ด ํ์ํ๋ค๊ณ ํ ๊ฒ์ด๋ค.
๋ค์ฏ์งธ, ๋ถ๊ฐ๊ฐ์นํจ์จ์ฑ์์๋ ๊ท๋ชจ์ ๋ํ ์์ต์ฆ๊ฐ(irs)๊ฐ ๋ง์ด ๋ํ๋์ง๋ง ๋งค์ถ์ก ๋ชจํ์์๋ ๊ท๋ชจ์ ๋ํ ์์ต๊ฐ์(drs)๊ฐ ๋ง์ด ๋ํ๋๋ค๋ ๊ฒ์ ์ ์ ์์๋ค. ์ด๋ฌํ ํ์์ ๋ถ๊ฐ๊ฐ์น๊ฐ ์์ธํ ๊ธฐ์
๋ค์ด ๋ง์ ๊ฒฝ์ฐ์ ๋ํ๋๋ ํ์์ผ๋ก ํด์๋๋ค. ๋งค์ถ์ก๋๋น ๋ถ๊ฐ๊ฐ์น๊ฐ ์ ์ ๊ฒฝ์ฐ์๋ ํฌ์
๋ฌผ๋๋น ์ฐ์ถ์์ ๊ท๋ชจ์ ์ฆ๊ฐ์ ๋ํด ํจ์จ์ฑ์ฆ๋๊ฐ ๋ํ๋ ์ ์์ง๋ง ๋งค์ถ์ก์ ๊ฒฝ์ฐ ๋ถ๊ฐ๊ฐ์น๋๋น ๋งค์ฐ ํฌ๊ฒ ๋๋ฉด ๋งค์ถ์ก์ธก๋ฉด์์์ ๊ท๋ชจ์ ์ฆ๊ฐ๋ ์์ต๊ฐ์๋ก ๋ํ๋ ์ ์๋ค๋ ์ ์ ์์ฌํ๋ค. ํด์ด์ฐ์
์ ํน์ฑ์ ๋ถ๊ฒฝ๊ธฐ๊ฐ ์ฌ ๊ฒฝ์ฐ ๊ตฌ์กฐ์กฐ์ ์ด ๋ฆ๊ฒ ๋ํ๋๋ ํ์์ ๋ชฉ๊ฒฉํ๋ค. ์ ๋ฐ๋งค๊ฐ์ด ์ฝ์ง ์๊ณ ๊ณ ์ฉ๋ ๊ธ๊ฒฉํ๊ฒ ์ค์ด์ง ์ด๋ ค์ด ํ์ค์ด ํด์ด์ฐ์
์ ํน์ฑ์ด๋ค. ๊ทธ๋ฌ๋ ์ฐ๋ฆฌ๋๋ผ ํด์ด์ฐ์
์์ ๊ฒฝํํ ๊ฒ์ฒ๋ผ ๋งค์ถ์กํจ์จ์ฑ์์ ๊ท๋ชจ์์ต์ด ์ฆ๋๋ ์ ์๋ ๋ฐฉ์์ด ๋ชจ์๋์ด์ผ ํ ๊ฒ์ผ๋ก ์๊ฐ๋๋ค. ์ด๋ฅผ ์ํด์ ์ฐ์
๋ณ๋ก ๊ธฐ์
๋ณ๋ก ๋ถ๋ฅ๋ฅผ ํ์ฌ ์์ก์ฐ์
๋ณ ๊ธฐ์
๋ณ ๊ท๋ชจ์์ต์ ๋ํ ์๋ฃ๋ฅผ ํ๋ณดํ๊ณ ์ด ์๋ฃ์ ๊ธฐ์ดํ ์์ฌ๊ฒฐ์ ์ด ์ด๋ฃจ์ด์ง๋๋ก ํ ํ์์ฑ์ด ์๋ค. ๊ท๋ชจ์์ต์ ๋ํ ์ ๋ขฐ์ฑ ์๋ ์๋ฃ๋ฅผ ๋ฐํ์ผ๋ก ๊ธฐ์
์ ๋์๋ฅ๋ ฅ์ ํฅ์์ํฌ ํ์์ฑ์ด ์๋ค๋ ๊ฒ์ด๋ค.์ 1 ์ฅ ์ ๋ก 1
1.1 ์ฐ๊ตฌ๋ฐฐ๊ฒฝ ๋ฐ ๋ชฉ์ 1
1.2 ์ฐ๊ตฌ๋ด์ฉ ๋ฐ ์ฐ๊ตฌ๋ฐฉ๋ฒ 6
์ 2 ์ฅ ์ ํ์ฐ๊ตฌ 8
์ 3 ์ฅ ํจ์จ์ฑ์ธก์ ์ ์ด๋ก ์ ๋ฐฐ๊ฒฝ 12
3.1 DEA๋ชจํ 12
3.1.1 CCR ๋ชจํ 14
3.1.2 BCC ๋ชจํ 15
3.1.3 ๊ท๋ชจ์ ํจ์จ์ฑ 16
3.2 ํจ์จ์ฑ ๊ฒฐ์ ์์ธ ๋ถ์๋ชจํ 18
์ 4 ์ฅ ๊ตญ๋ด ์์์ด์ก์
์ ๋ํ ํจ์จ์ฑ ๋ฐ ๊ฒฐ์ ์์ธ ๋ถ์ 20
4.1 ๋ณ์์ค์ 20
4.1.1 ์ฐ๊ตฌ๋์ ๋ฐ ์๋ฃ์์ง ๋ฐฉ๋ฒ 22
4.1.2 ๊ธฐ์ด ํต๊ณ๋ 23
4.2 ํจ์จ์ฑ๋ถ์ 24
4.2.1 ์์์ด์ก์
๋งค์ถ์ก ๋ชจํ์ ํจ์จ์ฑ ๋ถ์ 24
4.2.1.1 ๊ธฐ์ ํจ์จ์ฑ๋ถ์(CRS) 24
4.2.1.2 ์๊ธฐ์ ํจ์จ์ฑ(VRS) 29
4.2.1.3 ๊ท๋ชจ์ ํจ์จ์ฑ(SCALE) 34
4.2.1.4 ๊ท๋ชจ ์์ต 39
4.2.1.5 ์์์ด์ก์
๋งค์ถ์ก ๋ชจํ ํจ์จ์ฑ ๋น๊ต 42
4.2.2 ๋ถ๊ฐ๊ฐ์น ๋ชจํ 44
4.2.2.1 ๊ธฐ์ ํจ์จ์ฑ(CRS) 44
4.2.2.2 ์๊ธฐ์ ํจ์จ์ฑ(VRS) 49
4.2.2.3 ๊ท๋ชจ์ ํจ์จ์ฑ(SCALE) 54
4.2.2.4 ๊ท๋ชจ ์์ต 59
4.2.2.5 ์์์ด์ก์
๋ถ๊ฐ๊ฐ์น ๋ชจํ ํจ์จ์ฑ ๋น๊ต 62
4.2.3 ์์์ด์ก์
๋งค์ถ์ก ํจ์จ์ฑ๊ณผ ๋ถ๊ฐ๊ฐ์น ํจ์จ์ฑ๋น๊ต 64
4.3 ๋น์ ๊ท์ง๊ณ ์ฉ๋ณํ๊ฐ ํจ์จ์ฑ์ ๋ฏธ์น๋ ์ํฅ๋ถ์ 68
4.3.1 ๋งค์ถ์ก ๋ชจํ์ ํ๊ท๋ถ์ 69
4.3.1.1 ๊ธฐ์ ํจ์จ์ฑ ์ํฅ์์ธ๋ถ์ 69
4.3.1.2 ์๊ธฐ์ ํจ์จ์ฑ ์ํฅ์์ธ๋ถ์ 70
4.3.1.3 ๊ท๋ชจ์ ํจ์จ์ฑ ์ํฅ์์ธ๋ถ์ 71
4.3.2 ๋ถ๊ฐ๊ฐ์น๋ชจํ์ ์ํฅ ์์ธ ๋ถ์ 72
4.3.2.1 ๊ธฐ์ ํจ์จ์ฑ ์ํฅ์์ธ๋ถ์ 72
4.3.2.2 ์๊ธฐ์ ํจ์จ์ฑ ์ํฅ์์ธ๋ถ์ 73
4.3.2.3 ๊ท๋ชจ์ ํจ์จ์ฑ ์ํฅ์์ธ๋ถ์ 74
์ 5 ์ฅ ๊ฒฐ ๋ก 75
5.1 ์ฐ๊ตฌ์์ฝ 75
5.2 ์ ์ฑ
์ ํจ์ 78
5.3 ์ฐ๊ตฌ์ ํ๊ณ์ 80
์ฐธ ๊ณ ๋ฌธ ํ 82
๊ตญ๋ด๋ฌธํ 82
ํด์ธ๋ฌธํ 85
๊ตญ ๋ฌธ ์ด ๋ก 89Docto
๊ธ์ ๊ฒฐํฉ์ ์ ์ข ๋ฅ์ ๋ฐ๋ฅธ ์ฐ์ญ ์ซ๋์ ์ ํด๋๋ ์ฑ ํน์ฑ์ ์ฐจ์ด์ ์ ๋ํ ์ฐ๊ตฌ :๊ตฌ๋ฆฌ๊ฒฐํฉ์ ์ ์ฃผ์ฒ ๊ฒฐํฉ์ ๋ฅผ ์ค์ฌ์ผ๋ก
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ผ๋ฌธ(์์ฌ)--์์ธ๋ํ๊ต ๋ํ์ :๊ธฐ๊ณ์ค๊ณํ๊ณผ,1995.Maste