• Journal of the Korean Society of Propulsion Engineers
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• v.22 no.5
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• pp.73-80
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• 2018

At the early stages of development of high-speed propulsion systems, associated uncertainties cannot be easily modeled into probabilistic distributions, owing to the lack of test data, cost, and difficulty of simulating real-flight environments on the ground. To tackle this issue, in this research, the combustion efficiencies of dual-combustion ramjet engines are assumed to have been provided by experts and quantified by evidence theory. Using quantified uncertainty, the inlet area and combustor exit are optimized while satisfying reliability margins of thrust and thermal choking. The result shows a reasonable design of the engine under uncertain circumstances.

• International journal of advanced smart convergence
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• v.11 no.4
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• pp.104-119
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• 2022

Life safety awareness level diagnosis is necessary for customized safety education and continuous safety awareness. As the starting stage of safety education for each life cycle, a scale that has verified the reliability and validity of high school students' life safety awareness has not yet been developed. In this context, the purpose of this study is to develop and validate the life safety awareness scale of high school students and to analyze interindividual differences. Questionnaire data was collected from April to June 2022 from 834 students in the first, second, and third grades of high schools in △△ city in Jeollabuk-do. A final 25-item scale was developed using the preliminary survey, preliminary test, the main test, descriptive statistical analysis, and exploratory and confirmatory factor analysis. This scale consists of four sub-factors: 'safety prevention', 'safety knowledge', 'safety preparation', and 'safety protection'. Good reliability and validity were verified by analysis of content validity and construct validity. The generalizability of the scale was verified by crossover validation between the search group and the crossover group. Based on the interindividual differences analysis, although there was a difference between genders in life safety awareness, there was no difference by grade level and academic achievement. This study is significant in developing the first valid scale that can measure high school students' life safety awareness and providing the necessity and rationale for life safety education by life cycle considering individual gender differences.

• Korean Journal of Construction Engineering and Management
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• v.18 no.6
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• pp.26-37
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• 2017

Preliminary scheduling at the initial stage of planning phase is usually performed with limited information and details. Therefore, the reliability and accuracy of preliminary scheduling is affected by personal experiences and skills of the schedule planners, and it requires enormous managerial effort (or workload). Reusing of historical data of the similar projects is important for efficient preliminary scheduling. However, understanding the structure of historical data and applying them to a new project requires a great deal of experience and knowledge. In this context, this paper propose a framework and methodology for automated preliminary schedule generation based on historical database. The proposed methodology and framework enables to automatically generate CPM schedules for airport projects in the early planning stage in order to enhance the reliability and to reduce the workload by using structured knowledge and experience.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.9 no.3
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• pp.47-61
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• 2014

Many angel investors hesitate to invest in early-stage company. Most early-stage company has no sales and only R&D step project or in early approach of market. So it's impossible to evaluate early-stage company quantitatively. Therefore many angel investors depend on CEO's tendency to evaluate company and make decision for investment. The purpose of this study is discover the entrepreneurial characteristics of CEO and the importance level which affect on the angel investors decision making factors for investment. To identify the factors of entrepreneurial characteristics, survey was conducted by Delphi Technique which is involved by 20 experts who is angel investment club members, venture capitalists, CEOs and officers. Three rounds of survey results derived 10 elements of entrepreneurial characteristics for investment decision making factors including reliability, risk sensitivity, passion, perseverance, integrity, leadership, startup experience, organizational management skills, innovation and social networking. In addition, this study derived the importance level of elements of entrepreneurial characteristics based on the AHP(Analytic Hierarchy Process) theory and maintained the logical consistency by pair-wise comparison for each element. As a result of analyzing the importance of entrepreneurial characteristics, the sequence is reliability (18.1%), integrity (15.9%), leadership (11.7%), organizational management skills (10.0%), social networking (9.5%), passion(9.1%), perseverance(8.4%), innovation(8.1%), startup experience(5.3%) and risk sensitivity(3.9%) respectively. The significance of this study is somewhat decrease limit of the uncertainty arising from angel investors and angel investors can help a decision making, by discover factors of entrepreneurial characteristics that can be called the biggest influencing factors among Investor's investment decision-making In early stage companies and compare importance.

• Korean Journal of Construction Engineering and Management
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• v.9 no.6
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• pp.175-184
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• 2008

For deciding the profitability and feasibility of the construction project, the schematic estimation has to not only link the design decision-making but also estimate the cost with reliability. The prototype-based schematic estimation system was developed for easily linking with design-making and supports to evaluate the design alternatives in the design development stage but didn't consider the cost estimated by parameter and additional work items by users. This research presents the object-parameter based schematic estimation model in the design development stage that can lead to accurately estimate the cost by using historical data from the high-storied office buildings. For the development of the proposed model for schematic estimation, after analyzing and classifying the work items from the Bills of Quantities(BOQs) and drawings of historical data, this research proposed the methods of estimating cost in accordance with attributes of each work item. In addition, a case study is performed for the effectiveness as comparing the previous estimating method with the proposed model.

• Nuclear Engineering and Technology
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• v.31 no.2
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• pp.192-201
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• 1999

This paper proposes an improved signal processing strategy for accurate feedwater flowrate estimation in nuclear power plants. It is generally known that ∼2% thermal power errors occur due to fouling Phenomena in feedwater flowmeters. In the strategy Proposed, the noises included in feedwater flowrate signal are classified into rapidly varying noises and gradually varying noises according to the characteristics in a frequency domain. The estimation precision is enhanced by introducing a low pass filter with the wavelet analysis against rapidly varying noises, and an autoassociative neural network which takes charge of the correction of only gradually varying noises. The modified multivariate stratification sampling using the concept of time stratification and MAXIMIN criteria is developed to overcome the shortcoming of a general random sampling. In addition the multi-stage robust training method is developed to increase the quality and reliability of training signals. Some validations using the simulated data from a micro-simulator were carried out. In the validation tests, the proposed methodology removed both rapidly varying noises and gradually varying noises respectively in each de-noising step, and 5.54% root mean square errors of initial noisy signals were decreased to 0.674% after de-noising. These results indicate that it is possible to estimate the reactor thermal power more elaborately by adopting this strategy.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2008.11a
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• pp.159-165
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• 2008

For deciding the profitability and feasibility of the construction project, the schematic estimation has to not only link the design decision-making but also estimate the cost with reliability. The Object-based schematic estimation system was developed for easily linking with design-making and supports to evaluate the design alternatives in the design development stage but didn't consider the cost estimated by object supplementary and parameter work item. This research presents the Integrated Object-Parameter Schematic Estimation Model in the design development stage that can lead to more accurately estimate the cost through analyzing historical data from the high-storied office buildings. For the development of the proposed model for schematic estimation, after analyzing and classifying the work items from the Bills of Quantities(BOQs) and drawings of historical data, this research proposed the methods of estimating cost in accordance with attributes of each work item using regression analysis. In addition, a case study is performed for the effectiveness as comparing the proposed model with the previous estimating model.

• Environmental Engineering Research
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• v.17 no.1
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• pp.27-34
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• 2012

This paper deals with the evaluation of environmental impact of rail and road transport in South Korea. A framework of energy input-output analysis is employed to estimate the total energy consumption and $CO_2$ emission in acquiring and using a life cycle of passenger and freight transport activity. The reliability of $CO_2$ emission based on uncertainty values is assessed by means of a Monte Carlo simulation. The results show that on a passenger-kilometers basis, passenger roads have life cycle emissions about 1.5 times those of rail, while that ratio is ten times greater when the scope of evaluation regards the tailpipe. In the case of freight transport, on a million ton-kilometers basis, the value for road mode is estimated to be about three times compared to those of rail mode. The results also show that the main contribution of $CO_2$ emission for road transport is the operation stage, accounting for 70%; however, the main contribution for rail transport is the construction and supply chain stage, accounting for over 50% emission.

• Korean Journal of Materials Research
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• v.20 no.3
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• pp.148-154
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• 2010

It is necessary to develop new methods to prevent catastrophic failure of structural material in order to avoid accidents and conserve natural and energy resources. Design of intelligent materials with a self-diagnosing function to prevent fatal fracture of structural materials was achieved by smart composites consisting of carbon fiber tows or carbon powders with a small value of ultimate elongation and glass fiber tows with a large value of ultimate elongation. The changes in electrical resistance of CF-GFRP/GFRP (carbon fiber and glass fiber-reinforced plastics/glass fiber-reinforced plastics) composites increased abruptly with increasing strain, and a tremendous change was seen at the transition point where carbon fiber tows were broken. Therefore, the composites were not to monitor damage from the early stage. On the other hand, the change in electrical resistance of CP-GFRP/GFRP (carbon powder dispersed in glass fiber-reinforced plastics/glass fiber-reinforced plastics) composites increased almost linearly in proportion to strain. CP-GFRP/GFRP composites are superior to CF-GFRP/GFRP composites in terms of their capability to monitor damage by measuring change in electrical resistance from the early stage of damage. However, the former was inferior to the latter as an application because of the difficulties of mass production and high cost. A method based on monitoring damage by measuring changes in the electrical resistance of structural materials is promising for improved reliability of the material.

• The Journal of the Convergence on Culture Technology
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• v.8 no.2
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• pp.385-391
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• 2022

Quality evaluation of beef carcasses is an important issue in the livestock industry. Recently, through the AI monitor system based on artificial intelligence, the quality manager can receive help in making accurate decisions based on the analysis of beef carcass images or result information. This artificial intelligence dataset is an important factor in judging performance. Existing datasets may have different surface orientation or resolution. In this paper, we proposed a two-stage classification model that can efficiently manage the grades of beef carcass image using deep learning. And to overcome the problem of the various conditions of the image, a new dataset of 1,300 images was constructed. The recognition rate of deep network for 5-grade classification using the new dataset was 72.5%. Two-stage evaluation is a method to increase reliability by taking advantage of the large difference between grades 1++, 1+, and grades 1 and 2 and 3. With two experiments using the proposed two stage model, the recognition rates of 73.7% and 77.2% were obtained. As this, The proposed method will be an efficient method if we have a dataset with 100% recognition rate in the first stage.