• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.4
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• pp.479-488
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• 2019

The flight test operational procedure artifact includes mission planning, execution methods, and safety measures for each step of test progress. As the development of guided missiles has become more advanced and strategic, flight test has become increasingly complex and broadened. Therefore, increased reliability of the flight test operation procedures was required to ensure test safety. Particularly, the design of the flight test operational procedures required verification through M&S to predict and prepare for the uncertainty in a new test. The relevant studies have published the optimal framework development for flight tests and the model-based improvements of flight test processes, but they lacked the specificity to be applied directly to the flight test operational procedures. In addition, the flight test operational procedures, which consist of document bases, have caused problems such as limitations of analysis capabilities, insensitive expressions, and lack of scalability for the behavior and performance analysis of test resources. To improve these problems, this paper proposes how to design operational procedure of guided missile flight test system by applying MBSE(Model-based Systems Engineering). This research has improved reliability by increasing the ability to analyze the behavior and performance of test resources, and increased efficiency with the scalability applicable to multiple flight tests. That can be also used continuously for the guided missile flight tests that will be developed in the future.

• Journal of the Korean Chemical Society
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• v.63 no.3
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• pp.166-182
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• 2019

This study examined teachers' difficulties that they encountered free semester science assessment and their problem solutions. Fifteen science teachers who had experiences of free semester teaching and assessment were selected by convenience sampling in this study. The participant teachers mentioned difficulties of accurate scoring in student self/peer assessment and suggested solutions of providing studetns with detailed assessment criteria and opportunities to practice assessment. The participant teachers mentioned a lack of objective assessment criteria for affective domain and suggested solutions of providing criteria prior to assessment and developing assessment framework. The participant teachers mentioned a lack of assessment tools and references for perforamnce assesement. The participant teachers mentioned difficulties of a large teacherstudent ratio for providng feedbacks to students and suggested solutions of decreasing teacher-student ratio and teaching load. The participant teachers mentioned difficulties of identifying student characteristics for assessment reporting and suggested solutions of decreaing teacher work load. The participant teachers mentioned a lack of teacher understanding of process based assessment and inactive attitude to performance assessment and suggested solutions of professional learning community and improving teacher perceptions on performance assessment. The participant teachers mentioned difficulties of a large teacher-student ratio and a lack of time for implementing assessment methods that they learned from professional development programs. With both teacher self-efforts and systematic support, these problems would be solved and success of free semester assessment would be achieved.

• Journal of the Korea Society of Computer and Information
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• v.26 no.4
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• pp.1-9
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• 2021

In this paper, we propose a training algorithm of support vector machine (SVM) with a sensitive variable. Although machine learning models enable automatic decision making in the real world applications, regulations prohibit sensitive information from being used to protect privacy. In particular, the privacy protection of the legally protected attributes such as race, gender, and disability is compulsory. We present an efficient least square SVM (LSSVM) training algorithm using a fully homomorphic encryption (FHE) to protect a partial sensitive attribute. Our framework posits that data owner has both non-sensitive attributes and a sensitive attribute while machine learning service provider (MLSP) can get non-sensitive attributes and an encrypted sensitive attribute. As a result, data owner can obtain the encrypted model parameters without exposing their sensitive information to MLSP. In the inference phase, both non-sensitive attributes and a sensitive attribute are encrypted, and all computations should be conducted on encrypted domain. Through the experiments on real data, we identify that our proposed method enables to implement privacy-preserving sensitive LSSVM with FHE that has comparable performance with the original LSSVM algorithm. In addition, we demonstrate that the efficient sensitive LSSVM with FHE significantly improves the computational cost with a small degradation of performance.

• Journal of Korea Water Resources Association
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• v.55 no.3
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• pp.205-215
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• 2022

Amidst the global climate crisis, dam operation policies formulated under the stationary climate assumption could lead to unsatisfactory water management. In this work, we assessed status-quo performance of the Yongdam Dam in Korea under various climatic stresses in flood risk reduction and water supply reliability for 2021-2040. To this end, we employed a decision-centric framework equipped with a stochastic weather generator, a conceptual streamflow model, and a machine-learning reservoir operation rule. By imposing 294 climate perturbations to dam release simulations, we found that the current operation rule of the Yongdam dam could redundantly secure water storage, while inefficiently enhancing the supply reliability. On the other hand, flood risks were likely to increase substantially due to rising mean and variability of daily precipitation. Here, we argue that the current operation rules of the Yongdam Dam seem to be overly focused on securing water storage, and thus need to be adjusted to efficiently improve supply reliability and reduce flood risks in downstream areas.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.50 no.7
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• pp.445-454
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• 2022

Ice accretion on the aircraft components, such as wings, fuselage, and empennage, can occur when the aircraft encounters a cloud zone with high humidity and low temperature. The prevention of ice accretion is important because it causes a decrease in the aerodynamic performance and flight stability, thus leading to fatal safety problems. In this study, a shape design optimization of a multi-element airfoil is performed to minimize the amount of ice accretion on the high-lift device including leading-edge slat, main element, and trailing-edge flap. The design optimization framework proposed in this paper consists of four major parts: air flow, droplet impingement and ice accretion simulations and gradient-free optimization algorithm. Reynolds-averaged Navier-Stokes (RANS) simulation is used to predict the aerodynamic performance and flow field around the multi-element airfoil at the angle of attack 8°. Droplet impingement and ice accretion simulations are conducted using the multi-physics computational analysis tool. The objective function is to minimize the total mass of ice accretion and the design variables are the deflection angle, gap, and overhang of the flap and slat. Kriging surrogate model is used to construct the response surface, providing rapid approximations of time-consuming function evaluation, and genetic algorithm is employed to find the optimal solution. As a result of optimization, the total mass of ice accretion on the optimized multielement airfoil is reduced by about 8% compared to the baseline configuration.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.2
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• pp.183-200
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• 2022

A variety of simulation approaches based on automated driving technologies have been proposed to develop traffic operations strategies to prevent traffic crashes and alleviate congestion. The maneuver of simulated autonomous vehicles (AVs) needs to be realistic and be effectively differentiated from the behavior of manually driven vehicles (MVs). However, the verification of simulated AV maneuvers is limited due to the difficulty in collecting actual AVs trajectory and interaction data with MVs. The purpose of this study is to develop a methodology to evaluate the suitability of AV maneuvers based on both driving and traffic simulation experiments. The proposed evaluation framework includes the requirements for the behavior of individual AVs and the traffic stream performance resulting from the interactions with surrounding vehicles. A driving simulation approach is adopted to evaluate the feasibility of maneuvering of individual AVs. Meanwhile, traffic simulations are used to evaluate whether the impact of AVs on the performance of traffic stream is reasonable. The outcome of this study is expected to be used as a fundamental for the design and evaluation of transportation systems using automated driving technologies.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.35 no.5
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• pp.249-257
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• 2022

This paper presents an improved computational framework for the direct-solution-based finite element tearing and interconnecting (FETI) algorithm. The FETI-local algorithm is further improved herein, and localized Lagrange multipliers are used to define the interface among its subdomains. Selective inverse entry computation, using a property of the Boolean matrix, is employed for the computation of the subdomain interface stiffness and load, in which the original FETI-local algorithm requires a full matrix inverse computation of a high computational cost. In the global interface computation step, the original serial computation is replaced by a parallel multi-frontal method. The performance of the improved FETI-local algorithm was evaluated using a numerical example with 64 million degrees of freedom (DOFs). The computational time was reduced by up to 97.8% compared to that of the original algorithm. In addition, further stable and improved scalability was obtained in terms of a speed-up indicator. Furthermore, a performance comparison was conducted to evaluate the differences between the proposed algorithm and commercial software ANSYS using a large-scale computation with 432 million DOFs. Although ANSYS is superior in terms of computational time, the proposed algorithm has an advantage in terms of the speed-up increase per processor increase.

• The Korean Journal of Franchise Management
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• v.13 no.3
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• pp.31-43
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• 2022

Purpose: Service quality is a topic of constant interest in marketing research and practitioners. Service quality is an important factor influencing performance even in the context of coffee shops, and research on service quality management strategies continues by coffee shop researchers and practitioners. The service quality of coffee shops is a source of competitive advantage and is an important factor in enhancing customer and business performance. This study aims to identify the effects of cognitive evaluation on emotional and behavioral responses using a cognitive-emotional-behavioral framework and SOR model in the coffee shop context. Cognitive evaluation (service quality) consists of tangibles, responsiveness, assurance, reliability, and empathy dimensions. Research design, data, and methodology: In the proposed model, positive and negative emotions and satisfaction mediate the relationship between service quality and money to spend and visit frequency. The data were collected from customers who visited a coffee shop within the last 1 month. The survey was conducted for about one month. Among a total of 300 distributed questionnaires 261 responses were used for data analysis. The data were analyzed using frequency analysis, measurement model analysis, and structural equation modeling analysis with SPSS 28.0 and SmartPLS 4.0. Results: Tangibles, responsiveness, assurance, and empathy had significant positive effects on positive emotion, while only reliability had a significant negative effect on negative emotion. Both positive and negative emotions had significant positive effects on customer satisfaction, but not on money to spend and visit frequency. Lastly, customer satisfaction had significant positive effects on money to spend and visit frequency. Conclusions: The study revealed the relative weight of cognitive factors on customer emotions and confirmed the validity of SOR model. The fact that tangibility is the most important factor in increasing positive emotions and reliability is the most important factor in reducing negative emotions provides a direction for emotional branding strategies using the service quality mix of coffee shops. This study confirmed the full mediating role of satisfaction between positive and negative emotions and consumer behaviors (money to spend and visit frequency). This infers that when a coffee shop increases customer satisfaction through customer emotion management, the customer's money to spend and visit frequency in the coffee shop increases.

• Journal of the Korean Society of Earth Science Education
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• v.16 no.2
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• pp.182-195
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• 2023

The purpose of this study was to analyze reports from research middle schools based on the criteria for process-focused assessment to find out how the characteristics of process-focused assessment were being implemented in middle school science classes. The analysis criteria for the characteristics of process-focused assessment (integration of lessons and assessments, evaluation elements and methods, content and timing of feedback, and learner growth management) were extracted. Using the analysis framework, the result reports of seven research middle schools for process-focused assessment were analyzed. In terms of integration of lessons and assessments, when the process-focused assessment was operated, the class and evaluation plan were well implemented based on the curriculum achievement standards, but the process-focused assessment was recognized as a performance evaluation. In terms of evaluation elements and methods, the evaluation element for knowledge was the main component, and competency was presented in the planning stage, but competency was not dealt with in class execution. The evaluation method was biased toward teacher-centered observation evaluation and written test, and the setting of scoring criteria for each evaluation element was insufficient. In terms of the content and timing of feedback, feedback was mainly provided based on achievement confirmation, but no case was found in which scaffolding was provided at an appropriate time for insufficient parts in the learning process. In terms of the learner's growth management, the competencies cultivated through science classes were included in the detailed subject specialties of the school record. However, little was shown in the report on how to systematically manage the process of developing learners' competencies and reflect the evaluation results to teachers' class improvement.

• Journal of Broadcast Engineering
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• v.27 no.1
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• pp.31-43
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• 2022

Traditional video compression has developed so far based on hybrid compression methods through motion prediction, residual coding, and quantization. With the rapid development of technology through artificial neural networks in recent years, research on image compression and video compression based on artificial neural networks is also progressing rapidly, showing competitiveness compared to the performance of traditional video compression codecs. In this paper, a new method capable of improving the performance of such an artificial neural network-based video compression model is presented. Basically, we take the rate-distortion optimization method using the auto-encoder and entropy model adopted by the existing learned video compression model and shifts some components of the latent information that are difficult for entropy model to estimate when transmitting compressed latent representation to the decoder side from the encoder side, and finally compensates the distortion of lost information. In this way, the existing neural network based video compression framework, MFVC (Motion Free Video Compression) is improved and the BDBR (Bjøntegaard Delta-Rate) calculated based on H.264 is nearly twice the amount of bits (-27%) of MFVC (-14%). The proposed method has the advantage of being widely applicable to neural network based image or video compression technologies, not only to MFVC, but also to models using latent information and entropy model.