• International conference on construction engineering and project management
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• 2009.05a
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• pp.409-416
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• 2009

This paper presents a tool called Quality-Cost optimization system (QCOS), which integrates Multi-Objective Genetic Algorithm (MOGA) and Quality Function Deployment (QFD), for tradeoff between quality and cost of the unitized metal curtain-wall unit. A construction owner as the external customer pursues to maximize the quality of the curtain-wall unit. However, the contractor as the internal customer pursues to minimize the cost involved in designing, manufacturing and installing the curtain-wall unit. It is crucial for project manager to find the tradeoff point which satisfies the conflicting interests pursued by the both parties. The system would be beneficial to establish a quality plan satisfying the both parties. Survey questionnaires were administered to the construction owner who has an experience of curtain-wall project, the architects who are the independent assessor, and the contractors who were involved in curtain-wall design and installation. The Customer Requirements (CRs) and their importance weights, the relationship between CRs and Technical Attributes (TAs) consisting of a curtain-wall unit, and the cost ratios of each components consisting curtain-wall unit are obtained from the three groups mentioned previously. The data obtained from the surveys were used as the QFD input to compute the Owner Satisfaction (OS) and Contractor Satisfaction (CS). MOGA is applied to optimize resource allocation under limited budget when multi-objectives, OS and CS, are pursued at the same time. The deterministic multi-objective optimization method using MOGA and QFD is extended to stochastic model to better deal with the uncertainties of QFD input and the variability of QFD output. A case study demonstrates the system and verifies the system conformance.

• Journal of Hydrogen and New Energy
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• v.33 no.6
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• pp.636-642
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• 2022

The "Hydrogen Economic Activation Road map" was announced in 2019, and hydrogen demand is expected to exceed 470,000 tons per year in 2022 and keep increasing. Under this circumstance, it has become important to understand the greenhouse gas (GHG) emissions associated with various hydrogen production pathways. In this study, the evaluation of life cycle GHG emissions regarding the hydrogen produced as by-product from coke oven gas (COG) in steel mill is conducted. To cover the possible range of operations, three literatures were reviewed and their data of inputs and outputs for the process were adopted for calculation. Life cycle inventories and emission factors were mostly referred to GaBi and Intergovernmental Panel on Climate Change (IPCC) guidelines, respectively. When there are multiple products from a single process, the energy allocation method was applied. Based on these sources and the assumptions, the life cycle emission values of COG-based hydrogen were found to be 3.8 to 4.7 kg/CO₂-eq./kg-H₂.

• The Journal of the Convergence on Culture Technology
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• v.9 no.1
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• pp.463-469
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• 2023

The aim of this study is to provide basic data that can contribute to improving successful clinical adaptation and reducing turnover of new graduate nurses by analyzing research related to reality shock experienced by new graduate nurses using text network analysis. The topics of reality shock experienced by new graduate nurses were extracted from 115 papers published in domestic and foreign journals from January 2002 to December 2021. Articles were retrieved from 6 databases (Korean DB: DBpia, KISS, RISS /International DB: Web of science, Springer, Scopus). Keywords were extracted from the abstract and organized using semantic morphemes. Network analysis and topic modeling for subject knowledge structure analysis were performed using NetMiner 4.5.0 program. The core keywords included 'new graduate nurses', 'reality shock', 'transition', 'student nurse', 'experience', 'practice', 'work environment', 'role', 'care' and 'education'. In recent articles on reality shock experienced by new graduate nurses, three major topics were extracted by LDA (Latent Dirichlet Allocation) techniques: 'turnover', 'work environment', 'experience of transition'. Based on this research, the necessity of interventional research that can effectively reduce the reality shock experienced by new graduate nurses and successfully help clinical adaptation is suggested.

• Asia-Pacific Journal of Business
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• v.13 no.4
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• pp.45-59
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• 2022

Purpose - This study investigates the performance of investment strategies incorporating estimated stock market cycle based on a lead-lag relationship between business cycle and stock market cycle, thereby deriving empirical implications on risk management. Design/methodology/approach - The data period ranges from June 1953 to September 2022 and de-trended short rate, term spread, credit spread, stock market volatility are considered as major input variables to estimate business cycle and stock market cycle by applying probit model. Based on the estimated stock market cycle, two types of strategies are constructed and their performance relative to the benchmark is empirically examined. Findings Two types of strategies based on stock market cycle are considered: The first strategy is to long(short) on stocks when stock market stage is expected to be an expansion(a recession), and the second one is to long on stocks(bonds) when expecting an expansion(a recession). The empirical results show that the strategies based on stock market cycle outperforms a simple buy and hold strategy in both in-sample and out-of-sample investigation. Also the out-of-sample evidence suggests that the second strategy which is in line with asset allocation is more profitable than the first one. Research implications or Originality The strategies considered in this study are based on the estimated stock market cycle which only depends on a few easily available financial variables, thereby making easier to establish such a strategy. It implies that investors enhance investment performance by constructing a relatively simple trading strategies if they set their position on stocks or choose which asset class to buy conditioning on stock market cycle.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.3D
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• pp.307-315
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• 2008

The aim of this study is to develop travel time estimation model by using Self-Organized Neural network(in brief, SON) algorithm. Travel time data based on vehicles equipped with GPS and number-plate matching collected from National road number 3 (between Jangji-IC and Gonjiam-IC), which is pilot section of National Highway Traffic Management System were employed. We found that the accuracies of travel time are related to location of detector, the length of road section and land-use properties. In this paper, we try to develop travel time estimation using SON to remedy defects of existing neural network method, which could not additional learning and efficient structure modification. Furthermore, we knew that the estimation accuracy of travel time is superior to optimum located detectors than based on existing located detectors. We can expect the results of this study will make use of location allocation of detectors in highway.

• Asia Marketing Journal
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• v.6 no.2
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• pp.55-68
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• 2004

After Nakanishi and Cooper(1982) suggested a way of transforming the complicated nonlinear MCI model into a simple linear form, the application of MCI model has been increased. However, the use of MCI model in Korea is quite limited. The goal of this paper is to demonstrate the practical application of MCI(Multiplicative Competitive Interaction) model to a consumer goods industry. MCI model is a form of the attraction model explaining the relation between marketing mix variables and market share. In this study, multiple sources of empirical data are incorporated in the model formulation stage. In the estimation process, the equity estimation is applied to solve the possible multi-collinearity problem among marketing mix variables. Results from the fitted model suggest meaningful managerial implications for the management of brand equity and the allocation of resources among marketing mix variables.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.4
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• pp.152-159
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• 2023

This study endeavors to enrich investment prospects in cryptocurrency by establishing a rationale for investment decisions. The primary objective involves evaluating the predictability of four prominent cryptocurrencies - Bitcoin, Ethereum, Litecoin, and EOS - and scrutinizing the efficacy of trading strategies developed based on the prediction model. To identify the most effective prediction model for each cryptocurrency annually, we employed three methodologies - AutoRegressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), and Prophet - representing traditional statistics and artificial intelligence. These methods were applied across diverse periods and time intervals. The result suggested that Prophet trained on the previous 28 days' price history at 15-minute intervals generally yielded the highest performance. The results were validated through a random selection of 100 days (20 target dates per year) spanning from January 1st, 2018, to December 31st, 2022. The trading strategies were formulated based on the optimal-performing prediction model, grounded in the simple principle of assigning greater weight to more predictable assets. When the forecasting model indicates an upward trend, it is recommended to acquire the cryptocurrency with the investment amount determined by its performance. Experimental results consistently demonstrated that the proposed trading strategy yields higher returns compared to an equal portfolio employing a buy-and-hold strategy. The cryptocurrency trading model introduced in this paper carries two significant implications. Firstly, it facilitates the evolution of cryptocurrencies from speculative assets to investment instruments. Secondly, it plays a crucial role in advancing deep learning-based investment strategies by providing sound evidence for portfolio allocation. This addresses the black box issue, a notable weakness in deep learning, offering increased transparency to the model.

• Journal of Internet Computing and Services
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• v.24 no.6
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• pp.57-64
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• 2023

This study analyzed the perceptions of elementary and secondary school teachers regarding the importance of SW/AI education in fostering students' fundamental knowledge and the necessity of integrating SW/AI into education. A total of 830 elementary and secondary school teachers were selected as study subjects using the judgment sampling method. The analysis of survey data revealed that elementary and secondary teachers exhibited a strong awareness of the importance and necessity of SW/AI education, irrespective of school characteristics, region, educational experience, or prior involvement in SW and AI education. Nevertheless, the primary reasons for not implementing SW/AI education were identified as excessive workload and a lack of pedagogical expertise. An analysis of opinions on the essential conditions for implementing SW/AI education revealed that workload reduction, budget support, teacher training to enhance teacher competency, content distribution, expansion of subject-linked courses, and dedicated instructional time allocation were the major influencing factors. These findings indicate a significant demand for comprehensive instructional support and teacher capacity-building programs.

• Journal of Korea Society of Industrial Information Systems
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• v.29 no.1
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• pp.31-48
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• 2024

The domestic automotive industry must consider a strategic shift from traditional automotive component manufacturing to align with future trends such as connectivity, autonomous driving, sharing, and electrification. This research conducted topic modeling on R&D projects in the future automotive sector funded by the Ministry of Trade, Industry, and Energy from 2013 to 2021. We found that topics such as sensors, communication, driver assistance technology, and battery and power technology remained consistently prominent throughout the entire period. Conversely, topics like high-strength lightweight chassis were observed only in the first period, while topics like AI, big data, and hydrogen fuel cells gained increasing importance in the second and third periods. Furthermore, this research analyzed the areas of concentrated investment for each period based on topic-specific government investment amounts and investment growth rates.

• Korean Journal of Radiology
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• v.24 no.7
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• pp.626-639
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• 2023

Objective: To investigate the association of clinical, pathologic, and magnetic resonance imaging (MRI) variables with progressive disease (PD) during neoadjuvant chemotherapy (NAC) and distant metastasis-free survival (DMFS) in patients with triple-negative breast cancer (TNBC). Materials and Methods: This single-center retrospective study included 252 women with TNBC who underwent NAC between 2010 and 2019. Clinical, pathologic, and treatment data were collected. Two radiologists analyzed the pre-NAC MRI. After random allocation to the development and validation sets in a 2:1 ratio, we developed models to predict PD and DMFS using logistic regression and Cox proportional hazard regression, respectively, and validated them. Results: Among the 252 patients (age, 48.3 ± 10.7 years; 168 in the development set; 84 in the validation set), PD was occurred in 17 patients and 9 patients in the development and validation sets, respectively. In the clinical-pathologic-MRI model, the metaplastic histology (odds ratio [OR], 8.0; P = 0.032), Ki-67 index (OR, 1.02; P = 0.044), and subcutaneous edema (OR, 30.6; P = 0.004) were independently associated with PD in the development set. The clinical-pathologic-MRI model showed a higher area under the receiver-operating characteristic curve (AUC) than the clinical-pathologic model (AUC: 0.69 vs. 0.54; P = 0.017) for predicting PD in the validation set. Distant metastases occurred in 49 patients and 18 patients in the development and validation sets, respectively. Residual disease in both the breast and lymph nodes (hazard ratio [HR], 6.0; P = 0.005) and the presence of lymphovascular invasion (HR, 3.3; P < 0.001) were independently associated with DMFS. The model consisting of these pathologic variables showed a Harrell's C-index of 0.86 in the validation set. Conclusion: The clinical-pathologic-MRI model, which considered subcutaneous edema observed using MRI, performed better than the clinical-pathologic model for predicting PD. However, MRI did not independently contribute to the prediction of DMFS.