• International conference on construction engineering and project management
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• 2022.06a
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• pp.736-743
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• 2022

Construction workers experience a high rate of fatal incidents from mobile equipment in the industry. One of the major causes is the decline in the acoustic condition of workers due to the constant exposure to construction noise. Previous studies have proposed various ways in which audio sensing and machine learning techniques can be used to track equipment's movement on the construction site but not on the audibility of safety signals. This study develops a novel framework to help automate safety surveillance in the construction site. This is done by detecting the audio sound at a different signal-to-noise ratio of -10db, -5db, 0db, 5db, and 10db to notify the worker of imminent dangers of mobile equipment. The scope of this study is focused on developing a signal processing model to help improve the audible sense of mobile equipment for workers. This study includes three-phase: (a) collect audio data of construction equipment, (b) develop a novel audio-based machine learning model for automated detection of collision hazards to be integrated into intelligent hearing protection devices, and (c) conduct field experiments to investigate the system' efficiency and latency. The outcomes showed that the proposed model detects equipment correctly and can timely notify the workers of hazardous situations.

• Asia pacific journal of information systems
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• v.32 no.2
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• pp.226-248
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• 2022

The existing model for sentiment analysis of product reviews learned from past data and new data was labeled based on training. But new data was never used by the existing system for making a decision. The proposed Aspect-based multi-agent Deep Reinforcement learning Sentiment Analysis (ADRSA) model learned from its very first data without the help of any training dataset and labeled a sentence with aspect category and sentiment polarity. It keeps on learning from the new data and updates its knowledge for improving its intelligence. The decision of the proposed system changed over time based on the new data. So, the accuracy of the sentiment analysis using deep reinforcement learning was improved over supervised learning and unsupervised learning methods. Hence, the sentiments of premium customers on a particular site can be explored to other customers effectively. A dynamic environment with a strong knowledge base can help the system to remember the sentences and usage State Action Reward State Action (SARSA) algorithm with Bidirectional Encoder Representations from Transformers (BERT) model improved the performance of the proposed system in terms of accuracy when compared to the state of art methods.

• Journal of Soil and Groundwater Environment
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• v.9 no.2
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• pp.48-53
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• 2004

This study is objected to estimate the variation of the coefficient of leachate according to designs in landfill cover systems. Design (a) is the unsanitary landfill cover system with 50 cm soil. But Design (b), (c) are sanitary cover systems which are composed of soil top layer, drainage layer, barrier liner(Design (b): Geomembrane(1.5 mm) and compacted clay liner(30 cm), Design (c) compacted clay liner(45 cm)), gas venting layer. Quantity of leachate estimates Rational Method generally and depend on the coefficient of leachate, on one of the factors in Rational Method largely. The coefficient of leachate is defined as the leachate production ratio result from incident precipitation. To estimate the variation of the coefficient of leachate, the authors use HELP(Hydrologic Evaluation of Landfill Performance) Simulation and Pilot Test. As a result of HELP Simulation, the coefficient of leachate is 0.36∼0.42 in Design (a) and 0.03∼0.15 in Design (b), (c) according to designs in landfill cover systems and quality of barrier liner placement. These numerical values are similar to 0.13 with the coefficient of leachate in Pilot Test.

• Journal of Korean Academy of Nursing
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• v.36 no.4
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• pp.662-670
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• 2006

Purpose. This study was performed to develop and test a decision-tree model of treatment-seeking behaviors about when Korean patients visit a doctor after experiencing stroke symptoms. Methods. The study used methodological triangulation. The model was developed based on qualitative data collected from in-depth interviews with 18 stroke patients. The model was tested using quantitative data collected from interviews and a structured questionnaire involving 150 stroke patients. The predictability of the decision-tree model was quantified as the proportion of participants who followed the pathway predicted by the model. Results. Decision outcomes of the model were categorized into immediate and delayed treatment-seeking behavior. The model was influenced by lowered consciousness, social-group influences, perceived seriousness of symptoms, past history of hypertension or stroke, and barriers to hospital visits. The predictability of the model was found to be 90.7%. Conclusions. The results from this study can help healthcare personnel understand the education needs of stroke patients regarding treatment-seeking behaviors, and hence aid in the development of educational strategies for stroke patients.

• Journal of the Korea Computer Industry Society
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• v.7 no.5
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• pp.521-536
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• 2006

The Blended Learning of K-12 education can he complimented to exceed the limits of the traditional face-to-face instructional method and can help learners have experiences with both various interactions and different learning experiences. Specifically, in K-12 education blended learning has a strength in being able to integrate on-line instruction with off-line instruction and actively help learners emerge with a breadth of knowledge gained collaboratively.<중략>For the objective of the study, that is, an investigation by a literature review process, the mode) of the process of collaboratively emerging knowledge as well as the alternative model of the concept can be applied as a developmental research method, which can improve practical procedure, process, and prescription. For overcoming the model and the real situation, this study was applied with Checkland(1999)'s soft system methodology, using$\square$the comparison of the model and real situations$\square$part and$\square$defining the alternatives$\square$of Checkland(1999)'s methods. In conclusion, analyzing the conceptual model and real situation helps in the development of a model which can minimize the discrepancy between the conceptual model and the real situation within K-12 education.

• Journal of the Ergonomics Society of Korea
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• v.33 no.3
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• pp.175-189
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• 2014

Objective: The goal of this research is to develop new user behavior model using user motion analysis with microscopic perspective for attracting user's participation in interactive space. Background: The interactive space is 'human's place', which is made up of complex elements of digital virtual space and traditional analog and physical environment based on human-computer interaction system. Human behavior has changed in it at the same time. If the user couldn't make participation in interaction, the purpose of the system is not met, which reduces its effect. Therefore, we need to focus on interactive space that is potential future direction from a new point of view. Method: For this research, we would discuss and study fields of interactive space; (1) finding definition of interactive space and studying background of theory about it. (2) providing base of user behavior model with study of user's context that is to be user information and motion. (3) examining user motion, classify basic motion type and making user participation behavior model in phases. Results: Through this process, user's basic twenty motions which are systematized are taken as a standard for analysis of interaction process and participation in interactive space. Then, 'NK-$I^5$ (I Five)' model is developed for user participation types in interactive space. There are five phases of user participation behavior: Imperception, Interest, Involvement, Immersion, and Influence. In this analysis, three indicators which are time, motion types, and user relationship are found to be related to participation. Conclusion: The capabilities and limitation of this research is discussed to attract user participation. This paper focuses especially on contribution of design to lead user's participation in interactive system and expectation to help adapt to user centered design of various interactive space with new aspect of user behavior research. Application: The results of the 'NK-$I^5$ (I Five)' model might help to realize successful interactive space based on user centered design.

• Proceedings of the Korean Institute of Interior Design Conference
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• 1999.04a
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• pp.133-136
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• 1999

This study intends to develop an evaluation model which could be used as an effective tool for criticizing the model house of multi-family housing. The evaluation model has been developed based on the theoretical framework suggested by previous results of research related on hosing design, which comprises the following four components: 1) the paradigm of future home and life-style, 2) marketing effects of housing developer, 3) goals and principles of housing design, and 4) results of user-preference study. In this study, an evaluation model was suggested as a preliminary form which would be modified in detail after series of tests in the field. The evaluation model will be used provide standardized criteria for the quality of model houses, and eventually help to improve the quality of multi-family housing design by balanced information from theorist, user and developer.

• Communications for Statistical Applications and Methods
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• v.23 no.2
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• pp.179-187
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• 2016

The fixed effects panel probit model faces "incidental parameters problem" because it has a property that the number of parameters to be estimated will increase with sample size. The maximum likelihood estimation fails to give a consistent estimator of slope parameter. Unlike the panel regression model, it is not feasible to find an orthogonal reparameterization of fixed effects to get a consistent estimator. In this note, a hierarchical Bayesian model is proposed. The model is essentially equivalent to the frequentist's random effects model, but the individual specific effects are estimable with the help of Gibbs sampling. The Bayesian estimator is shown to reduce reduced the small sample bias. The maximum likelihood estimator in the random effects model is also efficient, which contradicts Green (2004)'s conclusion.

• Journal of Industrial Convergence
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• v.2 no.2
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• pp.127-147
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• 2004

This paper examines the time-series relations among expected return, risk, and book-to-market(B/M) at the portfolio level. The time-series analysis is a natural alternative to cross-sectional regressions. An alternative feature of the time-series regressions is that they focus on changes in expected returns, not on average returns. Using the time-series analysis, we can directly test whether the three-factor model explains time-varying expected returns better than the characteristic-based model. These results should help distinguish between the risk and mispricing stories. We find that B/M is strongly associated with changes in risk, as measured by the Fama and French(1993) three-factor model. After controlling for changes in risk, B/M contains little additional information about expected returns. The evidence suggests that the three-factor model explains time-varying expected returns better than the characteristic-based model.

• International Journal of Advanced Culture Technology
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• v.11 no.3
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• pp.297-301
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• 2023

The study identified the various causes of odor problems, the discomfort they cause, and the importance of the public health and environmental issues associated with them. To solve the odor problem, you must identify the cause and perform an accurate analysis. Therefore, we proposed a CNN-Transformer hybrid model (CTHM) that combines CNN and Transformer and evaluated its performance. It was evaluated using a dataset consisting of 120,000 odor samples, and experimental results showed that CTHM achieved an accuracy of 93.000%, a precision of 92.553%, a recall of 94.167%, an F1 score of 92.880%, and an RMSE of 0.276. Our results showed that CTHM was suitable for odor analysis and had excellent prediction performance. Utilization of this model is expected to help address odor problems and alleviate public health and environmental concerns.