• IE interfaces
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• v.13 no.2
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• pp.204-210
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• 2000

This paper deals with the problem of mixed-model sequencing on an assembly line. In this sequencing problem we want to minimize the risk of the conveyor stoppage and the total utility work. This paper applies genetic algorithm to solve the mixed-model sequencing problem which is formulated as an integer programming. The solution we get from this algorithm is compared with the solution of Tsai(1995)'s.

• Journal of Korea Water Resources Association
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• v.55 no.7
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• pp.495-504
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• 2022

With the recent development of artificial intelligence, a Long Short-Term Memory (LSTM) model that is efficient with time-series analysis is being used to increase the accuracy of predicting the inflow of dams. In this study, we predict the inflow of the Soyang River dam, using the LSTM model with the Sequence-to-Sequence (LSTM-s2s) and attention mechanism (LSTM-s2s with attention) that can further improve the LSTM performance. Hourly inflow, temperature, and precipitation data from 2013 to 2020 were used to train the model, and validate and test for evaluating the performance of the models. As a result, the LSTM-s2s with attention showed better performance than the LSTM-s2s in general as well as in predicting a peak value. Both models captured the inflow pattern during the peaks but detailed hourly variability is limitedly simulated. We conclude that the proposed LSTM-s2s with attention can improve inflow forecasting despite its limits in hourly prediction.

• Journal of KIISE:Software and Applications
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• v.35 no.9
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• pp.554-561
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• 2008

Prediction technique of user's intention can be used as a post-processing method for reducing the search space of an automatic speech recognizer. Prediction technique of system's intention can be used as a pre-processing method for generating a flexible sentence. To satisfy these practical needs, we propose a statistical model to predict speakers' intentions that are generalized into pairs of a speech act and a concept sequence. Contrary to the previous model using simple n-gram statistic of speech acts, the proposed model represents a dialogue history of a current utterance to a feature set with various linguistic levels (i.e. n-grams of speech act and a concept sequence pairs, clue words, and state information of a domain frame). Then, the proposed model predicts the intention of the next utterance by using the feature set as inputs of CRFs (Conditional Random Fields). In the experiment in a schedule management domain, The proposed model showed the precision of 76.25% on prediction of user's speech act and the precision of 64.21% on prediction of user's concept sequence. The proposed model also showed the precision of 88.11% on prediction of system's speech act and the Precision of 87.19% on prediction of system's concept sequence. In addition, the proposed model showed 29.32% higher average precision than the previous model.

• The Journal of Korean Academic Society of Nursing Education
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• v.20 no.2
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• pp.212-222
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• 2014

Purpose: The purpose of this study was to evaluate the nursing students' clinical judgment skills in simulation using Tanner's Clinical Judgment Model. Method: Forty-five teams of a total 93 nursing students participated in a post-operative patient care scenario using human patient simulator. Data were collected from students' responses in scenario and guided reflective journaling according to the framework of Tanner's model which comprised noticing, interpreting, responding, and reflecting on response. Data were analyzed using descriptive statistics. Results: The students' responses of the situation were in accordance with the goals of scenario, i.e. relieving patient' pain and preventing pulmonary complications. However, most of students needed clinical cues and focused on a given clue to solve the issues. They were lack of ability to collect additional information as well as connect the relevant clues in simulated clinical situation. Conclusion: The nursing students have difficulty in what they notice, how they interpret finding, and respond appropriately to the situation. The simulation training using Tanner's model could provide faculty and nursing students with an effective teaching and learning strategy to develop the clinical judgment skills.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.49 no.2
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• pp.9-14
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• 2012

In this study, multi-finger RF MOSFET substrate parameters are accurately extracted by using S-parameters measured from common source-bulk and common source-gate test structures. Using this extraction method, the accuracy of an asymmetrical model with three substrate resistances is verified by observing better agreement with measured Y-parameters than a simple model with a single substrate resistance. The modeled S-parameters of the asymmetrical model also show excellent agreement with measured ones up to 20GHz.

• Culinary science and hospitality research
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• v.23 no.3
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• pp.15-28
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• 2017

The purpose of this study is to examine the influence of workplace harassment in foodservice industry culinary staff's on organizational silence, counterproductive work behavior and turnover intent. Based on total 234 samples obtained from empirical research, this study tested the reliability and fitness of the research model and verified a total of 5 hypotheses using the AMOS program. Using a structural equation model (SEM), hypothesized relationships in the model were tested simultaneously. The proposed model provided an adequate fit the data, $x^2=75.936$ (p<.001), df=41, CMIN/DF=1.852, GFI=.946, AGFI=.913, NFI=.914, TLI=.944, CFI=.958, RMSEA=.060. The model's fit, as indicated by these indexes, was deemed satisfactory, thus providing a good basis for testing the hypothesized paths. The SEM showed that the relational workplace harassment (${\beta}=.957$) had a positive significant influence on organizational silence, organizational silence (${\beta}=.934$) had a positive significant influence on counterproductive work behavior. Also, counterproductive work behavior (${\beta}=.815$) had a positive significant influence on turnover intention. The moderating effects on gender and job status did not show significant effect. Limitations and future research directions are also discussed.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2018.05a
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• pp.41-42
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• 2018

This study is about the prediction of ship's roll motion characteristic which has been used for evaluating ship's seakeeping performance. In order to obtain the ship's roll RAO during voyage, this paper utilized machine learning-based surrogate model. By comparing the prediction result data of surrogate model with test data, we suggest the best approximation technique and data sampling interval of the surrogate model appropriate for predicting the ships' roll motion characteristic.

• Journal of the Korean Society of Clothing and Textiles
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• v.35 no.12
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• pp.1440-1453
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• 2011

Proper consumer segmentation is receiving more attention from industry professionals as markets become more diverse and consumer-centered. Researchers have recognized the limitations of the traditional cluster analysis technique and this research study analyzes market segmentation using Mixture-model or latent-class segmentation. This study used a questionnaire to determine the characteristics of clothing shoppers using a new technique that proved its superiority over traditional techniques. Questions included items measuring fashion shopping behavior, store choice criteria, apparel consumption styles, price perception by product type, and demographic characteristics. Data were collected from 1074 males and females in their 20s and 30s through an online survey. SPSS 16.0 and Latent GOLD 4.0 were used to analyze the data. The ideal typology of clothing shoppers using the Mixture-model were: 'brand loyalty orientated group', 'group of conservative late 30s', 'group of pleasure-emotion early 20s', 'value oriented consumer product with high-income group', 'group of eco/symbol oriented consumer', and 'group of utility/goal oriented male consumer'. This study showed differences in fashion product purchasing behavior by conducting market segmentation for clothing shoppers using the Mixture-model.

• Journal of The Korean Association For Science Education
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• v.28 no.8
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• pp.796-813
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• 2008

A good understanding of how gifted science students understand physics is important to developing and delivering effective curriculum for gifted science students. This dissertation reports on a systematic investigation of gifted science students' reasoning model in learning physics. An analysis of videotaped class work, written work and interviews indicate that I will discuss the framework to characterize student reasoning. There are three main groups of students. The first group of gifted science students holds several different understandings of a single concept and apply them inconsistently to the tasks related to that concept. Most of these students hold the Aristotelian Model about Newton's second law. In this case, I define this reasoning model as the manifold model. The second group of gifted science students hold a unitary understanding of a single concept and apply it consistently to several tasks. Most of these students hold a Newtonian Model about Newton's second law. In this case, I define this reasoning model as the coherence model. Finally, some gifted science students have a manifold model with several different perceptions of a single concept and apply them inconsistently to tasks related to the concept. Most of these students hold the Aristotelian Model about Newton's second law. In this case, I define this reasoning model as the coherence model.

• Journal of the Korea Institute of Military Science and Technology
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• v.14 no.5
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• pp.818-824
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• 2011

In this paper, we proposed a phase noise model of a frequency synthesizer for a radar system. Especially, it was proposed a phase noise model in a DAS(Direct Analog Synthesizer) and a frequency up converter system using Leeson's model. The proposed phase noise model was derived from the measurement data of model 1 and evaluated by adapting to model 2 and model 3 frequency synthesizers. The prediction phase noise by modeling was totally matched to the measured data and the effective analysis of the phase noise was done in a frequency synthesizer and a frequency converter of radar system.