• The Journal of Korean Association of Computer Education
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• v.14 no.4
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• pp.63-70
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• 2011

Recommender systems, which predict and recommend items that may possibly draw users' interests, have been applied in various fields as e-commerce systems are widespread. Collaborative filtering, one of the major methodologies of recommender systems, recommends either items similar to those preferred by the user, or items preferred by the other similar user. Therefore, two problems determine its performance; one is correct estimation of similarity and the other is predicting the real rating of the recommended item. This study addresses the latter problem. Previous studies predict the real rating based on the mean of the ratings, but this study proposes a prediction based on the range of the ratings and investigates its performance through experiments. As a result, it is demonstrated that the proposed method improves the mean absolute error significantly, compared to the previous method.

• The Journal of the Korea Contents Association
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• v.8 no.9
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• pp.139-149
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• 2008

The Police are classed special service on civil service system. Most of special service officers are applied special personal law. It is reason that organization and mission are different from general public officials. The police performance evaluation is enforced in dissimilar form with another civil services for these cause. This study proposed to verification formality about appraisal result to desirable operation of police performance evaluation system. At system enforcement early, it may be desirable that verification is limited finally supervisors rating.

• International conference on construction engineering and project management
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• 2009.05a
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• pp.550-557
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• 2009

The concepts of 'sustainable development', 'sustainable construction' and 'green building' have been elevated to priority levels in all types and phases of construction project development worldwide. Consultants and contractors are now required to seriously consider the impact of their operations on the natural environment and the society, and consequently adopt sustainable construction practices in the development process to minimize and mitigate the negative impacts of their activities. However, existing sustainability rating tools apply to the design, post-construction and operation phases of a building; no tool exists for the rating of the performance of the contractor or the project team at the construction phase. This study aimed to develop a model for evaluating the sustainability of construction operations, drawing on the global best practice standards on sustainability. Practical applications of the model were carried out through case studies to evaluate the performances of fifteen construction firms in New Zealand. The developed model and the outcomes of the case studies were presented, including potential areas of weaknesses, strengths, constraints to achievement or adoption of sustainable construction practices and areas for improvement in the operations of the firms. The successful application of the developed model in practice shows its usefulness and ease of application. It is therefore recommended for adoption as a simple but effective system for measuring and reporting on sustainability performance or sustainability of construction operations of firms in New Zealand and elsewhere.

• Applied Chemistry for Engineering
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• v.32 no.4
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• pp.423-430
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• 2021

In order to evaluate the fire risk and fire risk rating of wood for construction materials, this study focused on fire performance index-III (FPI-III), fire growth index-III (FGI-III), and fire risk index-IV (FRI-IV) according to Chung's equations-III and -IV. Western red cedar, needle fir, ash, and maple were used as the specimens. The fire characteristics were investigated using a cone calorimeter (ISO 5660-1) equipment on the specimen. The FPI-III measured after the combustion reaction was 0.86 to 12.77 based on polymethylmethacrylate (PMMA). The FGI-III was found to be 0.63 to 5.26 based on PMMA. The fire rating according to the FRI-IV, which is the fire rating index, was 0.05 to 6.12, and the western red cedar was 122.4 times higher than that of the maple. The fire risk rating according to the FRI-IV increased in the order of maple, ash, needle fir, PMMA and western red cedar. The CO peak concentration of all specimens was measured as 103 to 162 ppm, and it was 2.1 to 3.2 times higher than 50 ppm, the permissible exposure limits of the US occupational safety and health administration. Materials such as western red cedar, which have a low bulk density and contain a large amount of volatile organic substances, have a low FPI-III and a high FGI-III, so they have a high fire risk rating.

• Journal of the Korea Society of Computer and Information
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• v.23 no.8
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• pp.123-131
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• 2018

The purpose of this study is to investigate the clinical nursing competence and self-efficacy of 4th and 2nd semester nursing college students who completed most of the performance-based nursing education curriculum. It was attempted to develop competency evaluation and competency-based curriculum. The collected data were analyzed using descriptive statistics, t-test, one-way ANOVA, $scheff{\bar{e}}$ test, Pearson's correlation coefficients and Stepwise multiple regression in SPSS WIN 24.0 program. The findings of this study were as follows. 1) The knowledge level of essential basic nursing skills received a score of 88.95. The overall average score of clinical performance was 3.15 out of 5. The mean score of self-efficacy was $4.14{\pm}0.57$ points on 6 points 2) Among the general characteristics of subjects, 'motivation of major selection' and 'satisfaction of practice time' differed in the knowledge of essential basic nursing skills, 'religion' and 'health status' differed in clinical performance ability and 'interpersonal relationship', 'motivation of major selection', 'major satisfaction', 'satisfaction of practice time', 'nursing satisfaction', 'desired working period' and 'average rating' differed in self-efficacy. 3) The self-efficacy showed a significant positive correlation with the clinical nursing competency including the knowledge of essential basic nursing skills and clinical performance ability. The nursing satisfaction, clinical performance ability, the knowledge of essential basic nursing skills, interpersonal relationship and average rating influenced significantly and explained 23.7% of the subjects' self-efficacy.

• The Journal of Engineering Geology
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• v.29 no.4
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• pp.393-403
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• 2019

The water-storage performance of an intake weir can be evaluated by stage-discharge ratings. The stage-discharge rating of a gabion weir depends on the physical characteristics of the filling materials. This study reviewed existing discharge formulae for the evaluation of the water-storage performance of gabion weirs. A previously published relationship between the characteristics of filling materials and experimental constants was adapted for stage-discharge rating. The mean size of the filling material is the most influential factor for the water intake and water-storage performance of gabion weirs.

• The Journal of Information Systems
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• v.29 no.1
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• pp.241-265
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• 2020

Purpose Various machine learning techniques are used to implement for predicting corporate credit. However, previous research doesn't utilize time series input features and has a limited prediction timing. Furthermore, in the case of corporate bond credit rating forecast, corporate sample is limited because only large companies are selected for corporate bond credit rating. To address limitations of prior research, this study attempts to implement a predictive model with more sample companies, which can adjust the forecasting point at the present time by using the credit score information and corporate information in time series. Design/methodology/approach To implement this forecasting model, this study uses the sample of 2,191 companies with KIS credit scores for 18 years from 2000 to 2017. For improving the performance of the predictive model, various financial and non-financial features are applied as input variables in a time series through a sliding window technique. In addition, this research also tests various machine learning techniques that were traditionally used to increase the validity of analysis results, and the deep learning technique that is being actively researched of late. Findings RNN-based stateful LSTM model shows good performance in credit rating prediction. By extending the forecasting time point, we find how the performance of the predictive model changes over time and evaluate the feature groups in the short and long terms. In comparison with other studies, the results of 5 classification prediction through label reclassification show good performance relatively. In addition, about 90% accuracy is found in the bad credit forecasts.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.11
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• pp.5321-5334
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• 2019

Data mining technology is frequently used in identifying the intention of users over a variety of information contexts. Since relevant terms are mainly hidden in text data, it is necessary to extract them. Quantification is required in order to interpret user preference in association with other structured data. This paper proposes rating and comments mining to identify user priority and obtain improved ratings. Structured data (location and rating) and unstructured data (comments) are collected and priority is derived by analyzing statistics and employing TF-IDF. In addition, the improved ratings are generated by applying priority categories based on materialized ratings through Sentiment-Oriented Point-wise Mutual Information (SO-PMI)-based emotion analysis. In this paper, an experiment was carried out by collecting ratings and comments on "place" and by applying them. We confirmed that the proposed mining method is 1.2 times better than the conventional methods that do not reflect priorities and that the performance is improved to almost 2 times when the number to be predicted is small.

• Journal of Multimedia Information System
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• v.3 no.4
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• pp.155-160
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• 2016

The Pulse Coupled Neural Network (PCNN) is a kind of neural network models that consists of spiking neurons and local connections. The PCNN was originally proposed as a model that can reproduce the synchronous phenomena of the neurons in the cat visual cortex. Recently, the PCNN has been applied to the various image processing applications, e.g., image segmentation, edge detection, pattern recognition, and so on. The method for the image matching using the PCNN had been proposed as one of the valuable applications of the PCNN. In this method, the Genetic Algorithm is applied to the PCNN parameter learning for the image matching. In this study, we propose the method of the similar image rating using the PCNN. In our method, the Genetic Algorithm based method is applied to the parameter learning of the PCNN. We show the performance of our method by simulations. From the simulation results, we evaluate the efficiency and the general versatility of our parameter learning method.

• Proceedings of the SAREK Conference
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• 2009.06a
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• pp.706-711
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• 2009

Energy shortage and environmental pollution caused by fossil fuels are very serious problem. Especially buildings have consumed more and more energy, and buildings are spend up to 25% of total energy consumption. So we should prepare alternatives to save energy in buildings. In apartment houses, The efficiency of thermal insulators and window glasses is very important to curtail heating energy. In this study, the energy rating of Apartment building is evaluated by applying various thermal insulators and window glasses.