• Journal of Hospice and Palliative Care
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• v.25 no.1
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• pp.25-41
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• 2022

Purpose: This study investigated trends of nursing research on life-sustaining treatment in South Korea. Methods: The period for data search was set from January 2018 to December 2020. The major search terms used were advance directives and life-sustaining treatment. Of the 492 records identified in the initial search, 461 articles were excluded for various reasons. A total of 31 records were included in the final qualitative analysis. Results: Sixteen studies had nursing students as study subjects, while nine studies had nurses as study subjects. The majority of the studies employed cross-sectional descriptive surveys as their research design. The major themes that emerged from the studies were as follows: attitudes toward withdrawal of life-sustaining treatment, knowledge of and attitudes toward advance directives, perceptions of a good death, and nurses' attitude toward life support care. Most of the studies reviewed concluded that attitudes toward withdrawal of life-sustaining treatment significantly impacted both knowledge of and attitudes toward advance directives and perceptions of a good death. Conclusion: To date, Korea still lacks extensive nursing research concerning life support care. Further research is needed to provide systematic education for nursing ethics and life support care, as well as the introduction of a specialist course. Furthermore, a multidisciplinary approach is necessary to provide diverse support systems and policy measures. In particular, since nurses are directly responsible for providing life support care, nurses' roles should be expanded in accordance with the Act on Decisions on Life-Sustaining Treatment.

• Journal of Service Research and Studies
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• v.3 no.2
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• pp.53-60
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• 2013

Datamining from the large data in the form of various techniques for obtaining information have been developed. In recent years one of the most sought areas of pattern recognition and machine learning method is created with most of existing learning algorithms based on categorical attributes to a rule or decision model. However, the real-world data, it may consist of numeric attributes in many cases. In addition it contains attributes with numerical values to the normal categorical attribute. In this case, therefore, it is required processes in order to use the data to learn an appropriate value for the type attribute. In this paper, the domain of the numeric attributes are divided into several segments using learning algorithm techniques of discritization. It is described Clustering with other data mining techniques. Large amount of first cluster with characteristics is similar records from the database into smaller groups that split multiple given finite patterns in the pattern space. It is close to each other of a set of patterns that together make up a bunch. Among the set without specifying a particular category in a given data by extracting a pattern. It will be described similar grouping of data clustering technique to classify the data.

• Journal of Intelligence and Information Systems
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• v.28 no.4
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• pp.119-133
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• 2022

One of the most important current features of food related industry is the growth of food delivery service. Another notable food related culture is, with the advent of Youtube, the popularity of Mukbang, which refers to content that records eating. Based on these background, this study intended to focus on two things. First, we tried to see the impact of Youtube Mukbang and the sentiments of Mukbang comments on the number of related food deliveries. Next, we tried to set up the predictive modeling of chicken delivery order with machine learning method. We used Youtube Mukbang comments data as well as weather related data as main independent variables. The dependent variable used in this study is the number of delivery order of fried chicken. The period of data used in this study is from June 3, 2015 to September 30, 2019, and a total of 1,580 data were used. For the predictive modeling, we used machine learning methods such as linear regression, ridge, lasso, random forest, and gradient boost. We found that the sentiment of Youtube Mukbang and comments have impacts on the number of delivery orders. The prediction model with Mukban data we set up in this study had better performances than the existing models without Mukbang data. We also tried to suggest managerial implications to the food delivery service industry.

• The Korean Journal of Archival Studies
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• no.74
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• pp.187-222
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• 2022

The aim of this study was to analyze the operation status of administrative information datasets of private universities and present improvement plans. For the system of private universities, the generation, correction, and deletion of functions, development types, and data were analyzed politically. As a result of the analysis, it has one or more administrative information systems and uses the academic management system in common, and the system is often developed on its own through the university's infrastructure, and data is deleted by the person in charge, but the regulations are not clear. To solve these problems, it was proposed to revise the EA portal to properly investigate the current status of the administrative information system of private universities, manage records centering on systems without data correction, and revise internal regulations to conduct education.

• Journal of Powder Materials
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• v.29 no.6
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• pp.459-467
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• 2022

Soft magnetic powder materials are used throughout industries such as motors and power converters. When manufacturing Fe-based soft magnetic composites, the size and shape of the soft magnetic powder and the microstructure in the powder are closely related to the magnetic properties. In this study, Fe-Si-Al-P alloy powders were manufactured using various manufacturing process parameter sets, and the process parameters of the vacuum induction melt gas atomization process were set as melt temperature, atomization gas pressure, and gas flow rate. Process variable data that records are converted into 6 types of data for each powder recovery section. Process variable data that recorded minute changes were converted into 6 types of data and used as input variables. As output variables, a total of 6 types were designated by measuring the particle size, flowability, apparent density, and sphericity of the manufactured powders according to the process variable conditions. The sensitivity of the input and output variables was analyzed through the Pearson correlation coefficient, and a total of 6 powder characteristics were analyzed by artificial neural network model. The prediction results were compared with the results through linear regression analysis and response surface methodology, respectively.

• Journal of KIISE:Databases
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• v.35 no.3
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• pp.286-295
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• 2008

Multimedia consumer electronics(CE) devices, such as MP3 players, PMPs, and digital cameras, are electronic equipments used to record, play or create multimedia data. Most multimedia CE devices provide uses with the ability to search multimedia stored in the device and browse the search results. One of the unique requirements in multimedia CE devices is that the search results displayed in the screen must be restored quickly when the device powers off and later back on. For this purpose, the existing methods (1) re-execute the original search query, and (2) move the cursor to the original position in the search results. However, this approach may be inefficient when the number of records in the result set is large. In this paper, we propose an efficient method for multimedia CE devices that can quickly restore the search results displayed in the screen when the device powers off and later back on. The proposed method can retrieve the original search results in the screen quickly by saving and loading some information about the query evaluation plan. Though the performance evaluation, we show that the proposed method provides excellent performance regardless of the number of records in tile result set or the original cursor position.

• Proceedings of the Earthquake Engineering Society of Korea Conference
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• 1998.10a
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• pp.421-435
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• 1998

An empirical formula for estimating duration magnitude(MD)is determined by analyzing 619 epicentral distance-duration data set, obtained from earthquakes of 1989-1998 recorded at the KMA network. Based on two assumptions: 1) observed signal duration decreases with increasing epicentral distance, and 2) seismographs of KMA are set at low-gain and therefore inclusion of sensitivity correction term in the equation is not necessary, scaling predicted duration at epicenter to Tsuboi's local magnitude yielded the duration magnitude equation: MD =2.0292$\times$log$\tau$＋0.00123Δ-1.4017 for 1/0$\leq$ML$\leq$5.0, where $\tau$is total signal duration(sec)and Δis epicentral distance(km). Event by event comparison of ML values against MD estimates for t152 events shows that for events having a same ML the difference in MD estimates reaches as high as 1.1 magnitude units. So, to test the usefulness of the duration magnitude equation, we have calculated ML-MD relations by which duration magnitude estimates are converted to local magnitudes ("predicted" ML, say) which are then compared with the directly determined local magnitude values. Except for events with stations where duration is anomalously reestimates(predicted ML) which are in an agreement within a 0.2 magnitude units with the corresponding ML values. Although this study could gain some insights into magnitudes of the past events, we still need to re-examine all the observables in order to obtain more reliable and precise information about magnitude and hypocenter location. So we will pursue a new local-magnitude scaling, as well as refinement of the duration magnitude equation, starting soon with re-reading the amplitudes-arrival time records of (and hence relocating) 250+earthquakes of 1979-present recorded at the KMA network. Thus, with more reliable and precise earthquake parameters determined we would better understand the recent seismicity and related tectonic process within and adjacent region to the Korean peninsula.peninsula.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.2
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• pp.538-561
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• 2020

Recommender Systems (RecSys) have a major role in e-commerce for recommending products, which they may like for every user and thus improve their business aspects. Although many types of RecSyss are there in the research field, the state of the art RecSys has focused on finding the user similarity based on sequence (e.g. purchase history, movie-watching history) analyzing and prediction techniques like Recurrent Neural Network in Deep learning. That is RecSys has considered as a sequence prediction problem. However, evaluation of similarities among the customers is challenging while considering temporal aspects, context and multi-component ratings of the item-records in the customer sequences. For addressing this issue, we are proposing a Deep Learning based model which learns customer similarity directly from the sequence to sequence similarity as well as item to item similarity by considering all features of the item, contexts, and rating components using Dynamic Temporal Warping(DTW) distance measure for dynamic temporal matching and 2D-GRU (Two Dimensional-Gated Recurrent Unit) architecture. This will overcome the limitation of non-linearity in the time dimension while measuring the similarity, and the find patterns more accurately and speedily from temporal and spatial contexts. Experiment on the real world movie data set LDOS-CoMoDa demonstrates the efficacy and promising utility of the proposed personalized RecSys architecture.

• Earthquakes and Structures
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• v.8 no.5
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• pp.957-976
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• 2015

Recently, two energy-based response parameters, i.e., the absolute and the relative elastic input energy equivalent velocity, have been receiving a lot of research attention. Several studies, in fact, have demonstrated the potential of these intensity measures in the prediction of the seismic structural response. Although some ground motion prediction equations have been developed for these parameters, they only provide marginal distributions without information about the joint occurrence of the spectral values at different periods. In order to build new prediction models for the two equivalent velocities, a large set of ground motion records is used to calculate the correlation coefficients between the response spectral values corresponding to different periods and components of the ground motion. Then, functional forms adopted in models from the literature are calibrated to fit the obtained data. A new functional form is proposed to improve the predictions of the considered models from the literature. The components of the ground motion considered in this study are the two horizontal ones only. Potential uses of the proposed equations in addition to the prediction of the correlation coefficients of the equivalent velocity spectral values are shown, such as the prediction of derived intensity measures and the development of conditional mean spectra.

• KIPS Transactions on Software and Data Engineering
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• v.4 no.2
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• pp.71-76
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• 2015

In similarity join processing, a general technique employs a generation-verification framework, which includes two phases: the first phase generates a set of candidate pairs from a collection of records; and the second phase verifies each candidate pair by computing real similarity. In order to reduce the number of candidate pairs in the verification phase, the median of one record of each candidate pair is used as a filter in this paper to test whether the other record can has the proper number of overlapped tokens. We propose a similarity join algorithm with the median filter, and show that the proposed algorithm has better performance in execution time than recent algorithms without the filter through extensive experiments on real-world datasets.