• Journal of Korean Academy of Nursing Administration
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• v.14 no.2
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• pp.107-117
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• 2008

Purpose: The aim of this study was to testify effectiveness and adaptability of the job characteristics model in nursing organization. Methods: The subjects of this study were 250 nurses who were working in the 2 general hospitals located in Metropolitan city area. The data were collected by self-reporting questionnaires. The data were analyzed using descriptive statistics and path analysis. Results: The modified path model revealed a highly fitness of the data in the overall fitness indexes. The prediction power of modified model was from 44% to 58%, which was very high. The highest predict factors of organizational commitment were identified meaning of empowerment and feedback of job characteristics. The highest predict factors of job satisfaction were identified impact of empowerment and autonomy of job characteristics. Conclusion: With these findings, it was suggested that the nursing job-redesign plan focused on nursing feedback and autonomy among the job characteristics was needed to increase the nurse’ empowerment as well as nursing organizational effectiveness.

• The Journal of Industrial Distribution & Business
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• v.12 no.6
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• pp.23-35
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• 2021

Purpose: This paper aims to investigate whether consumers' mental simulation and self-brand connections influence purchase intention and how the characteristics of advertisings' nonverbal information (congruence among multisensory cues) and verbal messages (self-referencing point and narrative structure) jointly shape mental simulation and self-brand connections. Research design, data and methodology: This paper develops a sportswear advertising and totally collected 225 data through the online survey platform "WenJuanXing". To exam the hypotheses in this paper, structural equation model is conducted in AMOS 21.0 via using 210 valid data. Results: The findings reveal that consumers who engage in mental simulation or establish the connections between them and the brands are more likely to present high purchase intention. Moreover, the characteristics of congruence among multisensory cues, self-referencing points and narrative structure can not only facilitate consumers' mental simulation but also encourage consumers to create connections between them and the brands. Conclusions: This paper develops the advertising research via exploring the characteristics of advertisings' nonverbal information (multisensory cues) and verbal messages simultaneously. And suggesting that both of consumers' mental simulation and self-brand connections are the important approaches for advertisers to effectively increase consumers' purchase intention. Finally, the limitations and suggestions are concluded for the future research.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2217-2220
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• 2003

It is known that an EEG is characterized by the unique and personal characteristics of an individual. Little research has been done to take into account these personal characteristics when analyzing EEG signals. Often the EEG has frequency components which can describe most of the significant characteristics. These combinations are often unique like individual human beings and yet they have an underlying basic characteristics as well. We think that these combinations are the personal characteristics frequency components of the EEG. In this seminar, the EEG analysis method by using the Genetic Algorithms (GA), Factor Analysis (FA), and the Neural Networks (NN) is proposed. The GA is used for selecting the personal characteristic frequency components. The FA is used for extracting the characteristics data of the EEG. The NN is used for estimating the characteristics data of the EEG. Finally, in order to show the effectiveness of the proposed method, classifying the EEG pattern is carried out via computer simulations. The EEG pattern is evaluated under 4 conditions: listening to Rock music, Schmaltzy Japanese ballad music, Healing music, and Classical music. The results, when personal characteristics frequency components are NOT used, gave over 80 % accuracy versus a 95 % accuracy when personal characteristics frequency components are used. This result of our experiment shows the effectiveness of the proposed method.

• Journal of Animal Science and Technology
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• v.66 no.2
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• pp.251-265
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• 2024

Meat derived from skeletal muscles of animals is a highly nutritious type of food, and different meat types differ in nutritional, sensory, and quality properties. This study was conducted to compare the results of previous studies on the muscle fiber characteristics of major porcine skeletal muscles to the end of providing basic data for understanding differences in physicochemical and nutritional properties between different porcine muscle types (or meat cuts). Specifically, the muscle fiber characteristics between 19 major porcine skeletal muscles were compared. The muscle fibers that constitute porcine skeletal muscle can be classified into several types based on their contractile and metabolic characteristics. In addition, the muscle fiber characteristics, including size, composition, and density, of each muscle type were investigated and a technology based on these muscle fiber characteristics for improving meat quality or preventing quality deterioration was briefly discussed. This comparative review revealed that differences in muscle fiber characteristics are primarily responsible for the differences in quality between pork cuts (muscle types) and also suggested that data on muscle fiber characteristics can be used to develop optimal meat storage and packaging technologies for each meat cut (or muscle type).

• Korean Management Science Review
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• v.20 no.2
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• pp.1-16
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• 2003

Previous research on data extraction and integration for data warehousing has concentrated mainly on the relational DBMS or partly on the object-oriented DBMS. Mostly, it describes issues related with the change data (deltas) capture and the incremental update by using the triggering technique of active database systems. But, little attention has been paid to data extraction approaches from other types of source systems like hierarchical DBMS, etc. and from source systems without triggering capability. This paper argues, from the practical point of view, that we need to consider not only the types of information sources and capabilities of ETT tools but also other factors of source systems such as operational characteristics (i.e., whether they support DBMS log, user log or no log, timestamp), and DBMS characteristics (i.e., whether they have the triggering capability or not, etc), in order to find out appropriate data extraction techniques that could be applied to different source systems. Having applied several different data extraction techniques (e.g., DBMS log, user log, triggering, timestamp-based extraction, file comparison) to S bank's source systems (e.g., IMS, DB2, ORACLE, and SAM file), we discovered that data extraction techniques available in a commercial ETT tool do not completely support data extraction from the DBMS log of IMS system. For such IMS systems, a new date extraction technique is proposed which first creates Index database and then updates the data warehouse using the Index database. We illustrates this technique using an example application.

• Journal of the Korean Society for information Management
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• v.34 no.4
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• pp.59-79
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• 2017

This study analyzed the type, subject and open level of research data in the field of library and information science field shared by Figshare, and statistically analyzed the characteristics of data with relatively high recyclability. The results of the analysis showed that datasets and papers were most common data types, and open access and research data were the most common keywords of data, and that 70% of the data were published in a form that can not be processed mechanically such as pdf. As a result of analysis of the relationship between characteristics of research data and degree of sharing, open access areas such as APC (Article Processing Charge) were found to be most common in the subject. However in data type, gray literature such as paper found to be highly utilized rather than dataset.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.11
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• pp.433-440
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• 2019

Recently, machine learning and data mining have been used for many disease prediction and diagnosis. Chronic diseases account for about 80% of the total mortality rate and are increasing gradually. In previous studies, the predictive model for chronic diseases use data such as blood glucose, blood pressure, and insulin levels. In this paper, world's first research, verifies the relationship between dyslipidemia and facial characteristics, and develops the predictive model using machine learning based facial characteristics. Clinical data were obtained from 5390 adult Korean men, and using hypertriglyceridemia and facial characteristics data. Hypertriglyceridemia is a measure of dyslipidemia. The result of this study, find the facial characteristics that highly correlated with hypertriglyceridemia. FD_43_143_aD (p<0.0001, Area Under the receiver operating characteristics Curve(AUC)=0.652) is the best indicator of this study. FD_43_143_aD means distance between mandibular. The model based on this result obtained AUC value of 0.662. These results will provide a basis for predicting various diseases with only facial characteristics in the screening stage of disease epidemiology and public health in the future.

• Journal of Sericultural and Entomological Science
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• v.43 no.1
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• pp.29-32
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• 2001

At present, more than 300 races of the Silkworm are conserved and used as valuable genetic resources. But because of the uneffectiveness of manual data management, faster and systematic data base construction is needed. So, development of silkworm genetic resources management program has been begun and the result can be practically used. When developing the program, Visual basic was used for data input system construction, and MS Access for database. IIS(Internet Information System) and ASP(Active Server Page) was also used for searching data and information with Internet Web Server and Web Browser which is comfortable for constructing database and providing information. Data input item consists of 46 practical characteristics such as race name, moltinism, larval period and pupation percentage etc.. And these characteristics are classified with qualitative and quantitative character. Photographs of silkworm, cocoon and other related items were scanned and the image data was recorded on the database.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.11
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• pp.2817-2827
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• 1996

In this paper, we propose a new channel assignment scheme considering traffic characteristics in the CDMA cellular system. The object of proposed method is to reduce the hard handoff of data calls which are very sensitive to transmission errors. In this algorithm, we use three channel assignment policies. First, all of the data calls are assigned to the primary CDMA channel if possible. Second, priority for primary CDMA channel is given to data calls by assigning some primary CDMA traffic channels exclusively for data calls. Third, data calls are assigned to the CDMA channel most served by neighbor cells, when all of the primary CDMA traffic channels are used. A performance analysis shows the minimum soft handoff probability of data calls in handoff which is guarantied any cellconfiguration is increased above 40% by the adopting proposed algorithm.

• 한국지구물리탐사학회:학술대회논문집
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• 2003.11a
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• pp.340-343
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• 2003

This study presents a practical procedure for the Bayesian inversion of geophysical data by Markov chain Monte Carlo (MCMC) sampling and geostatistics. We have applied geostatistical techniques for the acquisition of prior model information, and then the MCMC method was adopted to infer the characteristics of the marginal distributions of model parameters. For the Bayesian inversion of dipole-dipole array resistivity data, we have used the indicator kriging and simulation techniques to generate cumulative density functions from Schlumberger array resistivity data and well logging data, and obtained prior information by cokriging and simulations from covariogram models. The indicator approach makes it possible to incorporate non-parametric information into the probabilistic density function. We have also adopted the MCMC approach, based on Gibbs sampling, to examine the characteristics of a posteriori probability density function and the marginal distribution of each parameter. This approach provides an effective way to treat Bayesian inversion of geophysical data and reduce the non-uniqueness by incorporating various prior information.