• Journal of Industrial Convergence
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• v.19 no.3
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• pp.1-12
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• 2021

Even though there have been so much practical interests in industry, the relevant empirical researches are not sufficient. In this study, we try to identify the problems of matrix organization structure in the semiconductor industry and make suggestions for improvements. Also, we try to find out whether there are differences in the perceptions of the problems among ranks and teams. This study was conducted to the researchers in the matrix organization structure of the H-corp. research institute. The problems we found are as follows. The researchers agreed that the matrix organization structure is appropriate when highly professional members for the development of next-generation semiconductors are participated in the projects. They showed strong wills to participate and succeed in projects. However, the researchers felt that the equipments and manpowers were not enough and too much tasks and workloads were assigned to both the managers and members Also, in an open ended question, the researchers pointed out the problems of the matrix organization structure such as 'weak project manager's authority', 'communication and teamwork issues', 'non-obvious work priorities', 'compensation and benefit system', 'lack of research manpower and equipment'. From the strengths and weaknesses of the matrix organization structure of the semiconductor industry, we provide some suggestions for improvements.

• Journal of Korean Academy of Nursing
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• v.51 no.6
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• pp.703-719
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• 2021

Purpose: We aimed to identify collaborative disaster governance through the demand and supply analysis of resources recognized by nurses during the COVID-19 pandemic. Methods: We used a descriptive study design with an online survey technique for data collection. The survey questions were developed based on focus group interviews with nurses responding to COVID-19 and expert validity testing. A 42-question online survey focusing on disaster governance was sent to nurses working in COVID-19 designated hospitals, public health offices, and schools. A total of 630 nurses participated in the survey. Demand and supply analysis was used to identify the specific components of disaster governance during a pandemic situation and analyze priority areas in disaster governance, as reported by nurses. Results: Demand and supply analysis showed that supplies procurement, cooperation, education, and environment factors clustered in the high demand and supply quadrant while labor condition, advocacy, emotional support, and workload adjustment factors clustered in the high demand but low supply quadrant, indicating a strong need in those areas of disaster governance among nurses. The nurses practicing at the public health offices and schools showed major components of disaster governance plotted in the second quadrant, indicating weak collaborative disaster governance. Conclusion: These findings show that there is an unbalanced distribution among nurses, resulting in major challenges in collaborative disaster governance during COVID-19. In the future and current pandemic, collaborative disaster governance, through improved distribution, will be useful for helping nurses to access more required resources and achieve effective pandemic response.

• Journal of Digital Convergence
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• v.20 no.2
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• pp.511-523
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• 2022

This study was conducted using non-face-to-face care technology for the elderly with mild dementia and the physically weak living in the community, as various methods of care for the elderly have been raised due to the prolonged COVID-19. The purpose of this study is a similar experimental study before and after the inequality control group to compare cognitive function, daily living activities, and the degree of depression by applying an AI robot integrated management program using. The data was collected from June 4 to September 17, 2021, and the survey results of 17 people in the experimental group and 18 in the control group were analyzed using the SPSS 25.0 program. As a result of the study, the experimental group was significant in language function, activities of daily living, and depression. In particular, the results showed a decrease in moderate to severe depression and mild depression. Cognitive function was significant with long-term care grade and daily living activity with family living together. Therefore, if such non-face-to-face care technology is introduced to the elderly care field in the 'With Corona era', it is thought that it will contribute to cognitive function training and depression reduction of the elderly.

• Journal of Industrial Convergence
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• v.20 no.1
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• pp.77-86
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• 2022

This study is a descriptive research study to understand the level of communication awareness between doctors and nurses, who are professional medical professionals, and the detailed areas and satisfaction of communication. Data were collected from 372 doctors and nurses at general hospitals located in C city from March to May 2021. The collected data were analyzed with descriptive statistics, mean and standard deviation, t-test, ANOVA, Scheffe test, and correlation analysis, using the SPSS/WIN 20.0 program. As a result, there was a difference in the awareness level of communication between doctor and nurse groups. Specifically, out of the detailed areas of communication recognized by doctors and nurses, there were statistically significant differences in openness(t=9.91), mutual understanding between occupations(t=5.25), and satisfaction(t=8.13) between the two groups. In addition, a positive correlation was found between the detailed areas and the communication satisfaction in both groups, showing that nurses have higher communication satisfaction with the higher openness(r=.72, p<.001), mutual understanding between occupations(r=.71, p<.001) and similarly, doctors also have higher communication satisfaction with the higher mutual understanding between occupations(r=.79, p<.001), timeliness(r=.73, p<.001). Therefore, these result suggest that it is necessary to develop a communication program that can effectively improve the weak areas such as mutual understanding between occupations and openness in nurses and doctors in order to ensure patient safety and provide quality medical care.

• The Journal of the Convergence on Culture Technology
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• v.8 no.5
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• pp.677-684
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• 2022

This study is a basic study to evaluate the ecological environment of Changwon City. The study site was 6 ecotourism districts in Changwon-si, and the ecotourism index was evaluated through direct visits and interviews with the person in charge through preliminary research and various literature data from June to July 2021. There are six ecotourism indicators: climate crisis, air quality improvement, water conservation, natural coexistence, citizen participation, and tourism resource management. When looking at the scores for the six ecotourism areas in Changwon, it received the highest score in the areas of natural coexistence and air quality improvement. However, the ecoregion received the lowest score in the water resource conservation category, indicating that the water resource conservation as a whole was weak. Next, tourism resource management, climate crisis, and citizen participation are at the same level in all regions. As a result of the evaluation through the low-carbon ecotourism index, the Changwon City low-carbon ecotourism certification system should first be operated as a measure to revitalize the ecotourism region. It is necessary to prepare a low-carbon ecotourism level for Changwon City. Second, it is very important to guide and promote ecotourism areas to revitalize ecotourism areas. Lastly, to operate a sustainable eco-environment area, it is necessary to operate using local governance above all else.

• The Journal of the Convergence on Culture Technology
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• v.8 no.5
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• pp.369-375
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• 2022

Due to the so-called COVID-19 pandemic, railway service management has also faced an unprecedented situation over the past. This study conducted a survey of customers using high-speed railways during the COVID-19 pandemic to explore the impact of infectious diseases on the railway service customer experience. As a result, customer satisfaction and loyalty increase as customers are more aware of the quarantine-related services provided by railway operators. The moderating role of customer's COVID-19 risk perception was examined as well because there are individual differences in the level of thinking dangerously about Covid-19. As a result, the perceived level of the service's quarantine-related services has a significant impact on customer satisfaction when the customer's risk perception of Covid-19 is at an appropriate level, but its impact is relatively weak when the customer's risk perception is significantly high. Eventually, only the complete extinction of COVID-19 risk will bring a complete recovery to the service industry. Nevertheless, during the epidemic period, it was confirmed that the main service characteristics are that the service operator thoroughly conducts quarantine activities and faithfully communicates with customers.

• Journal of Industrial Convergence
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• v.21 no.1
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• pp.159-165
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• 2023

While the frequency of seismic occurrence has been increasing recently, the domestic seismic response system is weak, the objective of this research is to compare and analyze the seismic vulnerability of buildings using statistical analysis and machine learning techniques. As the result of using statistical technique, the prediction accuracy of the developed model through the optimal scaling method showed about 87%. As the result of using machine learning technique, because the accuracy of Random Forest method is 94% in case of Train Set, 76.7% in case of Test Set, which is the highest accuracy among the 4 analyzed methods, Random Forest method was finally chosen. Therefore, Random Forest method was derived as the final machine learning technique. Accordingly, the statistical analysis technique showed higher accuracy of about 87%, whereas the machine learning technique showed the accuracy of about 76.7%. As the final result, among the 22,296 analyzed building data, the seismic vulnerabilities of 1,627(0.1%) buildings are expected as more dangerous when the statistical analysis technique is used, 10,146(49%) buildings showed the same rate, and the remaining 10,523(50%) buildings are expected as more dangerous when the machine learning technique is used. As the comparison of the results of using advanced machine learning techniques in addition to the existing statistical analysis techniques, in spatial analysis decisions, it is hoped that this research results help to prepare more reliable seismic countermeasures.

• The Journal of the Convergence on Culture Technology
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• v.9 no.1
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• pp.691-695
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• 2023

Currently, the utilization of measurement results of the automated measurement system is very low and is at the level of providing only fragmentary measurement results. In this study, we are going to study a structure behavior analysis 3D display system with high precision and reliability for automated measurement data obtained by constructing big data by transmitting massive data values measured in real time to the cloud and using a Python-based algorithm. As a result of the study, as a system that can evaluate the behavior of a structure to a manager in real time, it provides analysis data in real time without significant restrictions regardless of the type of measurement data and sensor, and derived it as a 3D display. In addition, it was analyzed that the manager could grasp the behavior graph of the structure in real time and more easily judge the derivation of the weak part of the structure through data analysis. In the future, by analyzing the behavior of structures in three dimensions using past and present data, it is expected that more effective measurement results can be obtained in terms of repair, reinforcement, and maintenance of realistic structures.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.39-70
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• 2017

Data-drive analytics techniques have been recently applied to public surveys. Instead of simply gathering survey results or expert opinions to research the preference for a recently launched product, enterprises need a way to collect and analyze various types of online data and then accurately figure out customer preferences. In the main concept of existing data-based survey methods, the sentiment lexicon for a particular domain is first constructed by domain experts who usually judge the positive, neutral, or negative meanings of the frequently used words from the collected text documents. In order to research the preference for a particular product, the existing approach collects (1) review posts, which are related to the product, from several product review web sites; (2) extracts sentences (or phrases) in the collection after the pre-processing step such as stemming and removal of stop words is performed; (3) classifies the polarity (either positive or negative sense) of each sentence (or phrase) based on the sentiment lexicon; and (4) estimates the positive and negative ratios of the product by dividing the total numbers of the positive and negative sentences (or phrases) by the total number of the sentences (or phrases) in the collection. Furthermore, the existing approach automatically finds important sentences (or phrases) including the positive and negative meaning to/against the product. As a motivated example, given a product like Sonata made by Hyundai Motors, customers often want to see the summary note including what positive points are in the 'car design' aspect as well as what negative points are in thesame aspect. They also want to gain more useful information regarding other aspects such as 'car quality', 'car performance', and 'car service.' Such an information will enable customers to make good choice when they attempt to purchase brand-new vehicles. In addition, automobile makers will be able to figure out the preference and positive/negative points for new models on market. In the near future, the weak points of the models will be improved by the sentiment analysis. For this, the existing approach computes the sentiment score of each sentence (or phrase) and then selects top-k sentences (or phrases) with the highest positive and negative scores. However, the existing approach has several shortcomings and is limited to apply to real applications. The main disadvantages of the existing approach is as follows: (1) The main aspects (e.g., car design, quality, performance, and service) to a product (e.g., Hyundai Sonata) are not considered. Through the sentiment analysis without considering aspects, as a result, the summary note including the positive and negative ratios of the product and top-k sentences (or phrases) with the highest sentiment scores in the entire corpus is just reported to customers and car makers. This approach is not enough and main aspects of the target product need to be considered in the sentiment analysis. (2) In general, since the same word has different meanings across different domains, the sentiment lexicon which is proper to each domain needs to be constructed. The efficient way to construct the sentiment lexicon per domain is required because the sentiment lexicon construction is labor intensive and time consuming. To address the above problems, in this article, we propose a novel product reputation mining algorithm that (1) extracts topics hidden in review documents written by customers; (2) mines main aspects based on the extracted topics; (3) measures the positive and negative ratios of the product using the aspects; and (4) presents the digest in which a few important sentences with the positive and negative meanings are listed in each aspect. Unlike the existing approach, using hidden topics makes experts construct the sentimental lexicon easily and quickly. Furthermore, reinforcing topic semantics, we can improve the accuracy of the product reputation mining algorithms more largely than that of the existing approach. In the experiments, we collected large review documents to the domestic vehicles such as K5, SM5, and Avante; measured the positive and negative ratios of the three cars; showed top-k positive and negative summaries per aspect; and conducted statistical analysis. Our experimental results clearly show the effectiveness of the proposed method, compared with the existing method.

• Journal of Animal Science and Technology
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• v.55 no.5
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• pp.359-365
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• 2013

Estimate breeding value can be used as single trait animal model was developed directly using the Fortran language program. The program is based on data computed by using the indirect method repeatedly. The program develops a common algorithm and imprves efficiency. Algorithm efficiency was compared between the two programs. Estimated using the solution is easy to farm and brand the service, pedigree data base was associated with the development of an improved system. The existing program that uses the single trait animal model and the comparative analysis of efficiency is weak because the estimation of the solution and the conventional algorithm programmed through regular formulation involve many repetition; therefore, the newly developed algorithm was conducted to improve speed by reducing the repetition. Single trait animal model was used to analyze Gauss-Seidel iteration method, and the aforesaid two algorithms were compared thorough the mixed model equation which is used the most commonly in estimating the current breeding value by applying the procedures such as the preparation of information necessary for modelling, removal of duplicative data, verifying the parent information of based population in the pedigree data, and assigning sequential numbers, etc. The existing conventional algorithm is the method for reading and recording the data by utilizing the successive repetitive sentences, while new algorithm is the method for directly generating the left hand side for estimation based on effect. Two programs were developed to ensure the accurate evaluation. BLUPF90 and MTDFREML were compared using the estimated solution. In relation to the pearson and spearman correlation, the estimated breeding value correlation coefficients were highest among all traits over 99.5%. Depending on the breeding value of the high correlation in Model I and Model II, accurate evaluation can be found. The number of iteration to convergence was 2,568 in Model I and 1,038 in Model II. The speed of solving was 256.008 seconds in Model I and 235.729 seconds in Model II. Model II had a speed of approximately 10% more than Model I. Therefore, it is considered to be much more effective to analyze large data through the improved algorithm than the existing method. If the corresponding program is systemized and utilized for the consulting of farm and industrial services, it would make contribution to the early selection of individual, shorten the generation, and cultivation of superior groups, and help develop the Hanwoo industry further through the improvement of breeding value based enhancement, ultimately paving the way for the country to evolve into an advanced livestock country.