• International Journal of Quality Innovation
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• v.6 no.1
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• pp.24-34
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• 2005

This research topic evaluates the effectiveness and importance of environmental protection in the 21st century in light of the increasing demand on the earth's natural resources and the pressures on economic and industrial development to provide dynamic power. As the world and our fellow citizens become more conscious of environmental protection, companies are under greater pressure whilst pursuing economic growth. Therefore, domestic manufacturers have been devoting efforts to promote environmental management. This research conducts survey using questionnaires on the operational performance of the manufacturers who have ISO 14000 series accreditation and certification. The survey considers five dimensions/functions within a manufacturer, financial management, human resources management, production management, and marketing management. A total of 35 indices are used for analysis of the effects that the location, history, industry, number of employees, amount of capital, and revenue may have on the performance. This research targets the manufacturers approved of ISO 14000 series certification by Environment Administration Association. The statistical methods deployed are descriptive statistics, T-test, and single factor analysis of variance used for analysis. The conclusions reveal that a certain level of performance has been achieved in every dimension. After T-test, all the indices have reached a significant level. The indications are that ISO 14001 benefits all manufacturers the level of benefits however varies from company to company.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.1
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• pp.391-406
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• 2015

Social media have emerged as new communication channels between consumers and companies that generate a large volume of unstructured text data. This social media content, which contains consumers' opinions and interests, is recognized as valuable material from which businesses can mine useful information; consequently, many researchers have reported on opinion-mining frameworks, methods, techniques, and tools for business intelligence over various industries. These studies sometimes focused on how to use opinion mining in business fields or emphasized methods of analyzing content to achieve results that are more accurate. They also considered how to visualize the results to ensure easier understanding. However, we found that such approaches are often technically complex and insufficiently user-friendly to help with business decisions and planning. Therefore, in this study we attempt to formulate a more comprehensive and practical methodology to conduct social media opinion mining and apply our methodology to a case study of the oldest instant noodle product in Korea. We also present graphical tools and visualized outputs that include volume and sentiment graphs, time-series graphs, a topic word cloud, a heat map, and a valence tree map with a classification. Our resources are from public-domain social media content such as blogs, forum messages, and news articles that we analyze with natural language processing, statistics, and graphics packages in the freeware R project environment. We believe our methodology and visualization outputs can provide a practical and reliable guide for immediate use, not just in the food industry but other industries as well.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.1
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• pp.392-412
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• 2018

Due to the semantic gap problem across different modalities, automatically retrieval from multimedia information still faces a main challenge. It is desirable to provide an effective joint model to bridge the gap and organize the relationships between them. In this work, we develop a deep image annotation and classification by fusing multi-modal semantic topics (DAC_mmst) model, which has the capacity for finding visual and non-visual topics by jointly modeling the image and loosely related text for deep image annotation while simultaneously learning and predicting the class label. More specifically, DAC_mmst depends on a non-parametric Bayesian model for estimating the best number of visual topics that can perfectly explain the image. To evaluate the effectiveness of our proposed algorithm, we collect a real-world dataset to conduct various experiments. The experimental results show our proposed DAC_mmst performs favorably in perplexity, image annotation and classification accuracy, comparing to several state-of-the-art methods.

• Journal of the Korean Data and Information Science Society
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• v.21 no.2
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• pp.263-270
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• 2010

Data mining is the process of sorting through large amounts of data and picking out useful information. An important goal of data mining is to discover, define and determine the relationship between several variables. Association rule mining is an important research topic in data mining. An association rule technique finds the relation among each items in massive volume database. Association rule technique consists of two steps: finding frequent itemsets and then extracting interesting rules from the frequent itemsets. Some interestingness measures have been developed in association rule mining. Interestingness measures are useful in that it shows the causes for pruning uninteresting rules statistically or logically. This paper explores some problems for two interestingness measures, confidence and net confidence, and then propose a decision process for right association rule generation using these interestingness measures.

• Journal of the Korean Data and Information Science Society
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• v.28 no.2
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• pp.309-316
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• 2017

In baseball, estimation of winning percentage is critical and many studies for this topic have been actively performed. Pairwise winning percentage estimation using Pythagorean winning percentages of individual teams against other individual teams has the property that the sum of estimated winning percentage totals must be a constant. In this paper, we consider two types of pairwise estimation including linear formula and Pythagorean formula to the Korean baseball data of seasons from 2013 to 2016 under the criterions of RMSE and MAD. In conclusion, pairwise Pythagorean methods have the smaller RMSE and MAD than traditional Pythagorean methods. We suggest the optimal pairwise Pythagorean formula with a fixed exponent. Also we show that there are very little differences of RMSE and MAD between variation in exponent values.

• Communications of Mathematical Education
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• v.22 no.4
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• pp.417-444
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• 2008

Many research papers on the college students' functional concept show they have poor understanding on this topic. To compare the results with that of Korean students, four interrelated topics are chosen: How do they understand the concept of function?; what are their misconceptions including epistemological obstacles?; How do the function concepts develop and are acquired? For this a survey has been conducted to 95 engineering students just before they start Calculus course. We have done research on other major areas including psychology, economics and statistics to see how function is defined in these areas. Function definitions from US math text books are also introduced. Based on the these and the survey, some suggestions are made on the new curriculum which treat function as a correspondence relation. Vertical line test should be added to the Algebra II/Pre calculus course to check the univalent property.

• Smart Structures and Systems
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• v.14 no.2
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• pp.85-104
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• 2014

Damage detection is a challenging, complex, and at the same time very important research topic in civil engineering. Identifying the location and severity of damage in a structure, as well as the global effects of local damage on the performance of the structure are fundamental elements of damage detection algorithms. Local damage detection is essential for structural health monitoring since local damages can propagate and become detrimental to the functionality of the entire structure. Existing studies present several methods which utilize sensor data, and track global changes in the structure. The challenging issue for these methods is to be sensitive enough in identifYing local damage. Autoregressive models with exogenous terms (ARX) are a popular class of modeling approaches which are the basis for a large group of local damage detection algorithms. This study presents an algorithm, called Influence-based Damage Detection Algorithm (IDDA), which is developed for identification of local damage based on regression of the vibration responses. The formulation of the algorithm and the post-processing statistical framework is presented and its performance is validated through implementation on an experimental beam-column connection which is instrumented by dense-clustered wired and wireless sensor networks. While implementing the algorithm, two different sensor networks with different sensing qualities are utilized and the results are compared. Based on the comparison of the results, the effect of sensor noise on the performance of the proposed algorithm is observed and discussed in this paper.

• Perspectives in Nursing Science
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• v.16 no.2
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• pp.55-64
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• 2019

Purpose: This study examined the knowledge and perception of hospice-palliative care and terminal care stress among pediatric nurses, and the relationships among these variables. Methods: In this descriptive research study, 154 pediatric nurses who experienced terminal care at least once were surveyed. This study used three scales, including the Palliative Care Quiz for Nursing (PCQN), Perception of Hospice-Palliative Care, and Terminal care stress. Data analyses using SPSS 22.0 included descriptive statistics, independent t-test, one-way ANOVA, Mann-Whitney U test, Pearson's correlation coefficient, and stepwise multiple linear regression. Results: Terminal care stress experienced by the pediatric nurses was significantly related to the perception of hospice-palliative care; the hospice-palliative care education program enhanced the knowledge and perception of hospice-palliative care. Conclusion: Hospice-palliative care education programs should be developed and provided for pediatric nurses to improve pediatric hospice-palliative care. Additionally, further research on this topic is required because the present results are inconsistent with previous and current researches.

• Research in Community and Public Health Nursing
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• v.30 no.1
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• pp.57-68
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• 2019

Purpose: This study was performed to determine effects of a structure-centered cooperative learning safety education program based on blended learning for elementary school students. Methods: The study is developed in nonequivalent control group non-synchronized design. The subjects included 24 sixth grade students in the experimental group and 23 sixth grade students in the control group, respectively. To prevent diffusion of the experiment, it was carried out from May 20th to June 24th in 2015 with the control group and the other from August 26th to September 30th in 2015 with the experimental group. It was performed on experimental group after the structure-centered cooperative learning safety education program based on blended learning once a week for 6weeks. Data were analyzed by using descriptive statistics, paired t-test and independent t-test. Results: After the intervention, the experimental group showed significant increases in the self-directed learning attitudes and safety behavior compared to the control group except for the academic self-efficacy. Conclusion: The results indicate that the structure-centered cooperative learning safety education program based on blended learning program is effective in safety education for 6th graders.

• Journal of Information Processing Systems
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• v.15 no.4
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• pp.797-819
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• 2019

Internet of Things (IoT) is the paradigm of network of Internet-connected things as objects that constantly sense the physical world and share the data for further processing. At the core of IoT lies the early technology of radio frequency identification (RFID), which provides accurate location tracking of real-world objects. With its small size and convenience, RFID tags can be attached to everyday items such as books, clothes, furniture and the like as well as to animals, plants, and even humans. This phenomenon is the beginning of new applications and services for the industry and consumer market. IoT is regarded as a fourth industrial revolution because of its massive coverage of services around the world from smart homes to artificial intelligence-enabled smart driving cars, Internet-enabled medical equipment, etc. It is estimated that there will be several dozens of billions of IoT devices ready and operating until 2020 around the world. Despite the growing statistics, however, IoT has security vulnerabilities that must be addressed appropriately to avoid causing damage in the future. As such, we mention some fields of study as a future topic at the end of the survey. Consequently, in this comprehensive survey of IoT, we will cover the architecture of IoT with various layered models, security characteristics, potential applications, and related supporting technologies of IoT such as 5G, MEC, cloud, WSN, etc., including the economic perspective of IoT and its future directions.