• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.11C
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• pp.1518-1526
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• 2004

In this paper, an algorithm using wavelet transform for detecting a cut that is a radical scene transition point, and fade and dissolve that are gradual scene transition points is proposed. The conventional methods using wavelet transform for this purpose is using features in both spatial and frequency domain. But in the proposed algorithm, the color space of an input image is converted to YUV and then luminance component Y is transformed in frequency domain using 2-level lifting. Then, the histogram of only low frequency subband that may contain some spatial domain features is compared with the previous one. Edges obtained from other higher bands can be divided into global, semi-global and local regions and the histogram of each edge region is compared. The experimental results show the performance improvement of about 17% in recall and 18% in precision and also show a good performance in fade and dissolve detection.

• Design & Manufacturing
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• v.10 no.2
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• pp.18-23
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• 2016

Injection molded thermoplastic parts may need to be coated to facilitate paint adhesion, or to satisfy other surface property requirements, such as appearance, durability, and weather resistance. In this paper, a two-component polyurethane metering system was developed for the simultaneous injection and surface coating of a plastic substrate. The system was composed of storage tanks, feed pumps, axial piston pumps, mixing head. The tank was designed to be double-jacket structured and fabricated for polyol and isocyanate, respectively. A temperature chamber was used to maintain the material temperature to be $80^{\circ}C$ during flowing from storage tank to mixing head. Inside the chamber, feed pump, low pressure filter, high pressure pump, high pressure filter, pressure sensor, flow meter were installed. A mixing head of L-type was used for homogeneous mixing of polyol and isocyanate. Inside the mixing head, a cartridge heater and a temperature sensor were installed to control the temperature of the materials. The flow rate of axial-piston pump was controlled by using closed-loop feedback control algorithm. The input flow-rates were compared with the measured values. The output error was 6.7% for open-loop control, whereas the error was below 2.2% for closed-loop control. In addition, the pressure generated through mixing-head nozzle increased with increasing flow rate. It was found that the pressure drop between metering pump and mixing-head nozzle was almost 10 bar.

• Safety and Health at Work
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• v.12 no.1
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• pp.35-41
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• 2021

Introduction: Training standards for long-haul truck drivers (LHTD) are rapidly evolving in Canada, yet the opinions of the drivers themselves have not been adequately considered. The purpose was to survey LHTD on their work training history and to examine LHTD perceptions of driver training and licensing protocols. Methods: LHTD were recruited across two Western Canadian provinces from seven different truck stops. The sample completed 207 surveys and 67 semi-structured interviews. Results: The average age of the participants was 52.5 ± 11.5 years (range 24-79); 96% were men. Approximately 33% of the LHTD had at least one crash. Those who did not receive formal driver training were significantly more likely to crash than those who had received training. Participants stated that current training standards are inadequate for the industry, particularly for new drivers. According to participants, entry-level curriculums should consist of both classroom and practical training, as well as on-road observation with a senior mentor. LHTD reported that many new drivers are not equipped to drive in various contexts and settings (e.g., mountains, slippery roads). Conclusions: LHTD are not confident in the current training guidelines for novice truck drivers. Revisions to the training curriculum and standardization across Canada should be considered. Practical Application: A federal mandatory entry-level training program is needed in Canada to ensure that all new LHTD ascertain the necessary skills to drive safely. Such a program requires government involvement and input from LHTD to facilitate appropriate licensure and consistent training for all drivers.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.8
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• pp.2278-2291
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• 2023

Imported ornamental fish should be quarantined because they can have dangerous diseases depending on their habitat. The quarantine requires a lot of time because quarantine officers collect various information on the imported ornamental fish. Inefficient quarantine processes reduce its work efficiency and accuracy. Also, long-time quarantine causes the death of environmentally sensitive ornamental fish and huge financial losses. To improve existing quarantine systems, information on ornamental fish was collected and structured, and a server was established to develop quarantine performance support software equipped with a text search engine. However, the long names of ornamental fish in general can cause many typos and time bottlenecks when we type search words for the target fish information. Therefore, we need a technique that can correct typos. Typical typo character calibration compares input text with all characters in a calibrated candidate text dictionary. However, this approach requires computational power proportional to the number of typos, resulting in slow processing time and low calibration accuracy performance. Therefore, to improve the calibration accuracy of characters, we propose a fusion system of simple Artificial Neural Network (ANN) models and character preprocessing methods that accelerate the process by minimizing the computation of the models. We also propose a typo character generation method used for training the ANN models. Simulation results show that the proposed typo character correction system is about 6 times faster than the conventional method and has 10% higher accuracy.

• The Journal of Korean Academic Society of Nursing Education
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• v.29 no.1
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• pp.27-40
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• 2023

Purpose: The study aimed to examine the concept and attributes of the quality of clinical nursing education for new graduate nurses. Methods: This study adopted a hybrid model introduced by Schwartz-Barcott and Kim. In the theoretical stage, the meaning and attributes of the quality of clinical nursing education for new graduate nurses were determined by analyzing eight articles. In the fieldwork stage, data were collected using semi-structured interviews with five new graduate nurses and seven experienced nurses. The data were analyzed by qualitative content analysis methods developed by Elo and Kyngӓs. In the final analysis, a final result was arrived at comparing, contrasting, and integrating the attributes of the concepts derived in the theoretical and field-work stages. Results: The quality of clinical nursing education for new graduate nurses was identified as excellence or the standard of education for new graduate nurses that would support them in adapting to clinical settings and transitioning to professional nurses. The attributes of the quality of clinical nursing education were founded to possess three dimensions, six categories, and 18 attributes. The multidimensional attributes of the quality of clinical nursing education for new graduate nurses were confirmed as education resources, design, method, content, evaluation, interaction, and outcome under the three dimensions of input, process, and output. Conclusion: The concept and nature of the quality of clinical nursing education observed in this study can be utilized as a basis for the future development, evaluation, and improvement of clinical nursing education for new graduate nurses in healthcare organizations.

• Radiation Oncology Journal
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• v.19 no.4
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• pp.369-380
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• 2001

Purpose : By developing on-line statistics program to record the information of radiation oncology to share the information with internet. It is possible to supply basic reference data for administrative plans to improve radiation oncology. Materials and methods : The information of radiation oncology statistics had been collected by paper forms about 52 hospitals in the past. Now, we can input the data by internet web browsers. The statistics program used windows NT 4.0 operation system, Internal Information Server 4.0 (IIS4.0) as a web server and the Microsoft Access MDB. We used Structured Query Language (SQL), Visual Basic, VBScript and JAVAScript to display the statistics according to years and hospitals. Results : This program shows present conditions about man power, research, therapy machines, technics, brachytherapy, clinic statistics, radiation safety management, institution, quality assurance and radioisotopes in radiation oncology department. The database consists of 38 inputs and 6 outputs windows. Statistical output windows can be increased continuously according to user's need. Conclusion : We have developed statistics program to process all of the data in department of radiation oncology for reference information. Users easily could input the data by internet web browsers and share the information.

• Economic and Environmental Geology
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• v.48 no.6
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• pp.509-523
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• 2015

This study introduces newly implemented geological well logs database for Jeju public water wells, built for a research project focusing on integrated hydrogeology database of Jeju Island. A detailed analysis of the existing 1,200 Jeju Island geological logs for the public wells developed since 1970 revealed six major indications to be improved for their use in Jeju geological logs DB construction: (1) lack of uniformity in rock name classification, (2) poor definitions of pyroclastic deposits and sand and gravel layers, (3) lack of well borehole aquifer information, (4) lack of information on well screen installation in many water wells, (5) differences by person in geological logging descriptions. A new Jeju geological logs DB enabling standardized input and output formats has been implemented to overcome the above indications by reestablishing the names of Jeju volcanic and sedimentary rocks and utilizing a commercial, database-based input structured, geological log program. The newly designed database structure in geological log program enables users to store a large number of geology, well drilling, and test data at the standardized DB input structure. Also, well borehole groundwater and aquifer test data can be easily added without modifying the existing database structure. Thus, the newly implemented geological logs DB could be a standardized DB for a large number of Jeju existing public wells and new wells to be developed in the future at Jeju Island. Also, the new geological logs DB will be a basis for ongoing project 'Developing GIS-based integrated interpretation system for Jeju Island hydrogeology'.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.1-17
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• 2019

Because stock price forecasting is an important issue both academically and practically, research in stock price prediction has been actively conducted. The stock price forecasting research is classified into using structured data and using unstructured data. With structured data such as historical stock price and financial statements, past studies usually used technical analysis approach and fundamental analysis. In the big data era, the amount of information has rapidly increased, and the artificial intelligence methodology that can find meaning by quantifying string information, which is an unstructured data that takes up a large amount of information, has developed rapidly. With these developments, many attempts with unstructured data are being made to predict stock prices through online news by applying text mining to stock price forecasts. The stock price prediction methodology adopted in many papers is to forecast stock prices with the news of the target companies to be forecasted. However, according to previous research, not only news of a target company affects its stock price, but news of companies that are related to the company can also affect the stock price. However, finding a highly relevant company is not easy because of the market-wide impact and random signs. Thus, existing studies have found highly relevant companies based primarily on pre-determined international industry classification standards. However, according to recent research, global industry classification standard has different homogeneity within the sectors, and it leads to a limitation that forecasting stock prices by taking them all together without considering only relevant companies can adversely affect predictive performance. To overcome the limitation, we first used random matrix theory with text mining for stock prediction. Wherever the dimension of data is large, the classical limit theorems are no longer suitable, because the statistical efficiency will be reduced. Therefore, a simple correlation analysis in the financial market does not mean the true correlation. To solve the issue, we adopt random matrix theory, which is mainly used in econophysics, to remove market-wide effects and random signals and find a true correlation between companies. With the true correlation, we perform cluster analysis to find relevant companies. Also, based on the clustering analysis, we used multiple kernel learning algorithm, which is an ensemble of support vector machine to incorporate the effects of the target firm and its relevant firms simultaneously. Each kernel was assigned to predict stock prices with features of financial news of the target firm and its relevant firms. The results of this study are as follows. The results of this paper are as follows. (1) Following the existing research flow, we confirmed that it is an effective way to forecast stock prices using news from relevant companies. (2) When looking for a relevant company, looking for it in the wrong way can lower AI prediction performance. (3) The proposed approach with random matrix theory shows better performance than previous studies if cluster analysis is performed based on the true correlation by removing market-wide effects and random signals. The contribution of this study is as follows. First, this study shows that random matrix theory, which is used mainly in economic physics, can be combined with artificial intelligence to produce good methodologies. This suggests that it is important not only to develop AI algorithms but also to adopt physics theory. This extends the existing research that presented the methodology by integrating artificial intelligence with complex system theory through transfer entropy. Second, this study stressed that finding the right companies in the stock market is an important issue. This suggests that it is not only important to study artificial intelligence algorithms, but how to theoretically adjust the input values. Third, we confirmed that firms classified as Global Industrial Classification Standard (GICS) might have low relevance and suggested it is necessary to theoretically define the relevance rather than simply finding it in the GICS.

• Applied Chemistry for Engineering
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• v.18 no.3
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• pp.205-212
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• 2007

In this work, polyacrylonitrile (PAN) fiber was prepared by electrospinning methods from dimethyl formamide solutions with various conditions, such as 8~20 kV applied voltage, 5~15 wt% PAN concentration, and 15 cm tip-to-collector distance (TCD). The nanofibers were stabilized by oxidation at $250^{\circ}C$ for 1 h, and then subsequently carbonized at $800{\sim}1000^{\circ}C$ for 1 h. The structured characteristics of the nanofibers before and after carbonization were studied by Fourier transform infrared spectroscopy. The resulting diameter distribution and morphologies of the nanofiber were evaluated by scanning electron microscope analysis. The electrochemical behaviors of the nanofiber were observed by cyclic voltammetry tests. From the results, the diameter of electrospinning nanofibers was predominantly influenced by the concentration of polymer solution and the applied voltage. The average diameter of the fibers was decreased with increasing the polymer concentration up to 10wt%. It was also found that the nanofibers with uniform diameter distribution and fine diameter could be achieved at 15kV input voltage and 15 cm TCD.

• Journal of Physiology & Pathology in Korean Medicine
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• v.25 no.5
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• pp.765-772
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• 2011

Knowledge which is represented by formal logic are widely used in many domains such like artificial intelligence, information retrieval, e-commerce and so on. And for medical field, medical documentary records retrieval, information systems in hospitals, medical data sharing, remote treatment and expert systems need knowledge representation technology. To retrieve information intellectually and provide advanced information services, systematically controlled mechanism is needed to represent and share knowledge. Importantly, medical expert's knowledge should be represented in a form that is understandable to computers and also to humans to be applied to the medical information system supporting decision making. And it should have a suitable and efficient structure for its own purposes including reasoning, extendability of knowledge, management of data, accuracy of expressions, diversity, and so on. we call it ontology which can be processed with machines. We can use the ontology to represent traditional medicine knowledge in structured and systematic way with visualization, then also it can also be used education materials. Hence, the authors developed an Shanghanlun ontology by way of showing an example, so that we suggested a methodology for ontology development and also a model to structure the traditional medical knowledge. And this result can be used for student to learn Shanghanlun by graphical representation of it's knowledge. We analyzed the text of Shanghanlun to construct relational database including it's original text, symptoms and herb formulars. And then we classified the terms following some criterion, confirmed the structure of the ontology to describe semantic relations between the terms, especially we developed the ontology considering visual representation. The ontology developed in this study provides database showing fomulas, herbs, symptoms, the name of diseases and the text written in Shanghanlun. It's easy to retrieve contents by their semantic relations so that it is convenient to search knowledge of Shanghanlun and to learn it. It can display the related concepts by searching terms and provides expanded information with a simple click. It has some limitations such as standardization problems, short coverage of pattern(證), and error in chinese characters input. But we believe this research can be used for basic foundation to make traditional medicine more structural and systematic, to develop application softwares, and also to applied it in Shanghanlun educations.