• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.109-130
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• 2011

One of the major problems in the area of data mining is the size of the data, as most data set has huge volume these days. Streams of data are normally accumulated into data storages or databases. Transactions in internet, mobile devices and ubiquitous environment produce streams of data continuously. Some data set are just buried un-used inside huge data storage due to its huge size. Some data set is quickly lost as soon as it is created as it is not saved due to many reasons. How to use this large size data and to use data on stream efficiently are challenging questions in the study of data mining. Stream data is a data set that is accumulated to the data storage from a data source continuously. The size of this data set, in many cases, becomes increasingly large over time. To mine information from this massive data, it takes too many resources such as storage, money and time. These unique characteristics of the stream data make it difficult and expensive to store all the stream data sets accumulated over time. Otherwise, if one uses only recent or partial of data to mine information or pattern, there can be losses of valuable information, which can be useful. To avoid these problems, this study suggests a method efficiently accumulates information or patterns in the form of rule set over time. A rule set is mined from a data set in stream and this rule set is accumulated into a master rule set storage, which is also a model for real-time decision making. One of the main advantages of this method is that it takes much smaller storage space compared to the traditional method, which saves the whole data set. Another advantage of using this method is that the accumulated rule set is used as a prediction model. Prompt response to the request from users is possible anytime as the rule set is ready anytime to be used to make decisions. This makes real-time decision making possible, which is the greatest advantage of this method. Based on theories of ensemble approaches, combination of many different models can produce better prediction model in performance. The consolidated rule set actually covers all the data set while the traditional sampling approach only covers part of the whole data set. This study uses a stock market data that has a heterogeneous data set as the characteristic of data varies over time. The indexes in stock market data can fluctuate in different situations whenever there is an event influencing the stock market index. Therefore the variance of the values in each variable is large compared to that of the homogeneous data set. Prediction with heterogeneous data set is naturally much more difficult, compared to that of homogeneous data set as it is more difficult to predict in unpredictable situation. This study tests two general mining approaches and compare prediction performances of these two suggested methods with the method we suggest in this study. The first approach is inducing a rule set from the recent data set to predict new data set. The seocnd one is inducing a rule set from all the data which have been accumulated from the beginning every time one has to predict new data set. We found neither of these two is as good as the method of accumulated rule set in its performance. Furthermore, the study shows experiments with different prediction models. The first approach is building a prediction model only with more important rule sets and the second approach is the method using all the rule sets by assigning weights on the rules based on their performance. The second approach shows better performance compared to the first one. The experiments also show that the suggested method in this study can be an efficient approach for mining information and pattern with stream data. This method has a limitation of bounding its application to stock market data. More dynamic real-time steam data set is desirable for the application of this method. There is also another problem in this study. When the number of rules is increasing over time, it has to manage special rules such as redundant rules or conflicting rules efficiently.

• Journal of Internet Computing and Services
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• v.25 no.1
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• pp.99-107
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• 2024

Along with economic growth and industrial development, there is an increasing demand for various electronic components and device production of semiconductor, SMT component, and electrical battery products. However, these products may contain foreign substances coming from manufacturing process such as iron, aluminum, plastic and so on, which could lead to serious problems or malfunctioning of the product, and fire on the electric vehicle. To solve these problems, it is necessary to determine whether there are foreign materials inside the product, and may tests have been done by means of non-destructive testing methodology such as ultrasound ot X-ray. Nevertheless, there are technical challenges and limitation in acquiring X-ray images and determining the presence of foreign materials. In particular Small-sized or low-density foreign materials may not be visible even when X-ray equipment is used, and noise can also make it difficult to detect foreign objects. Moreover, in order to meet the manufacturing speed requirement, the x-ray acquisition time should be reduced, which can result in the very low signal- to-noise ratio(SNR) lowering the foreign material detection accuracy. Therefore, in this paper, we propose a five-step approach to overcome the limitations of low resolution, which make it challenging to detect foreign substances. Firstly, global contrast of X-ray images are increased through histogram stretching methodology. Second, to strengthen the high frequency signal and local contrast, we applied local contrast enhancement technique. Third, to improve the edge clearness, Unsharp masking is applied to enhance edges, making objects more visible. Forth, the super-resolution method of the Residual Dense Block (RDB) is used for noise reduction and image enhancement. Last, the Yolov5 algorithm is employed to train and detect foreign objects after learning. Using the proposed method in this study, experimental results show an improvement of more than 10% in performance metrics such as precision compared to low-density images.

• Journal of Intelligence and Information Systems
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• v.22 no.4
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• pp.109-122
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• 2016

Recently, big data analysis has developed into a field of interest to individuals and non-experts as well as companies and professionals. Accordingly, it is utilized for marketing and social problem solving by analyzing the data currently opened or collected directly. In Korea, various companies and individuals are challenging big data analysis, but it is difficult from the initial stage of analysis due to limitation of big data disclosure and collection difficulties. Nowadays, the system improvement for big data activation and big data disclosure services are variously carried out in Korea and abroad, and services for opening public data such as domestic government 3.0 (data.go.kr) are mainly implemented. In addition to the efforts made by the government, services that share data held by corporations or individuals are running, but it is difficult to find useful data because of the lack of shared data. In addition, big data traffic problems can occur because it is necessary to download and examine the entire data in order to grasp the attributes and simple information about the shared data. Therefore, We need for a new system for big data processing and utilization. First, big data pre-analysis technology is needed as a way to solve big data sharing problem. Pre-analysis is a concept proposed in this paper in order to solve the problem of sharing big data, and it means to provide users with the results generated by pre-analyzing the data in advance. Through preliminary analysis, it is possible to improve the usability of big data by providing information that can grasp the properties and characteristics of big data when the data user searches for big data. In addition, by sharing the summary data or sample data generated through the pre-analysis, it is possible to solve the security problem that may occur when the original data is disclosed, thereby enabling the big data sharing between the data provider and the data user. Second, it is necessary to quickly generate appropriate preprocessing results according to the level of disclosure or network status of raw data and to provide the results to users through big data distribution processing using spark. Third, in order to solve the problem of big traffic, the system monitors the traffic of the network in real time. When preprocessing the data requested by the user, preprocessing to a size available in the current network and transmitting it to the user is required so that no big traffic occurs. In this paper, we present various data sizes according to the level of disclosure through pre - analysis. This method is expected to show a low traffic volume when compared with the conventional method of sharing only raw data in a large number of systems. In this paper, we describe how to solve problems that occur when big data is released and used, and to help facilitate sharing and analysis. The client-server model uses SPARK for fast analysis and processing of user requests. Server Agent and a Client Agent, each of which is deployed on the Server and Client side. The Server Agent is a necessary agent for the data provider and performs preliminary analysis of big data to generate Data Descriptor with information of Sample Data, Summary Data, and Raw Data. In addition, it performs fast and efficient big data preprocessing through big data distribution processing and continuously monitors network traffic. The Client Agent is an agent placed on the data user side. It can search the big data through the Data Descriptor which is the result of the pre-analysis and can quickly search the data. The desired data can be requested from the server to download the big data according to the level of disclosure. It separates the Server Agent and the client agent when the data provider publishes the data for data to be used by the user. In particular, we focus on the Big Data Sharing, Distributed Big Data Processing, Big Traffic problem, and construct the detailed module of the client - server model and present the design method of each module. The system designed on the basis of the proposed model, the user who acquires the data analyzes the data in the desired direction or preprocesses the new data. By analyzing the newly processed data through the server agent, the data user changes its role as the data provider. The data provider can also obtain useful statistical information from the Data Descriptor of the data it discloses and become a data user to perform new analysis using the sample data. In this way, raw data is processed and processed big data is utilized by the user, thereby forming a natural shared environment. The role of data provider and data user is not distinguished, and provides an ideal shared service that enables everyone to be a provider and a user. The client-server model solves the problem of sharing big data and provides a free sharing environment to securely big data disclosure and provides an ideal shared service to easily find big data.

• Journal of the Korean Society of Radiology
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• v.10 no.6
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• pp.403-409
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• 2016

MRI scan is a useful method in the diagnosis of musculoskeletal excellent contrast of the organization. Depending on the patient's musculoskeletal examinations state the type of aids provided the aid is used there is also challenging as well as the costs do not vary. This study was produced by the use of 3D printing technology, an MRI aids. Aids in the production process, then through 3D modeling and then convert stl files using (3D MAX.2014, Fusion360) slicing programs (Cubicreater 2.1ver., Cura 15.4ver) converted to G-code printed on the FDM scheme (Cubicon Style, output was MICRO MAKE). Output is, but in the FDM to evaluate the SNR on the MRI images were compared to the test is the case before use, and then to produce a Water Phantom case of a PLA, ABS, a TPU thickness 3mm, using aids before, It was evaluated in a clinical image after qualitatively. Obtaining an image of SNR Warter Phantom appeared to have been evaluated as T1 NON $123.778{\pm}28.492$, PLA $123.522{\pm}28.373$, ABS $124.461{\pm}25.716$, TPU $124.843{\pm}27.272$. T2 NON $127.421{\pm}26.949$, was rated as PLA $124.501{\pm}27.768$, ABS $128.663{\pm}26.549$, TPU $130.171{\pm}25.998$. The results did not show statistically significant differences. The use of assistive devices before and after images Clinical evaluation method palliative $3.20{\pm}0.88$, $3.95{\pm}0.76$ after using the aids used to aid improved the quality of the image. Production of the auxiliary mechanism using a future 3D printing is expected are thought to be used clinically, it can be an aid making safe and comfortable than the inspection of the patient is an alternative to improve the problems of the aids used in the conventional do.

• Journal of the Korean Academic Society of Industrial Cluster
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• v.1 no.1
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• pp.67-86
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• 2007

Wonju Medical Equipment Industry, despite of its short history, poor sales and weak manpower and so on, have shown remarkable outcomes in a relatively short period. At the end of 2007, totally 79 enterprises (only 4.6% of whole enterprises in Korea) made 10% of the nationwide production and 15% of the nationwide exports with an annual average growth rate of 66.7%, contributing domestic medical equipment industry tremendously. In addition, many leading medical equipment enterprises in various fields already moved or plan to move to Wonju, accelerating Wonju Medical Equipment Cluster. Wonju Medical Equipment Industry Cluster now enters into the growth stage, getting out of the initial business setup stage. Especially, the nomination of Wonju cluster project from the government accelerates networking (e.g. the development of the universal parts, the establishment of the mutual collaboration model among enterprises, and the mutual marketing), making a rapid growth in Wonju Medical Equipment Industry. Wonju Medical Equipment Industry Cluster revealed positive outcomes despite of the weakness in investment size and infra-structure comparing with the other medical industry cluster in the advanced country, while many domestic enterprises pursued their own growth models and thus failed to promote the international competitive power. Wonju Medical Equipment Industry has been developed rapidly. However, there are many challenging problems to support enterprises: small R&D investment and thus weak technology power, difficulties in recruiting R&D engineers, and poor marketing capabilities, financial infrastructure & policies, and network architecture. In order to develop a world-competitive medical equipment industry cluster at Wonju, the complement of infrastructures, the technology innovation, the mutual marketing, and the network expansion to support enterprises are further required. Wonju' s experiences in developing medical equipment industry so far suggest that our own flexible cluster model considering the industry structure and maturity for different regions should be developed, and specific action plans from the local and central governments based on their systematic strategies for industry development should be implemented in order to build world-competitive industry clusters in Korea.

• Korean Journal of Cognitive Science
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• v.28 no.3
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• pp.149-171
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• 2017

In this research, based on the fact that spatial ability is important for the achievement in the STEM fields, and technological innovation, Purdue Spatial Visualization Test-Rotation has been used to investigate engineering freshmen's spatial ability and gender differences. Students who have taken advanced mathematics courses in high school(those who have taken type B math test in Korean SAT test) and students with general math courses(those who have taken type A in Korean SAT-Math test) are included in this study to find out the relationship between mathematics achievement and spatial ability. Finding out the strategies taken by students was another aim of this study. This strategic differences between high achievers and lower achievers, male and female students were analyzed from students' self report. Spatial ability test score was highest in the SAT-Math type B male students, decreased in the order of type A male students, type B female students, and lastly type A female students. There was no substantial difference between second and third groups. In each group, male students' average score was 8~10% higher than female students, which affirms 2015's results. The correlation between spatial ability and mathematics achievement was negligible in each group, but male students' math score and spatial ability score were higher than that of female students. This can be interpreted that there is some correlation between these two. Strategic choices can vary in the continuous spectrum with analytic method and holistic method at both ends. From students' self report, using Mann-Witney test, it turned out that there exists strategic differences between male and female students. Male students have a tendency to use holistic strategy more often than female students. I also found that the strategy choice did not vary greatly among all score groups. For the perfect score groups, both female and male students used holistic strategy most frequently. For low achieving groups, there is an evidence that these students overuse one method compared to average or high achieving groups, which turned out to be less effective. Based on these, I suggest that low achieving students need to have more chances to adopt efficient strategies and to practice challenging problems to improve their spatial abilities.

• Korean Journal of Cleft Lip And Palate
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• v.3 no.1
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• pp.23-31
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• 2000

Objective: When an alveolar cleft is too large to close with adjacent mucobuccal flaps or large secondary fistula following a primary bilateral palatoplasty exists, a one-stage procedure for bone grafting becomes challenging. In such a case, we used the tongue flap to repair the fistula and cleft alveolus in the first stage, and bone grafting to the cleft defect was performed in the second stage several months later. The purpose of this paper is to report our experiences with the use of an anteriorly-based Y-shaped tongue flap to fit the palatal and labial alveolar defects and the ultimate result of the bone graft. Patients: A series of 14 patients underwent surgery of this type from January 1994 to December 1998.The average age of the patients was 15.8 years old (range: 5 to 28 years old). The mean period of follow-up following the 2nd stage bone raft operation was 45.9 months (range: 9 to 68 months). In nine of the 14 cases, the long-fork type of a Yshaped tongue flap was used for extended coverage of the labial side alveolar defects with the palatal fistula in the remaining cases the short-forked design was used. Results: All cases demonstrated a good clinical result after the initial repair of cleft alveolus and palatal fistula. There was no fistula recurrence, although Partial necrosis of distal margin in long-forked tongue flap was occurred in one case. Furthermore, the bone graft, which was performed an average of 8 months after the tongue flap repair, was always successful. Occasionally, the transferred tongue tissue was bulging and interfering with the hygienic care of nearby teeth; however, these problems were able to be solved with proper contour-pasty performed afterwards. No donor site complications such as sensory disturbance, change in taste, limitations in tongue movement, normal speech impairments or tongue disfigurement were encountered. Conclusion: This two-stage reconstruction of a bilateral cleft alveolus using a Y-shaped tongue flap and iliac bone graft was very successful. It may be indicated for a bilateral cleft alveolus patient where the direct closure of the cleft defect with adjacent tissue or the buccal flap is not easy due to scarred fibrotic mucosa and/or accompanied residual palatal fistula.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.14 no.7
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• pp.586-603
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• 2004

One of the subtle problems that make noise control difficult for engineers is “the invisibility of noise or sound.” The visual image of noise often helps to determine an appropriate means for noise control. There have been many attempts to fulfill this rather challenging objective. Theoretical or numerical means to visualize the sound field have been attempted and as a result, a great deal of progress has been accomplished, for example in the field of visualization of turbulent noise. However, most of the numerical methods are not quite ready to be applied practically to noise control issues. In the meantime, fast progress has made it possible instrumentally by using multiple microphones and fast signal processing systems, although these systems are not perfect but are useful. The state of the art system is recently available but still has many problematic issues : for example, how we can implement the visualized noise field. The constructed noise or sound picture always consists of bias and random errors, and consequently it is often difficult to determine the origin of the noise and the spatial shape of noise, as highlighted in the title. The first part of this paper introduces a brief history, which is associated with “sound visualization,” from Leonardo da Vinci's famous drawing on vortex street (Fig. 1) to modern acoustic holography and what has been accomplished by a line or surface array. The second part introduces the difficulties and the recent studies. These include de-Dopplerization and do-reverberation methods. The former is essential for visualizing a moving noise source, such as cars or trains. The latter relates to what produces noise in a room or closed space. Another mar issue associated this sound/noise visualization is whether or not Ivecan distinguish mutual dependence of noise in space : for example, we are asked to answer the question, “Can we see two birds singing or one bird with two beaks?"

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.16 no.4
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• pp.85-99
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• 2021

Due to the continuous unemployment problem, the number of jobs is gradually decreasing, and entrepreneurship is emerging as an alternative. This is because, despite the government operating various start-up support programs to build a start-up-friendly culture, young entrepreneurs cannot endure the valley of death and disappear. Therefore, through this study, we intend to provide implications by analyzing the impact on Entrepreneurial satisfaction, which is essential for continuously running a business, and life satisfaction, which can act as a social awareness. This study was conducted with 573 non-wage workers who belonged to the founders among the participants of the 'College Graduation Occupational Migration Path Survey(GOMS)' survey provided by the Korea Employment Information Service. In order to analyze the relationship between entrepreneurial competency and job fit, Entrepreneurial satisfaction, and life satisfaction, the analysis was conducted using the SPSS 23.0 program. The main research results are summarized as follows. First, entrepreneurial competency has a positive effect on Entrepreneurial satisfaction and life satisfaction. Second, job fit indicates a moderating role in the relationship between entrepreneurial competency and Entrepreneurial satisfaction. Third, start-up satisfaction appears to have a partial mediating role in the relationship between entrepreneurial competency and life satisfaction. Fourth, as a result of analyzing the difference between groups according to the type of start-up(single/partnership), the group that worked together showed higher Entrepreneurial satisfaction and life satisfaction. The main implications of this study are: First, in order to increase the Entrepreneurial satisfaction and life satisfaction of university graduates who are the subject of the study, it will be necessary to design a program that can diagnose and enhance the entrepreneurial competency of students at the university level. Second, entrepreneurial competency is a basic intrinsic factor that founders must have, and it should act as an important evaluation factor when selecting founders for support programs from start-up support organizations as well as founders. Third, it is necessary to maintain mutual trust by documenting problems (positions, wages, management rights, distribution of profits, etc.) that may occur in joint ventures with objective data. Fourth, it is necessary to establish an environment in which the MZ generation, armed with the challenging spirit and creativity, can continue to take on challenges even if they fail.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.925-938
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• 2022

Agricultural reservoirs are an important water resource nationwide and vulnerable to abnormal climate effects such as drought caused by climate change. Therefore, it is required enhanced management for appropriate operation. Although water-level tracking is necessary through continuous monitoring, it is challenging to measure and observe on-site due to practical problems. This study presents an objective comparison between multiple AI models for water-body extraction using radar images that have the advantages of wide coverage, and frequent revisit time. The proposed methods in this study used Sentinel-1 Synthetic Aperture Radar (SAR) images, and unlike common methods of water extraction based on optical images, they are suitable for long-term monitoring because they are less affected by the weather conditions. We built four AI models such as Support Vector Machine (SVM), Random Forest (RF), Artificial Neural Network (ANN), and Automated Machine Learning (AutoML) using drone images, sentinel-1 SAR and DSM data. There are total of 22 reservoirs of less than 1 million tons for the study, including small and medium-sized reservoirs with an effective storage capacity of less than 300,000 tons. 45 images from 22 reservoirs were used for model training and verification, and the results show that the AutoML model was 0.01 to 0.03 better in the water Intersection over Union (IoU) than the other three models, with Accuracy=0.92 and mIoU=0.81 in a test. As the result, AutoML performed as well as the classical machine learning methods and it is expected that the applicability of the water-body extraction technique by AutoML to monitor reservoirs automatically.