• Journal of the Korea Academia-Industrial cooperation Society
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• v.8 no.2
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• pp.286-296
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• 2007

In this paper, we have proposed on the Matching Table of 'Type of If Business - Class of IT Techniques' to train If manpower as an educational-industrial cooperation. The Type of If Business(TITB) are classified according to ${\triangle}$IT Services, ${\triangle}$IT Devices, and ${\triangle}$Software & Computer related Services which are guided by KAIT(Korea Association of Information & Telecommunication). The Class of IT Techniques(CITT) are brought from IITA(Institute for Information Technology Advancement). We have suggested on the element techniques from the Class of If Techniques(CITT) according to the Type of IT Business(TITB) and proposed an educational-industrial cooperation program to line up 'Type of IT Business(TITB) - Class of IT Techniques(CITT) - University(Department/Major)'. The deduction process of the core education program is presented to show the educational-industrial cooperation between company(Type of IT Business : Digital Contents Development Service) and university(Major of Multimedia & Contents) in the Busan area. The Matching Table of 'Type of IT Business(TITB) - Class of IT Techniques(CITT)' varies as the way of classification according to IT Business and IT Techniques. The research on the real and exact Matching Table of 'Type of IT Business(TITB) - Class of IT Techniques(CITT)' is necessary for educational-industrial cooperation by the trends of techniques and the changes of IT market share.

• Journal of KIISE:Computing Practices and Letters
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• v.8 no.2
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• pp.174-182
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• 2002

The object-oriented design methods and their CASE tools are widely used in practice by many real-time software developers. However, object-oriented CASE tools require an additional step of identifying tasks from a given design model. Unfortunately, it is difficult to automate this step for a couple of reasons: (1) there are inherent discrepancies between objects and tasks; and (2) it is hard to derive tasks while maximizing real-time schedulability since this problem makes a non-trivial optimization problem. As a result, in practical object-oriented CASE tools, task identification is usually performed in an ad-hoc manner using hints provided by human designers. In this paper, we present a systematic, schedulability-aware approach that can help mapping real-time object-oriented models to multi-threaded implementations. In our approach, a task contains a group of mutually exclusive transactions that may possess different periods and deadline. For this new task model, we provide a new schedulability analysis algorithm. We also show how the run-time system is implemented and how executable code is generated in our frame work. We have performed a case study. It shows the difficulty of task derivation problem and the utility of the automated synthesis of implementations as well as the Inappropriateness of the single-threaded implementations.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.125-140
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• 2018

As one of the processes in the manufacturing industry, quality inspection inspects the intermediate products or final products to separate the good-quality goods that meet the quality management standard and the defective goods that do not. The manual inspection of quality in a mass production system may result in low consistency and efficiency. Therefore, the quality inspection of mass-produced products involves automatic checking and classifying by the machines in many processes. Although there are many preceding studies on improving or optimizing the process using the data generated in the production process, there have been many constraints with regard to actual implementation due to the technical limitations of processing a large volume of data in real time. The recent research studies on big data have improved the data processing technology and enabled collecting, processing, and analyzing process data in real time. This paper aims to propose the process and details of applying big data for quality inspection and examine the applicability of the proposed method to the dairy industry. We review the previous studies and propose a big data analysis procedure that is applicable to the manufacturing sector. To assess the feasibility of the proposed method, we applied two methods to one of the quality inspection processes in the dairy industry: convolutional neural network and random forest. We collected, processed, and analyzed the images of caps and straws in real time, and then determined whether the products were defective or not. The result confirmed that there was a drastic increase in classification accuracy compared to the quality inspection performed in the past.

• Journal of Korea Technology Innovation Society
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• v.10 no.1
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• pp.22-46
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• 2007

This article introduces a recent innovation in Korea's human resources development policy in the SW sector. Facing serious problems in cultivating SW engineers such as a mismatch in supply and demand of SW workers, shortage of globally competitive SW professionals, and insufficient education and training of university graduates, the Korean government has decided to adopt a new paradigm in national SW engineering education, based on supply chain management (SCM) in manufacturing. SCM has been a major component of the corporate competitive strategy, enhancing organizational productiveness and responsiveness in a highly competitive environment. It weighs improving competitiveness of the supply chain as a whole via long-term commitment to supply chain relationships and a cooperative, integrated approach to business processes. These characteristics of SCM are believed to provide insight into a more effective IT education and university-industry collaboration. On the basis of the SCM literature, a framework for industry-oriented SW human resources development is designed, and then applied in the case of nurturing computer-software engineers in Korea. This approach is expected to fumish valuable implications not only to Korean policy makers, but also to other countries making similar efforts to enhance the effectiveness and flexibility in human resources development. The construction of SCM-based SW HRD model is first trial to apply SCM into SW HRD field. The model is divided into three kinds of primary activities and two kinds of supportive activities in the field of value chain such as SW HRD Council, SW demand and supply plan establishment and the integration of SW engineering capabilities that contribute the reduction of the skill and job matching through SW HR demand and supply collaboration.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.6
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• pp.315-320
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• 2017

Estimating the correct pose of augmented objects on the real camera view efficiently is one of the most important questions in image tracking area. In computer vision, Homography is used for camera pose estimation in augmented reality system with markerless. To estimating Homography, several algorithm like SURF features which extracted from images are used. Based on extracted features, Homography is estimated. For this purpose, RANSAC algorithm is well used to estimate homography and DCS-RANSAC algorithm is researched which apply constraints dynamically based on Constraint Satisfaction Problem to improve performance. In DCS-RANSAC, however, the dataset is based on pattern of feature distribution of images manually, so this algorithm cannot classify the input image, pattern of feature distribution is not recognized in DCS-RANSAC algorithm, which lead to reduce it's performance. To improve this problem, we suggest the KCS-RANSAC algorithm using K-means clustering in CS-RANSAC to cluster the images automatically based on pattern of feature distribution and apply constraints to each image groups. The suggested algorithm cluster the images automatically and apply the constraints to each clustered image groups. The experiment result shows that our KCS-RANSAC algorithm outperformed the DCS-RANSAC algorithm in terms of speed, accuracy, and inlier rate.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.12
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• pp.491-498
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• 2019

Digital twin is a technology that virtualizes physical objects of the real world on a computer. It is used by collecting sensor data through IoT, and using the collected data to connect physical objects and virtual objects in both directions. It has an advantage of minimizing risk by tuning an operation of virtual model through simulation and responding to varying environment by exploiting experiments in advance. Recently, artificial intelligence and machine learning technologies have been attracting attention, so that tendency to virtualize a behavior of physical objects, observe virtual models, and apply various scenarios is increasing. In particular, recognition of each robot's motion is needed to build digital twin for co-robot which is a heart of industry 4.0 factory automation. Compared with modeling based research for recognizing motion of co-robot, there are few attempts to predict motion based on sensor data. Therefore, in this paper, an experimental environment for collecting current and inertia data in co-robot to detect the motion of the robot is built, and a motion prediction model based on the collected sensor data is proposed. The proposed method classifies the co-robot's motion commands into 9 types based on joint position and uses current and inertial sensor values to predict them by accumulated learning. The data used for accumulating learning is the sensor values that are collected when the co-robot operates with margin in input parameters of the motion commands. Through this, the model is constructed to predict not only the nine movements along the same path but also the movements along the similar path. As a result of learning using SVM, the accuracy, precision, and recall factors of the model were evaluated as 97% on average.

• Proceedings of the KSRS Conference
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• v.2
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• pp.551-554
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• 2006

House management is very important in water resource protection in order to provide sustainable drinking water for about four millions population in northern Taiwan. House management can be a simple job that can be done without any ingredient of remote sensing or geographic information systems. Remote sensing and GIS integration for house management can provide more efficient management prescription when land use enforcement, soil and water conservation, sewage management, garbage collection, and reforestation have to be managed simultaneously. The objective of this paper was to integrate remote sensing and GIS to manage houses in a water resource protection district. More than four thousand houses have been surveyed and created as a house data base. Site map of every single house and very detail information consisting of address, ownership, date of creation, building materials, acreages floor by floor, parcel information, and types of house condition. Some houses have their photos in different directions. One house has its own card consists these information and these attributes were created into a house data base. Site maps of all houses were created with the same coordinates system as parcel maps, topographic maps, sewage maps, and city planning maps. Visual Basic.NET, Visual C#.NET have been implemented to develop computer programs for house information inquiry and maps overlay among house maps and other GIS map layers. Remote sensing techniques have been implemented to generate the background information of a single house in the past 15 years. Digital orthophoto maps at a scale of 1:5000 overlay with house site maps are very useful in determination of a house was there or not for a given year. Satellite images if their resolutions good enough are also very useful in this type of daily government operations. The developed house management systems can work with commercial GIS software such as ArcView and ArcPad. Remote sensing provided image information of a single house whether it was there or not in a given year. GIS provided overlay and inquiry functions to automatically extract attributes of a given house by ownership, address, and so on when certain house management prescriptions have to be made by government agency. File format is the key component that makes remote sensing and GIS integration smoothly. The developed house management systems are user friendly and can be modified to meet needs encountered in a single task of a government technician.

• Journal of Acupuncture Research
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• v.21 no.1
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• pp.111-118
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• 2004

Background and purpose: Opposing-needling technique involves selecting acupoints at unaffected limb. The aim of this study was to evaluate the effect of LI4-LI11 electrical acupuncture at unaffected limb on the cerebral blood flow in ischemic stroke patients using SPECT Methods: We selected 9 ischemic stroke patients. Baseline brain SPECT was done with triple head gamma camera(MultiSPECT3, Siemens, USA) after intravenous administration of 925 MBq of Tc-99m ECD). Fifteen-minute electro-acupuncture at Hapgok(LI 4) and Gokji(LI 11) were applied on unaffected upper limb of subjects. The same dose of Tc-99m ECD was injected during the electro-acupuncture, and the second SPECT images were obtained. Using the computer software(ICON 7.1, Siemens, USA), 3 SPECT slices(upper, middle, lower) surrounding the brain lesion were selected and each slice was divided by 10-16 brain regions. Asymmetry indexes were analyzed in each brain region. We regarded$\geq$10% changes of asymmetry index between before and after electro-acupuncture as significance. Results: Seven Patients(77.8%) had significantly increased perfusion and 2(22.2%) didn't show increased perfusion in post-acupuncture scans compared to pre-acupuncture scans(baseline). The regions of CBF improvement were mostly frontal lobes and anterior temporal lobes. Conclusions: This study demonstrated that LI4-LI11 electro-acupuncture at unaffected limb increased regional cerebral blood perfusion to the corresponding brain areas in ischemic stroke patients.

• The Journal of Society for e-Business Studies
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• v.22 no.1
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• pp.65-87
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• 2017

We propose a method to analyze the user reviews and ratings of the products in the online shopping mall and automatically extracts the features of the products to determine the characteristics of a product. By judging whether a rating is given by a specific feature of a product, our method distributes the score to each feature. Conventional methods force users to wastes time reading overflowing number of reviews and ratings to decide whether to buy the product or not. Moreover, it is difficult to grasp the merits and demerits of the product, because of the way reviews and ratings are provided. It is structured in a way that it is impossible to decide which rating is given to the which characteristics of the product. Therefore, in this paper, to resolve this problem, we propose a method to automatically extract the feature of the product from the user review and distribute the score to appropriate characteristics of the product by calculating the rating of each feature from the overall rating. proposed method collects product reviews and ratings, conducts morphological analysis, and extracts features and emotional words of the products. In addition, a method for determining the polarity of a sentence in which the feature appears is given a weight value for each feature. results of the experiment and the questionnaires comparing the existing methods show the usefulness of the proposed method. We also validates the results by comparing the analysis conducted by the product review experts.

• Journal of Digital Convergence
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• v.15 no.6
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• pp.133-143
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• 2017

Data is the most important asset in the financial sector. On average, 71 percent of financial institutions generate competitive advantage over data analysis. In particular, in the card industry, the card transaction data is widely used in the development of merchant information, economic fluctuations, and information services by analyzing patterns of consumer behavior and preference trends of all customers. However, creation of new value through fusion of data is insufficient. This study introduces the analysis and forecasting of consumption trends of credit card companies which convergently analyzed the social data and the sales data of the company's own. BC Card developed an algorithm for linking card and social data with trend profiling, and developed a visualization system for analysis contents. In order to verify the performance, BC card analyzed the trends related to 'Six Pocket' and conducted th pilot marketing campaign. As a result, they increased marketing multiplier by 40~100%. This study has implications for creating a methodology and case for analyzing the convergence of structured and unstructured data analysis that have been done separately in the past. This will provide useful implications for future trends not only in card industry but also in other industries.