• Journal of KIISE:Software and Applications
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• v.33 no.6
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• pp.550-563
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• 2006

Software product line engineering is a method that prepares for the future reuse and supports to seamless reuse in application development process. Commonality and variability play central roles in all product line development processes. Reusable assets will become core assets by explicitly representing C&V. Indeed, the variabilities that art identified at each phase of core assets development have different levels of abstraction. In the past, these variabilities have been handled in an implicit manner and without distinguishing the characteristics of each core assets. In addition, previous approaches have depended on the experience and intuition of a domain expert to recognize commonality and variability. In this paper, we suggest a 2-dimensional analyzing method that analyzes the variabilities of core assets in software product line. In horizontal analysis process, the variation types are analyzed in requirements, architecture, and component that are produced at each phase of development process. In vertical analysis process, variations are analyzed in different abstract levels, in which the region of commonality is identified and the variation points are refined. By this method, the traceability of variations between core assets will be possible and core assets can be reused seamlessly.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.10
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• pp.465-472
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• 2017

In the past, researchers mainly used the supervised learning technique of machine learning to analyze power data and investigated the identification of patterns through the data mining technique. Data analysis research, however, faces its limitations with the old data classification and analysis techniques today when the size of electric power data has increased with the possible real-time provision of data. This study thus set out to propose a clustering architecture to analyze large-sized electric power data. The clustering process proposed in the study supplements the K-means algorithm, an unsupervised learning technique, for its problems and is capable of automating the entire process from the collection of electric power data to their analysis. In the present study, power data were categorized and analyzed in total three levels, which include the row data level, clustering level, and user interface level. In addition, the investigator identified K, the ideal number of clusters, based on principal component analysis and normal distribution and proposed an altered K-means algorithm to reduce data that would be categorized as ideal points in order to increase the efficiency of clustering.

• Current Research on Agriculture and Life Sciences
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• v.22
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• pp.19-27
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• 2004

Cultivation area of garlics is 9% of all cultivation area of vegetables. The amount of annual demand is increased in 1,000~1,500ton. Also, the amount of demand per person has a tendency to increase as above 10kg. So, garlics has become important crops in agriculture. The purpose of this study is to acquire the basic information to design the garlic seeder attached power tiller. We used Working Model 3D program to design an automatic aligning device which is the most important part of the garlic seeder. The results are as follows; 1. The optimum depth of garlics seeding was shown as 3cm. 2. The garlic seed has to he discharged from the garlic seeder at intervals of 0.75sec in order to seed at intervals of seeds of 15cm. 3. The optimum design factors of the automatic aligning device were shown as cylinder diameter of 4cm, cylinder gap of 1cm, revolution of 36rpm and inclined angle of cylinder of $8.4^{\circ}$.

• KIPS Transactions on Software and Data Engineering
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• v.1 no.2
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• pp.127-136
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• 2012

Many research of hand gesture recognition based on vision system have been conducted which enable user operate various electronic devices more easily. 3D position calculation and meaningful gesture classification from similar gestures should be executed to recognize hand gesture accurately. A simple and cost effective method of 3D position calculation and gesture spotting (a task to recognize meaningful gesture from other similar meaningless gestures) is described in this paper. 3D position is achieved by calculation of two cameras relative position through pan/tilt module and a marker regardless with the placed position. Fuzzy garbage model is proposed to provide a variable reference value to decide whether the user gesture is the command gesture or not. The reference is achieved from fuzzy command gesture model and fuzzy garbage model which returns the score that shows the degree of belonging to command gesture and garbage gesture respectively. Two-stage user adaptation is proposed that off-line (batch) adaptation for inter-personal difference and on-line (incremental) adaptation for intra-difference to enhance the performance. Experiment is conducted for 5 different users. The recognition rate of command (discriminate command gesture) is more than 95% when only one command like meaningless gesture exists and more than 85% when the command is mixed with many other similar gestures.

• KIPS Transactions on Software and Data Engineering
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• v.1 no.2
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• pp.109-114
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• 2012

Honeycombs are dense structures that small cysts, which generally have about 2~10 mm in diameter, are surrounded by the wall of fibrosis. When honeycomb is found in the patients, the incidence of acute exacerbation is generally very high. Thus, the observation and quantitative measurement of honeycomb are considered as a significant marker for clinical diagnosis. In this point of view, we propose an automatic segmentation method using morphological image processing and assessment of the degree of clustering techniques. Firstly, image noises were removed by the Gaussian filtering and then a morphological dilation method was applied to segment lung regions. Secondly, honeycomb cyst candidates were detected through the 8-neighborhood pixel exploration, and then non-cyst regions were removed using the region growing method and wall pattern testing. Lastly, final honeycomb regions were segmented through the extraction of dense regions which are consisted of two or more cysts using cluster analysis. The proposed method applied to 80 High resolution computed tomography (HRCT) images and achieved a sensitivity of 89.4% and PPV (Positive Predictive Value) of 72.2%.

• KIPS Transactions on Software and Data Engineering
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• v.4 no.10
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• pp.465-472
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• 2015

We propose a novel plane detection in disparity space and evaluate its performance. Our method simplifies and makes scenes in disparity space easily dealt with by approximating various surfaces as planes. Moreover, the approximated planes can be represented in the same size as in the real world, and can be employed for obstacle detection and camera pose estimation. Using a stereo matching technique, our method first creates a disparity image which consists of binocular disparity values at xy-coordinates in the image. Slants of disparity values are estimated by exploiting a line simplification algorithm which allows our method to reflect global changes against x or y axis. According to pairs of x and y slants, we label the disparity image. 4-connected disparities with the same label are grouped, on which least squared model estimates plane parameters. N plane models with the largest group of disparity values which satisfy their plane parameters are chosen. We quantitatively and qualitatively evaluate our plane detection. The result shows 97.9%와 86.6% of quality in our experiment respectively on cones and cylinders. Proposed method excellently extracts planes from Middlebury and KITTI dataset which are typically used for evaluation of stereo matching algorithms.

• KIPS Transactions on Software and Data Engineering
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• v.4 no.10
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• pp.431-438
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• 2015

In this paper we address the symmetric-invariant problem in boundary image matching. Supporting symmetric transformation is an important factor in boundary image matching to get more intuitive and more accurate matching results. However, the previous boundary image matching handled rotation transformation only without considering symmetric transformation. In this paper, we propose symmetric-invariant boundary image matching which supports the symmetric transformation as well as the rotation transformation. For this, we define the concept of image symmetry and formally prove that rotation-invariant matching of using a symmetric image always returns the same result for every symmetric angle. For efficient symmetric transformation, we also present how to efficiently extract the symmetric time-series from an image boundary. Finally, we formally prove that our symmetric-invariant matching produces the same result for two approaches: one is using the time-series extracted from the symmetric image; another is using the time-series directly obtained from the original image time-series by symmetric transformation. Experimental results show that the proposed symmetric-invariant boundary image matching obtains more accurate and intuitive results than the previous rotation-invariant boundary image matching. These results mean that our symmetric-invariant solution is an excellent approach that solves the image symmetry problem in time-series domain.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.3
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• pp.161-172
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• 2013

Metadata registry (MDR) is the international standard, developed by ISO/IEC for exchange and sharing data between databases. Many MDR systems are used in diverse domains such as medical service, bibliography, environment for sharing and integrating data. However, those systems have different physical structures individually because the MDR standard defines only the metamodel for registering and storing metadata. It causes heterogeneity between the system structures and requires additional cost to maintain interoperability. ISO/IEC 13249-8 Metadata Registry Access (MRA) is developing as an international standard to provide a consistent access facility to data stored in different metadata registries. However, MRA does not consider the relationships between the concepts (classes) defined in the MDR specification. It causes that incorrect query results returned from MDR systems. It also requires additional cost of modeling and rewriting queries to reflect each physical model. Therefore, this paper suggests eMRA which considers the relationships between the concepts in MDR. The comparative evaluations are described to show the advantages of eMRA. eMRA has superior performance in query modeling and referential integrity than MRA defined by the relationship between the concept of MDR.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.10
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• pp.651-660
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• 2013

Object oriented design brought up improvement of productivity and software quality by adopting some concepts such as inheritance and encapsulation. However, both the number of software's classes and object couplings are increasing as the software volume is becoming larger. The object coupling between classes is closely related with software complexity, and high complexity causes decreasing software quality. In order to solve the object coupling issue, IT-field researchers adopt a component based development and software quality metrics. The component based development requires explicit representation of dependencies between classes and the software quality metrics evaluates quality of software. As part of the research, we intend to gain a basic data that will be used on decomposing software. We focused on properties of the linkage between classes rather than previous studies evaluated and accumulated the qualities of individual classes. Our method exploits machine learning technique to analyze the properties of linkage and predict the strength of dependency between classes, as a new perspective on analyzing software property.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.10
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• pp.723-730
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• 2013

This paper presents the design of an arm gesture recognition system using Kinect sensor. A variety of methods have been proposed for gesture recognition, ranging from the use of Dynamic Time Warping(DTW) to Hidden Markov Models(HMM). Our system learns a unique HMM corresponding to each arm gesture from a set of sequential skeleton data. Whenever the same gesture is performed, the trajectory of each joint captured by Kinect sensor may much differ from the previous, depending on the length and/or the orientation of the subject's arm. In order to obtain the robust performance independent of these conditions, the proposed system executes the feature transformation, in which the feature vectors of joint positions are transformed into those of angles between joints. To improve the computational efficiency for learning and using HMMs, our system also performs the k-means clustering to get one-dimensional integer sequences as inputs for discrete HMMs from high-dimensional real-number observation vectors. The dimension reduction and discretization can help our system use HMMs efficiently to recognize gestures in real-time environments. Finally, we demonstrate the recognition performance of our system through some experiments using two different datasets.