• The Journal of Society for e-Business Studies
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• v.12 no.3
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• pp.33-47
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• 2007

The requirements have been changed during development progresses, since it is impossible to define all of software requirements. These requirements change leads to mistakes because the developers cannot completely understand the software's structure and behavior, or they cannot discover all parts affected by a change. Requirement changes have to be managed and assessed to ensure that they are feasible, make economic sense and contribute to the business needs of the customer organization. We propose a feature-oriented requirements change management method to manage requirements change with value analysis and feature-oriented traceability links including intermediate catalysis using features. Our approach offers two contributions to the study of requirements change: (1) We define requirements change tree to make user requirements change request generalize by feature level. (2) We provide overall process such as change request normalization, change impact analysis, solution dealing with change request, change request implementation, change request evaluation. In addition, we especially present the results of a case study which is carried out in asset management portal system in details.

• The Journal of the Acoustical Society of Korea
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• v.22 no.4
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• pp.323-328
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• 2003

This study describes an energy contour generation method for Korean n systems. We propose a vector-regression tree, which is a vector version of a scalar regression tree. A vector-regression tree predicts a response vector for an unknown feature vector. In our study, the tree yields a vector containing ten sampled energy values for each phone. After collecting 500 sentences and its corresponding speech corpus, we trained trees on 300 sentences and tested them on 200 sentences. We construct a bagged tree and a born again one to improve the performance of contour prediction. In the experiment, we got a 0.803 correlation coefficient for the observed and predicted energy values.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2006.11a
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• pp.407-410
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• 2006

Decision tree is one of the most effective and widely used methods for building classification model. Researchers from various disciplines such as statistics, machine teaming, pattern recognition, and data mining have considered the decision tree method as an effective solution to their field problems. In this paper, an application of decision tree method to classify the faults of induction motors is proposed. The original data from experiment is dealt with feature calculation to get the useful information as attributes. These data are then assigned the classes which are based on our experience before becoming data inputs for decision tree. The total 9 classes are defined. An implementation of decision tree written in Matlab is used for four data sets with good performance results

• Spatial Information Research
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• v.15 no.2
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• pp.181-187
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• 2007

To make digital terrain data, automatic correlation matching by stereo airborne/satellite images has been researched. The result of automatic correlation matching has a limit on extracting exact ground height because of angle of sensor, tree of height. Therefore, the amount of editing works depend on the distribution of spatial feature in images as well as image quality. This paper shows that the automatic correlation matching result was affected by density and height of tree.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 1999.05a
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• pp.234-238
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• 1999

The correlation between shape of electrical trees and partial discharge(PD) pulses in low density polyethylene(LDPE) were discussed. We observed growth feature of electrical tree by using optical microscope. On the basis of experimental results of measurements of trees occurring in the needle-plane arrangement with needle shape void and without needle shape void , statistical quantities are derived, which are relevant to PD pulse amplitude and phase. The PD quantities detected by partial discharge detector. we were analyzed q-n distribution pattern and $\psi$ -q-n distribution pattern. In this experiment, electrical trees in the needle-plane arrangement with needle shape void propagated branch type tree and in the needle-plane arrangement without needle shape void propagated bush type tree

• Transactions of the Korean Society of Mechanical Engineers A
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• v.31 no.6 s.261
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• pp.651-658
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• 2007

A new tree-structured neural network classifier is proposed for the automatic real-time inspection of cold-rolled steel strip surface defects. The defects are classified into 3 groups such as area type, disk type, area & line type in the first stage of the tree-structured neural network. The defects are classified in more detail into 11 major defect types which are considered as serious defects in the second stage of neural network. The tree-structured neural network classifier consists of 4 different neural networks and optimum features are selected for each neural network classifier by using SFFS algorithm and correlation test. The developed classifier demonstrates very plausible result which is compatible with commercial products having high world-wide market shares.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.1
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• pp.95-104
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• 2022

A TPR-tree is a well-known indexing structure that is developed to answer queries about the current or future time locations of moving objects. For the purpose of space efficiency, the TPR-tree employs the notion of VBR (velocity bounding rectangle)so that a regionalrectangle presents varying positions of a group of moving objects. Since the rectangle computed from a VBR always encloses the possible maximum range of an indexed object group, a search process only has to follow VBR-based rectangles overlapped with a given query range, while searching toward candidate leaf nodes. Although the TPR-tree index shows up its space efficiency, it easily suffers from the problem of dead space that results from fast and constant expansions of VBR-based rectangles. Against this, the TPR-tree index is enforced to update leaf nodes for reducing dead spaces within them. Such an update-prone feature of the TPR-tree becomes more problematic when the tree is saved in flash storage. This is because flash storage has very expensive update costs. To solve this problem, we propose a new Bloom filter based caching scheme that is useful for reducing updates in a flash TPR-tree. Since the proposed scheme can efficiently control the frequency of updates on a leaf node, it can offer good performance for indexing moving objects in modern flash storage.

• Journal of Electrical Engineering and Technology
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• v.13 no.2
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• pp.961-968
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• 2018

Recently, researches on automatic recognition of human activities have been actively carried out with the emergence of various intelligent systems. Since a large amount of visual data can be secured through Closed Circuit Television, it is required to recognize human behavior in a dynamic situation rather than a static situation. In this paper, we propose new intelligent human activity recognition model using the trajectory information extracted from the video sequence. The proposed model consists of three steps: segmentation and partitioning of trajectory step, feature extraction step, and behavioral learning step. First, the entire trajectory is fuzzy partitioned according to the motion characteristics, and then temporal features and spatial features are extracted. Using the extracted features, four pedestrian behaviors were modeled by decision tree learning algorithm and performance evaluation was performed. The experiments in this paper were conducted using Caviar data sets. Experimental results show that trajectory provides good activity recognition accuracy by extracting instantaneous property and distinctive regional property.

• Proceedings of the Korean Society of Computer Information Conference
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• 2008.06a
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• pp.33-38
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• 2008

최근 컴퓨터비전이나 로봇 공학 분야에서 가격이 저렴한 웹캠을 이용한 영상, 즉 2차원 영상으로부터 물체를 인식하는 연구가 활발히 이루어지고 있다. 이러한 로봇이나 비전에서 물체를 찾아내는 여러 가지 방향들이 제시되고 있으며, 지속적으로 로봇은 사람과 유사해져가고 있다. 이를 실현하기 위해서는 사람이 사과를 보고 사과라고 알기 때문에 사과라고 인식하듯이 로봇 또한 미리 알고 있어야 한다는 가정 하에 내용기반의 물체인식이 필요하다. 그러나 엄청난 양의 내용의 데이터베이스가 필요하다. 그래서 용량은 하드웨어기술로 커버가 가능하지만 화면상에 있는 물체들을 빠르게 데이터베이스상의 자료와 매칭이 되어야한다. 본 논문에서는 이미지를 SIFT(Scale Invariant Feature Transform)알고리즘으로 BTS(Binary Search Tree)로 트리구조의 데이터베이스를 구축하여 많은 양의 데이터베이스 중 빠르게 검색하여 화면에 있는 물체를 인식하는 방법을 제안하였다.

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.239-240
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• 2006

In this study, we examined all peaks of confocal Raman spectra as peaks are the most important features for discrimination between basal cell carcinoma (BCC) and normal tissue (NOR). 14 peaks were extracted from these peaks using decision tree. For dimension reduction, frequently selected 4 peaks were chosen. They are located at 1014, 1095, 1439, $1523cm^{-1}$. These peaks were used as an input feature of the multilayer perceptron networks (MLP). According to the experimental results, MLP gave classification error rate of about 6.5%.