• The Journal of Korean Institute of Communications and Information Sciences
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• v.39B no.12
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• pp.842-855
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• 2014

Recently, the progress of smart phone market has retarded by oversupply therefore wearable computer has been the focus of new growth engine. Wearable computing system is a complex fusion of a variety of technologies such as wireless network, embedded, sensor and new material. Because these technologies involves utilization and mobility in addition to quality characteristic in existing software, application of ISO/IEC 9126 is not perfect when assessing quality of wearable computer. In this study, author suggested new quality assessment model for wearable computer by sorting quality attribute in ISO/IEC 9126 and adding new quality attribute. For this, author investigated features and functional requirements related to wearable computer. and then author suggested quality standard and metrics by identifying quality characteristic. Author confirmed practicality of quality assessment model by using suggested model in scenario and comparing quality assessment of three goods such as company S, L, G. This quality assessment model is expected to use guidelines for assessing quality of wearable computer.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09b
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• pp.53-56
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• 2003

유사도 혹은 자료간의 거리 개념은 많은 기계학습 알고리즘에서 사용되고 있는 중요한 측정개념이다 하지만 입력되는 자료의 속성들중 순서가 정의되지 않은 범주형 속성이 포함되어 있는 경우, 자료간의 유사도나 거리 측정에 어려움이 따른다. 비거리 기반의 알고리즘들의 경우-C4.5, CART-거리의 측정없이 작동할 수 있지만, 거리기반의 알고리즘들의 경우 범주형 속성의 거리 정보 결여로 효과적으로 적용될 수 없는 문제점을 갖고 있다. 본 논문에서는 이러한 범주형 자료들간 거리 측정을 자료 집합의 특성을 충분히 고려한 방법을 제안한다. 이를 위해 자료 집합의 선험적인 정보를 필요로 한다. 이런 선험적 정보인 조건부 확률을 기반으로한 거리 측정방법을 제시하고 오류 피드백을 통해서 속성 간 거리 측정을 최적화 하려고 노력한다. 주어진 자료 집합에 대해 서로 다른 두 범주형 값이 목적 속성에 대해서 유사한 분포를 보인다면 이들 값들은 비교적 가까운 거리로 결정한다 이렇게 결정된 거리를 기반으로 학습 단계를 진행하며 이때 발생한 오류들에 대해 피드백 작업을 진행한다. UCI Machine Learning Repository의 자료들을 이용한 실험 결과를 통해 제안한 거리 측정 방법의 우수한 성능을 확인하였다.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.32 no.1
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• pp.20-25
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• 2009

Mahalanobis Taguchi System (MTS) is a pattern information technology, which has been used in different diagnostic applications to make quantitative decisions by constructing a multivariate measurement scale using data analytic methods without any assumption regarding statistical distribution. The MTS performs Taguchi's fractional factorial design based on the Mahahlanobis Distance (MS) as a performance metric. In this work, MTS is used for analyzing Wisconsin Breast Cancer data which has ten attributes. Ten different tests are conducted for the data to determine if the patient has cancer or not. Also, MTS is used for reducing the number of test to define the relationship between each attribute and diagnosis result. The accuracy of diagnosis is compare with two different previous research.

• Proceedings of the Safety Management and Science Conference
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• 2009.11a
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• pp.99-107
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• 2009

Mahalanobis Taguchi-System (MTS) has been used in different diagnostic applications to make quantitative decisions by constructing a multivariate system using data analytic methods without any assumption regarding statistical distribution. MTS performs Taguchi's fractional factorial design based on the Mahahlanobis distance as a performance metric. In this study, MTS used for analyzing automotive ride satisfaction, which measured as a CSR(Customer Satisfaction Rating). The automobile which has a good CSR score treated as a normal group for constructing Mahalanobis space. The results of this research show that two attribute (Impact Hardness and Memory Shake) have a minus gain value and can be removed from further analysis. With the linear regression model, the difference of CSR between using all 6 attributes and just using significant 4 attributes compared.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.30 no.1
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• pp.41-47
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• 2007

Mahalanobis Taguchi-System (MTS) is a pattern information technology, which has been used in different diagnostic ap plications to make quantitative decisions by constructing a multivariate system using data analytic methods without any as sumption regarding statistical distribution. MTS performs Taguchi's fractional factorial design based on the Mahalanobis distance as a performance metric In this work, MTS used for analyzing automotive brake feeling system, which measured as a brake feel index (BFI) from 9 attributes. The automobile which has a good BFI score treated as a normal group for constructing Mahalanobis space. The results of this research show that two attributes (Pre load & Max deceleration) have a minus gain value and can be removed from further analysis. The difference of MD value between using all 9 attributes and just using significant attribute compared.

• Communications of the Korean Mathematical Society
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• v.34 no.3
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• pp.855-861
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• 2019

The geodesics on the round 2-sphere $S^2$ are all simple closed curves of equal length. In 1903 Otto Zoll introduced other Riemannian surfaces with the same property. After that, his name is attached to the Riemannian manifolds whose geodesics are all simple closed curves of the same length. The question that "whether or not the set of Zoll metrics on 2-sphere $S^2$ is connected?" is still an outstanding open problem in the theory of Zoll manifolds. In the present paper, continuing the work of D. Jane for the case of the Ricci flow, we show that a naive application of some famous geometric flows does not work to answer this problem. In fact, we identify an attribute of Zoll manifolds and prove that along the geometric flows this quantity no longer reflects a Zoll metric. At the end, we will establish an alternative proof of this fact.

• Journal of Computing Science and Engineering
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• v.1 no.2
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• pp.177-194
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• 2007

Index selection is one of the most important decisions to take in the physical design of relational data warehouses. Indices reduce significantly the cost of processing complex OLAP queries, but require storage cost and induce maintenance overhead. Two main types of indices are available: mono-attribute indices (e.g., B-tree, bitmap, hash, etc.) and multi-attribute indices (join indices, bitmap join indices). To optimize star join queries characterized by joins between a large fact table and multiple dimension tables and selections on dimension tables, bitmap join indices are well adapted. They require less storage cost due to their binary representation. However, selecting these indices is a difficult task due to the exponential number of candidate attributes to be indexed. Most of approaches for index selection follow two main steps: (1) pruning the search space (i.e., reducing the number of candidate attributes) and (2) selecting indices using the pruned search space. In this paper, we first propose a data mining driven approach to prune the search space of bitmap join index selection problem. As opposed to an existing our technique that only uses frequency of attributes in queries as a pruning metric, our technique uses not only frequencies, but also other parameters such as the size of dimension tables involved in the indexing process, size of each dimension tuple, and page size on disk. We then define a greedy algorithm to select bitmap join indices that minimize processing cost and verify storage constraint. Finally, in order to evaluate the efficiency of our approach, we compare it with some existing techniques.

• Journal of KIISE:Software and Applications
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• v.33 no.3
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• pp.277-288
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• 2006

Product line engineering (PLE) is a new effective approach to software reuse, where applications are generated by instantiating a core asset which is a large-grained reuse unit. Hence, a core asset is a key element of PLE, and therefore the reusability of the core asset largely determines the success of PLE projects. A tore asset is a reusable part not a whole system, and supports not only variable functions but also common functions. However, there are limitations to evaluate reusability of core asset that has these unique characteristics. This paper proposes a comprehensive quality system for evaluating the reusability of core assets, based on ISO/IEC 9126. We first identify the key characteristics of core assets, and derive the set of quality attributes that characterizes the reusability of core assets. finally, we define metrics for each quality attribute. In addition, we provide guidelines for applying the metrics and perform a case study based on rental product line. Using the proposed quality system, reusability of core assets can be more effectively and correctly evaluated.

• Journal of KIISE:Software and Applications
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• v.30 no.7_8
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• pp.618-641
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• 2003

Various quality models using quality factor, quality criteria and metrics have been proposed in order to evaluate quality of software products. However, a customized quality model which is specific to the characteristics of software component is required. In this paper, we propose external quality prediction techniques enable us to predict what external quality the final software product will have by using metrics as with internal attributes of software in development. We also propose a model not only for measuring quality by using metrics but also for applying internal attributes of ISO 9126 into artifacts of software component development.

• Asian-Australasian Journal of Animal Sciences
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• v.32 no.7
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• pp.1015-1026
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• 2019

Objective: The objective of this study was to evaluate three different degrees of white striping (WS) addressing their automatic assessment and customer acceptance. The WS classification was performed based on a computer vision system (CVS), exploring different machine learning (ML) algorithms and the most important image features. Moreover, it was verified by consumer acceptance and purchase intent. Methods: The samples for image analysis were classified by trained specialists, according to severity degrees regarding visual and firmness aspects. Samples were obtained with a digital camera, and 25 features were extracted from these images. ML algorithms were applied aiming to induce a model capable of classifying the samples into three severity degrees. In addition, two sensory analyses were performed: 75 samples properly grilled were used for the first sensory test, and 9 photos for the second. All tests were performed using a 10-cm hybrid hedonic scale (acceptance test) and a 5-point scale (purchase intention). Results: The information gain metric ranked 13 attributes. However, just one type of image feature was not enough to describe the phenomenon. The classification models support vector machine, fuzzy-W, and random forest showed the best results with similar general accuracy (86.4%). The worst performance was obtained by multilayer perceptron (70.9%) with the high error rate in normal (NORM) sample predictions. The sensory analysis of acceptance verified that WS myopathy negatively affects the texture of the broiler breast fillets when grilled and the appearance attribute of the raw samples, which influenced the purchase intention scores of raw samples. Conclusion: The proposed system has proved to be adequate (fast and accurate) for the classification of WS samples. The sensory analysis of acceptance showed that WS myopathy negatively affects the tenderness of the broiler breast fillets when grilled, while the appearance attribute of the raw samples eventually influenced purchase intentions.