• Title/Summary/Keyword: Feature evaluation

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Effects of Temporal Distance on Brand Extension Evaluation: Applying the Construal-Level Perspective to Brand Extensions

  • Park, Kiwan
    • Asia Marketing Journal
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    • 제17권1호
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    • pp.97-121
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    • 2015
  • In this research, we examine whether and why temporal distance influences evaluations of two different types of brand extensions: concept-based extensions, defined as extensions primarily based on the importance or relevance of brand concepts to extension products; and similarity-based extensions, defined as extensions primarily based on the amount of feature similarity at the product-category level. In Study 1, we test the hypothesis that concept-based extensions are evaluated more favorably when they are framed to launch in the distant rather than in the near future, whereas similaritybased extensions are evaluated more favorably when they are framed to launch in the near rather than in the distant future. In Study 2, we confirm that this time-dependent differential evaluation is driven by the difference in construal level between the bases of the two types of extensions - i.e., brand-concept consistency and product-category feature similarity. As such, we find that conceptbased extensions are evaluated more favorably under the abstract than concrete mindset, whereas similarity-based extensions are evaluated more favorably under the concrete than abstract mindset. In Study 3, we extend to the case for a broad brand (i.e., brands that market products across multiple categories), finding that making accessible a specific product category of a broad parent brand influences evaluations of near-future, but not distant-future, brand extensions. Combined together, our findings suggest that temporal distance influences brand extension evaluation through its effect on the importance placed on brand concepts and feature similarity. That is, consumers rely on different bases to evaluate brand extensions, depending on their perception of when the extensions take place and on under what mindset they are placed. This research makes theoretical contributions to the brand extension research by identifying one important determinant to brand extension evaluation and also uncovering its underlying dynamics. It also contributes to expanding the scope of the construal level theory by putting forth a novel interpretation of two bases of perceived fit in terms of construal level. Marketers who are about to launch and advertise brand extensions may benefit by considering temporal-distance information in determining what content to deliver about extensions in their communication efforts. Conceptual relation of a parent brand to extensions needs to be emphasized in the distant future, whereas feature similarity should be highlighted in the near future.

오스테나이트계 스테인리스강 304 용접부의 초음파 형상 인식 평가를 위한 카오스 시뮬레이터의 구축 (Construction fo chaos simulator for ultrasonic pattern recognition evaluation of weld zone in austenitic stainless steel 304)

  • 이원;윤인식;장영권
    • Journal of Welding and Joining
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    • 제16권5호
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    • pp.108-118
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    • 1998
  • This study proposes th analysis and evaluation method of time series ultrasonic signal using the chaos feature extraction for ultrasonic pattern recognition. Features extracted from time series data using the chaos time series signal analyze quantitatively weld defects. For this purpose, analysis objective in this study is fractal dimension and Lyapunov exponent. Trajectory changes in the strange attractor indicated that even same type of defects carried substantial difference in chaosity resulting from distance shifts such as 0.5 and 1.0 skip distance. Such differences in chaosity enables the evaluation of unique features of defects in the weld zone. In quantitative chaos feature extraction, feature values of 4.511 and 0.091 in the case of side hole and 4.539 and 0.115 in the case of vertical hole were proposed on the basis of fractal dimension and Lyapunov exponent. Proposed chaos feature extraction in this study can enhances ultrasonic pattern recognition results from defect signals of weld zone such as side hole and vertical hole.

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6dB Drop법에 의한 용접 결함 초음파 신호의 카오스성 평가 (Chaoticity Evaluation of Ultrasonic Signals in Welding Defects by 6dB Drop Method)

  • 이원;윤인식
    • 대한기계학회논문집A
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    • 제23권7호
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    • pp.1065-1074
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    • 1999
  • This study proposes the analysis and evaluation method of time series ultrasonic signal using the chaotic feature extraction for ultrasonic pattern recognition. Features extracted from time series data using the chaotic time series signal analysis quantitatively welding defects. For this purpose analysis objective in this study is fractal dimension and Lyapunov exponent. Trajectory changes in the strange attractor indicated that even same type of defects carried substantial difference in chaoticity resulting from distance shills such as 0.5 and 1.0 skip distance. Such differences in chaoticity enables the evaluation of unique features of defects in the weld zone. In experiment fractal(correlation) dimension and Lyapunov exponent extracted from 6dB ultrasonic defect signals of weld zone showed chaoticity. In quantitative chaotic feature extraction, feature values(mean values) of 4.2690 and 0.0907 in the case of porosity and 4.2432 and 0.0888 in the case of incomplete penetration were proposed on the basis of fractal dimension and Lyapunov exponent. Proposed chaotic feature extraction in this study enhances ultrasonic pattern recognition results from defect signals of weld zone such as vertical hole.

패턴 인식에서 특징 선택을 위한 개미 군락 최적화 (Ant Colony Optimization for Feature Selection in Pattern Recognition)

  • 오일석;이진선
    • 한국콘텐츠학회논문지
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    • 제10권5호
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    • pp.1-9
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    • 2010
  • 이 논문은 특징 선택에 사용되는 개미 군락 최적화의 수렴 특성을 개선하기 위해 선택적 평가라는 새로운 기법을 제시한다. 이 방법은 불필요하거나 가능성이 덜한 후보 해를 배제함으로써 계산량을 줄인다. 이 방법은, 그런 해를 찾아내는데 사용할 수 있는 페로몬 정보 때문에 구현이 가능하다. 문제 크기에 따른 알고리즘의 적용가능성을 판단할 목적으로, 특징 선택에 사용되는 세 가지 알고리즘인 탐욕 알고리즘, 유전 알고리즘, 그리고 개미 군락 최적화의 계산 시간을 분석한다. 엄밀한 분석을 위해 원자 연산이라는 개념을 사용한다. 실험 결과는 선택적 평가를 채택한 개미 군락 최적화가 계산 시간과 인식 성능 모두에서 우수함을 보여준다.

뇌파를 이용한 의자의 쾌적성 평가 기술에 관한 연구 (A Study on Comfortableness Evaluation Technique of Chairs using Electroencephalogram)

  • 김동준
    • 대한전기학회논문지:시스템및제어부문D
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    • 제52권12호
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    • pp.702-707
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    • 2003
  • This study describes a new technique for human sensibility evaluation using electroencephalogram(EEG). Production of EEG is assumed to be linear. The linear predictor coefficients and the linear cepstral coefficients of EEG are used as the feature parameters of sensibility and pattern classification performances of them are compared. Using the better parameter, a human sensibility evaluation algorithm is designed. The obtained results are as follows. The linear predictor coefficients showed the better performance in pattern classification than the linear cepstral coefficients. Then, using the linear predictor coefficients as the feature parameter, a human sensibility evaluation algorithm is developed at the base of a multi-layer neural network. This algorithm showed 90% of accuracy in comfortableness evaluation in spite of fluctuations in statistics of EEG signal.

Feature-Based Relation Classification Using Quantified Relatedness Information

  • Huang, Jin-Xia;Choi, Key-Sun;Kim, Chang-Hyun;Kim, Young-Kil
    • ETRI Journal
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    • 제32권3호
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    • pp.482-485
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    • 2010
  • Feature selection is very important for feature-based relation classification tasks. While most of the existing works on feature selection rely on linguistic information acquired using parsers, this letter proposes new features, including probabilistic and semantic relatedness features, to manifest the relatedness between patterns and certain relation types in an explicit way. The impact of each feature set is evaluated using both a chi-square estimator and a performance evaluation. The experiments show that the impact of relatedness features is superior to existing well-known linguistic features, and the contribution of relatedness features cannot be substituted using other normally used linguistic feature sets.

기계학습 기반의 실시간 악성코드 탐지를 위한 최적 특징 선택 방법 (An Optimal Feature Selection Method to Detect Malwares in Real Time Using Machine Learning)

  • 주진걸;정인선;강승호
    • 한국멀티미디어학회논문지
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    • 제22권2호
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    • pp.203-209
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    • 2019
  • The performance of an intelligent classifier for detecting malwares added to multimedia contents based on machine learning is highly dependent on the properties of feature set. Especially, in order to determine the malicious code in real time the size of feature set should be as short as possible without reducing the accuracy. In this paper, we introduce an optimal feature selection method to satisfy both high detection rate and the minimum length of feature set against the feature set provided by PEFeatureExtractor well known as a feature extraction tool. For the evaluation of the proposed method, we perform the experiments using Windows Portable Executables 32bits.

입술영역 분할을 위한 CIELuv 칼라 특징 분석 (Analysis of CIELuv Color feature for the Segmentation of the Lip Region)

  • 김정엽
    • 한국멀티미디어학회논문지
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    • 제22권1호
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    • pp.27-34
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    • 2019
  • In this paper, a new type of lip feature is proposed as distance metric in CIELUV color system. The performance of the proposed feature was tested on face image database, Helen dataset from University of Illinois. The test processes consists of three steps. The first step is feature extraction and second step is principal component analysis for the optimal projection of a feature vector. The final step is Otsu's threshold for a two-class problem. The performance of the proposed feature was better than conventional features. Performance metrics for the evaluation are OverLap and Segmentation Error. Best performance for the proposed feature was OverLap of 65% and 59 % of segmentation error. Conventional methods shows 80~95% for OverLap and 5~15% of segmentation error usually. In conventional cases, the face database is well calibrated and adjusted with the same background and illumination for the scene. The Helen dataset used in this paper is not calibrated or adjusted at all. These images are gathered from internet and therefore, there are no calibration and adjustment.

특징 선택에서 선택적 평가를 사용하는 개미 군집 최적화의 수렴 특성 (Convergence Characteristics of Ant Colony Optimization with Selective Evaluation in Feature Selection)

  • 이진선;오일석
    • 한국콘텐츠학회논문지
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    • 제11권10호
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    • pp.41-48
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    • 2011
  • 최근 특징 선택에서 개미군집 최적화를 위한 선택적 평가 기법이 제안되었다. 이 기법은 불필요하거나 가능성이 적은 후보 해를 실제 평가 과정에서 제외함으로써 계산량을 줄인다. 실험을 통해 이 기법의 우수성을 보였으나, 하나의 데이터만을 사용하였으므로 통계적으로 충분한 신뢰성을 보여주지 못한다. 이 논문의 목적은 선택적 평가 기법의 수렴 특성을 분석하고 결론의 신뢰성을 높이는 것이다. 실험을 위해 UCI 데이터베이스에서 필기, 의료, 음성에 관련된 세가지 데이터를 선택하였다. 이들의 특징 집합 크기는 256부터 617까지 분포한다. 통계적으로 안정된 데이터를 얻기 위해, 이들 각각에 대해 프로그램을 독립적으로 12번 실행하였다. 긴 시간에 걸친 수렴을 관찰하기 위해, 각각의 프로그램 실행은 72시간 동안 이루어졌다. 실험 데이터의 분석을 바탕으로, 선택적 평가 기법의 우수성에 대한 이유와 이 기법의 적용 범위에 대해 기술한다.

타격음을 이용한 복합재료 구조물의 비파괴 검사법 개발 (Development of Non-destructive Evaluation Method for Composite Structures using Tapping Sound)

  • 황준석;김승조
    • Composites Research
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    • 제17권1호
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    • pp.1-9
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    • 2004
  • 본 연구에서는 타격음을 이용한 비파괴 검사법을 제안하였다. 제안된 방법(tapping sound analysis)은 건강한 구조물과 손상된 구조물의 타격음의 차이를 분석하여 구조물의 손상 유무를 판단하는 방법이다. 타격음의 직접적인 비교는 비효율적이므로 타격음으로부터 특성을 추출하기 위해 wavelet packet transform에 기반한 특성추출법을 제안하였다. 또한 추출된 특성 자료를 바탕으로 손상의 유무를 판단하는 지표로서 특성 지수를 정의하였다. 제안된 방법의 타당성을 밝히기 위해 실험적인 검증을 수행하였다. 복합재료를 이용하여 건강한 구조물과 손상된 구조물을 제작하고 타격음을 측정하였다. 제안된 손상 판단 기법을 이용한 결과로부터 특성 지수에 의한 손상 판단의 타당성을 밝혔다.