• Journal of the Korean Wood Science and Technology
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• 제24권1호
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• pp.1-10
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• 1996

The frequency distribution diagrams of Korean diffuse-porous woods, 36 families, 75 genera, 145 species, 215 specimens in relation to habit and phenology were analyzed. As the habit character changes from shrub to tree, such quantitative features as vessel frequency, percentage of solitary vessels, length/diameter(L/D) ratio of vessel element decreased but tangential vessel diameter, fiber length/vessel element length(F/V) ratio increased. Qualitative features such as helical vessel wall thickening, diffuse distribution of longitudinal parenchyma, heterogeneous ray composition decreased, while alternate intervessel pits, libriform wood fiber, simple perforations increase. As the phenology character changes from evergreen to deciduous species, such quantitative features as percentage of solitary vessels, vessel element length and L/D ratio decreased but tangential vessel diameter, F/V ratio increased. Diffuse distribution of longitudinal parenchyma, heterogeneous ray composition, and crystals in qualitative features decreased, while alternate intervessel pits, libriform wood fiber, simple perforation of vessel element, ray width and ray height increased.

• Korean Journal of Radiology
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• 제25권1호
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• pp.43-54
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• 2024

Objective: To evaluate the added value of diffusion-weighted imaging (DWI)-based quantitative parameters to distinguish uterine sarcomas from atypical leiomyomas on preoperative magnetic resonance imaging (MRI). Materials and Methods: A total of 138 patients (age, 43.7 ± 10.3 years) with uterine sarcoma (n = 44) and atypical leiomyoma (n = 94) were retrospectively collected from four institutions. The cohort was randomly divided into training (84/138, 60.0%) and validation (54/138, 40.0%) sets. Two independent readers evaluated six qualitative MRI features and two DWI-based quantitative parameters for each index tumor. Multivariable logistic regression was used to identify the relevant qualitative MRI features. Diagnostic classifiers based on qualitative MRI features alone and in combination with DWI-based quantitative parameters were developed using a logistic regression algorithm. The diagnostic performance of the classifiers was evaluated using a cross-table analysis and calculation of the area under the receiver operating characteristic curve (AUC). Results: Mean apparent diffusion coefficient value of uterine sarcoma was lower than that of atypical leiomyoma (mean ± standard deviation, 0.94 ± 0.30 10^-3 mm²/s vs. 1.23 ± 0.25 10^-3 mm²/s; P < 0.001), and the relative contrast ratio was higher in the uterine sarcoma (8.16 ± 2.94 vs. 4.19 ± 2.66; P < 0.001). Selected qualitative MRI features included ill-defined margin (adjusted odds ratio [aOR], 17.9; 95% confidence interval [CI], 1.41-503, P = 0.040), intratumoral hemorrhage (aOR, 27.3; 95% CI, 3.74-596, P = 0.006), and absence of T2 dark area (aOR, 83.5; 95% CI, 12.4-1916, P < 0.001). The classifier that combined qualitative MRI features and DWI-based quantitative parameters showed significantly better performance than without DWI-based parameters in the validation set (AUC, 0.92 vs. 0.78; P < 0.001). Conclusion: The addition of DWI-based quantitative parameters to qualitative MRI features improved the diagnostic performance of the logistic regression classifier in differentiating uterine sarcomas from atypical leiomyomas on preoperative MRI.

• 디지털융복합연구
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• 제11권9호
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• pp.301-310
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• 2013

본 연구는 대학생들의 올바른 스마트폰 사용 문화의 확립을 위해, 대학생의 스마트폰 사용 특성에 관한 경험을 조사하였다. 본 연구의 참여자는 총 9명의 대학생들이고, 이들을 대상으로 심층 인터뷰를 실시하였다. 수집된 자료는 질적 연구 방법을 사용하여 분석하였다. 분석 결과, 다음과 같은 5개의 주제군과 18개의 주제들이 도출되었다. 5개의 주제군들은 1. 스마트폰 사용 배경 2. 스마트폰 애착 요인 3. 나만의 세상 구축, 4. 인간관계의 신풍속도 출현 5. 스마트폰 사용의 부담요인 등이다. 이상의 내용을 바탕으로, 대학생들의 스마트폰의 올바른 사용에 관한 실천적 제언과 후속 연구를 제안하였다.

• International Journal of Contents
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• 제2권4호
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• pp.1-7
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• 2006

The goal of Ambient Intelligence (AmI) is to build a smart environment for users where they are supported in some of their activities by many interaction mechanisms. The diversity of AmI characteristics requires special support from Operating Systems (OSes). In this paper, in order to support a conscious choice of an operating system for any specific AmI application, features requested by AmI systems were characterized and defined considering various applications. Then, characteristics of existing Operating Systems have been investigated in the context of AmI application support to relate their key characteristics to the typical requirements of AmI systems. Qualitative mapping table between AmI characteristics and as features has been proposed with an illustration of how to use it. As no as completely covers the range of characteristics required by AmI systems, challenging issues are summarized for the development of a new as and a product line of OSes.

• 말소리와 음성과학
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• 제6권1호
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• pp.71-76
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• 2014

In this paper voice quality of normal speech is qualitatively classified by five components of breathy, creaky, rough, nasal, and thin/thick voice. To determine whether a correlation exists between a subjective measure of voice and an objective measure of voice, each voice is perceptually evaluated using the 1/2/3 scale by speech processing specialists and acoustically analyzed using speech analysis tools such as the Praat, MDVP, and VoiceSauce. The speech parameters include features related to speech source and vocal tract filter. Statistical analysis uses a two-independent-samples non-parametric test. Experimental results show that statistical analysis identified a significant correlation between the speech feature parameters and the components of voice quality.

• 디지털콘텐츠학회 논문지
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• 제12권3호
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• pp.319-327
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• 2011

클라우드 컴퓨팅은 인터넷상에 기업이나 소비자들에게 IT서비스를 제공하기 위한 유연하고 비용 효과적인 검증된 전송 플랫폼으로, 계산이나 데이터가 데스크탑이나 포터블 PC로부터 대규모 데이터센터로 이동하는 계기가 되었다. 이와 같은 클라우드 컴퓨팅 서비스의 활성화를 위해서는 현재 제공되거나 향후 제공될 서비스가 현재와 미래의 고객을 만족시키기 위해 개선되어야 할 서비스 품질요건에 대한 분석이 필수적이다. 본 연구는 클라우드 서비스 환경에서 서비스품질 규명을 위한 연구의 일환으로서, 탐색적 방법을 사용하여 기존 다양한 문헌을 분석하였으며, 이로부터 7가지의 클라우드 컴퓨팅의 서비스 특성과 세부속성들을 정의하였다.

• 한국CDE학회논문집
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• 제9권1호
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• pp.51-61
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• 2004

Mechanical parts are often grouped into part families based on the similarity of their shapes, to support efficient manufacturing process planning and design modification. The 2-part sequence papers present similarity assessment techniques to support part family classification for machined parts. These exploit the multiple feature decompositions obtained by the feature recognition method using convex decomposition. Convex decomposition provides a hierarchical volumetric representation of a part, organized in an outside-in hierarchy. It provides local accessibility directions, which supports abstract and qualitative similarity assessment. It is converted to a Form Feature Decomposition (FFD), which represents a part using form features intrinsic to the shape of the part. This supports abstract and qualitative similarity assessment using positive feature volumes.. FFD is converted to Negative Feature Decomposition (NFD), which represents a part as a base component and negative machining features. This supports a detailed, quantitative similarity assessment technique that measures the similarity between machined parts and associated machining processes implied by two parts' NFDs. Features of the NFD are organized into branch groups to capture the NFD hierarchy and feature interrelations. Branch groups of two parts' NFDs are matched to obtain pairs, and then features within each pair of branch groups are compared, exploiting feature type, size, machining direction, and other information relevant to machining processes. This paper, the second one of the two companion papers, describes the similarity assessment method using NFD.

• 한국CDE학회논문집
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• 제9권1호
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• pp.44-50
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• 2004

Mechanical parts are often grouped into part families based on the similarity of their shapes, to support efficient manufacturing process planning and design modification. The 2-part sequence papers present similarity assessment techniques to support part family classification for machined parts. These exploit the multiple feature decompositions obtained by the feature recognition method using convex decomposition. Convex decomposition provides a hierarchical volumetric representation of a part, organized in an outside-in hierarchy. It provides local accessibility directions, which supports abstract and qualitative similarity assessment. It is converted to a Form Feature Decomposition (FFD), which represents a part using form features intrinsic to the shape of the part. This supports abstract and qualitative similarity assessment using positive feature volumes. FFD is converted to Negative Feature Decomposition (NFD), which represents a part as a base component and negative machining features. This supports a detailed, quantitative similarity assessment technique that measures the similarity between machined parts and associated machining processes implied by two parts' NFDs. Features of the NFD are organized into branch groups to capture the NFD hierarchy and feature interrelations. Branch groups of two parts' NFDs are matched to obtain pairs, and then features within each pair of branch groups are compared, exploiting feature type, size, machining direction, and other information relevant to machining processes. This paper, the first one of the two companion papers, describes the similarity assessment methods using convex decomposition and FFD.

• Korean Journal of Radiology
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• 제24권6호
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• pp.498-511
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• 2023

Objective: To evaluate the diagnostic performance of chest computed tomography (CT)-based qualitative and radiomics models for predicting residual axillary nodal metastasis after neoadjuvant chemotherapy (NAC) for patients with clinically node-positive breast cancer. Materials and Methods: This retrospective study included 226 women (mean age, 51.4 years) with clinically node-positive breast cancer treated with NAC followed by surgery between January 2015 and July 2021. Patients were randomly divided into the training and test sets (4:1 ratio). The following predictive models were built: a qualitative CT feature model using logistic regression based on qualitative imaging features of axillary nodes from the pooled data obtained using the visual interpretations of three radiologists; three radiomics models using radiomics features from three (intranodal, perinodal, and combined) different regions of interest (ROIs) delineated on pre-NAC CT and post-NAC CT using a gradient-boosting classifier; and fusion models integrating clinicopathologic factors with the qualitative CT feature model (referred to as clinical-qualitative CT feature models) or with the combined ROI radiomics model (referred to as clinical-radiomics models). The area under the curve (AUC) was used to assess and compare the model performance. Results: Clinical N stage, biological subtype, and primary tumor response indicated by imaging were associated with residual nodal metastasis during the multivariable analysis (all P < 0.05). The AUCs of the qualitative CT feature model and radiomics models (intranodal, perinodal, and combined ROI models) according to post-NAC CT were 0.642, 0.812, 0.762, and 0.832, respectively. The AUCs of the clinical-qualitative CT feature model and clinical-radiomics model according to post-NAC CT were 0.740 and 0.866, respectively. Conclusion: CT-based predictive models showed good diagnostic performance for predicting residual nodal metastasis after NAC. Quantitative radiomics analysis may provide a higher level of performance than qualitative CT features models. Larger multicenter studies should be conducted to confirm their performance.

• Bulletin of the Korean Chemical Society
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• 제29권3호
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• pp.656-662
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• 2008

Antagonists of the d -opioid receptor are effective in overcoming resistance against analgesic drugs such as morphine. To identify novel antagonists of the d -opioid receptor that display high potency and low resistance, we performed 3D-QSAR analysis using chemical feature-based pharmacophore models. Chemical features for d -opioid receptor antagonists were generated using quantitative (Catalyst/HypoGen) and qualitative (Catalyst/HipHop) approaches. For HypoGen analysis, we collected 16 peptide and 16 non-peptide antagonists as the training set. The best-fit pharmacophore hypotheses of the two antagonist models comprised identical features, including a hydrophobic aromatic (HAR), a hydrophobic (HY), and a positive ionizable (PI) function. The training set of the HipHop model was constructed with three launched opioid drugs. The best hypothesis from HipHop included four features: an HAR, an HY, a hydrogen bond donor (HBD), and a PI function. Based on these results, we confirm that HY, HAR and PI features are essential for effective antagonism of the d -opioid receptor, and determine the appropriate pharmacophore to design such antagonists.