• Title/Summary/Keyword: discrimination accuracy

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An acoustic and perceptual investigation of the vowel length contrast in Korean

  • Lee, Goun;Shin, Dong-Jin
    • Phonetics and Speech Sciences
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    • v.8 no.1
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    • pp.37-44
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    • 2016
  • The goal of the current study is to investigate how the sound change is reflected in production or in perception, and what the effect of lexical frequency is on the loss of sound contrasts. Specifically, the current study examined whether the vowel length contrasts are retained in Korean speakers' productions, and whether Korean listeners can distinguish vowel length minimal pairs in their perception. Two production experiments and two perception experiments investigated this. For production tests, twelve Korean native speakers in their 20s and 40s completed a read-aloud task as well as a map-task. The results showed that, regardless of their age group, all Korean speakers produced vowel length contrasts with a small but significant differences in the read-aloud test. Interestingly, the difference between long and short vowels has disappeared in the map task, indicating that the speech mode affects producing vowel length contrasts. For perception tests, thirty-three Korean listeners completed a discrimination and a forced-choice identification test. The results showed that Korean listeners still have a perceptual sensitivity to distinguish lexical meaning of the vowel length minimal pair. We also found that the identification accuracy was affected by the word frequency, showing a higher identification accuracy in high- and mid- frequency words than low frequency words. Taken together, the current study demonstrated that the speech mode (read-aloud vs. spontaneous) affects the production of the sound undergoing a language change; and word frequency affects the sound change in speech perception.

Discrimination of Parkinson's Disease from Essential Tremor using Acceleration based Tremor Analysis (가속도계를 이용한 진전현상의 분석을 통한 파킨슨병과 본태성 진전의 판별)

  • Lee, Hongji;Lee, Woongwoo;Jeon, Hyoseon;Kim, Sangkyong;Kim, Hanbyul;Jeon, Beom S.;Park, Kwangsuk
    • Journal of Biomedical Engineering Research
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    • v.36 no.4
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    • pp.103-108
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    • 2015
  • Discrimination of Parkinson's disease (PD) from Essential tremor (ET) is often misdiagnosed in clinical practice. Since tremor is time-varying signal, and dominant and harmonic frequencies are shown in tremor only with moderate or severe symptom, there are some limitations to use frequency related features. Moreover, patients with PD or ET can suffer from both resting tremor and postural tremor. In this study, 28 patients with PD and 17 patients with ET were enrolled. Tremor was measured with accelerations on the more affected hand during resting and postural conditions. The ratio of root mean square (RMS) of resting tremor to RMS of postural tremor, the mean coefficients of autocorrelation function (ACF), and the mean of differences of two adjacent coefficients of ACF at resting and postural were calculated and compared between PD and ET. The performance showed 98% accuracy with support vector machine and leave-one-out cross validation. In addition, the method accurately differentiated the patients with tremor-dominant PD from patients with ET, with 100% accuracy. Therefore, the developed algorithm can assist clinicians in diagnosing and categorizing patients with tremor, especially, patients with mild symptom or the early stage of a disease, for proper treatment.

Feature Extraction and Fusion for land-Cover Discrimination with Multi-Temporal SAR Data (다중 시기 SAR 자료를 이용한 토지 피복 구분을 위한 특징 추출과 융합)

  • Park No-Wook;Lee Hoonyol;Chi Kwang-Hoon
    • Korean Journal of Remote Sensing
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    • v.21 no.2
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    • pp.145-162
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    • 2005
  • To improve the accuracy of land-cover discrimination in SAB data classification, this paper presents a methodology that includes feature extraction and fusion steps with multi-temporal SAR data. Three features including average backscattering coefficient, temporal variability and coherence are extracted from multi-temporal SAR data by considering the temporal behaviors of backscattering characteristics of SAR sensors. Dempster-Shafer theory of evidence(D-S theory) and fuzzy logic are applied to effectively integrate those features. Especially, a feature-driven heuristic approach to mass function assignment in D-S theory is applied and various fuzzy combination operators are tested in fuzzy logic fusion. As experimental results on a multi-temporal Radarsat-1 data set, the features considered in this paper could provide complementary information and thus effectively discriminated water, paddy and urban areas. However, it was difficult to discriminate forest and dry fields. From an information fusion methodological point of view, the D-S theory and fuzzy combination operators except the fuzzy Max and Algebraic Sum operators showed similar land-cover accuracy statistics.

Discrimination of Pasture Spices for Italian Ryegrass, Perennial Ryegrass and Tall Fescue Using Near Infrared Spectroscopy (근적외선분광법을 이용한 이탈리안 라이그라스, 페레니얼 라이그라스,톨 페스큐 종자의 초종 판별)

  • Park, Hyung Soo;Choi, Ki Choon;Kim, Ji Hye;So, Min Jeong;Lee, Ki Won;Lee, Sang Hoon
    • Journal of The Korean Society of Grassland and Forage Science
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    • v.35 no.2
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    • pp.125-130
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    • 2015
  • The objective of this study was to investigate the feasibility of using near infrared spectroscopy (NIRS) to discriminate between grass spices. A combination of NIRS and chemometrics was used to discriminate between Italian ryegrass, perennial ryegrass, and tall fescue seeds. A total of 240 samples were used to develop the best discriminant equation, whereby three spectra range (visible, NIR, and full range) were applied within a 680 nm to 2500 nm wavelength. The calibration equation for the discriminant analysis was developed using partial least square (PLS) regression and discrimination equation (DE) analysis. A PLS discriminant analysis model for the three spectra range that was developed with the mathematic pretreatment "1,8,8,1" successfully discriminated between Italian ryegrass, perennial ryegrass, and tall fescue. An external validation indicated that all of the samples were discriminated correctly. The discriminant accuracy was shown as 68%, 78%, and 73% for Italian ryegrass, perennial ryegrass, and tall fescue, respectively, with the NIR full-range spectra. The results demonstrate the usefulness of the NIRS-chemometrics combination as a rapid method for the discrimination of grass species by seed.

Discrimination model for cultivation origin of paper mulberry bast fiber and Hanji based on NIR and MIR spectral data combined with PLS-DA (닥나무 인피섬유와 한지의 원산지 판별모델 개발을 위한 NIR 및 MIR 스펙트럼 데이터의 PLS-DA 적용)

  • Jang, Kyung-Ju;Jung, So-Yoon;Go, In-Hee;Jeong, Seon-Hwa
    • Analytical Science and Technology
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    • v.32 no.1
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    • pp.7-16
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    • 2019
  • The objective of this study was the development of a discrimination model for the cultivational origin of paper mulberry bast fiber and Hanji using near infrared (NIR) and mid infrared (MIR) spectroscopy combined with partial least squares discriminant analysis (PLS-DA). Paper mulberry bast fiber was purchased in 10 different regions of Korea, and used to make Hanji. PLS-DA was performed using pre-treated FT-NIR and FT-MIR spectral data for paper mulberry bast fiber and Hanji. PLS-DA of paper mulberry bast fiber and Hanji samples, using FT-NIR spectral data, showed 100 % performance in cross validation and the confusion matrix (accuracy, sensitivity, and specificity). The discrimination models showed four regional groups which demonstrated clearer separation and much superior score plots in the NIR spectral data-based model than in the MIR spectral data-based model. Furthermore, the discrimination model based on the NIR spectral data of paper mulberry bast fiber had highly similar score morphology to that of the discrimination model based on the NIR spectral data of Hanji.

The discrimination model for the pattern identification diagnosis of the stroke (중풍의 변증 진단을 위한 판별모형)

  • Kang, Byeong-Kab;Kang, Kyung-Won;Park, Sae-Wook;Kim, Bo-Young;Kim, Jeong-Chul;Go, Mi-Mi;Seol, In-Chan;Jo, Hyun-Kyung;Lee, In;Choi, Sun-Mi
    • Korean Journal of Oriental Medicine
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    • v.13 no.2 s.20
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    • pp.59-63
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    • 2007
  • The purpose of this study was to diagnosis that what patterns identification using the statistical method. Discriminant analysis using the medical specialist and resident pattern identification agree case in stroke patients within 1 month of onset. The agreement rate of dificiency of Gi(75%), heat-transformation(74%), dampphlegm syndrome(69%), deficiency of Eum(51%) and syndrome of blood stagnation(43%) are respectively 0.75, 0.74, 0.69, 0.51 and 0.43 in medical specialist and using linear discriminant function pattern identification are same. The study of inspection, pulse feeling and palpitation will be continued to evaluate concordance rate. Discrimination model will be make to get higher Accuracy and prediction, it means becomes the help in pattern identification diagnosis objectivity and scientific.

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Discrimination of cultivation ages and cultivars of ginseng leaves using Fourier transform infrared spectroscopy combined with multivariate analysis

  • Kwon, Yong-Kook;Ahn, Myung Suk;Park, Jong Suk;Liu, Jang Ryol;In, Dong Su;Min, Byung Whan;Kim, Suk Weon
    • Journal of Ginseng Research
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    • v.38 no.1
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    • pp.52-58
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    • 2014
  • To determine whether Fourier transform (FT)-IR spectral analysis combined with multivariate analysis of whole-cell extracts from ginseng leaves can be applied as a high-throughput discrimination system of cultivation ages and cultivars, a total of total 480 leaf samples belonging to 12 categories corresponding to four different cultivars (Yunpung, Kumpung, Chunpung, and an open-pollinated variety) and three different cultivation ages (1 yr, 2 yr, and 3 yr) were subjected to FT-IR. The spectral data were analyzed by principal component analysis and partial least squares-discriminant analysis. A dendrogram based on hierarchical clustering analysis of the FT-IR spectral data on ginseng leaves showed that leaf samples were initially segregated into three groups in a cultivation age-dependent manner. Then, within the same cultivation age group, leaf samples were clustered into four subgroups in a cultivar-dependent manner. The overall prediction accuracy for discrimination of cultivars and cultivation ages was 94.8% in a cross-validation test. These results clearly show that the FT-IR spectra combined with multivariate analysis from ginseng leaves can be applied as an alternative tool for discriminating of ginseng cultivars and cultivation ages. Therefore, we suggest that this result could be used as a rapid and reliable F1 hybrid seed-screening tool for accelerating the conventional breeding of ginseng.

Skin Lesion Image Segmentation Based on Adversarial Networks

  • Wang, Ning;Peng, Yanjun;Wang, Yuanhong;Wang, Meiling
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.12 no.6
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    • pp.2826-2840
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    • 2018
  • Traditional methods based active contours or region merging are powerless in processing images with blurring border or hair occlusion. In this paper, a structure based convolutional neural networks is proposed to solve segmentation of skin lesion image. The structure mainly consists of two networks which are segmentation net and discrimination net. The segmentation net is designed based U-net that used to generate the mask of lesion, while the discrimination net is designed with only convolutional layers that used to determine whether input image is from ground truth labels or generated images. Images were obtained from "Skin Lesion Analysis Toward Melanoma Detection" challenge which was hosted by ISBI 2016 conference. We achieved segmentation average accuracy of 0.97, dice coefficient of 0.94 and Jaccard index of 0.89 which outperform the other existed state-of-the-art segmentation networks, including winner of ISBI 2016 challenge for skin melanoma segmentation.

Discrimination of Geographical Origin of Mushroom (Tricholoma matsutake) using Near Infrared Spectroscopy (근적외선 분광광도법을 이용한 송이버섯의 원산지 판별)

  • Lee, Nam-Youn;Bae, Hey-Ree;Noh, Bong-Soo
    • Korean Journal of Food Science and Technology
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    • v.38 no.6
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    • pp.835-837
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    • 2006
  • The geographical origin of Tricholoma matsutake mushrooms was studied using near-infrared spectroscopy. Modified partial least-square regression analyses were used to discriminate geographical origin. Two-hundred fifty-six of 259 actual domestic Tricholoma matsutake were classified as domestic produce, Sixty of 81 actual imported mushrooms were correctly classified as imported, while the other 21 imported from North Korea were not clearly classified. The accuracy of geographical origin discrimination was 92.94% The correlation coefficient, standard error of calibration, and standard error of prediction from modified partial least-square regression analysis were 0.84, 15.10% and 18.30% respectively.

Determination of Leaf Color and Health State of Lettuce using Machine Vision (기계시각을 이용한 상추의 엽색 및 건강상태 판정)

  • Lee, J.W.
    • Journal of Biosystems Engineering
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    • v.32 no.4
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    • pp.256-262
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    • 2007
  • Image processing systems have been used to measure the plant parameters such as size, shape and structure of plants. There are yet some limited applications for evaluating plant colors due to illumination conditions. This study was focused to present adaptive methods to analyze plant leaf color regardless of illumination conditions. Color patches attached on the calibration bars were selected to represent leaf colors of lettuces and to test a possibility of health monitoring of lettuces. Repeatability of assigning leaf colors to color patches was investigated by two-tailed t-test for paired comparison. It resulted that there were no differences of assignment histogram between two images of one lettuce that were acquired at different light conditions. It supported that use of the calibration bars proposed for leaf color analysis provided color constancy, which was one of the most important issues in a video color analysis. A health discrimination equation was developed to classify lettuces into one of two classes, SOUND group and POOR group, using the machine vision. The classification accuracy of the developed health discrimination equation was 80.8%, compared to farmers' decision. This study could provide a feasible method to develop a standard color chart for evaluating leaf colors of plants and plant health monitoring system using the machine vision.