• Proceedings of the IEEK Conference
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• summer
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• pp.447-450
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• 2004

This paper proposes remotely sensed image classification method by fuzzy c-means clustering algorithm using average intra-cluster distance. The average intra-cluster distance acquires an average of the vector set belong to each cluster and proportionates to its size and density. We perform classification according to pixel's membership grade by cluster center of fuzzy c-means clustering using the mean-values of training data about each class. Fuzzy c-means algorithm considered membership degree for inter-cluster of each class. And then, we validate degree of overlap between clusters. A pixel which has a high degree of overlap applies to the maximum likelihood classification method. Finally, we decide category by comparing with fuzzy membership degree and likelihood rate. The proposed method is applied to IKONOS remote sensing satellite image for the verifying test.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.5
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• pp.7-13
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• 2020

This paper focuses on detecting abnormal patterns of respiration of humans. In this study, a contact-based device was used to acquire both normal and abnormal respiration signals. To this end, this paper reports the development of a monitoring system to investigate the respiratory status of humans in a normal environment. This work aims to classify the respiratory status, i.e., normal and abnormal status, quantitatively. The respiration signal is acquired using a contact-based medical device (BIOBPAC), and noise reduction is carried out before classifying the respiratory status. To reduce noise, a mixed filter that combines the Savitzky-Golay filter and Median filter is applied to the acquired respiration signals. The inter-class distance is maximized, and the intra-class distance is minimized. The proposed algorithm is straightforward and can be applied to a practical environment. In addition, the experimental results are provided to substantiate the proposed approach.

• Journal of Korea Multimedia Society
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• v.22 no.8
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• pp.844-851
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• 2019

In this paper, we propose a loss function in CNN that introduces inter-class amplitudes to increase inter-class loss and reduce intra-class loss to increase of face recognition performance. This loss function increases the distance between the classes and decreases the distance in the class, thereby improving the performance of the face recognition finally. It is confirmed that the accuracy of face recognition for visible light image of proposed loss function is 99.62%, which is better than other loss functions. We also applied it to face recognition of visible and near-infrared complex images to obtain satisfactory results of 99.76%.

• Physical Therapy Korea
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• v.21 no.3
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• pp.73-79
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• 2014

The purposes of the current study were to (1) estimate the inter-rater agreement for visual assessment of scapular downward rotation (SDR), (2) develop the scapular downward rotation index (SDRI) as a method to measure SDR objectively and quantitatively, and (3) analyze the intra- and inter-rater reliability of the SDRI. Twenty subjects with scapular downward rotation syndrome (SDRS) were recruited for this study. The visual assessment and the measurement for the SDRI were conducted by two examiners in two sessions each. The SDRI [$(a-b){\div}a{\times}100$] is calculated with the measurement of two linear distances: One is a perpendicular distance from the root of the scapular spine to the thoracic mid-line (a), and the other is a perpendicular distance from the inferior angle of the scapula to the thoracic mid-line (b). Cohen's kappa coefficient was calculated to estimate the inter-rater agreement for visual assessment. Intra-class correlation coefficients (ICCs) with a 95% confidence interval (CI), the standard error of measurement, and minimal detectable differences were calculated to assess intra- and inter-rater reliability of SDR measurement using the SDRI. The results indicated that the kappa coefficient of inter-rater agreement for visual assessment was fair (${\kappa}=.21$). The intra-rater reliability of SDR measurement using the SDRI was excellent for examiner 1 (ICC=.92, 95% CI=.78~.97) and good for examiner 2 (ICC=.82, 95% CI=.55~.93). The inter-rater reliability was moderate (ICC=.73, 95% CI=.32~.89). These findings showed that SDR measurement using the SDRI for subjects with SDRS may be considered reliable and better than the visual assessment.

• Earthquakes and Structures
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• v.11 no.3
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• pp.505-516
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• 2016

This paper evaluates the scaling of ground motions recorded from nine intermediate-depth earthquakes produced in the Vrancea seismic zone in Romania. The considered ground motion database consists of 363 horizontal recordings obtained on soil classes B and C (according to Eurocode 8). An analysis of the inter- and intra-event spectral accelerations is performed in order to gain information regarding the magnitude and distance scaling of the Vrancea ground motions. The analyses reveal a significant influence of the earthquake magnitude and focal depth on the distance scaling and different magnitude and distance scaling for the two soil classes. A linear magnitude and distance scaling is inferred from the results for the range of magnitudes $5.2{\leq}M_W{\leq}7.1$. The results obtained are checked through stochastic simulations and the influence of the stress drop and kappa values on the ground motion levels is assessed. In addition, five ground motion models which were tested in other studies using recordings from Vrancea earthquakes are analyzed in order to evaluate their corresponding host stress drop and kappa. The results show generally a direct connection between the host kappa values and the host stress drop values. Moreover, all the ground motion models depict magnitude dependent host kappa and stress drop levels.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.6
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• pp.2098-2114
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• 2021

Light intensity variation is one of the key factors which affect the accuracy of vehicle face re-identification, so in order to improve the robustness of vehicle face features to light intensity variation, a Nonnegative Matrix Factorization model with the constraint of image acquisition time difference is proposed. First, the original features vectors of all pairs of positive samples which are used for training are placed in two original feature matrices respectively, where the same columns of the two matrices represent the same vehicle; Then, the new features obtained after decomposition are divided into stable and variable features proportionally, where the constraints of intra-class similarity and inter-class difference are imposed on the stable feature, and the constraint of image acquisition time difference is imposed on the variable feature; At last, vehicle face matching is achieved through calculating the cosine distance of stable features. Experimental results show that the average False Reject Rate and the average False Accept Rate of the proposed algorithm can be reduced to 0.14 and 0.11 respectively on five different datasets, and even sometimes under the large difference of light intensities, the vehicle face image can be still recognized accurately, which verifies that the extracted features have good robustness to light variation.

• International journal of advanced smart convergence
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• v.7 no.4
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• pp.40-49
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• 2018

We propose a more stable robust recognition algorithm which detects faces reliably even in cases where there are changes in lighting and angle of view, as well it satisfies efficiency in calculation and detection performance. We propose detects the face area alone after normalization through pre-processing and obtains a feature vector using (PCA). The feature vector is applied to LDA and using Euclidean distance of intra-class variance and inter class variance in the 2nd dimension, the final analysis and matching is performed. Experimental results show that the proposed method has a wider distribution when the input image is rotated $45^{\circ}$ left / right. We can improve the recognition rate by applying this feature value to a single algorithm and complex algorithm, and it is possible to recognize in real time because it does not require much calculation amount due to dimensional reduction.

• Mycobiology
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• v.50 no.1
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• pp.55-65
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• 2022

Lichen is a symbiotic mutualism of mycobiont and photobiont that harbors diverse organisms including endolichenic fungi (ELF). Despite the taxonomic and ecological significance of ELF, no comparative investigation of an ELF community involving isolation of a pure culture and high-throughput sequencing has been conducted. Thus, we analyzed the ELF community in Parmotrema tinctorum by culture and metabarcoding. Alpha diversity of the ELF community was notably greater in metabarcoding than in culture-based analysis. Taxonomic proportions of the ELF community estimated by metabarcoding and by culture analyses showed remarkable differences: Sordariomycetes was the most dominant fungal class in culture-based analysis, while Dothideomycetes was the most abundant in metabarcoding analysis. Thirty-seven operational taxonomic units (OTUs) were commonly observed by culture-and metabarcoding-based analyses but relative abundances differed: most of common OTUs were underrepresented in metabarcoding. The ELF community differed in lichen segments and thalli in metabarcoding analysis. Dissimilarity of ELF community intra lichen thallus increased with thallus segment distance; inter-thallus ELF community dissimilarity was significantly greater than intra-thallus ELF community dissimilarity. Finally, we tested how many fungal sequence reads would be needed to ELF diversity with relationship assays between numbers of lichen segments and saturation patterns of OTU richness and sample coverage. At least 6000 sequence reads per lichen thallus were sufficient for prediction of overall ELF community diversity and 50,000 reads per thallus were enough to observe rare taxa of ELF.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.7
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• pp.3172-3193
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• 2018

Recently, several studies have shown that linear representation based approaches are very effective and efficient for image classification. One of these linear-representation-based approaches is the Collaborative representation (CR) method. The existing algorithms based on CR have two major problems that degrade their classification performance. First problem arises due to the limited number of available training samples. The large variations, caused by illumintion and expression changes, among query and training samples leads to poor classification performance. Second problem occurs when an image is partially noised (contiguous occlusion), as some part of the given image become corrupt the classification performance also degrades. We aim to extend the collaborative representation framework under limited training samples face recognition problem. Our proposed solution will generate virtual samples and intra-class variations from training data to model the variations effectively between query and training samples. For robust classification, the image patches have been utilized to compute representation to address partial occlusion as it leads to more accurate classification results. The proposed method computes representation based on local regions in the images as opposed to CR, which computes representation based on global solution involving entire images. Furthermore, the proposed solution also integrates the locality structure into CR, using Euclidian distance between the query and training samples. Intuitively, if the query sample can be represented by selecting its nearest neighbours, lie on a same linear subspace then the resulting representation will be more discriminate and accurately classify the query sample. Hence our proposed framework model the limited sample face recognition problem into sufficient training samples problem using virtual samples and intra-class variations, generated from training samples that will result in improved classification accuracy as evident from experimental results. Moreover, it compute representation based on local image patches for robust classification and is expected to greatly increase the classification performance for face recognition task.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.4
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• pp.819-833
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• 2013

Computing global features based on local features using a bio-inspired framework has shown promising performance. However, for some tough applications with large intra-class variances, a single local feature is inadequate to represent all the attributes of the images. To integrate the complementary abilities of multiple local features, in this paper we have extended the efficacy of the bio-inspired framework, HMAX, to adapt heterogeneous features for global feature extraction. Given multiple global features, we propose an approach, designated as parameterized metric learning, for high dimensional feature fusion. The fusion parameters are solved by maximizing the canonical correlation with respect to the parameters. Experimental results show that our method achieves significant improvements over the benchmark bio-inspired framework, HMAX, and other related methods on the Caltech dataset, under varying numbers of training samples and feature elements.