• Archives of Plastic Surgery
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• v.34 no.1
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• pp.134-136
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• 2007

Purpose: Erysipelas is a bacterial infection of the dermis and hypodermis, mostly of streptococcal origin, and erysipelas of upper extremity following breast cancer treatment has never been reported in the Korean literature. Methods: 39-year-old female presented to our hospital complaining of fever and painful swelling of her left upper extremity. She had a history of breast cancer and was treated with breast conserving surgery with axillary lymph node dissection, chemotherapy, and radiation. On physical examination, her left upper extremity showed vesicle, bullae, local heatness and erythema with well-defined margin. With these distinctive features of a skin lesion, we gave a diagnosis of erysipelas and started treatment with intravenous antibiotics. Results: Resolution of the signs and symptoms of erysipelas occurred after 7 days of treatment. Conclusion: The diagnosis of erysipelas with distinctive feature of skin lesion is essential and we emphasize that the prevention of any trauma are very important in these patients for prophylactic measures.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.1
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• pp.213-231
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• 2014

Existing image search systems allow users to search images by keywords, or by example images through content-based image retrieval (CBIR). On the other hand, users might learn more relevant textual information about an image from its text captions or surrounding contexts within documents or Web pages. Without such contexts, it's difficult to extract semantic description directly from the image content. In this paper, we propose an annotation watermarking system for users to embed text descriptions, and retrieve more relevant textual information from similar images. First, tags associated with an image are converted by two-dimensional code and embedded into the image by discrete wavelet transform (DWT). Next, for images without annotations, similar images can be obtained by CBIR techniques and embedded annotations can be extracted. Specifically, we use global features such as color ratios and dominant sub-image colors for preliminary filtering. Then, local features such as Scale-Invariant Feature Transform (SIFT) descriptors are extracted for similarity matching. This design can achieve good effectiveness with reasonable processing time in practical systems. Our experimental results showed good accuracy in retrieving similar images and extracting relevant tags from similar images.

• Journal of Korea Multimedia Society
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• v.10 no.2
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• pp.179-186
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• 2007

This paper proposes a novel method for face detection method using Zernike moments. To detect the faces in an image, local regions in multiscale sliding windows are classified into face and non-face by a neural network, and input features of the neural network consist of Zernike moments. Feature dimension is reduced as the reconstruction capability of orthogonal moment. In addition, because the magnitude of Zernike moment is invariant to rotation, a tilted human face can be detected. Even so the detection rate of the proposed method about head on face is less than experiments using intensity features, the result of our method about rotated faces is more robust. If the additional compensation and features are utilized, the proposed scheme may be best suited for the later stage of classification.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.11 no.4
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• pp.131-143
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• 2012

In this paper, we propose a drowsy driver detection system with a novel eye state detection method that is adaptive to various vehicle environment such as glasses, light and so forth using SURF(Speed Up Robust Feature) which can extract quickly local features from images. Also the performance of eye state detection is improved as individual three eye-state templates of each driver can be made using Bayesian inference. The experimental results under various environment with average 98.1% and 96.1% detection rate in the daytime and at night respectively and those in the opened ZJU database with average 97.8% detection rate show that the proposed method outperforms the current state-of-the-art.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.5
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• pp.997-1003
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• 2007

In this paper, we propose a global approach for automatic inspection of defects in the polarizer film image. The proposed method does not rely on local feature of the defect. It is based on a global image reconstruction scheme using the singular value decomposition(SVD). SVD is used to decompose the image and then obtain a diagonal matrix of the singular values. Among the singular values, the first singular value is used to reconstruct a image. In reconstructed image, the normal pixels in background region have a different characteristics from the pixels in defect region. It is obtained the ratio of pixels in the reconstructed image to ones in the original image and then the defects are detected based on the the statistical process of the ratio. The experiment results show that the proposed method is efficient for defect inspection of polarizer lam image.

• Bulletin of the Korean Chemical Society
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• v.19 no.1
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• pp.79-82
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• 1998

Novel coordination polymers of general form $[Co(NCS)_2(Py_2L)_2]_n\; (Py_2L=trans-1,2-bis(4-pyridyl)ethylene$ (bpee), 1; 1,2-bis(4-pyridyl)ethane (bpea), 2) have been synthesized by slow diffusion of aqueous solution of $Co(NCS)_2$ with ethanolic solution of appropriate spacer ligand, $Py_2L$, in a mole ratio of 1 : 2. X-ray analyses on both 1 and 2 have provided similar unit and infinite structures with space group Ibam. The local geometry around the cobalt(Ⅱ) atoms is an octahedral arrangement with two NCS groups in trans position (N-Co-N, 180.0° (1); 180.0°(2)) and four pyridine units in propeller fashion. Each spacer ligand connects two cobalt(Ⅱ) ions defining the edges of a $[Co(II)]_4$ rhombus. The most fascinating feature is the occurrence of perpendicular interwoven of independent square grid sheets: this is, one molecular network is perpendicularly interpenetrated through the centers of the $[Co(II)]_4$ rhombuses of another independent network with all of the cobalt(Ⅱ) atoms in a coplanar sheet. The physicochemical properties of the present unique structures were studied.

• Journal of Information Processing Systems
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• v.17 no.4
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• pp.690-706
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• 2021

Classification is a substantial operation in data mining, and each element is distributed taking into account its feature values in the corresponding class. Metaheuristics have been widely used in attempts to solve satellite image classification problems. This article proposes a hybrid approach, the flower pigeons-inspired optimization algorithm (FPIO), and the local search method of the flower pollination algorithm is integrated into the pigeon-inspired algorithm. The efficiency and power of the proposed FPIO approach are displayed with a series of images, supported by computational results that demonstrate the cogency of the proposed classification method on satellite imagery. For this work, the Davies-Bouldin Index is used as an objective function. FPIO is applied to different types of images (synthetic, Alsat-2B, and Sentinel-2). Moreover, a comparative experiment between FPIO and the genetic algorithm genetic algorithm is conducted. Experimental results showed that GA outperformed FPIO in matters of time computing. However, FPIO provided better quality results with less confusion. The overall experimental results demonstrate that the proposed approach is an efficient method for satellite imagery classification.

• Journal of Information Processing Systems
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• v.17 no.4
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• pp.818-833
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• 2021

To address the problems of low precision rate, insufficient feature extraction, and poor contextual ability in existing text sentiment analysis methods, a mixed model account of a CNN-BiLSTM-TE (convolutional neural network, bidirectional long short-term memory, and topic extraction) model was proposed. First, Chinese text data was converted into vectors through the method of transfer learning by Word2Vec. Second, local features were extracted by the CNN model. Then, contextual information was extracted by the BiLSTM neural network and the emotional tendency was obtained using softmax. Finally, topics were extracted by the term frequency-inverse document frequency and K-means. Compared with the CNN, BiLSTM, and gate recurrent unit (GRU) models, the CNN-BiLSTM-TE model's F1-score was higher than other models by 0.0147, 0.006, and 0.0052, respectively. Then compared with CNN-LSTM, LSTM-CNN, and BiLSTM-CNN models, the F1-score was higher by 0.0071, 0.0038, and 0.0049, respectively. Experimental results showed that the CNN-BiLSTM-TE model can effectively improve various indicators in application. Lastly, performed scalability verification through a takeaway dataset, which has great value in practical applications.

• Journal of the Korea Society of Computer and Information
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• v.24 no.3
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• pp.77-86
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• 2019

The advent of Internet of Things (IoT) makes common nowadays computing environments involving programming not a single computer but several heterogeneous distributed computers together. Developing programs separately, one for each computer, increases programmer burden and testing all the programs become more complex. To address the challenge, this paper proposes an RPC-based unified programming language, UniPy, for development of MGC (eMbedded, Gateway, and Cloud) applications in IoT systems configured with popular computers such as Arduino, Raspberry Pi, and Web-based DB server. UniPy offers programmers a view of classes as locations and a very simple form of remote procedure call mechanism. Our UniPy compiler automatically splits a UniPy program into small pieces of the program at different locations supporting the necessary RPC mechanism. An advantage of UniPy programs is to permit programmers to write local codes the same as for a single computer requiring no extra knowledge due to having unified programming models, which is very different from the existing research works such as Fabryq and Ravel. Also, the structure of UniPy programs allows programmers to test them by directly executing them before splitting, which is a feature that has never been emphasized yet.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.2129-2147
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• 2019

This paper introduces an adaptive face recognition method based on a Novel Incremental Kernel Nonparametric Discriminant Analysis (IKNDA) that is able to learn through time. More precisely, the IKNDA has the advantage of incrementally reducing data dimension, in a discriminative manner, as new samples are added asynchronously. Thus, it handles dynamic and large data in a better way. In order to perform face recognition effectively, we combine the Gabor features and the ordinal measures to extract the facial features that are coded across local parts, as visual primitives. The variegated ordinal measures are extraught from Gabor filtering responses. Then, the histogram of these primitives, across a variety of facial zones, is intermingled to procure a feature vector. This latter's dimension is slimmed down using PCA. Finally, the latter is treated as a facial vector input for the advanced IKNDA. A comparative evaluation of the IKNDA is performed for face recognition, besides, for other classification endeavors, in a decontextualized evaluation schemes. In such a scheme, we compare the IKNDA model to some relevant state-of-the-art incremental and batch discriminant models. Experimental results show that the IKNDA outperforms these discriminant models and is better tool to improve face recognition performance.