• Journal of Biomedical Engineering Research
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• v.17 no.1
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• pp.109-120
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• 1996

Cardiac function is evaluated quantitatively using angiographic images via the analysis of the shape change or the heart wall boundaries. To kin with, boundary defection or ESLV(End Systolic Lert Ventricular) and EDLV(End Diastolic Left Ventricular) is essential for the quantitative analysis of cardiac function. The boundary detection methods proposed in the past were almost semi-automatic. Intervention by a knowledgeable human operator was still required Of con, manual tracing of the boundaries is currently used for subsequent analysis and diagnosis. This method would not cut excessive time, labor, and subjectivity associated with manual intervention by a human operator. EDLV images have noncontiguous and ambiguous edge signal on some boundary regions. In this paper, we propose a new method for automated detection of boundaries in noncontiguous and ambiguous EDLV images. The boundary detection scheme which based on a priori knowledge information is divided into two steps. The first step is to detect the candidate edge points of EDLV using ESLV boundaries. The second step is to correct detected boundaries of EDLV using the LV shape. We developed the algorithm of modifying EDLV boundaries defined adaptive modifier. We experimented the method proposed in this paper and compared our proposed method with the manual method in detecting boundaries of EDLV. In the areas within estimated boundaries of EDLV, the percentage of error was about 1.4%. We verified the useflilness and obtained the satisfying results througll the experiments of the proposed method.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.19 no.4
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• pp.669-678
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• 1994

This paper presents a new data predistortion technique with memory to compensate for the nonlinearities of high-power amplifiers (HPA`s) in digital radio systems employing QAM signal formats. In contrast with the conventional data predistortion technique which is designed to reduce nonlinearity of memoryless HPA`s, the proposed technique in this paper compensates not only for nonlinear warping of the signal constellation but also for clustering of the signal points caused by transmitter pulse sharping filter with memory. A practical implementation method which can reduce the size of memory at the predistortion stage is described by utilizing symmetry of QAM constellation format and Modulo-4 operation.

• Korean Journal of Agricultural Science
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• v.48 no.1
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• pp.179-189
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• 2021

Plants need essential mineral elements to favorably develop and to complete their life cycle. Despite the irreplaceable roles of microelements, they are often ignored due to the relative importance of macroelements with their influence on crop growth and development. We focused on the changes in primary metabolites in the leaves and roots of bell pepper plants under 6 microelements-deficient conditions: Copper (Cu), Zinc (Zn), Iron (Fe), Manganese (Mn), Boron (B) and Molybdenum (Mo). Bell pepper plants were grown in hydroponic containers, and individual elements were adjusted to 1/10-strength of Hoagland nutrient solution. A remarkable perturbation in the abundance of the primary metabolites was observed for the Fe and B and the Mn and B deficiencies in the leaves and roots, respectively. The metabolites with up-accumulation in the Fe-deficient leaves were glucose, fructose, xylose, glutamine, asparagine and serine. In contrast, the Mn deficiency also resulted in a higher accumulation of glucose, fructose, xylose, galactose, serine, glycine, β-alanine, alanine and valine in the roots. The B deficiency noticeably accumulated alanine, valine and phenylalanine in the roots while it showed a substantial decrease in glucose, fructose and xylose. These results show that the primary metabolism could be seriously disturbed due to a microelement deficiency, and the alteration may be either the specific or adaptive responses of bell pepper plants.

• Journal of Ecology and Environment
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• v.45 no.2
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• pp.78-87
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• 2021

Background: To understand shade and wind effects on seedling traits of common reed (Phragmites australis), we conducted a mesocosm experiment manipulating day length (10 h daytime a day as open canopy conditions or 6 h daytime a day as partially closed canopy conditions) and wind speed (0 m/s as windless conditions or 4 m/s as windy conditions). Results: Most values of functional traits of leaf blades, culms, and biomass production of P. australis were higher under long day length. In particular, we found sole positive effects of long day length in several functional traits such as internode and leaf blade lengths and the values of above-ground dry weight (DW), rhizome DW, and total DW. Wind-induced effects on functional traits were different depending on functional traits. Wind contributed to relatively low values of chlorophyll contents, angles between leaf blades, mean culm height, and maximum culm height. In contrast, wind contributed to relatively high values of culm density and below-ground DW. Conclusions: Although wind appeared to inhibit the vertical growth of P. australis through physiological and morphological changes in leaf blades, it seemed that P. australis might compensate the inhibited vertical growth with increased horizontal growth such as more numerous culms, indicating a highly adaptive characteristic of P. australis in terms of phenotypic plasticity under windy environments.

• Journal of Multimedia Information System
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• v.8 no.2
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• pp.79-84
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• 2021

Detecting brain tumors of different sizes is a challenging task. This study aimed to identify brain tumors using detection algorithms. Most studies in this area use segmentation; however, we utilized detection owing to its advantages. Data were obtained from 64 patients and 11,200 MR images. The deep learning model used was RetinaNet, which is based on ResNet152. The model learned three different types of pre-processing images: normal, general histogram equalization, and contrast-limited adaptive histogram equalization (CLAHE). The three types of images were compared to determine the pre-processing technique that exhibits the best performance in the deep learning algorithms. During pre-processing, we converted the MR images from DICOM to JPG format. Additionally, we regulated the window level and width. The model compared the pre-processed images to determine which images showed adequate performance; CLAHE showed the best performance, with a sensitivity of 81.79%. The RetinaNet model for detecting brain tumors through deep learning algorithms demonstrated satisfactory performance in finding lesions. In future, we plan to develop a new model for improving the detection performance using well-processed data. This study lays the groundwork for future detection technologies that can help doctors find lesions more easily in clinical tasks.

• International Journal of Industrial Entomology and Biomaterials
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• v.43 no.2
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• pp.37-44
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• 2021

Mulberry silkworm is classified into uni, bi and multivoltine based on the frequency of diapause incidence. The variation in the incidence of diapause in bivoltine silkworm provides a unique opportunity to study the process of evolution of adaptive plasticity towards seasonal variations. The diapause expression in bivoltine silkworm is highly variable and is determined by environmental factors experienced by the maternal generation. Diapause in natural populations is functionally associated with the overwintering mechanism that facilitates survival in harsh winter conditions. In contrast, under standard commercial rearing conditions, the domesticated bivoltine silkworm is known to enter diapause in every generation. This paper presents a short review of the literature dealing with the role of temperature, photoperiod, diapause hormone and its receptor in diapause induction. Also, we briefly review the incidence of non-diapause eggs in bivoltine silkworm under controlled conditions.

• Structural Engineering and Mechanics
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• v.80 no.6
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• pp.711-726
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• 2021

This paper presents a multiple parameters-based recursive methodology for the reliability quantification of civil structures subjected to meteorological disasters. Recognizing the challenge associated with characterizing at a single stroke all the meteorological disasters that may hit a structure during its service life, the proposed methodology by contrast features a multiparameter recursive mechanism to describe the meteorological demand of the structure. The benefit of the arrangements is that the essentially inevitable deviation of the practically observed meteorological data from those in the existing model can be mitigated in an adaptive manner. In particular, the implications of potential climate change to the relevant reliability of civil structures are allowed for. The application of the formulated methodology of recursive reliability quantification is illustrated by first considering the reliability quantification of a linear shear frame against simulated strong wind loads. A parametric study is engaged in this application to examine the effect of some hyperparameters in the configured hierarchical model. Further, the application is extended to a nonlinear hysteretic shear frame involving some field-observed cyclone data, and the incompleteness of the relevant structural diagnosis data that may arise in reality is taken into account. Also investigated is another application scenario where the reliability of a building envelope is assessed under hailstone impacts, and the emphasis is to demonstrate the recursive incorporation of newly obtained meteorological data.

• International Journal of Computer Science & Network Security
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• v.24 no.1
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• pp.119-126
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• 2024

Disease caused by the coronavirus (COVID-19) is sweeping the globe. There are numerous methods for identifying this disease using a chest imaging. Computerized Tomography (CT) chest scans are used in this study to detect COVID-19 disease using a pretrain Convolutional Neural Network (CNN) ResNet50. This model is based on image dataset taken from two hospitals and used to identify Covid-19 illnesses. The pre-train CNN (ResNet50) architecture was used for feature extraction, and then fully connected layers were used for classification, yielding 97%, 96%, 96%, 96% for accuracy, precision, recall, and F1-score, respectively. When combining the feature extraction techniques with the Back Propagation Neural Network (BPNN), it produced accuracy, precision, recall, and F1-scores of 92.5%, 83%, 92%, and 87.3%. In our suggested approach, we use a preprocessing phase to improve accuracy. The image was enhanced using the Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm, which was followed by cropping the image before feature extraction with ResNet50. Finally, a fully connected layer was added for classification, with results of 99.1%, 98.7%, 99%, 98.8% in terms of accuracy, precision, recall, and F1-score.

• IMMUNE NETWORK
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• v.22 no.4
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• pp.32.1-32.20
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• 2022

Sjögren syndrome (SS) is a chronic autoimmune disorder that primarily targets the salivary and lacrimal glands. The pathology of these exocrine glands is characterized by periductal focal lymphocytic infiltrates, and both T cell-mediated tissue injury and autoantibodies that interfere with the secretion process underlie glandular hypofunction. In addition to these adaptive mechanisms, multiple innate immune pathways are dysregulated, particularly in the salivary gland epithelium. Our understanding of the pathogenetic mechanisms of SS has substantially improved during the past decade. In contrast to viral infection, bacterial infection has never been considered in the pathogenesis of SS. In this review, oral dysbiosis associated with SS and evidence for bacterial infection of the salivary glands in SS were reviewed. In addition, the potential contributions of bacterial infection to innate activation of ductal epithelial cells, plasmacytoid dendritic cells, and B cells and to the breach of tolerance via bystander activation of autoreactive T cells and molecular mimicry were discussed. The added roles of bacteria may extend our understanding of the pathogenetic mechanisms and therapeutic approaches for this autoimmune exocrinopathy.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.44 no.2 s.314
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• pp.116-125
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• 2007

In this paper, we propose a illumination environment adaptive real-time surveillance system for security of important area such as military bases, prisons, and strategic infra structures. The proposed system recognizes movement of objects on the bright environments as well as in dark illumination. The procedure of proposed system may be summarized as follows. First, the system discriminates between bright and dark with input image distribution. Then, if the input image is dark, the system has a pre-processing. The Multi-scale Retinex Color Restoration(MSRCR) is processed to enhance the contrast of image captured in dark environments. Secondly, the enhanced input image is subtracted with the revised background image. And then, we take a morphology image processing to obtain objects correctly. Finally, each bounding box enclosing each objects are tracked. The center point of each bounding box obtained by the proposed algorithm provides more accurate tracking information. Experimental results show that the proposed system provides good performance even though an object moves very fast and the background is quite dark.