• Journal of the Korean Institute of Oriental Medical Informatics
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• v.15 no.2
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• pp.1-6
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• 2009

The discipline of terminology is based on its own theoretical principles and consists primarily of the following aspects: analysing the concepts and concept structures used in a field or domain of activity, identifying the terms assigned to the concepts, in the case of bilingual or multilingual terminology, establishing correspondences between terms in the various languages, creating new terms, as required. The word properties has syntax, morphology and orthography. The syntax is that how words are put together. The morphology is consist of inflection, derivation, and compounding. The orthography is spelling. Otherwise, the terms of TKM(Traditional Korean Medicine) is two important element of visual character and phonetic notation. A visual character consist of spell, sort words, stop words, etc. For example, that is a case of sort words in which this '다한', '한다', '多汗', '汗多' as same. A phonetic notation consist of palatalization, initial law, etc. For example, that is a case of palatalization in which this '수족랭', '수족냉', '手足冷', '手足冷' as same. Therefore, to enlarged reference terminology is a method by term's normalization. For such a reason, TKM's terms of normalization is necessary.

• Journal of Korea Multimedia Society
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• v.23 no.8
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• pp.977-985
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• 2020

Aiming at the problem of complex feature extraction and low accuracy in human action recognition, this paper proposed a network structure combining batch normalization algorithm with GoogLeNet network model. Applying Batch Normalization idea in the field of image classification to action recognition field, it improved the algorithm by normalizing the network input training sample by mini-batch. For convolutional network, RGB image was the spatial input, and stacked optical flows was the temporal input. Then, it fused the spatio-temporal networks to get the final action recognition result. It trained and evaluated the architecture on the standard video actions benchmarks of UCF101 and HMDB51, which achieved the accuracy of 93.42% and 67.82%. The results show that the improved convolutional neural network has a significant improvement in improving the recognition rate and has obvious advantages in action recognition.

• Journal of the Korean Physical Society
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• v.73 no.10
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• pp.1452-1457
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• 2018

Inclusion of the ground state of $^{16}O$ is investigated for a study of elastic ${\alpha}-^{12}C$ scattering for the l = 0 channel at low energies in effective field theory. We employ a Markov chain Monte Carlo method for the parameter fitting and find that the uncertainties of the fitted parameters are significantly improved compared to those of our previous study. We then calculate the asymptotic normalization constants of the $0^+$ states of $^{16}O$ and compare them with the experimental data and the previous theoretical estimates. We discuss implications of the results of the present work.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.6
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• pp.374-382
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• 2020

The purpose of this study is to improve the learning speed of an ammunition stockpile reliability classification artificial neural network model by proposing a normalization method that reduces the number of input variables based on the characteristic of Ammunition Stockpile Reliability Program (ASRP) data without loss of classification performance. Ammunition's performance requirements are specified in the Korea Defense Specification (KDS) and Ammunition Stockpile reliability Test Procedure (ASTP). Based on the characteristic of the ASRP data, input variables can be normalized to estimate the lot percent nonconforming or failure rate. To maintain the unitary hypercube condition of the input variables, min-max normalization method is also used. Area Under the ROC Curve (AUC) of general min-max normalization and proposed 2-step normalization is over 0.95 and speed-up for marching learning based on ASRP field data is improved 1.74 ~ 1.99 times depending on the numbers of training data and of hidden layer's node.

• Korean Journal of Remote Sensing
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• v.36 no.2_1
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• pp.179-197
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• 2020

Topographic normalization reduces the terrain effects on reflectance by adjusting the brightness values of the image pixels to be equal if the pixels cover the same land-cover. Topographic effects are induced by the imaging conditions and tend to be large in high mountainousregions. Therefore, image analysis on mountainous terrain such as estimation of wildfire damage assessment requires appropriate topographic normalization techniques to yield accurate image processing results. However, most of the previous studies focused on the evaluation of topographic normalization on satellite images with moderate-low spatial resolution. Thus, the alleviation of topographic effects on multi-temporal high-resolution images was not dealt enough. In this study, the evaluation of terrain normalization was performed for each band to select the optimal technical combinations for rapid and accurate wildfire damage assessment using PlanetScope images. PlanetScope has considerable potential in the disaster management field as it satisfies the rapid image acquisition by providing the 3 m resolution daily image with global coverage. For comparison of topographic normalization techniques, seven widely used methods were employed on both pre-fire and post-fire images. The analysis on bi-temporal images suggests the optimal combination of techniques which can be applied on images with different land-cover composition. Then, the vegetation index was calculated from the images after the topographic normalization with the proposed method. The wildfire damage detection results were obtained by thresholding the index and showed improvementsin detection accuracy for both object-based and pixel-based image analysis. In addition, the burn severity map was constructed to verify the effects oftopographic correction on a continuous distribution of brightness values.

• Asian-Australasian Journal of Animal Sciences
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• v.33 no.12
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• pp.2021-2030
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• 2020

Objective: Quantitative polymerase chain reaction (qPCR) has been extensively used in the field of mesenchymal stem cell (MSC) research to elucidate their characteristics and clinical potential by normalization of target genes against reference genes (RGs), which are believed to be stably expressed irrespective of various experimental conditions. However, the expression of RGs is also variable depending on the experimental conditions, which may lead to false or contradictory conclusions upon normalization. Due to the current lack of information for a clear list of stable RGs in bovine MSCs, we conducted this study to identify suitable RGs in bovine MSCs. Methods: The cycle threshold values of ten traditionally used RGs (18S ribosomal RNA [18S], beta-2-microglobulin [B2M], H2A histone family, member Z [H2A], peptidylprolyl isomerase A [PPIA], ribosomal protein 4 [RPL4], succinate dehydrogenase complex, subunit A [SDHA], beta actin [ACTB], glyceraldehyde-3-phosphate dehydrogenase [GAPDH], TATA box binding protein [TBP], and hypoxanthine phosphoribosyltrasnfrase1 [HPRT1]) in bovine bone marrow-derived MSCs (bBMMSCs) were validated for their stabilities using three types of RG evaluation algorithms (geNorm, Normfinder, and Bestkeeper). The effect of validated RGs was then verified by normalization of lineage-specific genes (fatty acid binding protein 4 [FABP4] and osteonectin [ON]) expressions during differentiations of bBMMSCs or POU class 5 homeobox 1 (OCT4) expression between bBMMSCs and dermal skins. Results: Based on the results obtained for the three most stable RGs from geNorm (TBP, RPL4, and H2A), Normfinder (TBP, RPL4, and SDHA), and Bestkeeper (TBP, RPL4, and SDHA), it was comprehensively determined that TBP and RPL4 were the most stable RGs in bBMMSCs. However, traditional RGs were suggested to be the least stable (18S) or moderately stable (GAPDH and ACTB) in bBMMSCs. Normalization of FABP4 or ON against TBP, RPL4, and 18S presented significant differences during differentiation of bBMMSCs. However, although significantly low expression of OCT4 was detected in dermal skins compared to that in bBMMSCs when TBP and RPL4 were used in normalization, normalization against 18S exhibited no significance. Conclusion: This study proposes that TBP and RPL4 were suitable as stable RGs for qPCR study in bovine MSCs.

• The Journal of Korean Society for Radiation Therapy
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• v.14 no.1
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• pp.79-84
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• 2002

The main cause factor for effective the output, especially in small & irregular shaped field of electron beam therapy, are collimation system, insert block diameter and energy. In the absorption deose of treatment fields, we should consider the lateral build-up ratio (LBR), which the ratio of dose at a point at depth for a given circular field to the dose at the same point for a 'broad-field', for the same incident fluence and profile. The LBR data for a small circular field are used to extract radial spread of the pencil beam, ${\sigma}$, as a function of depth and energy. It's based on elementary pencil beam. We consider availability of the factor, ${\sigma}$, in the small & irregular fields electron beam treatment.

• Land and Housing Review
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• v.4 no.1
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• pp.125-131
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• 2013

To predict the real bearing capacity and settlement of the shallow foundation the plate load test results were used. But there is no field estimation method about igneous weathered soil and rock. Therefore, to predict the settlement equation, the plate load test about igneous weathered soil and rock was done in this study. To analyze the load ~ relative settlement curve by normalization, it did not use normal analysis method, but the load ~ relative settlement (s/B, s : settlement, B : breadth of plate) was used. As a result of normalization by load ~ relative settlement conception, the curve was regular regardless of plate diameter and it was suggested the relationship of in-situ soil condition and results.

• International Journal of Computer Science & Network Security
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• v.21 no.9
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• pp.51-62
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• 2021

Plant disease is one of the issues that can create losses in the production and economy of the agricultural sector. Early detection of this disease for finding solutions and treatments is still a challenge in the sustainable agriculture field. Currently, image processing techniques and machine learning methods have been applied to detect plant diseases successfully. However, the effectiveness of these methods still needs to be improved, especially in multiclass plant diseases classification. In this paper, a convolutional neural network with a batch normalization-based deep learning approach for classifying plant diseases is used to develop an automatic diagnostic assistance system for leaf diseases. The significance of using deep learning technology is to make the system be end-to-end, automatic, accurate, less expensive, and more convenient to detect plant diseases from their leaves. For evaluating the proposed model, an experiment is conducted on a public dataset contains 20654 images with 15 plant diseases. The experimental validation results on 20% of the dataset showed that the model is able to classify the 15 plant diseases labels with 96.4% testing accuracy and 0.168 testing loss. These results confirmed the applicability and effectiveness of the proposed model for the plant disease detection task.

• Journal of The Korea Institute of Healthcare Architecture
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• v.19 no.2
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• pp.41-49
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• 2013

Purpose: Economic and social pressures are driving Korea to reform its mental health services. However, it is not easy for the governments to find to the proper method for the mental health service network. This study is to find the mental health service network in Japan. Methods: The survey was conducted by researches and field studies. 1) Researches for mental health service network and facilities. 2) Field study is for Mental Health and Welfare Network in Tokyo. Results: The result of this study can be summarized into three points. The first one, Reform measures are beginning to promote the concept of "normalization" in japanese society. The second one, Facilities of Mental health and welfare system designed by level that can be providing places for people with mental problems. The third one, Facilities consist of barrier-free environment for people with mental problems.