• Journal of Aquaculture
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• v.16 no.1
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• pp.1-7
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• 2003

A new technique combining forced-feeding and dorsal aorta cannulation was developed to monitor concentration of nutritions in the blood circulation and their metabolites in rainbow trout. To study the effect of dorsal aorta cannulation on stress, 30 rainbow trout (523$\pm$5.4 g; Mean$\pm$SD) were divided into 6 groups of 5 individuals each. A group was anesthetized and blood samples were taken at 0, 3, 6, 12, 24 or 48 h after dorsal aorta cannulation. Hematocrit peaked at 6 h and returned to 0 values by 12 h after dorsal aorta cannulation. Plasma cortisol and glucose concentrations also peaked at 6 h and returned to 0 values by 48 h after dorsal aorta cannulation. Based on the plasma cortisol and glucose concentrations, the rainbow trout recovered from the operation of dorsal aorta cannulation within 48 h. To compare the patterns of plasma free amino acid concentrations after force-feeding in the fish with dorsal aorta cannulation, 5 dorsal aorta cannulated individuals (511$\pm$6.2 g) were kept in a cage. After 48 h starvation, they were anesthetized and blood samples were taken at 0, 4, 8, 12, 24, 36 or 48 h after forced-feeding. The concentration of all plasma free amino acids, except isoleucine, leucine, phenylalanine, and tryptophan, also peaked at 4 h and returned to 0 values by 24 h after feeding. The combined technique allows forced-feeding and repeated sampling of blood in rainbow trout with minimum stress.

• Applied Microscopy
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• v.2 no.1
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• pp.39-46
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• 1972

There are many kinds of microscopes suitable for general studies; optical microscopes(OM), conventional transmission electron microscopes (TEM), and scanning electron microscopes(SEM). The optical microscopes and the conventional transmission electron microscopes are very familiar. The images of these microscopes are directly formed on an image plane with one or more image forming lenses. On the other hand, the image of the scanning electron microscope is formed on a fluorescent screen of a cathode ray tube using a scanning system similar to television technique. In this paper, the features and some applications of the scanning electron microscope will be discussed briefly. The recently available scanning electron microscope, combining a resolution of about $200{\AA}$ with great depth of field, is favorable when compared to the replica technique. It avoids the problem of specimen damage and the introduction of artifacts. In addition, it permits the examination of many samples that can not be replicated, and provides a broader range of information. The scanning electron microscope has found application in diverse fields of study including biology, chemistry, materials science, semiconductor technology, and many others. In scanning electron microscopy, the secondary electron method. the backscattererd electron method, and the electromotive force method are most widely used, and the transmitted electron method will become more useful. Change-over of magnification can be easily done by controlling the scanning width of the electron probe. It is possible. to continuously vary the magnification over the range from 100 times to 1.00,000 times without readjustment of focusing. Conclusion: With the development of a scanning. electron microscope, it is now possible to observe almost all-information produced through interactions between substances and electrons in the form of image. When the probe is properly focused on the specimen, changing magnification of specimen orientation does not require any change in focus. This is quite different from the conventional transmission electron microscope. It is worthwhile to note that the typical probe currents of $10^{-10}$ to $10^{-12}\;{\AA}$ are for below the $10^{-5}$ to $10^{-7}\;{\AA}$ of a conventional. transmission microscope. This reduces specimen contamination and specimen damage due to heatings. Outstanding features of the scanning electron microscope include the 'stereoscopic observation of a bulky or fiber specimen in high resolution' and 'observation of potential distribution and electromotive force in semiconductor devices'.

• 한국지구물리탐사학회:학술대회논문집
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• 2008.10a
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• pp.173-178
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• 2008

In this paper, we will introduce rock physics modeling technique, which interrelate reservoir properties with seismic properties, and apply the technique to the Donghae-1 gas reservoir. From well-log data analysis, we obtained velocityporosity (Vp-$\phi$) relations for each formation. These relations can used to predict porosity from seismic data. In addition, we analyzed permeability data, which were obtained from core measurements and computational rock physics simulations. We then obtained permeability-porosity ($\kappa-\phi$) relations. Combining $\kappa-\phi$ with Vp-$\phi$ relations, we finally present quantitative Vp-$\kappa$ relations. As to Vp-$\phi$ modeling, we found that the degree of diagenesis and clay contents increase with depth. As to Vp-$\kappa$ relations, though \kappa-\phi relations are almost identical for all formations, we could obtain distinct Vp-$\kappa$ relations due to Vp-$\phi$ variations. In conclusion, the rock physics modeling, which bridges between seismic properties and reservoir properties, can be a very robust tool for quantitative reservoir characterization with less uncertainty.

• The Journal of the Acoustical Society of Korea
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• v.33 no.2
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• pp.118-125
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• 2014

An approximation of speed of sound in the measurement plane is essential for the inverse estimation of temperature. To this end, an inverse problem relating the measured retarded time data in between set of sensors and actuators array located on the wall is formulated. The involved transfer matrix and its coefficient vectors approximate speed of sound of the measurement plane by using the radial basis function with finite number of interpolation points deployed inside the target field. Then, the temperature field can be reconstructed by using spatial interpolation technique, which can achieve high spatial resolution with proper number of interpolation points. A large number of retarded time data of acoustic paths in between sensors and arrays are needed to obtain accurate reconstruction result. However, the shortage of interpolation points due to practical limitations can cause the decrease of spatial resolution and deterioration of the reconstruction result. In this works, a regeneration for obtaining the additional retarded time data for an arbitrary acoustic path is suggested to overcome the shortage of interpolation points. By applying the regeneration technique, many interpolation points can be deployed inside the field by increasing the number of retarded time data. As a simulation example, two rectangular duct sections having arbitrary temperature distribution are reconstructed by two different data set: measured data only, combination of measured and regenerated data. The result shows a decrease in reconstruction error by 15 % by combining the original and regenerated retarded time data.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.33 no.2
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• pp.129-136
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• 2020

In this study, we develop a sampling-based seismic probabilistic risk assessment (SPRA) quantification technique that can accurately consider a partially dependent condition of component seismic fragility information. Specifically, the SPRA quantification method is proposed by combining the advantages of two representative methodologies: EPRI seismic fragility and JAERI seismic fragility input-based quantification. The most important feature of the proposed method is that it performs a SPRA using a sampling technique by transforming the EPRI seismic fragility input into JAERI seismic fragility input. When the proposed sampling-based approach was applied to an example of simple system and to a SPRA problem of a nuclear power plant, it was observed that the proposed method yields approximately similar system seismic fragility and seismic risk results as those of the exact solution. Therefore, it is believed that the approach proposed in this study can be used as a useful tool for accurately assessing seismic risks, considering the partial seismic dependence among the components; the existing SPRA method cannot handle such partial dependencies.

• Journal of Ginseng Research
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• v.41 no.3
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• pp.353-360
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• 2017

Background: Endophytic fungi play an important role in balancing the ecosystem and boosting host growth. In the present study, we investigated the endophytic fungal diversity of healthy Panax notoginseng and evaluated its potential antimicrobial activity against five major phytopathogens causing root-rot of P. notoginseng. Methods: A culture-dependent technique, combining morphological and molecular methods, was used to analyze endophytic fungal diversity. A double-layer agar technique was used to challenge the phytopathogens of P. notoginseng. Results: A total of 89 fungi were obtained from the roots, stems, leaves, and seeds of P. notoginseng, and 41 isolates representing different morphotypes were selected for taxonomic characterization. The fungal isolates belonged to Ascomycota (96.6%) and Zygomycota (3.4%). All isolates were classified to 23 genera and an unknown taxon belonging to Sordariomycetes. The number of isolates obtained from different tissues ranged from 12 to 42 for leaves and roots, respectively. The selected endophytic fungal isolates were challenged by the root-rot pathogens Alternaria panax, Fusarium oxysporum, Fusarium solani, Phoma herbarum, and Mycocentrospora acerina. Twenty-six of the 41 isolates (63.4%) exhibited activity against at least one of the pathogens tested. Conclusion: Our results suggested that P. notoginseng harbors diversified endophytic fungi that would provide a basis for the identification of new bioactive compounds, and for effective biocontrol of notoginseng root rot.

• International Journal of Knowledge Content Development & Technology
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• v.10 no.3
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• pp.33-50
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• 2020

Knowledge recipes are packages of knowledge which arise from the process of combining the knowledge assets in the organization in distinctive ways. This involves converting them into useful outputs which are the ideal core competitive advantage enablers for companies. The major objective of this study was to propose a knowledge recipe for financial-sector state corporations in Kenya. The study adopted a convergent parallel mixed methods research design. Quantitative and qualitative data were collected using questionnaires and key informant interviews. The target population of the study was 1574 respondents drawn from all financial state corporations. A multistage sampling technique was used for the study. The first phase involved purposive sampling of the organizations to be studied whereby the four state corporations namely: Capital Markets Authority, Competition Authority of Kenya, Kenya Investment Authority, and Kenya Revenue Authority were identified. The second phase entailed stratified sampling of the respondents in three strata namely senior management team, knowledge management team, and general staff. The authors used a census of all senior management team and knowledge management staff while a simple random sampling technique was used for the general staff. By use of the Krejcie and Morgan table, the actual sample size was 358 respondents from all the four organizations. Data were collected using questionnaires and interview schedules. The qualitative data were analyzed using content analysis while the quantitative data were analyzed by the use of Ms. Excel and VOSviewer and presented using pie charts, bar graphs, and tables. The response rate for this study was 257 (72%). The study revealed that while most employees in the financial sector organizations understand their knowledge needs, knowledge types, knowledge uses and knowledge gaps, they do not have a universal knowledge recipe to facilitate effective knowledge management in their organizations. Consequently, the authors propose a universal knowledge recipe for the state corporations in the financial sector in Kenya. The ingredients of the recipe are legal-knowledge (18%), financial knowledge (15%), administrative knowledge (11%), best practice (10%), lessons learnt (8%), human resource knowledge (8%), research and statistics knowledge (7%), product knowledge (6%), policy and procedure knowledge (5%), ICT knowledge (4%), investor knowledge (3%), markets knowledge (2%), general knowledge (2%) and regulatory framework knowledge (1%).

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.164-175
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• 2019

The Tor Tong Daeng Irrigation Project with the irrigation area of 61,400 hectares is located in the Ping Basin of the Upper Central Plain of Thailand where farmers depended on both surface water and groundwater. In the drought year, water storage in the Bhumipol Dam is inadequate to allocate water for agriculture, and caused water deficit in many irrigation projects. Farmers need to find extra sources of water such as water from farm pond or groundwater as a supplement. The operation of Bhumipol Dam and irrigation demand estimation are vital for irrigation water allocation to help solve water shortage issue in the irrigation project. The study aims to determine the smart dam operation system to mitigate water shortage in this irrigation project via introduction of machine learning to improve dam operation and irrigation demand estimation via soil moisture estimation from satellite images. Via ANN technique application, the inflows to the dam are generated from the upstream rain gauge stations using past 10 years daily rainfall data. The input vectors for ANN model are identified base on regression and principal component analysis. The structure of ANN (length of training data, the type of activation functions, the number of hidden nodes and training methods) is determined from the statistics performance between measurements and ANN outputs. On the other hands, the irrigation demand will be estimated by using satellite images, LANDSAT. The Enhanced Vegetation Index (EVI) and Temperature Vegetation Dryness Index (TVDI) values are estimated from the plant growth stage and soil moisture. The values are calibrated and verified with the field plant growth stages and soil moisture data in the year 2017-2018. The irrigation demand in the irrigation project is then estimated from the plant growth stage and soil moisture in the area. With the estimated dam inflow and irrigation demand, the dam operation will manage the water release in the better manner compared with the past operational data. The results show how smart system concept was applied and improve dam operation by using inflow estimation from ANN technique combining with irrigation demand estimation from satellite images when compared with the past operation data which is an initial step to develop the smart dam operation system in Thailand.

• Korean Journal of Remote Sensing
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• v.37 no.3
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• pp.367-378
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• 2021

Field compost is a representative non-point pollution source for livestock. If the field compost flows into the water system due to rainfall, nutrients such as phosphorus and nitrogen contained in the field compost can adversely affect the water quality of the river. In this paper, we propose a method for detecting field compost using unmanned aerial vehicle images and deep learning-based semantic segmentation. Based on 39 ortho images acquired in the study area, about 30,000 data were obtained through data augmentation. Then, the accuracy was evaluated by applying the semantic segmentation algorithm developed based on U-net and the filtering technique of Open CV. As a result of the accuracy evaluation, the pixel accuracy was 99.97%, the precision was 83.80%, the recall rate was 60.95%, and the F1-Score was 70.57%. The low recall compared to precision is due to the underestimation of compost pixels when there is a small proportion of compost pixels at the edges of the image. After, It seems that accuracy can be improved by combining additional data sets with additional bands other than the RGB band.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.1
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• pp.51-58
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• 2023

In this paper, we propose an organ segmentation technique for the automatic extraction of medical diagnostic indicators from X-ray images. In order to calculate diagnostic indicators of heart disease and spinal disease such as VHS(vertebral heart scale) and Cobb angle, it is necessary to accurately segment the thoracic spine, carina, and heart in a chest X-ray image. A deep neural network model in which the high-resolution representation of the image for each layer and the structure converted into a low-resolution feature map are connected in parallel was adopted. This structure enables the relative position information in the image to be effectively reflected in the segmentation process. It is shown that learning performance can be improved by combining the OCR module, in which pixel information and object information are mutually interacted in a multi-step process, and the channel attention module, which allows each channel of the network to be reflected as different weight values. In addition, a method of augmenting learning data is presented in order to provide robust performance against changes in the position, shape, and size of the subject in the X-ray image. The effectiveness of the proposed theory was evaluated through an experiment using 145 human chest X-ray images and 118 animal X-ray images.