• Journal of KIISE
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• v.42 no.10
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• pp.1239-1246
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• 2015

Cell counting is extensively used to analyze cell growth in biomedical research, and as a result automated cell counting methods have been developed to provide a more convenient and means to analyze cell growth. However, there are still many challenges to improving the accuracy of the cell counting for cells that proliferate abnormally, divide rapidly, and cluster easily, such as cancer cells. In this paper, we present an automated cell counting method for HeLa cells, which are used as reference for cancer research. We recognize and classify the morphological conditions of the cells by using a cell segmentation algorithm based on cell membrane extraction, and we then apply a cell back-tracking algorithm to improve the cell counting accuracy in cell clusters that have indistinct cell boundary lines. The experimental results indicate that our proposed segmentation method can identify each of the cells more accurately when compared to existing methods and, consequently, can improve the cell counting accuracy.

• Proceedings of the KIEE Conference
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• 1992.07b
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• pp.820-824
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• 1992

An accurate method for extracting both Si film doping concentration and front or back silicon-to-oxide fixed charge density of fully depleted SOI devices is proposed. The method utilizes the current-to-voltage and capacitance-to-voltage characteristics of both SOI NMOSFET and PMOSFET which have the same doping concentration. The Si film doping concentration and the front or back silicon-to-oxide fixed charge density are extracted by mainpulating the respective threshold voltages of the SOI NMOSFET and PMOSFET according to the back surface condition (accumulation or inversion) and the capacitance-to-voltage characteristics of the SOI PMOSFET. Device simulations show that the proposed method has less than 10% errors for wide variations of the film doping concentration and the front or the back silicon-to-oxide fixed charge density.

• Nuclear Engineering and Technology
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• v.51 no.4
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• pp.1163-1168
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• 2019

Hydrodynamic properties have been investigated for promising extraction systems: $0.05mol\;L^{-1}$ solutions of di(N-ethyl-4-ethylanilide) of 2,2'-bipyridine-6,6'-dicarboxylic acid, di(N-ethyl-4-fluoroanilide) of 2,6-pyridinedicarboxylic acid and di(N-ethyl-4-hexylanilide) of 2,2'-bipyridine-6,6'-dicarboxylic acid in meta-nitrobenzotrifluoride (F-3) or trifluoromethylphenyl sulfone (FS-13) diluents. To evaluate the perspectives for their use as extraction mixtures at the final stage of the nuclear fuel cycle, the change in density, viscosity, surface tension, and phase separation rate under irradiation with accelerated electrons was studied. The concentrations of extractants in the irradiated mixtures have been determined and the radiation-chemical yields have been calculated. Irradiation significantly decreases the phase separation rate at the stages of extraction and back extraction for all the studied systems. The viscosity of the DYP-7 solution in FS-13 increase above the values suitable for its use in extraction processes.

• Korean Journal of Remote Sensing
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• v.39 no.2
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• pp.157-168
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• 2023

Image segmentation and supervised classification techniques are widely used to monitor the ground surface using high-resolution remote sensing images. In order to classify various objects, a process of defining a class corresponding to each object and selecting samples belonging to each class is required. Existing methods for extracting class samples should select a sufficient number of samples having similar intensity characteristics for each class. This process depends on the user's visual identification and takes a lot of time. Representative samples of the class extracted are likely to vary depending on the user, and as a result, the classification performance is greatly affected by the class sample extraction result. In this study, we propose an image classification technique that minimizes user intervention when extracting class samples by applying the histogram back-projection technique and has consistent intensity characteristics of samples belonging to classes. The proposed classification technique using histogram back-projection showed improved classification accuracy in both the experiment using hue subchannels of the hue saturation value transformed image from Compact Advanced Satellite 500-1 imagery and the experiment using the original image compared to the technique that did not use histogram back-projection.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.1
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• pp.33-45
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• 2011

In this paper, we present a method for extracting video objects efficiently by using the modified graph cut algorithm based on contour information. First, we extract objects at the first frame by an automatic object extraction algorithm or the user interaction. To estimate the objects' contours at the current frame, motion information of objects' contour in the previous frame is analyzed. Block-based histogram back-projection is conducted along the estimated contour point. Each color model of objects and background can be generated from back-projection images. The probabilities of links between neighboring pixels are decided by the logarithmic based distance transform map obtained from the estimated contour image. Energy of the graph is defined by predefined color models and logarithmic distance transform map. Finally, the object is extracted by minimizing the energy. Experimental results of various test images show that our algorithm works more accurately than other methods.

• KSBB Journal
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• v.8 no.1
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• pp.83-88
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• 1993

Lipase was separated using reverse mlcelles in a spray column. The 50 mM AOT-Isooctane solution was used as reverse micellar solution for the extraction of lipase (crude containing 25% Protein). Ionic strength was controlled by KCl(0.1M KCl for extraction, 0.5M KCl for back exlractlon). Acetate buffer and phosphate buffer were used for control of pH. The efficiencies of extraction and stripping were 30% and 50%. An increase of circulation did not change the efficiency of extraction in forward extraction. The optimum flow rate was around 0.10ml/sec.

• Journal of Radiation Industry
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• v.5 no.2
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• pp.101-106
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• 2011

The separation of germanium and gallium ion with metal oxide was introduced into the development of $^{68}Ge/^{68}Ga$ generator. Germanium and gallium within mixed solution were respectively separated by using a liquid-liquid extraction and a column chromatographic method. The separation of Ge within high concentrated hydrochloric and sulfuric acid was conducted by the extraction to $CCl_4$ and the back-extraction to 0.05 M HCl. An optimum condition of the extraction by $CCl_4$ was in 5~7 M HCl and efficiency was around 80%. The gallium was selectively separated by using $Al_2O_3$ among metal oxides as sorbents from the mixed solution in 0.04~0.10 M HCl condition.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2000.11c
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• pp.715-721
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• 2000

In this research, rule and neuro net based boundary extraction algorithm was developed. Extracting boundary of the interest, lean tissue, is essential for the quality evaluation of the beef based on color machine vision. Major quality features of the beef are size, marveling state of the lean tissue, color of the fat, and thickness of back fat. To evaluate the beef quality, extracting of loin parts from the sectional image of beef rib is crucial and the first step. Since its boundary is not clear and very difficult to trace, neural network model was developed to isolate loin parts from the entire image input. At the stage of training network, normalized color image data was used. Model reference of boundary was determined by binary feature extraction algorithm using R(red) channel. And 100 sub-images(selected from maximum extended boundary rectangle 11${\times}$11 masks) were used as training data set. Each mask has information on the curvature of boundary. The basic rule in boundary extraction is the adaptation of the known curvature of the boundary. The structured model reference and neural net based boundary extraction algorithm was developed and implemented to the beef image and results were analyzed.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.6
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• pp.67-75
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• 1993

ECG pattern was classified using a back-propagation neural network. An improved feature extractor of ECG is proposed for better classification capability. It is consisted of preprocessing ECG signal by an FIR filter faster than conventional one by a factor of 5. QRS complex recognition by moving-window integration, and peak extraction by quadratic approximation. Since the FIR filter had a periodic frequency spectrum, only one-fifth of usual processing time was required. Also, segmentation of ECG signal followed by quadratic approximation of each segment enabled accurate detection of both P and T waves. When improtant features were extracted and fed into back-propagation neural network for pattern classification, the required number of nodes in hidden and input layers was reduced compared to using raw data as an input, also reducing the necessary time for study. Accurate pattern classification was possible by an appropriate feature selection.

• Proceedings of the PSK Conference
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• 2001.10a
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• pp.174.2-175
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• 2001