• Journal of the Korean Electrochemical Society
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• v.25 no.1
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• pp.13-21
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• 2022

There is an increasing interest in monitoring of specific biomarker for determining progression of a disease or efficacy of a treatment. Conventional method for quantification of specific biomarkers as enzyme linked immunosorbent assay (ELISA) has high material costs, long incubation periods, requires large volume of samples and involves special instruments, which necessitates clinical samples to be sent to a lab. This paper reports on the development of an electrochemical biosensor to measure total immunoglobulin E (IgE), a marker of asthma disease that varies with age, gender, and disease in concentrations from 0.3-1000 ng/mL with consuming 20 µL volume of whole blood sample. The sensor provides rapid, accurate, easy, point-of-care measurement of IgE, also, sequential monitoring of total IgE with ovalbumin (OVA) induced mice is another application of sensor. Taken together, these results provide an alternative way for detection of biomarkers in whole blood with low volumes and long-term ex-vivo assessments for understanding the progression of a disease.

• ETRI Journal
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• v.44 no.2
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• pp.286-299
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• 2022

Human activity recognition in real time is a challenging task. Recently, a plethora of studies has been proposed using deep learning architectures. The implementation of these architectures requires the high computing power of the machine and a massive database. However, handcrafted features-based machine learning models need less computing power and very accurate where features are effectively extracted. In this study, we propose a handcrafted model based on three-dimensional sequential skeleton data. The human body skeleton movement over a frame is computed through joint positions in a frame. The joints of these skeletal frames are projected into two-dimensional space, forming a "movement polygon." These polygons are further transformed into a one-dimensional space by computing amplitudes at different angles from the centroid of polygons. The feature vector is formed by the sampling of these amplitudes at different angles. The performance of the algorithm is evaluated using a support vector machine on four public datasets: MSR Action3D, Berkeley MHAD, TST Fall Detection, and NTU-RGB+D, and the highest accuracies achieved on these datasets are 94.13%, 93.34%, 95.7%, and 86.8%, respectively. These accuracies are compared with similar state-of-the-art and show superior performance.

• Geomechanics and Engineering
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• v.34 no.1
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• pp.29-41
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• 2023

The 3D geospatial modeling of geotechnical information can aid in understanding the geotechnical characteristic values of the continuous subsurface at construction sites. In this study, a geostatistical optimization model for the three-dimensional (3D) mapping of subsurface stratification and the SPT-N value based on a trial-and-error rule was developed and applied to a dam emergency spillway site in South Korea. Geospatial database development for a geotechnical investigation, reconstitution of the target grid volume, and detection of outliers in the borehole dataset were implemented prior to the 3D modeling. For the site-specific subsurface stratification of the engineering geo-layer, we developed an integration method for the borehole and geophysical survey datasets based on the geostatistical optimization procedure of ordinary kriging and sequential Gaussian simulation (SGS) by comparing their cross-validation-based prediction residuals. We also developed an optimization technique based on SGS for estimating the 3D geometry of the SPT-N value. This method involves quantitatively testing the reliability of SGS and selecting the realizations with a high estimation accuracy. Boring tests were performed for validation, and the proposed method yielded more accurate prediction results and reproduced the spatial distribution of geotechnical information more effectively than the conventional geostatistical approach.

• The Journal of the Acoustical Society of Korea
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• v.29 no.2
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• pp.133-140
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• 2010

We improve the performance of cardiac disorder classification by adding new temporal features extracted from continuous heart sound signals. We add three kinds of novel temporal features to a conventional feature based on mel-frequency cepstral coefficients (MFCC): Heart sound envelope, murmur probabilities, and murmur amplitude variation. In cardiac disorder classification and detection experiments, we evaluate the contribution of the proposed features to classification accuracy and select proper temporal features using the sequential feature selection method. The selected features are shown to improve classification accuracy significantly and consistently for neural network-based pattern classifiers such as multi-layer perceptron (MLP), support vector machine (SVM), and extreme learning machine (ELM).

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.3C
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• pp.374-386
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• 2004

The mammogram provides the way to observe detailed internal organization of breasts to radiologists for the early detection. This paper is mainly focused on efficiently detecting the Microcalcification's Region Of Interest(ROI)s. Breast cancers can be caused from either microcalcifications or masses. Microcalcifications are appeared in a digital mammogram as tiny dots that have a little higher gray levels than their surrounding pixels. We can roughly determine the area which possibly contain microcalifications. In general, it is very challenging to find all the microcalcifications in a digital mammogram, because they are similar to some tissue parts of a breast. To efficiently detect microcalcifications ROI, we used four sequential processes; preprocessing for breast area detection, modified multilevel thresholding, ROI selection using simple thresholding filters and final ROI selection with two stages of neural networks. The filtering process with boundary conditions removes easily-distinguishable tissues while keeping all microcalcifications so that it cleans the thresholded mammogram images and speeds up the later processing by the average of 86％. The first neural network shows the average of 96.66％ recognition rate. The second neural network performs better by showing the average recognition rate 98.26％. By removing all tissues while keeping microcalcifications as much as possible, the next parts of a CAD system for detecting breast cancers can become much simpler.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.10
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• pp.1331-1340
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• 2020

Object detection in maritime means that the captain detects floating objects that has a risk of colliding with the ship using the computer automatically and as accurately as human eyes. In conventional ships, the presence and distance of objects are determined through radar waves. However, it cannot identify the shape and type. In contrast, with the development of AI, cameras help accurately identify obstacles on the sea route with excellent performance in detecting or recognizing objects. The computer must calculate high-volume pixels to analyze digital images. However, the CPU is specialized for sequential processing; the processing speed is very slow, and smooth service support or security is not guaranteed. Accordingly, this study developed maritime object detection software and implemented it with FPGA to accelerate the processing of large-scale computations. Additionally, the system implementation was improved through embedded boards and FPGA interface, achieving 30 times faster performance than the existing algorithm and a three-times faster entire system.

• Journal of Internet Computing and Services
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• v.11 no.6
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• pp.61-72
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• 2010

Spectrum scarcity problem and increasing spectrum demand for new wireless applications have embossed the importance of cognitive radio technology; the technology enables the sharing of channels among secondary (unlicensed) and primary (licensed) users on a non-interference basis after sensing the vacant channel. To enhance the accuracy of sensing, distributed spectrum sensing is proposed. However, it is necessary to provide the robustness against the compromised sensing nodes in the distributed spectrum sensing. RDSS, a fusion mechanism based on the reputation of sensing nodes and WSPRT (weighted sequential probability ratio test), was proposed. However, in RDSS, the execution number of WSPRT could increase according to the order of inputted sensing values, and the fast defense against the forged values is difficult. In this paper, we propose an enhanced fusion mechanism to input the sensing values in reputation order and exclude the sensing values with the high possibility to be compromised, using the trend of reputation variation. We evaluate our mechanism through simulation. The results show that our mechanism improves the robustness against attack with the smaller number of sensing values and more accurate detection ratio than RDSS.

• Applied Biological Chemistry
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• v.45 no.3
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• pp.152-156
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• 2002

Soils near abandoned zinc mines were known to be contaminated with arsenic-rich mining by-products. To examine the potential impacts of arsenic- contaminated soils on plant growth, surface soils were subjected to sequential extraction. Results revealed that 54% and 74% total As and 74% total extractable As were bound to iron hydrous oxide, and water soluble fraction was below detection limit. Arsenic faction extracted using the Koran standard method(dissolution of metals via treatment of 1 N HCI) was strongly correlated with the Fe-bound As fraction ($r^2=0.884**$). Arsenic level in rice plant roots was the highest with a maximum value of 154.9 mg/kg, whereas it was below 0.6 mg/kg in grains. Arsenic level in rice plant roots was strongly correlated with those of Al-bound As ($r^2=0.821**$) and 1N HCI-extractable As levels ($r^2=0.801**$).

• Journal of the Korean Chemical Society
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• v.41 no.5
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• pp.256-265
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• 1997

A rapid and sequential method was studied, which can determine nitrite, nitrate and ammonium ion in soil or water samples with flow injection analysis. Geometric factors including injection volume, length of the reaction coil and flow rate of carrier solution were investigated prior to sample measurement. Nitrite was determined at 540 nm by Griess reaction producing azo dye between N-(1-naphthylethylenediamine dihydrochloride) and sulfanilamide. Nitrate was also measured under the help of reduction mechanism toward nitrite with hydrazine. Ammonium was analyzed at 440 nm with Nessler's reagent. At the optimum condition, the detection limit(S/N=3) has been shown 0.1 ㎍/mL N(NO2-), 0.4 ㎍/mL N(NO3-) and 0.3 ㎍/mL N(NH4+) respectively. The results measured by colorimetry, ion chromatography and FIA were compared showing 80%-125% reasonable match each other. Injection throughput rate could be performed better than 30 times per hour.

• The Plant Pathology Journal
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• v.22 no.1
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• pp.21-27
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• 2006

We immunized BALB/c mice with purified Cucumber green mottle mosaic virus isolate HY1 (CGMMV-HY1). Through the selection of positive clones that were grown on the HAT medium, four sensitive monoclonal clones (CG99-01, CG99-02, CG99-03, and CG99-04) were selected from 500 Hypoxanthine-guanine phosphoribosyltransferase positive hybridoma cells. Four sensitive clones of CGMMV-HYI were determined as IgM type of the subclass of mouse immunoglobulins Ig group. The titer of monoclonal antiserum against CGMMVHY1 was estimated 1:12,800 by the indirect ELISA. Although monoclonal antibodies (MAbs) from CG99-01 and from CG99-04 cross-reacted with Zucchini green mottle mosaic virus and Kyuri green mottle mosaic virus, MAb from the cell line CG99-03 was highly specific to CGMMV. No MAbs cross-reacted with Cucumber mosaic virus-Fny. Only CG99-04 reacted with Pepper mild mottle virus weakly and CG99-02 reacted with both CGMMV and KGMMV. CGMMV was detected from the rind of watermelon fruit by DAS-ELISA of CGMMV-HY1, but not from the flesh of watermelon. Average seed transmission rate of CGMMV in watermelon was $24\%$ from symptomatic watermelon collected from 5 regions of Gyeongnam province. CGMMV was detected by DAS-ELISA with specific MAb of CGMMVHY1 periodically from root stock, during the sequential process for nursery seedling in Haman. Necrotic spots on cotyledons of root stock seedling progressed to reveal the typical symptomatology on the primary leaves of scion upon grafting. Here, we have established MAb based ELISA system, which could accurately detect CGMMV from watermelon seeds, nursery seedlings, transplants and field samples from greenhouse or open out door field as well.