• Proceedings of the KSR Conference
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• 1999.05a
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• pp.185-191
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• 1999

This paper deals with a method to predict the noise level inside metro electric cars running a single-line tunnel at the speed of 80km per hour using ray tracing method, a kind of ray acoustics generally used for a high-frequency and air-born noise analysis. The interior of the car including a under-frame, seats, side doors, end doors, door-pockets, side panels, end panel, a roof panel and so on is modeled. And in order to describe the noise power coming inside, artificial noise sources are designated using sound transmission loss data of each section measured from simple tests and external noise level. The noise level inside the car is calculated and its properties are investigated. The results satisfy the criteria on noise level inside the car.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.757-759
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• 1996

This paper proposes an application of artificial neural networks to the bus-bar separation in a substation for radial network operation. For the effective bus-bar operation, the insecurity index of transmission line load is introduced. For the radial network operation. the constraints of bus-bar switch is formulated in the performance function with the insecurity index. The determination of bus-bar switching is to find the states of 0 or 1 in the circuit breakers. In this paper, it is tested that the bus-bar separation of binary optimization problem can be solved by Hopfield networks with adequate manipulations.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1032-1032
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• 2001

Artificial Neural Network (ANN) calibration techniques have been used commercially for agricultural applications since the mid-nineties. Global models, based on transmission data from 850 to 1050 nm, are used routinely to measure protein and moisture in wheat and barley and also moisture in triticale, rye, and oats. These models are currently used commercially in approx. 15 countries throughout the world. Results concerning earlier European ANN models are being published elsewhere. Some of the findings from that study will be discussed here. ANN models have also been developed for coarsely ground samples of compound feed and feed ingredients, again measured in transmission mode from 850 to 1050 nm. The performance of models for pig- and poultry feed will be discussed briefly. These models were developed from a very large data set (more than 20,000 records), and cover a very broad range of finished products. The prediction curves are linear over the entire range for protein, fat moisture, fibre, and starch (measured only on poultry feed), and accuracy is in line with the performance of smaller models based on Partial Least Squares (PLS). A simple bias adjustment is sufficient for calibration transfer across instruments. Recently, we have investigated the possible use of ANN for a different type of NIR spectrometer, based on reflectance data from 1100 to 2500 nm. In one study, based on data for protein, fat, and moisture measured on unground compound feed samples, dedicated ANN models for specific product classes (cattle feed, pig feed, broiler feed, and layers feed) gave moderately better Standard Errors of Prediction (SEP) compared to modified PLS (MPLS). However, if the four product classes were combined into one general calibration model, the performance of the ANN model deteriorated only slightly compared to the class-specific models, while the SEP values for the MPLS predictions doubled. Brix value in molasses is a measure of sugar content. Even with a huge dataset, PLS models were not sufficiently accurate for commercial use. In contrast an ANN model based on the same data improved the accuracy considerably and straightened out non-linearity in the prediction plot. The work of Mr. David Funk (GIPSA, U. S. Department of Agriculture) who has studied the influence of various types of spectral distortions on ANN- and PLS models, thereby providing comparative information on the robustness of these models towards instrument differences, will be discussed. This study was based on data from different classes of North American wheat measured in transmission from 850 to 1050 nm. The distortions studied included the effect of absorbance offset pathlength variation, presence of stray light bandwidth, and wavelength stretch and offset (either individually or combined). It was shown that a global ANN model was much less sensitive to most perturbations than class-specific GIPSA PLS calibrations. It is concluded that ANN models based on large data sets offer substantial advantages over PLS models with respect to accuracy, range of materials that can be handled by a single calibration, stability, transferability, and sensitivity to perturbations.

• Journal of IKEEE
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• v.27 no.4
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• pp.601-609
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• 2023

In this paper, we propose an artificial intelligence-based high-speed fault diagnosis method using an FPGA in the event of a ground fault in a DC system. When applying artificial intelligence algorithms to fault diagnosis, a substantial amount of computation and real-time data processing are required. By employing an FPGA with AI-based high-speed fault diagnosis, the DC breaker can operate more rapidly, thereby reducing the breaking capacity of the DC breaker. therefore, in this paper, an intelligent high-speed diagnosis algorithm was implemented by collecting fault data through fault simulation of a DC system using Matlab/Simulink. Subsequently, the proposed intelligent high-speed fault diagnosis algorithm was applied to the FPGA, and performance verification was conducted.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.15 no.3
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• pp.74-82
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• 2001

To reduce the maintenance expense and the possibility of electric outage and/or accident, which causes the decrease in stability and reliability of transmission/distribution line, most of all, accurate measurement for the degree of contamination should be preceded. But the conventional method (brush wiping method), which is recommended in IEC 60815, has sow significant problem in the aspect of man power, expense, error, and so forth. In this investigation, we purpose the development of a new type automatic measuring apparatus, which could measure the degree of contamination on the surface of insulator in outdoor condition. To design and evaluate the apparatus, a FLUX 2D is used, and various laboratory tests, artificial contamination tests, were carried to proof the actual performance. With the result of these effort, we can get the meaningful conclusion to develope a new type automatic apparatus for contamination measurement.

• Computers and Concrete
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• v.33 no.5
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• pp.621-630
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• 2024

This study presents novel electromagnetic wave-based methods for evaluating the integrity of cement-based structures using time domain reflectometry (TDR). Two cement-based plates with embedded rebars are prepared under sound and defective conditions. TDR tests are carried out using transmission lines with various numbers of artificial joints, and electromagnetic waves are measured to assess the integrity of the plates. The experimental results show that the travel time of electromagnetic waves is consistently longer in sound plates than in defective ones, and an increase in the reflection coefficients is observed in the defect zone of the defective plates. Electromagnetic wave velocities are higher in the defective plates, especially when connectors are present in the transmission line. A novel approach based on the area of the reflection coefficient provides larger areas in the defective plates, and the attenuation effect of the electromagnetic waves induces a difference in the areas of the reflection coefficient between the two defect conditions. An alternative method using the centroid of the defect zone slightly overestimates the location of the defect zone. The length of the defect zone is estimated using the defect ratio and wave velocities of cement, air, and plate. The length of the defect zone can also be calculated using the travel times within the plate, total measured length of the plate, and wave velocities in the cement and air. Therefore, the electromagnetic wave-based methods proposed in this study may be useful for estimating the location and length of defect zones by considering attenuation effects.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.233-247
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• 2023

Recently, research on detecting objects in hidden spaces beyond the direct line-of-sight of observers has received attention. Most studies use optical equipment that utilizes the directional of light, but sound that has both diffraction and directional is also suitable for non-line-of-sight(NLOS) research. In this paper, we propose a novel method of detecting objects in non-line-of-sight (NLOS) areas using acoustic signals in the audible frequency range. We developed a deep learning model that extracts information from the NLOS area by inputting only acoustic signals and predicts the properties and location of hidden objects. Additionally, for the training and evaluation of the deep learning model, we collected data by varying the signal transmission and reception location for a total of 11 objects. We show that the deep learning model demonstrates outstanding performance in detecting objects in the NLOS area using acoustic signals. We observed that the performance decreases as the distance between the signal collection location and the reflecting wall, and the performance improves through the combination of signals collected from multiple locations. Finally, we propose the optimal conditions for detecting objects in the NLOS area using acoustic signals.

• Journal of Ginseng Research
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• v.43 no.2
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• pp.261-271
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• 2019

Background: Protopanaxatriol (PPT) is an aglycone of ginsenosides, which has high medicinal values. Production of PPT from natural ginseng plants requires artificial deglycosylation procedures of ginsenosides via enzymatic or physicochemical treatments. Metabolic engineering could be an efficient technology for production of ginsenoside sapogenin. For PPT biosynthesis in Panax ginseng, damarenediol-II synthase (PgDDS) and two cytochrome P450 enzymes (CYP716A47 and CYP716A53v2) are essentially required. Methods: Transgenic tobacco co-overexpressing P. ginseng PgDDS, CYP716A47, and CYP716A53v2 was constructed via Agrobacterium-mediated transformation. Results: Expression of the three introduced genes in transgenic tobacco lines was confirmed by Reverse transcription-polymerase chain reaction (RT-PCR). Analysis of liquid chromatography showed three new peaks, dammarenediol-II (DD), protopanaxadiol (PPD), and PPT, in leaves of transgenic tobacco. Transgenic tobacco (line 6) contained $2.8{\mu}g/g$ dry weight (DW), $7.3{\mu}g/g$ DW, and $11.6{\mu}g/g$ DW of PPT, PPD, and DD in leaves, respectively. Production of PPT was achieved via cell suspension culture and was highly affected by auxin treatment. The content of PPT in cell suspension was increased 37.25-fold compared with that of leaves of the transgenic tobacco. Transgenic tobacco was not able to set seeds because of microspore degeneration in anthers. Transmission electron microscopy analysis revealed that cells of phloem tissue situated in the center of the anther showed an abnormally condensed nuclei and degenerated mitochondria. Conclusion: We successfully achieved the production of PPT in transgenic tobacco. The possible factors deriving male sterility in transgenic tobacco are discussed.

• Journal of the Korean Society for Nondestructive Testing
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• v.20 no.6
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• pp.538-544
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• 2000

A phased array ultrasonic nondestructive inspection system is being developed to obtain images of the interior of steel structures by modifying a medical ultrasound imaging system. The medical system consists of 64 individual transceiver channels that can drive 128 array elements. Several modifications of the system were required mainly due to the change of sound speed. It was necessary to fabricate array transducers for steel structure and to obtain A-scan signal that is necessary for the nondestructive testing. Boundary diffraction wave model was used for the prediction of radiation beam field from array transducers, which provided guidelines to design array transducers. And a RF data acquisition board was fabricated for the A-scan signal acquisition along a selected un line within an image. For the proper beam forming in the transmission and reception for steel structure, delay time was controlled. To demonstrate the performance of the developed system and fabricated transducers, images of artificial specimens and A-scan signals for selected scan lines were obtained in a real time fashion.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.9
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• pp.317-322
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• 2022

The rapid development of information technology is also bringing about many changes in the medical environment. In particular, it is leading the rapid change of medical image information systems using big data and artificial intelligence (AI). The prescription delivery system (OCS), which consists of an electronic medical record (EMR) and a medical image storage and transmission system (PACS), has rapidly changed the medical environment from analog to digital. When combined with multiple solutions, PACS represents a new direction for advancement in security, interoperability, efficiency and automation. Among them, the combination with artificial intelligence (AI) using big data that can improve the quality of images is actively progressing. In particular, AI PACS, a system that can assist in reading medical images using deep learning technology, was developed in cooperation with universities and industries and is being used in hospitals. As such, in line with the rapid changes in the medical image information system in the medical environment, structural changes in the medical market and changes in medical policies to cope with them are also necessary. On the other hand, medical image information is based on a digital medical image transmission device (DICOM) format method, and is divided into a tomographic volume image, a volume image, and a cross-sectional image, a two-dimensional image, according to a generation method. In addition, recently, many medical institutions are rushing to introduce the next-generation integrated medical information system by promoting smart hospital services. The next-generation integrated medical information system is built as a solution that integrates EMR, electronic consent, big data, AI, precision medicine, and interworking with external institutions. It aims to realize research. Korea's medical image information system is at a world-class level thanks to advanced IT technology and government policies. In particular, the PACS solution is the only field exporting medical information technology to the world. In this study, along with the analysis of the medical image information system using big data, the current trend was grasped based on the historical background of the introduction of the medical image information system in Korea, and the future development direction was predicted. In the future, based on DICOM big data accumulated over 20 years, we plan to conduct research that can increase the image read rate by using AI and deep learning algorithms.