• Journal of the Korean Society for Heat Treatment
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• v.26 no.4
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• pp.185-190
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• 2013

Influence of solution treatment (T4) and peak-aging (T6) on damping capacity was investigated in permanent-mold cast Mg-3%Nd alloy. In as-cast state, the microstructure was characterized by eutectic $Mg_{12}Nd$ intermetallic phase network in the intergranular region. T4 treatment resulted in a dissolution of the eutectic particles, but small amount of the particles still remained in the microstructure. After T6 treatment, nano-sized ${\beta}^{\prime}(Mg_{12}Nd)$ particles were precipitated within the matrix. T4 microstructure showed higher damping capacity than as-cast and T6 ones. In view of the microstructural features, this may well be associated with the dissolution of second-phase particles which play a role in pinning the dislocations acting as a damping source.

• Journal of IKEEE
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• v.26 no.4
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• pp.545-552
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• 2022

The number of patent application filing for a specific technology has a good relation with the technology's life cycle and future industry development on that area. So industry and governments are highly interested in forecasting the number of patent application filing in order to take appropriate preparations in advance. In this paper, a new method based on the bidirectional long short-term memory(LSTM), a kind of recurrent neural network(RNN), is proposed to improve the forecasting accuracy compared to related methods. Compared with the Bass model which is one of conventional diffusion modeling methods, the proposed method shows the 16% higher performance with the Korean patent filing data on the five selected technology areas.

• Journal of the Korea Society of Computer and Information
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• v.23 no.7
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• pp.19-26
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• 2018

This paper presents a structuring process of unstructured social network service (SNS) messages on rail services. We crawl messages about rail services posted on SNS and extract keywords indicating date and time, rail operating company, station name, direction, and rail service types from each message. Among them, the rail service types are classified by machine learning according to predefined rail service types, and the rest are extracted by regular expressions. Words are converted into vector representations using Word2Vec and a conventional Convolutional Neural Network (CNN) is used for training and classification. For performance measurement, our experimental results show a comparison with a TF-IDF and Support Vector Machine (SVM) approach. This structured information in the database and can be easily used for services for railway users.

• Membrane Journal
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• v.26 no.1
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• pp.26-31
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• 2016

Polycaprolactone (PCL) has been fabricated into the membrane type scaffolds of 3 dimensional pore network for the tissue engineering applications by the blade method of salt (NaCl) leaching and solution casting. In this study, the experimental designs have each conditions of drying temperature, salt particle size, salt content. The modified dispensing pump connected up to homogenizing mixer system is used for mixing the $PCL/CHCl_3$ solution and NaCl particles. The membrane fabricated use by the film applicator to poured mixed solution on the glass plate. The great pore by NaCl particles and the small pore by the evaporated $CHCl_3$ in the frame wall of great pores are multiply formed in membrane scaffolds.

• KIEE International Transactions on Power Engineering
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• v.11A no.4
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• pp.21-26
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• 2001

As competition is introduced in the electricity supply industry, congestion becomes a more important issue. Congestion in a transmission network occurs due to an operating condition that causes limit violations on the transmission capacities. Congestion leads to inefficient use of the system, or causes additional costs (Congestion cost). One way to reduce this inefficiency or congestion cost is to control the transmission flow through the installation of UPFC (Unified Power Flow Controller). This paper also deals with an optimal siting of the UPFC for reducing congestion cost by using shadow prices. A performance index for an optimal siting is defined as a combination of line flow sensitivities and shadow prices. The proposed algorithm is applied to the sample system with a condition, which is concerning the quadratic cost functions. Test results show that the siting of the UPFC is optimal to minimize the congestion cost by the proposed algorithm.

• Korean Journal of Remote Sensing
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• v.36 no.2_1
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• pp.199-215
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• 2020

A seagrass that is marine vascular plants plays an important role in the marine ecosystem, so periodic monitoring ofseagrass habitatsis being performed. Recently, the use of dronesthat can easily acquire very high-resolution imagery is increasing to efficiently monitor seagrass habitats. And deep learning based on a convolutional neural network has shown excellent performance in semantic segmentation. So, studies applied to deep learning models have been actively conducted in remote sensing. However, the segmentation accuracy was different due to the hyperparameter, various deep learning models and imagery. And the normalization of the image and the tile and batch size are also not standardized. So,seagrass habitats were segmented from drone-borne imagery using a deep learning that shows excellent performance in this study. And it compared and analyzed the results focused on normalization and tile size. For comparison of the results according to the normalization, tile and batch size, a grayscale image and grayscale imagery converted to Z-score and Min-Max normalization methods were used. And the tile size isincreased at a specific interval while the batch size is allowed the memory size to be used as much as possible. As a result, IoU was 0.26 ~ 0.4 higher than that of Z-score normalized imagery than other imagery. Also, it wasfound that the difference to 0.09 depending on the tile and batch size. The results were different according to the normalization, tile and batch. Therefore, this experiment found that these factors should have a suitable decision process.

• Journal of the Korean Society for Precision Engineering
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• v.26 no.11
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• pp.99-107
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• 2009

Since a long work piece influences the natural frequency of the entire system with a miniature high speed spindle, a bar-feeder is used for a long work piece to improve the vibration characteristics of a spindle system. Therefore, it is very important to design optimally support positions between a bar-feeder and a long work piece for a miniature high speed spindle system. The goal of the current paper is to present an optimization method for the design of support positions between a bar-feeder and a long work piece. This optimization method is effectively composed of the method of design of experiment (DOE), the artificial neural network (ANN) and the genetic algorithm (GA). First, finite element models which include a high speed spindle, a long work piece and the support conditions of a bar-feeder were generated from the orthogonal array of the DOE method, and then the results of natural vibration analysis using FEM were provided for the learning inputs of the neural network. Finally, the design of bar-feeder support positions was optimized by the genetic algorithm method using the neural network approximations.

• Ocean and Polar Research
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• v.35 no.1
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• pp.15-26
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• 2013

The toxic, Harmful Algal Blooms (HABs) caused by the Cochlodinium polykrikoides have a serious impact on the coastal waters of Korea. In this study, the acoustic detection system was developed for rapid HABs detection, based on the acoustic backscattering properties of the C. polykrikoides. The developed system was mainly composed of a pulser-receiver board, a signal processor board, a control board, a network board, a power board, ultrasonic sensors (3.5 and 5.0 MHz), an environmental sensor, GPS, and a land-based control unit. To evaluate the performance of the system, a trail was done at a laboratory, and two in situ trials were conducted: (1) when there was no red tide, and (2) when there was red tide. In the laboratory evaluation, the system performed well in accordance with the number of C. polykrikoides in the received level. Second, under the condition when there was no red tide in the field, there was a good correlation between the acoustic data and sampling data. Finally, under the condition when there was red tide in the field, the system successfully worked at various densities in accordance with the number of C. polykrikoides, and the results corresponded with the sampling data and monitoring result of NFRDI (National Fisheries Research & Development Institute). From the laboratory and field evaluations, the developed acoustic detection system for early detecting HABs has demonstrated that it could be a significant system to monitor the occurrence of HABs in coastal regions.

• Journal of Ocean Engineering and Technology
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• v.36 no.1
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• pp.32-40
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• 2022

Underwater optical images face various limitations that degrade the image quality compared with optical images taken in our atmosphere. Attenuation according to the wavelength of light and reflection by very small floating objects cause low contrast, blurry clarity, and color degradation in underwater images. We constructed an image data of the Korean sea and enhanced it by learning the characteristics of underwater images using the deep learning techniques of CycleGAN (cycle-consistent adversarial network), UGAN (underwater GAN), FUnIE-GAN (fast underwater image enhancement GAN). In addition, the underwater optical image was enhanced using the image processing technique of Image Fusion. For a quantitative performance comparison, UIQM (underwater image quality measure), which evaluates the performance of the enhancement in terms of colorfulness, sharpness, and contrast, and UCIQE (underwater color image quality evaluation), which evaluates the performance in terms of chroma, luminance, and saturation were calculated. For 100 underwater images taken in Korean seas, the average UIQMs of CycleGAN, UGAN, and FUnIE-GAN were 3.91, 3.42, and 2.66, respectively, and the average UCIQEs were measured to be 29.9, 26.77, and 22.88, respectively. The average UIQM and UCIQE of Image Fusion were 3.63 and 23.59, respectively. CycleGAN and UGAN qualitatively and quantitatively improved the image quality in various underwater environments, and FUnIE-GAN had performance differences depending on the underwater environment. Image Fusion showed good performance in terms of color correction and sharpness enhancement. It is expected that this method can be used for monitoring underwater works and the autonomous operation of unmanned vehicles by improving the visibility of underwater situations more accurately.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.7
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• pp.17-26
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• 2016

This work proposes a 12b 60MS/s 0.18um CMOS Flash-SAR ADC for various systems such as wireless communications and portable video processing systems. The proposed Flash-SAR ADC alleviates the weakness of a conventional SAR ADC that the operation speed proportionally increases with a resolution by deciding upper 4bits first with a high-speed flash ADC before deciding lower 9bits with a low-power SAR ADC. The proposed ADC removes a sampling-time mismatch by using the C-R DAC in the SAR ADC as the combined sampling network instead of a T/H circuit which restricts a high speed operation. An interpolation technique implemented in the flash ADC halves the required number of pre-amplifiers, while a switched-bias power reduction scheme minimizes the power consumption of the flash ADC during the SAR operation. The TSPC based D-flip flop in the SAR logic for high-speed operation reduces the propagation delay by 55% and the required number of transistors by half compared to the conventional static D-flip flop. The prototype ADC in a 0.18um CMOS demonstrates a measured DNL and INL within 1.33LSB and 1.90LSB, with a maximum SNDR and SFDR of 58.27dB and 69.29dB at 60MS/s, respectively. The ADC occupies an active die area of $0.54mm^2$ and consumes 5.4mW at a 1.8V supply.