• Journal of the Korea Society for Simulation
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• v.17 no.4
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• pp.89-97
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• 2008

In wireless personal area networks (WPANs), the successful design of channel time allocation algorithm is a key factor in guaranteeing the various quality of service (QoS) requirements for the stringent real-time constraints of multimedia services. In this paper we propose a channel time allocation algorithm for achieving a high quality transmission and high channel error tolerance of MPEG stream in the IEEE 802.15.3 high-rate wireless PANs. Our algorithm exploits the characteristics of MPEG stream. When a new MPEG stream arrives, a DEV models it by the traffic envelope and delivers the traffic envelope to the piconet coordinator (PNC) along with the channel time request. The PNC performs channel time allocation according to the envelope. Performance of the proposed scheme is investigated by simulation and analysis. Our results show that compared to conventional scheme, the proposed scheme is very effective and provides a good performance under typical channel error conditions.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.3
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• pp.123-138
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• 2023

The Fourth Industrial Revolution and sensor technology have led to increased utilization of sensor data. In our modern society, data complexity is rising, and the extraction of valuable information has become crucial with the rapid changes in information technology (IT). Recurrent neural networks (RNN) and long short-term memory (LSTM) models have shown remarkable performance in natural language processing (NLP) and time series prediction. Consequently, there is a strong expectation that models excelling in NLP will also excel in time series prediction. However, current research on Transformer models for time series prediction remains limited. Traditional RNN and LSTM models have demonstrated superior performance compared to Transformers in big data analysis. Nevertheless, with continuous advancements in Transformer models, such as GPT-2 (Generative Pre-trained Transformer 2) and ProphetNet, they have gained attention in the field of time series prediction. This study aims to evaluate the classification performance and interval prediction of remaining useful life (RUL) using an advanced Transformer model. The performance of each model will be utilized to establish a health index (HI) for cutting blades, enabling real-time monitoring of machine health. The results are expected to provide valuable insights for machine monitoring, evaluation, and management, confirming the effectiveness of advanced Transformer models in time series analysis when applied in industrial settings.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.27 no.4
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• pp.505-513
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• 2009

Recently, researches on the construction information for construction of high-tech cities are under performed. RTLS(Real-Time location system) for gathering the dynamic location information of construction resources in construction sites, such as workers, materials and equipments, is one of the developments. Especially, construction resources can be managed efficiently with the dynamic location information and the improvement of safety and the reduction of cost are expected. To introduce the RTLS to the construction sites, the installation location of AP(Access Point) must be simulated using the 3-dimensional visibility analysis considering the propagation distance of AP. In this research, 3-dimensional signal simulation software based on the spatial data using surveying terrestrial LiDAR is developed. The simulated results are compared with the signal strength of field experiments for 4 test sites. As a result, the signal strength from the propagation model is most similar to that of field experiment at the front of the main building and the maul playground of Konkuk University. The visibilities in that sites are higher than other sites. among 4 test sites.

• Korean Journal of Geomatics
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• v.3 no.1
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• pp.33-41
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• 2003

Every year, several typhoons hit the Korean peninsula and cause severe damage. For the prevention and accurate estimation of these damages, real time or almost real time flood information is essential. Because of weather conditions, images taken by optic sensors or LIDAR are sometimes not appropriate for an accurate estimation of water areas during typhoon. In this case SAR (Synthetic Aperture Radar) images which are independent of weather condition can be useful for the estimation of flood areas. To get detailed information about floods from satellite imagery, accurate classification of water areas is the most important step. A commonly- and widely-used classification methods is the ML(Maximum Likelihood) method which assumes that the distribution of brightness values of the images follows a Gaussian distribution. The distribution of brightness values of the SAR image, however, usually does not follow a Gaussian distribution. For this reason, in this study the ANN (Artificial Neural Networks) method independent of the statistical characteristics of images is applied to the SAR imagery. RADARS A TSAR images are primarily used for extraction of water areas, and DEM (Digital Elevation Model) is used as supplementary data to evaluate the ground undulation effect. Water areas are also extracted from KOMPSAT image achieved by optic sensors for comparison purpose. Both ANN and ML methods are applied to flat and mountainous areas to extract water areas. The estimated areas from satellite imagery are compared with those of manually extracted results. As a result, the ANN classifier performs better than the ML method when only the SAR image was used as input data, except for mountainous areas. When DEM was used as supplementary data for classification of SAR images, there was a 5.64% accuracy improvement for mountainous area, and a similar result of 0.24% accuracy improvement for flat areas using artificial neural networks.

• Transactions of the Society of Information Storage Systems
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• v.1 no.2
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• pp.155-160
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• 2005

In this paper, the channel decoder promising reliable data retrieving in noisy holographic channel has been developed for holographic WORM(write once read many) system. It covers various DSP(digital signal processing) blocks, such as align mark detector, adaptive channel equalizer, modulation decoder and ECC(error correction code) decoder. The specific schemes of DSP are designed to reduce the effect of noises in holographic WORM(H-WORM) system, particularly in prototype of DAEWOO electronics(DEPROTO). For real time data retrieving, the channel decoder is redesigned for FPGA(field programmable gate array) based hardware, where DSP blocks calculate in parallel sense with memory buffers between blocks and controllers for driving peripherals of FPGA. As an input source of the experiments, MPEG2 TS(transport stream) data was used and recorded to DEPROTO system. During retrieving, the CCD(charge coupled device), capturing device of DEPROTO, detects retrieved images and transmits signals of them to the FPGA of hardware channel decoder. Finally, the output data stream of the channel decoder was transferred to the MPEG decoding board for monitoring video signals. The experimental results showed the error corrected BER(bit error rate) of less than $10^{-9}$, from the raw BER of DEPROTO, about $10^{-3}$. With the developed hardware channel decoder, the real-time video demonstration was possible during the experiments. The operating clock of the FPGA was 60 MHz, of which speed was capable of decoding up to 120 mega channel bits per sec.

• Journal of Microbiology and Biotechnology
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• v.22 no.9
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• pp.1193-1201
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• 2012

The analysis and quantification of ammonia-oxidizing bacteria (AOB) is crucial, as they initiate the biological removal of ammonia-nitrogen from sewage. Previous methods for analyzing the microbial community structure, which involve the plating of samples or culture media over agar plates, have been inadequate because many microorganisms found in a sewage plant are unculturable. In this study, to exclusively detect AOB, the analysis was carried out via denaturing gradient gel electrophoresis using a primer specific to the amoA gene, which is one of the functional genes known as ammonia monooxygenase. An AOB consortium (S1 sample) that could oxidize an unprecedented 100% of ammonia in 24 h was obtained from sewage sludge. In addition, real-time PCR was used to quantify the AOB. Results of the microbial community analysis in terms of carbon utilization ability of samples showed that the aeration tank water sample (S2), influent water sample (S3), and effluent water sample (S4) used all the 31 substrates considered, whereas the AOB consortium (S1) used only Tween 80, D-galacturonic acid, itaconic acid, D-malic acid, and $_L$-serine after 192 h. The largest concentration of AOB was detected in S1 ($7.6{\times}10^6copies/{\mu}l$), followed by S2 ($3.2{\times}10^6copies/{\mu}l$), S4 ($2.8{\times}10^6copies/{\mu}l$), and S3 ($2.4{\times}10^6copies/{\mu}l$).

• Proceedings of the KSME Conference
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• 2007.05a
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• pp.1537-1541
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• 2007

We injection molded a wedge type of plate with micro prizm patterns on its surface and investigated the fidelity of replication of the micro pattern depending on the process parameter such as mold temperature, melt temperature, injection rate or packing pressure. The size of the size of the $90^{\circ}$ prizm pattern is $50{\mu}m$ and the size of the plate is about 300㎜${\times}$200㎜. The thicknesses are 2.6㎜. and 0.7mm at each edge of the wedge type of plate. The fidelity of the replication turned out quite different according to the process parameters and location of the patterns on the plate. We measured the cavity pressure and temperature in real-time during the molding to analyze the effect of the local melt pressure and temperature on the micro pattern replication.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.8
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• pp.1032-1038
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• 2021

Rate of penetration(ROP) is one of the important variables for maximizing the drilling performance. In order to maximize drilling efficiency, it is necessary to increase the drilling speed, and real-time ROP prediction is important so that the driller can identify problems during drilling. The ROP has a high correlation with the drillstring rotational speed, weight on bit, and flow rate. In this paper, the ROP was predicted using a data-driven supervised learning model trained from the drilling efficiency parameters. As a result of comparison through the performance evaluation metrics of the regression model, the root mean square error(RMSE) of the RF model was 4.20 and the mean absolute percentage error(MAPE) was 9.08%, confirming the best predictive performance. The proposed method can be used as a base model for ROP prediction when constructing a real-time drilling operation guide system.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.12
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• pp.6349-6355
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• 2012

Previous studies showed that Math1 homologous to human Hath1 can cause mouse goblet cells to differentiate. In this context it is important that the majority of colon cancers have few goblet cells. In the present study, the potential role of Hath1 in colon carcinogenesis was investigated. Sections of paraffin-embedded tissues were used to investigate the goblet cell population of normal colon mucosa, mucosa adjacent colon cancer and colon cancer samples from 48 patients. Hath1 and Muc2 expression in these samples were tested by immunohistochemistry, quantitative real-time reverse transcription -PCR and Western blotting. After the recombinant plasmid, pcDNA3.1(+)-Hath1 had been transfected into HT29 colon cancer cells, three clones were selected randomly to test the levels of Hath1 mRNA, Muc2 mRNA, Hath1, Muc2, cyclin D1 and p27 by quantitative real-time reverse transcription-PCR and Western blotting. Moreover, the proliferative ability of HT29 cells introduced with Hath1 was assessed by means of colony formation assay and xenografting. Expression of Hath1, Muc2, cyclin D1 and p27 in the xenograft tumors was also detected by Western blotting. No goblet cells were to be found in colon cancer and levels of Hath1 mRNA and Hath1, Muc2 mRNA and Muc2 were significantly down-regulated. Hath1 could decrease cyclin D1, increase p27 and Muc2 in HT29 cells and inhibit their proliferation. Hath1 may be an anti-oncogene in colon carcinogenesis.

• Korean Journal of Agricultural Science
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• v.47 no.2
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• pp.315-326
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• 2020

In this study, a real-time crop recognition system was developed for an unmanned farm machine for upland farming. The crop recognition system was developed based on a stereo camera, and an image processing framework was proposed that consists of disparity matching, localization of crop area, and estimation of crop height with coordinate transformations. The performance was evaluated by attaching the crop recognition system to a tractor for five representative crops (cabbage, potato, sesame, radish, and soybean). The test condition was set at 3 levels of distances to the crop (100, 150, and 200 cm) and 5 levels of camera height (42, 44, 46, 48, and 50 cm). The mean relative error (MRE) was used to compare the height between the measured and estimated results. As a result, the MRE of Chinese cabbage was the lowest at 1.70%, and the MRE of soybean was the highest at 4.97%. It is considered that the MRE of the crop which has more similar distribution lower. the results showed that all crop height was estimated with less than 5% MRE. The developed crop recognition system can be applied to various agricultural machinery which enhances the accuracy of crop detection and its performance in various illumination conditions.