• Journal of the Korea Institute of Military Science and Technology
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• v.17 no.5
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• pp.561-568
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• 2014

The Republic of Korea Army is using R-NAD of MIL-STD-188-220 as a Media Access Control protocol. Under urgent situations, almost all stations transmit data frames and then the network will reach a saturation state. Several articles have been devoted to the study of R-NAD performance. However, most of them focus on comparing the performance of some NADs using network simulation tools. We propose an analytical model to compute the throughput of R-NAD under the assumption of a network traffic saturation. Analytical results were verified by Monte Carlo methods. We have shown that the performance of a success probability and an average idle time remains almost unchanged as the total number of stations increases. We have also shown that Type 1/2/4 operation mode outperforms Type 3 operation mode in throughput. The results showed that the system with a squelch detection achieved a better performance than the one without it. The longer DATA time had a higher throughput.

• Journal of Korean Ophthalmic Optics Society
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• v.12 no.4
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• pp.71-76
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• 2007

The aim of this study was to investigate level of understanding of symptoms appearing after wearing rigid gas permeable contact lens for optometrists, and if they were educated high about rigid gas permeable contact lens, they could increase the probability of wearing success of rigid gas permeable contact lens as dividing into an adaptative symptoms and an abnormal symptoms. For 96 optometrists a questionnaire about apparatuses, protocol and experience for prescription of rigid gas permeable contact lens and level of understanding of symptoms appearing after wearing rigid gas permeable contact lens was performed. Results showed that level of understanding of rigid gas permeable contact lens was very low, and education was performed for optometrists.

• Asia-Pacific Journal of Atmospheric Sciences
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• v.54 no.4
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• pp.527-544
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• 2018

This paper presents a nighttime sea fog detection algorithm incorporating unsupervised learning technique. The algorithm is based on data sets that combine brightness temperatures from the $3.7{\mu}m$ and $10.8{\mu}m$ channels of the meteorological imager (MI) onboard the Communication, Ocean and Meteorological Satellite (COMS), with sea surface temperature from the Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA). Previous algorithms generally employed threshold values including the brightness temperature difference between the near infrared and infrared. The threshold values were previously determined from climatological analysis or model simulation. Although this method using predetermined thresholds is very simple and effective in detecting low cloud, it has difficulty in distinguishing fog from stratus because they share similar characteristics of particle size and altitude. In order to improve this, the unsupervised learning approach, which allows a more effective interpretation from the insufficient information, has been utilized. The unsupervised learning method employed in this paper is the expectation-maximization (EM) algorithm that is widely used in incomplete data problems. It identifies distinguishing features of the data by organizing and optimizing the data. This allows for the application of optimal threshold values for fog detection by considering the characteristics of a specific domain. The algorithm has been evaluated using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) vertical profile products, which showed promising results within a local domain with probability of detection (POD) of 0.753 and critical success index (CSI) of 0.477, respectively.

• Journal of Construction Engineering and Project Management
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• v.10 no.1
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• pp.1-15
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• 2020

With the increased dependence on subcontracting in the construction industry, the operational relationship between main contractors and subcontractor plays an imperative role in successful project delivery. Consequently, improving this relationship increases the probability of project success and enhancing project performance. A wide range of research has confirmed that partnering improves the main contractor-subcontractor relationship. Though the positive impact of partnering on project performance is supported by a plethora of evidence, the guiding theory on practical partnering process steps is limited. The study aimed at improving subcontracting in the construction industry through a partnering process relevant to Zambia guided by factors obtained from industry expects. Questionnaire surveys and Semi-structured interview were adopted to investigate the perception of construction industry professionals and academics towards the main contractor-subcontractor relationship along with improvement factors. The findings showed that the relationship between main contractors and subcontractors on most projects in Zambia is unsatisfactory therefore justifying attention. Top factors that can enhance the main contractor-subcontractor relationship were identified. From the factors deduced and guidelines on partnering best practices, a project partnering model was developed.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.134-134
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• 2021

Precipitation is a crucial component of water cycle and play a key role in hydrological processes. Traditionally, gauge-based precipitation is the main method to achieve high accuracy of rainfall estimation, but its distribution is sparsely in mountainous areas. Recently, satellite-based precipitation products (SPPs) provide grid-based precipitation with spatio-temporal variability, but SPPs contain a lot of uncertainty in estimated precipitation, and the spatial resolution quite coarse. To overcome these limitations, this study aims to generate new grid-based daily precipitation using Automatic weather system (AWS) in Korea and multiple SPPs(i.e. CHIRPSv2, CMORPH, GSMaP, TRMMv7) during the period of 2003-2017. And this study used a machine learning based Random Forest (RF) model for generating new merging precipitation. In addition, several statistical linear merging methods are used to compare with the results of the RF model. In order to investigate the efficiency of RF, observed data from 64 observed Automated Synoptic Observation System (ASOS) were collected to evaluate the accuracy of the products through Kling-Gupta efficiency (KGE), probability of detection (POD), false alarm rate (FAR), and critical success index (CSI). As a result, the new precipitation generated through the random forest model showed higher accuracy than each satellite rainfall product and spatio-temporal variability was better reflected than other statistical merging methods. Therefore, a random forest-based ensemble satellite precipitation product can be efficiently used for hydrological simulations in ungauged basins such as the Mekong River.

• Journal of the Korean Operations Research and Management Science Society
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• v.18 no.1
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• pp.119-126
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• 1993

We study a manufacturing process that is quite common in semiconductor wafer fabrication of semiconductor chip production. A machine is used to process a job consisting of J wafers. Each job requires a setup, and the i$_{th}$ setup for a job is sucessful with probability P$_{i}$. The setup is prone to failure, which results in the loss of expensive wafers. Therefore, a tiral run is first conducted on a small batch. If the set up is successful, the test is passed and the balance of the job can be processed. If the setup is unsuccessful, the exposed wafers are lost to scrap and the mask is realigned. The process then repeats on the balance of the job. We call this as send-ahead policy and consider general policies in which the number of wafers that are sent shead depend on the cost of the raw wafer, the sequence of success probabilities, and the balance of the job. We model this process and determine the expected number of good wafers per job,the expected time to process a job, and the long run average throughput. An algorithm to minimize the cost per good wafer subject to a demand constraint is provided.d.d.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• 2007.11a
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• pp.599-602
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• 2007

To enhance the competition of National power, the investment of SOC is an essential factor for infrastructure construction. The capital for SOC is so gigantic and the national finance is limited, engagement of various subject is necessary. So, Government is promoting the private sector to invest capital in SOC development. Nevertheless the increasement of SOC investment, various problems were issued in selection & operation of BTL. Therefore, to raise the probability of success for BTL project, escalation, unification and specialization of BTL affairs, BTL selection process were suggested in this study.

• Journal of the Korean Society of Systems Engineering
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• v.19 no.2
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• pp.18-31
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• 2023

Exploring artificial intelligence and machine learning for nuclear safety has witnessed increased interest in recent years. To contribute to this area of research, a machine learning model capable of accurately predicting nuclear power plant response with minimal computational cost is proposed. To develop a robust machine learning model, the Best Estimate Plus Uncertainty (BEPU) approach was used to generate a database to train three models and select the best of the three. The BEPU analysis was performed by coupling Dakota platform with the best estimate thermal hydraulics code RELAP/SCDAPSIM/MOD 3.4. The Code Scaling Applicability and Uncertainty approach was adopted, along with Wilks' theorem to obtain a statistically representative sample that satisfies the USNRC 95/95 rule with 95% probability and 95% confidence level. The generated database was used to train three models based on Recurrent Neural Networks; specifically, Long Short-Term Memory, Gated Recurrent Unit, and a hybrid model with Long Short-Term Memory coupled to Convolutional Neural Network. In this paper, the System Engineering approach was utilized to identify requirements, stakeholders, and functional and physical architecture to develop this project and ensure success in verification and validation activities necessary to ensure the efficient development of ML meta-models capable of predicting of the nuclear power plant response.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37A no.9
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• pp.746-752
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• 2012

SVC layered video is consists of a Base Layer (BL) and Enhancement Layer (EL). Without the base layer decoding, the higher EL layer can not be decoded. Therefore, successful transfer of the BL is important factor for improving the SVC video data. In this paper, we propose a network coding of layered video to improve success decoding probability with the importance order of the video data over a multi-relay system. We shows that formula analysis and experimental results of the proposed network coding scheme. In addition, we shows performance of video quality according to the number of relays.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.19 no.4
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• pp.605-613
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• 1994

In this paper, we evaluate performance of multiple access for distributed wireless communication network by CSMA protocol. It is envident that the existence of hidden node in an environment degrades the performance of CSMA. In order to improve performance due to the problem of hidden node, the previous paper used random multiple access protocols a as such as ISMA, BTMA, BCMA. In this paper, We propose a protocol that we can improve performance by allowing node to sense the carrier of any other transmission on the channel in the distributed wireless communication networks The probability of transmission success was obtained by steady stats analysis under given assumptions. We confirmed that hidden node problem be virtually elimated by using a new protocol.