• Journal of Korea Water Resources Association
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• v.36 no.1
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• pp.105-115
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• 2003

The computation model which evaluates combined hydraulic and mechanical reliability, is developed to analyze the integrated reliability in water distribution system. The hydraulic reliability is calculated by considering uncertain variables like water demand, hydraulic pressure, pipe roughness as random variables according to proper distribution type. The mechanical reliability is evaluated by analyzing the effect of pipe network with sequential failure of network components. The result of this study model applied to the real pipe network shows that this model can be used to simulate the uncertain factors effectively in real pipe network. Therefore, The pipe-line engineers can design and manage the network system with more quantitative reliability, through applying this model to reliable pipe network design and diagnosis of existing systems.

• Journal of the Korean Society for Precision Engineering
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• v.17 no.1
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• pp.129-137
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• 2000

In this paper, a new method of noncontact measurement has been developed for a 3 dimensional topography in semiconductor wafer, implementing a new optical probe based on the precision defocus measurement. The developed technique consists of the new optical probe, precision stages, and the measurement/control system. The basic principle of the technique is to use the reflected slit beam from the specimen surface, and to measure the deviation of the specimen surface. The defocusing distance can be measured by the reflected slit beam, where the defocused image is measured by the proposed optical probe, giving very high resolution. The distance measuring formula has been proposed for the developed probe, using the laws of geometric optics. The precision calibration technique has been applied, giving about 10 nanometer resolution and 72 nanometer of four sigma uncertainty. In order to quantitize the micro pattern in the specimen surface, some efficient analysis algorithms have been developed to analyse the 3D topography pattern and some parameters of the surface. The developed system has been successfully applied to measure the wafer surface, demonstrating the line scanning feature and excellent 3 dimensional measurement capability.

• Journal of the korean society of oceanography
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• v.36 no.1
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• pp.1-8
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• 2001

An in-situ four-electrode contact resistivity probe system was designed, and field-tested in submarine sediments. Seismic survey was also performed to support and compare the results of electric survey. The probe was designed to be driven to selected depths below the seafloor using a Vibracore system. The four insulated electrodes were, spaced equidistant across the wedge, were extended beyond the probe tip to minimize effects of sediment disturbance by the wedge insertion. In-situ measurements of resistivity were recorded on board by precision electronic equipment consisting of signal generators and processors, and by temperature-monitoring systems. Overall limits of Uncertainty at respective depths below the seafloor are up to ${\pm}$10 of the measured values. Best estimates of conductivity are considered to be ${\pm}$3 percent of the reported values. Resistivity measurements were made at six sites in carbonate sediments to a maximum depth of penetration of about 5 m. Average values of conductivity range between 0.88 and 1.21 mho/m. The results show the seabed is composed of alternating layers of relatively high-conductivity material (0.8 to 1.4 mho/m) in thicknesses of more or less one meter and layers about 30 cm thick having relatively low conductivities (0.4 to 0.8 mho/m).

• Journal of Korea Multimedia Society
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• v.19 no.8
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• pp.1516-1529
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• 2016

While global manufacturing is becoming more competitive due to variety of customer demand, increase in production cost and uncertainty in resource availability, the future ability of manufacturing industries depends upon the implementation of Smart Factory. With the convergence of new information and communication technology, Smart Factory enables manufacturers to respond quickly to customer demand and minimize resource usage while maximizing productivity performance. This paper presents the development of a big data analytics platform architecture for Smart Factory. As this platform represents a conceptual software structure needed to implement data-driven decision-making mechanism in shop floors, it enables the creation and use of diagnosis, prediction and optimization models through the use of data analytics and big data. The completion of implementing the platform will help manufacturers: 1) acquire an advanced technology towards manufacturing intelligence, 2) implement a cost-effective analytics environment through the use of standardized data interfaces and open-source solutions, 3) obtain a technical reference for time-efficiently implementing an analytics modeling environment, and 4) eventually improve productivity performance in manufacturing systems. This paper also presents a technical architecture for big data infrastructure, which we are implementing, and a case study to demonstrate energy-predictive analytics in a machine tool system.

• Journal of Korea Port Economic Association
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• v.22 no.3
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• pp.43-57
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• 2006

Responsiveness to the uncertainty of SCM system shows its competitiveness. In order to secure SCM competitiveness, RFID-related projects aim to enhance both system visibility and process automation. Nowadays, we conduct RFID technology-oriented researches very actively; however, quantitative ROI analysis model from the perspective of SCM does not exist yet, which helps decide the introduction of technology. Therefore, our study suggests a ROI analysis model for the adoption of RFID technology, and we demonstrate its usefulness using the real world data that is taken from one of the government-funded RFID projects in Korea.

• Nuclear Engineering and Technology
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• v.40 no.4
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• pp.299-304
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• 2008

A detection system was set up to measure the neutron generation rate of a recently developed D-D neutron generator. The system is composed of a Si detector, He-3 detector, and electronics for pulse height analysis. The neutron generation rate was measured by counting protons using the Si detector, and the data was crosschecked by counting neutrons with the He-3 detector. The efficiencies of the Si and He-3 detectors were calibrated independently by using a standard alpha particle source $^{241}Am$ and a bare isotopic neutron source $^{252}Cf$, respectively. The effect of the cross-sectional difference between the D(d,p)T and $D(d,n)^3He$ reactions was evaluated for the case of a thick target. The neutron generation rate was theoretically corrected for the anisotropic emission of protons and neutrons in the D-D reactions. The attenuations of neutron on the path to the He-3 detector by the target assembly and vacuum flange of the neutron generator were considered by the Monte Carlo method using the MCNP 4C2 code. As a result, the neutron generation rate based on the Si detector measurement was determined with a relative uncertainty of ${\pm}5%$, and the two rates measured by both detectors corroborated within 20%.

• Nuclear Engineering and Technology
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• v.53 no.10
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• pp.3359-3366
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• 2021

An engineered barrier system (EBS) for the deep geological disposal of high-level radioactive waste (HLW) is composed of a disposal canister, buffer material, gap-filling material, and backfill material. As the buffer fills the empty space between the disposal canisters and the near-field rock mass, heat energy from the canisters is released to the surrounding buffer material. It is vital that this heat energy is rapidly dissipated to the near-field rock mass, and thus the thermal conductivity of the buffer is a key parameter to consider when evaluating the safety of the overall disposal system. Therefore, to take into consideration the sizeable amount of heat being released from such canisters, this study investigated the thermal conductivity of Korean compacted bentonites and its variation within a temperature range of 25 ℃ to 80-90 ℃. As a result, thermal conductivity increased by 5-20% as the temperature increased. Furthermore, temperature had a greater effect under higher degrees of saturation and a lower impact under higher dry densities. This study also conducted a regression analysis with 147 sets of data to estimate the thermal conductivity of the compacted bentonite considering the initial dry density, water content, and variations in temperature. Furthermore, the Kriging method was adopted to establish an uncertainty metamodel of thermal conductivity to verify the regression model. The R² value of the regression model was 0.925, and the regression model and metamodel showed similar results.

• Asia-Pacific Journal of Atmospheric Sciences
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• v.54 no.4
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• pp.611-622
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• 2018

Observed climate data are processed under the assumption that their time series are stationary, as in multi-step temperature and precipitation prediction, which usually leads to low prediction accuracy. If a climate system model is based on a single prediction model, the prediction results contain significant uncertainty. In order to overcome this drawback, this study uses a method that integrates ensemble prediction and a stepwise regression model based on a mean-valued generation function. In addition, it utilizes empirical mode decomposition (EMD), which is a new method of handling time series. First, a non-stationary time series is decomposed into a series of intrinsic mode functions (IMFs), which are stationary and multi-scale. Then, a different prediction model is constructed for each component of the IMF using numerical ensemble prediction combined with stepwise regression analysis. Finally, the results are fit to a linear regression model, and a short-term climate prediction system is established using the Visual Studio development platform. The model is validated using temperature data from February 1957 to 2005 from 88 weather stations in Guangxi, China. The results show that compared to single-model prediction methods, the EMD and ensemble prediction model is more effective for forecasting climate change and abrupt climate shifts when using historical data for multi-step prediction.

• Smart Structures and Systems
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• v.30 no.6
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• pp.583-599
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• 2022

In this paper, a multi-objective wireless sensor network configuration optimization method is proposed. The proposed method aims to determine the optimal information and lifespan wireless sensor network for structural health monitoring of large-scale infrastructures. In particular, cluster-based wireless sensor networks with multi-type of sensors are considered. To optimize the lifetime of the wireless sensor network, a cluster-based network optimization algorithm that optimizes the arrangement of cluster heads and base station is developed. On the other hand, based on the Bayesian inference, the uncertainty of the estimated parameters can be quantified. The coefficient of variance of the estimated parameters can be obtained, which is utilized as a holistic measure to evaluate the estimation accuracy of sensor configurations with multi-type of sensors. The proposed method provides the optimal wireless sensor network configuration that satisfies the required estimation accuracy with the longest lifetime. The proposed method is illustrated by designing the optimal wireless sensor network configuration of a cable-stayed bridge and a space truss.

• Journal of the Korean Society of Systems Engineering
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• v.18 no.2
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• pp.11-23
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• 2022

Although artificial intelligence(AI) is a key technology in the digital transformation among the emerging technologies, there are concerns about the use of AI, so many countries have been trying to set up a proper regulation system. This study analyzes the cases of the regulation policies on AI in USA, EU and Korea with the aim to set up and improve proper AI policies and strategies in Korea. In USA, the establishment of the code of ethics for the use of AI is led by private sector. On the other side, Europe is strengthening competitiveness in the AI industry by consolidating regulations that are dispersed by EU members. Korea has also prepared and promoted policies for AI ethics, copyright and privacy protection at the national level and trying to change to a negative regulation system and improve regulations to close the gap between the leading countries and Korea in AI. Moreover, this study analyzed the course of policy changes of AI regulation policy centered on ACF(Advocacy Coalition Framework) model of Sabatier. Through this study, it proposes hyper-scale AI regulation policy recommendations for improving competitiveness and commercialization in Korea. This study is significant in that it can contribute to increasing the predictability of policy makers who have difficulties due to uncertainty and ambiguity in establishing regulatory policies caused by the emergence of hyper-scale artificial intelligence.