• Journal of the Korea Institute of Military Science and Technology
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• v.10 no.2
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• pp.31-37
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• 2007

This paper presents a ballistic lead-computation method which utilizes automatic video tracking, tracking assistance and roll uncoupling. The method is able to improve the firing accuracy of army fighting vehicles such as main battle tanks. In the experiment, the efficiency of the proposed method is evaluated by an error analysis in real operating environment. The proposed method has been applied to the fire control system of a military vehicle and proved through the development test of the vehicle.

• Journal of Korea Trade
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• v.23 no.2
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• pp.34-45
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• 2019

Purpose - This study investigates the lead-lag relations between the prices of major commodities imported into Korea and corresponding shipping freight rates. This paper aims to provide implications for cross-market causal relations between related economic segments. Design/Methodology - For economic long-run equilibrium between commodity prices and freights, a Johansen (1988) cointegration test is employed first. Then, Granger (1987) causality tests are performed under the vector error correction model (VECM) framework. Findings - The results indicate that the direction of causality varies by raw materials, which is attributable to different economic mechanisms in the corresponding shipping transportation sectors. In addition, the significance of causality becomes blurred during the post-2008 period. Practical Implication - Corporate managers in commodity trading, steelmaking, power generation, and oil refinery sectors can take advantage of the findings in this study as identifying leading economic indicators can be helpful for decision making in both short- and long-term strategies. Originality/value - This study is the first attempt to analyze the inter-relations between commodity prices and corresponding freight rates focusing on raw material imports of Korea.

• Korean Journal of Computational Design and Engineering
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• v.14 no.1
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• pp.18-24
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• 2009

This paper proposes an analytical method of evaluating the maximum error by modeling the exact tool path for the tool traverse singular region in five-axis machining. It is known that the NC data from the inverse kinematics transformation of 5-axis machining can generate singular positions where incoherent movements of the rotary axes can appear. These lead to unexpected errors and abrupt operations, resulting in scoring on the machined surface. To resolve this problem, previous methods have calculated several tool positions during a singular operation, using inverse kinematics equations to predict tool trajectory and approximate the maximum error. This type of numerical approach, configuring the tool trajectory, requires much computation time to obtain a sufficient number of tool positions in a region. We have derived an analytical equation for the tool trajectory in a singular area by modeling the tool operation into a linear and a nonlinear part that is a general form of the tool trajectory in the singular area and that is suitable for all types of five-axis machine tools. In addition, we have evaluated the maximum tool-path error exactly, using our analytical model. Our algorithm can be used to modify NC data, making the operation smoother and bringing any errors to within tolerance.

• IEMEK Journal of Embedded Systems and Applications
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• v.11 no.5
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• pp.305-312
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• 2016

Modern embedded systems are typically operated by the rechargeable batteries in our daily life. Since charge of batteries is considered as an time consuming task, there have been extensive efforts to manage the charge time from the perspective of materials, circuits, and systems. Estimation of battery charge time is one of the essential information to design the charge circuitry. A compact macro model for the constant-current and constant-voltage charge protocol was recently introduced, which gives us a quick estimation of charge time with similar shape to the famous Peukert's law for discharge time estimation. The CC-CV charging protocol is widely used for Lithium-based batteries and Lead-acid batteries. In this paper, we characterize the lead-acid battery by measurement to extract the model coefficients, which was not covered by the previous studies. By our proposed model, the key coefficient Kcc results in 1.18-1.31, which is little bit higher than that of Lithium batteries. The accuracy of our model is within the range of ${\pm}10%$ error, which is compatible with the other studies such as Peukert's law.

• Journal of Welding and Joining
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• v.35 no.3
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• pp.21-27
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• 2017

A soldering temperature of ($235{\pm}3$) $^{\circ}C$ is described in ISO 9455-15 for the copper corrosion test. However, this temperature is not suitable for performing lead-free solder pastes. We evaluated the compatibility of a lead-free solder paste in the experimental conditions of (Liquidus temperature + ($35{\pm}3$)) $^{\circ}C$. Based on the results after a Cu corrosion test, a proper temperature for Pb-free soldering was (melting point+($35{\pm}3$)) $^{\circ}C$. Criteria used to evaluate corrosion due to discoloration of flux residue is described in ISO 9455-15, but a more quantitative evaluation standard is needed. In this study, experimental error level was estimated by analyzing flux residue after a corrosion test for 72, 500 hours of specimens using EDS analysis with acceleration voltage. It was determined that the copper area at the flux residue boundary is suitable for the EDS analysis area.

• Journal of Magnetics
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• v.22 no.1
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• pp.34-42
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• 2017

Material defects in the Steam Generator Tubes (SGT) of sodium cooled fast breeder reactor (PFBR) can lead to leakage of water into sodium. The water and sodium reaction will lead to major accidents. Therefore, the examination of steam generator tubes for the early detection of defects is an important requirement for safety and economic considerations. In this work, the Magnetic Flux Leakage (MFL) based Non Destructive Testing (NDT) technique is used to perform the defect detection process. The rectangular notch defects on the outer surface of steam generator tubes are modeled using COMSOL multiphysics 4.3a software. The obtained MFL images are de-noised to improve the integrity of flaw related information. Grey Level Co-occurrence Matrix (GLCM) features are extracted from MFL images and taken as input parameter to train the neural network. A comparative study on characterization have been carried out using feed-forward back propagation (FFBP) and cascade-forward back propagation (CFBP) algorithms. The results of both algorithms are evaluated with Mean Square Error (MSE) as a prediction performance measure. The average percentage error for length, depth and width are also computed. The result shows that the feed-forward back propagation network model performs better in characterizing the defects.

• Nuclear Engineering and Technology
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• v.55 no.10
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• pp.3709-3715
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• 2023

It is an important work to measure the dimensions of underwater spent fuel assemblies in the nuclear power industry during the overhaul, to judging whether the spent fuel assemblies can continue to be used. In this paper, a three dimensional reconstruction method for underwater spent fuel assemblies of nuclear reactor based on linear structured light is proposed, and the topography and size measurement was carried out based on the reconstructed 3D model. Multiple linear structured light sensors are used to obtain contour size data, and the shape data of the whole spent fuel assembly can be collected by one-dimensional scanning motion. In this paper, we also presented a corrected model to correct the measurement error introduced by lead-glass and water is corrected. Then, we set up an underwater measurement system for spent fuel assembly based on this method. Finally, an underwater measurement experiment is carried out to verify the 3D reconstruction ability and measurement ability of the system, and the measurement error is less than ±0.05 mm.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.6 no.1
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• pp.88-96
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• 1996

This study was conducted to establish model between lead and ZPP concentration in blood of workers exposed to lead. Workers employed in secondary smelting manufacturing industry showed $85.1{\mu}g/dl$ of blood lead level, exceeding $60{\mu}g/dl$, the Criteria for Removal defined by Occupational Safety and Health Act of Korea. Average blood lead level of workers in the battery manufacturing industry was $51.3{\mu}g/dl$, locating between $40{\mu}g/dl$ and $60{\mu}g/dl$, the Criteria for Requiring Medical Removal. Blood lead level of in the litharge and radiator manufacturing industry was below $40{\mu}g/dl$, the Criteria Requiring Temporary Medical Removal. Blood lead levels of workers by industry were Significantly different(p<0.05). 50(21 %) showed blood lead levels above $60{\mu}g/dl$, the Criteria for Removal and 66(27.7 %) showed blood lead levels between the Criteria for Requiring Medical Removal, $40-60{\mu}g/dl$. Thus, approximately 50 percent of workers indicated blood lead levels above $40{\mu}g/dl$, the Criteria Requiring Temporary Medical Removal and should receive medical examination and consultation including biological monitoring. Average ZPP level of workers employed in the secondary smelting industry was $186.2{\mu}g/dl$, exceeding above $150{\mu}g/dl$, the Criteria for Removal. Seventy seven of all workers(32.3 %) showed ZPP level above $100-150{\mu}g/dl$, the Criteria for Requiring Medical Removal. The most appropriate model for predicting ZPP in blood was log-linear regression model. Log linear regression models between lead and ZPP concentrations in blood was Log ZPP(${\mu}g/dl$) = -0.2340 + 1.2270 Log Pb-B(${\mu}g/dl$)(standard error of estimate: 0,089, ${\gamma}^2=0.4456$, n=238, P=0.0001), Blood-in-lead explained 44.56 % of the variance in log(ZPP in blood).

• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.597-599
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• 2006

In this paper, we propose an artificial intelligence method to compensate the nonlinearity error which occurs in the heterodyne laser interferometer. Some superior properties such as long measurement range, ultra-precise resolution and various system set-up lead the laser interferometer to be a practical displacement measurement apparatus in various industry and research area. In ultra-precise measurement such as nanometer or subnanometer scale, however, the accuracy is limited by the nonlinearity error caused by the optical parts. The feedforward neural network trained by back-propagation with a capacitive sensor as a reference signal minimizes the nonlinearity error and we demonstrate the effectiveness of our proppsed algorithm through some experimental results.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.29B no.1
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• pp.94-101
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• 1992

The multilayer perceptron, trained by the error back-propagation learning rule, has been known as a mapping network which can represent arbitrary functions. However depending on the complexity of a function and the initial weights of the multilayer perceptron, the error back-propagation learning may fall into a local minimum or a flat area which may require a long learning time or lead to unsuccessful learning. To solve such difficulties in training the multilayer perceptron by standard error back-propagation learning rule, the paper proposes a learning method which progressively enlarges the learning domain from a small area to the entire region. The proposed method is devised from the investigation on the roles of hidden nodes and connection weights in the multilayer perceptron which approximates a function of one variable. The validity of the proposed method was illustrated through simulations for a function of one variable and a function of two variable with many extremal points.