• Transactions of the Korean Society of Mechanical Engineers B
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• v.38 no.2
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• pp.107-114
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• 2014

This paper proposes a common rail pressure control algorithm for passenger car diesel engines. For handling the parameter-varying characteristics of common rail systems, the quantitative feedback theory (QFT) is applied to the design of a robust rail pressure control algorithm. The driving current of the pressure control valve and the common rail pressure are used as the input/output variables for the common rail system model. The model parameter uncertainty ranges are identified through experiments. Rail pressure controller requirements in terms of tracking performance, robust stability, and disturbance rejection are defined on a Nichols chart, and these requirements are fulfilled by designing a compensator and a prefilter in the QFT framework. The proposed common rail pressure control algorithm is validated through engine experiments. The experimental results show that the proposed rail pressure controller has a good degree of consistency under various operating conditions, and it successfully satisfies the requirements for reference tracking and disturbance rejection.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1539-1544
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• 2005

The gauge control of the fishing mill is very important because more and more accurately sized hot rolled coils are demanded by customers recently. Because the mill constant and the plasticity coefficient vary with the specifications of the mill, the classification of steel, the strip width, the strip thickness and the slab temperature, the variation of these parameters should be considered in the automatic gauge control system(AGC). Generally, the AGC gain is used to minimize the effect of the uncertain parameters. In a practical field, operators set the AGC gain as a constant value calculated by FSU (Finishing-mill Set-Up model) and it is not changed during the operating time. In this paper, the thickness data signals that occupy different frequency bands are respectively extracted by adaptive filters and then the main cause of the thickness variation is analyzed. Additionally, the AGC gain is adaptively tuned to reduce this variation using the online tuning model. Especially ANFIS(Adaptive-Neuro-based Fuzzy Interface System) which unifies both fuzzy logics and neural networks, is used for this gain adjustment system because fuzzy logics use the professionals' experiences about the uncertainty and the nonlinearity of the system. Simulation is performed by using POSCO's data and the results show that proposed on-line gain adjustment algorithm has a good performance.

• Journal of Environmental Science International
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• v.26 no.9
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• pp.1031-1043
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• 2017

Recently severe drought caused the water shortage around the western parts of Chungcheongnamdo province, South Korea. A Diversion tunnel from the Geum river to the Boryong dam, which is the water supply dam for these areas has been proposed to solve this problem. This study examined hydraulic impacts on the Geum river associated with the diversion plan assuming the severe drought condition of 2015 would persist for the simulation period of 2016. The hydraulic simulation model was verified using hydrologic and hydraulic data including hourly discharges of the Geum river and its 8 tributaries, fluctuation of tidal level at the mouth of the river, withdrawals and return flows and operation records of the Geum river barrage since Feb. 1, 2015 through May 31, 2015. For the upstream boundary condition of the Geum river predicted inflow series using the nonlinear regression equation for 2015 discharge data was used. In order to estimate the effects of uncertainty in inflow prediction to the results total four inflow series consisting of upper limit flow, expected flow, lower limit flow and instream flow were used to examine hydraulic impacts of the diversion plan. The simulation showed that in cases of upper limit and expected flows there would be no problem in taking water from the Geum river mouth with a minimum water surface level of EL(+) 1.44 m. Meanwhile, the simulation also showed that in cases of lower limit flow and instream flow there would be some problems not only in taking water for water supply from the mouth of the Geum river but also operating the diversion facility itself with minimum water surface levels of EL(+) 0.94, 0.72, 0.43, and 0.14 m for the lower limit flow without/with diversion and the instream flow without/with diversion, respectively.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.14 no.4
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• pp.151-159
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• 2015

In this study, we developed an automated contact-type thickness measurement machine that continuously and precisely measures the thickness of a PCB module product using multi-LVDT sensors. The system contains a measurement part to automatically measure the thickness in real time according to the set conditions with an alignment supply unit and unloading unit to separate OK and NG products. The sensors were calibrated before assembly in the measuring machine, and precision and accuracy performance tests were also performed to reduce uncertainty errors in the measurement machine. In the calibration test, the precision errors of the LVDT sensor were determined to be $1-3{\mu}m$ as 0.1% at the measuring range. A measurement error of 0.8 mm and 1.0 mm thickness test standards were found to be $1{\mu}m$ and $4{\mu}m$, and the standard deviations of two 1.0 mm products were measured as $14{\mu}m$ and $8{\mu}m$, respectively. In the measurement system analysis, the accuracies of test PCB standards were found to be $2{\mu}m$ and $3{\mu}m$, respectively. From the results of gage repeatability and reproducibility (R & R) crossed, we found that the machine is suitable for the measurement and process control in the mass production line as 7.92% of total gage R & R and in seven distinct categories. The maximum operating speed was limited at 13 pcs/min, showing a value good enough to measure.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.34 no.6
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• pp.743-749
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• 2010

This paper proposes a derating design method for improving system reliability by using a probabilistic approach. In the proposed design, the focus is upon system levels in determining derated levels of stresses such as temperature and current, unlike recent design approaches that focus on component levels. System reliability is evaluated using component reliability metrics that are given as functions of time and unknown stresses; this evaluation is based on a series system-reliability model. The variation in stress, which was not considered in previous derating designs, is introduced in the present design to account for the uncertainty in both environmental and operating conditions at the customer' hands. Optimization problems for system reliability improvement are formulated and solved using FORM to determine the best derating design. An example of a derating design for an electrical system shows the details of the proposed method and its applicability to systems design for reliability improvement.

• Transactions of the Korean Society of Mechanical Engineers
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• v.16 no.4
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• pp.675-683
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• 1992

Mapping and navigation system based on certainty grids for an autonomous mobile robt operating in unknown and unstructured environment is described. The system uses sonar range data to build a map of robot's surroundings. The range data from sonar sensor are integrated into a probability map that is composed of two dimensional grids which contain the probabilities of being occupied by the objects in the environment. A Bayesian model is used to estimate the uncertainty of the sensor information and to update the existing probability map with new range data. The resulting two dimensional map is used for path planning and navigation. In this paper, the Bayesian updating model which was successfully simulated in our earlier work is implemented on a mobile robot and is shown to be valid in the real world through experiment. This paper also proposes a technique for reducing for reducing specular reflection problem of sonar system which seriousely deteriorates the map quality, and a new path planning method based on weighted distance, which enables the robot to efficiently navigate in an unknown area.

• Fire Science and Engineering
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• v.24 no.6
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• pp.28-33
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• 2010

The present study develops a numerical model based on the computational fluid dynamics technique to analyse the thermal flow characteristics of large scale fire calorimeter and examine the characteristics of primary parameters affecting on the uncertainty of heat release rate measurement. ANSYS CFX version 12.1 which is a commercial CFD package is used to solve the governing equations of the thermal flow field and the eddy dissipation combustion model and P-1 radiation model are applied to simulate the fire driven flow. The numerical results shows that the horizontal duct system with $90^{\circ}$ bend duct was shown relatively high deviated asymmetric flow profiles at the sampling location and the deviation of the velocity field was higher than that of the temperature and species quantities. The present study shows that the computational model can be applicable to optimize the design process and operating condition of the large scale fire calorimeter based on the understanding of the detail flow field.

• Journal of the Society of Naval Architects of Korea
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• v.57 no.2
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• pp.80-87
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• 2020

As the Arctic sea ice decreases due to various reasons such as global warming, the demand for ships and offshore structures operating in the Arctic region is steadily increasing. In the case of sea ice, the anisotropy is caused by the uncertainty inside the material. For most of the research, nevertheless, estimating the ice load has been treated deterministically. With regard to this, in this paper, a four-point bending strength analysis of an ice specimen was attempted using a stochastic finite element method. First, spatial distribution of the material properties used in the yield criterion was assumed to be a multivariate Gaussian random field. After that, a direct method, which is a sort of stochastic finite element method, and a sensitivity method using the sensitivity of response for random variables were proposed for calculating the probabilistic distribution of ice specimen strength. A parametric study was conducted with different mean vectors and correlation lengths for each material property used in the above procedure. The calculation time was about ten seconds for the direct method and about three minutes for the sensitivity methods. As the cohesion and correlation length increased, the mean value of the critical load and the standard deviation increased. On the contrary, they decreased as the friction angle increased. Also, in all cases, the direct and sensitivity methods yielded very similar results.

• Journal of the Korean Solar Energy Society
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• v.34 no.4
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• pp.23-29
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• 2014

Recently, in power system studies, Multiple Energy Carriers (MECs) such as Energy Hub has been broadly utilized in power system planners and operators. Particularly, Energy Hub performs one of the most important role as the intermediate in implementing the MECs. However, it still needs to be put under examination in both modeling and operating concerns. For instance, a probabilistic optimization model is treated by a robust global optimization technique such as multi-agent genetic algorithm (MAGA) which can support the online economic dispatch of MECs. MAGA also reduces the inevitable uncertainty caused by the integration of selected input energy carriers. However, MAGA only considers current state of the integration of selected input energy carriers in conjunctive with the condition of smart grid environments for decision making in Energy Hub. Thus, in this paper, we propose an immune algorithm based Multiple Energy Carriers System which can adopt the learning process in order to make a self decision making in Energy Hub. In particular, the proposed immune algorithm considers the previous state, the current state, and the future state of the selected input energy carriers in order to predict the next decision making of Energy Hub based on the probabilistic optimization model. The below figure shows the proposed immune algorithm based Multiple Energy Carriers System. Finally, we will compare the online economic dispatch of MECs of two algorithms such as MAGA and immune algorithm based MECs by using Real Time Digital Simulator (RTDS).

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.10 no.6
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• pp.99-106
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• 2015

This is a case study on organizational learning with action learning. It targets B industry belonging to Shoe manufacturer. We apply action learning techniques as consulting skills to promote the organization of specific learning activities. Action Learning solves the challenges faced by the company with the ability to enhance the member while participating in the program. Therefore, it is a good methodology to overcome the uncertainty environment. Through a case study, in the maturing process of a learning organization can see the conditions that are necessary for the ongoing maintenance of that identity, organizational learning activities. Findings to the continued operation of the enterprise learning organization suggest the establishment of a learning organization, and direction and strategic importance. Systems and learning environments should be built and then repeat the process of practice to master the new learning organization. It suggests to learn a new organizations operating methods that require repetition of the course of action.