• Journal of Navigation and Port Research
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• v.40 no.5
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• pp.241-247
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• 2016

The tracking filter plays a key role in the accurate estimation and prediction of maneuvering a vessel's position and velocity when attempting to enhance safety by avoiding collision. Therefore, in order to achieve accurate estimation and prediction, many oceangoing vessels are equipped with the Automatic Radar Plotting Aid (ARPA) system. However, the accuracy of prediction depends on the tracking filter's ability to reduce noise and maintain a stable transient response. The purpose of this paper is to derive the optimal values of the gain parameters used in tracking a High Dynamic Warship. The algorithm employs a ${\alpha}-{\beta}-{\gamma}$ filter to provide accurate estimates and updates of the state variables, that is, positions, velocity and acceleration of the high dynamic warship based on previously observed values. In this study, the filtering coefficients ${\alpha}$, ${\beta}$ and ${\gamma}$ are determined from set values of the damping parameter, ${\xi}$. Optimization of the damping parameter, ${\xi}$, is achieved experimentally by plotting the residual error against different values of the damping parameter to determine the least value of the damping parameter that results in the optimum smoothing coefficients leading to a reduction in the noise corruption effect. Further investigation of the performance of the filter indicates that optimal smoothing coefficients depend on the initial and average velocity of the target.

• Ocean Systems Engineering
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• v.7 no.2
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• pp.75-87
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• 2017

A breakwater has always been an ideal option to prevent shoreline erosion due to wave action as well as to maintain the tranquility in the lagoon area. The effects of the impinging wave on the structure could be analyzed and evaluated by several physical and numerical methods. An alternate approach to the numerical methods in the prediction of performance of a breakwater is Artificial Intelligence (AI) tools. In the recent decade many researchers have implemented several Artificial Intelligence (AI) tools in the prediction of performance, stability number and scour of breakwaters. This paper is a comprehensive review which serves as a guide to the current state of the art knowledge in application of soft computing techniques in breakwaters. This study aims to provide a detailed review of different soft computing techniques used in the prediction of performance of different breakwaters considering various combinations of input and response variables.

• Transactions of the Korean hydrogen and new energy society
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• v.21 no.5
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• pp.460-469
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• 2010

To develop coal gasfication system, many studies have been actively conducted to describe the simulation of steady state. Now, it is necessary to study the gasification system not only in steady state but also in dynamic state to elucidate abnormal condition such as start-up, shut-down, disturbance, and develop control logic. In this study, a model was proposed with process simulation in dynamic state being conducted using a chemical process simulation tool, where a heat and mass transfer model in the gasifier is incorporated, The proposed model was verified by comparison of the results of the simulation with those available from NETL (National Energy Technology Laboratory) report under steady state condition. The simulation results were that the coal gas efficiency was 80.7%, gas thermal efficiency was 95.4%, which indicated the error was under 1 %. Also, the compositions of syngas were similar to those of the NETL report. Controlled variables of the proposed model was verified by increasing oxygen flow rate to gasifier in order to validate the dynamic state of the system. As a result, trends of major process variables were resonable when oxygen flow rate increased by 5% from the steady state value. Coal flow rate to gasifier and quench gas flow rate were increased, and flow rate of liquid slag was also increased. The proposed model in this study is able to be used for the prediction of gasification of various coals and dynamic analysis of coal gasification.

• Journal of the Korean Geotechnical Society
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• v.33 no.6
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• pp.27-36
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• 2017

Gravel compaction pile method has been widely used to improve the soft ground on the land or sea as one of the soft ground improvement technique. The ultimate bearing capacity of the ground reinforced by gravel compaction piles is affected by the soil strength, the replacement ratio of pile, construction conditions, and so on, and various prediction equations have been proposed to predict this. However, the prediction of the ultimate bearing capacity using the existing models has a very large error and variation, and it is not suitable for practical design. In this study, multiple regression analysis was performed using field loading test results to predict the ultimate bearing capacity of ground reinforced by gravel compaction pile, and the most efficient input variables are selected through evaluation of error by leave one out cross validation, and a multiple regression equation for the prediction of ultimate bearing capacity was proposed. In addition, the prediction error was evaluated by applying artificial neural network using the selected input variables, and the results were compared with those of the existing model.

• Proceedings of the Korea Water Resources Association Conference
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• 2005.05b
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• pp.220-224
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• 2005

칼만필터 알고리즘을 분포형 유출모형에 적용하였다. 관측 유량과 상태변수인 유역내 저류량을 갱신하고자 Q-S curve를 도입하였고, 갱신된 저류량과 모형에 의해 모의된 저류량의 비율을 유역 내 각 지점의 수위에 적용하므로써 분포화 된 상태변수를 효율적으로 갱신하였다. 갱신된 상태변수와 상태변수 오차의 시간갱신은 몬테 카를로 시뮬레이션을 이용하여 모의하였다.

• 제어로봇시스템학회:학술대회논문집
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• 1986.10a
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• pp.216-224
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• 1986

This paper treats the modelling and the control of the burnthrough point control system for an industrial sintering process. First, a state-space model is derived by defining new unconventional variables. A simple control law is proposed, which consists of the modified receding horizon control law and the least-squares prediction algorithm. The stability and the tracking properties of this control law are proved. The real-time experiments are carried out in a POSCO sintering plant and satisfactory results are presented in this paper. Before the real-time experiments, computer simulations are done and their results are also given for the comparison with the real-time experiments.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 1999.03b
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• pp.203-206
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• 1999

The shift of flow behavior due to dynamic recrystallization during hot forming process is investigated, A series of load relaxation and compression tests has been conducted at various temperatures Constitutive relations and recrystallization behaviors were formulated from the mechanical test results, The consideration of dynamic recrystallization during a specific forming process was implemented to commercial FEM package by conditioned remeshing and remapping of state variables. Improvement of Load-Stroke prediction was validated by comparison with experimental results.

• Journal of the Korean Society of Safety
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• v.21 no.6 s.78
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• pp.74-81
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• 2006

Probabilistic Risk Assessment considering statistically random variables is performed for the preliminary design of a Arch Bridge. Component reliabilities of girders have been evaluated using the response surfaces of the design variables at the selected critical sections based on the maximum shear and negative moment locations. Response Surface Method(RSM) is successfully applied for reliability analyses for this relatively small probability of failure of the complex structure, which is hard to be obtained by Monte-Carlo Simulations or by First Order Second Moment Method that can not easily calculate the derivative terms of implicit limit state functions. For the analysis of system reliability, parallel resistance system composed of girders is changed into parallel series connection system. The upper and lower probabilities of failure for the structural system have been evaluated and compared with the suggested prediction method for the combination of failure modes. The suggested prediction method for the combination of failure modes reveals the unexpected combinations of element failures in significantly reduced time and efforts compared with the previous permutation method or system reliability analysis method.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2007.04a
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• pp.295-300
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• 2007

Probabilistic Risk Assessment considering statistically random variables is performed for the preliminary design of a Arch Bridge. Component reliabilities of girders have been evaluated using the response surfaces of the design variables at the selected critical sections based on the maximum shear and negative moment locations. Response Surface Method (RSM) is successfully applied for reliability analyses for this relatively small probability of failure of the complex structure, which is hard to be obtained by Monte-Carlo Simulations or by First Order Second Moment Method that can not easily calculate the derivative terms of implicit limit state functions. For the analysis of system reliability, parallel resistance system composed of girders is changed into parallel series connection system. The upper and lower probabilities of failure for the structural system have been evaluated and compared with the suggested prediction method for the combination of failure modes. The suggested prediction method for the combination of failure modes reveals the unexpected combinations of element failures in significant]y reduced time and efforts compared with the previous permutation method or system reliability analysis method.

• International Journal of Advanced Culture Technology
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• v.11 no.2
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• pp.249-255
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• 2023

This study aims to empirically confirm the effect and impact of community care design research centered on domestic space and environment on health promotion, diagnosis treatment, disease management, rehabilitation, and mitigation through the year of publication and perspective. To this end, based on 1,227 space and environment design studies from 2,144 community care design research data conducted for about 20 years from 2002 to 2022, when care services began in earnest through the long-term care system for the elderly, SPSS 26.0 was used to create a 'Multi-layer Perceptron' artificial neural network structure model was predicted and neural network analysis was performed. Research Results First, as a result of checking studies in each field of health care by year, there is a significant difference with the number of studies related to health promotion being the highest. Second, the five perspectives are region, time, dimension, function, and content perspective. As a result of inputting these variables as independent variables and analyzing their importance in the artificial neural network, the function perspective had the most influence, followed by the region > content > dimension > time perspective.