• Journal of the Society of Naval Architects of Korea
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• v.50 no.1
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• pp.33-40
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• 2013

Since most of yacht sails are made of thin fabric, they form cambered sail shape that can efficiently generate lift power by aerodynamic interaction and by external force delivered from supporting structures such as mast and boom. When the incident flow and external force alter in terms of volume or condition, the shape of sail also change. This deformation in shape has impact on the peripheral flow and aerodynamic interaction of the sail, and thus it is related to the deformation of the sail in shape again. Therefore, the precise optimization of aerodynamic performance of sail requires fluid-structure interaction (FSI) analysis. In this study, the simplified sail without camber was under experiment for one-way FSI that uses the result of flow analysis to the structural analysis as load condition in an attempt to fluid-structure interaction phenomenon. To confirm the validity of the analytical methods and the reliability of numerical computation, the difference in deformation by the number of finite element was compared. This study reproduced the boundary conditions that sail could have by rigs such as mast and boom and looked into the deformation of sail. Sail has non-linear deformation such as wrinkles because it is made of a thin fabric material. Thus non-linear structural analysis was conducted and the results were compared with those of analysis on elastic material.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.4
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• pp.554-561
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• 2013

In this paper, we introduce a modeling and a process simulator developed with the aid of pRBFNNs for activated sludge process in the sewage treatment system. Activated sludge process(ASP) of sewage treatment system facilities is a process that handles biological treatment reaction and is a very complex system with non-linear characteristics. In this paper, we carry out modeling by using essential ASP factors such as water effluent quality, the manipulated value of various pumps, and water inflow quality, and so on. Intelligent algorithms used for constructing process simulator are developed by considering multi-output polynomial radial basis function Neural Networks(pRBFNNs) as well as Fuzzy C-Means clustering and Particle Swarm Optimization. Here, the apexes of the antecedent gaussian functions of fuzzy rules are decided by C-means clustering algorithm and the apexes of the consequent part of fuzzy rules are learned by using back-propagation based on gradient decent method. Also, the parameters related to the fuzzy model are optimized by means of particle swarm optimization. The coefficients of the consequent polynomial of fuzzy rules and performance index are considered by the Least Square Estimation and Mean Squared Error. The descriptions of developed process simulator architecture and ensuing operation method are handled.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.39 no.6
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• pp.853-858
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• 2019

One of the methods which can enhance the efficiency of railroad network is construction of interconnection branch line for several route to share one railway. In Korea, this method already has been implemented or excuted as project level. This study suggests a network design model and a solution algorithm to choice most proper site to construction it and determine the priority of branch lines which can be considered in planning level, not project level. The model is a non-linear optimization program which minimize total cost-construction cost, operating cost and passengers' travel cost. The decision variables are a binary variable to explain whether construction or not and its direction and a integer variable of the frequencies of travel routes. The solution algorithm-problem solution and route choice and also the result of implementation for example network are suggested. This result can be more advanced after application in real network and calibration of parameters.

• Industrial Engineering and Management Systems
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• v.4 no.1
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• pp.54-67
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• 2005

This paper provides an evaluation of an optimization-based, multiple-input double-output (MIDO) run-to-run (R2R) control scheme for general semiconductor manufacturing processes. The controller in this research, termed adaptive dual response optimizing controller (ADROC), can serve as a process optimizer as well as a recipe regulator between consecutive runs of wafer fabrication. In evaluation, it is assumed that the equipment model could be appropriately described by a pair of second-order polynomial functions in terms of a set of controllable variables. Of practical relevance is to consider a drifting effect in the equipment model since in common semiconductor practice the process tends to drift due to machine aging and tool wearing. We select a typical application of R2R control to chemical mechanical planarization (CMP) in semiconductor manufacturing in this evaluation, and there are five different CMP process scenarios demonstrated, including mean shift, variance increase, and IMA disturbances. For the controller, ADROC, an on-line estimation technique is implemented in a self-tuning (ST) control manner for the adaptation purpose. Subsequently, an ad hoc global optimization algorithm based on the dual response approach, arising from the response surface methodology (RSM) literature, is used to seek the optimum recipe within the acceptability region for the execution of next run. The main components of ADROC are described and its control performance is assessed. It reveals from the evaluation that ADROC can provide excellent control actions for the MIDO R2R situations even though the process exhibits complicated, nonlinear interaction effects between control variables, and the drifting disturbances.

• Journal of Astronomy and Space Sciences
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• v.38 no.3
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• pp.157-164
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• 2021

We describe a method for the in-orbit calibration of body-mounted magnetometers based on the CHAOS-7 geomagnetic field model. The code is designed to find the true calibration parameters autonomously by using only the onboard magnetometer data and the corresponding CHAOS outputs. As the model output and satellite data have different coordinate systems, they are first transformed to a Star Tracker Coordinate (STC). Then, non-linear optimization processes are run to minimize the differences between the CHAOS-7 model and satellite data in the STC. The process finally searches out a suite of calibration parameters that can maximize the model-data agreement. These parameters include the instrument gain, offset, axis orthogonality, and Euler rotation matrices between the magnetometer frame and the STC. To validate the performance of the Python code, we first produce pseudo satellite data by convoluting CHAOS-7 model outputs with a prescribed set of the 'true' calibration parameters. Then, we let the code autonomously undistort the pseudo satellite data through optimization processes, which ultimately track down the initially prescribed calibration parameters. The reconstructed parameters are in good agreement with the prescribed (true) ones, which demonstrates that the code can be used for actual instrument data calibration. This study is performed using Python 3.8.5, NumPy 1.19.2, SciPy 1.6, AstroPy 4.2, SpacePy 0.2.1, and ChaosmagPy 0.5 including the CHAOS-7.6 geomagnetic field model. This code will be utilized for processing NextSat-1 and Small scale magNetospheric and Ionospheric Plasma Experiment (SNIPE) data in the future.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.1B
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• pp.27-38
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• 2006

The storage function model (SFM) has been used for the flood forecasting in Korea. The SFM has a simple calculation process and it is known that the model is more reasonable than linear model because it considers non-linearity of flood runoff. However, the determination of parameters is very difficult. In general, the trial and error method which is an manual calibration by the decision of a model manager. This study calibrated the parameters by the trial and error method and optimization technique. The calibrated parameters were compared with the representative parameters which are used in the Flood Control Centers in Korea. Also, the evaluation indexes on objective functions and calibration methods for the comparative analysis of simulation efficiency. As a result, the Genetic Algorithm showed the smallest variation in objective functions and, in this study, it is known that the objective function of SSR (Sum of Squared of Residual) is the best one for the flood forecasting.

• Ocean Systems Engineering
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• v.6 no.3
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• pp.265-287
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• 2016

The change of the global performance of a turret-moored FPSO (Floating Production Storage Offloading) with DP (Dynamic Positioning) control is simulated, analyzed, and compared for two different internal turret location cases; bow and midship. Both collinear and non-collinear 100-yr GOM (Gulf of Mexico) storm environments and three cases (mooring-only, with DP position control, with DP position+heading control) are considered. The horizontal trajectory, 6DOF (degree of freedom) motions, fairlead mooring and riser tension, and fuel consumptions are compared. The PID (Proportional-Integral-Derivative) controller based on LQR (linear quadratic regulator) theory and the thrust-allocation algorithm which is based on the penalty optimization theory are implemented in the fully-coupled time-domain hull-mooring-riser-DP simulation program. Both in collinear and non-collinear 100-yr WWC (wind-wave-current) environments, the advantage of mid-ship turret is demonstrated by the significant reduction in heave at the turret location due to the minimal coupling with pitch mode, which is beneficial to mooring and riser design. However, in the non-collinear WWC environment, the mid-turret case exhibits unfavorable weathervaning characteristics, which can be reduced by employing DP position and heading controls as demonstrated in the present case studies. The present study also reveals the plausible cause of the failure of mid-turret Gryphon Alpha FPSO in milder environment than its survival condition.

• Food Science and Preservation
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• v.20 no.6
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• pp.810-817
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• 2013

This study investigated the properties of rice snack puffed in a microwave oven after drying its dough according to Baekjinju soaking time and additional soybean milk. The optimum conditions for the non-fried rice snack using Baekjinju wetted flour were determined through the design of an experiment using response surface methodology. The independent variables were the Baekjinju soaking time and the additional soybean milk, and the dependent variables were the weight, volume, density, expansibility, Hunter's color value, hardness, and sensory properties. The quadratic model was chosen for the weight, density, expansibility, b value, and hardness. The two-factor interaction model was chosen for the volume, flavor, appearance, and overall preference. The linear model was chosen for the L value, taste, and texture. The weight was increased to longer than 11.26 days with the increase in the rice soaking. The volume, expansibility, L value, and b value increased with the increase in the rice soaking time and in the additional soybean milk ratio. However, the density was decreased was in reverse. The hardness increased most, with the rice soaking time rising from 5.28 to 8.53 days and the soybean milk additional ratio increasing from 5.34 to 20.26%. The sensory properties improved as rice soaking time decreased, and the soybean milk additional ratio was revered. As for the desirability, the optimal formulation of the dough of non-fried rice snack was achieved by mixing 200 g of Baekjinju flour with a 0.69 days rice soaking time and a 26.67% soybean milk of rice ratio according to weight.

• Journal of the Korean Data and Information Science Society
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• v.28 no.6
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• pp.1229-1244
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• 2017

In recent years, as demand for data-based analytical methodologies increases in various fields, optimization methods have been developed to handle them. In particular, various constraints required for problems in statistics and machine learning can be solved by convex optimization. Alternating direction method of multipliers (ADMM) can effectively deal with linear constraints, and it can be effectively used as a parallel optimization algorithm. ADMM is an approximation algorithm that solves complex original problems by dividing and combining the partial problems that are easier to optimize than original problems. It is useful for optimizing non-smooth or composite objective functions. It is widely used in statistical and machine learning because it can systematically construct algorithms based on dual theory and proximal operator. In this paper, we will examine applications of ADMM algorithm in various fields related to statistics, and focus on two major points: (1) splitting strategy of objective function, and (2) role of the proximal operator in explaining the Lagrangian method and its dual problem. In this case, we introduce methodologies that utilize regularization. Simulation results are presented to demonstrate effectiveness of the lasso.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.8
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• pp.2774-2796
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• 2015

Quality-of-service (QoS) provisioning for a cognitive mesh network (CMN) with heterogeneous services has become a challenging area of research in recent days. Considering both real-time (RT) and non-real-time (NRT) traffic in a multihop CMN, [1] studied cross-layer resource management, including joint access control, route selection, and resource allocation. Due to the complexity of the formulated resource allocation problems, which are mixed-integer non-linear programming, a low-complexity yet efficient algorithm was proposed there to approximately solve the formulated optimization problems. In contrast, in this work, we present an application of genetic algorithm (GA) to re-address the hard resource allocation problems studied in [1]. Novel initialization, selection, crossover, and mutation operations are designed such that solutions with enough randomness can be generated and converge with as less number of attempts as possible, thus improving the efficiency of the algorithm effectively. Simulation results show the effectiveness of the newly proposed GA-based algorithm. Furthermore, by comparing the performance of the newly proposed algorithm with the one proposed in [1], more insights have been obtained in terms of the tradeoff among QoS provisioning for RT traffic, throughput maximization for NRT traffic, and time complexity of an algorithm for resource allocation in a multihop network such as CMN.