• Journal of the Korea Institute of Military Science and Technology
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• v.27 no.3
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• pp.416-427
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• 2024

The wake-homing underwater guided weapon that detects and tracks wake generated during voyage of a surface ship is impossible to avoid with the present acoustic deception torpedo defense system. Therefore, research on bubble-generating wake decoy is necessary to deceive wake-homing underwater guided weapon. Experiments in various environments are required to verify the effective operation method and performance of the wake decoy, but performance verification through underwater experiment is limited. In this paper, we develop a simulator for an torpedo defense system of surface ship, which is applied bubble-generating wake decoy, against acoustic, wake, and hybrid homing underwater guided weapon attack. The simulator includes surface ship model, acoustic decoy(static, mobile) model, bubble-generating wake decoy model, search and motion model of underwater guided weapon and so on. By integrating various models, MATLAB GUI simulator was developed. Through the simulation results for various environmental variables by this simulator, it is judged that effective operation method and performance verification of the bubble-generating wake decoy can be performed.

• Journal of ILASS-Korea
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• v.27 no.2
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• pp.57-65
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• 2022

The purpose of this study is to use machine learning to build a model capable of predicting the flash boiling spray characteristics. In this study, the flash boiling spray was visualized using Shadowgraph visualization technology, and then the spray image was processed with MATLAB to obtain quantitative data of spray characteristics. The experimental conditions were used as input, and the spray characteristics were used as output to train the machine learning model. For the machine learning model, the XGB (extreme gradient boosting) algorithm was used. Finally, the performance of machine learning model was evaluated using R² and RMSE (root mean square error). In order to have enough data to train the machine learning model, this study used 12 injectors with different design parameters, and set various fuel temperatures and ambient pressures, resulting in about 12,000 data. By comparing the performance of the model with different amounts of training data, it was found that the number of training data must reach at least 7,000 before the model can show optimal performance. The model showed different prediction performances for different spray characteristics. Compared with the upstream spray angle and the downstream spray angle, the model had the best prediction performance for the spray tip penetration. In addition, the prediction performance of the model showed a relatively poor trend in the initial stage of injection and the final stage of injection. The model performance is expired to be further enhanced by optimizing the hyper-parameters input into the model.

• KIEAE Journal
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• v.16 no.3
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• pp.89-94
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• 2016

Purpose: This study aimed at developing an artificial neural network (ANN) model to predict the optimal start moment of the setback temperature during the normal occupied period of a building. Method: For achieving this objective, three major steps were conducted: the development of an initial ANN model, optimization of the initial model, and performance tests of the optimized model. The development and performance testing of the ANN model were conducted through numerical simulation methods using transient systems simulation (TRNSYS) and matrix laboratory (MATLAB) software. Result: The results analysis in the development and test processes revealed that the indoor temperature, outdoor temperature, and temperature difference from the setback temperature presented strong relationship with the optimal start moment of the setback temperature; thus, these variables were used as input neurons in the ANN model. The optimal values for the number of hidden layers, number of hidden neurons, learning rate, and moment were found to be 4, 9, 0.6, and 0.9, respectively, and these values were applied to the optimized ANN model. The optimized model proved its prediction accuracy with the very storing statistical correlation between the predicted values from the ANN model and the simulated values in the TRNSYS model. Thus, the optimized model showed its potential to be applied in the control algorithm.

• KIEAE Journal
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• v.17 no.5
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• pp.69-76
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• 2017

Purpose: This study aimed at developing an Artificial Neural Network (ANN) model for predicting the amount of cooling energy consumption of the variable refrigerant flow (VRF) cooling system by the different set-points of the control variables, such as supply air temperature of air handling unit (AHU), condenser fluid temperature, condenser fluid pressure, and refrigerant evaporation temperature. Applying the predicted results for the different set-points, the control algorithm, which embedded the ANN model, will determine the most energy efficient control strategy. Method: The ANN model was developed and tested its prediction accuracy by using matrix laboratory (MATLAB) and its neural network toolbox. The field data sets were collected for the model training and performance evaluation. For completing the prediction model, three major steps were conducted - i) initial model development including input variable selection, ii) model optimization, and iii) performance evaluation. Result: Eight meaningful input variables were selected in the initial model development such as outdoor temperature, outdoor humidity, indoor temperature, cooling load of the previous cycle, supply air temperature of AHU, condenser fluid temperature, condenser fluid pressure, and refrigerant evaporation temperature. The initial model was optimized to have 2 hidden layers with 15 hidden neurons each, 0.3 learning rate, and 0.3 momentum. The optimized model proved its prediction accuracy with stable prediction results.

• The Transactions of the Korean Institute of Power Electronics
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• v.11 no.1
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• pp.79-89
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• 2006

Particularly the photovoltaic systems are preferred because the output is extracted to the useful electric energy. However, the output characteristics of photovoltaic(PV) systems using solar cell or array depend on the weather conditions. The assistant equipment which emulates the solar cell characteristics that can be controlled arbitrarily by researcher is required to the researchers for reliable experimental data. To solve these problems, it is necessary to research a solar cell model of which output characteristics varied by setting the weather conditions such as insolation levels and temperatures. Therefore, this paper was presented that improved model which is based on interpolation model. To verified the improved model, it is confirmed using the simulation of MATLAB. Also, the experiment was performed by the characteristics of virtual implemented solar cell(VISC) system with the proposed solar cell model. It could be confirmed that there exists actual ewer within 5% between actual solar cell and VISC system.

• Transactions of the Korean Society of Automotive Engineers
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• v.21 no.1
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• pp.43-50
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• 2013

The stringent emission regulation and future shortage of fossil fuel motivate the research of alternative powertrain. In this study, a system of proton exchange membrane fuel cell has been modeled to analyze the performance of the fuel cell system for automotive application. The model is composed of the fuel cell stack, air compressor, humidifier, and intercooler, and hydrogen supply which are implemented by using the Matlab/Simulink(R). Fuel cell stack model is empirical model but the water transport model is included so that the system performance can be predicted over various humidity conditions. On the other hand, the model of air compressor is composed of motor, static air compressor, and some manifolds so that the motor dynamics and manifold dynamics can be investigated. Since the model is concentrated on the strategic operation of compressor to reduce the power consumption, other balance of components (BOP) are modeled to be static components. Since the air compressor model is empirical model which is based on curve fitting of experiments, the stack model is validated with the commercial software and the experiments. The dynamics of air compressor is investigated over unit change of system load. The results shows that the power consumption of air compressor is about 12% to 25% of stack gross power and dynamic response should be reduced to optimize the system operation.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.5
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• pp.537-542
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• 2014

This paper proposes a control system to keep the balance of a unicycle robot. The robot consists of the disk and wheel, for balancing and driving respectively, and the tile angle is measured and used for balancing by the IMU sensor. A PID controller is designed based on a non-model based algorithm to prove that it is possible to control the unicycle robot without any approximated linear system model such as the sliding mode control algorithm. The PID controller has the advantage that it is simple to design the controller and it does not require an unnecessary complex formula. In this paper, assuming that the pitch and roll axis are dynamically decoupled, each of the two controllers are designed separately. A reaction wheel pendulum method is used for the control of the roll axis, that is, for balancing and an inverted pendulum concept is used for the control of the pitch axis. To confirm the performance of the proposed controllers using MATLAB Simulink, the dynamic equations of the robot are derived.

• Korean Journal of Computational Design and Engineering
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• v.20 no.3
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• pp.298-311
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• 2015

Nowadays, automatic digging operation of an excavator is a big challenge due to the complexity of digging environment, the hardness of soil and buried obstacles into the ground. In order to achieve the maximum soil bucket volume, this paper introduces a novel engineering model that was developed as a virtual excavator in the design phase. Through this model, the designs of mechanical and control systems for autonomous excavator are executed and modified easily before developing in real testbed. Based on a concept of an autonomous excavation, a mechanical system of excavator was first designed in SOLIDWORKS, and a soil model also was modeled by finite-element analysis in ANSYS, both modeled models were then exported to ADAMS environment to investigate the digging behavior through virtual simulation. An intelligent control strategy was generated in MATLAB/Simulink to control the excavator operation. The simulation results were demonstrated by effectiveness of the proposed excavator robot in testing scenarios with many soil types and obstacles.

• Journal of international Conference on Electrical Machines and Systems
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• v.1 no.4
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• pp.405-411
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• 2012

This paper deals with direct torque control of an induction motor (IM) with constant switching frequency. The desired torque is obtained from the speed controller which is designed using the IP controller. Decoupling control of torque and flux is developed based on the energy model of IM using the IP controller strategies. The desired d-axis and q-axis stator voltage components are obtained from the designed controller, which decouples torque and flux. The constant switching frequency can be applied using space-vector pulse width modulation, since the desired stator voltage can be known from the decoupling torque and flux controllers. In order to achieve stable operation of the proposed IP controllers, the gains of the controllers are chosen by setting the poles in negative (left) half of s-plane and by choosing the rising time for the response of the step function. The proposed controller was verified in simulations using Matlab/Simulink and results have proven excellent performance. It was found that the proposed IP controllers can provide excellent performance to track the desired torque and speed and to reject the disturbance of load.

• Computers and Concrete
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• v.22 no.4
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• pp.419-437
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• 2018

The compressive strength of self-compacting concrete (SCC) containing fly ash (FA) is highly related to its constituents. The principal purpose of this paper is to investigate the efficiency of hybrid fuzzy radial basis function neural network with biogeography-based optimization (FRBFNN-BBO) for predicting the compressive strength of SCC containing FA based on its mix design i.e., cement, fly ash, water, fine aggregate, coarse aggregate, superplasticizer, and age. In this regard, biogeography-based optimization (BBO) is applied for the optimal design of fuzzy radial basis function neural network (FRBFNN) and the proposed model, implemented in a MATLAB environment, is constructed, trained and tested using 338 available sets of data obtained from 24 different published literature sources. Moreover, the artificial neural network and three types of radial basis function neural network models are applied to compare the efficiency of the proposed model. The statistical analysis results strongly showed that the proposed FRBFNN-BBO model has good performance in desirable accuracy for predicting the compressive strength of SCC with fly ash.