• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.18 no.6 s.121
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• pp.602-611
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• 2007

In this paper, the forward link of UHF RFID system is modeled in accordance with the EPCglobal class 1 generation 2(EPCglobal C1G2) UHF Radio-Frequency Identity protocol specification at $860{\sim}960MHz$. Based on the constructed model, characteristics on the forward link signal for the EPCglobal C1G2 RFID reader are simulated with the help of a MATLAB softwarein order to extract the design parameters of a transmit digital filter which meets the Korean RFID regulations. Herein, the forward link model is consisted of PIE source coding, transmit digital filter, modulation, local oscillator, and antenna. From the simulation results, the ranges of three design parameters(roll-off factor, cutoff frequency, the number of tabs) for transmit digital filter are obtained with different modulation techniques and the Tari(type a reference interval) values. Finally, DSB/SSB-ASK modulation technique can not satisfy the EPCglobal C1G2 specification when Tari equals to $6.25{\mu}sec$ in a multiple-reader environment. Consequently this paper can provide a guideline for design parameters of a RFID reader as well as the basic scheme of analyzing frequency interference problems in RFID environments, including multiple-reader and dense-reader environments.

• Korean Chemical Engineering Research
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• v.61 no.4
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• pp.542-549
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• 2023

Since the growing interest in surrogate modeling, there has been continuous research aimed at simulating nonlinear chemical processes using data-driven machine learning. However, the opaque nature of machine learning models, which limits their interpretability, poses a challenge for their practical application in industry. Therefore, this study aims to analyze chemical processes using Explainable Artificial Intelligence (XAI), a concept that improves interpretability while ensuring model accuracy. While conventional sensitivity analysis of chemical processes has been limited to calculating and ranking the sensitivity indices of variables, we propose a methodology that utilizes XAI to not only perform global and local sensitivity analysis, but also examine the interactions among variables to gain physical insights from the data. For the ammonia synthesis process, which is the target process of the case study, we set the temperature of the preheater leading to the first reactor and the split ratio of the cold shot to the three reactors as process variables. By integrating Matlab and Aspen Plus, we obtained data on ammonia production and the maximum temperatures of the three reactors while systematically varying the process variables. We then trained tree-based models and performed sensitivity analysis using the SHAP technique, one of the XAI methods, on the most accurate model. The global sensitivity analysis showed that the preheater temperature had the greatest effect, and the local sensitivity analysis provided insights for defining the ranges of process variables to improve productivity and prevent overheating. By constructing alternative models for chemical processes and using XAI for sensitivity analysis, this work contributes to providing both quantitative and qualitative feedback for process optimization.

• IEIE Transactions on Smart Processing and Computing
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• v.5 no.1
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• pp.35-42
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• 2016

Developments in power electronics have enabled the widespread application of Pulse Width Modulation (PWM) inverters, notably for connecting renewable systems to the grid. This study demonstrates that a high-quality power can be achieved using a stand-alone inverter, whereby the comparison between the power quality of the stand-alone inverter with battery storage (off-grid) and the power quality of the utility network is presented. Multi-loop control techniques for a single phase stand-alone inverter are used. A capacitor current control is used to give active damping and enhance the transient and steady state inverter performance. A capacitor current control is cheaper than the inductor current control, where a small current sensing resistor is used. The output voltage control is used to improve the system performance and also control the output voltage. The inner control loop uses a proportional gain current controller and the outer loop is implemented using internal model control proportional-integral-derivative to ensure stability. The optimal controls are achieved by using the Sisotool tool in MATLAB/Simulink. The outcome of the control scheme of the numerical model of the stand-alone inverter has a smooth and good dynamic performance, but also a strong robustness to load variations. The numerical model of the stand-alone inverter and its power quality are presented, and the power quality is shown to meet the IEEE 519-2014. Furthermore, the power quality of the off-grid system is measured experimentally and compared with the grid power, showing power quality of off-grid system to be better than that of the utility network.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.40 no.2
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• pp.229-236
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• 2016

For this research, the trajectory of a projectile was simulated via the multibody dynamics analysis of a self-propelled mortar. The dynamic model was composed of a mortar model and a vehicle model, and was simulated using the RecurDyn program. Interior ballistic was applied to the mortar model, and exterior ballistic was conducted by Matlab using the simulation results of the interior trajectory. Through repetitive Monte-Carlo simulations, the accuracy of the mortar was analyzed by considering variations in the aiming angle and vehicle dynamic response.

• Safety and Health at Work
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• v.11 no.3
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• pp.322-334
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• 2020

Background: Spontaneous combustion of coal is one of the factors which causes direct or indirect gas and dust explosion, mine fire, the release of toxic gases, loss of reserve, and loss of miners' life. To avoid these incidents, the prediction of spontaneous combustion is essential. The safety of miner's in the mining field can be assured if the prediction of a coal fire is carried out at an early stage. Method: Adularya Underground Coal Mine which is fully mechanized with longwall mining method was selected as a case study area. The data collected for 2017, by sensors from ten gas monitoring stations were used for the simulation and prediction of a coal fire. In this study, the fuzzy logic model is used because of the uncertainties, nonlinearity, and imprecise variables in the data. For coal fire prediction, CO, O₂, N₂, and temperature were used as input variables whereas fire intensity was considered as the output variable.The simulation of the model is carried out using the Mamdani inference system and run by the Fuzzy Logic Toolbox in MATLAB. Results: The results showed that the fuzzy logic system is more reliable in predicting fire intensity with respect to uncertainties and nonlinearities of the data. It also indicates that the 1409 and 610/2B gas station points have a greater chance of causing spontaneous combustion and therefore require a precautional measure. Conclusion: The fuzzy logic model shows higher probability in predicting fire intensity with the simultaneous application of many variables compared with Graham's index.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.1
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• pp.21-28
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• 2010

This paper describes the mathematical model and controller design for Manta-type Unmanned Underwater Test Vehicle (MUUTV) with 6 DOF nonlinear dynamic equations. The mathematical model contains hydrodynamic forces and moments expressed in terms of a set of hydrodynamic coefficients which were obtained through the PMM (Planar Motion Mechanism) test. Based on the 6 DOF dynamic equations, numerical simulations have been performed to analyze the dynamic performances of the MUUTV. In addition, using the mathematical model PID and sliding mode controller are constructed for the diving and steering maneuver. Simulation results show that the control performances of the MUUTV and compared with these of NPS (Naval Postgraduate School) AUV II.

• Korean Journal of Applied Biomechanics
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• v.27 no.1
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• pp.9-18
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• 2017

Objective: The purpose of this study was to identify kinematic variables that govern successful performance and judges' scores and to establish correlative relationships among those of Yurchenko layout with a full twist in female vaults. Method: Four video cameras with sampling rate of 60 Hz collected 32 motion data of Yurchenko vaults from twenty-two female participants (age: $18.6{\pm}3.6years$, height: $153.0{\pm}6.5cm$, mass: $44.7{\pm}7.3kg$) during national competition. Posting processing and calculations of kinematic variables were performed in Kwon 3D XP and $Matlab^{(R)}$ programs. Correlation and regression analyses were applied to find the relationships between the obtained scores and kinematic variables. Deterministic model (Hay & Reid, 1988) was used to investigate the strength of correlative relationships among kinematic variables. Results: The obtained scores from the judges' decision were mainly affected by post-flight peak height, horse contact time, knee angle at landing, and horse takeoff angle. Strong blocking during horse contact was required to get successful performance and obtain high scores. Modified deterministic model showed that round-off entrance and takeoff angles and resultant velocity of the center of mass (CM) during the roundoff phase were the starting variables affecting performance in the following kinematics. Knee angle at landing, a highly influential variable on the obtained point, was only determined by judges' decision without significant correlative relationship with previous kinematic variables. Conclusion: The obtained scores highly depended on kinematic variables of post-flight and horse contact phases that were affected by those from the previous phases including round-off postures and resultant velocity of the body center of mass.

• Journal of Biomedical Engineering Research
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• v.29 no.3
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• pp.212-221
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• 2008

The total hip replacement (THR) has been used as the most effective way to restore the function of damaged hip joint. However, various factors have caused some side effects after the THR. Unfortunately, the success of the THR have been decided only by the proficiency of surgeons so far. Hence, It is necessary to find the way to minimize the side effect caused by those factors. The purpose of this study was to suggest the definite data, which can be used to design and choose the optimal hip implant. Using finite element analysis (FEA), the biomechanical condition of bone cement was evaluated. Stress patterns were analyzed in three conditions: cement mantle, procimal femur and stem-cement contact surface. Additionally, micro-motion was analyzed in the stem-cement contact surface. The 3-D femur model was reconstructed from 2-D computerized tomography (CT) images. Raw CT images were preprocessed by image processing technique (i.e. edge detection). In this study, automated edge detection system was created by MATLAB coding for effective and rapid image processing. The 3-D femur model was reconstructed based on anatomical parameters. The stem shape was designed using that parameters. The analysis of the finite element models was performed with the variation of parameters. The biomechanical influence of each parameter was analyzed and derived optimal parameters. Moreover, the results of FE A using commercial stem model (Zimmer's V erSys) were similar to the results of stem model that was used in this study. Through the study, the improved designs and optimal factors for clinical application were suggested. We expect that the results can suggest solutions to minimize various side effects.

• Journal of Power Electronics
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• v.17 no.4
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• pp.1014-1026
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• 2017

The traditional fault diagnosis method for photovoltaic (PV) inverters has a difficult time meeting the requirements of the current complex systems. Its main weakness lies in the study of nonlinear systems. In addition, its diagnosis time is long and its accuracy is low. To solve these problems, a hidden Markov model (HMM) is used that has unique advantages in terms of its training model and its recognition for diagnosing faults. However, the initial value of the HMM has a great influence on the model, and it is possible to achieve a local minimum in the training process. Therefore, a genetic algorithm is used to optimize the initial value and to achieve global optimization. In this paper, the HMM is combined with a genetic algorithm (GHMM) for PV inverter fault diagnosis. First Matlab is used to implement the genetic algorithm and to determine the optimal HMM initial value. Then a Baum-Welch algorithm is used for iterative training. Finally, a Viterbi algorithm is used for fault identification. Experimental results show that the correct PV inverter fault recognition rate by the HMM is about 10% higher than that of traditional methods. Using the GHMM, the correct recognition rate is further increased by approximately 13%, and the diagnosis time is greatly reduced. Therefore, the GHMM is faster and more accurate in diagnosing PV inverter faults.

• Journal of the Korean Institute of Intelligent Systems
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• v.27 no.1
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• pp.35-41
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• 2017

This paper presents the development of a monitoring and control system for a vehicle cluster using a model-based design technique. For MBD(model-based design), MATLAB GUI(Graphic User Interface), M programs, simulink, state flow, and tool boxes are used to monitor a number of data such as warning, interrupts, and etc. connected to a real vehicle cluster. As a monitoring tool, a PC(Personal Computer) station interworks with the real vehicle cluster through the interface commands of tool boxes. Thus, unlike existing text-based designs, the MBD based vehicle cluster system provides very easy algorithm updates and addition, since it offers a number of blocks and state flow programs for each functional actions. Furthermore, the proposed MBD technique reduces the required time and cost for the development and modification of a vehicle cluster, because of verification and validation of the cluster algorithm on the monitor through a PC.