• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.5C
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• pp.355-364
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• 2012

Target processing mechanisms are necessary to collect target information, real-time data fusion, and tactical environment recognition for cooperative engagement ability. Among these mechanisms, the target tracking starts from predicting state of speed, acceleration, and location by using sensors' measurements. However, it can be a problem to give the reliability because the measurements have a certain uncertainty. Thus, a technique which uses multiple sensors is needed to detect the target and increase the reliability. Also, data fusion technique is necessary to process the data which is provided from heterogeneous sensors for target tracking. In this paper, a target tracking algorithm is proposed based on probabilistic data association(PDA) by fusing radar and ESM sensor measurements. The radar sensor's azimuth and range measurements and the ESM sensor's bearing-only measurement are associated by the measurement fusion method. After gating associated measurements, state estimation of the target is performed by PDA filter. The simulation results show that the proposed algorithm provides improved estimation under linear and circular target motions.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.2
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• pp.307-314
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• 2011

A new predistortion technique for dynamic spectrum allocation systems such as cognitive radio (CR) is proposed. The system model considered in this paper occupies a small band at a time, but the center frequency can be changed in the wide range of frequency. In this scenario. the front-end filter may not eliminate the harmonics of the power amplifier (PA) output. The proposed PD reduces the spectral regrowth of the fundamental signal at the carrier frequency (${\omega}_0$) and removes the harmonics ($2{\omega}_0$, $3{\omega}_0$, ...) at the same time. The proposed PD structure is composed of multiple predistorters (PDs) centered at integer multiples of ${\omega}_0$. The PD at ${\omega}_0$ is for removing spectral regrowth of the fundamental signal, and the others are for harmonic reduction. In the proposed PD structure, parameters of PDs are found jointly. Simulation results show that the spectral regrowth can be reduced by 20dB, and the 2nd and 3rd harmonics can be reduced down to -70dB from the power of the fundamental signal.

• Journal of Navigation and Port Research
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• v.32 no.6
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• pp.459-464
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• 2008

In this paper, we present the model-based fuzzy controller for container cranes which effectively performs set-point tracking control of trolley and anti-swaying control under system parameter and disturbance changes. The first part of this paper focuses on the development of Takagi-Sugeno (T-S) fuzzy modeling in a nonlinear container crane system. Parameters of the membership functions are adjusted by a RCGA to have same dynamic characteristics with nonlinear model of a container crane. In the second part, we present a design methodology of the model-based fuzzy controller. Sub-controllers are designed using LQ control theory for each subsystem in fuzzy model and then the proposed controller is performed with the combination of these sub-controllers by fuzzy IF-THEN rules. In the results of simulation, the fuzzy model showed almost similar dynamic characteristics compared to the outputs of the nonlinear container crane model. Also, the model-based fuzzy controller showed not only the fast settling time for the change in parameter and disturbance, but also stable and robust control performances without any steady-state error.

• Journal of the Korean Chemical Society
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• v.38 no.9
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• pp.621-627
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• 1994

Two crystal structures of dehydrated, $Ag^{+}$ and $Ca^{2+}$-exchanged zeolite A treated at $250^{\circ}C$ with 0.15 torr of Cs vapor have been determined by single-crystal X-ray diffraction technique in the cubic space group $Pm{\bar\3m$ at $21(1)^{\circ}C$ (a = 12.344(2) $\AA$ and 12.304(2) $\AA$). Their structures were refined to the final error indices, R (weighted), of 0.091 with 180 reflections, and 0.093 with 179 reflections, respectively, for which I > $3\sigma(I).$ In each structure, Cs species are found at four different crystallographic sites: 3 $Cs^{+}$ ions per unit cell are located at 8-ring centers, ca. 6.81∼7.14 $Cs^{+}$ ions are found on opposite 6-rings on threefold axes in the large cavity, ca. 1.93∼2.03 $Cs^{+}$ ions are found on threefold axes in the sodalite unit, and 0.53∼0.66 $Cs^{+}$ ions lie on opposite 4-rings. Also, ca. 4.12∼4.27 Ag atoms are located near the center of the large cavity. In these structures, excess cesium atoms in a unit cell are associated with other $Cs^{+}$ ions on a single threefold axis to form the linear cationic cluster $(Cs_4)^{3+}$. By blocking 8-rings, the $Cs^{+}$ ions may have prevented silver atoms from migrating out of the structure. The Ag atoms are likely to have formed hexasilver clusters at the centers of the large cavities. Each hexasilver cluster is stabilized by coordination to 14 $Cs^{+}$ ions.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.29 no.11
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• pp.828-833
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• 2018

It is well known that reflected waves and resonance affect phase distortion. In addition, phase delay can be distorted by antenna impedance. In this study, we analyze the phase delay variation caused by the antenna impedance, considering mutual coupling effects. In addition, we confirm the beam steering characteristics. When was -10 dB and -7 dB, the maximum phase delay error was $18.5^{\circ}$ and $26.5^{\circ}$, respectively. The Monte Carlo simulation with an eight-element linear array antenna demonstrated that the RMS error of the beam steering angle ranged from $0.19^{\circ}$ to $0.4^{\circ}$, and the standard deviation ranged from $0.14^{\circ}$ to $0.33^{\circ}$ when the beam steering angle was in the range of $0^{\circ}$ to $30^{\circ}$, with the uniformly distributed phase error of $18.5^{\circ}$ and $26.5^{\circ}$. The side lobe level increased from 0.74 dB to 1.21 dB by the phase error from the theoretical value of -12.8 dB, with a standard deviation of 0.31 dB to 0.51 dB. This is verified by designing an eight-element spiral array antenna.

• Korean Journal of Optics and Photonics
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• v.30 no.4
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• pp.154-158
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• 2019

We have used a mid-IR (mid-infrared) continuous-wave (cw) optical parametric oscillator (OPO), developed previously and described in Ref. 12, to build a performance-evaluation setup for a mid-IR spectrometer. The used CW OPO had a wavelength tuning range of $ 2.5-3.6{\mu}m$ using a pump laser with a wavelength of 1064 nm and a fan-out MgO-doped periodically poled lithium niobate (MgO:PPLN) nonlinear crystal in a concentric cavity design. The OPO was combined with a near-IR integrating sphere and a Fourier-transform IR optical spectrum analyzer to build a performance-evaluation setup for mid-IR spectrometers. We applied this performance-evaluation setup to evaluating a mid-IR spectrometer developed domestically, and demonstrated the capability of evaluating the performance, such as spectral resolution, signal-to-noise ratio, spectral stray light, and so on, based on this setup.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.12
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• pp.47-53
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• 2018

Various types of robotic arms are being used for industrial purposes, particularly with the small production of multi-products, and the importance of the gripper, which can be used in industrial fields, is increasing. This study evaluated a variable stiffness mechanism gripper that can change the stiffness using the nonlinearity of a flexible material. A prototype of the gripper was fabricated and examined to confirm the change in stiffness. The previous gripper was unable to grip objects in some situations with three variable stiffness mechanism. In addition, these mechanisms were not balanced and rarely rotated when the object was gripped. Therefore, a new type of gripper was needed to solve this problem. Inspired by the movements of the human palm and Venus Flytrap, a new type of a variable stiffness soft robot hand was designed. The possibility of grasping could be increased by interlocking the palm folding mechanism by pulling the tendon attached to the variable stiffness mechanism. The soft robotic hand was used to grasp objects of various shapes and weights more stably than the previous variable stiffness mechanism gripper. This new variable stiffness soft robot hand can be used selectively depending on the application and environment to be used.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.2
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• pp.651-658
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• 2021

Because bolts with anti-loosening coatings are used mainly for joining safety-related components in automobiles, accurate automatic screening of these coatings is essential to detect defects efficiently. The performance of the convolutional neural network (CNN) used in a previous study [Identification of bolt coating defects using CNN and Grad-CAM] increased with increasing number of data for the analysis of image patterns and characteristics. On the other hand, obtaining the necessary amount of data for coated bolts is difficult, making training time-consuming. In this paper, resorting to the same VGG16 model as in a previous study, transfer learning was applied to decrease the training time and achieve the same or better accuracy with fewer data. The classifier was trained, considering the number of training data for this study and its similarity with ImageNet data. In conjunction with the fully connected layer, the highest accuracy was achieved (95%). To enhance the performance further, the last convolution layer and the classifier were fine-tuned, which resulted in a 2% increase in accuracy (97%). This shows that the learning time can be reduced by transfer learning and fine-tuning while maintaining a high screening accuracy.

• Journal of Environmental Impact Assessment
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• v.29 no.6
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• pp.403-413
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• 2020

A mathematical model comprised with eight simultaneous quasi-linear partial differential equations was suggested to provide optimal chlorination strategy. Upstream weighted finite element method was employed to construct multidimensional numerical code. The code was verified against measured concentrations in three type of reactors. Boundary conditions and reaction rate were calibrated for the sixteen cases of experimental results to regenerate the measured values. Eight reaction rate coefficients were estimated from the modeling result. The reaction rate coefficients were expressed in terms of pH and temperature. Automatic optimal algorithm was invented to estimate the reaction rate coefficients by minimizing the sum of squares of the numerical errors and combined with the model. In order to minimize the concentration of chlorine and pollutants at the final usage sites, a real-time predictive control system is imperative which can predict the water quality variables from the chlorine disinfection process at the water purification plant to the customer by means of a model and operate the disinfection process according to the influent water quality. This model can be used to build such a system in water treatment plants.

• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.1
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• pp.92-111
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• 2021

As fine dust negatively affects disease, industry and economy, the people are sensitive to fine dust. Therefore, if the occurrence of fine dust can be predicted, countermeasures can be prepared in advance, which can be helpful for life and economy. Fine dust is affected by the weather and the degree of concentration of fine dust emission sources. The industrial sector has the largest amount of fine dust emissions, and in industrial complexes, factories emit a lot of fine dust as fine dust emission sources. This study targets regions with old industrial complexes in local cities. The purpose of this study is to explore the factors that cause fine dust and develop a predictive model that can predict the occurrence of fine dust. weather data and fine dust data were used, and variables that influence the generation of fine dust were extracted through multiple regression analysis. Based on the results of multiple regression analysis, a model with high predictive power was extracted by learning with a machine learning regression learner model. The performance of the model was confirmed using test data. As a result, the models with high predictive power were linear regression model, Gaussian process regression model, and support vector machine. The proportion of training data and predictive power were not proportional. In addition, the average value of the difference between the predicted value and the measured value was not large, but when the measured value was high, the predictive power was decreased. The results of this study can be developed as a more systematic and precise fine dust prediction service by combining meteorological data and urban big data through local government data hubs. Lastly, it will be an opportunity to promote the development of smart industrial complexes.