• Korean Journal of Remote Sensing
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• v.40 no.1
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• pp.93-101
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• 2024

Accurate field crop classification is essential for various agricultural applications, yet existing methods face challenges due to diverse crop types and complex field conditions. This study aimed to address these issues by combining support vector machine (SVM) models with multi-seasonal unmanned aerial vehicle (UAV) images, texture information extracted from Gray Level Co-occurrence Matrix (GLCM), and RGB spectral data. Twelve high-resolution UAV image captures spanned March-October 2021, while field surveys on three dates provided ground truth data. We focused on data from August (-A), September (-S), and October (-O) images and trained four support vector classifier (SVC) models (SVC-A, SVC-S, SVC-O, SVC-AS) using visual bands and eight GLCM features. Farm maps provided by the Ministry of Agriculture, Food and Rural Affairs proved efficient for open-field crop identification and served as a reference for accuracy comparison. Our analysis showcased the significant impact of hyperparameter tuning (C and gamma) on SVM model performance, requiring careful optimization for each scenario. Importantly, we identified models exhibiting distinct high-accuracy zones, with SVC-O trained on October data achieving the highest overall and individual crop classification accuracy. This success likely stems from its ability to capture distinct texture information from mature crops.Incorporating GLCM features proved highly effective for all models,significantly boosting classification accuracy.Among these features, homogeneity, entropy, and correlation consistently demonstrated the most impactful contribution. However, balancing accuracy with computational efficiency and feature selection remains crucial for practical application. Performance analysis revealed that SVC-O achieved exceptional results in overall and individual crop classification, while soybeans and rice were consistently classified well by all models. Challenges were encountered with cabbage due to its early growth stage and low field cover density. The study demonstrates the potential of utilizing farm maps and GLCM features in conjunction with SVM models for accurate field crop classification. Careful parameter tuning and model selection based on specific scenarios are key for optimizing performance in real-world applications.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.5
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• pp.223-229
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• 2023

This Paper propose the method that measure main system parameters for PI(proportional-integral) current control of a.c. motor adopting the vector control technique. For current control, the PI control input is could be tuning by several selective methods. Among the several methods, the method that using the main system parameters, wire resistance and inductance, are frequently used. In this study, the technique to dissect and measure these two system parameters through the results of simple feedback control. This analytic measurement method is measuring parameters step by step dissecting the results of P control using simple proportional feedback gain about the unit step or multiple step reference command. This strategy is an real time analytic measurement method that calculate current control gains of torque component and flux component both for vector control of A.C. motor without introducing the further measurement circuits and complex measuring algorithms.

• Smart Structures and Systems
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• v.32 no.5
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• pp.319-338
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• 2023

The study presents a new hybrid data-driven method by combining radial basis functions neural networks (RBF-NN) with the Jaya algorithm (JA) to provide effective structural health monitoring of arch dams. The novelty of this approach lies in that only one user-defined parameter is required and thus can increase its effectiveness and efficiency, as compared to other machine learning techniques that often require processing a large amount of training and testing model parameters and hyper-parameters, with high time-consuming. This approach seeks rapid damage detection in arch dams under dynamic conditions, to prevent potential disasters, by utilizing the RBF-NNN to seamlessly integrate the dynamic elastic modulus (DEM) and modal parameters (such as natural frequency and mode shape) as damage indicators. To determine the dynamic characteristics of the arch dam, the JA sequentially optimizes an objective function rooted in vibration-based data sets. Two case studies of hyperbolic concrete arch dams were carefully designed using finite element simulation to demonstrate the effectiveness of the RBF-NN model, in conjunction with the Jaya algorithm. The testing results demonstrated that the proposed methods could exhibit significant computational time-savings, while effectively detecting damage in arch dam structures with complex nonlinearities. Furthermore, despite training data contaminated with a high level of noise, the RBF-NN and JA fusion remained the robustness, with high accuracy.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.6
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• pp.1055-1065
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• 2023

Recently, various studies using deep reinforcement learning (deep RL) technology have been conducted to solve complex problems using big data collected at industrial internet of things. Deep RL uses reinforcement learning"s trial-and-error algorithms and cumulative compensation functions to generate and learn its own data and quickly explore neural network structures and parameter decisions. However, studies so far have shown that the larger the size of the learning data is, the higher are the memory usage and search time, and the lower is the accuracy. In this study, model-agnostic learning for efficient federated deep RL was utilized to solve privacy invasion by increasing robustness as 55.9% and achieve 97.8% accuracy, an improvement of 5.5% compared with the comparative optimization-based meta learning models, and to reduce the delay time by 28.9% on average.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.6
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• pp.434-440
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• 2023

This paper propose the method that measure the main system parameters for current control of a.c. motor adopting the vector control technique. The automatical method that tuning PI control gains for current control of servo motors are used frequently through the information of main system parameters, wire resistance and inductance. In this study, the techniques to measure these two system parameters through the control of 3-phase inverter are presented. These control and measuring method are implemented by measuring output phase current obtained as a results of the step current control using simple proportional feedback input. Moreover, this method use freewheeling current of inverter at special switching mode for measuring inductance. This analytic strategy is could measure and calculate the system parameters without the complex measurement algorithm and new additional measuring circuits. That is could measure the total resistance and total inductance including wiring resistance and conduction resistance of switching devices using real driving circuits to control the motors.

• Journal of the Korean Chemical Society
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• v.53 no.3
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• pp.257-265
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• 2009

The structures and metal affinities of the binding configurations of $Cu^{+}$ and $Cu^{2+}$ to proline have been investigated using the hybrid three-parameter Density Functional Theory(DFT/B3LYP). We found that the metal-proline bonding and the energy ordering of several conformers were very different in $Cu^{+}$-proline and $Cu^{2+}$-proline. For $Cu^{+}$-proline, the ground state structure was found to have a bidentated coordination in which $Cu^{+}$ was coordinated to the carbonyl oxygen and imino group nitrogen of neutral proline. On the contrary, the ground state structure of $Cu^{2+}$-proline involves chelation between the two oxygens of the carboxylate group in a zwitterionic proline. The metal ion affinity of proline of the most stable $Cu^{+}$-proline complex was calculated as 76.0 kcal/mol at 6-311++G(d,p) level, whereas the $Cu^{2+}$ ion affinity of proline was calculated as 258.5 kcal/mol.

• Journal of Advanced Navigation Technology
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• v.28 no.3
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• pp.278-287
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• 2024

In this paper, telemetry link budget model for small launch vehicle is proposed, and telemetry link budget simulator is implemented. The proposed link budget model consist of geometry model and propagation loss model. The geometry model is calculation of look angle between ground station and small launch vehicle. The propagation loss model consist of free space loss, polarization loss, and de-pointing loss which are appropriate to the small launch vehicle flight environment. The proposed propagation loss model can be calculate propagation loss without complex calculation of propagation environments. The link budget simulator is implemented in MATLAB. The simulator calculate look angle, free space loss, polarization loss parameter, de-pointing loss and received signal level in ground station by using position of ground station, routing of small launch vehicle, 3-D radiation patterns of antennas.

• Wind and Structures
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• v.38 no.3
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• pp.215-229
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• 2024

Guyed mast structures exhibit characteristics such as high flexibility, low mass, small damping ratio, and large aspect ratio, leading to a complex wind-induced vibration response mechanism. This study analyzed the time- and frequency-domain characteristics of the wind-induced response of a guyed mast structure using measured acceleration response data obtained from the Shenzhen Meteorological Gradient Tower (SZMGT). Firstly, 734 sets of 1-hour acceleration samples measured from 0:00 October 1, 2021, to 0:00 November 1, 2021, were selected to study the vibration shapes of the mast and the characteristics of the generalized extreme value (GEV) distribution. Secondly, six sets of typical samples with different vibration intensities were further selected to explore the Gaussian property and modal parameter characteristics of the mast. Finally, the modal parameters of the SZMGT are identified and the identification results are verified by finite element analysis. The findings revealed that the guyed mast vibration shape exhibits remarkable diversity, which increases nonlinearly along the height in most cases and reaches a maximum at the top of the tower. Moreover, the GEV distribution characteristics of the 734 sets of samples are closer to the Weibull distribution. The probability distribution of the structural wind vibration response under strong wind is in good agreement with the Gaussian distribution. The structural response of the mast under wind loading exhibits multiple modes. As the structural response escalates, the first three orders of modal energy in the tower display a gradual increase in proportion.

• Tunnel and Underground Space
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• v.34 no.2
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• pp.143-153
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• 2024

Tunnel Boring Machine (TBM) method is a tunnel excavation method that produces lower levels of noise and vibration during excavation compared to drilling and blasting methods, and it offers higher stability. It is increasingly being applied to tunnel projects worldwide. The disc cutter is an excavation tool mounted on the cutterhead of a TBM, which constantly interacts with the ground at the tunnel face, inevitably leading to wear. In this study quantitatively predicted disc cutter wear using geological conditions, TBM operational parameters, and machine learning algorithms. Among the input variables for predicting disc cutter wear, the Uniaxial Compressive Strength (UCS) is considerably limited compared to machine and wear data, so the UCS estimation for the entire section was first conducted using TBM machine data, and then the prediction of the Coefficient of Wearing rate(CW) was performed with the completed data. Comparing the performance of CW prediction models, the XGBoost model showed the highest performance, and SHapley Additive exPlanation (SHAP) analysis was conducted to interpret the complex prediction model.

• Nuclear Engineering and Technology
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• v.56 no.6
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• pp.2220-2238
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• 2024

As a typical active equipment, pump machinery is widely used in nuclear power plants. Although the mechanism of pump machinery in nuclear power plants is similar to that of conventional pumps, the safety and reliability requirements of nuclear pumps are higher in complex operating environments. Once there is significant performance degradation or failure, it may cause huge security risks and economic losses. There are many pumps mechanical parameters, and it is very important to explore the correlation between multi-dimensional variables and condition. Therefore, a condition monitoring model based on Deep Denoising Autoencoder (DDAE) is constructed in this paper. This model not only ensures low false positive rate, but also realizes early abnormal monitoring and location. In order to alleviate the influence of parameter time-varying effect on the model in long-term monitoring, this paper combined equidistant sampling strategy and DDAE model to enhance the monitoring efficiency. By using the simulation data of reactor coolant pump and the actual centrifugal pump data, the monitoring and positioning capabilities of the proposed scheme under normal and abnormal conditions were verified. This paper has important reference significance for improving the intelligent operation and maintenance efficiency of nuclear power plants.