• 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.

• Transactions of Materials Processing
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• v.12 no.1
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• pp.18-25
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• 2003

This paper explains the process design for improving tool life in the conventional hot forging process. The thermal load and the thermal softening are happened by contact between the hotter billet and the cooler tools in hot forging process. Tool life decreases considerably due to the softening of the surface layer of a tool was caused by a high thermal load and long contact time between the tools and the billet. Also, tool life is to a large extent limited by wear, heat crack and plastic deformation in hot forging process. Above all, the main factors which affect die accuracy and tool life we wear and the plastic deformation of a tool. The newly developed techniques for predicting tool life are applied to estimate the production quantity for a spindle component and these techniques can be applied to improve the tool life in hot forging process.

• Journal of the Optical Society of Korea
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• v.19 no.1
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• pp.22-28
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• 2015

We propose a new method for improving the phase contrast of a multiphase digital holographic microscope using a spatial light modulator (SLM). Using the SLM as the annulus, our method improves the light contrast of the object edge to achieve higher accuracy. We demonstrate a digital holographic microscopy technique that provides a 30% improvement in the phase contrast compared to conventional microscopy, which utilizes a mechanical annulus. The phase-contrast improvement allows the 3D reconstructed hologram to be determined more precisely.

• Journal of the Korean Institute of Telematics and Electronics A
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• v.31A no.5
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• pp.50-57
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• 1994

The analysis of the two-tone intermodulation distortion of multistage two-ports with gain and mismatching losses is presented with simplified two-port analyses and statistical viewpoint. The uncertainty obtained from unknown phase angles of the intermodulation distortion signals to the system designer is reduced using stochastic process, hence improving the accuracy of the solution. Based on the dc power dependance of third-order intercept point of each stage, the new efficient method for improving the total intercept point is also suggested with only the relation of dc power and available power gain criteria. Experimental verification on specific amplifiers used for cellular mobile communication comparing predicted and measured intercept points for various power conditions is presented.

• Proceedings of the KSME Conference
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• 2001.11a
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• pp.367-371
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• 2001

In the shape optimization based on the finite element method, the accuracy of finite element analysis of a given structure is important to determine the final shape. In case of a bending dominant problem, finite element solutions by the full integration scheme are not reliable because of the locking phenomenon. Furthermore, in the process of shape optimization, the mesh distortion is large due to the change of the structure outline: therefore, we cannot guarantee the accurate result unless the finite element itself is accurate. We approach to more accurate shape optimization to diminish these inaccuracies by improving the existing finite element. The shape optimization using the modified finite element is applied to a two-dimensional simple beam. Results show that the modified finite element have improved the optimization results.

• Journal of Information Technology Applications and Management
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• v.23 no.1
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• pp.45-59
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• 2016

An ensemble classifier is a method that combines output of multiple classifiers. It has been widely accepted that ensemble classifiers can improve the prediction accuracy. Recently, ensemble techniques have been successfully applied to the bankruptcy prediction. Bagging and random subspace are the most popular ensemble techniques. Bagging and random subspace have proved to be very effective in improving the generalization ability respectively. However, there are few studies which have focused on the integration of bagging and random subspace. In this study, we proposed a new hybrid ensemble model to integrate bagging and random subspace method using genetic algorithm for improving the performance of the model. The proposed model is applied to the bankruptcy prediction for Korean companies and compared with other models in this study. The experimental results showed that the proposed model performs better than the other models such as the single classifier, the original ensemble model and the simple hybrid model.

• Journal of Information Technology Services
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• v.15 no.1
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• pp.153-166
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• 2016

Ensemble classification is an approach that combines individually trained classifiers in order to improve prediction accuracy over individual classifiers. Ensemble techniques have been shown to be very effective in improving the generalization ability of the classifier. But base classifiers need to be as accurate and diverse as possible in order to enhance the generalization abilities of an ensemble model. Bagging is one of the most popular ensemble methods. In bagging, the different training data subsets are randomly drawn with replacement from the original training dataset. Base classifiers are trained on the different bootstrap samples. In this study we proposed a new bagging variant ensemble model, Randomized Bagging (RBagging) for improving the standard bagging ensemble model. The proposed model was applied to the bankruptcy prediction problem using a real data set and the results were compared with those of the other models. The experimental results showed that the proposed model outperformed the standard bagging model.

• Journal of the Korean Society for Precision Engineering
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• v.28 no.4
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• pp.505-512
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• 2011

Linear permanent magnet synchronous motors utilize high-energy product permanent magnet to produce high thrust, velocity and acceleration. Such motors are finding applications requiring high positioning accuracy and speed response, for example, machine tools, in the absence of mechanical gears and ball screw systems. A disadvantage of the linear motors is high power loss in comparison with rotary motors. For the application of the linear motors to machine tools, it is required to use water coolers and to improve the thermal behavior through insulation and structure optimization or control strategies. This paper presents the function of the secondary part of the linear synchronous motor as to the thermal behavior and the improving method. The result shows cooling pipe combined with an insulation layer is a suitable design for improving of the thermal behavior.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.05a
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• pp.872-875
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• 2009

In a bankruptcy prediction model, the accuracy is one of crucial performance measures due to its significant economic impacts. Ensemble is one of widely used methods for improving the performance of classification and prediction models. Two popular ensemble methods, Bagging and Boosting, have been applied with great success to various machine learning problems using mostly decision trees as base classifiers. In this paper, we analyze the performance of boosted neural networks for improving the performance of traditional neural networks on bankruptcy prediction tasks. Experimental results on Korean firms indicated that the boosted neural networks showed the improved performance over traditional neural networks.

• Journal of the Korean Society of Systems Engineering
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• v.17 no.2
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• pp.37-48
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• 2021

Improving the plant protection system against unforeseen changes/transients during operation is essential to maintain plant safety. Under this condition, it requires rapid and accurate signal processing. The use of an Intellectual Property (IP) core for floating point calculations for Safety Critical MMIS can make numerical computations easier and more precise, improving system accuracy. It can represent and manipulate rational numbers as well as a much broader range of values with dynamic range in nuclear power plant. Systems engineering approach (SE) is used through the development process, it helps to reduce complexity and avoid omissions and invalid assumptions as delivers a better understanding of the stakeholders needs. For the implementation on the FPGA target board, the 32-bit floating-point arithmetic with IEEE-754 standards has designed using Simulink model in Matlab for all operations of addition, subtraction, multiplication and division and VHDL code generated.