• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2258-2263
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• 2003

This paper presents the design and development of an optimal fuzzy logic controller (FLC) for a laser tracking system. An optimal intelligent fuzzy logic controller was founded on integral criterion of the fuzzy models and three-dimensional fuzzy control. Research had been also concentrated on the methods for multivariable fuzzy models for the purposes of real-time process. Simulation results have shown remarkable tracking performance of this fuzzy PID controller.

• Proceedings of the KIEE Conference
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• 2005.07a
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• pp.175-177
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• 2005

This paper analyzes transient behavior due to switching overvoltage in 22.9kV combined distribution systems. Computer models are consisted of distribution overhead line model, underground cable model and surge-arrester model in this paper. The computer models are made by EMTP/ATPDraw simulation and Line constants are calculated by ATP_LCC. This paper analyzes the various parameters affecting. These factors include closing angle and cable length.

• Communications for Statistical Applications and Methods
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• v.6 no.1
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• pp.117-130
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• 1999

Nayak(1994) suggested a combined randomized response model that combined the Warner's model and greenberg et al.'s model. In this paper we extend Nayak's model to two sample case of including unknown unrelated character also propose some combined models such W-M model and G-M model that modify the Nayak's model. We suggest the efficiency conditions of our models for Nayak's model, also find the efficiency condition of G-M model for the W-M model.

• Advances in Computational Design
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• v.5 no.3
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• pp.291-304
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• 2020

In this paper, we develop models to design the airfoil using Multilayer Feed-forward Artificial Neural Network (MFANN) and Support Vector Regression model (SVR). The aerodynamic coefficients corresponding to series of airfoil are stored in a database along with the airfoil coordinates. A neural network is created with aerodynamic coefficient as input to produce the airfoil coordinates as output. The performance of the models have been evaluated. The results show that the SVR model yields the lowest prediction error.

• International Journal of Precision Engineering and Manufacturing
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• v.1 no.2
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• pp.43-47
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• 2000

When estimating fatigue crack growth (FCG) life of structures, the use of crack growth models and knowledge of the values of their corresponding parameters are of vital importance. Inconsistency in using models with appropriate parameters can lead to enormous errors in FCG life prediction. In this paper examples are analyzed and compared with test results to show the possible problems, Consistency checks are necessary for avoiding some pitfalls, and also necessary for verifying the correct performance and accuracy of the used computer program.

• ETRI Journal
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• v.32 no.1
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• pp.160-162
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• 2010

For the acoustic models of embedded speech recognition systems, hidden Markov models (HMMs) are usually quantized and the original full space distributions are represented by combinations of a few quantized distribution prototypes. We propose a maximum likelihood objective function to train the quantized distribution prototypes. The experimental results show that the new training algorithm and the link structure adaptation scheme for the quantized HMMs reduce the word recognition error rate by 20.0%.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.05a
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• pp.168-169
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• 2019

Trusted Execution Environment(TEE), such as Intel SGX, AMD Secure Processor and ARM TrustZone, has recently been a rising issue. Trusted Execution Environment provides a secure and independent code execution, hardware-based, environment for untrusted OS. In this paper, we show that Trusted Execution Environment's research trends on its vulnerability and attack models. We classify the previous attack models, and summarize mitigations for each TEE environment.

• Journal of Information Processing Systems
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• v.4 no.1
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• pp.33-40
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• 2008

Traceability has been held as an important factor in testing activities as well as model-driven development. Vertical traceability affords us opportunities to improve manageability from models and test cases to a code in testing and debugging phase. This paper represents a vertical test method which connects a system test level and an integration test level in testing stage by using UML. An experiment how traceability works to effectively focus on error spots has been included by using concrete examples of tracing from models to the code.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.15 no.3
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• pp.192-199
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• 2015

Heterogeneous cellular networks are gaining momentum in industry and the research community, and are attracting the attention of standard bodies such as 3GPP LTE and IEEE 802.16j, whose objectives are to increase the capacity and coverage of cellular networks. In this article, we provide an overview of expansion strategies, optimal locations of base stations with different characteristics, and radio-planning models.

• Korean Journal of Remote Sensing
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• v.39 no.5_4
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• pp.1111-1123
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• 2023

In recent times, semantic image segmentation methods using deep learning models have been widely used for monitoring changes in surface attributes using remote sensing imagery. To enhance the performance of various UNet-based deep learning models, including the prominent UNet model, it is imperative to have a sufficiently large training dataset. However, enlarging the training dataset not only escalates the hardware requirements for processing but also significantly increases the time required for training. To address these issues, transfer learning is used as an effective approach, enabling performance improvement of models even in the absence of massive training datasets. In this paper we present three transfer learning models, UNet-ResNet50, UNet-VGG19, and CBAM-DRUNet-VGG19, which are combined with the representative pretrained models of VGG19 model and ResNet50 model. We applied these models to building extraction tasks and analyzed the accuracy improvements resulting from the application of transfer learning. Considering the substantial impact of learning rate on the performance of deep learning models, we also analyzed performance variations of each model based on different learning rate settings. We employed three datasets, namely Kompsat-3A dataset, WHU dataset, and INRIA dataset for evaluating the performance of building extraction results. The average accuracy improvements for the three dataset types, in comparison to the UNet model, were 5.1% for the UNet-ResNet50 model, while both UNet-VGG19 and CBAM-DRUNet-VGG19 models achieved a 7.2% improvement.