• 제목/요약/키워드: computer models

검색결과 3,894건 처리시간 0.032초

DESIGN AND DEVELOPMENT OF AN OPTIMAL INTELLIGENT FUZZY LOGIC CONTROLLER FOR LASER TRACKING SYSTEM

  • Lu, Jia;Cannady, James
    • 제어로봇시스템학회:학술대회논문집
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    • 제어로봇시스템학회 2003년도 ICCAS
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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.

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혼합배전계통에서 EMTP/APTDraw를 이용한 개폐서지 해석에 관한 연구 (A Study and Analysis on the Switching Surge Using a EMTP/ATPDraw in the Combined Distribution System)

  • 이장근;이종범
    • 대한전기학회:학술대회논문집
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    • 대한전기학회 2005년도 제36회 하계학술대회 논문집 A
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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.

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Modified Nayak's Randomized Response Model

  • Lee, Gi-Sung;Hong, Ki-Hak
    • Communications for Statistical Applications and Methods
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    • 제6권1호
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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.

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Predicting the 2-dimensional airfoil by using machine learning methods

  • Thinakaran, K.;Rajasekar, R.;Santhi, K.;Nalini, M.
    • Advances in Computational Design
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    • 제5권3호
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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.

Issues When Estimating Fatigue Life of Structures

  • Lee, Ouk-Sub;Chen, Zhi-wei
    • International Journal of Precision Engineering and Manufacturing
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    • 제1권2호
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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.

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Maximum Likelihood Training and Adaptation of Embedded Speech Recognizers for Mobile Environments

  • Cho, Young-Kyu;Yook, Dong-Suk
    • ETRI Journal
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    • 제32권1호
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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%.

A Survey of Trusted Execution Environment Security

  • Yoon, Hyundo;Hur, Junbeom
    • 한국정보처리학회:학술대회논문집
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    • 한국정보처리학회 2019년도 춘계학술발표대회
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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.

An Experiment of Traceability-Driven System Testing

  • Choi, Eun-Man;Seo, Kwang-Ik
    • Journal of Information Processing Systems
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    • 제4권1호
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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.

Comparison of Radio Wave Propagation Models for Mobile Networks

  • Altayeva, Aigerim Bakatkaliyevna;Cho, Young Im
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • 제15권3호
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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.

전이학습을 이용한 UNet 기반 건물 추출 딥러닝 모델의 학습률에 따른 성능 향상 분석 (Performance Improvement Analysis of Building Extraction Deep Learning Model Based on UNet Using Transfer Learning at Different Learning Rates)

  • 예철수;안영만;백태웅;김경태
    • 대한원격탐사학회지
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    • 제39권5_4호
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    • pp.1111-1123
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    • 2023
  • 원격탐사 영상을 이용한 지표 속성의 변화를 모니터링 하기 위해서 딥러닝(deep learning) 모델을 이용한 의미론적 영상 분할 방법이 최근에 널리 사용되고 있다. 대표적인 의미론적 영상 분할 딥러닝 모델인 UNet 모델을 비롯하여 다양한 종류의 UNet 기반의 딥러닝 모델들의 성능 향상을 위해서는 학습 데이터셋의 크기가 충분해야 한다. 학습 데이터셋의 크기가 커지면 이를 처리하는 하드웨어 요구 사항도 커지고 학습에 소요되는 시간도 크게 증가되는 문제점이 발생한다. 이런 문제를 해결할 수 있는 방법인 전이학습은 대규모의 학습 데이터 셋이 없어도 모델 성능을 향상시킬 수 있는 효과적인 방법이다. 본 논문에서는 UNet 기반의 딥러닝 모델들을 대표적인 사전 학습 모델(pretrained model)인 VGG19 모델 및 ResNet50 모델과 결합한 세 종류의 전이학습 모델인 UNet-ResNet50 모델, UNet-VGG19 모델, CBAM-DRUNet-VGG19 모델을 제시하고 이를 건물 추출에 적용하여 전이학습 적용에 따른 정확도 향상을 분석하였다. 딥러닝 모델의 성능이 학습률의 영향을 많이 받는 점을 고려하여 학습률 설정에 따른 각 모델별 성능 변화도 함께 분석하였다. 건물 추출 결과의 성능 평가를 위해서 Kompsat-3A 데이터셋, WHU 데이터셋, INRIA 데이터셋을 사용하였으며 세 종류의 데이터셋에 대한 정확도 향상의 평균은 UNet 모델 대비 UNet-ResNet50 모델이 5.1%, UNet-VGG19 모델과 CBAM-DRUNet-VGG19 모델은 동일하게 7.2%의 결과를 얻었다.