• Industrial Engineering and Management Systems
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• v.4 no.1
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• pp.54-67
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• 2005

This paper provides an evaluation of an optimization-based, multiple-input double-output (MIDO) run-to-run (R2R) control scheme for general semiconductor manufacturing processes. The controller in this research, termed adaptive dual response optimizing controller (ADROC), can serve as a process optimizer as well as a recipe regulator between consecutive runs of wafer fabrication. In evaluation, it is assumed that the equipment model could be appropriately described by a pair of second-order polynomial functions in terms of a set of controllable variables. Of practical relevance is to consider a drifting effect in the equipment model since in common semiconductor practice the process tends to drift due to machine aging and tool wearing. We select a typical application of R2R control to chemical mechanical planarization (CMP) in semiconductor manufacturing in this evaluation, and there are five different CMP process scenarios demonstrated, including mean shift, variance increase, and IMA disturbances. For the controller, ADROC, an on-line estimation technique is implemented in a self-tuning (ST) control manner for the adaptation purpose. Subsequently, an ad hoc global optimization algorithm based on the dual response approach, arising from the response surface methodology (RSM) literature, is used to seek the optimum recipe within the acceptability region for the execution of next run. The main components of ADROC are described and its control performance is assessed. It reveals from the evaluation that ADROC can provide excellent control actions for the MIDO R2R situations even though the process exhibits complicated, nonlinear interaction effects between control variables, and the drifting disturbances.

• KIISE Transactions on Computing Practices
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• v.21 no.7
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• pp.488-493
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• 2015

An AddSIM(Adaptive distributed and parallel Simulation environment for Interoperable and reusable Models) is an integrated engagement simulation environment with high-resolution weapon system models for estimation and analysis of their performance and effectiveness. AddSIM can simultaneously handle the continuous dynamical system models based on continuous time, and command, control(C2) and network system models based on a discrete event. To accommodate legacies based on DEVS(Discrete Event System Specification) modeling, DEVS legacies must first be converted into AddSIM models. This paper describes how to implement DEVS models on AddSIM. In this study a method of mapping from hierarchical DEVS models to AddSIM players was developed: The hierarchical DEVS model should be flattened into a one layered model and four DEVS functions of the model, external transition, internal transition, output and time advance, should be mapped into functions of the AddSIM player.

• The KIPS Transactions:PartB
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• v.15B no.3
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• pp.205-210
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• 2008

A new approach is proposed for restoration of corrupted regions and segmentation in natural text images. The challenge is to fill in the corrupted regions on the basis of color feature analysis by second order symmetric stick tensor. It is show how feature analysis can benefit from analyzing features using tensor voting with chromatic and achromatic components. The proposed method is applied to text images corrupted by manifold types of various noises. Firstly, we decompose an image into chromatic and achromatic components to analyze images. Secondly, selected feature vectors are analyzed by second-order symmetric stick tensor. And tensors are redefined by voting information with neighbor voters, while restore the corrupted regions. Lastly, mode estimation and segmentation are performed by adaptive mean shift and separated clustering method respectively. This approach is automatically done, thereby allowing to easily fill-in corrupted regions containing completely different structures and surrounding backgrounds. Applications of proposed method include the restoration of damaged text images; removal of superimposed noises or streaks. We so can see that proposed approach is efficient and robust in terms of restoring and segmenting text images corrupted.

• Proceedings of the Korea Water Resources Association Conference
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• 2012.05a
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• pp.128-132
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• 2012

물은 생물의 생존을 위해 필수적인 요소로 인류가 시작된 이래로 물을 효율적으로 이용하고 안전하게 관리하기 위한 노력은 계속되어 왔다. 최근 지구 온난화가 주요 원인으로 알려진 국지성 집중호우의 피해는 매우 심각하며, 이로 인해 치수에 대한 중요성은 날로 커지고 있다. 지금까지 사용해 왔던 홍수 예 경보 과정은 특정 지점의 유출량을 예측하기 위해서 강우-유출 모형을 운영하였다. 그러나 물리적 모형의 경우 운영에 필요한 매개변수의 결정과정이 복잡하고, 매개변수 결정을 위해 많은 자료를 필요로 한다. 또한 그 매개변수의 결정과정은 많은 불확실성을 포함하고 있어서 모형의 운영을 위한 전처리과정과 계산과정을 거치는 동안 발생한 오차가 누적되어 결과물 속에는 많은 오차가 포함되어 있다. 본 연구에서는 기존의 홍수 예 경보 시스템의 문제점과 불확실성을 최대한 감소시키고 더 우수한 유출량 예측을 위해 neuro-fuzzy 추론 기법을 이용한 모형인 ANFIS(Adaptive Neuro-Fuzzy Inference System)를 사용하여 하천수위를 예측하였다. ANFIS는 신경회로망과 퍼지이론을 결합한 기법으로 신경회로망의 구조와 학습 능력을 이용하여 제어환경에서 획득한 입 출력 정보로부터 언어변수의 membership 함수와 제어규칙을 제어 대상에 적합하도록 자동으로 조종하는 기법이다. 본 연구에서는 ANFIS를 사용하여 탄천 하류에 위치한 대곡교의 수위를 예측하였다. 분석을 위해 2007년부터 2011년까지의 탄천 유역의 관측 강우자료와 수위 자료 중 강우강도와 지속시간, 강우 형태에 따라 7개의 강우사상을 선정하였다. 학습자료 및 보정자료의 변화에 따른 예측 오차를 비교하여 모형의 적용성과 적정성을 평가하였다. 적용결과 입력자료 구성의 경우 해당 시간의 강우량 및 수위자료와 10분 전 강우자료를 이용한 모델이 가장 우수한 예측을 보였고, 학습자료의 경우 자료의 길이가 길고, 최대홍수량이 큰 경우 가장 우수한 예측 결과를 보였다. 본 연구의 적용결과 가장 우수한 모형의 경우 30분 예측 첨두수위 오차는 0.32%, RMSE는 0.05m 이고 예측시간이 길어짐에 따라 오차가 비선형적으로 증가하는 경향을 보였다.

• Journal of Korea Water Resources Association
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• v.40 no.1 s.174
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• pp.1-10
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• 2007

Reallocation procedure of multipurpose reservoir storage capacity between flood control and conservation is presented as an alternative to secure more water resources. Storage reallocation is an adaptive management mechanism for converting existing normal pool level of reservoirs to more beneficial uses without requirement for physical alteration. This study is intended to develop a reservoir storage reallocation methodology that allows increased water supply storage without minimizing adverse impacts on flood control. The methodology consists of flood control reservoir simulation for inflows with various return periods, flow routing from reservoir to a potential damage site, analyzing river carrying capacity, and reservoir yields estimation for reallocated storages. For the flood control model, a simulation model called Rigid ROM(Reservoir Operation Method) and HEC-5 are used. The approach is illustrated by applying it to two reservoirs system in Geum River basin. Especially with and without new project conditions are considered to analyze trade-offs between competing objectives.

• Journal of Digital Convergence
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• v.17 no.10
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• pp.251-258
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• 2019

In the future, various products are created in various fields using artificial intelligence. In this age, it is a very important problem to know the operation principle of artificial intelligence learning method and to use it correctly. This paper introduces artificial intelligence learning methods that have been known so far. Learning of artificial intelligence is based on the fixed point iteration method of mathematics. The GD(Gradient Descent) method, which adjusts the convergence speed based on the fixed point iteration method, the Momentum method to summate the amount of gradient, and finally, the Adam method that mixed these methods. This paper describes the advantages and disadvantages of each method. In particularly, the Adam method having adaptivity controls learning ability of machine learning. And we analyze how these methods affect digital signals. The changes in the learning process of digital signals are the basis of accurate application and accurate judgment in the future work and research using artificial intelligence.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.2
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• pp.714-720
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• 2021

The ITL (information theoretic learning) based on the kernel density estimation method that has successfully been applied to machine learning and signal processing applications has a drawback of severe sensitiveness in choosing proper kernel sizes. For the maximization of correntropy criterion (MCC) as one of the ITL-type criteria, several methods of adapting the remaining kernel size ( ) after removing the term have been studied. In this paper, it is shown that the main cause of sensitivity in choosing the kernel size derives from the term and that the adaptive adjustment of in the remaining terms leads to approach the absolute value of error, which prevents the weight adjustment from continuing. Thus, it is proposed that choosing an appropriate constant as the kernel size for the remaining terms is more effective. In addition, the experiment results when compared to the conventional algorithm show that the proposed method enhances learning performance by about 2dB of steady state MSE with the same convergence rate. In an experiment for channel models, the proposed method enhances performance by 4 dB so that the proposed method is more suitable for more complex or inferior conditions.

• Structural Engineering and Mechanics
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• v.83 no.3
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• pp.293-304
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• 2022

In this work, a novel reliability approach for combined high and low cycle fatigue (CCF) estimation is developed by combining active learning strategy with least squares support vector machines (LS-SVM) (named as ALS-SVM) surrogate model to address the multi-resources uncertainties, including working loads, material properties and model itself. Initially, a new active learner function combining LS-SVM approach with Monte Carlo simulation (MCS) is presented to improve computational efficiency with fewer calls to the performance function. To consider the uncertainty of surrogate model at candidate sample points, the learning function employs k-fold cross validation method and introduces the predicted variance to sequentially select sampling. Following that, low cycle fatigue (LCF) loads and high cycle fatigue (HCF) loads are firstly estimated based on the training samples extracted from finite element (FE) simulations, and their simulated responses together with the sample points of model parameters in Coffin-Manson formula are selected as the MC samples to establish ALS-SVM model. In this analysis, the MC samples are substituted to predict the CCF reliability of turbine blades by using the built ALS-SVM model. Through the comparison of the two approaches, it is indicated that the reliability model by linear cumulative damage rule provides a non-conservative result compared with that by the proposed one. In addition, the results demonstrate that ALS-SVM is an effective analysis method holding high computational efficiency with small training samples to gain accurate fatigue reliability.

• Journal of Astronomy and Space Sciences
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• v.39 no.3
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• pp.117-126
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• 2022

The Ionospheric Anomaly Monitoring by Magnetometer And Plasma-probe (IAMMAP) is one of the scientific instruments for the Compact Advanced Satellite 500-3 (CAS 500-3) which is planned to be launched by Korean Space Launch Vehicle in 2024. The main scientific objective of IAMMAP is to understand the complicated correlation between the equatorial electro-jet (EEJ) and the equatorial ionization anomaly (EIA) which play important roles in the dynamics of the ionospheric plasma in the dayside equator region. IAMMAP consists of an impedance probe (IP) for precise plasma measurement and magnetometers for EEJ current estimation. The designated sun-synchronous orbit along the quasi-meridional plane makes the instrument suitable for studying the EIA and EEJ. The newly-devised IP is expected to obtain the electron density of the ionosphere with unprecedented precision by measuring the upper-hybrid frequency (f_UHR) of the ionospheric plasma, which is not affected by the satellite geometry, the spacecraft potential, or contamination unlike conventional Langmuir probes. A set of temperature-tolerant precision fluxgate magnetometers, called Adaptive In-phase MAGnetometer, is employed also for studying the complicated current system in the ionosphere and magnetosphere, which is particularly related with the EEJ caused by the potential difference along the zonal direction.

• Journal of radiological science and technology
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• v.46 no.4
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• pp.277-285
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• 2023

In this study, pneumonia identification networks with the small number of layers were constructed by using chest X-ray images. The networks had similar trainable-parameters, and the performance of the trained models was quantitatively evaluated with the modification of the network architectures. A total of 6 networks were constructed: convolutional neural network (CNN), VGGNet, GoogleNet, residual network with identity blocks, ResNet with bottleneck blocks and ResNet with identity and bottleneck blocks. Trainable parameters for the 6 networks were set in a range of 273,921-294,817 by adjusting the output channels of convolution layers. The network training was implemented with binary cross entropy (BCE) loss function, sigmoid activation function, adaptive moment estimation (Adam) optimizer and 100 epochs. The performance of the trained models was evaluated in terms of training time, accuracy, precision, recall, specificity and F1-score. The results showed that the trained models with the small number of layers precisely detect pneumonia from chest X-ray images. In particular, the overall quantitative performance of the trained models based on the ResNets was above 0.9, and the performance levels were similar or superior to those based on the CNN, VGGNet and GoogleNet. Also, the residual blocks affected the performance of the trained models based on the ResNets. Therefore, in this study, we demonstrated that the object detection networks with the small number of layers are suitable for detecting pneumonia using chest X-ray images. And, the trained models based on the ResNets can be optimized by applying appropriate residual-blocks.