• 제어로봇시스템학회논문지
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• 제8권11호
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• pp.976-983
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• 2002

We presents a modified variable dimension filter with input estimation for maneuvering target tracking. The conventional variable dimension filter with input estimation(VDIE) consists of the input estimation(IE) technique and the variable dimension(VD) filter. In the VDIE, the IE technique is used for estimation of a maneuver onset time and its magnitude in the least square sense. The detection of the maneuver is declared according to the estimated magnitude of the maneuver. The VD filter structure is applied for the adaptation to the maneuver of the target after compensating the filter parameter with respect to the estimated maneuver when the detection of the maneuver is declared. The VDIE is known as one of the best maneuvering target tracking filter based on a single filter. However, it requires too much computational burden since the IE technique is performed at every sampling instance and thus it is computationally inefficient. We propose another variable dimension filter with input estimation named 'Modified VDIE' which combines VD filter with If technique. Modified VDIE has less computational load than the original one by separating maneuver detection and input estimation. Simulation results show that the proposed VDIE is more efficient and outperforms in terms of computational load.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.93.3-93
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• 2001

We propose the cascade PID type fuzzy control method for a good performance such as robustness. The one of proposed method, the first stage have two input variables of an error and a derivative error, and one output variable, and the next stage have two input variables of the output of first stage and an integral error, and one output variable, have two stages. The other, the first stage has one input of an error, and one output variable, and the second stage have two input of the output of first stage and a derivative error, and one output variable, and the third stage have two input of the output of the second stage and an integer error, and one output variable ...

• 생태와환경
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• 제43권1호
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• pp.161-167
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• 2010

In this study, we implemented an experimental approach of ecological model development in order to emphasize the importance of input variable selection with respect to time-delayed arrangement between input and output variables. Time-series modeling requires relevant input variable selection for the prediction of a specific output variable (e.g. density of a species). Inadequate variable utility for input often causes increase of model construction time and low efficiency of developed model when applied to real world representation. Therefore, for future prediction, researchers have to decide number of time-delay (e.g. months, weeks or days; t-n) to predict a certain phenomenon at current time t. We prepared a total of 3,900 equation models produced by Time-Series Optimized Genetic Programming (TSOGP) algorithm, for the prediction of monthly averaged density of a potamic phytoplankton species Stephanodiscus hantzschii, considering future prediction from 0- (no future prediction) to 12-months ahead (interval by 1 month; 300 equations per each month-delay). From the investigation of model structure, input variable selectivity was obviously affected by the time-delay arrangement, and the model predictability was related with the type of input variables. From the results, we can conclude that, although Machine Learning (ML) algorithms which have popularly been used in Ecological Informatics (EI) provide high performance in future prediction of ecological entities, the efficiency of models would be lowered unless relevant input variables are selectively used.

• 전기의세계
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• 제23권2호
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• pp.46-54
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• 1974

The purposes of this paper are to deal with the design of m-input, m-output linear systems by the state variable feedback, and to extend the design capability of the state variable feedback design. The design requirements are decoupling and the exact realigation of desired transfer functions. Some methods are proposed to insert series compensators in the fixed plant in the cases when series compensators are needed to meet the input-output transfer matrix specification. The method for adding series compensators to the input channels of the fixed plant is shown by examples to lead both to the loss of the ability to decouple the augmented plant by the state variable feedback, and to the loss of desired zeroes. A method which avoids these two hazards is developed in which series compensators are put on the output channels of the fixed plant: it is proved that the augmented plant is F-invariant. By treating each subsystem individually, the designer can apply some of the previous developed knowledge of the state variable design of single-input, single-output systems.

• Journal of Welding and Joining
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• 제21권4호
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• pp.56-62
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• 2003

MIG brazing is used for many parts without melting base metal because of high productivity. Pulsed MIG brazing can be used to further reduce heat input and to improve the process stability. However, a significant amount of zinc in galvanized sheet steel is burned off in the area of brazes. Therefore, the brazing method to reduce the heat input is needed. In the brazing for galvanized sheet steel, variable polarity AC pulse MIG arc brazing can be applied to more decrease the heat input by setting EN-ratio adequately. In this research, we studied for the variable polarity AC pulse MIG arc brazing to decrease the heat input by using ERCuSi-A wire. As the result of increasing EN-ratio, melting ratio of base metal and burning off of zinc were reduced in galvanized sheet steel.

• Communications for Statistical Applications and Methods
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• 제10권1호
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• pp.239-246
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• 2003

Statisticians, or data miners, are often requested to assess the importances of input variables in the given supervised learning model. For the purpose, one may rely on separate ad hoc measures depending on modeling types, such as linear regressions, the neural networks or trees. Consequently, the conceptual consistency in input variable importance measures is lacking, so that the measures cannot be directly used in comparing different types of models, which is often done in data mining processes, In this short communication, we propose a unified approach to the importance measurement of input variables. Our method uses sensitivity analysis which begins by perturbing the values of input variables and monitors the output change. Research scope is limited to the models for continuous output, although it is not difficult to extend the method to supervised learning models for categorical outcomes.

• Journal of the Korean Data and Information Science Society
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• 제24권1호
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• pp.201-209
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• 2013

For censored regression, it is often the case that some input variables are not important, while some input variables are more important than others. We propose a novel algorithm for selecting such important input variables for censored kernel regression, which is based on the penalized regression with the weighted quadratic loss function for the censored data, where the weight is computed from the empirical survival function of the censoring variable. We employ the weighted version of ANOVA decomposition kernels to choose optimal subset of important input variables. Experimental results are then presented which indicate the performance of the proposed variable selection method.

• 한국항행학회논문지
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• 제24권1호
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• pp.53-60
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• 2020

본 논문에서는 가변 데이터 전송 가능한 텔레메트리 송신기의 구조에 대해 연구하였다. 가변 데이터 전송을 위하여 가변 컷오프 특성을 가지는 VPMF (variable pre-modulation filter)와 가변입력 샘플레이트 변환기 (variable input sample rate converter)가 결합된 구조를 제안한다. VPMF는 일반적인 PMF (pre-modulation filter)와 동일하게 변조 전 신호의 고주파 성분을 억제하여 RF (radio frequency)신호의 대역을 제한하는 특성을 지니며, 추가적으로 입력 데이터율에 따라 가변 컷오프 특성을 갖는다. 가변입력 샘플레이트 변환기는 입력 데이터율 변경 시 추가적인 구조변경 없이 입력 데이터율과 무관하게 출력을 일정한 샘플링 속도로 변환하는 기능을 갖는다. 추가적으로 실시간 변화하는 데이터양에 대응하기 위해 VPMF와 가변입력 샘플레이트 변환기를 능동적으로 제어하는 소프트웨어 프로그램 기법을 제안한다. 제안 방법을 적용하여 시뮬레이션과 실제 제작을 통하여 입력 데이터율 390 kbps ~ 25 Mbps 범위에서 IRIG (inter-range instrumentation group) 표준의 99% 전력 대역폭, null-to-null 대역폭, 1st IMD (inter modulation distortion) 비교 결과 규격을 만족하는 데이터 전송이 가능함을 확인하였다.

• 대한산업공학회지
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• 제27권1호
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• pp.47-53
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• 2001

In this paper, we compare the classification performances of both ensemble and clustering algorithms (Data Bagging, Variable Selection Bagging, Parameter Combining, Clustering) to logistic regression in consideration of various characteristics of input data. Four factors used to simulate the logistic model are (1) correlation among input variables (2) variance of observation (3) training data size and (4) input-output function. In view of the unknown relationship between input and output function, we use a Taguchi design to improve the practicality of our study results by letting it as a noise factor. Experimental study results indicate the following: When the level of the variance is medium, Bagging & Parameter Combining performs worse than Logistic Regression, Variable Selection Bagging and Clustering. However, classification performances of Logistic Regression, Variable Selection Bagging, Bagging and Clustering are not significantly different when the variance of input data is either small or large. When there is strong correlation in input variables, Variable Selection Bagging outperforms both Logistic Regression and Parameter combining. In general, Parameter Combining algorithm appears to be the worst at our disappointment.

• 한국품질경영학회:학술대회논문집
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• 한국품질경영학회 2006년도 추계 학술대회
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• pp.195-198
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• 2006

In this paper, we propose a 2dimension Control Chart. Suggested which Control chart augments Schwart Control chart which is 1-dimensional and Independent variable and dependent variable interrelationship by using control chart. Schwart control chart cannot use input variable and output variable together. In this paper, we try to analysis input variable and output variable dependent and effect. So called 2-dimensional control char.