• 제목/요약/키워드: Input Variable

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Maneuvering Target Tracking Using Modified Variable Dimension Filter with Input Estimation (수정된 가변차원 입력추정 필터를 이용한 기동표적 추적)

  • 안병완;최재원;황태현;송택렬
    • Journal of Institute of Control, Robotics and Systems
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    • v.8 no.11
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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.

The Cascade PID Type Fuzzy Control Method

  • Lee, Jung-Hoon;Ki whan Eom;Lee, Yong-Gu
    • 제어로봇시스템학회:학술대회논문집
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    • 2001.10a
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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 ...

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Relationship among Degree of Time-delay, Input Variables, and Model Predictability in the Development Process of Non-linear Ecological Model in a River Ecosystem (비선형 시계열 하천생태모형 개발과정 중 시간지연단계와 입력변수, 모형 예측성 간 관계평가)

  • Jeong, Kwang-Seuk;Kim, Dong-Kyun;Yoon, Ju-Duk;La, Geung-Hwan;Kim, Hyun-Woo;Joo, Gea-Jae
    • Korean Journal of Ecology and Environment
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    • v.43 no.1
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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.

The Decoupling And Design Of Linear Multivariable Control Systems By State Variable Feedback (상태변수피이드백에 의한 선형다변수제어시스템의 분할식설계에 관한 연구)

  • 황창선
    • 전기의세계
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    • v.23 no.2
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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.

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The Arc Brazing by Variable Polarity AC Pulse MIG Welding Machine (극성가변 AC 펄스 MIG용접기를 이용한 아크 브레이징)

  • 조상명;공현상
    • Journal of Welding and Joining
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    • v.21 no.4
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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.

Input Variable Importance in Supervised Learning Models

  • Huh, Myung-Hoe;Lee, Yong Goo
    • Communications for Statistical Applications and Methods
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    • v.10 no.1
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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.

Variable selection in censored kernel regression

  • Choi, Kook-Lyeol;Shim, Jooyong
    • Journal of the Korean Data and Information Science Society
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    • v.24 no.1
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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.

The Telemetry Transmitter with Variable Data rate Transmission (가변 데이터 전송 가능한 텔레메트리(Telemetry) 송신기)

  • Kim, Jang-Hee;Hong, Seung-Hyun;Park, Byong-Kwan;Kim, Bok-ki;Kim, Hyo-Jong
    • Journal of Advanced Navigation Technology
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    • v.24 no.1
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    • pp.53-60
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    • 2020
  • In this paper, We have studied the structure of a Telemetry Transmitter capable of transmitting variable data rates. This paper proposed a structure combining variable pre-modulation filter with cutoff characteristic with variable input sample rate converter. Variable pre-modulation filter has the same characteristics as pre-modulation filter and is converted to a constant sampling rate without structural changes according to the variable input data rate. We propose a software program that actively controls variable pre-modulation filter and variable input sample rate converter to respond to real-time changing data.

Comparing Classification Accuracy of Ensemble and Clustering Algorithms Based on Taguchi Design (다구찌 디자인을 이용한 앙상블 및 군집분석 분류 성능 비교)

  • Shin, Hyung-Won;Sohn, So-Young
    • Journal of Korean Institute of Industrial Engineers
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    • v.27 no.1
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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.

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Study on 2 dimensional Control Chart and Search interrelation Independent variable and dependent variable by using control chart considered simultaneously (2차원 관리도와 관리도를 이용한 독립변수와 종속변수의 관계연구)

  • Ree, Sang-Bok;Kim, Myung-Hoon
    • Proceedings of the Korean Society for Quality Management Conference
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    • 2006.11a
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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.

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