• 한국철도학회논문집
• /
• 제9권5호
• /
• pp.601-605
• /
• 2006

Reliability of the Railway signaling system which is safety critical is determined by reliability of hardware and software. Reliability of hardware is easily predicted and demonstrated through lots of different studies and environmental tests, while that of software is estimated by the iterative test outcomes so estimates of reliability will depend on the inputs. Combinations of inputs to and outputs from the software may be mostly combinatoric and therefore all the combinations could not be tested. As a result, it has been more important to calculate reliability by means of a simpler method. This paper identifies the reliability prediction equation applicable to reliability prediction for railway signaling system software, and performs the simulation of onboard equipment of automatic train control for high speed train to review reliability prediction and validity.

• 한국소성가공학회:학술대회논문집
• /
• 한국소성가공학회 2004년도 제5회 압연심포지엄 신 시장 개척을 위한 압연기술
• /
• pp.368-377
• /
• 2004

A mathematical model was developed for the prediction of the average temperature and RDT(RM Delivery temperature) in a roughing mill. The model consisted of three parts as follows (1) The intermediate numerical model calculated the deformation and heat transfer phenomena in the rolling: region by steady state FEM and the heat transfer phenomena in the interpass region by unsteady state FEM (2) The Off-line prediction model was derived from non-linear regression analysis based on the results of intermediate numerical model considering the various rolling conditions, (3) Using the heat flux in rolling region, temperature profile along thickness direction was calculated. For validation of the presented model, the rolling force per pass and RDT measued in on-line process was compared with those of model and the results showed close agreement with the existing data. In order to demonstrate the effectiveness of the proposed model, the various rolling conditions was tested.

• 전기의세계
• /
• 제49권9호
• /
• pp.9-16
• /
• 2000

A receding horizon $H_{\infty}$ predictive control method is derived by solving a min-max problem in non-recursive forms. The min-max cost index is converted to a quadratic form which for systems with input saturation can be minimized using QP. Through the use of closed-loop prediction the prediction of states the use of closed-loop prediction the prediction of states in the presence of disturbances are made non-conservative and it become possible to get a tighter $H_{\infty}$ norm bound. Stability conditions and $H_{\infty}$ norm bounds on disturbance rejection are obtained in infinite horizon sence. Polyhedral types of feasible sets for sets and disturbances are adopted to deal with the input constraints. The weight selection procedures are given in terms of LMIs and the algorithm is formulated so that it can be solved via QP. This work is a modified version of an earlier work which was based on ellipsoidal type feasible sets[15].

• 대한전기학회논문지:시스템및제어부문D
• /
• 제55권3호
• /
• pp.98-109
• /
• 2006

Fire control system(FCS) errors can be classified as hardware errors, filter prediction errors, effective ballistic function errors, and aiming errors. Among these errors, the filter prediction errors are the most significant error sources. To reduce them, a target future position calculation method using the acceleration estimate is suggested and it is compared with the constant velocity target prediction method. Simulation results show that the suggested method has better performance than the constant velocity prediction method. Target tracking algorithm is established with multiple target tracking filters based on IMM structure.

• KIEE International Transaction on Systems and Control
• /
• 제2D권2호
• /
• pp.125-134
• /
• 2002

This paper proposes a method for identifying temporal pattern clusters to predict events in time series. Instead of predicting future values of the time series, the proposed method forecasts specific events that may be arbitrarily defined by the user. The prediction is defined by an event characterization function, which is the target of prediction. The events are predicted when the time series belong to temporal pattern clusters. To identify the optimal temporal pattern clusters, fuzzy goal programming is formulated to combine multiple objectives and solved by an adaptive differential evolution technique that can overcome the sensitivity problem of control parameters in conventional differential evolution. To evaluate the prediction method, five test examples are considered. The adaptive differential evolution is also tested for twelve optimization problems.

• International Journal of Computer Science & Network Security
• /
• 제21권9호
• /
• pp.103-108
• /
• 2021

The emergence of new technologies and their implementation by different manufacturers of electronic devices are experiencing an ascending trend. Most of the time, these protocols are expected to reach a certain degree of maturity, and electronic equipment manufacturers use simplified communication standards and interfaces that have already reached maturity in terms of their development such as ModBUS, KNX or CAN. This paper proposes an IoT solution of the Smart Home type based on an Analysis and Prediction System. A data acquisition component was implemented and there was defined an algorithm for the analysis and prediction of actions based on the values collected from the data update component and the data logger records.

• 대한산업공학회지
• /
• 제31권4호
• /
• pp.334-340
• /
• 2005

A mouse is an important input device that is used in most of all computer works. A mouse control time prediction model was proposed in this study. Especially, the model described the time of mouse control that made a cursor to move within path constraints. The model was developed by a laboratory experiment. Cursor movement times were measured in 36 task conditions; 3 levels of path length, 3 levels of path width and 4 levels of target's width. 12 subjects participated in all conditions. The time of cursor movement with path constraints could be better explained by the combination of Fitts' law with steering law($r^2=0.947$) than by the other models; Fitts' law($r^2=0.740$), Steering law($r^2=0.633$) and Crossman's model($r^2=0.897$). The proposed model is expected to be used in menu design or computer game design.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 1999년도 하계학술대회 논문집 B
• /
• pp.933-935
• /
• 1999

In this paper, we present model predictive control scheme based on neural network to control discrete-time chaotic systems. We use a feedforward neural network as nonlinear prediction model. The training algorithm used is an adaptive backpropagation algorithm that tunes the connection weights. And control signal is obtained by using gradient descent (GD), some kind of LMS method. We identify that the system identification results through model prediction control have a great effect on control performance. Finally, simulation results show that the proposed control algorithm performs much better than the conventional controller.

• Journal of Advanced Marine Engineering and Technology
• /
• 제20권3호
• /
• pp.286-286
• /
• 1996

A neural predictive tracking system for the control of structure-unknown dynamic system is presented. The control system comprises a neural network modelling mechanism for the the forward and inverse dynamics of a plant to be controlled, a feedforward controller, feedback controller, and an error prediction mechanism. The feedforward controller, a neural network model of the inverse dynamics, generates feedforward control signal to the plant. The feedback control signal is produced by the error prediction mechanism. The error predictor adopts the neural network models of the forward and inverse dynamics. Simulation results are presented to demonstrate the applicability of the proposed scheme to predictive tracking control problems.

• Journal of Advanced Marine Engineering and Technology
• /
• 제20권3호
• /
• pp.154-161
• /
• 1996

A neural predictive tracking system for the control of structure-unknown dynamic system is presented. The control system comprises a neural network modelling mechanism for the the forward and inverse dynamics of a plant to be controlled, a feedforward controller, feedback controller, and an error prediction mechanism. The feedforward controller, a neural network model of the inverse dynamics, generates feedforward control signal to the plant. The feedback control signal is produced by the error prediction mechanism. The error predictor adopts the neural network models of the forward and inverse dynamics. Simulation results are presented to demonstrate the applicability of the proposed scheme to predictive tracking control problems.