• Transactions of the Korean Society of Automotive Engineers
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• v.17 no.5
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• pp.26-30
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• 2009

In this paper, a robust model based fault detection for varying temperature is proposed, To develop a robust force estimation model, it needs temperature information because the force sensor's output is affected by a temperature variation. If an EPB system does not include a temperature sensor, the model has a much larger error than an EPB system with a built-in temperature sensor. Therefore, the temperature is estimated by using Ohm's law. The force model is applied with a motor current, battery voltage, operation mode, and the estimated temperature to detect a force sensor's abnormal signal fault. The residual is calculated by comparing the value of the measured force and the estimated force. Fault information is collected by using the output of the evaluated residual with the adaptive thresholds. A proposed robust model based fault detection for varying temperature was verified by HILS (Hardware in the Loop Simulation).

• Journal of the Korean Society for Precision Engineering
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• v.15 no.2
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• pp.146-153
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• 1998

NC machines with 4 or 5 axes are capable of various tool approach motions, which makes interference-free and high machinablity machining possible. This paper deals with how to integrate these two advantages (interference-free and high machinability machining) in multi-axes NC machining with a ball-end mill. Feasible tool approach region at a point on a surface is first computed, then among which an approach direction is determined so as to minimize the cutting force required. Tool and spindle volumes are considered in computing the feasible tool approach region, and the computing time is improved by trans-forming surface patches into minimal enclosing spheres. A cutting force prediction model is used for estimating the cutting force. The algorithm is developed so as to be applied to 4- or 5-axes NC machining in common.

• Wind and Structures
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• v.32 no.5
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• pp.487-499
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• 2021

The wind-blown sand effect on the high-speed train is investigated. Unsteady RANS equation and the SST k-ω turbulent model coupled with the discrete phase model (DPM) are utilized to simulate the two-phase of air-sand. Sand impact force is calculated based on the Hertzian impact theory. The different cases, including various wind velocity, train speed, sand particle diameter, were simulated. The train's flow field characteristics and the sand impact force were analyzed. The results show that the sand environment makes the pressure increase under different wind velocity and train speed situations. Sand impact force increases with the increasing train speed and sand particle diameter under the same particle mass flow rate. The train aerodynamic force connected with sand impact force when the train running in the wind-sand environment were compared with the aerodynamic force when the train running in the pure wind environment. The results show that the head car longitudinal force increase with wind speed increasing. When the crosswind speed is larger than 35m/s, the effect of the wind- sand environment on the train increases obviously. The longitudinal force of head car increases 23% and lateral force of tail increases 12% comparing to the pure wind environment. The sand concentration in air is the most important factor which influences the sand impact force on the train.

• Journal of Industrial Technology
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• v.28 no.A
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• pp.155-161
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• 2008

Muscle force prediction in forward dynamic analysis of human motion depends many muscle parameters associated with muscle actuation. This research studies the effects of various parameters of Hill type muscle model using the simple hand raising motion. Motion analysis is carried out using motion capture system, and each muscle force is recorded for comparison with muscle model generated muscle force. Using Hill type muscle model, muscle force for generating the same hand rasing motion was setup adjusting 5 activation parameters. The test showed the importance of activation parameters on the accurate generation of muscle force.

• The Transactions of the Korean Institute of Power Electronics
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• v.4 no.5
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• pp.455-461
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• 1999

In this paper, we present the 3DOF force-rei1ecting interface which allows to acquire force of objc'Ct within a a virtual environment. This system is comlxlsed of device, virtual environment model, and force-rei1ecting r rendering algorithm. We design a J DOF force reflecting device using the pc$\alpha$allel linkage, torque shared by W wire, and the controller of system applied by impedance control algorithm. The force reflecting behaviour i implemented as a function position is equivalent to controlling the mechanical impedance felt by the user. E Especially how force should be supplied to user, we know using a God-Object algorithm As we experiment a system implement$\varepsilon$d by the interface of 3D virtual object and 3DOF force reJll'Cting i interface, we can feel a contact, non contact of :)D virtual object surface and sensin앙 of push button model.utton model.

• Journal of Auto-vehicle Safety Association
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• v.5 no.2
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• pp.32-38
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• 2013

This paper presents collision avoidance using model predictive control algorithm. A model predictive control algorithm determines lateral tire force and yaw moment and steering angle input and differential braking input is determined from lateral tire force and yaw moment. A constraint for model predictive control is designed for obstacle avoidance. A objective function is designed to minimize lateral tire force and yaw moment input and to follow changed lane after collision avoidance. The performance of proposed algorithm has been investigated via computer simulation conducted to vehicle dynamic software CARSIM and Matlab/Simulink.

• Structural Engineering and Mechanics
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• v.46 no.5
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• pp.673-693
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• 2013

This paper presents a systematic design and training procedure for the feed-forward back-propagation neural network (NN) modeling of both forward and inverse behavior of a rotary magnetorheological (MR) damper based on experimental data. For the forward damper model, with damper force as output, an optimization procedure demonstrates accurate training of the NN architecture with only current and velocity as input states. For the inverse damper model, with current as output, the absolute value of velocity and force are used as input states to avoid negative current spikes when tracking a desired damper force. The forward and inverse damper models are trained and validated experimentally, combining a limited number of harmonic displacement records, and constant and half-sinusoidal current records. In general the validation shows accurate results for both forward and inverse damper models, where the observed modeling errors for the inverse model can be related to knocking effects in the measured force due to the bearing plays between hydraulic piston and MR damper rod. Finally, the validated models are used to emulate pure viscous damping. Comparison of numerical and experimental results demonstrates good agreement in the post-yield region of the MR damper, while the main error of the inverse NN occurs in the pre-yield region where the inverse NN overestimates the current to track the desired viscous force.

• Structural Engineering and Mechanics
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• v.5 no.1
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• pp.1-19
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• 1997

A numerical procedure for dynamic analysis of structures including lateral-torsional coupling, axial force effect and soil-structure interaction is presented in this study. A simple soil-structure system model has been designed for microcomputer applications capable of reflecting both kinematic and inertial soil-foundation interaction as well as the effect of this interaction on the superstructure response. A parametric study focusing on inertial soil-structure interaction is carried out through a simplified nine-degree of freedom building model with different foundation conditions. The inertial soil-structure interaction and axial force effects on a 20-storey building excited by an Australian earthquake is analysed through its top floor displacement time history and envelope values of structural maximum displacement and shear force.

• Journal of The Korean Association For Science Education
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• v.36 no.3
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• pp.423-434
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• 2016

The purpose of this study is to investigate preservice earth science teachers' mental models through applications of Coriolis force experiment apparatus. After the root of preconception was examined by face to face interviews based on the questionnaire, five preservice earth science teachers were finally selected for this study. The mental models about concept of Coriolis force was classified into naive mental model, static unstable mental model, dynamic unstable mental model, and scientific mental model through the result of individual interviews and their drawings. According to the mental model analysis about Coriolis' force conception, students C and M showed naive mental model about concept of Coriolis force before experiment. After the experiment, student M's model changed to static unstable mental model. Student C's model improved to dynamic unstable mental model. In adiition, students D and O's model improved from static unstable mental model to dynamic unstable mental model. In the case of student B, the dynamic unstable mental model was maintained after the experiment, however, student B's preconception changed to scientific concept. It turned out that a change occurred from low mental model level to integrated mental model after the application of the developed Coriolis' force experiment apparatus. According to the results, national curriculum is similar to static unstable mental model and the result of developed Coriolis' force experiment apparatus is similar to dynamic unstable mental model. It is suggested that it become the theoretical foundation to develop more comfortable and advanced Coriolis force experiment apparatus by improving the experiment apparatus.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.5
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• pp.364-371
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• 2016

We developed a prediction model of assistance force for the opening/closing of an automobile door depending on the condition of the parking ground. The candidates of the learning models for the operating assistance force were compared to determine the proper force according to the slope and user's force, etc. The reduced experimental model was developed to obtain learning data for the estimation model. The learning algorithm was composed to predict the assistance force to incorporate real assistance force data. Among these algorithms, an Artificial Neural Network (ANN) and Support Vector Machine(SVM) were applied and the adaptability was compared between these models. The SVM provided more adaptability for the learning process of the door assistance force prediction. This paper proposes a system for determining the assistance force to control a door motor to compensate for the deviation of required door force in the slope condition, as needed in the plane condition.