• Journal of the Korea Institute of Military Science and Technology
• /
• v.18 no.6
• /
• pp.816-821
• /
• 2015

In this paper, an in-flight prediction method of thrust profiles for solid rocket motors is proposed. Actually, it is very difficult to have detailed information about the performance of the rocket motors beforehand because it is quite sensitive to combustion environments. To overcome this problem, we have developed an algorithm for generating in-flight prediction of rocket motor performance in realistic environments via a reference burnback profile and accelerations measured at a short time-interval just after launch. The performance is evaluated through a lot of flight test results.

• The Transactions of the Korean Institute of Electrical Engineers D
• /
• v.55 no.3
• /
• 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.

• ETRI Journal
• /
• v.45 no.2
• /
• pp.329-337
• /
• 2023

For safe last-mile autonomous robot delivery services in complex environments, rapid and accurate collision prediction and detection is vital. This study proposes a suitable neural network model that relies on multiple navigation sensors. A light detection and ranging technique is used to measure the relative distances to potential collision obstacles along the robot's path of motion, and an accelerometer is used to detect impacts. The proposed method tightly couples relative distance and acceleration time-series data in a complementary fashion to minimize errors. A long short-term memory, fully connected layer, and SoftMax function are integrated to train and classify the rapidly changing collision countermeasure state during robot motion. Simulation results show that the proposed method effectively performs collision prediction and detection for various obstacles.

• Smart Structures and Systems
• /
• v.21 no.4
• /
• pp.521-535
• /
• 2018

The main purpose of this paper is to predict missing absolute out-of-plane displacements and failure limits of infill walls by artificial neural network (ANN) models. For this purpose, two shake table experiments are performed. These experiments are conducted on a 1:1 scale one-bay one-story reinforced concrete frame (RCF) with an infill wall. One of the experimental models is composed of unreinforced brick model (URB) enclosures with an RCF and other is composed of an infill wall with bed joint reinforcement (BJR) enclosures with an RCF. An artificial earthquake load is applied with four acceleration levels to the URB model and with five acceleration levels to the BJR model. After a certain acceleration level, the accelerometers are detached from the wall to prevent damage to them. The removal of these instruments results in missing data. The missing absolute maximum out-of-plane displacements are predicted with ANN models. Failure of the infill wall in the out-of-plane direction is also predicted at the 0.79 g acceleration level. An accuracy of 99% is obtained for the available data. In addition, a benchmark analysis with multiple regression is performed. This study validates that the ANN-based procedure estimates missing experimental data more accurately than multiple regression models.

• Journal of the Korean Society of Manufacturing Technology Engineers
• /
• v.23 no.3
• /
• pp.223-229
• /
• 2014

In this paper, the natural frequency and damping ratio are analyzed with the acceleration signal of an Euler-Bernoulli beam using the impact hammer test. The results are presented according to crack depth and position using the recursive least squares method. The results are compared and investigated with FEM analysis of CATIA. Both methods agree well with each other regarding the natural mode characteristics. The captured acceleration can be used for the calculation of the natural frequency and damping ratio using time series methods that are based on the measured acceleration. Using these data, a recursive time series model with the acceleration signal was configured and the behaviors of the natural frequency and damping ratio were investigated and analyzed. Finally, the results can be used for the prediction of crack position and depth under different crack conditions for an Euler-Bernoulli beam.

• Structural Engineering and Mechanics
• /
• v.45 no.1
• /
• pp.53-67
• /
• 2013

This study aimed to develop a model to accurately predict the acceleration of structural systems during an earthquake. The acceleration and applied force of a structure were measured at current time step and the velocity and displacement were estimated through linear integration. These data were used as input to predict the structural acceleration at next time step. The computation tool used was the Volterra/Wiener neural network (VWNN) which contained the mathematical model to predict the acceleration. For alleviating problems of relatively large-dimensional and nonlinear systems, the VWNN model was utilized as the signal processing tool, including the Taylor series components in the input nodes of the neural network. The number of the intermediate layer nodes in the neural network model, containing the training and simulation stage, was evaluated and optimized. Discussions on the influences of the gradient descent with adaptive learning rate algorithm and the Levenberg-Marquardt algorithm, both for determining the network weights, on prediction errors were provided. During the simulation stage, different earthquake excitations were tested with the optimized settings acquired from the training stage to find out which of the algorithms would result in the smallest error, to determine a proper simulation model.

• Earthquakes and Structures
• /
• v.8 no.3
• /
• pp.681-697
• /
• 2015

Iran as one of the countries located on the Alpine-Himalayan seismic belt has recently experienced a few number of catastrophic earthquakes. A well-known index of how buildings are affected by earthquakes is through assessment of probable Peak Ground Acceleration (PGA) and structures' response spectra. In this research, active faults around Kerman and Birjand, two major cities in eastern parts of Iran, have been considered. Seismic catalogues are gathered to categorize effects of surrounding faults on seismicity of the region. These catalogues were further refined with respect to time and space based on Knopoff-Gardner algorithm in order to increase statistical independency of events. Probabilistic Seismic Hazard Analysis (PSHA) has been estimated for each of cities regarding 50, 100, 200 and 500 years of structures' effective life-span. These results subsequently have been compared with Deterministic Seismic Hazard Analysis (DSHA). It has been observed that DSHA not necessarily suggests upper bound of PSHA results. Furthermore, based on spectral Ground Motion Prediction Equations (GMPEs), Uniform Hazard Spectra (UHS) and spectral acceleration were provided for 2% and 10% levels of probability of exceedance. The results show that increasing source-to-site distance leads to spectral acceleration reduction regarding each fault. In addition, the spectral acceleration rate of variation would increase if the source-to-site distance decreases.

• Journal of the Society of Naval Architects of Korea
• /
• v.48 no.6
• /
• pp.552-559
• /
• 2011

An interest in Arbitrary Lagrangian Eulerian (ALE) finite element methods has been increased due to more accurate responses in Fluid-Structure Interaction(FSI) problems. The multi-material ALE approach was applied to the prediction of the acceleration response of free-fall lifeboat, and its responses were compared to those of the single-material ALE one. It could be found that even though there was no big difference in the simulation responses of two methods, the single-material and multi-material ALE ones, the latter multi-material ALE method showed a little bit more close response to those of experimental results compared to the former single-material ALE one, especially in the x- and z-direction acceleration responses. Through this study, it could be found that several parameters in the ALE algorithms have to be examined more carefully for a good structural safety assessment of FSI problems.

• Journal of the Korea Institute of Military Science and Technology
• /
• v.26 no.3
• /
• pp.262-272
• /
• 2023

A polymer-based mechanical low-pass filter(m-LPF) for high-g accelerometers makes it possible to remove high-frequency transient noises from acceleration signals, thus ensuring repeatable and reliable measurement on high-g acceleration. We establish a prediction model for performance of m-LPF by combining a fundamental vibration model with the fractional derivative standard linear solid(FD SLS) model describing the storage modulus and loss modulus of polymers. Here, the FD SLS model is modified to consider the effect of m-LPF shape factor (i.e., thickness) on storage modulus and loss modulus. The prediction accuracy is verified by comparing the displacement transmissibility(or cut-off frequency) estimated using our model with that measured from 3 kinds of polymers(polysulfide rubber(PSR), silicone rubber(SR), and polydimethylsiloxane(PDMS)). Our findings will contribute a significant growth of m-LPF for high-g accelerometers.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2012.04a
• /
• pp.847-848
• /
• 2012