• Smart Structures and Systems
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• v.16 no.2
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• pp.295-328
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• 2015

In recent years the monitoring of structural behavior through acquisition of vibrational data has become common practice. In addition, recent advances in sensor development have made the collection of diverse dynamic information feasible. Other than the commonly collected acceleration information, Global Position System (GPS) receivers and non-contact, optical techniques have also allowed for the synchronous collection of highly accurate displacement data. The fusion of this heterogeneous information is crucial for the successful monitoring and control of structural systems especially when aiming at real-time estimation. This task is not a straightforward one as measurements are inevitably corrupted with some percentage of noise, often leading to imprecise estimation. Quite commonly, the presence of noise in acceleration signals results in drifting estimates of displacement states, as a result of numerical integration. In this study, a new approach based on a time domain identification method, namely the Unscented Kalman Filter (UKF), is proposed for correcting the "drift effect" in displacement or rotation estimates in an online manner, i.e., on the fly as data is attained. The method relies on the introduction of artificial white noise (WN) observations into the filter equations, which is shown to achieve an online correction of the drift issue, thus yielding highly accurate motion data. The proposed approach is demonstrated for two cases; firstly, the illustrative example of a single degree of freedom linear oscillator is examined, where availability of acceleration measurements is exclusively assumed. Secondly, a field inspired implementation is presented for the torsional identification of a tall tower structure, where acceleration measurements are obtained at a high sampling rate and non-collocated GPS displacement measurements are assumed available at a lower sampling rate. A multi-rate Kalman Filter is incorporated into the analysis in order to successfully fuse data sampled at different rates.

• Journal of the Korean Institute of Landscape Architecture
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• v.30 no.2
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• pp.50-62
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• 2002

From modernism to post-modernism, the practice in the design field often reduced the complexity of environment and to remove variety. However, contemporary ideas of space have been changed. The current thought premise is that the environment is mutable and is evolving according to inner and outer forces and elements. Therefore, leading designers recognize that the environment is complex in itself while anticipating a new theory explaining on-going trends. The idea of fold formulated by Gilles Deleuze can provide a theoretical base for new environmental design in constrat to current design practices. The fold is a hybrid by accommodating complex relations within an object. It carries a dynamic world view through continual process and yields a topological space against absolute space like Euclid geometry. The characteristics of the fold can be paraphrased as rhizome, stratification and smooth space. Rhizome forms a non-hierarchial connection like networking in internet space. Stratification is a kind of superimposition of autonomous potential layers within a single object. Smooth space is a free space and event oriented space keeping non-linear form. This study tried to incorporate the idea of fold to environmental design methods and design process in order to make space which can correspond with complex environment and topological form. In the design process adapted to fold theory, rhizome analysis accepts the complexity of environment and stratification strategy embraces the possibility of accidental use. As a result, the designed park carries a monadic image and produces an ambiguous space. Lastly, smooth space makes topological space unlike Euclid geometry and is free space comosed by the user themselves. Transporting the idea of fold into environmental design could be an alterative way for indeterminate and flexible design to accept new identity of place. Therefore, this study accepts the concept of incidental morphogenesis to make space based on the complexity of environment. The designed space based on the idea of fold searches to create free event space determined by user rather than designated by designer.

• Transactions of the Korean Society of Mechanical Engineers
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• v.17 no.1
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• pp.101-111
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• 1993

Many processes such as machining, injection-moulding and metal-forming are usually operated by hydraulic servo-systems. The dynamic characteristics of these systems are complex and highly non-linear and are often subjected to the uncertain external disturbances associated with the processes. Consequently, the conventional approach to the controller design for these systems may not guarantee accurate tracking control performance. An effective neuro-fuzzy controller is proposed to realize an accurate hydraulic servo-system regardless of the uncertainties and the external disturbances. For this purpose, first, we develop a simplified fuzzy logic controller which have multidimensional and unsymmetric membership functions. Secondly, we develop a neural network which consists of the parameters of the fuzzy logic controller. It is show that the neural network has both learning capability and linguistic representation capability. The proposed controller was implemented on a hydraulic servo-system. Feedback error learning architecture is adopted which uses the feedback error directly without passing through the dynamics or inverse transfer function of the hydraulic servo-system to train the neuro-fuzzy controller. A series of simulations was performed for the position-tracking control of the system subjected to external disturbances. The results of simulations show that regardless of inherent non-linearities and disturbances, an accuracy tracking-control performance is obtained using the proposed neuro-fuzzy controller.

• Korean Chemical Engineering Research
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• v.43 no.2
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• pp.230-235
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• 2005

Reversed-phase high-performance liquid chromatography (RP-HPLC) was used to determine the equilibrium isotherm of single and multi-components of dUrd(2'-deoxyuridine), dGuo(2'-deoxyguanosine), and dAdo(2'-deoxyadenosine) of 2'-deoxyribonucleosides by dynamic method. The composition of mobile phase was 90/10 vol.% (water/MeOH). With an increase in the injection volumes, the retention times were shorter and the peak shapes were triangle-shaped, so Langmuir-type isotherm was assumed. The Langmuir adsorption parameters were estimated by PIM (pulsed-input method), and the competitive Langmuir adsorption isotherm was further utilized. For the sample of the dUrd and dGuo whose retention times were relatively short, the agreement of between the calculated value and experimental data was fairly good in both single and multi-components, but for the dAdo, the last eluting component, some deviations were caused by non-linear and non-ideal properties.

• Journal of the Korea Convergence Society
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• v.3 no.4
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• pp.35-44
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• 2012

In this study, a simulation program is developed in order to investigate non steady-state cornering performance of 6WD/6WS special-purpose vehicles. 6WD vehicles are believed to have good performance on off-the-road maneuvering and to have fail-safe capabilities. But the cornering performances of 6WS vehicles are not well understood in the related literature. In this paper, 6WD/6WS vehicles are modeled as a 18 DOF system which includes non-linear vehicle dynamics, tire models, and kinematic effects. Then the vehicle model is constructed into a simulation program using the MATLAB/SIMULINK so that input/output and vehicle parameters can be changed easily with the modulated approach. Cornering performance of the 6WS vehicle is analyzed for brake steering and pivoting, respectively. Simulation results show that cornering performance depends on the middle-wheel steering as well as front/rear wheel steering. In addition, a new 6WS control law is proposed in order to minimize the sideslip angle. Lane change simulation results demonstrate the advantage of 6WS vehicles with the proposed control law.

• Journal of the Korea institute for structural maintenance and inspection
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• v.20 no.4
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• pp.1-8
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• 2016

Recently, buildings tend to be large size, complex shape and functional. As the size of buildings is becoming massive, the need for structural health monitoring(SHM) technique is ever-increasing. Various SHM techniques have been studied for buildings which have different dynamic characteristics and are influenced by various external loads. Generally, the visual inspection and non-destructive test for an accessible point of structures are performed by experts. But nowadays, the system is required which is online measurement and detect risk elements automatically without blind spots on structures. In this study, in order to consider the response of non-linear structures, proposed a signal feature extraction and the adaptive threshold setting algorithm utilized to determine the abnormal behavior by using statistical methods such as control chart, root mean square deviation, generalized extremely distribution. And the performance of that was validated by using the acceleration response of structures during earthquakes measuring system of forced vibration tests and actual operation.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.47 no.10
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• pp.712-719
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• 2019

This paper explores an implementation of finite element analysis and experiment in the design process of a tape spring hinge used for various CubeSat missions. Tape spring hinges consist of short-length hardened-steel strips with one-sided curvature, and thus the behavior is subject to large deformation with unpredicted non-linearity. Precise dimensions of a commercial tape spring are traced by the use of high-resolution digital camera, and thin-shell FEM analysis is conducted using ABAQUS program. Based on the rotation-moment analysis suggested in previous studies, parametric analysis is conducted by adjusting the contributing factors such as strip thickness and the subtended angle of the cross section. Finally the behaviors are investigated by both analytical and non-linear finite element methods, and the results are compared with the simple measurements. Further studies suggest a possible application in dynamic characteristics of hinges during CubeSat operations.

• Structural Engineering and Mechanics
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• v.85 no.2
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• pp.197-206
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• 2023

The deterioration caused by chloride penetration and carbonation plays a significant role in a concrete structure in a marine environment. The chloride corrosion in some marine concrete structures is invisible but can be dangerous in a sudden collapse. Therefore, as a novelty, this research investigates the ability of a non-destructive damage detection method named the Power Spectral Density (PSD) to diagnose damages caused only by chloride ions in concrete structures. Furthermore, the accuracy of this method in estimating the amount of annual damage caused by chloride in various parts and positions exposed to seawater was investigated. For this purpose, the RC Arosa bridge in Spain, which connects the island to the mainland via seawater, was numerically modeled and analyzed. As the first step, each element's bridge position was calculated, along with the chloride corrosion percentage in the reinforcements. The next step predicted the existence, location, and timing of damage to the entire concrete part of the bridge based on the amount of rebar corrosion each year. The PSD method was used to monitor the annual loss of reinforcement cross-section area, changes in dynamic characteristics such as stiffness and mass, and each year of the bridge structure's life using sensitivity equations and the linear least squares algorithm. This study showed that using different approaches to the PSD method based on rebar chloride corrosion and assuming 10% errors in software analysis can help predict the location and almost exact amount of damage zones over time.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.139-153
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• 2017

Corporate bankruptcy can cause great losses not only to stakeholders but also to many related sectors in society. Through the economic crises, bankruptcy have increased and bankruptcy prediction models have become more and more important. Therefore, corporate bankruptcy has been regarded as one of the major topics of research in business management. Also, many studies in the industry are in progress and important. Previous studies attempted to utilize various methodologies to improve the bankruptcy prediction accuracy and to resolve the overfitting problem, such as Multivariate Discriminant Analysis (MDA), Generalized Linear Model (GLM). These methods are based on statistics. Recently, researchers have used machine learning methodologies such as Support Vector Machine (SVM), Artificial Neural Network (ANN). Furthermore, fuzzy theory and genetic algorithms were used. Because of this change, many of bankruptcy models are developed. Also, performance has been improved. In general, the company's financial and accounting information will change over time. Likewise, the market situation also changes, so there are many difficulties in predicting bankruptcy only with information at a certain point in time. However, even though traditional research has problems that don't take into account the time effect, dynamic model has not been studied much. When we ignore the time effect, we get the biased results. So the static model may not be suitable for predicting bankruptcy. Thus, using the dynamic model, there is a possibility that bankruptcy prediction model is improved. In this paper, we propose RNN (Recurrent Neural Network) which is one of the deep learning methodologies. The RNN learns time series data and the performance is known to be good. Prior to experiment, we selected non-financial firms listed on the KOSPI, KOSDAQ and KONEX markets from 2010 to 2016 for the estimation of the bankruptcy prediction model and the comparison of forecasting performance. In order to prevent a mistake of predicting bankruptcy by using the financial information already reflected in the deterioration of the financial condition of the company, the financial information was collected with a lag of two years, and the default period was defined from January to December of the year. Then we defined the bankruptcy. The bankruptcy we defined is the abolition of the listing due to sluggish earnings. We confirmed abolition of the list at KIND that is corporate stock information website. Then we selected variables at previous papers. The first set of variables are Z-score variables. These variables have become traditional variables in predicting bankruptcy. The second set of variables are dynamic variable set. Finally we selected 240 normal companies and 226 bankrupt companies at the first variable set. Likewise, we selected 229 normal companies and 226 bankrupt companies at the second variable set. We created a model that reflects dynamic changes in time-series financial data and by comparing the suggested model with the analysis of existing bankruptcy predictive models, we found that the suggested model could help to improve the accuracy of bankruptcy predictions. We used financial data in KIS Value (Financial database) and selected Multivariate Discriminant Analysis (MDA), Generalized Linear Model called logistic regression (GLM), Support Vector Machine (SVM), Artificial Neural Network (ANN) model as benchmark. The result of the experiment proved that RNN's performance was better than comparative model. The accuracy of RNN was high in both sets of variables and the Area Under the Curve (AUC) value was also high. Also when we saw the hit-ratio table, the ratio of RNNs that predicted a poor company to be bankrupt was higher than that of other comparative models. However the limitation of this paper is that an overfitting problem occurs during RNN learning. But we expect to be able to solve the overfitting problem by selecting more learning data and appropriate variables. From these result, it is expected that this research will contribute to the development of a bankruptcy prediction by proposing a new dynamic model.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.12 no.2
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• pp.43-54
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• 1992

For shallow arches with large dynamic loading, linear analysis is no longer considered as practical and accurate. In this study, a method is presented for the dynamic analysis of shallow arches in which geometric nonlinearity must be considered. A program is developed for the analysis of the nonlinear dynamic behavior and for evaluation of critical buckling loads of shallow arches. Geometric nonlinearity is modeled using Lagrangian description of the motion. The finite element analysis procedure is used to solve the dynamic equation of motion and Newmark method is adopted in the approximation of time integration. A shallow arch subject to radial step loads is analyzed. The results are compared with those from other researches to verify the developed program. The behavior of arches is analyzed using the non-dimensional time, load, and shape parameters. It is shown that geometric nonlinearity should be considered in the analysis of shallow arches and probability of buckling failure is getting higher as arches are getting shallower. It is confirmed that arches with the same shape parameter have the same deflection ratio at the same time parameter when arches are loaded with the same parametric load. In addition, it is proved that buckling of arches with the same shape parameter occurs at the same load parameter. Circular arches, which are under a single or uniform normal load, are analyzed for comparison. A parabolic arch with radial step load is also analyzed. It is verified that the developed program is applicable for those problems.