• 제어로봇시스템학회:학술대회논문집
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• 1992.10b
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• pp.124-129
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• 1992

The goal of the proposed Intelligent Assisting System - IAS is to assist human operators in an intelligent way, while leaving decision and goal planning instances for the human. To realize the IAS the very important issue of manipulation skill identification and analysis has to be solved, which then is stored in a Skill Data Base. Using this data base the IAS is able to perform complex manipulations on the motion control level and to assist the human operator flexibly. We propose a model for manipulation skill based on the dynamics of the grip transformation matrix, which describes the dynamic transformation between object space and finger joint space. Interaction with a virtual world simulator allows the calculation and feedback of appropriate forces through controlled actuators of the sensor glove with 10 degrees-of-freedom. To solve the sensor glove calibration problem, we learn the nonlinear calibration mapping by an artificial neural network(ANN). In this paper we also describe the experimental system setup of the skill acquisition and transfer system as a first approach to the IAS. Some simple manipulation examples and simulation results show the feasibility of the proposed manipulation skill model.

• Corrosion Science and Technology
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• v.20 no.2
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• pp.62-68
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• 2021

Artificial neural network techniques show an excellent ability to predict the data (output) for various complex characteristics (input). It is primarily specialized to solve nonlinear relationship problems. This study is an experimental investigation that applies artificial neural network techniques and an experimental design to predict the cyclic polarization curves of the super-austenitic stainless steel AL-6XN alloy with sensitization. A cyclic polarization test was conducted in a 3.5% NaCl solution based on an experimental design matrix with various factors (degree of sensitization, temperature, pH) and their levels, and a total of 36 cyclic polarization data were acquired. The 36 cyclic polarization patterns were used as training data for the artificial neural network model. As a result, the supervised learning algorithms with back-propagation showed high learning and prediction performances. The model showed an excellent training performance (R²=0.998) and a considerable prediction performance (R²=0.812) for the conditions that were not included in the training data.

• Advances in materials Research
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• v.9 no.4
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• pp.251-263
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• 2020

α-mangostin imprinted polymers have been synthesized by a non-covalent imprinting approach with α-mangostin as a template molecule. The α-mangostin molecularly imprinted polymers (MIPs) prepared by radical polymerization using methacrylic acid, ethlylene glycol dimethacrylate, benzoyl peroxide, and acetonitrile, as a monomer, crosslinker, initiator, and porogen, respectively. The template was removed by using methanol:acetic acid 90:10 (v/v). The physical characteristics of the polymers were investigated by Fourier Transform Infrared spectroscopy (FTIR), scanning electron microscopy (SEM), and thermogravimetric analysis (TGA). The rebinding studies were carried out by batch methods. The results exhibited that the MIPs was able to adsorb the α-mangostin at pH 2 and the contact time of 180 min. The kinetic adsorption data of α-mangostin performed the pseudo-second order model and followed the Langmuir isotherm model with the adsorption capacity of 16.19 mg·g-1. MIPs applied as a sorbent material in solid-phase extraction, namely molecularly imprinted solid-phase extraction (MISPE) and it shows the ability for enrichment and clean-up of α-mangostin from the complex matrix in medicinal herbal product and crude extract of mangosteen (Garcinia mangostana L.) pericarp. Both samples, respectively, which were spiked with α-mangostin gives recovery more than 90% after through by MISPE in all concentration ranges.

• Journal of Auto-vehicle Safety Association
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• v.13 no.4
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• pp.7-13
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• 2021

This paper presents an vulnerable road user (VRU) classification and tracking algorithm using vision and LiDAR sensor fusion method for urban autonomous driving. The classification and tracking for vulnerable road users such as pedestrian, bicycle, and motorcycle are essential for autonomous driving in complex urban environments. In this paper, a real-time object image detection algorithm called Yolo and object tracking algorithm from LiDAR point cloud are fused in the high level. The proposed algorithm consists of four parts. First, the object bounding boxes on the pixel coordinate, which is obtained from YOLO, are transformed into the local coordinate of subject vehicle using the homography matrix. Second, a LiDAR point cloud is clustered based on Euclidean distance and the clusters are associated using GNN. In addition, the states of clusters including position, heading angle, velocity and acceleration information are estimated using geometric model free approach (GMFA) in real-time. Finally, the each LiDAR track is matched with a vision track using angle information of transformed vision track and assigned a classification id. The proposed fusion algorithm is evaluated via real vehicle test in the urban environment.

• Earthquakes and Structures
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• v.24 no.6
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• pp.393-404
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• 2023

This paper presents the application of hybrid-simulation-based adapter elements for the non-linear two-scale analysis of reinforced concrete frames with masonry infills under seismic-like demands. The approach provides communication and distribution of the computations carried out by two or more remote or locally distributed numerical models connected through the OpenFresco Framework. The modeling consists of a global analysis formed by macro-elements to represent frames and walls, and to reduce global degrees of freedom, portions of the structure that require advanced analysis are substituted by experimental elements and dimensional couplings acting as interfaces with their respective sub-assemblies. The local sub-assemblies are modeled by solid finite elements where the non-linear behavior of concrete matrix and masonry infill adopt a continuum damage representation and the reinforcement steel a discrete one, the conditions at interfaces between concrete and masonry are considered through a contact model. The methodology is illustrated through the analysis of a frame-wall system subjected to lateral loads comparing the results of using macro-elements, finite element model and experimental observations. Finally, to further assess and validate the methodology proposed, the paper presents the pushover analysis of two more complex structures applying both modeling scales to obtain their corresponding capacity curves.

• Transactions of Materials Processing
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• v.32 no.1
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• pp.28-34
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• 2023

In this research, a representative volume element (RVE)-based FE Model is presented to estimate the mechanical properties of additively manufactured continuous fiber-reinforced composites with different fiber orientations. To construct the model, an ABAQUS Python script has been implemented to produce matrix and fiber in the desired orientations at the RVE. A script has also been developed to apply the periodic boundary conditions to the RVE. Experimental tests were conducted to validate the numerical models. Tensile specimens with the fiber directions aligned in the 0, 45, and 90 degrees to the loading direction were manufactured using a continuous fiber 3D printer and tensile tests were performed in the three directions. Tensile tests were also simulated using the RVE models. The predicted Young's moduli compared well with the measurements: the Young's modulus prediction accuracy values were 83.73, 97.70, and 92.92 percent for the specimens in the 0, 45, and 90 degrees, respectively. The proposed method with periodic boundary conditions precisely evaluated the elastic properties of additively manufactured continuous fiber-reinforced composites with complex microstructures.

• Proceedings of the Korea Water Resources Association Conference
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• 2008.05a
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• pp.694-698
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• 2008

In hydrology, it is a difficult task to deal with multivariate time series such as modeling streamflows of an entire complex river system. Normal distribution based model such as MARMA (Multivariate Autorgressive Moving average) has been a major approach for modeling the multivariate time series. There are some limitations for the normal based models. One of them might be the unfavorable data-transformation forcing that the data follow the normal distribution. Furthermore, the high dimension multivariate model requires the very large parameter matrix. As an alternative, one might be decomposing the multivariate data into independent components and modeling it individually. In 1985, Lins used Principal Component Analysis (PCA). The five scores, the decomposed data from the original data, were taken and were formulated individually. The one of the five scores were modeled with AR-2 while the others are modeled with AR-1 model. From the time series analysis using the scores of the five components, he noted "principal component time series might provide a relatively simple and meaningful alternative to conventional large MARMA models". This study is inspired from the researcher's quote to develop a multivariate simulation model. The multivariate simulation model is suggested here using Principal Component Analysis (PCA) and Independent Component Analysis (ICA). Three modeling step is applied for simulation. (1) PCA is used to decompose the correlated multivariate data into the uncorrelated data while ICA decomposes the data into independent components. Here, the autocorrelation structure of the decomposed data is still dominant, which is inherited from the data of the original domain. (2) Each component is resampled by block bootstrapping or K-nearest neighbor. (3) The resampled components bring back to original domain. From using the suggested approach one might expect that a) the simulated data are different with the historical data, b) no data transformation is required (in case of ICA), c) a complex system can be decomposed into independent component and modeled individually. The model with PCA and ICA are compared with the various statistics such as the basic statistics (mean, standard deviation, skewness, autocorrelation), and reservoir-related statistics, kernel density estimate.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.7
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• pp.881-886
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• 2005

This paper presents intelligent digital redesign method of global approach for hybrid state space fuzzy-model-based controllers. For effectiveness and stabilization of continuous-time uncertain nonlinear systems under discrete-time controller, Takagi-Sugeno(TS) fuzzy model is used to represent tile complex system. And global approach design problems viewed as a convex optimization problem that we minimize the error of the norm bounds between nonlinearly interpolated linear operators to be matched. Also, by using the power series, we analyzed nonlinear system's uncertain parts more precisely. When a sampling period is sufficiently small, the conversion of a continuous-time structured uncertain nonlinear system to an equivalent discrete-time system have proper reason. Sufficiently conditions for the global state-matching of tile digitally controlled system are formulated in terms of linear matrix inequalities (LMIs). Finally, a TS fuzzy model for the chaotic Lorentz system is used as an example to guarantee the stability and effectiveness of the proposed method.

• Korean Journal of Construction Engineering and Management
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• v.9 no.5
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• pp.137-148
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• 2008

Using PMIS(Project Management Information System) plays an important role in systematically planning and effectively managing the complex and large-sized construction projects. In spite of the importance of PMIS, on-site engineers, who are the main users of PMIS, have not used PMIS enthusiastically because PMIS has been evaluated and improved by not the viewpoint of the system users but the viewpoint of system managers and head office manager. Therefore, this study developed an assessment model to evaluate PMIS with the viewpoint of the on-site engineers, so previously developed PMIS can be evaluated by the on-site engineers with importance and satisfaction elements. It is anticipated that the effective use of the developed assessment model might increase the utilization of existing PMIS as well as develop the construction industry.

• The Korean Journal of Applied Statistics
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• v.29 no.4
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• pp.581-594
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• 2016

We introduce innovations state space exponential smoothing models (ISS-ESM) that can analyze time series with multiple seasonal patterns. Especially, in order to control complex structure existing in the multiple patterns, the model equations use a matrix consisting of seasonal updating parameters. It enables us to group the seasonal parameters according to their similarity. Because of the grouped parameters, we can accomplish the principle of parsimony. Further, the ISS-ESM can potentially accommodate any number of multiple seasonal patterns. The models are applied to predict electricity demand in Korea that is observed on hourly basis, and we compare their performance with that of the traditional exponential smoothing methods. It is observed that the ISS-ESM are superior to the traditional methods in terms of the prediction and the interpretability of seasonal patterns.