• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.2
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• pp.251-256
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• 2005

Up to now, a number of models have been proposed and discussed to describe a wide range of inelastic behaviors of materials. The fetal problem of using such models is however the existence of model errors, and the problem remains inevitably as far as a material model is written explicitly. In this paper, the authors define the implicit constitutive model and propose an implicit viscoplastic constitutive model using neural networks. In their modeling, inelastic material behaviors are generalized in a state space representation and the state space form is constructed by a neural network using input output data sets. A technique to extract the input-output data from experimental data is also described. The proposed model was first generated from pseudo-experimental data created by one of the widely used constitutive models and was found to replace the model well. Then, having been tested with the actual experimental data, the proposed model resulted in a negligible amount of model errors indicating its superiority to all the existing explicit models in accuracy.

• Journal of the Korean Institute of Telematics and Electronics
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• v.26 no.9
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• pp.1308-1316
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• 1989

A simplified 10-element GaAs MESFET equivalent circuit model has been presented which is suitable for the design of ultrabroad-band microwave small-signal amplification, the these circuit element values are extracted from measured S-parameters using complex-curve fitting algorithm. Packaged GaAs MESFET equivalent circuits are composed of intrinsic \ulcornermodel and several extrinsic elements at microwave frequencies, of which the largest effects are caused by package lead inductances. If these are eliminated from measured S-parameters, newly obtained S-parameters are closed to intrinsic \ulcornermodel, and the rest element values can be easily extracted. The modeling results applied to the packaged GaAs MESFET NE71083 are almost equal between the measure S-parameters and the mideled S-parameters within b 2% errors from DC to 8GHz, and errors are increased to \ulcorner% upto 12GHz wide bandwidth.

• Journal of KSNVE
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• v.9 no.5
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• pp.975-983
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• 1999

This paper describes the dynamic characteristics of the joint structures in case of using the simplified beam model in the F. E. analysis. The modeling errors, when replace the shell with the beam, are investigated through F. E. normal modes analysis. Normal mode analysis were performed to obtain the natural frequencies of the L and T shaped joints with various type of channels. The results were analyzed to access the effects of the models on the accuracy of F.E. analysis by identifying the geometric factors which cause the error. The geometric factors considered are joint angle, channel length, thickness and area ratio of the hollow section to the filled one. The joint stiffness evaluation technique is developed in this study using normal modes analysis with Lumped Mass. With this method, the progressively improved results of F. E. analysis are obtained using the simplified beam model. The static and normal modes analysis are performed with the joint stiffness values obtained by the Kazunori Shimonkakis' virtual stiffness method and the proposed method and these simplified modeling errors are compared.

• Journal of Korean Society of Transportation
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• v.14 no.2
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• pp.119-136
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• 1996

The evaluation of risk level or possibility of traffic accidents is a fundamental task in reducing the dangers associated with current transportation system. However, due to the lack of data and basic researches for identifying such factors, evaluations so far have been undertaken by only the experts who can use their judgements well in this regard. Here comes the motivation this thesis to evaluate such risk level more or less in an automatic manner. The purpose of this thesis is to test the fuzzy-logic theory in evaluating the risk level of traffic accidents. In modeling the process of expert's logical inference of risk level determination, only the geometric features have been considered for the simplicity of the modeling. They are the visibility of road surface, horizontal alignment, vertical grade, diverging point, and the location of pedestrain crossing. At the same time, among some inference methods, fuzzy composition inference method has been employed as a back-bone inference mechanism. In calibration, the proposed model used four sites' data. After that, using calibrated model, six sites' risk levels have been identified. The results of the six sites' outcomes were quite similar to those of real world other than some errors caused by the enforcement of the model's output. But it seems that this kind of errors can be overcome in the future if some other factors such as driver characteristics, traffic environment, and traffic control conditions have been considered. Futhermore, the application of site's specific time series data would produce better results.

• Journal of the Ergonomics Society of Korea
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• v.30 no.1
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• pp.41-53
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• 2011

Objective: The purpose of this paper is to introduce new research trends of human error and accident modeling and to suggest future promising research directions in those areas. Background: Various methods and techniques have been developed to understand the nature of human errors, to classify them, to analyze their causes, to prevent their negative effects, and to use their concepts during design process. However, it has been reported that they are impractical and ineffective for modern complex systems, and new research approaches are needed to secure the safety of those systems. Method: Six different perspectives to study human error and system safety are explained, and then seven recent research trends are introduced in relation to the six perspectives. The implications of the new research trends and viable research directions based on them are discussed from a cognitive systems engineering point of view. Results: Traditional methods for analyzing human errors and identifying causes of accidents have critical limitations in complex systems, and recent research trends seem to provide some insights and clues for overcoming them. Conclusion: Recent research trends of human error and accident modeling emphasize different concepts and viewpoints, which include systems thinking, sociotechnical perspective, ecological modelling, system resilience, and safety culture. Application: The research topics explained in this paper will help researchers to establish future research programmes.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.11
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• pp.5194-5201
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• 2011

In this paper, an adaptive nonlinear friction compensation scheme for second-order nonlinear mechanical system with a partially known nonlinear dynamic friction is proposed to achieve asymptotic position and velocity tracking in the absence of disturbances and modeling errors. It is also shown that even with disturbances and modeling errors, in contrast to existing other adaptive control schemes, by proper adjustment of design parameters, reduced error bounds on position and velocity tracking can be achieved.

• Food Science of Animal Resources
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• v.39 no.1
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• pp.114-120
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• 2019

Response surface methodology (RSM) is a useful set of statistical techniques for modeling and optimizing responses in research studies of food science. As a design for a response surface experiment, a central composite design (CCD) with multiple runs at the center point is frequently used. However, sometimes there exist situations where some among the responses at the center point are outliers and these outliers are overlooked. Since the responses from center runs are those from the same experimental conditions, there should be no outliers at the center point. Outliers at the center point ruin statistical analysis. Thus, the responses at the center point need to be looked at, and if outliers are observed, they have to be examined. If the reasons for the outliers are not errors in measuring or typing, such outliers need to be deleted. If the outliers are due to such errors, they have to be corrected. Through a re-analysis of a dataset published in the Korean Journal for Food Science of Animal Resources, we have shown that outlier elimination resulted in the increase of the maximum possible R-square that the modeling of the data can obtain, which enables us to improve the quality of response surface analysis.

• Journal of Drive and Control
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• v.18 no.2
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• pp.38-45
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• 2021

In recent years, the demand for an onboard scale system which can directly monitor load distribution and overload of vehicles has increased. Depending on the suspension type of the vehicle, the onboard scale system could use different types of sensors, such as, angle sensors, pressure sensors, load cells, etc. In the case of a vehicle equipped with leaf spring suspension system, the load of the vehicle is measured by using the deflection or displacement of the leaf spring. Leaf springs have hysteresis characteristics that vary in displacement depending on the load state. These characteristics cause load measurement errors when moving or removing cargoes. Therefore, this study aimed at developing an onboard scale device for cargo vehicles equipped with leaf springs. A sectional modeling method which can reduce measurement errors caused by hysteresis characteristics was also proposed.

• Progress in Superconductivity
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• v.4 no.1
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• pp.19-26
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• 2002

We have earlier developed a 40-channel SQUID system. An important figure of merit of a MEG system is the localization error, within which the underlying current source can be localized. With this system, we investigated the localization error in terms of the standard deviation of the coordinates of the ECDs and the systematic error due to inadequate modeling. To do this, we made localization of single current dipoles from tangential components of auditory evoked fields. Equivalent current dipoles (ECD) at N1m peak were estimated based on a locally fitted spherical conductor model. In addition, we made skull phantom and simulation measurements to investigate the contribution of various errors to the localization error. It was found that the background noise was the main source of the errors that could explain the observed standard deviation. Further, the amount of systematic error, when modeling the head with a spherical conductor, was much less than the standard deviation due to the background noise. We also demonstrated the performance of the system by measuring the evoked fields to grammatical violation in sentence comprehension.

• Journal of the Korean Society of Industry Convergence
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• v.27 no.2_2
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• pp.445-456
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• 2024

This study aims to suggest service improvement opportunities by analyzing user review data of the top three OTT service apps(Netflix, Coupang Play, and TVING) on Google Play Store. To achieve this objective, we proposed a framework for uncovering service opportunities through the analysis of negative user reviews from OTT service providers. The framework involves automating the labeling of identified topics and generating service improvement opportunities using topic modeling and prompt engineering, leveraging GPT-4, a generative AI model. Consequently, we pinpointed five dissatisfaction topics for Netflix and TVING, and nine for Coupang Play. Common issues include "video playback errors", "app installation and update errors", "subscription and payment" problems, and concerns regarding "content quality". The commonly identified service enhancement opportunities include "enhancing and diversifying content quality". "optimizing video quality and data usage", "ensuring compatibility with external devices", and "streamlining payment and cancellation processes". In contrast to prior research, this study introduces a novel research framework leveraging generative AI to label topics and propose improvement strategies based on the derived topics. This is noteworthy as it identifies actionable service opportunities aimed at enhancing service competitiveness and satisfaction, instead of merely outlining topics.