• Journal of Biomedical Engineering Research
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• v.34 no.4
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• pp.218-225
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• 2013

In this paper hair bundle feature model and integration method for hair cell models were proposed. The proposed hair bundle feature model was based on spring-damper-mass model. Input of integrated vestibular hair cell model was frequency and output was interspike interval of hair cell that was reflected the feature of hair bundles. Irregular afferents that had a great gain variation showed reduction of negative stiffness section. Regular afferents that had a small gain variation, however, showed same feature with base negative stiffness feature. As a result, integrated vestibular hair cell model showed almost the same modeling data with experimental data in the modeled eleven frequency bands. It is verified that the proposed model is a good model for hair bundle feature modeling.

• Structural Engineering and Mechanics
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• v.15 no.3
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• pp.299-314
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• 2003

This investigation presents an efficient method for identifying modal characteristics from the measured displacement, velocity and acceleration signals of multiple channels on structural systems. A Vector Backward Auto-Regressive model (VBAR) that describes the relationship between the output information in different time steps is used to establish a backward state equation. Generally, the accuracy of the identified dynamic characteristics can be improved by increasing the order of the Auto-Regressive model (AR) in cases of measurement of data under noisy circumstances. However, a higher-order AR model also induces more numerical modes, only some of which are the system modes. The proposed VBAR model provides a clear characteristic boundary to separate the system modes from the spurious modes. A numerical example of a lumped-mass model with three DOFs was established to verify the applicability and effectiveness of the proposed method. Finally, an offshore platform model was experimentally employed as an application case to confirm the proposed VBAR method can be applied to real-world structures.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1343-1346
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• 2005

In the cold rolling mill, coil breakage that generated in rolling process makes the various types of troubles such as the degradation of productivity and the damage of equipment. Recent researches were done by the mechanical analysis such as the analysis of roll chattering or strip inclining and the prevention of breakage that detects the crack of coil. But they could cover some kind of breakages. The prediction of Coil breakage was very complicated and occurred rarely. We propose to build effective prediction modes for coil breakage in rolling process, based on data mining model. We proposed three prediction models for coil breakage: (1) decision tree based model, (2) regression based model and (3) neural network based model. To reduce model parameters, we selected important variables related to the occurrence of coil breakage from the attributes of coil setup by using the methods such as decision tree, variable selection and the choice of domain experts. We developed these prediction models and chose the best model among them using SEMMA process that proposed in SAS E-miner environment. We estimated model accuracy by scoring the prediction model with the posterior probability. We also have developed a software tool to analyze the data and generate the proposed prediction models either automatically and in a user-driven manner. It also has an effective visualization feature that is based on PCA (Principle Component Analysis).

• Journal of Korean Society of Industrial and Systems Engineering
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• v.39 no.1
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• pp.105-115
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• 2016

Ensemble classification involves combining individually trained classifiers to yield more accurate prediction, compared with individual models. Ensemble techniques are very useful for improving the generalization ability of classifiers. The random subspace ensemble technique is a simple but effective method for constructing ensemble classifiers; it involves randomly drawing some of the features from each classifier in the ensemble. The instance selection technique involves selecting critical instances while deleting and removing irrelevant and noisy instances from the original dataset. The instance selection and random subspace methods are both well known in the field of data mining and have proven to be very effective in many applications. However, few studies have focused on integrating the instance selection and random subspace methods. Therefore, this study proposed a new hybrid ensemble model that integrates instance selection and random subspace techniques using genetic algorithms (GAs) to improve the performance of a random subspace ensemble model. GAs are used to select optimal (or near optimal) instances, which are used as input data for the random subspace ensemble model. The proposed model was applied to both Kaggle credit data and corporate credit data, and the results were compared with those of other models to investigate performance in terms of classification accuracy, levels of diversity, and average classification rates of base classifiers in the ensemble. The experimental results demonstrated that the proposed model outperformed other models including the single model, the instance selection model, and the original random subspace ensemble model.

• Structural Engineering and Mechanics
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• v.30 no.5
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• pp.619-645
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• 2008

This study presents the efficiency of simulating structural systems using a method that combines a simplified component model (SCM) and rigorous component model (RCM). To achieve a realistic simulation of structural systems, a numerical model must be adequately capturing the detailed behaviors of real systems at various scales. However, capturing all details represented within an entire structural system by very fine meshes is practically impossible due to technological limitations on computational engineering. Therefore, this research develops an approach to simulate large-scale structural systems that combines a simplified global model with multiple detailed component models adjusted to various scales. Each correlated multi-scale simulation model is linked to others using a multi-level hierarchical modeling simulation method. Simulations are performed using nonlinear finite element analysis. The proposed method is applied in an analysis of a simple reinforced concrete structure and the Reuipu Elementary School (an existing structure), with analysis results then compared to actual onsite observations. The proposed method obtained results very close to onsite observations, indicating the efficiency of the proposed model in simulating structural system behavior.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.16 no.1
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• pp.139-147
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• 1998

The national topographic basedata should meet a variety of user requirements. To generate, maintain and handle the national topographic basedata in economic and effective way, the nationally supported research works on data model, structure and feature classification system should be intensively undertaken by government agency, research institute and university. This paper presents the technical definitions of conceptual and logical model for proposed data model representing digital map data. The key aspects of the proposed data model are flexibility for accommodating user requirements as well as step-by-step approach for modification as necessary m recent future. Conclusively, the proposed data model is a transitional data model between simple graphical model and object oriented data model.

• Transactions of the Korean Society of Automotive Engineers
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• v.12 no.4
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• pp.154-162
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• 2004

In this study, a mathematical model for analyzing the shifting transients of the passenger car with an automatic transmission is proposed. The proposed model comprises a power transmission system and a vehicle system, which are coupled. In order to extract the modeling parameters, on-road car test is carried out. The model is composed of a detailed powertrain, an engine/AT housing, a simplified suspension system, tires and a vehicle body model. On the test, the vehicle accelerations and pitch ratio are measured by using accelerometers and a gyro sensor. The speeds, the brake signal, and the throttle position are taken from sensors which already exist in the vehicle. Considering natural ftequencies, which is calculated from the measured accelerations, and the characteristic equation, vehicle model parameters are identified. Dynamic behaviors during upshift or downshift are simulated using the proposed vehicle model. By comparing and analyzing the simulation result and on-road car test data, the vibration of the Engine/AT housing influences the shifting transients. The effect of model parameters are also studied. Among model parameters, the location of engine mountings influences the vibration of the vehicle body.

• Wind and Structures
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• v.15 no.5
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• pp.409-421
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• 2012

In this paper, the method of introducing additional source/sink terms in the turbulence and momentum transport equations was applied to appropriately model the effect of the tree canopy. At first, the new additional source term for the turbulence frequency ${\omega}$ equation in the SST k-${\omega}$ model was proposed through theoretical analogy. Then the new source/sink term model for the SST k-${\omega}$ model was numerically verified. At last, the proposed source term model was adopted in the wind environment optimal design of the twin high-rise buildings of CABR (China Academy of Building Research). Based on the numerical simulations, the technical measure to ameliorate the wind environment was proposed. Using the new inflow boundary conditions developed in the previous studies, it was concluded that the theoretically reasonable source term model of the SST k-${\omega}$ model was applicable for modeling the tree canopy flow and accurate numerical results are obtained.

• Journal of Electrical Engineering and Technology
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• v.13 no.5
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• pp.1874-1885
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• 2018

The customer side operation is getting more complex in a smart grid environment because of the adoption of renewable resources. In performing energy management planning or scheduling, it is essential to forecast non-controllable resources accurately and robustly. The PV system is one of the common renewable energy resources in customer side. Its output depends on weather and physical characteristics of the PV system. Thus, weather information is essential to predict the amount of PV system output. However, weather forecast usually does not include enough solar irradiation information. In this study, a PV system power output prediction model (PPM) under limited weather information is proposed. In the proposed model, meteorological radiation model (MRM) is used to improve cloud cover radiation model (CRM) to consider the seasonal effect of the target region. The results of the proposed model are compared to the result of the conventional CRM prediction method on the PV generation obtained from a field test site. With the PPM, root mean square error (RMSE), and mean absolute error (MAE) are improved by 23.43% and 33.76%, respectively, compared to CRM for all days; while in clear days, they are improved by 53.36% and 62.90%, respectively.

• The Journal of Korea Robotics Society
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• v.11 no.2
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• pp.92-99
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• 2016

An electro-hydraulic actuator (EHA) is widely used in industrial motion systems and the increasing bandwidth of EHA position control is important issue. The model-inverse feedforward controller is known to extend the bandwidth of system. When the system has non-minimum phase (NMP) zeros, direct model inversion makes system unstable. To overcome this problem, an approximate model-inverse method is used. A representative approximate model inversion method is zero phase error tracking control (ZPETC). However, if zeros locate right half plane of z-plane, the approximate inverse model amplifies the high-frequency response. In this paper, to solve the problem of ZPETC, an adaptive model-inverse control is proposed. The adaptive algorithm updates feedforward term in real-time. The effectiveness of the proposed adaptive model-inverse position control strategy is verified by comparison with typical proportional-integral (PI) control and feedforward control by experiments. As a result, the proposed adaptive controller extends the bandwidth of EHA position control.