• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.18 no.2
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• pp.157-168
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• 2000

MDD(Mountainous District Development) has more available area than flatland come into solution for many urban problem arisen from the insufficiency of urban-purpose land and space. When develope the mountainous district, the development must be a development considered the viewscape. To efficiently accomplish the MDD viewscape was considered, firstly we have to know which viewscape element effect on many observers when they rate the viewscpe preference. As a basical study for MDD viewscape was considered, we used GSIS and ANN(Artificial Neural Network) in cause analysis of viewscape preference. Many observers firstly influenced by“height difference”that represent the difference between elevation of a mountainous and buildings, and secondly floor-area-ratio has influence on viewscape preference, thirdly many observer affected by building coverage among many viewscape elecments considered in this study.

• Proceedings of the KIEE Conference
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• 2009.04b
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• pp.32-34
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• 2009

The field oriented control of induction motors is widely used in high performance applications. However, detuning caused by parameter disturbance still limits the performance of these drives. In order to accomplish variable speed operation, conventional PI-like controllers are commonly used. These controllers provide limited good Performance over a wide range of operation, even under ideal field oriented conditions. This paper is proposed model reference adaptive fuzzy control(MFC) and artificial neural network(ANN) based on the vector controlled induction motor drive system. Also, this paper is proposed control of speed and current using fuzzy adaption mechanism(FAM), MFC and estimation of speed using ANN. The proposed control algorithm is applied to induction motor drive system using FAM, MFC and ANN controller. Also, this paper is proposed the analysis results to verify the effectiveness of this controller.

• Journal of Electrical Engineering and Technology
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• v.13 no.3
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• pp.1089-1098
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• 2018

This study aims at developing and applying a hybrid model to the wind power prediction (WPP). The hybrid model for a very-short-term WPP (VSTWPP) is achieved through analytical data, multiple linear regressions and least square methods (MLR&LS). The data used in our hybrid model are based on the historical records of wind power from an offshore region. In this model, the WPP is achieved in four steps: 1) transforming historical data into ratios; 2) predicting the wind power using the ratios; 3) predicting rectification ratios by the total wind power; 4) predicting the wind power using the proposed rectification method. The proposed method includes one-step and multi-step predictions. The WPP is tested by applying different models, such as the autoregressive moving average (ARMA), support vector machine (SVM), and artificial neural network (ANN). The results of all these models confirmed the validity of the proposed hybrid model in terms of error as well as its effectiveness. Furthermore, forecasting errors are compared to depict a highly variable WPP, and the correlations between the actual and predicted wind powers are shown. Simulations are carried out to definitely prove the feasibility and excellent performance of the proposed method for the VSTWPP versus that of the SVM, ANN and ARMA models.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.25 no.5
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• pp.22-33
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• 2011

An induction motor operated with a conventional direct self controller(DSC) shows a sluggish response during startup and under changes of torque command. Fuzzy logic controller(FLC) is used in conjection with DSC to minimize these problems. A FLC chooses the switching states based on a set of fuzzy variables. Flux position, error in flux magnitude and error in torque are used as fuzzy state variables. Fuzzy rules are determinated by observing the vector diagram of flux and currents. This paper proposes hybrid fuzzy controller for direct torque control(DTC) of induction motor drives. The speed controller is based on adaptive fuzzy learning controller(AFLC), which provide high dynamics performances both in transient and steady state response. Flux position, error in flux magnitude and error in torque are used as FLC state variables. The speed is estimated with model reference adaptive system(MRAS) based on artificial neural network(ANN) trained on-line by a back-propagation algorithm. This paper is controlled speed using hybrid fuzzy controller(HFC) and estimation of speed using ANN. The performance of the proposed induction motor drive with HFC controller and ANN is verified by analysis results at various operation conditions.

• Proceedings of the Korea Information Processing Society Conference
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• 2018.05a
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• pp.242-243
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• 2018

무선 통신기술이 발전함에 따라 위치기반 서비스에 대한 관심 또한 증가하고 있다. 그 중 저전력 블루투스 기술을 사용한 비콘(Beacon)은 실내 위치인식이 불가능한 GPS와 달리 실내에서도 측위가 가능하여 사용성이 주목 받고 있다. 그러나 비콘으로부터 수신되는 RSSI(Received Signal Strength Indication) 값은 여러 환경요소로부터 영향을 받기 때문에 RSSI값을 기반으로 한 거리측정이 실제거리와의 오차가 크게 나타난다. 이에 따른 문제를 해결하기 위한 기존의 연구들이 존재하지만 평균적으로 10m이하의 거리에서 2m의 오차를 나타내고 있다. 본 연구에서는 RSSI의 오차를 줄이기 위해 확장 칼만 필터와 신호 안정화 필터를 사용하여 Raw Data를 전처리 한 후 산출된 Cleaned Data를 기반으로 각 거리단위에 최적화된 ANN(Artificial Neural Network)모델을 생성하여 거리를 측정하는 기법을 제안한다.

• Journal of the Korean Geosynthetics Society
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• v.17 no.4
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• pp.251-265
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• 2018

This paper concerns the development of an optimized NATM secondary lining design system for a subsea tunnel. The subsea tunnel is normally laid down under the sea water and submarine ground which consists of soil or rock. The design system is the series of process which can predict lining member forces by ANN (artificial neural network system), analyze suitable section for the designated ground, construction and tunnel conditions. Finally, this lining design system aims to be connected for designing the subsea tunnel automatically. The lining member forces are predicted based on the ANN which was calculated by a FEM (finite element analysis) and it helps designers determine its lining dimension easily without any further FEM calculations.

• Geomechanics and Engineering
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• v.29 no.3
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• pp.249-258
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• 2022

A disc cutter is an excavation tool on a tunnel boring machine (TBM) cutterhead; it crushes and cuts rock mass while the machine excavates using the cutterhead's rotational movement. Disc cutter wear occurs naturally. Thus, along with the management of downtime and excavation efficiency, abrasioned disc cutters need to be replaced at the proper time; otherwise, the construction period could be delayed and the cost could increase. The most common prediction models for TBM performance and for the disc cutter lifetime have been proposed by the Colorado School of Mines and Norwegian University of Science and Technology. However, design parameters of existing models do not well correspond to the field values when a TBM encounters complex and difficult ground conditions in the field. Thus, this study proposes a series of machine learning models to predict the disc cutter lifetime of a shield TBM using the excavation (machine) data during operation which is response to the rock mass. This study utilizes five different machine learning techniques: four types of classification models (i.e., K-Nearest Neighbors (KNN), Support Vector Machine, Decision Tree, and Staking Ensemble Model) and one artificial neural network (ANN) model. The KNN model was found to be the best model among the four classification models, affording the highest recall of 81%. The ANN model also predicted the wear rate of disc cutters reasonably well.

• Computers and Concrete
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• v.25 no.2
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• pp.119-132
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• 2020

There is a growing interest in the use of by-product materials such as ceramics as alternative materials in construction. The aim of this study is to investigate the mechanical properties and durability of sustainable concrete containing waste ceramic powder (WCP), and to predict the results using artificial neural network (ANN). In this order, different water to binder (W/B) ratios of 0.3, 0.4, and 0.5 were considered, and in each W/B ratio, a percentage of cement (between 5-50%) was replaced with WCP. Compressive and tensile strengths, water absorption, electrical resistivity and rapid chloride permeability (RCP) of the concrete specimens having WCP were evaluated by related experimental tests. The results showed that by replacing 20% of the cement by WCP, the concrete achieves compressive and tensile strengths, more than 95% of those of the control concrete, in the long term. This percentage increases with decreasing W/B ratio. In general, by increasing the percentage of WCP replacement, all durability parameters are significantly improved. In order to validate and suggest a suitable tool for predicting the characteristics of the concrete, ANN model along with various multivariate regression methods were applied. The comparison of the proposed ANN with the regression methods indicates good accuracy of the developed ANN in predicting the mechanical properties and durability of this type of concrete. According to the results, the accuracy of ANN model for estimating the durability parameters did not significantly follow the number of hidden nodes.

• Economic and Environmental Geology
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• v.46 no.4
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• pp.301-312
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• 2013

Landslides are caused by complex interaction among a large number of interrelated factors such as topography, geology, forest and soils. In this study, a comparative study was carried out using fuzzy relationship method and artificial neural network to evaluate landslide susceptibility. For landslide susceptibility mapping, maps of the landslide occurrence locations, slope angle, aspect, curvature, lithology, soil drainage, soil depth, soil texture, forest type, forest age, forest diameter and forest density were constructed from the spatial data sets. In fuzzy relation analysis, the membership values for each category of thematic layers have been determined using the cosine amplitude method. Then the integration of different thematic layers to produce landslide susceptibility map was performed by Cartesian product operation. In artificial neural network analysis, the relative weight values for causative factors were determined by back propagation algorithm. Landslide susceptibility maps prepared by two approaches were validated by ROC(Receiver Operating Characteristic) curve and AUC(Area Under the Curve). Based on the validation results, both approaches show excellent performance to predict the landslide susceptibility but the performance of the artificial neural network was superior in this study area.

• Journal of Power Electronics
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• v.8 no.3
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• pp.280-290
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• 2008

This paper deals with an investigation and evaluation of the performance of a state observer based Permanent Magnet Synchronous Motor (PMSM) drive controlled by PI (Proportional Integral), PID (Proportional Integral and Derivative), SMC (sliding mode control), ANN (Artificial neural network) and FLC (Fuzzy logic) speed controllers. A detailed study of the steady state and dynamic performance of estimated speed and angle is given to demonstrate the capability of the controllers.