• Structural Engineering and Mechanics
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• 제60권3호
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• pp.471-488
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• 2016

This study predicts the strength of rotary brace damper by analyzing a new set of probabilistic models using the usual method of multiple linear regressions (MLR) and advanced machine-learning methods of multivariate adaptive regression splines (MARS), Rotary brace damper can be easily assembled with high energy-dissipation capability. To investigate the behavior of this damper in structures, a steel frame is modeled with this device subjected to monotonic and cyclic loading. Several response parameters are considered, and the performance of damper in reducing each response is evaluated. MLR and MARS methods were used to predict the strength of this damper. Displacement was determined to be the most effective parameter of damper strength, whereas the thickness did not exhibit any effect. Adding thickness parameter as inputs to MARS and MLR models did not increase the accuracies of the models in predicting the strength of this damper. The MARS model with a root mean square error (RMSE) of 0.127 and mean absolute error (MAE) of 0.090 performed better than the MLR model with an RMSE of 0.221 and MAE of 0.181.

• 한국조명전기설비학회:학술대회논문집
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• 한국조명전기설비학회 2007년도 추계학술대회 논문집
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• pp.307-312
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• 2007

The paper is proposed artificial neural network(ANN) sensorless control of induction motor drive with fuzzy learning control-fuzzy neural network(FLC-FNN) controller. The hybrid combination of neural network and fuzzy control will produce a powerful representation flexibility and numerical processing capability. Also, this paper is proposed speed control of induction motor using FLC-FNN and estimation of speed using ANN controller The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The error between the desired state variable and the actual one is back-propagated to adjust the rotor speed, so that the actual state variable will coincide with the desired one. The proposed control algorithm is applied to induction motor drive system controlled FLC-FNN and ANN controller, Also, this paper is proposed the analysis results to verify the effectiveness of the FLC-FNN and ANN controller.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1995년도 추계학술대회 95 KFIS Workshop Realization of Human Friendly System Based on Soft Computiong Techniques
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• pp.124-140
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• 1995

The merit of fuzzy rule based systems stems from their capability of encoding qualitative knowledge of experts into quantitative rules. Recent advancement in automatic tuning or self-organization of fuzzy rules from experimental data further enhances their power, allowing the integration of the top-down encoding of knowledge with the bottom-up learning of rules. In this paper, methods of self-organizing fuzzy rules and of performing defuzzification and inference is presented based on a multi-resolution radial basis function network. The network learns an arbitrary input-output mapping from sample distribution as the union of hyper-ellipsoidal clusters of various locations, sizes and shapes. The hyper-ellipsoidal clusters, representing fuzzy rules, are self-organized based of global competition in such a way as to ensute uniform mapping errors. The cooperative interpolation among the multiple clusters associated with a mapping allows the network to perform a bidirectional many-to-many mapping, representing a particular from of defuzzification. Finally, an inference engine is constructed for the network to search for an optimal chain of rules or situation transitions under the constraint of transition feasibilities imposed by the learned mapping. Applications of the proposed network to skill acquisition are shown.

• 대한의용생체공학회:의공학회지
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• 제19권1호
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• pp.39-46
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• 1998

본 연구에서 복잡한 비선형적 특성을 갖는 공압식 좌심실보조장치의 모델링과 제어에 인공신경망을 제안하였다. 일반적으로 좌심실보조장치는 비선형이 보상되어야 하는데 인공신경망은 학습능력에 의해 비선형 동적 시스템의 제어에 적용될 수 있다. 인공신경망 모델링을 통해 좌심실 보조장치의 동적 모델을 모델링하고 이를 기반으로 하여 인공신경망 제어기가 설계되었다. 제안된 알고리즘을 이용한 좌심실보조장치의 모델링과 제어성능 및 유효성은 컴퓨터 시뮬레이션에 의해 증명되었다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제4권1호
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• pp.111-118
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• 2004

In this paper, we present a novel approach for the structure of Fuzzy Neural Network(FNN) based on wavelet function and apply this network structure to the solution of the tracking problem for mobile robots. Generally, the wavelet fuzzy model(WFM) has the advantage of the wavelet transform by constituting the fuzzy basis function(FBF) and the conclusion part to equalize the linear combination of FBF with the linear combination of wavelet functions. However, it is very difficult to identify the fuzzy rules and to tune the membership functions of the fuzzy reasoning mechanism. Neural networks, on the other hand, utilize their learning capability for automatic identification and tuning. Therefore, we design a wavelet based FNN structure(WFNN) that merges these advantages of neural network, fuzzy model and wavelet transform. The basic idea of our wavelet based FNN is to realize the process of fuzzy reasoning of wavelet fuzzy system by the structure of a neural network and to make the parameters of fuzzy reasoning be expressed by the connection weights of a neural network. And our network can automatically identify the fuzzy rules by modifying the connection weights of the networks via the gradient descent scheme. To verify the efficiency of our network structure, we evaluate the tracking performance for mobile robot and compare it with those of the FNN and the WFM.

• International Journal of Quality Innovation
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• 제6권1호
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• pp.109-118
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• 2005

The parameter design is the most emphasized measure by researchers for a new products development. It is critical for makers to achieve simultaneously in both the time-to-market production and the quality enhancement. However, there are difficulties in practical application, such as (1) complexity and nonlinear relationships co-existed among the system's inputs, outputs and control parameters, (2) interactions occurred among parameters, (3) where the adjustment factors of Taguchi's two-phase optimization procedure cannot be sure to exist in practice, and (4) for some reasons, the data became lost or were never available. For these incomplete data, the Taguchi methods cannot treat them well. Neural networks have a learning capability of fault tolerance and model free characteristics. These characteristics support the neural networks as a competitive tool in processing multivariable input-output implementation. The successful fields include diagnostics, robotics, scheduling, decision-making, prediction, etc. This research is a case study of spherical annealing model. In the beginning, an original model is used to pre-fix a model of parameter design. Then neural networks are introduced to achieve another model. Study results showed both of them could perform the highest spherical level of quality.

• 한국멀티미디어학회논문지
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• 제2권4호
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• pp.490-495
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• 1999

본 논문에서는 인공 신경망과 퍼지 논리의 장점을 뉴런 구조에 적용하여 학습 속도가 마르며 수렴률을 향상시키는 방법을 제안한다. 인공신경망의 벤치 마크로 사용되는 XOR문제 n 비트 parity문제와 현실적인 이미지 응용을 위해 자동차 번호 판에서 숫자 이미지에 적용시켜 보았다. 실험결과, 모든 자료 값과 목표 값에 대해서 항상 수렴을 보장하는 것은 아니다. 그렇지만, 학습 속도가 빠르며 수렴률의 향상을 보였다. 제안된 방법은 임의의 충으로 확장이 가능하다. 여기서는 단층의 경우만을 고려하여 빠른 속도와 방대한 이미지에 대해서 빠른 처리를 가능하게 한다.

• 한국군사과학기술학회지
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• 제23권2호
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• pp.115-124
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• 2020

Due to the increasing threats of submarines from North Korea and other countries, ROK Navy should improve the detection capability of submarines. There are two ways to detect submarines : acoustic detection and non-acoustic detection. Since the acoustic-detection way has limitations in spite of its usefulness, it should have the complementary way. The non-acoustic detection is the way to detect submarines which are operating mast sets such as periscopes and snorkels by non-acoustic sensors. So, this paper proposes a new submarine non-acoustic detection model using an ensemble of Convolutional Neural Network models in order to automate the non-acoustic detection. The proposed model is trained to classify targets as 4 classes which are submarines, flag buoys, lighted buoys, small boats. Based on the numerical study with 10,287 images, we confirm the proposed model can achieve 91.5 % test accuracy for the non-acoustic detection of submarines.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2007년도 추계학술대회 논문집 전기기기 및 에너지변환시스템부문
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• pp.123-125
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• 2007

The paper is proposed artificial neural network(ANN) sensorless control of induction motor drive with fuzzy learning control-fuzzy neural network(FLC-FNN)controller. The hybrid combination of neural network and fuzzy control will produce a powerful representation flexibility and numerical processing capability. Also, this paper is proposed speed control of induction motor using FLC-FNN and estimation of speed using ANN controller. This paper is proposed the analysis results to verify the effectiveness of the FLC-FNN and ANN controller.

• Industrial Engineering and Management Systems
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• 제7권3호
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• pp.214-227
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• 2008

Today's dynamic competitiveness requires an organization to improve its performance measurement and management. Quality Management Systems (QMS) abound, the main ones being: ISO series, Malcolm Baldridge National Quality Award (MBNQA), European Forum for Quality Management (EFQM), Six Sigma Business Scorecard and the Balanced Scorecard. Based on the literature, the IPMMM (Integrated Performance Measurement and Management Model) identified 7 key synthesized factors: leadership, strategy management and policy, customer and market, learning and growth, partnership and resources, internal processes and business results that are employed to investigate the key performance indicators of a car assembler using the Delphi methodology. In the 2 rounds of Delphi panels consisting of 20 senior management personnel, the $1^{st}$ round of 198 indicators in the IPMMM yielded 90 indicators. The $2^{nd}$ round yielded 43 performance indicators with 18 rated as critical based on the % assigned in the $1^{st}$ and $2^{nd}$ priority rating of "very important factor" and "key performance indicator" that must be ranked high on both of the priorities. The very critical indicators appeared to be: defect percentage and first time capability (tie in $1^{st}$ place) and revenue, goal setting, customer satisfaction index, on-time delivery, brand image, return on investment, Claim Occurrence Ratio, and debt being ranked from $3^{rd}$ to $10^{th}$. It can be surmised that an organization can identify and develop an appropriate set of performance indicators through the Delphi methodology and implement and manage them based on the Balanced Scorecard.