• Smart Structures and Systems
• /
• 제26권1호
• /
• pp.63-75
• /
• 2020

Substructure pseudo-dynamic hybrid simulation (SPDHS) combining the advantages of physical experiments and numerical simulation has become an important testing method for evaluating the dynamic responses of structures. Various parameter identification methods have been proposed for online model updating. However, if there is large model gap between the assumed numerical models and the real models, the parameter identification methods will cause large prediction errors. This study presents an ANN (artificial neural network) method based on forgetting factor. During the SPDHS of model updating, a dynamic sample window is formed in each loading step with forgetting factor to keep balance between the new samples and historical ones. The effectiveness and anti-noise ability of this method are evaluated by numerical analysis of a six-story frame structure with BRBs (Buckling Restrained Brace). One BRB is simulated in OpenFresco as the experimental substructure, while the rest is modeled in MATLAB. The results show that ANN is able to present more hysteresis behaviors that do not exist in the initial assumed numerical models. It is demonstrated that the proposed method has good adaptability and prediction accuracy of restoring force even under different loading histories.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 1997년도 추계학술대회 논문집 학회본부
• /
• pp.600-602
• /
• 1997

In this study, a hybrid neural net consisting of an Adaptive LVQ(ALVQ) algorithm and MLP is proposed to perform speaker identification task. ALVQ is a new learning procedure using adaptively feature vector sequence instead of only one feature vector in training codebooks initialized by LBG algorithm and the optimization criterion of this method is consistent with the speaker classification decision rule. ALVQ aims at providing a compressed, geometrically consistent data representation. It is fit to cover irregular data distributions and computes the distance of the input vector sequence from its nodes. On the other hand, MLP aim at a data representation to fit to discriminate patterns belonging to different classes. It has been shown that MLP nets can approximate Bayesian "optimal" classifiers with high precision, and their output values can be related a-posteriori class probabilities. The different characteristics of these neural models make it possible to devise hybrid neural net systems, consisting of classification modules based on these two different philosophies. The proposed method is compared with LBG algorithm, LVQ algorithm and MLP for performance.

• Nuclear Engineering and Technology
• /
• 제56권9호
• /
• pp.3491-3500
• /
• 2024

In this work, the hybrid vibro-acoustic model reduction technique that is a physical-modal combined formulation is proposed to accelerate the finite element model updating process of the vibro-acoustic pipeline system. Particularly, the new formulation could provide an effective way of the model updating by preserving the physical DOFs for the direct calibration of the undetermined boundary conditions. The sensitivity based vibro-acoustic model updating is first conducted, and then the undetermined spring constant at the displacement boundary condition is then directly and effectively calibrated by using the proposed hybrid model reduction formulation. The proposed method is implemented in the real nuclear facility to evaluate its performance. In addition, an experimental implementation test using the inverse force identification process is also conducted to demonstrate the reliability of the generated vibro-acoustic FE model through the proposed method.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 1999년도 하계학술대회 논문집 B
• /
• pp.507-509
• /
• 1999

In this paper, we propose a new fuzzy model identification method that can yield a successful fuzzy rule base for fundamental approximations. The method in this paper uses a set of input-output data and is based on a hybrid messy genetic algorithm (mGA) with a fine-tuning scheme. The mGA processes variable-length strings, while standard GAs work with a fixed-length coding scheme. For successfully identifying a complex nonlinear system, we first use the mGA, which coarsely optimizes the structure and the parameters of the fuzzy inference system, and then the gradient descent method which tine tunes the identified fuzzy model. In order to demonstrate the superiority and efficiency of the proposed scheme, we finally show its application to a nonlinear approximation.

• Steel and Composite Structures
• /
• 제52권6호
• /
• pp.695-711
• /
• 2024

Suspension bridges are critical to urban transportation, but those in earthquake-prone areas face unique challenges. In the event of a moderate or strong earthquake, conventional linear theory-based approaches for detecting bridge damage become inadequate. This study presents an efficient method for identifying damage in suspension bridges using time history nonlinear inelastic analysis. A practical advanced analysis program is employed to model cable-supported bridges with low computational cost, generating a dataset for four hybrid models: PSO-DT, PSO-RF, PSO-XGB, and PSO-CGB. These models combine decision tree (DT), random forest (RF), extreme gradient boosting (XGB), and categorical gradient boosting (CGB) with particle swarm optimization (PSO) to capture nonlinear correlations between displacement response and damage. Principal component analysis reduces dataset dimensions, and PSO selects the optimal model. A numerical case study of a suspension bridge under simulated earthquake conditions identifies PSO-XGB as the best model for predicting stiffness reduction. The results demonstrate the method's robustness for nonlinear damage detection in suspension bridges under earthquake excitation.

• 한국산업정보학회논문지
• /
• 제15권3호
• /
• pp.1-9
• /
• 2010

최근 유비쿼터스 환경 구축의 가장 핵심은 센서 네트워크와 RFID 시스템이다. 이 중 RFID 시스템은 태그의 전자정보를 RF 신호를 이용하여 리더에게 전송한다. RFID 시스템은 다중 태그의 존재로 인해 충돌이 발생하고 태그 인식 성능이 저하된다. 그래서 태그 충돌을 중재할 수 있는 방법이 필요하다. 본 논문은 태그 간 충돌을 줄이며 좀 더 빠른 태그 인식이 가능한 하이브리드 방법을 제안한다. 본 논문에서 제안하는 방법은 트리기반 알고리즘의 장점인 확실성을 기반으로 동작하며 충돌을 줄이기 위해 태그 아이디를 이용하여 전송 타임 슬롯을 결정한다. 시뮬레이션을 통한 성능평가에서 다른 트리기반의 알고리즘과 다른 하이브리드 알고리즘에 비하여 충돌 횟수와 쿼리 수에서 높은 성능을 가진다는 것을 보여준다.

• 한국어류학회지
• /
• 제29권1호
• /
• pp.1-12
• /
• 2017

낙동강 상류 지류인 황지천에서 쉬리(Coreoleuciscus splendidus)와 참쉬리(C. aeruginosa)의 종 간 자연잡종 개체를 채집하였다. 쉬리와 참쉬리의 종 간 잡종 개체는 외부형태 비교와 함께 핵 DNA의 RAG1 유전자(1,334 bp)와 미토콘드리아 DNA인 CO1 유전자(1,551 bp)를 이용한 염기서열 분석을 실시하였다. 외부형태 분석결과 잡종 개체는 등지느러미, 꼬리지느러미 및 뒷지느러미 3곳에서 지느러미 반문의 형태가 쉬리와 참쉬리의 중간 형태를 나타내었다. RAG1과 CO1 유전자를 이용한 분자계통 분석결과 황치천에 분포하는 쉬리속 어류는 쉬리, 참쉬리 두 종과 두 종 간의 잡종 개체군으로 구성되어 있음을 확인하였으며, CO1 유전자의 염기서열 분석결과 순종인 정교배체와 잡종인 상반교배체가 잘 구분되었다. 또한 RAG1 유전자 분석결과 13개의 염기서열 변이를 확인하였고, 잡종 개체는 9개의 염기서열에서 double peaks가 확인되었다. 유전학적 분석과 외부형태 변이 분석에 의해 쉬리와 참쉬리 사이에 잡종화가 발생한 것을 확인하였으나 잡종 F2세대와 잡종 F1 세대의 생식적 격리 여부는 확인하지 못하였다.

• 국제초고층학회논문집
• /
• 제4권1호
• /
• pp.9-25
• /
• 2015

Field measurements of various structures have been conducted for many purposes. Measurement data obtained by field measurement is very useful to determine vibration characteristics including dynamic characteristics such as the damping ratio, natural frequency, and mode shape of a structure. In addition, results of field measurements and modal identification can be used for modal updating of FEM analysis, for checking the efficiency of damping devices and so on. This paper shows some examples of field measurements and modal identification for structural health monitoring. As the first example, changes of dynamic characteristics of a 15-story office building in four construction stages from the foundation stage to completion are described. The dynamic characteristics of each construction stage were modeled as accurately as possible by FEM, and the stiffness of the main structural frame was evaluated and the FEM results were compared with measurements performed on non-load-bearing elements. Simple FEM modal updating was also applied. As the next example, full-scale measurements were also carried out on a high-rise chimney, and the efficiency of the tuned mass damper was investigated by using two kinds of modal identification techniques. Good correspondence was shown with vibration characteristics obtained by the 2DOF-RD technique and the Frequency Domain Decomposition method. As the last example, the wind-induced response using RTK-GPS and the feasibility of hybrid use of FEM analysis and RTK-GPS for confirming the integrity of structures during strong typhoons were shown. The member stresses obtained by hybrid use of FEM analysis and RTK-GPS were close to the member stresses measured by strain gauges.

• 한국HCI학회:학술대회논문집
• /
• 한국HCI학회 2008년도 학술대회 1부
• /
• pp.190-194
• /
• 2008

본 논문은 지능형 서비스 로봇의 여러 기술들 중에서 기본적인 기술인 화자식별 기술에 관한 내용이다. 화자식별 기술은 화자의 음성신호를 이용하여 등록된 화자들 중에서 가장 유사한 화자를 찾아내는 것이다. 기존의 mel-frequency cepstral coefficient 를 이용한 화자식별 시스템은 무잡음 환경에서는 높은 성능을 보장하지만 잡음환경에서는 성능이 급격하게 떨어진다. 이렇게 잡음환경에서 성능이 떨어지는 요인은 등록환경과 식별환경이 다른 불일치문제 때문이다. 본 논문에서는 불일치문제를 해결하기 위해 relative autocorrelation sequence mel-frequency cepstral coefficient 를 사용하였다. 또한, 기존의 relative autocorrelation sequence mel-frequency cepstral coefficient 의 제한된 정보문제와 잔여잡음문제를 해결하기 위해 멀티스트리밍 방법과 멀티스트리밍 방법에 특정벡터 재결합 방법을 결합한 하이브리드 방법을 제한 하였다. 실험결과 제한된 방법들이 기존의 특정벡터보다 잡음환경에서 높은 화자식별 성능을 보여주었다.

• 대한임베디드공학회논문지
• /
• 제12권6호
• /
• pp.395-403
• /
• 2017

Image processing for object detection and identification has been studied for supply chain management application with various approaches. Among them, feature pointed detection algorithm is used to track an object or to recognize a position in automated supply chain systems and a depth image based feature point detection is recently highlighted in the application. The result of feature point detection is easily influenced by image noise. Also, the depth image has noise itself and it also affects to the accuracy of the detection results. In order to solve these problems, we propose a novel hybrid filtering mechanism for depth image based feature point detection, it shows better performance compared with conventional hybrid filtering mechanism.