• Structural Monitoring and Maintenance
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• 제7권4호
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• pp.283-294
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• 2020

In this paper, an adaptive MEMD based modal identification technique for linear time-invariant systems is proposed employing multiple vibration measurements. Traditional empirical mode decomposition (EMD) suffers from mode-mixing during sifting operations to identify intrinsic mode functions (IMF). MEMD performs better in this context as it considers multi-channel data and projects them into a n-dimensional hypercube to evaluate the IMFs. Using this technique, modal parameters of the structural system are identified. It is observed that MEMD has superior performance compared to its traditional counterpart. However, it still suffers from mild mode-mixing in higher modes where the energy contents are low. To avoid this problem, an adaptive filtering scheme is proposed to decompose the interfering modes. The Proposed modified scheme is then applied to vibrations of a reinforced concrete road bridge. Results presented in this study show that the proposed MEMD based approach coupled with the filtering technique can effectively identify the parameters of the dominant modes present in the structural response with a significant level of accuracy.

• 토지주택연구
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• 제4권1호
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• pp.89-98
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• 2013

최근 도시의 정책적 패러다임이 지속가능한 도시성장관리 중심으로 전환됨에 따라 교통정책도 승용차중심에서 대중교통중심으로 정책변화가 요구되고 있다. 이러한 배경 하에 복합환승센터의 도입은 매우 필연적이라고 볼 수 있다. 따라서 국가에서 주도하는 국가기간망중심의 환승센터 뿐 만 아니라 광역 및 지역중심의 환승시설에 대한 도입도 필요한 실정이다. 이에 본 연구에서는 효과적인 복합환승시설 도입을 위해 다양한 지역적 특성(영향권, 입지위치, 도시규모, 도시기능)과 환승시설기능(환승공간 이용방법, 연계교통수단)을 고려하여 66개의 사례를 유형화하였다. 또한 통계적인 유의성 검정을 통해 영향권-연계교통수단-도시기능조합과 도시규모-입지위치조합의 2가지 복합화된 유형을 제시하였다. 유형분류를 이용한 기존의 66개 사례지역을 정리해 본 결과 국내외 모두 비교적 규모가 큰 대도시를 중심으로 지역간 연계를 기반으로 하는 복합환승시설이 주류를 이루고 있음을 알 수 있다. 본 연구의 결과를 통해 향후 복합환승시설에 대한 자료가 지속적으로 축적되어질 경우 좀 더 신뢰성이 높은 유형분류의 결과를 기대해 본다.

• Journal of Construction Engineering and Project Management
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• 제5권4호
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• pp.16-22
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• 2015

This paper presents an automated computer vision-based system to update BIM data by leveraging multi-modal visual data collected from existing buildings under inspection. Currently, visual inspections are conducted for building envelopes or mechanical systems, and auditors analyze energy-related contextual information to examine if their performance is maintained as expected by the design. By translating 3D surface thermal profiles into energy performance metrics such as actual R-values at point-level and by mapping such properties to the associated BIM elements using XML Document Object Model (DOM), the proposed method shortens the energy performance modeling gap between the architectural information in the as-designed BIM and the as-is building condition, which improve the reliability of building energy analysis. Several case studies were conducted to experimentally evaluate their impact on BIM-based energy analysis to calculate energy load. The experimental results on existing buildings show that (1) the point-level thermography-based thermal resistance measurement can be automatically matched with the associated BIM elements; and (2) their corresponding thermal properties are automatically updated in gbXML schema. This paper provides practitioners with insight to uncover the fundamentals of how multi-modal visual data can be used to improve the accuracy of building energy modeling for retrofit analysis. Open research challenges and lessons learned from real-world case studies are discussed in detail.

• 국제학술발표논문집
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• The 6th International Conference on Construction Engineering and Project Management
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• pp.532-536
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• 2015

This paper presents an automated computer vision-based system to update BIM data by leveraging multi-modal visual data collected from existing buildings under inspection. Currently, visual inspections are conducted for building envelopes or mechanical systems, and auditors analyze energy-related contextual information to examine if their performance is maintained as expected by the design. By translating 3D surface thermal profiles into energy performance metrics such as actual R-values at point-level and by mapping such properties to the associated BIM elements using XML Document Object Model (DOM), the proposed method shortens the energy performance modeling gap between the architectural information in the as-designed BIM and the as-is building condition, which improve the reliability of building energy analysis. The experimental results on existing buildings show that (1) the point-level thermography-based thermal resistance measurement can be automatically matched with the associated BIM elements; and (2) their corresponding thermal properties are automatically updated in gbXML schema. This paper provides practitioners with insight to uncover the fundamentals of how multi-modal visual data can be used to improve the accuracy of building energy modeling for retrofit analysis. Open research challenges and lessons learned from real-world case studies are discussed in detail.

• 소음진동
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• 제8권1호
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• pp.139-145
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• 1998

A great deal of effort has been invested in upgrading the performance and the efficiency of dynamic analysis of mechanical structures. Using experimental modal analysis(EMA) or finite element analysis(FEA) data of mechanical structures, the performance and efficiency can be effectively evaluated. In order to analyze complex structures such as automobiles and aircrafts, for the sake of computing efficiency, the dynamic substructuring techniques that allow to predict the dynamic behavior of a structure are widely used. Through linking a modal model obtained from EMA and an analytical model obtained from FEA, the best conditioned strucutres can be proposed. In this study, a new algorithm of substructre synthesis method, Multi-FRF synthesis method, is proposed to analyze a structure composed of many substructures.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2023년도 추계학술발표대회
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• pp.526-527
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• 2023

Speech emotion recognition(SER) is one of the interesting topics in the machine learning field. By developing multi-modal speech emotion recognition system, we can get numerous benefits. This paper explain about fusing BERT as the text recognizer and CNN as the speech recognizer to built a multi-modal SER system.

• 대한조선학회논문집
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• 제50권2호
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• pp.69-78
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• 2013

This study is constructed to investigate the sloshing effect on the motions of a two-dimensional rectangular cylinder experimentally and numerically. The modes of motion under consideration are sway and roll, and also experimental cases are divided by two categories; 1-DoF roll motion and 2-DoF motion (Coupling sway and roll). It is found that the sway response is considerably affected by the motion of the fluid, particularly near the sloshing natural frequency, while the roll response changes comparatively small. The dominant mode of motion is analyzed for 2-DoF experiments as well. The measured data for 1-DoF motions is compared with numerical results obtained by the Multi-modal approach. The numerical schemes vary in detail with the number of dominant sloshing modes; i.e. there is a single dominant mode for the Single-dominant method, while the Model 2 method assumes that the first two modes are superior. For the roll motion, numerical results obtained by the two different methods are relatively in good agreement with the experiments, and these two results are similar in most wave frequency range. However, the discrepancies are apparent where the fluid motion is not governed by a single mode. But both of numerical methods over-predict the motion at the vicinity of the sloshing natural frequency. In order to correct the discrepancy, the modal damping needs to be investigated more precisely. Furthermore, another multi-modal approach, such as the Boussinesq-type method, seems to be required in the region of the intermediate liquid.

• 한국전자거래학회지
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• 제21권1호
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• pp.147-163
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• 2016

스마트 기기 사용자의 성별 정보는 성공적인 개인화 서비스를 위해 중요하며, 스마트 기기로부터 수집된 멀티 모달 로그 데이터는 사용자의 성별 예측에 중요한 근거가 된다. 하지만 각 멀티 모달 데이터의 특성에 따라 다른 방식으로 성별 예측을 수행해야 한다. 따라서 본 연구에서는 스마트 기기로부터 발생한 로그 데이터 중 텍스트, 어플리케이션, 가속도 데이터에 기반한 각기 다른 분류기의 예측 결과를 다수결 방식으로 앙상블하여 최종 성별을 예측하는 기법을 제안한다. 텍스트 데이터를 이용한 분류기는 데이터 유출에 의한 사생활 침해 문제를 최소화하기 위해 웹 문서로부터 각 성별의 특징적 단어 집합을 도출하고 이를 기기로 전송하여 사용자의 기기 내에서 성별 분류를 수행한다. 어플리케이션 데이터에 기반한 분류기는 사용자가 실행한 어플리케이션들에 성별을 부여하고 높은 비율을 차지하는 성별로 사용자의 성별을 예측한다. 가속도 기반 분류기는 성별에 따른 사용자의 가속도 데이터 인스턴스를 학습한 SVM 모델을 사용하여 주어진 성별을 분류한다. 자체 제작한 안드로이드 어플리케이션을 통해 수집된 실제 스마트 기기 로그 데이터를 사용하여 제안하는 기법을 평가하였으며 그 결과 높은 예측 성능을 보였다.

• 대한후두음성언어의학회지
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• 제13권2호
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• pp.132-138
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• 2002

Background and Objectives : The complex physiologic structure of the larynx can vibrate in three or more different ways that yield acuostically and perceptually distinct vocal quality. The purpose of this study is to examine the normal range of voice parameters in Multi-Channel Phonatory Function Analyzer and investigate the difference of voice parameters according to the phonatory patterns. Materials and Methods : Forty normal adult speakers (20 men and 20 women) with age ranging from third to forth decades pronounce low, comfortable, and high tone /a/ ; comfortable tone /${\ae}$/, /i/, /o/, and /u/ : fry, falsetto. Voice was analyzed by Newly developed multi-channel phonatory function analyzer. Results : The normal range of voice parameters in this system was similar to the existing data. Fry shows high jitter and falsetto low SQ. Fry and falsetto show low OQ in men but no difference in women. Jitter, OQ and SQ were different between men and women in modal register, whereas there was no gender difference in fry and falsetto. In frequency magnitude spectrum and EGG, modal register, fry and falsetto have distinguishing pattern. Conclusions : Modal register, fry and falsetto are distinguishable in voice parameters and show different vibratory patterns.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권3호
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• pp.670-684
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• 2024

3D Cross-Modal Retrieval (3DCMR) is a task that retrieves 3D objects regardless of modalities, such as images, meshes, and point clouds. One of the most prominent methods used for 3DCMR is the Cross-Modal Center Loss Function (CLF) which applies the conventional center loss strategy for 3D cross-modal search and retrieval. Since CLF is based on center loss, the center features in CLF are also susceptible to subtle changes in hyperparameters and external inferences. For instance, performance degradation is observed when the batch size is too small. Furthermore, the Mean Squared Error (MSE) used in CLF is unable to adapt to changes in batch size and is vulnerable to data variations that occur during actual inference due to the use of simple Euclidean distance between multi-modal features. To address the problems that arise from small batch training, we propose a Noisy Center Loss (NCL) method to estimate the optimal center features. In addition, we apply the simple Siamese representation learning method (SimSiam) during optimal center feature estimation to compare projected features, making the proposed method robust to changes in batch size and variations in data. As a result, the proposed approach demonstrates improved performance in ModelNet40 dataset compared to the conventional methods.