• Advances in concrete construction
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• 제1권3호
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• pp.227-237
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• 2013

Modal testing, widely accepted and applied method for determining the dynamic characteristics of structures for operational conditions, uses known or unknown vibrations in structures. The method's common applications includes estimation of dynamic characteristics and also damage detection and monitoring of structural performance. In this study, the structural identification of concrete arch dams is determined using ambient vibration tests which is one of the modal testing methods. For the purpose, several ambient vibration tests are conducted to an arch dam. Sensitive accelerometers were placed on the different points of the crest and a gallery of the dam, and signals are collected for the process. Enhanced Frequency Domain Decomposition technique is used for the extraction of natural frequencies, mode shapes and damping ratios. A total of eight natural frequencies are attained by experimentally for each test setup, which ranges between 0-12 Hz. The results obtained from each ambient vibration tests are presented and compared with each other in detail. There is a good agreement between the results for all measurements. However, the theoretical fundamental frequency of Berke Arch Dam is a little different from the experimental.

• 센서학회지
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• 제33권2호
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• pp.78-85
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• 2024

We proposed a method for visual place recognition that represents images using objects as visual words. Visual words represent the various objects present in urban environments. To detect various objects within the images, we implemented and used a zero-shot detector based on a large-scale image language model. This zero-shot detector enables the detection of various objects in urban environments without additional training. In the process of creating histograms using the proposed method, frequency-based weighting was applied to consider the importance of each object. Through experiments with open datasets, the potential of the proposed method was demonstrated by comparing it with another method, even in situations involving environmental or viewpoint changes.

• Journal of Electrical Engineering and Technology
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• 제12권6호
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• pp.2106-2117
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• 2017

An effective statistical feature extraction approach of data sampling of fault in the combined transmission system is presented in this paper. The proposed algorithm leads to high accuracy at minimum cost to predict fault location and fault type classification. This algorithm requires impedance measurement data from one end of the transmission line. Modal decomposition is used to extract positive sequence impedance. Then, the fault signal is decomposed by using discrete wavelet transform. Statistical sampling is used to extract appropriate fault features as benchmark of decomposed signal to train classifier. Support Vector Machine (SVM) is used to illustrate the performance of statistical sampling performance. The overall time of sampling is not exceeding 1 1/4 cycles, taking into account the interval time. The proposed method takes two steps of sampling. The first step takes 3/4 cycle of during-fault and the second step takes 1/4 cycle of post fault impedance. The interval time between the two steps is assumed to be 1/4 cycle. Extensive studies using MATLAB software show accurate fault location estimation and fault type classification of the proposed method. The classifier result is presented and compared with well-established travelling wave methods and the performance of the algorithms are analyzed and discussed.

• Journal of Information Processing Systems
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• 제17권4호
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• pp.754-771
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• 2021

In the task of continuous dimension emotion recognition, the parts that highlight the emotional expression are not the same in each mode, and the influences of different modes on the emotional state is also different. Therefore, this paper studies the fusion of the two most important modes in emotional recognition (voice and visual expression), and proposes a two-mode dual-modal emotion recognition method combined with the attention mechanism of the improved AlexNet network. After a simple preprocessing of the audio signal and the video signal, respectively, the first step is to use the prior knowledge to realize the extraction of audio characteristics. Then, facial expression features are extracted by the improved AlexNet network. Finally, the multimodal attention mechanism is used to fuse facial expression features and audio features, and the improved loss function is used to optimize the modal missing problem, so as to improve the robustness of the model and the performance of emotion recognition. The experimental results show that the concordance coefficient of the proposed model in the two dimensions of arousal and valence (concordance correlation coefficient) were 0.729 and 0.718, respectively, which are superior to several comparative algorithms.

• 반도체디스플레이기술학회지
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• 제17권3호
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• pp.113-116
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• 2018

This paper proposes a mobile image search system which uses a sensor information of smart phone, and enables running in a variety of environments, which is implemented on Android platform. The implemented system deals with a new image descriptor using combination of the visual feature (CEDD) with EXIF attributes in the target of JPEG image, and image matching scheme, which is optimized to the mobile platform. Experimental result shows that the proposed method exhibited a significant improved searching results of around 80% in precision in the large image database. Considering the performance such as processing time and precision, we think that the proposed method can be used in other application field.

• Wind and Structures
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• 제22권3호
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• pp.273-290
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• 2016

The flutter derivatives of bridge decks can be efficiently identified using the experimentally and/or numerically coupled forced vibration method. This paper addresses the issue of inherent requirement for adopting different frequencies of three modes in this method. The aerostatic force components and the inertia of force and moment are mathematically proved to exert no influence on identification results if the signal length (t) is integer (n=1,2,3...) times of the least common multiple (T) of three modal periods. It is one important contribution to flutter derivatives identification theory and engineering practice in this study. Therefore, it is unnecessary to worry about the determination accuracy of aerostatic force and inertia of force and moment. The influences of signal length, amplitude, and frequency ratio on flutter derivative are thoroughly investigated using a bridge example. If the signal length t is too short, the extraction results may be completely wrong, and particular attention should be paid to this issue. The signal length t=nT ($n{\geq}5$) is strongly recommended for improving parameter identification accuracy. The proposed viewpoints and conclusions are of great significance for better understanding the essences of flutter derivative identification through coupled forced vibration method.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2007년도 춘계학술대회논문집
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• pp.264-268
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• 2007

Proper orthogonal decomposition(POD) is a method for extracting bases for modal decomposition from the ensemble of signals. We verified the connection of the proper orthogonal modes(POMs) and the linear normal modes(LNMs) through MATLAB simulation for the simple cantilever and AFM microcantilever models. Using the POMs, we can analyze and model effectively the dynamic mode of AFM microcantievers.

• 대한기계학회논문집A
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• 제25권2호
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• pp.198-205
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• 2001

Recently, almost all hard disk drives employ the rotary actuator system. The performance of an HDD depends on the accuracy and speed of tracking motion. We study the dynamics of head-suspension assembly during track seek. We develop the numerical analysis program to study the dynamic characteristics of HDD suspension system considering the air bearing effects. The track seek simulation by using the developed program helps to estimate the effect of the suspension vibration on the air bearing dynamics. We calculate the behaviour of the air bearing for the given track seek profile and calculate the positioning error during track seek process due to the lateral deflection of the suspension.

• 정보처리학회논문지B
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• 제9B권5호
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• pp.563-570
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• 2002

본 논문은 MPEG4 SNHC의 얼굴 모델 인코딩을 구현하기 위하여 연속된 2차원 영상으로부터 얼굴영역을 검출하고, 얼굴의 특징데이터들을 추출한 후, 얼굴의 3차원 모양 및 움직임 정보를 복원하는 알고리즘과 결과를 제시한다. 얼굴 영역 검출을 위해서 영상의 거리, 피부색상, 움직임 색상정보등을 융합시킨 멀티모달합성의 방법이 사용되었다. 결정된 얼굴영역에서는 MPEG4의 FDP(Face Definition Parameter) 에서 제시된 특징점 위치중 23개의 주요 얼굴 특징점을 추출하며 추출성능을 향상시키기 위하여 GSCD(Generalized Skin Color Distribution), BWCD(Black and White Color Distribution)등의 움직임색상 변환기법과 형태연산 방법이 제시되었다. 추출된 2차원 얼팔 특징점들로부터 얼굴의 3차원 모양, 움직임 정보를 복원하기 위하여 준원근 카메라 모델을 적용하여 SVD(Singular Value Decomposition)에 의한 인수분해연산을 수행하였다. 본 논문에서 제시된 방법들의 성능을 객관적으로 평가하기 위하여 크기와 위치가 알려진 3차원 물체에 대해 실험을 행하였으며, 복원된 얼굴의 움직임 정보는 MPEG4 FAP(Face Animation Parameter)로 변환된 후, 인터넷상에서 확인이 가능한 가상얼굴모델에 인코딩되어 실제 얼굴파 일치하는 모습을 확인하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권10호
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• pp.2768-2787
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• 2023

As an important research direction of the application of computer science in the medical field, the automatic generation technology of radiology report has attracted wide attention in the academic community. Because the proportion of normal regions in radiology images is much larger than that of abnormal regions, words describing diseases are often masked by other words, resulting in significant feature loss during the calculation process, which affects the quality of generated reports. In addition, the huge difference between visual features and semantic features causes traditional multi-modal fusion method to fail to generate long narrative structures consisting of multiple sentences, which are required for medical reports. To address these challenges, we propose a multi-feature optimization Transformer (MFOT) for generating radiology reports. In detail, a multi-dimensional mapping attention (MDMA) module is designed to encode the visual grid features from different dimensions to reduce the loss of primary features in the encoding process; a feature pre-fusion (FP) module is constructed to enhance the interaction ability between multi-modal features, so as to generate a reasonably structured radiology report; a detail enhanced attention (DEA) module is proposed to enhance the extraction and utilization of key features and reduce the loss of key features. In conclusion, we evaluate the performance of our proposed model against prevailing mainstream models by utilizing widely-recognized radiology report datasets, namely IU X-Ray and MIMIC-CXR. The experimental outcomes demonstrate that our model achieves SOTA performance on both datasets, compared with the base model, the average improvement of six key indicators is 19.9% and 18.0% respectively. These findings substantiate the efficacy of our model in the domain of automated radiology report generation.