• 한국디지털건축인테리어학회논문집
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• 제12권3호
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• pp.81-89
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• 2012

Luminance is the most important quantity in lighting design and illuminating engineering. There are three methods for measuring luminance; using a conventional luminance meter, through the illuminance measurement and subsequent calculations and using digital imaging photometer. Recently, HDRI(High Dynamic Range Imaging) technique introduces a new method of capturing luminance values in a lighting environment. The radiance maps from HDRI are commonly used as visual environment maps for lighting analysis applications. For the HDRI, HDR software is needed to create HDR image. Currently, there is number of HDR software available. The purpose of this paper is to investigate whether a luminance map can be accurately captured by the various types of HDR software which include HDR Shop and Photoshop. To accomplish this goal a set of experiments was conducted. In order to assess the luminance values of the HDR image from HDR software, the values had to be compared to the ones obtained with conventional methods of luminance measurement.

• Structural Engineering and Mechanics
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• 제85권2호
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• pp.207-216
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• 2023

Three time-discontinuous Galerkin quadrature element methods (TDGQEMs) are developed for structural dynamic problems. The weak-form time-discontinuous Galerkin (TDG) statements, which are capable of capturing possible displacement and/or velocity discontinuities, are employed to formulate the three types of quadrature elements, i.e., single-field, single-field/least-squares and two-field. Gauss-Lobatto quadrature rule and the differential quadrature analog are used to turn the weak-form TDG statements into a system of algebraic equations. The stability, accuracy and numerical dissipation and dispersion properties of the formulated elements are examined. It is found that all the elements are unconditionally stable, the order of accuracy is equal to two times the element order minus one or two times the element order, and the high-order elements possess desired high numerical dissipation in the high-frequency domain and low numerical dissipation and dispersion in the low-frequency domain. Three fundamental numerical examples are investigated to demonstrate the effectiveness and high accuracy of the elements, as compared with the commonly used time integration schemes.

• Structural Engineering and Mechanics
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• 제84권3호
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• pp.323-335
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• 2022

The main idea of the framework is to seamlessly combine a reasonably accurate and fast surrogate model with the importance sampling strategy. Developing a surrogate model for predicting structures' dynamic responses is challenging because it involves high-dimensional inputs and outputs. For this purpose, a novel surrogate model based on cutting-edge deep learning architectures specialized for capturing temporal relationships within time-series data, namely Long-Short term memory layer and Transformer layer, is designed. After being properly trained, the surrogate model could be utilized in place of the finite element method to evaluate structures' responses without requiring any specialized software. On the other hand, the importance sampling is adopted to reduce the number of calculations required when computing the failure probability by drawing more relevant samples near critical areas. Thanks to the portability of the trained surrogate model, one can integrate the latter with the Importance sampling in a straightforward fashion, forming an efficient framework called TTIS, which represents double advantages: less number of calculations is needed, and the computational time of each calculation is significantly reduced. The proposed approach's applicability and efficiency are demonstrated through three examples with increasing complexity, involving a 1D beam, a 2D frame, and a 3D building structure. The results show that compared to the conventional Monte Carlo simulation, the proposed method can provide highly similar reliability results with a reduction of up to four orders of magnitudes in time complexity.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권1호
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• pp.233-262
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• 2024

Due to the rapid evolution of vehicular ad hoc networks (VANETs), effective communication and security are now essential components in providing secure and reliable vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication. However, due to their dynamic nature and potential threats, VANETs need to have strong security mechanisms. This paper presents a novel approach to improve VANET security by combining the Vehicular Delay-Tolerant Network (VDTN) protocol with the Deep Reinforcement Learning (DRL) technique known as the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm. A store-carry-forward method is used by the VDTN protocol to resolve the problems caused by inconsistent connectivity and disturbances in VANETs. The TD3 algorithm is employed for capturing and detecting Worm Hole Attack (WHA) behaviors in VANETs, thereby enhancing security measures. By combining these components, it is possible to create trustworthy and effective communication channels as well as successfully detect and stop rushing attacks inside the VANET. Extensive evaluations and simulations demonstrate the effectiveness of the proposed approach, enhancing both security and communication efficiency.

• 한국컴퓨터그래픽스학회논문지
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• 제18권2호
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• pp.1-8
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• 2012

HDR (high dynamic range) 영상은 일반적인 디지털 영상보다 훨씬 더 넓은 수치 범위로 빛의 노출을 저장한다. 따라서 실세계에 존재하는 광원들에 의해 표현되는 특정 장면에 내재된 빛의 세기를 매우 정확하게 저장할 수 있다. 이러한 HDR 영상을 빠르고 정확하게 촬영할 수 있는 전문가용 HDR 카메라가 개발되었으나 높은 가격으로 인해 아직까지 일반적인 작업환경에서 이용하기에는 어려움이 있다. 낮은 비용으로 HDR영상을 생성하는 일반적인 방법은 범용 디지털카메라를 이용해 동일한 장면을 서로 다른 노출로 반복 촬영하고 상용 소프트웨어에서 이들을 입력받아 하나의 HDR 영상으로 변환하는 것이다. 하지만 이러한 방법은 복잡하고 정확한 카메라 보정을 필요로 하는 작업이다. 더욱이 이 방법을 이용해 고급 영상 콘텐츠 제작을 위한 HDR 환경 맵을 생성하는 경우 더 섬세한 수작업과 시간 투자를 필요로 한다. 본 논문에서는 이러한 촬영 작업을자동화 하기위해 개발된 HDR 파노라마 환경 맵 제작 시스템에 대해 자세히 설명한다. 그리고 영상기반 라이팅 기법을 적용하여 3D 그래픽 모델을 2D 배경영상에 삽입하는 사실적합성 작업에서 본 시스템이 효과적으로 이용될 수 있음을 실제 사례를 통해 보인다.

• 한국컴퓨터그래픽스학회논문지
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• 제18권3호
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• pp.9-16
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• 2012

본 논문은 광학식 동작 포착 장비를 사용해 얼굴과 동작을 동시에 포착할 경우 발생하는 불완전한 표정 데이터의 복원에 관한 연구를 다룬다. 일반적으로 동작 포착과 표정 포착은 필요 해상도에서 차이가 나며, 이로 인해 동작과 표정을 동시에 포착하기 힘들었다. 본 연구에서는 표정과 동작의 동시 포착을 위해, 기존의 작은 마커를 촘촘히 얼굴에 부착하는 표정 포착 방식에서 탈피하여 적은 수의 마커만을 이용하여 표정을 포착하고, 이로부터 세밀한 얼굴 표정을 복원하는 방법을 제안한다. 본 방법의 핵심 아이디어는 얼굴 표정의 움직임을 미리 데이터베이스화하여, 적은 수의 마커로 표현된 얼굴 표정을 복원하는 것이다. 이를 위해 주성분분석을 사용하였으며, 제안된 기술을 실제 동적인 장면에 활용하여 표정의 복원이 잘 됨을 검증하였다.

• 한국터널지하공간학회 논문집
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• 제19권5호
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• pp.813-827
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• 2017

터널 내의 CCTV 영상은 동적으로 변화하는 요소들에 의해 영향을 받는 다양한 영상들을 촬영한다. 또한, 카메라의 상태 또한 관리 및 배치가 쉽지 않아 터널 내부 환경 변화에 따라 영상이 달라지는 경향이 있다. 본 논문에서는 터널 내에 설치된 CCTV 카메라 영상을 이용해 차량을 탐지하고 그 차량을 지속적으로 추적하는 새로운 방법을 소개한다. 터널 내 CCTV 카메라 영상은 모션블러 효과와 먼지로 인한 렌즈 흐려짐 효과로 인해 바로 차량을 탐지할 수 없다는 문제점이 있다. 본 논문에서는 이를 극복하기 위해 차영상/비-최대 억제 기법과 Haar Cascade 기법 등에 대한 효과 검토 실험을 제안하고 수행하였다. 본 논문에서 제안하는 방법을 통해 터널 내에 설치된 CCTV에서 차량의 탐지와 추적을 효과적으로 수행할 수 있으며 다양한 터널 유고상황을 자동으로 파악하기 위한 중요 정보를 확보할 수 있었다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권6호
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• pp.1530-1544
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• 2023

Data monitoring is an important foundation of modern science. In most cases, the monitoring data is time-series data, which has high application value. The deep learning algorithm has a strong nonlinear fitting capability, which enables the recognition of time series by capturing anomalous information in time series. At present, the research of time series recognition based on deep learning is especially important for data monitoring. Deep learning algorithms require a large amount of data for training. However, abnormal sample is a small sample in time series, which means the number of abnormal time series can seriously affect the accuracy of recognition algorithm because of class imbalance. In order to increase the number of abnormal sample, a data augmentation method called GANBATS (GAN-based Bi-LSTM and Attention for Time Series) is proposed. In GANBATS, Bi-LSTM is introduced to extract the timing features and then transfer features to the generator network of GANBATS.GANBATS also modifies the discriminator network by adding an attention mechanism to achieve global attention for time series. At the end of discriminator, GANBATS is adding averagepooling layer, which merges temporal features to boost the operational efficiency. In this paper, four time series datasets and five data augmentation algorithms are used for comparison experiments. The generated data are measured by PRD(Percent Root Mean Square Difference) and DTW(Dynamic Time Warping). The experimental results show that GANBATS reduces up to 26.22 in PRD metric and 9.45 in DTW metric. In addition, this paper uses different algorithms to reconstruct the datasets and compare them by classification accuracy. The classification accuracy is improved by 6.44%-12.96% on four time series datasets.

• 국제학술발표논문집
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• The 9th International Conference on Construction Engineering and Project Management
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• pp.1247-1248
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• 2022

Due to rapid urbanization and population growth, it has become crucial to analyze the various volumes and characteristics of pedestrian pathways to understand the capacity and level of service (LOS) for pathways to promote a better walking environment. Different indicators have been developed to measure pedestrian volume. The pedestrian level of service (PLOS), tailored to analyze pedestrian pathways based on the concept of the LOS in transportation in the Highway Capacity Manual, has been widely used. PLOS is a measurement concept used to assess the quality of pedestrian facilities, from grade A (best condition) to grade F (worst condition), based on the flow rate, average speed, occupied space, and other parameters. Since the original PLOS approach has been criticized for producing idealistic results, several modified versions of PLOS have also been developed. One of these modified versions is perceived PLOS, which measures the LOS for pathways by considering pedestrians' awareness levels. However, this method relies on survey-based measurements, making it difficult to continuously deploy the technique to all the pathways. To measure PLOS more quantitatively and continuously, researchers have adopted computer vision technologies to automatically assess pedestrian flows and PLOS from CCTV videos. However, there are drawbacks even with this method because CCTVs cannot be installed everywhere, e.g., in alleyways. Recently, a technique to monitor bio-signals, such as electrodermal activity (EDA), through wearable sensors that can measure physiological responses to external stimuli (e.g., when another pedestrian passes), has gained popularity. It has the potential to continuously measure perceived PLOS. In their previous experiment, the authors of this study found that there were many significant EDA responses in crowded places when other pedestrians acting as external stimuli passed by. Therefore, we hypothesized that the EDA responses would be significantly higher in places where relatively more dynamic objects pass, i.e., in crowded areas with low PLOS levels (e.g., level F). To this end, the authors conducted an experiment to confirm the validity of EDA in inferring the perceived PLOS. The EDA of the subjects was measured and analyzed while watching both the real-world and virtually created videos with different pedestrian volumes in a laboratory environment. The results showed the possibility of inferring the amount of pedestrian volume on the pathways by measuring the physiological reactions of pedestrians. Through further validation, the research outcome is expected to be used for EDA-based continuous measurement of perceived PLOS at the alley level, which will facilitate modifying the existing walking environments, e.g., constructing pathways with appropriate effective width based on pedestrian volume. Future research will examine the validity of the integrated use of EDA and acceleration signals to increase the accuracy of inferring the perceived PLOS by capturing both physiological and behavioral reactions when walking in a crowded area.