• Water Engineering Research
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• 제5권1호
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• pp.47-54
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• 2004

Several computer programs have been developed to make stochastically generated weather data from observed daily data. But they require fully dataset to run WGEN. Mostly, meterological data frequently have sporadic missing data as well as totally missing data. The modified WGEN has data filling algorithm for incomplete meterological datasets. Any other WGEN models have not the function of data filling. Modified WGEN with data filling algorithm is processing from the equation of Matalas for first order autoregressive process on a multi dimensional state with known cross and auto correlations among state variables. The parameters of the equation of Matalas are derived from existing dataset and derived parameters are adopted to fill data. In case of WGEN (Richardson and Wright, 1984), it is one of most widely used weather generators. But it has to be modified and added. It uses an exponential distribution to generate precipitation amounts. An exponential distribution is easier to describe the distribution of precipitation amounts. But precipitation data with using exponential distribution has not been expressed well. In this paper, generated precipitation data from WGEN and Modified WGEN were compared with corresponding measured data as statistic parameters. The modified WGEN adopted a formula of CLIGEN for WEPP (Water Erosion Prediction Project) in USDA in 1985. In this paper, the result of other parameters except precipitation is not introduced. It will be introduced through study of verification and review soon

• 국제초고층학회논문집
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• 제9권4호
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• pp.351-360
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• 2020

Monitoring civil structures periodically is necessary for ensuring the fitness of the structures. Cracks on inner and outer surfaces of the building plays a vital role in indicating the health of the building. Conventionally, human visual inspection techniques were carried up to human reachable altitudes. Monitoring of high rise infrastructures cannot be done using this primitive method. Also, there is a necessity for more accurate prediction of cracks on building surfaces for ensuring the health and safety of the building. The proposed research focused on developing an efficient crack classification model using Transfer Learning enabled EfficientNet (TL-EN) architecture. Though many other pre-trained models were available for crack classification, they rely on more number of training parameters for better accuracy. The TL-EN model attained an accuracy of 0.99 with less number of parameters on large dataset. A bench marked METU dataset with 40000 images were used to test and validate the proposed model. The surfaces of high rise buildings were investigated using vision enabled Unmanned Arial Vehicles (UAV). These UAV is fabricated with TL-EN model schema for capturing and analyzing the real time streaming video of building surfaces.

• 대한원격탐사학회지
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• 제38권6_1호
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• pp.1505-1514
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• 2022

영상정합은 다시기 및 다중센서 고해상도 위성영상을 효과적으로 활용하기 위해 필수적으로 선행되는 중요한 과정이다. 널리 각광받고 있는 딥러닝 기법은 위성영상에서 복잡하고 세밀한 특징을 추출하여 영상 간 빠르고 정확한 유사도 판별에 사용될 수 있음에도 불구하고, 학습자료의 양과 질이 결과에 영향을 미치는 딥러닝 모델의 한계와 고해상도 위성영상 기반 학습자료 구축의 어려움에 따라 고해상도 위성영상의 정합에는 제한적으로 적용되어 왔다. 이에 본 연구는 영상정합에서 가장 많은 시간을 소요하는 정합쌍 추출 과정에서 딥러닝 기반 기법의 적용성을 확인하기 위하여, 편향성이 존재하는 고해상도 위성영상 데이터베이스로부터 딥러닝 영상매칭 학습자료를 구축하고 학습자료의 구성이 정합쌍 추출 정확도에 미치는 영향을 분석하였다. 학습자료는 12장의 다시기 및 다중센서 고해상도 위성영상에 대하여 격자 기반의 Scale Invariant Feature Transform(SIFT) 알고리즘을 이용하여 추출한 영상쌍에 참과 거짓의 레이블(label)을 할당한 정합쌍과 오정합쌍의 집합으로 구축되도록 하였다. 구축된 학습자료로부터 정합쌍 추출을 위해 제안된 Siamese convolutional neural network (SCNN) 모델은 동일한 두 개의 합성곱 신경망 구조에 한 쌍을 이루는 두 영상을 하나씩 통과시킴으로써 학습을 진행하고 추출된 특징의 비교를 통해 유사도를 판별한다. 본 연구를 통해 고해상도 위성영상 데이터 베이스로부터 취득된 자료를 딥러닝 학습자료로 활용 가능하며 이종센서 영상을 적절히 조합하여 영상매칭 과정의 효율을 높일 수 있음을 확인하였다. 다중센서 고해상도 위성영상을 활용한 딥러닝 기반 영상매칭 기법은 안정적인 성능을 바탕으로 기존 수작업 기반의 특징 추출 방법을 대체하고, 나아가 통합적인 딥러닝 기반 영상정합 프레임워크로 발전될 것으로 기대한다.

• 한국측량학회지
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• 제36권1호
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• pp.17-24
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• 2018

DEM (Digital Elevation Model) is a useful dataset which represents the earth surface. Beside many applications, production and frequent update of DEM is a costly task. Recently global satellite based DEMs are available which has huge potential for application. To check the accuracy, this study compares two global DEMs: AW3D30 (Advanced Land Observing Satellite World 3D 30m) and SRTM30 (Shuttle Radar Topography Mission Global 30m) with reference resampled LiDAR DEM 30m in a test area around Chuncheon, Korea. The comparison analysis was based on statistics of each DEM, their difference, profiles, slope, basin and stream orders. As a result, it is found that SRTM30 and AW3D30 were much similar but inconsistent in the test area compared to the LiDAR30 DEM. In addition, SRTM30 shows less difference with LiDAR30 compared to the AW3D30 DEM. But, DEMs should be very carefully examined for area which has temporal or season changes. For basin and stream analysis, global DEMs can be used only for regional scale analysis not local large scales.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2006년도 Proceedings of ISRS 2006 PORSEC Volume I
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• pp.94-97
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• 2006

Timber stand age information of timber in industrialized plantation forest is generally collected by field surveying which is labor-intensive, time-consuming, and very costly. It is also inconsistent in analyses perspective. As an alternative, The objective of this research is to present a practical solution for estimating timber age of loblolly pine plantation using Landsat thematic mapper (TM) images, shuttle radar topography mission (SRTM), and national elevation dataset (NED). A multivariate regression model was developed based upon satellite image-based information (i.e.normalized difference vegetation index (NDVI), tasseled cap (TC) transformation, and derived tree heights). A residual studentized technique was applied to remove potential outliers. After that, a refined age estimation model with a correlation coefficient R-square of 84.6% was obtained. Finally, the feasibility test of estimated model was performed by comparing estimated and measured stand ages of timber plantations using test datasets of plantation stands (2,032 stands). The result shows that the proposed method of this study can estimate loblolly pine stand age within an error of $2{\sim}3$ years in an effective and consistent way in terms of time and cost.

• 한국환경과학회지
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• 제22권5호
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• pp.543-554
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• 2013

Acoustic Doppler Current Profilers (ADCPs) are increasingly popular in the river research and management communities being primarily used for estimation of stream flows. ADCPs capabilities, however, entail additional features that are not fully explored, such as morphological representation of river or reservoir bed based upon multi-beam depth measurements. In addition to flow velocity, ADCP measurements include river bathymetry information through the depth measurements acquired in individual 4 or 5 beams with a given oblique angle. Such sounding capability indicates that multi-beam ADCPs can be utilized as an efficient depth-sounder to be more capable than the conventional single-beam eco-sounders. The paper introduces the post-processing algorithms required to deal with raw ADCP bathymetry measurements including the following aspects: a) correcting the individual beam depths for tilt (pitch and roll); b) filtering outliers using SMART filters; d) transforming the corrected depths into geographical coordinates by UTM conversion; and, e) tag the beam detecting locations with the concurrent GPS information; f) spatial representation in a GIS package. The developed algorithms are applied for the ADCP bathymetric dataset acquired from Han-Cheon in Jeju Island to validate themselves applicability.

• Smart Structures and Systems
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• 제26권2호
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• pp.241-251
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• 2020

Due to the undeniable importance of approximating the concrete compressive strength (CSC) in civil engineering, this paper focuses on presenting four novel optimizations of multi-layer perceptron (MLP) neural network, namely artificial bee colony (ABC-MLP), grasshopper optimization algorithm (GOA-MLP), shuffled frog leaping algorithm (SFLA-MLP), and salp swarm algorithm (SSA-MLP) for predicting this crucial parameter. The used dataset consists of 103 rows of information concerning seven influential parameters (cement, slag, water, fly ash, superplasticizer, fine aggregate, and coarse aggregate). In this work, the best-fitted complexity of each ensemble is determined by a population-based sensitivity analysis. The GOA distinguished its self by the least complexity (population size = 50) and emerged as the second time-effective optimizer. Referring to the prediction results, all tested algorithms are able to construct reliable networks. However, the SSA (Correlation = 0.9652 and Error = 1.3939) and GOA (Correlation = 0.9629 and Error = 1.3922) performed more accurately than ABC (Correlation = 0.7060 and Error = 4.0161) and SFLA (Correlation = 0.8890 and Error = 2.5480). Therefore, the SSA-MLP and GOA-MLP can be promising alternatives to laboratorial and traditional CSC evaluative methods.

• Computers and Concrete
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• 제13권6호
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• pp.765-780
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• 2014

Many building codes use the empirical equation to determine fundamental period of vibration where in effect of length, width and the stiffness of the building is not explicitly accounted for. In the present study, ANN models are developed in three categories, varying the number of input parameters in each category. Input parameters are chosen to represent mass, stiffness and geometry of the buildings indirectly. Total numbers of 206 buildings are analyzed out of which, data set of 142 buildings is used to develop these models. It is demonstrated through developed ANN models that geometry of the building and the sizes of the columns are significant parameters in the dynamic analysis of building frames. The testing dataset of these three models is used to obtain the empirical relationship between the height of the building and fundamental period of vibration and compared with the similar equations proposed by other researchers. Experiments are conducted on Mild Steel frames using uniaxial shake table. It is seen that the values obtained through the ANN models are close to the experimental values. The validity of ANN technique is verified by experimental values.

• Smart Structures and Systems
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• 제29권1호
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• pp.141-151
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• 2022

In recent years, the industry and research communities have focused on developing autonomous crack inspection approaches, which mainly include image acquisition and crack detection. In these approaches, mobile devices such as cameras, drones or smartphones are utilized as sensing platforms to acquire structural images, and the deep learning (DL)-based methods are being developed as important crack detection approaches. However, the process of image acquisition and collection is time-consuming, which delays the inspection. Also, the present mobile devices such as smartphones can be not only a sensing platform but also a computing platform that can be embedded with deep neural networks (DNNs) to conduct on-site crack detection. Due to the limited computing resources of mobile devices, the size of the DNNs should be reduced to improve the computational efficiency. In this study, an architecture called pruned crack recognition network (PCR-Net) was developed for the detection of structural cracks. A dataset containing 11000 images was established based on the raw images from bridge inspections. A pruning method was introduced to reduce the size of the base architecture for the optimization of the model size. Comparative studies were conducted with image processing techniques (IPTs) and other DNNs for the evaluation of the performance of the proposed PCR-Net. Furthermore, a modularly designed framework that integrated the PCR-Net was developed to realize a DL-based crack detection application for smartphones. Finally, on-site crack detection experiments were carried out to validate the performance of the developed system of smartphone-based detection of structural cracks.

• Smart Structures and Systems
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• 제29권1호
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• pp.17-28
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• 2022

The cable component of cable-stayed bridges is gradually impacted by weather conditions, vehicle loads, and material corrosion. The stayed cable is a critical load-carrying part that closely affects the operational stability of a cable-stayed bridge. Damaged cables might lead to the bridge collapse due to their tension capacity reduction. Thus, it is necessary to develop structural health monitoring (SHM) techniques that accurately identify damaged cables. In this work, a combinational identification method of three efficient techniques, including statistical analysis, clustering, and neural network models, is proposed to detect the damaged cable in a cable-stayed bridge. The measured dataset from the bridge was initially preprocessed to remove the outlier channels. Then, the theory and application of each technique for damage detection were introduced. In general, the statistical approach extracts the parameters representing the damage within time series, and the clustering approach identifies the outliers from the data signals as damaged members, while the deep learning approach uses the nonlinear data dependencies in SHM for the training model. The performance of these approaches in classifying the damaged cable was assessed, and the combinational identification method was obtained using the voting ensemble. Finally, the combination method was compared with an existing outlier detection algorithm, support vector machines (SVM). The results demonstrate that the proposed method is robust and provides higher accuracy for the damaged cable detection in the cable-stayed bridge.