• Journal of Information Processing Systems
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• v.15 no.5
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• pp.1156-1170
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• 2019

In recent years, the problem of data drifted of the smart grid due to manual operation has been widely studied by researchers in the related domain areas. It has become an important research topic to effectively and reliably find the reasonable data needed in the Supervisory Control and Data Acquisition (SCADA) system has become an important research topic. This paper analyzes the data composition of the smart grid, and explains the power model in two smart grid applications, followed by an analysis on the application of each parameter in density-based spatial clustering of applications with noise (DBSCAN) algorithm. Then a comparison is carried out for the processing effects of the boxplot method, probability weight analysis method and DBSCAN clustering algorithm on the big data driven power grid. According to the comparison results, the performance of the DBSCAN algorithm outperforming other methods in processing effect. The experimental verification shows that the DBSCAN clustering algorithm can effectively screen the power grid data, thereby significantly improving the accuracy and reliability of the calculation result of the main grid's theoretical line loss.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.12
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• pp.5978-5999
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• 2018

Pedestrian detection is a challenging area in the intelligent vehicles domain. During the last years, many works have been proposed to efficiently detect motion in images. However, the problem becomes more complex when it comes to detecting moving areas while the vehicle is also moving. This paper presents a variational optical flow-based method for motion estimation in vehicular traffic scenarios. We introduce a framework for detecting motion areas with small and large displacements by computing optical flow using a multilevel architecture. The flow field is estimated at the shortest level and then successively computed until the largest level. We include a filtering parameter and a warping process using bicubic interpolation to combine the intermediate flow fields computed at each level during optimization to gain better performance. Furthermore, we find that by including a penalization function, our system is able to effectively reduce the presence of outliers and deal with all expected circumstances in real scenes. Experimental results are performed on various image sequences from Daimler Pedestrian Dataset that includes urban traffic scenarios. Our evaluation demonstrates that despite the complexity of the evaluated scenes, the motion areas with both moving and static camera can be effectively identified.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.5
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• pp.297-311
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• 2021

Around the world, there is an increasing interest in Digital Twin cities. Although geospatial data is critical for building a digital twin city, currently-established spatial data cannot be used directly for its implementation. Integration of geospatial data is vital in order to construct and simulate the virtual space. Existing studies for data integration have focused on data transformation. The conversion method is fundamental and convenient, but the information loss during this process remains a limitation. With this, standardization of the data model is an approach to solve the integration problem while hurdling conversion limitations. However, the standardization within indoor space data models is still insufficient compared to 3D building and city models. Therefore, in this study, we present a comparative analysis of data models commonly used in indoor space modeling as a basis for establishing a generic indoor space feature model. By comparing five models of IFC (Industry Foundation Classes), CityGML (City Geographic Markup Language), AIIM (ArcGIS Indoors Information Model), IMDF (Indoor Mapping Data Format), and OmniClass, we identify essential elements for modeling indoor space and the feature classes commonly included in the models. The proposed generic model can serve as a basis for developing further indoor feature models through specifying minimum required structure and feature classes.

• Nuclear Engineering and Technology
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• v.53 no.2
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• pp.430-438
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• 2021

The spatial distribution of neutron flux or reaction rate was calculated by cell or mesh tally in traditional Monte Carlo simulation. However, either cell or mesh tally leads to the increase of memory consumption and simulation time. In this paper, the function expansion tally (FET) method was developed in Reactor Monte Carlo code RMC to solve this problem. The FET method was applied to the tallies of neutron flux distributions of uranium block and PWR fuel rod models. Legendre polynomials were used in the axial direction, while Zernike polynomials were used in the radial direction. The results of flux, calculation time and memory consumption of different expansion orders were investigated, and compared with the mesh tally. Results showed that the continuous distribution of flux can be obtained by FET method. The flux distributions were consistent with that of mesh tally, while the memory consumption and simulation time can be effectively reduced. Finally, the convergence analysis of coefficients of polynomials were performed, and the selection strategy of FET order was proposed based on the statistics uncertainty of the coefficients. The proposed method can help to determine the order of FET, which was meaningful for the efficiency and accuracy of FET method.

• Journal of the Architectural Institute of Korea Planning & Design
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• v.35 no.1
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• pp.107-115
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• 2019

This research explores a historical trajectory of the Milwaukee Art Museum from its establishment to the third expansion over the last six decades. After established in 1957 by the architect, Eero Saarinen, this museum underwent three expansions led by three different architects including, David Kahler, Santiago Calatrava, and James Shields. Reasons for expanding the museum were varied. A lack of exhibition space, an absence of visual identity, and path interruption problem within urban contexts were the main factors of a series of expansion. Furthermore, this research points out that there are three issues in the process of the expansion. The first is connection issues between the downtown and the lakefront in which the museum has blocked the public flow. The second, there were controversies on the allocation of the expanded space among the main body of the decision including architects, curators, and stakeholders. The last one is relationships among architects. This is related to each architect's attitude toward the museum. Drawing on historical documents and interviews with the regional architects, I argue that the identity and values of the museum have changed over time through the expansions rather than having the invariable.

• Journal of the Architectural Institute of Korea Planning & Design
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• v.35 no.1
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• pp.117-125
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• 2019

The purpose of this study is to examine urban built environments associated with the walking behavior in order to identify the problem of urban infrastructure from the perspective of Universal design theory. Using the walkability criteria developed based on the findings of previous studies on Universal design theory and walking environment, the urban built environments in old town area of Seoul were evaluated. The results of the evaluation indicated that the pedestrian infrastructure in the study area had several shortcomings to improve in order to provide walking-friendly environments. First, the pedestrian infrastructure for visually impaired people were severely limited. Second, walking traffics in large portion of the street networks were interrupted by a variety of obstacles such as parked cars, signboards, and illegal street shops. Third, there were certain areas where the quality of pavement was not properly managed. In summary, this study provides an empirical data to understand the current situation of pedestrian infrastructure in the urban areas. Improvement of pedestrian infrastructure is needed to address the issues related to the mobility of the disabled people. It is also recommended to construct the GIS data on walking environment in order to develop and mange a long-term public investment plan for walking-friendly urban environments.

• Journal of Contemporary Eastern Asia
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• v.19 no.2
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• pp.148-168
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• 2020

In April, 2020, amid widespread fear of a second wave of infections of the novel coronavirus in China, local authorities in Guangzhou cracked down on the city's black population, resulting in mass evictions of Africans. The incident raises several questions about racism in China. How should we interpret this heavy-handed treatment of black people? Was this an isolated incident? What motivated such operations? In this article, I explain social control of Guangzhou's African communities as a problem of municipal politics. What underlies the government's heavy handed approach, I argue, are those communities' ties to rurality, which constitute a roadblock in the city's urban upgrade. Using Dengfeng Village, one of the best known African communities in China, as a case study, I show that efforts to upgrade the area by the local state and the real estate industry were frustrated by the community's status as an urban village. Africans, whom Chinese have historically associated with rurality, are seen as contributing to a space that has long been stigmatized as a spatial manifestation of rural people's lack of self-discipline. To better reveal the interconnection between social control and urban politics, I place official action in context of the history of the community's formation and the lived experience. This analysis of Dengfeng applies to various extents to other major African communities in Guangzhou.

• Journal of Positioning, Navigation, and Timing
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• v.10 no.1
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• pp.55-66
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• 2021

In this paper, we propose a signal propagation modeling technique for generating a positioning fingerprint DB based on Long Term Evolution (LTE) signals. When a DB is created based on the location-based signal information collected in an urban area, gaps in the DB due to uncollected areas occur. The spatial interpolation method for filling the gaps has limitations. In addition, the existing gap filling technique through signal propagation modeling does not reflect the signal attenuation characteristics according to directions occurring in urban areas by considering only the signal attenuation characteristics according to distance. To solve this problem, this paper proposes a Deep Neural Network (DNN)-based signal propagation functionalization technique that considers distance and direction together. To verify the performance of this technique, an experiment was conducted in Seocho-gu, Seoul. Based on the acquired signals, signal propagation characteristics were modeled for each method, and Root Mean Squared Errors (RMSE) was calculated using the verification data to perform comparative analysis. As a result, it was shown that the proposed technique is improved by about 4.284 dBm compared to the existing signal propagation model. Through this, it can be confirmed that the DNN-based signal propagation model proposed in this paper is excellent in performance, and it is expected that the positioning performance will be improved based on the fingerprint DB generated through it.

• Korean Journal of Remote Sensing
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• v.37 no.4
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• pp.763-776
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• 2021

This paper introduces a method of selecting priority update areas for subdivided land cover maps by training orthoimages and serial cadastral maps in a deep learning model. For the experiment, orthoimages and serial cadastral maps were obtained from the National Spatial Data Infrastructure Portal. Based on the VGG-16 model, 51,470 images were trained on 33 subdivided classifications within the experimental area and an accuracy evaluation was conducted. The overall accuracy was 61.42%. In addition, using the differences in the classification prediction probability of the misclassified polygon and the cosine similarity that numerically expresses the similarity of the land category features with the original subdivided land cover class, the cases were classified and the areas in which the boundary setting was incorrect and in which the image itself was determined to have a problem were identified as the priority update polygons that should be checked by operators.

• Nuclear Engineering and Technology
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• v.54 no.7
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• pp.2625-2634
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• 2022

Discretization errors are extremely challenging conundrums of discrete ordinates calculations for radiation transport problems with void regions. In previous work, we have presented a multi-collision source method (MCS) to overcome discretization errors, but the efficiency needs to be improved. This paper proposes a goal-oriented algorithm for the MCS method to adaptively determine the partitioning of the geometry and dynamically change the angular quadrature in remaining iterations. The importance factor based on the adjoint transport calculation obtains the response function to get a problem-dependent, goal-oriented spatial decomposition. The difference in the scalar fluxes from one high-order quadrature set to a lower one provides the error estimation as a driving force behind the dynamic quadrature. The goal-oriented algorithm allows optimizing by using ray-tracing technology or high-order quadrature sets in the first few iterations and arranging the integration order of the remaining iterations from high to low. The algorithm has been implemented in the 3D transport code ARES and was tested on the Kobayashi benchmarks. The numerical results show a reduction in computation time on these problems for the same desired level of accuracy as compared to the standard ARES code, and it has clear advantages over the traditional MCS method in solving radiation transport problems with reflective boundary conditions.