• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.7
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• pp.1437-1443
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• 2009

This paper is presented an simultaneous localization and mapping (SLAM) algorithm using ultrasonic for robot and electric compass, encoder, and gyro. Generally, localization based upon electric compass, encoder, and gyro can be measured just local position in workspace. However, actual robot must need an information of the absolute position in workspace to perform its mission, Absolute position in workspace could be calculated using SLAM algorithm. To implement SLAM in this paper, a map is built using ultrasonic sensor and hierarchical map building method. And then, we the map will be transformed into a feature map. The absolute position could be calculated using the feature map and map mapping method. As a test bed, we designed and construct an autonomous robot and showed the experimental performance of the proposed SLAM algorithm based on feature map. Experimental result, we verified that robot can found all absolute position on experiments using proposed SLAM algorithm.

• Journal of the Korean Institute of Rural Architecture
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• v.23 no.4
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• pp.1-12
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• 2021

Recently, the use of unmanned aerial vehicles (UAVs) is increasing in the field of land information acquisition and terrain exploration through high-altitude aerial photography. High-altitude aerial photography is suitable for large-scale geographic information collection, but has the disadvantage that it is difficult to accurately collect small-scale geographic information. Therefore, this study used low-altitude UAV to monitor changes in small rural spaces around rural resources, and the results are as follows. First, the low-altitude aerial imagery had a very high spatial resolution, so it was effective in reading and analyzing topographic features. Second, an area with a large number of aerial images and a complex topography had a large amount of point clouds to be extracted, and the number of point clouds affects the three-dimensional quality of rural space. Third, 3D mapping technology using point cloud is effective for monitoring rural space and rural resources because it enables observation and comparison of parts that cannot be read from general aerial images. In this study, the possibility of rural space analysis of low-altitude UAV was verified through aerial photography and analysis, and the effect of 3D mapping on rural space monitoring was visually analyzed. If data acquired by low-altitude UAV are used in various forms such as GIS analysis and topographic map production it is expected to be used as basic data for rural planning to maintain and preserve the rural environment.

• Journal of the Korean earth science society
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• v.32 no.3
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• pp.249-264
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• 2011

A multi-Gaussian kriging approach extended to space-time domain is presented for uncertainty modeling as well as time-series mapping of environmental variables. Within a multi-Gaussian framework, normal score transformed environmental variables are first decomposed into deterministic trend and stochastic residual components. After local temporal trend models are constructed, the parameters of the models are estimated and interpolated in space. Space-time correlation structures of stationary residual components are quantified using a product-sum space-time variogram model. The ccdf is modeled at all grid locations using this space-time variogram model and space-time kriging. Finally, e-type estimates and conditional variances are computed from the ccdf models for spatial mapping and uncertainty analysis, respectively. The proposed approach is illustrated through a case of time-series Particulate Matter 10 ($PM_{10}$) concentration mapping in Incheon Metropolitan city using monthly $PM_{10}$ concentrations at 13 stations for 3 years. It is shown that the proposed approach would generate reliable time-series $PM_{10}$ concentration maps with less mean bias and better prediction capability, compared to conventional spatial-only ordinary kriging. It is also demonstrated that the conditional variances and the probability exceeding a certain thresholding value would be useful information sources for interpretation.

• Asian Journal of Atmospheric Environment
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• v.12 no.3
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• pp.232-243
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• 2018

The construction, development and maintenance of an economically, environmentally and socially sustainable campus involves the integration of measuring tools and technical information that invites and encourages the community to know the actual state to generate positive actions for reducing the negative impacts over the local environment. At the Universidad Nacional de Colombia - Campus $Bogot{\acute{a}}$, a public area with daily traffic of more than 25000 people, the Environmental Management Bureau has committed with the monitoring of the noise pollution and air quality, as support to the campaigns aiming to reduce the pollutant emissions associated to the student's activities and campus operation. The target of this study is based in the implementation of mobile air quality and sonometry monitoring equipment, the mapping of the actual air quality and noise pollution inside the university campus as a novel methodology for a sub-area inside a megacity. This results and mapping are proposed as planning tool for the institution administrative sections. A mobile Kunak$^{(R)}$ Air & OPC air monitoring station with the capability to measure particulate matter $PM_{10}$, $PM_{2.5}$, Ozone ($O_3$), Sulfur Oxide ($SO_2$), Carbon Monoxide (CO) and Nitrogen Oxide ($NO_2$) as well as Temperature, Relative Humidity and Latitude and Longitude coordinates for the data georeferenciation; and a sonometer Cirrus$^{(R)}$ 162B Class 2 were used to perform the measurements. The measurements took place in conditions of academic activity and without it, with the aim of identify the impacts generated by the campus operation. Using the free code geographical information software QGIS$^{(R)}$ 2.18, the maps of each variable measured were developed, and the impacts generated by the operation of the campus were identified qualitative and quantitively. For the measured variables, an increase of around 21% for the $L_{Aeq}$ noise level and around 80% to 90% for air pollution were detected during the operation period.

• Journal of the Korean Association of Geographic Information Studies
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• v.8 no.4
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• pp.71-80
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• 2005

As local flash flood exceeding planned capacity occurs frequently, localized preparedness and response to flood inundation are increasingly important. Using XP-SWMM model and GIS techniques, this study analyzes inundation areas by local flash flood and develops rainfall standards for evacuation with the case of Sadang-Cheon area, a local stream and its nearby highly populated watershed in the southern part of metropolitan Seoul, Flood inundation areas overflowed from drainage systems are analyzed and mapped by amount of rainfall that is derived from reference levels of stream flow. Rainfall standards for evacuation are comprised of 'watch' (40mm/hr) in preparing for near-future inundation and 'evacuation' (65mm/hr) in responding to realized inundation. The methods suggested by this case study may be applied to other urban areas for sound flood prevention policy measures and thus risk minimization.

• Journal of the Korea Convergence Society
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• v.9 no.10
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• pp.45-53
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• 2018

Recently, studies about bilingual word embedding have been gaining much attention. However, bilingual word embedding with Korean is not actively pursued due to the difficulty in obtaining a sizable, high quality corpus. Local embeddings that can be applied to specific domains are relatively rare. Additionally, multi-word vocabulary is problematic due to the lack of one-to-one word-level correspondence in translation pairs. In this paper, we crawl 868,163 paragraphs from a Korean-English law corpus and propose three mapping strategies for word embedding. These strategies address the aforementioned issues including multi-word translation and improve translation pair quality on paragraph-aligned data. We demonstrate a twofold increase in translation pair quality compared to the global bilingual word embedding baseline.

• Journal of Environmental Science International
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• v.20 no.12
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• pp.1647-1661
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• 2011

The Government had devised legislation of Special Act and drew up guidelines for improving air quality in Seoul Metropolitan area. In 2007 local government of Seoul, Incheon and Gyeonggi conducted the results of application policy by reduced air pollutants emission for the first time. Although there was reduction of air pollutant emission in each local government, it was ineffective as expected using air pollution monitoring database. Therefore we worked out a way to prepare modeling input data using the results of enforcement plan. And we simulated surface $NO_2$ and PM10 before and after decrease in air pollutants emission and examine reduction effects of air pollution according to enforcement regulation except other influence, by using MM5-SMOKE-CMAQ system. Each local government calculated the amount of emission reduction under application policy, and we developed to prepare input data so as to apply to SMOKE system using emission reduction of enforcement plan. Distribution factor of emission reduction were classified into detailed source and fuel codes using code mapping method in order to allocate the decreased emission. The code mapping method also included a way to allocate spatial distribution by CAPSS distribution. According to predicted result using the reduction of NOx emission, $NO_2$ concentration was decreased from 19.1 ppb to 18.0 ppb in Seoul. In Gyeonggi and Incheon $NO^2$ concentrations were down to 0.65 ppb and 0.68 ppb after application of enforcement plan. PM10 concentration was reduced from 18.2 ${\mu}g/m^3$ to 17.5 ${\mu}g/m^3$ in Seoul. In Gyeonggi PM10 concentration was down to 0.51 ${\mu}g/m^3$ and in Incheon PM10 concentration was decreased about 0.47 ${\mu}g/m^3$ which was the lower concentration than any other cities.

• Journal of Environmental Impact Assessment
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• v.24 no.6
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• pp.593-606
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• 2015

This study was intended to develop Korean Environmental Sustainable City Index (ESCI) so that local governments can examine and identify urban environment issues and then come up with a policy to improve the environment and urban biodiversity for cities. Green City Index (GCI), Environmental Performance Index (EPI), and City Biodiversity Index (CBI) which have used worldwide were analyzed. Based on the result of analysis, evaluation indicators of ESCI were finally a total of 20 indicators under four categories, which are native biodiversity, living environment, ecosystem services, and governance and management. Then, five cities with biotope mapping and evaluation index were selected to apply ESCI for evaluation. In order to apply ESCI, local governments need to accumulate basic data. There should be a policy which requires local governments to build data for biotope mapping so that the rate of natural area, ecological network and permeable land surface can be evaluated. Indicators must be applied to be compliant with scale of the city and level of data building gradually.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.2
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• pp.907-923
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• 2020

For the severe haze situation in the Beijing-Tianjin-Hebei region, conventional fine particulate matter (PM2.5) concentration prediction methods based on pollutant data face problems such as incomplete data, which may lead to poor prediction performance. Therefore, this paper proposes a method of predicting the PM2.5 concentration based on image analysis technology that combines image data, which can reflect the original weather conditions, with currently popular machine learning methods. First, based on local parameter estimation, autoregressive (AR) model analysis and local estimation of the increase in image blur, we extract features from the weather images using an approach inspired by free energy and a no-reference robust metric model. Next, we compare the coefficient energy and contrast difference of each pixel in the AR model and then use the percentages to calculate the image sharpness to derive the overall mass fraction. Furthermore, the results are compared. The relationship between residual value and PM2.5 concentration is fitted by generalized Gauss distribution (GGD) model. Finally, nonlinear mapping is performed via the wavelet neural network (WNN) method to obtain the PM2.5 concentration. Experimental results obtained on real data show that the proposed method offers an improved prediction accuracy and lower root mean square error (RMSE).

• Progress in Superconductivity
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• v.6 no.2
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• pp.95-98
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• 2005

We present results of Raman spectroscopic studies of superconducting $YBa_2Cu_3O_7$ (YBCO) coated conductors. Raman scattering is used to characterize optical phonon modes, oxygen content, c-axis misalignment, and second phases of the YBCO coated conductors at a micro scale. A two-dimensional mapping of Raman spectra with transport properties has been performed to elucidate the effect of local propertied on current path and superconducting phase. The information taken from the local measurement will be useful for optimizing the process condition.