• Proceedings of the Korea Water Resources Association Conference
• /
• 2019.05a
• /
• pp.152-152
• /
• 2019

This study demonstrates a novel approach of remotely sensed estimates of stream flow at fifteen hydrological station in the Han River Basin, Korea. Multi-temporal data of the European Space Agency's Sentinel-1 SAR satellite from 19 January, 2015 to 25 August, 2018 is used to develop and validate the flow estimation model for each station. The flow estimation model is based on a power law relationship established between the remotely sensed surface area of water at a selected reach of the stream and the observed discharge. The satellite images were pre-processed for thermal noise, radiometric, speckle and terrain correction. The difference in SAR image brightness caused by the differences in SAR satellite look angle and atmospheric condition are corrected using the histogram matching technique. Selective area filtering is applied to identify the extent of the selected stream reach where the change in water surface area is highly sensitive to the change in stream discharge. Following this, an iterative procedure called the Optimum Threshold Classification Algorithm (OTC) is applied to the multi-temporal selective areas to extract a series of water surface areas. It is observed that the extracted water surface area and the stream discharge are related by the power law equation. A strong correlation coefficient ranging from 0.68 to 0.98 (mean=0.89) was observed for thirteen hydrological stations, while at two stations the relationship was highly affected by the hydraulic structures such as dam. It is further identified that the availability of remotely sensed data for a range of discharge conditions and the geometric properties of the selected stream reach such as the stream width and side slope influence the accuracy of the flow estimation model.

• The Journal of Bigdata
• /
• v.6 no.2
• /
• pp.39-49
• /
• 2021

Recently, large and diverse weather data are being collected by sensors from various sources. Efforts to predict the concentration of fine dust through machine learning are being made everywhere, and this study intends to compare PM10 and PM2.5 prediction models using data from 840 outdoor air meters installed throughout the city. Information can be provided in real time by predicting the concentration of fine dust after 5 minutes, and can be the basis for model development after 10 minutes, 30 minutes, and 1 hour. Data preprocessing was performed, such as noise removal and missing value replacement, and a derived variable that considers temporal and spatial variables was created. The parameters of the model were selected through the response surface method. XGBoost, Random Forest, and Deep Learning (Multilayer Perceptron) are used as predictive models to check the difference between fine dust concentration and predicted values, and to compare the performance between models.

• Asian Journal of Atmospheric Environment
• /
• v.12 no.3
• /
• pp.232-243
• /
• 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 Institute of Telematics and Electronics D
• /
• v.36D no.9
• /
• pp.56-63
• /
• 1999

Conventional method to improve the response time of the Cat-SPPCM was to increase fanning beam using cylindrical lens. But, in this case the temporal instability of this SPPCM is increased, and fanning beam plays a role as noise, so that it decreases the reflectivity. Thus, fanning beam must be controlled to improve the properties of SPPCM. In this paper, we propose the method to increase the reflectivity of SPPCM and decrease the response time by focusing line-shaped input beam into photorefractive crystal using cylindrical lens and decrease the temporal instability of output beam by using mixed-polarized beam instead of simple extraordinary polarized beam. Optical experiments show that the reflectivity of proposed SPPCM is increased twice and the response time is reduced by 15 times. Also, we observed that the temporal instability of SPPCM is reduced when the polarization angle of the mixed-polarized beam is between $10^{\circ}$ and $30^{\circ}$ . We used a $45^{\circ}-cut\;BaTiO_3$ crystal which has high electro-optical coefficients in optical experiments.

• Transactions of the Korean Society for Noise and Vibration Engineering
• /
• v.24 no.6
• /
• pp.462-469
• /
• 2014

After drilling operations at the offshore plant, crude oil is producted under high pressure. After that time, oil recovery is reduced, because the pressure of the pipe inside is low during the secondary produce. At that time injection sea water at the pipe inside through water injection pump that the device increase to recovery. A variety of mathematical analysis during the detailed design analysis was not made through the dynamics characteristic at the domestic company. 2D model has reliability of analysis results for the uncomplicated model. Also element and the node the number of significantly less than in the 3D model. So, the temporal part is very effective. In addition, depending on the quality of mesh 3D is a real model and FEM model occurs error. So, user needs a lot of skill. In this paper, a 2D finite element analysis was performed through the dynamics analysis and the study model was validated.

• Journal of Digital Contents Society
• /
• v.17 no.2
• /
• pp.89-96
• /
• 2016

SVC(Scalable Video Coding), which is one form among video encoding technologies, makes video streaming with the various frame rate, resolution, and video quality by combining three different scalability dimensions: temporal, spatial, and video quality scalability. As the above SVC-encoded video streaming consists of one base layer and several enhancement layers, and a wireless AP(Access Point) chooses and sends a suitable layer according to the received power from the receiving terminals in the changeable wireless network environment, the receiving terminals supporting SVC are able to receive video streaming with the appropriate resolution and quality according to their received powers. In this paper, after the performance analysis for the received power, packet loss rate, PSNR(Required Peak Signal to Noise Ratio), video quality level and amount of received video data based on the number of SVC layers was performed, an efficient method for selecting the number of SVC layer satisfying the RSNR and minimizing the amount of received video data is proposed.

• Journal of Biomedical Engineering Research
• /
• v.20 no.3
• /
• pp.291-297
• /
• 1999

Source consislency filtering (SCF) is very effective at removing nOIse when only one or a few leads among multi-lead ECG signals are contaminated. When the noise at one or only a few leads are statistically uncorrelated with signals at other leads, SCF seleclIvely removes the noise with a neglIgIble amount of distortion in the original signal waveform. In order to enhance the understanding of this new method, we describe the lheory and implementational details of SCF in this paper. Numerical implementation and test results of SCF on a multi-lead ECG dalabase show that SCF is a new kind of adaptive filters utilizmg spatial as well as temporal information in multi-c.hannel signals origmatmg from a single source. We also describe the limitations and future improvements in using SCF.

• Investigative Magnetic Resonance Imaging
• /
• v.23 no.2
• /
• pp.114-124
• /
• 2019

Purpose: We investigate biases in the assessments of left ventricular function (LVF), by compressed sensing (CS)-cine magnetic resonance imaging (MRI). Materials and Methods: Cardiovascular cine images with short axis view, were obtained for 8 volunteers without CS. LVFs were assessed with subsampled data, with compression factors (CF) of 2, 3, 4, and 8. A semi-automatic segmentation program was used, for the assessment. The assessments by 3 CS methods (ITSC, FOCUSS, and view sharing (VS)), were compared to those without CS. Bland-Altman analysis and paired t-test were used, for comparison. In addition, real-time CS-cine imaging was also performed, with CF of 2, 3, 4, and 8 for the same volunteers. Assessments of LVF were similarly made, for CS data. A fixed compensation technique is suggested, to reduce the bias. Results: The assessment of LVF by CS-cine, includes bias and random noise. Bias appeared much larger than random noise. Median of end-diastolic volume (EDV) with CS-cine (ITSC or FOCUSS) appeared -1.4% to -7.1% smaller, compared to that of standard cine, depending on CF from (2 to 8). End-systolic volume (ESV) appeared +1.6% to +14.3% larger, stroke volume (SV), -2.4% to -16.4% smaller, and ejection fraction (EF), -1.1% to -9.2% smaller, with P < 0.05. Bias was reduced from -5.6% to -1.8% for EF, by compensation applied to real-time CS-cine (CF = 8). Conclusion: Loss of temporal resolution by adopting missing data from nearby cardiac frames, causes an underestimation for EDV, and an overestimation for ESV, resulting in underestimations for SV and EF. The bias is not random. Thus it should be removed or reduced for better diagnosis. A fixed compensation is suggested, to reduce bias in the assessment of LVF.

• Journal of Internet Computing and Services
• /
• v.24 no.4
• /
• pp.65-76
• /
• 2023

This paper identifies the aspects of living environment elements (PM^2.5, PM¹⁰, Noise) throughout Seoul and the urban characteristics that affect them by utilizing the big data of the S-Dot sensors in Seoul, which has recently become a hot topic. In other words, it proposes a big data based urban computing research methodology and research direction to confirm the relationship between urban characteristics and living environments that directly affect citizens. The temporal range is from 2020 to 2021, which is the available range of time series data for S-Dot sensors, and the spatial range is throughout Seoul by 500mX500m GRID. First of all, as part of analyzing specific living environment patterns, simple trends through EDA are identified, and cluster analysis is conducted based on the trends. After that, in order to derive specific urban planning factors of each cluster, basic statistical analysis such as ANOVA, OLS and MNL analysis were conducted to confirm more specific characteristics. As a result of this study, cluster patterns of environment elements(PM^2.5, PM¹⁰, Noise) and urban factors that affect them are identified, and there are areas with relatively high or low long-term living environment values compared to other regions. The results of this study are believed to be a reference for urban planning management measures for vulnerable areas of living environment, and it is expected to be an exploratory study that can provide directions to urban computing field, especially related to environmental data in the future.

• ETRI Journal
• /
• v.37 no.2
• /
• pp.395-405
• /
• 2015

We address the problem of uncooperative person recognition through continuous monitoring. Multiple modalities, such as face, height, clothes color, and voice, can be used when attempting to recognize a person. In general, not all modalities are available for a given frame; furthermore, only some modalities will be useful as some frames in a video sequence are of a quality that is too low to be able to recognize a person. We propose a method that makes use of stochastic information updates of temporal modalities and environment estimators to improve person recognition performance. The environment estimators provide information on whether a given modality is reliable enough to be used in a particular instance; such indicators mean that we can easily identify and eliminate meaningless data, thus increasing the overall efficiency of the method. Our proposed method was tested using movie clips acquired under an unconstrained environment that included a wide variation of scale and rotation; illumination changes; uncontrolled distances from a camera to users (varying from 0.5 m to 5 m); and natural views of the human body with various types of noise. In this real and challenging scenario, our proposed method resulted in an outstanding performance.