• Spatial Information Research
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• v.22 no.1
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• pp.75-84
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• 2014

Power lines are one of the main obstacles causing an aircraft crash and thus their realtime detection is significantly important during flight. To avoid such flight obstacles, the use of LIDAR has been recently increasing thanks to its advantages that it is less sensitive to weather conditions and can operate in day and night. In this study, we suggest a fast method to detect power lines from LIDAR data for flight obstacle avoidance. The proposed method first extracts non-ground points by eliminating the points reflected from ground surfaces using a filtering process. Second, we calculate the eigenvalues for the covariance matrix from the coordinates of the generated non-ground points and obtain the ratio of eigenvalues. Based on the ratio of eigenvalues, we can classify the points on a linear structure. Finally, among them, we select the points forming horizontally long straight as power-line points. To verify the algorithm, we used both real and simulated data as the input data. From the experimental results, it is shown that the average detection rate and time are 80% and 0.2 second, respectively. If we would improve the method based on the experiment results from the various flight scenario, it will be effectively utilized for a flight obstacle avoidance system.

• Journal of the Korean Data and Information Science Society
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• v.28 no.6
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• pp.1427-1435
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• 2017

The smart farm is a remarkable system because it utilizes information and communication technologies in agriculture to bring high productivity and excellent qualities of crops. It automatically measures the growth environment of the crops and accumulates huge amounts of environmental information in real time growing in smart farms using multi-variable control of environmental factors. The statistical model using the collected big data will be helpful for decision making in order to control optimal growth environment of crops in smart farms. Using data collected from a smart farm of tomato, we carried out multiple regression analysis to determine the relationship between yield and environmental factors and to predict yield of tomato. In this study, appropriate parameter modification was made for environmental factors considering tomato growth. Using these new factors, we fit the model and derived the optimal environmental factors that affect the yields of tomato. Based on this, we could predict the yields of tomato. It is expected that growth environment can be controlled to improve tomato productivities by using statistical model.

• Journal of Advanced Navigation Technology
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• v.15 no.4
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• pp.523-535
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• 2011

The precipitable water vapor (PW) was estimated using Global Navigation Satellite System (GNSS) from several GNSS stations within the Korean Peninsula. Nearby radiosonde sites covering the GNSS stations were used for the comparison and validation of test results. GNSS data recorded under typical and severe weather conditions were used to generalize our approach. Based on the analysis, we have confirmed that the derived PW values from the GNSS observables were well agreed on the estimates from the radiosonde observables within 10 mm level. Assuming that the GNSS observables could be a good weather monitoring tool, we further tested the performance of the current WAAS tropospheric delay model, UNB3, in the Korean Peninsula. Especially, the wet zenith delays estimated from the GNSS observables and from UNB3 delay model were compared. Test results showed that the modelled approach for the troposphere (i.e., UNB3) did not perform well especially under the wet weather conditions in the Korean Peninsula. It was suggested that a new model or a near real-time model (e.g., based on regional model from GNSS or numerical weather model) would be highly desirable for the Korean WA-DGNSS to minimize the effects of the tropospheric delay and hence to achieve high precision vertical navigation solutions.

• Economic and Environmental Geology
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• v.41 no.6
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• pp.709-717
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• 2008

Rhizofiltration for cesium uptake by sunflowers (Helianthus annuus L.) and beans (Phaseolus vulgaris var.) was investigated for groundwater contamination. The cesium removal by sunflowers was greater than 98% of the total cesium in solution, and the uptake by beans was also greater than 99% within 24 hours of the rhizofiltration, showing that the rhizofiltration has a great capability to remove cesium from the contaminated water system. Experiments at various pH of solution indicated that a solution of pH $5{\sim}9$ yielded very high cesium accumulation in two plants. From the results of the analysis for cesium accumulation in plant parts, about 80% of cesium transferred into the plant from solution was accumulated in the root part and less than 20% of cesium existed in the shoot part (including leaves). Results suggest that only the roots of the fully grown plant used for rhizofiltration should be disposed or post-treated and thus the cost and time to treat massive amounts of grown plants could be dramatically reduced when sunflower and bean are used in the real field. The results of SEM and EDS analyses indicated that the most of cesium were accumulated in the root surface as a ionic phase rather than a soil precipitation phase.

• The Korean Journal of Applied Statistics
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• v.32 no.6
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• pp.795-835
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• 2019

Big data has been generated in various fields. Many companies have now tried to make profits by building a system capable of analyzing big data based on artificial intelligence (AI) techniques. Integrating AI technology has made analyzing and utilizing vast amounts of data increasingly valuable. In particular, demand forecasting with maximum accuracy is critical to government and business management in various fields such as finance, procurement, production and marketing. In this case, it is important to apply an appropriate model that considers the demand pattern for each field. It is possible to analyze complex patterns of real data that can also be enlarged by a traditional time series model or regression model. However, choosing the right model among the various models is difficult without prior knowledge. Many studies based on AI techniques such as machine learning and deep learning have been proven to overcome these problems. In addition, demand forecasting through the analysis of stereotyped data and unstructured data of images or texts has also shown high accuracy. This paper introduces important areas where demand forecasts are relatively active as well as introduces machine learning and deep learning techniques that consider the characteristics of each field.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.26 no.2
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• pp.225-236
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• 2016

Objectives: Nanomaterials have been used in various fields. As use of nanoproducts is increasing, workers dealing with nanomaterials are also gradually increasing. Exposure assessments for nanomaterials have been carried out for protection of worker's health in workplace. Exposure studies were mainly focused on manufacturing processes, but these studies on after-treatment processes such as refinement, weighing, and packing were insufficient. So, we investigated exposure characteristics of particles during after-treatment processes of $Al_2O_3$ fibers and Ni powders. Methods: Mass-production of Ni powder process was carried out in enclosed capture-type canopy hood. In a developing stage, $Al_2O_3$ was handled with a local ventilation unit. Exposure characteristics of particles were investigated for $Al_2O_3$ fiber and Ni powder processes during the periods of 10:00 to 16:00, 20 May 2014 and 13:00 to 16:00, 21 May 2014, respectively. Three real-time aerosol instruments were utilized in exposure assessment. A scanning mobility particle sizer(SMPS, nanoscan, model 3910, TSI) and an optical particle counter(OPC, portable aerosol spectrometer, model 1.109, Grimm) were used to determine the particle size distribution in the size range of 10-420 nm and $0.25-32{\mu}m$, respectively. In addition, a nanoparticle aerosol monitor(NAM, model 9000, TSI) was used to measure lung-deposited nanoparticle surface area. Membrane filters(isopore membrane filter, pore size of 100 nm) were also used for air sampling for the FE-SEM(model S-5000H, Hitachi) analysis using a personal sampling pump(model GilAir Plus by 2.5 L/min, Gilian). Conclusions: For Ni powder after-treatment process, only 27% increase in particle concentration was found during the process. However, for $Al_2O_3$ fiber after-treatment process, significant exposure(1.56-3.34 times) was observed during the process.

• Journal of Korean Society for Geospatial Information Science
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• v.16 no.1
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• pp.23-32
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• 2008

According to recent frequent local flash flood due to climate change, the very short-term rainfall forecast using remotely sensed rainfall like radar is necessary to establish. This research is to evaluate the feasibility of GIS-based distributed model coupled with radar rainfall, which can express temporal and spatial distribution, for multipurpose dam operation during flood season. $Vflo^{TM}$ model was used as physically based distributed hydrologic model. The study area was Yongdam dam basin ($930\;km^2$) and the 3 storm events of local convective rainfall in August 2005, and the typhoon.Ewiniar.and.Bilis.collected from Jindo radar was adopted for runoff simulation. Distributed rainfall consistent with hydrologic model grid resolution was generated by using K-RainVieux, pre-processor program for radar rainfall. The local bias correction for original radar rainfall shows reasonable results of which the percent error from the gauge observation is less than 2% and the bias value is $0.886{\sim}0.908$. The parameters for the $Vflo^{TM}$ were estimated from basic GIS data such as DEM, land cover and soil map. As a result of the 3 events of multiple peak hydrographs, the bias of total accumulated runoff and peak flow is less than 20%, which can provide a reasonable base for building operational real-time short-term rainfall-runoff forecast system.

• Journal of the Korean Association of Geographic Information Studies
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• v.15 no.1
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• pp.159-171
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• 2012

This study has developed a technology for river basin including the management of the data related with riverbed and the analysis of the riverbed maintenance based on the high-resolution imagery data and LiDAR (Light Detection and Raging) in order to enhance the utilization of river management by using RIMGIS(River Information Management GIS) and to acquire the advanced operation for river management. Using the detailed river topographical map specially designed in the form of LiDAR or high-resolution images, riverbed data and river bank channel information that are dynamically changed were informationized and established in the RIMGIS DB. At this stage, a monitoring techniques that is established in the river management system associated with RIMGIS and minimized the impact of riverbed maintenance (fluctuations) has been studied. In addition, functions and data structure of RIMGIS containing the information of geography and management of the river have been supplemented to make an improvement of the real-time management of the river. Furthermore, a technology that is capable of supplementing RIMGIS has been developed, making it feasible to maintain the river in use of structural method including an structural scheme of cross-section of the river by providing the information of riverbed and cross-section of the river. This is considered to solve an issue of insufficient data on accuracy and based on a lack of information of the river based on the two-dimensional lines, making it feasible to (steadily) improve the function of RIMGIS and to operate management tasks. Therefore, it is highly expected to enhance aforementioned technology of the river information management as a great foundation that maximizes the utilization of the river management to support RIMGIS for the development of national river management data.

• Physical Therapy Korea
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• v.23 no.3
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• pp.21-28
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• 2016

Background: The toe-spread-out (TSO) exercise has been introduced as a strengthening exercise for the abductor hallucis muscle in subjects with hallux valgus. Visual biofeedback using ultrasound imaging during exercise, may increase the ability to selectively contract the abductor hallucis muscle, compared with exercise alone. Objects: The aim of this study was to investigate the effects of ultrasound imaging visual feedback during the TSO exercise with respect to its influence on the angle of the first metatarsophalangeal joint (1st MPJ) and the cross-sectional area (CSA) of the abductor hallucis muscle in subjects with hallux valgus. Methods: Twenty-five healthy young subjects with a mean average age of 22.5 years, and a standard deviation of 2.3 years, were recruited for this study. Hallux valgus was defined as an angles greater than $15^{\circ}$ angle of 1st MPJ. Goniometric measurement was used to determine the angle of 1st MPJ. In addition, an ultrasound system was used to collect the CSA of the abductor hallucis muscle in each foot. The angle of the 1st MPJ and CSA of the abductor hallucis were measured in three positions; the resting position, during TSO exercise, and during TSO exercise in conjunction with real-time ultrasound imaging feedback. All data analyzed using a repeated analysis of variance with Bonferroni correction in order to compare the dependent variables in all three positions. Statistical level of significance was set up as p<.05. Results: The angle of the 1st MPJ was noted to be significantly reduced and the CSA of the abductor hallucis to be significantly greater during TSO exercise used in conjunction with ultrasound imaging visual feedback, compared to when the values were recorded during TSO exercise alone (p<.05). Conclusion: Based on these findings, it can be concluded that the application of ultrasound imaging visual feedback during TSO exercise is more effective in contracting selectively the abductor hallucis than the use of exercise alone.

• Journal of the Korean Association of Geographic Information Studies
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• v.15 no.3
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• pp.91-102
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• 2012

As the basis of the environmental ecological river management, this research developed a method of habitat classification using imagery information to understand a distribution characteristics of fish living in a natural river. First, topographic survey and investigation of discharge and water temperature were carried out to analyze hydraulic characteristics of fish habitat, and the unmanned aerial photography was applied to acquire river imagery at the observation time. Riffle, pool, and glide regions were selected as river habitat to analyze fish distribution characteristics. Analysis showed that the standard deviation of RGB on the riffle is higher than pool and glide because of fast stream flow. From the classification accuracy estimation on riffle region according to resolution and kernel size using the characteristics of standard deviation of RGB, the highest classification accuracy was 77.17% for resolution with 30cm and kernel size with 11. As the result of water temperature observation on pool and glide using infrared camera, they were $19.6{\sim}21.3^{\circ}C$ and $15.5{\sim}16.5^{\circ}C$ respectively with the differences of $4{\sim}5^{\circ}C$. Therefore it is possible to classify pool and glide region using the infrared photography information. The habitat classification to figure out fish distribution can be carried out more efficiently, if unmanned aerial photography system with RGB and infrared band is applied.