• Korean Journal of Remote Sensing
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• v.22 no.3
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• pp.183-197
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• 2006

Combinations of visible and near-infrared (NIR) bands in an image are widely used for estimating vegetation vigor and productivity. Using this approach to understand within-field grain crop variability could allow pre-harvest estimates of yield, and might enable mapping of yield variations without use of a combine yield monitor. The objective of this study was to estimate within-field variations in crop yield using vegetation indices derived from hyperspectral images. Hyperspectral images were acquired using an aerial sensor on multiple dates during the 2003 and 2004 cropping seasons for corn and soybean fields in central Missouri. Vegetation indices, including intensity normalized red (NR), intensity normalized green (NG), normalized difference vegetation index (NDVI), green NDVI (gNDVI), and soil-adjusted vegetation index (SAVI), were derived from the images using wavelengths from 440 nm to 850 nm, with bands selected using an iterative procedure. Accuracy of yield estimation models based on these vegetation indices was assessed by comparison with combine yield monitor data. In 2003, late-season NG provided the best estimation of both corn $(r^2\;=\;0.632)$ and soybean $(r^2\;=\;0.467)$ yields. Stepwise multiple linear regression using multiple hyperspectral bands was also used to estimate yield, and explained similar amounts of yield variation. Corn yield variability was better modeled than was soybean yield variability. Remote sensing was better able to estimate yields in the 2003 season when crop growth was limited by water availability, especially on drought-prone portions of the fields. In 2004, when timely rains during the growing season provided adequate moisture across entire fields and yield variability was less, remote sensing estimates of yield were much poorer $(r^2<0.3)$.

• Korean Journal of Remote Sensing
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• v.8 no.2
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• pp.93-104
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• 1992

Although the Transverse Mercator(TM) coordinate is used on standard topogrphic maps of Korea as a supplement to regular latitude-longitude coordinate, the use of this TM coordinate system is rather limited to a single coordinate zone that spans only two degrees of longitude. With growing applications of a variety of digiral geographic data, such as satellite remote sensor data, a Cartesian or rectangular coordinate system is more effective to deal with such data type than angular coordinate system. An unified rectangular coordinate system based on the Transverse Mercator projection is designed to cover the whole area of the Korea Peninsula as a single coordinate zone. Considering the width of the peninsula and the distribution of scale error, the origin of the coordinate is determined to 127$^{\circ}$30' east and 38$^{\circ}$ north. Coordinate conversion procedure is discussed along with the corresponding scale error term.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.5
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• pp.219-227
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• 2019

Manufacturing methods have been changed from labor-intensive methods to technological intensive methods centered on manufacturing facilities. As manufacturing facilities replace human labour, the importance of monitoring and managing manufacturing facilities is emphasized. In addition, Big Data technology has recently emerged as an important technology to discover new value from limited data. Therefore, changes in manufacturing industries have increased the need for smart factory that combines IoT, information and communication technologies, sensor data, and big data. In this paper, we present strategies for existing domestic manufacturing factory to becom big data based smart-factory through technologies for distributed storage and processing of manufacturing facility data in MongoDB in real time and visualization using R programming.

• The Journal of Bigdata
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• v.6 no.1
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• pp.161-178
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• 2021

The importance of 'Big Data' is increasing to the point that it is likened to '21st century crude oil'. For smart city IoT data, attention should be paid to quality control as the quality of data is associated with the quality of public services. However, data quality indicators presented through ISO/IEC organizations and domestic/foreign organizations are limited to the 'User' perspective. To complement these limitations, the study derives supplier-centric indicators and their priorities. After deriving 3 categories and 13 indicators of supplier-oriented smart city IoT data quality evaluation indicators, we derived the priority of indicator categories and data quality indicators through AHP analysis and investigated the feasibility of each indicator. The study can contribute to improving sensor data quality by presenting the basic requirements that data should have to individuals or companies performing the task. Furthermore, data quality control can be performed based on indicator priorities to provide improvements in quality control task efficiency.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.6
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• pp.535-544
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• 2018

Autonomous driving can be limited by only using sensors if the sensor is blocked by sudden changes in surrounding environments or large features such as heavy vehicles. In order to overcome the limitations, the precise road-map has been used additionally. This study was conducted to segment and classify road objects using 3D point cloud data acquired by terrestrial mobile mapping system provided by National Geographic Information Institute. For this study, the original 3D point cloud data were pre-processed and a filtering technique was selected to separate the ground and non-ground points. In addition, the road objects corresponding to the lanes, the street lights, the safety fences were initially segmented, and then the objects were classified using the support vector machine which is a kind of machine learning. For the training data for supervised classification, only the geometric elements and the height information using the eigenvalues extracted from the road objects were used. The overall accuracy of the classification results was 87% and the kappa coefficient was 0.795. It is expected that classification accuracy will be increased if various classification items are added not only geometric elements for classifying road objects in the future.

• Journal of the Korea Society of Computer and Information
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• v.24 no.9
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• pp.83-95
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• 2019

Recently, a variety of IoT data is collected by attaching geosensors to many vehicles that are on the road. IoT data basically has time and space information and is composed of various data such as temperature, humidity, fine dust, Co2, etc. Although a certain sensor data can be retrieved using time, latitude and longitude, which are keys to the IoT data, advanced search engines for IoT data to handle high-level user queries are still limited. There is also a problem with searching large amounts of IoT data without generating indexes, which wastes a great deal of time through sequential scans. In this paper, we propose a unified spatial index model that handles both grid and trajectory queries using a cell-based space-filling curve method. also it presents a visualization method that helps user grasp intuitively. The Trajectory query is to aggregate the traffic of the trajectory cells passed by taxi on the road searched by the user. The grid query is to find the cells on the road searched by the user and to aggregate the fine dust. Based on the generated spatial index, the user interface quickly summarizes the trajectory and grid queries for specific road and all roads, and proposes a Web-based prototype system that can be analyzed intuitively through road and heat map visualization.

• Journal of IKEEE
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• v.23 no.1
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• pp.242-248
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• 2019

Fire flame and smoke detection algorithm studies are challenging task in computer vision due to the variety of shapes, rapid spread and colors. The performance of a typical sensor based fire detection system is largely limited by environmental factors (indoor and fire locations). To solve this problem, a deep learning method is applied. Because it extracts the feature of the object using several methods, so that if a similar shape exists in the frame, it can be detected as false postive. This study proposes a new algorithm to reduce false positives by using frame similarity before using deep learning to decrease the false detection rate. Experimental results show that the fire detection performance is maintained and the false positives are reduced by applying the proposed method. It is confirmed that the proposed method has excellent false detection performance.

• Korean Journal of Materials Research
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• v.29 no.5
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• pp.336-341
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• 2019

The gas adsorption isotherm requires accurate measurement for the analysis of porous materials and is used as an index of surface area, pore distribution, and adsorption amount of gas. Basically, adsorption isotherms of porous materials are measured conventionally at 77K and 87K using liquid nitrogen and liquid argon. The cold volume calibration in this conventional method is done simply by splitting a sample cell into two zones (cold and warm volumes) by controlling the level sensor in a Dewar filled with liquid nitrogen or argon. As a result, BET measurement for textural properties is mainly limited to liquefied gases (i.e. $N_2$ or Ar) at atmospheric pressure. In order to independently investigate other gases (e.g. hydrogen isotopes) at cryogenic temperature, a novel temperature control system in the sample cell is required, and consequently cold volume calibration at various temperatures becomes more important. In this study, a cryocooler system is installed in a commercially available BET device to control the sample cell temperature, and the automated cold volume calibration method of temperature variation is introduced. This developed calibration method presents a reliable and reproducible method of cryogenic measurement for hydrogen isotope separation in porous materials, and also provides large flexibility for evaluating various other gases at various temperature.

• Journal of The Korean Society of Integrative Medicine
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• v.9 no.4
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• pp.71-83
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• 2021

Purpose : This study compared the differences in power and agility of athletes in each sports using visual response speed test (VRST) scores after conducting 10 weeks of body stability exercise (BSE) on elementary school athletes in handball, football, volleyball and conducted a post-hoc test on the measured values. The subjects of this study were baseball (n=27), taekwondo (n=22), and football (n=23) athletes with at least two years of athletic experience. A total of 72 elementary school athletes were measured by VRST after 10 weeks of BSE under the same conditions. Methods : For VRST measurement of the upper extremity, the right and left hands were alternately touched in the order the blazepod equipment lights were turned on. The number of touches for 15 seconds and response touch were measured. In the case of the measurement of lower extremity the left lower extremity was measured first when the Blaze pod equipment light came on. The average value was obtained by measuring 3 times using a measurement sensor with the position indicated in order to measure the upper arms and legs the same. Results : This study confirmed homogeneity among sports and that VRST improved after implementing BSE for sports. However, no statistically significant difference was identified when comparing VRST improvements between sports, and post-hoc test results showed no significant differences either. Conclusion : After applying the BSE program under the same conditions for 10 weeks to elementary school students who can improve their power and agility the most, the results of the examination using the Blaze pod showed that the power and agility of baseball, taekwondo, and soccer players were similarly improved. From the fact that there was no significant difference among sports, it could be inferred that the BES training program could improve VRST without being limited to some sports.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.15 no.5
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• pp.13-24
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• 2005

Due to the changes in the wireless information environment, there has been an increased demand for various types of information. Accordingly, many wireless communication technologies have been studied and developed. In particular, studies on ubiquitous communications are well underway. Lately, the focus has been on the Bluetooth technology due to its applicability in various environments. Applying Bluetooth connectivity to new environments such as ubiquitous or sensor networks requires finding new wars of using it. Thus, this research analyzed the vulnerability on the limited number of slaves in a piconet configuration through the current Bluetooth communication and proposed an expanded Bluetooth piconet formation method, regardless of the number of slaves inside the piconet even if it is not configured in a scatternet. In the proposed method, we applied a security service and resolved the vulnerabilities of the current piconet by configuring an expanded form of the current tree-shaped structure.