• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2022.11a
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• pp.113-114
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• 2022

This study demonstrates the performance of flexible surface wave resonators in spaces on a ship to overcome environmental limits like non-metallic walls where conventional surface wave resonators cannot installable. Although test results in plane structures show that the performance of conventional surface wave resonators are better than the flexible ones, the results are reversed in curved structures. Flexible surface wave resonators can be installed on metal-pipes that connects all spaces in a ship, and this will allow to build ultimate communication network all over the ship including the rooms like cabins or bridges that are enclosed in non-metallic walls.

• Korean Journal of Agricultural and Forest Meteorology
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• v.21 no.1
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• pp.29-41
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• 2019

Terrestrial Gross Primary Production (GPP) is the largest global carbon flux, and forest ecosystems are important because of the ability to store much more significant amounts of carbon than other terrestrial ecosystems. There have been several attempts to estimate GPP using mechanism-based models. However, mechanism-based models including biological, chemical, and physical processes are limited due to a lack of flexibility in predicting non-stationary ecological processes, which are caused by a local and global change. Instead mechanism-free methods are strongly recommended to estimate nonlinear dynamics that occur in nature like GPP. Therefore, we used the mechanism-free machine learning techniques to estimate the daily GPP. In this study, support vector machine (SVM), random forest (RF) and artificial neural network (ANN) were used and compared with the traditional multiple linear regression model (LM). MODIS products and meteorological parameters from eddy covariance data were employed to train the machine learning and LM models from 2006 to 2013. GPP prediction models were compared with daily GPP from eddy covariance measurement in a deciduous forest in South Korea in 2014 and 2015. Statistical analysis including correlation coefficient (R), root mean square error (RMSE) and mean squared error (MSE) were used to evaluate the performance of models. In general, the models from machine-learning algorithms (R = 0.85 - 0.93, MSE = 1.00 - 2.05, p < 0.001) showed better performance than linear regression model (R = 0.82 - 0.92, MSE = 1.24 - 2.45, p < 0.001). These results provide insight into high predictability and the possibility of expansion through the use of the mechanism-free machine-learning models and remote sensing for predicting non-stationary ecological processes such as seasonal GPP.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.1031-1042
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• 2023

In this study, a deep learning model was developed to predict the yield of cabbage and radish, one of the five major supply and demand management vegetables, using satellite images of Landsat 8. To predict the yield of cabbage and radish in Gangwon-do from 2015 to 2020, satellite images from June to September, the growing period of cabbage and radish, were used. Normalized difference vegetation index, enhanced vegetation index, lead area index, and land surface temperature were employed in this study as input data for the yield model. Crop yields can be effectively predicted using satellite images because satellites collect continuous spatiotemporal data on the global environment. Based on the model developed previous study, a model designed for input data was proposed in this study. Using time series satellite images, convolutional neural network, a deep learning model, was used to predict crop yield. Landsat 8 provides images every 16 days, but it is difficult to acquire images especially in summer due to the influence of weather such as clouds. As a result, yield prediction was conducted by splitting June to July into one part and August to September into two. Yield prediction was performed using a machine learning approach and reference models , and modeling performance was compared. The model's performance and early predictability were assessed using year-by-year cross-validation and early prediction. The findings of this study could be applied as basic studies to predict the yield of field crops in Korea.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.48 no.4
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• pp.35-42
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• 2011

Link quality assessment is a crucial part of sensor network formation to stably operate large-scale wireless sensor networks (WSNs). A stability of path consisting of several nodes strongly depends on all link quality between pair of consecutive nodes. Thus it is very important to assess the link quality on the stage of building a routing path. In this paper, we present a link quality assessment method, Hybrid Link Quality Metric (HQLM), which uses both of LQI and RSSI from RF chip of sensor nodes to minimize set-up time and energy consumption for network formation. The HQLM not only reduces the time and energy consumption, but also provides complementary cooperation of LQI and RSSI. In order to evaluate the validity and efficiency of the proposed method, we measure PDR (Packet Delivery Rate) by exchanging multiple messages and then, compare PDR to the result of HQLM for evaluation. From the research being carried out, we can conclude that the HQLM performs better than either LQI- or RSSI-based metric in terms of recall, precision, and matching on link quality.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.1
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• pp.23-33
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• 2020

Recently, high-resolution images can be easily acquired using UAV (Unmanned Aerial Vehicle), so that it is possible to produce small area observation and spatial information at low cost. In particular, research on the generation of cover maps in crop production areas is being actively conducted for monitoring the agricultural environment. As a result of comparing classification performance by applying RF(Random Forest), SVM(Support Vector Machine) and CNN(Convolutional Neural Network), deep learning classification method has many advantages in image classification. In particular, land cover classification using satellite images has the advantage of accuracy and time of classification using satellite image data set and pre-trained parameters. However, UAV images have different characteristics such as satellite images and spatial resolution, which makes it difficult to apply them. In order to solve this problem, we conducted a study on the application of deep learning algorithms that can be used for analyzing agricultural lands where UAV data sets and small-scale composite cover exist in Korea. In this study, we applied DeepLab V3 +, FC-DenseNet (Fully Convolutional DenseNets) and FRRN-B (Full-Resolution Residual Networks), the semantic image classification of the state-of-art algorithm, to UAV data set. As a result, DeepLab V3 + and FC-DenseNet have an overall accuracy of 97% and a Kappa coefficient of 0.92, which is higher than the conventional classification. The applicability of the cover classification using UAV images of small areas is shown.

• Korean Journal of Remote Sensing
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• v.25 no.1
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• pp.31-44
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• 2009

The objective of this study was to measure backscattering coefficients of paddy rice using a L-, C-, and X-band scatterometer system with full polarization and various angles during the rice growth period and to relate backscattering coefficients to rice growth parameters. Radar backscattering measurements of paddy rice field using multifrequency (L, C, and X) and full polarization were conducted at an experimental field located in National Academy of Agricultural Science (NAAS), Suwon, Korea. The scatterometer system consists of dual-polarimetric square horn antennas, HP8720D vector network analyzer ($20\;MHz{\sim}20\;GHz$), RF cables, and a personal computer that controls frequency, polarization and data storage. The backscattering coefficients were calculated by applying radar equation for the measured at incidence angles between $20^{\circ}$ and $60^{\circ}$ with $5^{\circ}$ interval for four polarization (HH, VV, HV, VH), respectively. We measured the temporal variations of backscattering coefficients of the rice crop at L-, C-, X-band during a rice growth period. In three bands, VV-polarized backscattering coefficients were higher than hh-polarized backscattering coefficients during rooting stage (mid-June) and HH-polarized backscattering coefficients were higher than VV-, HV/VH-polarized backscattering coefficients after panicle initiation stage (mid-July). Cross polarized backscattering coefficients in X-band increased towards the heading stage (mid-Aug) and thereafter saturated, again increased near the harvesting season. Backscattering coefficients of range at X-band were lower than that of L-, C-band. HH-, VV-polarized ${\sigma}^{\circ}$ steadily increased toward panicle initiation stage and thereafter decreased, and again increased near the harvesting season. We plotted the relationship between backscattering coefficients with L-, C-, X-band and rice growth parameters. Biomass was correlated with L-band hh-polarization at a large incident angle. LAI (Leaf Area Index) was highly correlated with C-band HH- and cross-polarizations. Grain weight was correlated with backscattering coefficients of X-band VV-polarization at a large incidence angle. X-band was sensitive to grain maturity during the post heading stage.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.138-142
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• 2004

In this paper, we present a new method for monitoring of ECU's sensor signals of vehicle. In order to measure the ECU's sensor signals, the interfaced circuit is designed to communicate ECU and the Embedded Linux is used to monitor communication result through Web the Embedded Linux system and this system is said "ECU Interface Part". In ECU Interface Part the interface circuit is designed to match voltage level between ECU and SA-1110 micro controller and interface circuit to communicate ECU according to the ISO, SAE communication protocol standard. Because Embedded Linux does not allow to access hardware directly in application level, anyone who wants to modify any low level hardware must develop device driver. To monitor ECU's sensor signals the most important thing is to match serial level between ECU and ECU Interface Part. It means to communicate correctly between two hardware we need to match voltage and signal level, and need to match baudrate. The voltage of SA-1110 is 0 ${\sim}$ +3.3V and ECU is 0 ${\sim}$ +12V and, ECU's communication Line K does multiple operation so, the interface circuit is used to match voltage and signal level. In Addition to ECU's baudrate is 10400bps, it's not standard baudrate in computer environment. So, we need to develop a device driver to control the interface circuit, and change baudrate. To monitor ECU's sensor signals through web there's a network socket program is working in Embedded Linux. It works as server program and manages user's connections and commands. Anyone who wants to monitor ECU's sensor signals he just only connect to Embedded Linux system with web browser then, Embedded Linux webserver will return the ActiveX webbased measurement software. It works in web browser and inits ECU, as a result it returns sensor signals through web. All the programs are developed with GCC(GNU C Compiler) and, webbased measurement software is developed with Borland C++ Builder.

• Journal of the Microelectronics and Packaging Society
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• v.24 no.3
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• pp.71-76
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• 2017

The electrical behavior and flexibility of the screen printed Ag circuits were investigated with infrared radiation sintering times and sintering temperatures. Electrical resistivity and radio frequency characteristics were evaluated by using the 4 point probe measurement and the network analyzer by using cascade's probe system, respectively. Electrical resistivity and radio frequency characteristics means that the direct current resistance and signal transmission properties of the printed Ag circuit. Flexibility of the screen printed Ag circuit was evaluated by measuring of electrical behavior during IPC sliding test. Failure mode of the Ag printed circuits was observed by using field emission scanning electron microscope and optical microscope. Electrical resistivity of the Ag circuits screen printed on Pl substrate was rapidly decreased with increasing sintering temperature and durations. The lowest electrical resistivity of Ag printed circuit was up to $3.8{\mu}{\Omega}{\cdot}cm$ at $250^{\circ}C$ for 45 min. The crack length arisen within the printed Ag circuit after $10{\times}10^4$ sliding numbers was 10 times longer than that of after $2.5{\times}10^4$ sliding numbers. Measured insertion loss and calculated insertion loss were in good agreements each other. Insertion loss of the printed Ag circuit was increased with increasing the number of sliding cycle.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.17 no.2 s.105
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• pp.89-94
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• 2006

A compact microstrip band-selective filter for ultra-wideband(UWB) radio system is proposed. The filter combines the traditional short-circuited stub highpass filter and coupled resonator band-stop filter on both sides of the mitered 50-ohm microstrip line. To realize the pseudo-highpass filtering characteristic over UWB frequency band(3.1 GHz to 10.6 GHz), a distributed highpass filter scheme is adopted. Three coupled resonators are utilized to obtain the band stop function at the desired frequency band. By meandering the coupled resonators, there is $29\;\%$ size reduction in footprint compared to the traditional band-stop filter using L-shaped resonators. The measured results show that the filter has a wide passband of $146.7\;\%$(2.1 GHz to 10.15 GHz) with low insertion loss and the stop band of $10.04\;\%$(5.2 GHz to 5.75 GHz) for 3-dB bandwidth. The measured group delay is less than 0.7 ns within the passband except the rejection band.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.11
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• pp.51-56
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• 2016

In this paper, we propose an Arduino-based plant monitoring system. The proposed system is based on the Arduino platform, using an environmental sensor and a pressure sensor for measuring temperature, humidity and illuminance in order to monitor the state of the environment and the facilities of the plant. Monitoring data are transmitted to a ZigBee coordinator connected to a server through a radio frequency transceiver. When using a pressure sensor and the environment sensor data stored on the host server, checking the pressure in the environment of the plant and equipment is intended to report any alarm status to the administrator. Using a grid line-based key distribution scheme, the authentication node dynamically generates a data key to protect the monitoring information. Applying a ZigBee wireless sensor network does not require additional wiring for the actual implementation of a plant monitoring system. Possible working-environment monitoring of an efficient plant can help analyze the cause of any failure by backtracking the working environment when a failure occurs. In addition, it is easy to expand or add a sensor function using the Arduino platform and an expansion board.