• Korean journal of applied entomology
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• v.60 no.1
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• pp.135-143
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• 2021

Aerospace and geospatial technologies have become more accessible by researchers and agricultural practitioners, and these technologies can play a pivotal role in transforming current pest management practices in agriculture and forestry. During the past 20 years, technologies including satellites, manned and unmanned aircraft, spectral sensors, information systems, and autonomous field equipment, have been used to detect pests and apply control measures site-specifically. Despite the availability of aerospace and geospatial technologies, along with big-data-driven artificial intelligence, applications of such technologies to apple IPM have not been realized yet. Using a case study conducted at the Korea Apple Research Institute, this article discusses the advances and limitations of current aerospace and geospatial technologies that can be used for improving apple IPM.

• Journal of the Korean Association of Geographic Information Studies
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• v.23 no.4
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• pp.234-252
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• 2020

This paper is to verify the effectiveness of 'Hybrid Disaster Management Strategy' that integrates 'RTM(Real-time Monitoring) based On-line' and 'UAV based Off-line' system. For landslide prone area where sensors were installed, the conventional way of risk management so far has entirely relied on RTM data collected from the field through the instrumentation devices. But it's not enough due to the limitation of'Pin-point sensor'which tend to provide with only the localized information where sensors have stayed fixed. It lacks, therefore, the whole picture to be grasped. In this paper, utilizing 'Digital Photogrammetry Software Pix4D', the possibility of inference for the deformation of ungauged area has been reviewed. For this purpose, actual measurement data from RTM were compared with the estimated value from 3D point cloud outcome by UAV, and the consequent results has shown very accurate in terms of RMSE.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.6
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• pp.583-590
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• 2021

This paper is a study on the digitization of an analog 3D pen. The term digital implies features such as homeostasis, transformability, combinability, reproducibility, and convenience of storage. One device that produces a combination of these digital characteristics is a 3D printer, but its industrial use is limited due to low productivity and limitations with materials and physical characteristics. In particular, improvements are required to use 3D printers, such as better user accessibility owing to expertise and skills in modeling software and printers. Complementing this fact is the 3D pen, which is excellent in portability and ease of use, but has a limitation in that it cannot be digitized. Therefore, in order to secure a digitalization capability and ease of use, and to secure the safety of printing materials that pose controversial hazards during the printing process, research problems and alternatives have been derived by combining food, and digitization was demonstrated with a newly developed 3D pen. In order to digitize the 3D pen, a sensor in a structured device detects the motion of an analog 3D pen, and this motion is converted into 3D data (X-Y-Z coordinate values) through a spatial analysis algorithm. To prove this method, the similarity was confirmed by visualization using MeshLab version 1.3.4. It is expected that this food pen can be used in youth education and senior healthcare programs in the future.

• The Journal of Korean Institute of Next Generation Computing
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• v.14 no.5
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• pp.31-39
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• 2018

Recently, there has been active studies to provide a user-friendly interface in a virtual reality environment by recognizing user hand gestures based on deep learning. However, most studies use separate sensors to obtain hand information or go through pre-process for efficient learning. It also fails to take into account changes in the external environment, such as changes in lighting or some of its hands being obscured. This paper proposes a hand gesture recognition method based on deep learning that is strong in external environments without the need for pre-process of RGB images obtained from general webcam. In this paper we improve the VGGNet and the GoogLeNet structures and compared the performance of each structure. The VGGNet and the GoogLeNet structures presented in this paper showed a recognition rate of 93.88% and 93.75%, respectively, based on data containing dim, partially obscured, or partially out-of-sight hand images. In terms of memory and speed, the GoogLeNet used about 3 times less memory than the VGGNet, and its processing speed was 10 times better. The results of this paper can be processed in real-time and used as a hand gesture interface in various areas such as games, education, and medical services in a virtual reality environment.

• The Journal of Korean Institute of Next Generation Computing
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• v.14 no.6
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• pp.57-65
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• 2018

Conventional RF signal-based indoor localization techniques such as BLE or Wi-Fi based fingerprinting method show considerable localization errors even in small-scale indoor environments due to unstable received signal strength(RSS) of RF signals. Therefore, it is difficult to apply the existing RF-based fingerprinting techniques to large-scale indoor environments such as airports and department stores. In this paper, instead of RF signal we use the geomagnetic sensor signal for indoor localization, whose signal strength is more stable than RF RSS. Although similar geomagnetic field values exist in indoor space, an object movement would experience a unique sequence of the geomagnetic field signals as the movement continues. We use a deep neural network model called the recurrent neural network (RNN), which is effective in recognizing time-varying sequences of sensor data, to track the user's location and movement path. To evaluate the performance of the proposed geomagnetic field based indoor positioning system (IPS), we constructed a magnetic field map for a campus testbed of about $94m{\times}26$ dimension and trained RNN using various potential movement paths and their location data extracted from the magnetic field map. By adjusting various hyperparameters, we could achieve an average localization error of 1.20 meters in the testbed.

• Journal of the Korea Society for Simulation
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• v.30 no.2
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• pp.11-22
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• 2021

Sonobuoy is widely used as a very important sensor in combat management system using P-3 patrol aircraft due to its advantages of rapid searching into wide exploration range. It is necessary to verify the performance of developed sonobuoy system using various maritime test data in order to be successfully applied in combat management system. But it is difficult to acquire various real maritime data because it needs much time and effort. Therefore we have developed in this paper a synthetic signal generator and a simulator that they can verify the performance of sonobuoy system and evaluate its operational effectiveness without conducting maritime test. We have synthesized target signals based on the characteristics of underwater sound sources, and then developed the synthesized signal generator which consider to sound propagation etc. like as underwater environment. And in the simulator development we use a HMI technique to enhance the convenience of operator, and design to verify the performance of sonobuoy system. The developed signal generator and simulator can be used as useful tools to evaluate the operational effectiveness such as optimal deployment of sonobuoy in combat management system using P-3 patrol aircraft.

• Journal of Advanced Navigation Technology
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• v.25 no.1
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• pp.68-77
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• 2021

In this paper, a GPS/MEMS IMU integrated navigation receiver module capable of operating in a high dynamic environment is designed and fabricated, and the results is confirmed. The designed module is composed of RF receiver unit, inertial measurement unit, signal processing unit, correlator, and navigation S/W. The RF receiver performs the functions of low noise amplification, frequency conversion, filtering, and automatic gain control. The inertial measurement unit collects measurement data from a MEMS class IMU applied with a 3-axis gyroscope, accelerometer, and geomagnetic sensor. In addition, it provides an interface to transmit to the navigation S/W. The signal processing unit and the correlator is implemented with FPGA logic to perform filtering and corrrelation value calculation. Navigation S/W is implemented using the internal CPU of the FPGA. The size of the manufactured module is 95.0×85.0×.12.5mm, the weight is 110g, and the navigation accuracy performance within the specification is confirmed in an environment of 1200m/s and acceleration of 10g.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.3
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• pp.537-542
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• 2022

There is a limit to the current indoor air quality (IAQ) monitoring method using fixed sensors and devices. A mobile robot for IAQ monitoring was developed by mounting IAQ monitoring sensors on a small multi-legged robot to minimize vibration and protect the sensors from vibration while robot moves. The developed mobile robot used a simple gait mechanism to enable the robot to move forward, backward, and turns only with the combination of forward and reverse rotation of the two DC motors. Due to the simple gait mechanism, not only IAQ data measurements but also gait motion control were processed using a single Arduino board. Because the mobile robot has small number of electronic components and low power consumption, a relatively low-capacity battery was mounted on the robot to reduce the weight of the battery. The weight of mobile robot is 1.4kg including links, various IAQ sensors, motors, and battery. The gait and turning speed of the mobile robot was measured at 3.75 cm/sec and 14.13 rad/sec. The maximum height where the robot leg could reach was 33 mm, but the mobile robot was able to overcome the bumps up to 24 mm.

• Journal of Navigation and Port Research
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• v.46 no.3
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• pp.280-288
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• 2022

This study examined the diagnostics of abnormalities and faults of equipment, whose rotational speed changes even during regular operation. The purpose of this study was to suggest a procedure that can properly apply machine learning to the time series data, comprising non-stationary characteristics as the rotational speed changes. Anomaly and fault diagnosis was performed using machine learning: k-Nearest Neighbor (k-NN), Support Vector Machine (SVM), and Random Forest. To compare the diagnostic accuracy, an autoencoder was used for anomaly detection and a convolution based Conv1D was additionally used for fault diagnosis. Feature vectors comprising statistical and frequency attributes were extracted, and normalization & dimensional reduction were applied to the extracted feature vectors. Changes in the diagnostic accuracy of machine learning according to feature selection, normalization, and dimensional reduction are explained. The hyperparameter optimization process and the layered structure are also described for each algorithm. Finally, results show that machine learning can accurately diagnose the failure of a variable-rotation machine under the appropriate feature treatment, although the convolution algorithms have been widely applied to the considered problem.

• Korean Journal of Remote Sensing
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• v.38 no.6_3
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• pp.1749-1760
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• 2022

In modern society, human and social damages caused by natural disasters and frequent disaster accidents have been increased year by year. Prompt access to dangerous disaster sites that are inaccessible or inaccessible using state-of-the-art Earth observation equipment such as satellites, drones, and survey robots, and timely collection and analysis of meaningful disaster information. It can play an important role in protecting people's property and life throughout the entire disaster management cycle, such as responding to disaster sites and establishing mid-to long-term recovery plans. This special issue introduces the National Disaster Management Research Institute (NDMI)'s disaster management technology that utilizes various Earth observation platforms, such as mobile survey vehicles equipped with close-range disaster site survey sensors, drones, and survey robots, as well as satellite technology, which is a tool of remote earth observation. Major research achievements include detection of damage from water disasters using Google Earth Engine, mid- and long-term time series observation, detection of reservoir water bodies using Sentinel-1 Synthetic Aperture Radar (SAR) images and artificial intelligence, analysis of resident movement patterns in case of forest fire disasters, and data analysis of disaster safety research. Efficient integrated management and utilization plan research results are summarized. In addition, research results on scientific investigation activities on the causes of disasters using drones and survey robots during the investigation of inaccessible and dangerous disaster sites were described.