• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.9 no.3
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• pp.285-290
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• 2016

In rural area, our farmers confront decreasing benefits owing to imported crops and increased cost. Recently, the government encourage the 6th Industry that merges farming, rural resources, and information and communication technology. Therefor the government makes an investment in supplying 'smart greenhouse' in which a farmer monitor growing crops and environment information to control growing condition. The objective of this study is developing an Moving Monitor and Control System for crops in green House. This system includes a movable sensing unit, a controlling unit, and a server PC unit. The movable sensing unit contains high resolution IP camera, temperature and humidity sensor and WiFi repeater. It rolls on a rail hanging beneath the ceiling of a green house. The controlling unit contains embedded PC, PLC module, WiFi router, and BLDC motor to drive the movable sensing unit. And the server PC unit contains a integrated farm management software and home pages and databases in which the images of crops and environment informations. The movable sensing unit moves widely in a green house and gathers lots of information. The server saves these informations and provides them to customers with the direct commercing web page. This system will help farmers to control house environment and sales their crops in online market. Eventually It will be helpful for farmers to increase their benefits.

• Journal of the Korean Association of Geographic Information Studies
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• v.27 no.1
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• pp.115-127
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• 2024

This research aimed to construct models with various structures based on the Transformer module and to perform land cover classification, thereby examining the applicability of the Transformer module. For the classification of land cover, the Unet model, which has a CNN structure, was selected as the base model, and a total of four deep learning models were constructed by combining both the encoder and decoder parts with the Transformer module. During the training process of the deep learning models, the training was repeated 10 times under the same conditions to evaluate the generalization performance. The evaluation of the classification accuracy of the deep learning models showed that the Model D, which utilized the Transformer module in both the encoder and decoder structures, achieved the highest overall accuracy with an average of approximately 89.4% and a Kappa coefficient average of about 73.2%. In terms of training time, models based on CNN were the most efficient. however, the use of Transformer-based models resulted in an average improvement of 0.5% in classification accuracy based on the Kappa coefficient. It is considered necessary to refine the model by considering various variables such as adjusting hyperparameters and image patch sizes during the integration process with CNN models. A common issue identified in all models during the land cover classification process was the difficulty in detecting small-scale objects. To improve this misclassification phenomenon, it is deemed necessary to explore the use of high-resolution input data and integrate multidimensional data that includes terrain and texture information.

• Journal of the Microelectronics and Packaging Society
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• v.14 no.2 s.43
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• pp.59-63
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• 2007

This paper presented a low-cost thick film gas sensor module, which was based on simple PCB (Printed Circuit Board) process. The proposed sensor module included a $NO_2/H_2$ gas sensor, a relative humidity sensor, and a heating element. The $NO_2/H_2$ gas and relative humidity sensors were realized by screen-printing $SnO_2,\;BaTiO_3$ nano-powders on IDTS (Interdigital Transducer) of a PCB substrate, respectively. At first 1% $H_2$ gas flowed into the sensor chamber. After 4 min, air filled the chamber while $H_2$ gas flow stopped. This experiment was performed repeatedly. The Identical procedure was used for the $NO_2$ detection. The result for sensing $H_2$ gas showed the increase of voltage from 0.8V to 3.5V due to the conductance increase and its reaction response time by hydrogen flow was 65 sec. $NO_2$ sensing results showed 2.7 V voltage drop due to the conductance decrease and its response time was 3 sec through a voltage monitoring.

• Korean Journal of Remote Sensing
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• v.39 no.6_2
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• pp.1529-1539
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• 2023

The data from Geostationary Ocean Color Imager-II (GOCI-II), which observes the color of the sea to monitor marine environments, undergoes various correction processes in the ground station system, producing data from Raw to Level-2 (L2). Quality issues arising at each processing stage accumulate step by step, leading to an amplification of errors in the satellite data. To address this, improvements were made to the GOCI-II ground station system to measure potential optical quality and geolocation accuracy errors in the Level-1A/B (L1A/B) data. A newly established Radiometric and Geometric Performance Assessment Module (RGPAM) now measures five optical quality factors and four geolocation accuracy factors in near real-time. Testing with GOCI-II data has shown that RGPAM's functions, including data processing, display and download of measurement results, work well. The performance metrics obtained through RGPAM are expected to serve as foundational data for real-time radiometric correction model enhancements, assessment of L1 data quality consistency, and the development of reprocessing strategies to address identified issues related to the GOCI-II detector's sensitivity degradation.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.593-596
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• 2005

The MSC is a remote sensing instrument with very high performance that is to be installed on KOMPSAT2 satellite. The MSC consists of EOS (Electro-Optic Subsystem), PMU (Payload Management Unit) and PDTS (Payload Data Transmission Subsystem). PMU controls and monitors all the other payload units by sending commands and collecting telemetry. PMU is in charge of interfacing between payload system and satellite bus system. PMU gets commands from ground-station via OBC (On-Board Computer) that is a main controller of the satellite bus system and sends telemetry to the ground-station via OBC. There is a processor module, called SBC (Single Board Computer) in the PMU. The SBC is a main controller of the MSC system. The main roles of the SBC are payload mission management, command validation and execution, telemetry collection and monitoring, ancillary data handling, event reporting, power control of payload sub-units and communication with these units. Intel's 80486DX2 processor has been used for the SBC. Due to the fact that the SBC plays important roles for imaging mission execution and handles a lot of control data that is required for payload operation, it is required to make analysis of the CPU load when it is in maximum operation mode. In this paper, the analysis and measurement results of the SBC throughput in the maximum operation mode.

• Korean Journal of Remote Sensing
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• v.20 no.6
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• pp.361-368
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• 2004

Hydraulic models has a module to calculate SCS-CN values in order to estimate amount of water flow, which can be done with remotely sensed data and GIS data. The choice of the ancillary data tends to determine the range of SCS-CN values. We compare the results of SCS-CN value with satellite data of different spatial resolution and with soil maps of different scale. Mokhyun river basin was chosen,partly because of availbility of water quality and quantity data, partly because of rapid changes in land use and land cover since last ten years. The average CN values were calculated with spatial resolutions of 2.5 meter and 30 meter, We could not find any different result due to spatial resolution of CN resolution but due to both soil maps and to land cover maps. Further studies should be done for more than two kinds of satellite data.

• Korean Journal of Remote Sensing
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• v.21 no.4
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• pp.317-327
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• 2005

The flood is the most general and frequently occurs among natural disasters. Generally flood by the rainfall which extends superexcellently for the occurrence but flash flood from severe rain storm gets up an absurd drowsiness at grade hour. This paper aims to 1 hour real-time flash flood and predict possibility at the area where is the possible flood will occur from the rainfall hour mountain after acquiring data in GIS(Geographic Information System) base by GcIUH(Geomorphoclimatic Instantaneous Unit Hydrograph). The flash flood occurrence is set up at 0.5m, 0.7m and 1.0m in standard depth. And this study suggests standard flood alarm which designed by probable flood according to duration time. The research result shows real-time flash flood evaluation has the suitable standard in the basin when comparing with the existing official warning announcement system considering topographical information.

• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.1061-1070
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• 2021

In this manuscript, we tried to improve the performance of the FC-DenseNet by applying an attention gate for the classification of cropping areas. The attention gate module could facilitate the learning of a deep learning model and improve the performance of the model by injecting of spatial/spectral weights to each feature map. Crop classification was performed in the onion and garlic regions using a proposed deep learning model in which an attention gate was added to the skip connection part of FC-DenseNet. Training data was produced using various PlanetScope satellite imagery, and preprocessing was applied to minimize the problem of imbalanced training dataset. As a result of the crop classification, it was verified that the proposed deep learning model can more effectively classify the onion and garlic regions than existing FC-DenseNet algorithm.

• Korean Journal of Remote Sensing
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• v.40 no.1
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• pp.115-122
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• 2024

Multi-object tracking (MOT) is a vital component in understanding the surrounding environments. Previous research has demonstrated that MOT can successfully detect and track surrounding objects. Nonetheless, inaccurate classification of the tracking objects remains a challenge that needs to be solved. When an object approaching from a distance is recognized, not only detection and tracking but also classification to determine the level of risk must be performed. However, considering the erroneous classification results obtained from the detection as the track class can lead to performance degradation problems. In this paper, we discuss the limitations of classification in tracking under the classification uncertainty of the detector. To address this problem, a class update module is proposed, which leverages the class uncertainty estimation of the detector to mitigate the classification error of the tracker. We evaluated our approach on the VisDrone-MOT2021 dataset,which includes multi-class and uncertain far-distance object tracking. We show that our method has low certainty at a distant object, and quickly classifies the class as the object approaches and the level of certainty increases.In this manner, our method outperforms previous approaches across different detectors. In particular, the You Only Look Once (YOLO)v8 detector shows a notable enhancement of 4.33 multi-object tracking accuracy (MOTA) in comparison to the previous state-of-the-art method. This intuitive insight improves MOT to track approaching objects from a distance and quickly classify them.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.493-496
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• 2005

The PMU (Payload Management Unit) in MSC (Multi-Spectral Camera) is the main subsystem for the management, control and power supply of the MSC payload operation. The PMU shall handle the communication with the BUS (Spacecraft) OBC (On Board Computer) for the command, the telemetry and the communications with the various MSC units. The PMU will perform that distributes power to the various MSC units, collects the telemetry reports from MSC units, performs thermal control of the EOS (Electro-Optical Subsystem), performs the NUC (Non-Uniformity Correction) function of the raw imagery data, and rearranges the pixel data and output it to the DCSU (Data Compression and Storage Unit). The BUS provides high voltage to the MSC. The PMU is connected to primary and redundant BUS power and distributes the high unregulated primary voltages for all MSC sub-units. The PSM (Power Supply Module) is an assembly in the PMU implements the interface between several channels on the input. The bus switches are used to prevent a single point system failure. Such a failure could need the PSS (Power Supply System) requirement to combine the two PSM boards' bus outputs in a wired-OR configuration. In such a configuration if one of the boards' output gets shorted to ground then the entire bus could fail thereby causing the entire MSC to fail. To prevent such a short from pulling down the system, the switch could be opened and disconnect the short from the bus. This switch operation is controlled by the BUS.