• Journal of the Korea Society of Computer and Information
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• v.14 no.2
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• pp.157-168
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• 2009

In this paper, a photovoltaic system is designed with a step up chopper and single phase PWM(Pulse Width Modulation) voltage source inverter. Where proposed Synchronous signal and control signal was processed by one-chip microprocessor for stable modulation. The step up chopper operates in continuous mode by adjusting the duty ratio so that the photovoltaic system tracks the maximum power point of solar cell without any influence on the variation of insolation and temperature because solar cell has typical voltage and current dropping character. The single phase PWM voltage source the inverter using inverter consists of complex type of electric power converter to compensate for the defect, that is, solar cell cannot be developed continuously by connecting with the source of electric power for ordinary use. It can cause the effect of saving electric power. from 10 to 20[%]. The single phase PWM voltage source inverter operates in situation that its output voltage is in same phase with the utility voltage. In order to enhance the efficiency of photovoltaic cells, photovoltaic positioning system using sensor and microprocessor was design so that the fixed type of photovoltaic cells and photovoltaic positioning system were compared. In result, photovoltaic positioning system can improved 5% than fixed type of photovoltaic cells. In addition, I connected extra power to the system through operating the system voltage and inverter power in a synchronized way by extracting the system voltage so that the phase of the system and the phase of single-phase inverter of PWM voltage type can be synchronized. And, It controlled in order to provide stable pier to the load and the system through maintaining high lurer factor and low output power of harmonics.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.6
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• pp.1009-1014
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• 2023

Comprehensive observations of the Euler method and the Lagrangian method were performed in order to obtain high-resolution observation data in space and time for the complex environment of new city. The two radiosondes, which measure meteorological parameters using Lagrangian methods, produced air pressure, wind speed and wind direction. They were generally consistent with each other even if the observation points or times were different. The temperature measured by the sensor exposed to the air during the day was relatively high as the altitude increased due to the influence of solar radiation. The temporal difference in wind direction and speed was found in the comparison of Euler's wind profiler data with radiosonde data. When the wind field is horizontally in homogeneous, this result implies the need to consider the advection component to compare the data of the two observation methods. In this study, a method of using observation data at different times for each altitude section depending on the observation period of the Euler method is proposed to effectively compare the data of the two observation methods.

• Korean Journal of Remote Sensing
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• v.40 no.4
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• pp.319-341
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• 2024

The utilization of multispectral imaging systems (MIS) in remote sensing has become crucial for large-scale agricultural operations, particularly for diagnosing plant health, monitoring crop growth, and estimating plant phenotypic traits through vegetation indices (VIs). However, environmental factors can significantly affect the accuracy of multispectral reflectance data, leading to potential errors in VIs and crop status assessments. This paper reviewed the complex interactions between environmental conditions and multispectral sensors emphasizing the importance of accounting for these factors to enhance the reliability of reflectance data in agricultural applications.An overview of the fundamentals of multispectral sensors and the operational principles behind vegetation index (VI) computation was reviewed. The review highlights the impact of environmental conditions, particularly solar zenith angle (SZA), on reflectance data quality. Higher SZA values increase cloud optical thickness and droplet concentration by 40-70%, affecting reflectance in the red (-0.01 to 0.02) and near-infrared (NIR) bands (-0.03 to 0.06), crucial for VI accuracy. An SZA of 45° is optimal for data collection, while atmospheric conditions, such as water vapor and aerosols, greatly influence reflectance data, affecting forest biomass estimates and agricultural assessments. During the COVID-19 lockdown,reduced atmospheric interference improved the accuracy of satellite image reflectance consistency. The NIR/Red edge ratio and water index emerged as the most stable indices, providing consistent measurements across different lighting conditions. Additionally, a simulated environment demonstrated that MIS surface reflectance can vary 10-20% with changes in aerosol optical thickness, 15-30% with water vapor levels, and up to 25% in NIR reflectance due to high wind speeds. Seasonal factors like temperature and humidity can cause up to a 15% change, highlighting the complexity of environmental impacts on remote sensing data. This review indicated the importance of precisely managing environmental factors to maintain the integrity of VIs calculations. Explaining the relationship between environmental variables and multispectral sensors offers valuable insights for optimizing the accuracy and reliability of remote sensing data in various agricultural applications.

• Journal of the Korean Association of Geographic Information Studies
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• v.21 no.1
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• pp.35-45
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• 2018

Image data collected by an airborne hyperspectral camera system have a great usability in coastal line mapping, detection of facilities composed of specific materials, detailed land use analysis, change monitoring and so forh in a complex coastal area because the system provides almost complete spectral and spatial information for each image pixel of tens to hundreds of spectral bands. A few approaches after classifying by a few approaches based on SAM(Spectral Angle Mapper) supervised classification were applied for extracting optimal land cover information from hyperspectral images acquired by CASI-1500 airborne hyperspectral camera on the object of a coastal area which includes both land and sea water areas. We applied three different approaches, that is to say firstly the classification approach of combined land and sea areas, secondly the reclassification approach after decompostion of land and sea areas from classification result of combined land and sea areas, and thirdly the land area-only classification approach using atmospheric correction images and compared classification results and accuracies. Land cover classification was conducted respectively by selecting not only four band images with the same wavelength range as IKONOS, QuickBird, KOMPSAT and GeoEye satelllite images but also eight band images with the same wavelength range as WorldView-2 from 48 band hyperspectral images and then compared with the classification result conducted with all of 48 band images. As a result, the reclassification approach after decompostion of land and sea areas from classification result of combined land and sea areas is more effective than classification approach of combined land and sea areas. It is showed the bigger the number of bands, the higher accuracy and reliability in the reclassification approach referred above. The results of higher spectral resolution showed asphalt or concrete roads was able to be classified more accurately.

• Journal of Korean Society of Forest Science
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• v.108 no.1
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• pp.40-49
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• 2019

Coarse woody debris (CWD), which is a component of the forest ecosystem, plays a major role in forest energy flow and nutrient cycling. In particular, CWD isolates carbon for a long time and is important in terms of slowing the rate of carbon released from the forest to the atmosphere. Therefore, this study measured the physiochemical characteristics and respiration rate ($R_{CWD}$) of CWD for Larix kaempferi and Pinus rigida in temperate forests in central Korea. In summer 2018, CWD samples from decay class (DC) I to IV were collected in the 14 forest stands. $R_{CWD}$ and physiochemical characteristics were measured using a closed chamber with a portable carbon dioxide sensor in the laboratory. In both species, as CWD decomposition progressed, the density ($D_{CWD}$) of the CWD decreased while the water content ($WC_{CWD}$) increased. Furthermore, the carbon concentrations did not significantly differ by DC, whereas the nitrogen concentration significantly increased and the C/N ratio decreased. The respiration rate of L. kaempferi CWD increased significantly up to DC IV, but for P. rigida it increased to DC II and then unchanged for DC II-IV. Accordingly, except for carbon concentration, all the measured characteristics showed a significant correlation with $R_{CWD}$. Multiple linear regression showed that $WC_{CWD}$ was the most influential factor on $R_{CWD}$. $WC_{CWD}$ affects $R_{CWD}$ by increasing microbial activity and is closely related to complex environmental factors such as temperature and light conditions. Therefore, it is necessary to study their correlation and estimate the time-series pattern of CWD moisture.

• Journal of Internet Computing and Services
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• v.23 no.5
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• pp.145-154
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• 2022

Automatic Target Recognition (ATR) technology is emerging as a core technology of Future Combat Systems (FCS). Conventional ATR is performed based on IMINT (image information) collected from the SAR sensor, and various image-based deep learning models are used. However, with the development of IT and sensing technology, even though data/information related to ATR is expanding to HUMINT (human information) and SIGINT (signal information), ATR still contains image oriented IMINT data only is being used. In complex and diversified battlefield situations, it is difficult to guarantee high-level ATR accuracy and generalization performance with image data alone. Therefore, we propose a knowledge graph-based ATR method that can utilize image and text data simultaneously in this paper. The main idea of the knowledge graph and deep model-based ATR method is to convert the ATR image and text into graphs according to the characteristics of each data, align it to the knowledge graph, and connect the heterogeneous ATR data through the knowledge graph. In order to convert the ATR image into a graph, an object-tag graph consisting of object tags as nodes is generated from the image by using the pre-trained image object recognition model and the vocabulary of the knowledge graph. On the other hand, the ATR text uses the pre-trained language model, TF-IDF, co-occurrence word graph, and the vocabulary of knowledge graph to generate a word graph composed of nodes with key vocabulary for the ATR. The generated two types of graphs are connected to the knowledge graph using the entity alignment model for improvement of the ATR performance from images and texts. To prove the superiority of the proposed method, 227 documents from web documents and 61,714 RDF triples from dbpedia were collected, and comparison experiments were performed on precision, recall, and f1-score in a perspective of the entity alignment..

• Korean Journal of Remote Sensing
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• v.39 no.5_2
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• pp.811-825
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• 2023

In accordance with the government's stance of actively promoting intelligent administrative service policies through data utilization, in the disaster and safety management field, it also is proceeding with disaster and safety management policies utilizing data and constructing systems for responding efficiently to new and complex disasters and establishing scientific and systematic safety policies. However, it is difficult to quickly and accurately grasp the on-site situation in the event of a disaster, and there are still limitations in providing information necessary for situation judgment and response only by displaying vast data. This paper focuses on deriving specific needs to make disaster situation management work more intelligent and efficient by utilizing intelligent information technology. Through individual interviews with workers at the Central Disaster and Safety Status Control Center, we investigated the scope of disaster situation management work and the main functions and usability of the geographic information system (GIS)-based integrated situation management system by practitioners in this process. In addition, the data built in the system was reclassified according to purpose and characteristics to check the status of data in the GIS-based integrated situation management system. To derive needed to make disaster situation management more intelligent and efficient by utilizing intelligent information technology, 3 strategies were established to quickly and accurately identify on-site situations, make data-based situation judgments, and support efficient situation management tasks, and implementation tasks were defined and task priorities were determined based on the importance of implementation tasks through analytic hierarchy process (AHP) analysis. As a result, 24 implementation tasks were derived, and to make situation management efficient, it is analyzed that the use of intelligent information technology is necessary for collecting, analyzing, and managing video and sensor data and tasks that can take a lot of time of be prone to errors when performed by humans, that is, collecting situation-related data and reporting tasks. We have a conclusion that among situation management intelligence strategies, we can perform to develop technologies for strategies being high important score, that is, quickly and accurately identifying on-site situations and efficient situation management work support.