• Atmosphere
• /
• v.15 no.3
• /
• pp.179-186
• /
• 2005

Due to several difficulties, a number of Automatic Weather Systems (AWS) operated by Korea Meteorological Administration (KMA) are located on the rooftop so that the forming of standard observation environment to obtain the accuracy is needed. Therefore, the air temperature of AWSs on the synthetic lawn and the concrete of the rooftop is compared with the standard observation temperature. The hourly mean temperature is obtained by monthly and hourly mean value and the difference of temperature is calculated according to the location, the weather phenomenon, and cloud amount. The maximum and the minimum temperatures are compared by the conditions, such as cloud amount, the existence of precipitation or not. Consequently, the temperature on the synthetic lawn is higher than it on the concrete so that it is difficult to obtain same effect from ASOS, on the contrary the installation of AWS on the synthetic lawn seem to be inadequate due to heat or cold source of the building.

• Journal of Information Processing Systems
• /
• v.20 no.3
• /
• pp.375-390
• /
• 2024

Edge computing architecture has effectively alleviated the computing pressure on cloud platforms, reduced network bandwidth consumption, and improved the quality of service for user experience; however, it has also introduced new security issues. Existing anomaly detection methods in big data scenarios with cloud-edge computing collaboration face several challenges, such as sample imbalance, difficulty in dealing with complex network traffic attacks, and difficulty in effectively training large-scale data or overly complex deep-learning network models. A lightweight deep-learning model was proposed to address these challenges. First, normalization on the user side was used to preprocess the traffic data. On the edge side, a trained Wasserstein generative adversarial network (WGAN) was used to supplement the data samples, which effectively alleviates the imbalance issue of a few types of samples while occupying a small amount of edge-computing resources. Finally, a trained lightweight deep learning network model is deployed on the edge side, and the preprocessed and expanded local data are used to fine-tune the trained model. This ensures that the data of each edge node are more consistent with the local characteristics, effectively improving the system's detection ability. In the designed lightweight deep learning network model, two sets of convolutional pooling layers of convolutional neural networks (CNN) were used to extract spatial features. The bidirectional long short-term memory network (BiLSTM) was used to collect time sequence features, and the weight of traffic features was adjusted through the attention mechanism, improving the model's ability to identify abnormal traffic features. The proposed model was experimentally demonstrated using the NSL-KDD, UNSW-NB15, and CIC-ISD2018 datasets. The accuracies of the proposed model on the three datasets were as high as 0.974, 0.925, and 0.953, respectively, showing superior accuracy to other comparative models. The proposed lightweight deep learning network model has good application prospects for anomaly traffic detection in cloud-edge collaborative computing architectures.

• Journal of Satellite, Information and Communications
• /
• v.8 no.3
• /
• pp.32-40
• /
• 2013

The Boltwood Cloud Sensor is meteorological sensor that is used to estimate an amount of clouds in the sky. This sensor will be installed for OWL(Optical Wide-field patroL) telescope and observatory system of Korea Astronomy and Space Science. Before applying this sensor to an observatory system, we performed test observations at Chungbuk University Observatory at Jincheon, Chungbuk. During the test run, a significant correlation between air temperature difference and the number of visible stars recorded in the CCD frames has not been found. This preliminary result can be attributed to test environment of the observation and our lack of knowledge on calculation algorithm as well as the hardware system of the Boltwood Cloud Sensor.In this paper, we present the procedure and the result of the performance test employing the cloud sensor.

• Journal of Navigation and Port Research
• /
• v.35 no.5
• /
• pp.387-392
• /
• 2011

The value of Photovoltaic as an independent power supply is small, but the city's carbon emissions reduction and for the reduction of fossil fuel use distributed power is the power source to a very high value. However, according to the weather conditions for solar power generation by power fluctuations because of the size distribution to be effective, the big swing for effectively controlling real-time monitoring should be made. But that depends on solar power generation solar radiation forecasts from the National Weather Service does not need to predict it, and this study, the diffuse sky radiation in the history of the solar radiation in the darkness of the clouds, thick and weather forecasts can be inferred from the atmospheric transmittance to announce this value is calculated to represent each weather forecast solar radiation and solar radiation predicted by substituting the expression And the measured solar radiation and CRM (Cloud Cover Radiation Model) technique with an expression of Kasten and Czeplak irradiation when compared to the calculated predictions were verified.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.37 no.6
• /
• pp.571-577
• /
• 2019

Since the introduction of digital cameras in an aerial-photogrammetry field on 2006, the technological paradigm related to the photogrammetry has been shifting from the analog types to digital types. However, current construction standard cost for the aerial-photogrammetry and the digital mapping are being mixed with analog-based concepts and digital-based methods. In the current standard cost, the monthly weather table is closely related to the calculation of the number of flying days in a taking of aerial photograph. The current monthly weather table uses the results calculated from the observation data of total cloud amount from 1999 to 2007. In this study, the monthly weather table was calculated using the total cloud data during ten years from 2009 to 2018. As a result, the newly calculated number of clear days for 29 stations was analyzed as 44 days decreased by 6 days. The maximum number of clear days decreased in Jinju as 23 days, and the highest decreased clearing days was February.

• Journal of Environmental Science International
• /
• v.5 no.5
• /
• pp.619-633
• /
• 1996

Intraseasonal variability of the tropical convection over the Indian/western Pacific is studied using the Geostationary Meteorological Satellite high cloud amount. This study is directed to find the tropical-extratropical interaction in the frequency range of intraseasonal and interannual variabilities of the summer monsoon occured over the domain of 90E-171W and 495-50N. Especially, in order to investigate the intraseasonal interaction of last Asia summer monsoon associated with the tropical convections in the high cloud amounts, the spatial and time structure of the intraseasonal oscillation for the movement-and the evolution of the large-scale connections are studied. To describe the spatial and the time evolution, the extended empirical orthogonal function analysis is applied. The first mode may be considered to a normal structure, indicating that the strong convection band over 90E-120E is extended to sastward but this mode was detected as the intraseasonal variability during summer monsoon. It is found that the dominant intraseasonal mode of the tropical convection consists of the spatial changes over a broad period range centered around 40~50days.

• KIISE Transactions on Computing Practices
• /
• v.23 no.1
• /
• pp.45-56
• /
• 2017

In astronomy, the amount of data generated using wide-field optical telescope has increased exponentially. However, the fixed-size small-scale computing environment and the complexity of data analysis tools, results in difficulties to process the massive observation data collected. To resolve this problem, we propose a cloud-based automation system for the efficient processing of the enormous data gathered. The proposed system consists of a Workflow Execution Manager which manages various workflow templates and controls the execution of workflows instantiated from theses templates, and an Elastic Resource Manager that dynamically adds/deletes computing resources, according to the amount of data analysis requests. To show the effectiveness of our proposed system, we exhaustively explored a board spectrum of experiments, like elastic resources allocation, system load, etc. Finally, we describe the best practice case of DEEP-SOUTH scheduling system as an example application.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2017.04a
• /
• pp.673-676
• /
• 2017

Accompanying the Internet of Things (IoT) is a demand of advanced applications and services utilizing the potential of the IoT environment. Monitoring the environment for a provision of context-aware services to the human beings is one of the new trends in our future life. The IoTivity Cloud is one of the most notable open-source platform bringing an opportunity to collect, analyze, and interpret a huge amount of data available in the IoT environment. Based on the IoTivity Cloud, we aim to develop a novel platform for comprehensive monitoring of a future network, which facilitates on-demand data collection to enable the network behavior prediction and the quality of user experience maintenance. In consideration of performance evaluation of the monitoring platform, this paper presents results of a preliminary test on the data acquisition/supply process in the IoTivity Cloud.

• Journal of Broadcast Engineering
• /
• v.25 no.1
• /
• pp.25-35
• /
• 2020

Recently, the development of image processing technology, as well as hardware performance, has been continuing the research on 3D point processing technology that provides users with free viewing angle and stereoscopic effect in various fields. Point cloud technology, which is a type of representation of 3D point, has attracted attention in various fields because it can acquired/expressed point precisely. However, since Hundreds of thousands, millions of point are required to represent one 3D point cloud content, there is a disadvantage that a larger amount of storage space is required than a conventional 2D content. For this reason, the MPEG (Moving Picture Experts Group), an international standardization organization, is continuing to research how to efficiently compress, store, and transmit 3D point cloud content to users. In this paper, a V-PCC bitstream generated by a V-PCC (Video-based Point Cloud Compression) encoder proposed by the MPEG-I (Immersive) group is composed of an MPU (Media Processing Unit) defined by the MMT. In addition, by extending the signaling message defined in the MMT standard, a parameter for a segmented transmission method of the 3D point cloud content by area and quality parameters considering the characteristic of the 3D point cloud content, so that the quality parameters can be selectively determined according to the user's request. Finally, in this paper, we verify the result through design/implementation of the verification platform based on the proposed technology.

• Journal of Hydrogen and New Energy
• /
• v.30 no.6
• /
• pp.499-504
• /
• 2019

Due to the rapid spread and low minimum ignition energy of hydrogen, rupture is highly likely to cause fire, explosion and major accidents. The self-ignition of high-pressure hydrogen is highly likely to ignite immediately when it leaks from an open space, resulting in jet fire. Results of the diffusion and leakage simulation show that jet effect occurs from the leakage source to a certain distance. And at the end of location, the vapor cloud explosion can be occurred due to the formation of hydrogen vapor clouds by built-up. In the result, it is important that depending on the time of ignition, a jet fire or a vapor cloud explosion may occur. Therefore, it is necessary to take into account jet effect by location of leakage source and establish a damage minimizing plan for the possible jet fire or vapor cloud explosion. And it is required to any kind of measurements such as an interlock system to prevent hydrogen leakage or minimize the amount of leakage when detecting leakage of gas.