• Journal of the Institute of Convergence Signal Processing
• /
• v.24 no.1
• /
• pp.42-50
• /
• 2023

The development of technology has achieved digital innovation throughout the media industry, including production techniques and editing technologies, and has brought diversity in the form of consumer viewing through the OTT service and streaming era. The convergence of big data and deep learning networks automatically generated text in format such as news articles, novels, and scripts, but there were insufficient studies that reflected the author's intention and generated story with contextually smooth. In this paper, we describe the flow of pictures in the storyboard with image caption generation techniques, and the automatic generation of story-tailored scenarios through language models. Image caption using CNN and Attention Mechanism, we generate sentences describing pictures on the storyboard, and input the generated sentences into the artificial intelligence natural language processing model KoGPT-2 in order to automatically generate scenarios that meet the planning intention. Through this paper, the author's intention and story customized scenarios are created in large quantities to alleviate the pain of content creation, and artificial intelligence participates in the overall process of digital content production to activate media intelligence.

• Korean Journal of Remote Sensing
• /
• v.39 no.5_4
• /
• pp.1135-1144
• /
• 2023

Many agricultural reservoirs in South Korea, constructed before 1970, have become aging facilities. The majority of small-scale reservoirs lack measurement systems to ascertain basic specifications and water levels, classifying them as unmeasured reservoirs. Furthermore, continuous sedimentation within the reservoirs and industrial development-induced water quality deterioration lead to reduced water supply capacity and changes in reservoir morphology. This study utilized Light Detection And Ranging (LiDAR) sensors, which provide elevation information and allow for the characterization of surface features, to construct high-resolution Digital Surface Model (DSM) and Digital Elevation Model (DEM) data of reservoir facilities. Additionally, bathymetric measurements based on multibeam echosounders were conducted to propose an updated approach for determining reservoir capacity. Drone-based LiDAR was employed to generate DSM and DEM data with a spatial resolution of 50 cm, enabling the display of elevations of hydraulic structures, such as embankments, spillways, and intake channels. Furthermore, using drone-based hyperspectral imagery, Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) were calculated to detect water bodies and verify differences from existing reservoir boundaries. The constructed high-resolution DEM data were integrated with bathymetric measurements to create underwater contour maps, which were used to generate a Triangulated Irregular Network (TIN). The TIN was utilized to calculate the inundation area and volume of the reservoir, yielding results highly consistent with basic specifications. Considering areas that were not surveyed due to underwater vegetation, it is anticipated that this data will be valuable for future updates of reservoir capacity information.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.31 no.3
• /
• pp.229-238
• /
• 2013

Development of methodologies to generate the small scale map from the large scale map using map generalization has huge importance in management of the digital topographic map, such as producing and updating maps. In this study, the selection methodology of map generalization for the road network data in digital topographic map is investigated and evaluated. The existing maps with 1:5,000 and 1:25,000 scales are compared and the criteria for selection of the road network data, which are the number of objects and the relative importance of road network, are analyzed by using the T$\ddot{o}$pfer's radical law and Logit model. The selection model derived from the analysis result is applied to the test data, and the road network data of 1:18,000 and 1:72,000 scales from the digital topographic map of 1:5,000 scale are generated. The generalized results showed that the road objects with relatively high importance are selected appropriately according to the target scale levels after the qualitative and quantitative evaluations.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.19 no.3
• /
• pp.155-160
• /
• 2018

The unmanned aerial vehicle (UAV) is used in various fields, such as land surveying, facility management, and disaster monitoring and restoration because it has low operational costs, fast data acquisition, and can generate a digital surface model (DSM). Recently, the UAV has been applied to process management in construction projects. Construction projects are widely distributed not only in urban areas but also in mountainous areas and rural areas where people are rarely in traffic or in vehicles. Projects range from a few hundred meters to several kilometers long. In order to perform a reconnaissance survey, a surveying method using a global positioning system (GPS) or a total station has mainly been used. However, these methods have a disadvantage in that a lot of time is required for data acquisition. This study's purpose is to evaluate the usability of a UAV DSM for surveying a construction area. Data was acquired using the UAV and a three-dimensional (3D) laser scanner, and the DSM of the construction site was created through data processing. The UAV DSM showed accuracy to within 30 cm based on the 3D laser scanner data, and a process comparison between the two work methods was able to present the usability of the UAV DSM in the field of construction surveying. Future utilization of the UAV DSM is expected to greatly improve the efficiency of work in construction projects.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.21 no.2
• /
• pp.366-372
• /
• 2020

The Army's rocket missile is a one-shot weapon system, which is produced and used for only one mission, and requires high reliability. While reliability analysis with failure data can result in underestimation of the life distribution, reliability analysis with all the non-failure data can result in overestimation of the life distribution. Under or overestimation of the life distribution can lead to cost increase by early disposal or complete observation of all rocket missiles. In order to overcome this problem, the Army suggests the guideline of the number of samples from non-failure data for reliability analysis with failure data. However, the currently used sampling method can generate errors for predicting the failure rate. To solve this problem, this study proposes a new sampling procedure for predicting a future failure rate using non-failure data. The comparison test between the currently used sampling method and the proposed sampling method is conducted and the result shows that the proposed sampling method can predict the future failure rate more accurately.

• Journal of KIISE
• /
• v.45 no.2
• /
• pp.113-125
• /
• 2018

Recently, with the advent of the Big Data era, we have gained the capability of acquiring vast amounts of knowledge from various fields. The collected knowledge is expressed by well-formed formula and in particular, OWL, a standard language of ontology, is a typical form of well-formed formula. The symbolic reasoning is actively being studied using large amounts of ontology data for extracting intrinsic information. However, most studies of this reasoning support the restricted rule expression based on Description Logic and they have limited applicability to the real world. Moreover, knowledge management for inaccurate information is required, since knowledge inferred from the wrong information will also generate more incorrect information based on the dependencies between the inference rules. Therefore, this paper suggests that the SWAT, knowledge management system should be combined with the SWRL (Semantic Web Rule Language) reasoning based on ATMS (Assumption-based Truth Maintenance System). Moreover, this system was constructed by combining with SWRL reasoning and ATMS for managing large ontology data based on the distributed In-memory framework. Based on this, the ATMS monitoring system allows users to easily detect and correct wrong knowledge. We used the LUBM (Lehigh University Benchmark) dataset for evaluating the suggested method which is managing the knowledge through the retraction of the wrong SWRL inference data on large data.

• KIPS Transactions on Software and Data Engineering
• /
• v.1 no.3
• /
• pp.159-168
• /
• 2012

Relation in the new IT environment, such as the SNS, Cloud, Web3.0, has become an important factor. And these relations generate a transaction. However, existing relational database and graph database does not processe graph structure representing the relationships and transactions. This paper, we propose the technique that can be processed concurrently graph structures and transactions in a scalable complex network system. The proposed technique simultaneously save and navigate graph structures and transactions using the Topic Maps data model. Topic Maps is one of ontology language to implement the semantic web(Web 3.0). It has been used as the navigator of the information through the association of the information resources. In this paper, the architecture of the proposed technique was implemented and design using Cassandra - one of column type NoSQL. It is to ensure that can handle up to Big Data-level data using distributed processing. Finally, the experiments showed about the process of storage and query about typical RDBMS Oracle and the proposed technique to the same data source and the same questions. It can show that is expressed by the relationship without the 'join' enough alternative to the role of the RDBMS.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.17 no.4
• /
• pp.40-51
• /
• 2014

This study aims to investigate urban inundation considering building footprints based on dual-drainage scheme. For this purpose, LiDAR data is cultivated to generate two original data set in terms of DEM with $1{\times}1$ meter and building layer of the study drainage area in Seoul and then the building layer is overlapped as vector polygon with the mesh data with the same size as DEM. Then, terrain data for modeling were re-sampled to reduce resolution as $10{\times}10$ meters. As results, the simulated depth without considering building footprints has a tendency to underestimate the inundation depth compared to observed data analized by CCTV imagery. Otherwise, the simulation result considering building footprints revealed definitely higher fitness. The difference of inundation depth came from the variation of inundation volume which was relevant to inundation extent. If the building footprints are enlarged, the possible inundation depth is increased, which results in being inundation depth higher because hydrological conditions such as rainfall depth are conservational. Otherwise, according to comparison of inundation extents, there were no significant difference but the case of considering building footprint was revealed slightly higher fitness. Thus, it is concluded that the considering building footprint for inundation analysis of urban watershed should be required to improve simulation accuracy synthetically.

• Korean Journal of Remote Sensing
• /
• v.36 no.2_2
• /
• pp.325-335
• /
• 2020

Forest canopy height can be used for estimate of above-ground forest biomass (AGB) by means of the allometric equation. The remote locations and harsh conditions of mangrove forests limit the number of field inventory data stations needed for large-scale modeling of carbon and biomass dynamics. Although active and passive spaceborne sensors have proven successful in mapping mangroves globally, the sensors generally have coarse spatial resolution and overlook small-scale features. Here we generate a 12 m spatial resolution mangrove canopy height map from TanDEM-X data acquired over the world largest intact mangrove forest located in the Sundarbans. With single-pol. TanDEM-X data from 2011 to 2013, the proposed technique makes use of the fact that the double-bounce scattering that occurs between the water and mangrove trees yields water surface level elevation over mangrove forest areas, thus allowing us to estimate forest height with the assumption of an underlying flat topography. Our observations have led to a large-scale mangrove canopy height map over the entire Sundarbans region at a 12 m spatial resolution. Our canopy height estimates were validated with ground measurements acquired in 2015, a correlation coefficient of 0.83 and a RMSE of 0.84 m. With globally available TanDEM-X data, the technique described here will potentially provide accurate global maps of mangrove canopy height at 12 m spatial resolution and provide crucial information for understanding biomass and carbon dynamics in the mangrove ecosystems.

• Journal of the Korean Data and Information Science Society
• /
• v.24 no.3
• /
• pp.523-532
• /
• 2013

Generally, data mining is the process of analyzing big data from different perspectives and summarizing it into useful information. The most widely used data mining technique is to generate association rules, and it finds the relevance between two items in a huge database. This technique has been used to find the relationship between each set of items based on the interestingness measures such as support, confidence, lift, etc. Among many interestingness measures, confidence is the most frequently used, but it has the drawback that it can not determine the direction of the association. The attributably pure confidence and compared confidence are able to determine the direction of the association, but their ranges are not [-1, +1]. So we can not interpret the degree of association operationally by their values. This paper propose a compared and attributably pure confidence to compensate for this drawback, and then describe some properties for a proposed measure. The comparative studies with confidence, compared confidence, attributably pure confidence, and a proposed measure are shown by numerical example. The results show that the a compared and attributably pure confidence is better than any other confidences.