• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.2
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• pp.63-70
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• 2022

Although 2D Object detection has been largely improved in the past years with the advance of deep learning methods and the use of large labeled image datasets, 3D object detection from 2D imagery is a challenging problem in a variety of applications such as robotics, due to the lack of data and diversity of appearances and shapes of objects within a category. Google has just announced the launch of Objectron that has a novel data pipeline using mobile augmented reality session data. However, it also is corresponding to 2D-driven 3D object detection technique. This study explores more mature 2D object detection method, and applies its 2D projection to Objectron 3D lifting system. Most object detection methods use bounding boxes to encode and represent the object shape and location. In this work, we explore a stochastic representation of object regions using Gaussian distributions. We also present a similarity measure for the Gaussian distributions based on the Hellinger Distance, which can be viewed as a stochastic Intersection-over-Union. Our experimental results show that the proposed Gaussian representations are closer to annotated segmentation masks in available datasets. Thus, less accuracy problem that is one of several limitations of Objectron can be relaxed.

• Korean Journal of Remote Sensing
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• v.38 no.2
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• pp.153-166
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• 2022

Waterstorage is one of the factorsthat most directly represent the amount of available water resources. Since the effects of drought can be more intuitively expressed, it is also used in variousstudies for drought evaluation. In a recent study, hydrological drought was evaluated through information on observing reservoirs with optical images. The short observation cycle and diversity of optical satellites provide a lot of data. However, there are some limitations because it is vulnerable to the influence of weather or the atmospheric environment. Therefore, thisstudy attempted to conduct a study on estimating the drought index using Synthetic Aperture Radar (SAR) image with relatively little influence from the observation environment. We produced the waterbody of Baekgok and Chopyeong reservoirs using SAR images of Sentinel-1 satellites and calculated the Reservoir Area Drought Index (RADI), a hydrological drought index. In order to validate the applicability of RADI to drought monitoring, it was compared with Reservoir Storage Drought Index (RSDI) based on measured storage. The two indices showed a very high correlation with the correlation coefficient, r=0.87, Area Under curve, AUC=0.97. These results show the possibility of regional-scale hydrological drought monitoring of SAR-based RADI. As the number of available SAR images increases in the future, it is expected that the utilization of drought monitoring will also increase.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1527-1539
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• 2022

As coastal erosion of the east coast is accelerating, the need for scientific and quantitative coastal erosion monitoring technology for a wide area increases. The traditional method for observing changes in the coast was precision monitoring based on field surveys, but it can only be applied to a small area. The airborne bathymetric Light Detection And Ranging (LiDAR) system is a technology that enables economical surveying of coastal and seabed topography in a wide area. In particular, it has the advantage of constructing topographical data for the intertidal zone, which is a major area of interest for coastal erosion monitoring. In this study, time series analysis of coastal seabed topography acquired in Aug, 2021 and Mar. 2022 on the littoral cell GW36 in Gangwon was performed using the Seahawk Airborne Bathymetric LiDAR (ABL) system. We quantitatively monitored the topographical changes by measuring the baseline length, shoreline and Digital Terrain Model (DTM) changes. Through this, the effectiveness of the ABL surveying technique was confirmed in coastal erosion monitoring.

• Journal of Korean Tunnelling and Underground Space Association
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• v.23 no.6
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• pp.559-575
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• 2021

Due to the increase in ground excavation work, the possibility of ground subsidence accidents is increasing. And it is very difficult to prevent these risk fundamentally through institutional reinforcement such as the special law for underground safety management. As for the various cases of urban ground excavation practice, the ground subsidence behavior characteristics which is predicted using various information before excavation showed a considerable difference that could not be ignored compared to the results real construction data. Changes in site conditions such as seasonal differences in design and construction period, changes in construction methods depending on the site conditions and long-term construction suspension due to various reasons could be considered as the main causes. As the countermeasures, the safety management system through various construction information is introduced, but there is still no suitable system which can predict the effect of excavation and risk assessment. In this study, a web-based system was developed in order to predict the degree of impact on the ground subsidence and surrounding structures in advance before ground excavation and evaluate the risk in the design and construction of urban ground excavation projects. A system was built using time series analysis technique that can predict the current and future behavior characteristics such as ground water level and settlement based on past field construction records with field monitoring data. It was presented as a geotechnical data visualization (GDV) technology for risk reduction and disaster management based on web-based system, Using this newly developed web-based assessment system, it is possible to predict ground excavation impact prediction and risk assessment.

• Journal of the Institute of Convergence Signal Processing
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• v.22 no.4
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• pp.179-184
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• 2021

Along with the development of the fourth industry, the public sector has increasingly paid more attention to search using drones and real-time monitoring, for various goals. The drones are used and researched to complete a variety of searching and monitoring missions, including search for missing persons, security, coastal patrol and monitoring, speed enforcement, highway and urban traffic monitoring, fire and wildfire monitoring, monitoring of illegal fishing in reservoirs and protest rally monitoring. Police stations, fire departments and military authorities, however, concentrate on the hardware part, so there are little research on efficient communication systems for the real-time monitoring of data collected from high-performance resolution and infrared thermal imagining cameras, and analysis programs suitable for special missions. In order to increase the efficiency of drones with the searching mission, this paper, therefore, attempts to propose an image analysis technique to increase the precision of search by producing image data suitable for searching missions, based on images obtained from drones and provide the foundation for improving relevant policies and establishing proper platforms, based on actual field cases and experiments.

• Korean Journal of Remote Sensing
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• v.38 no.5_1
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• pp.511-521
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• 2022

Cloud detection and removal from satellite images is an essential process for topographic observation and analysis. Threshold-based cloud detection techniques show stable performance because they detect using the physical characteristics of clouds, but they have the disadvantage of requiring all channels' images and long computational time. Cloud detection techniques using deep learning, which have been studied recently, show short computational time and excellent performance even using only four or less channel (RGB, NIR) images. In this paper, we confirm the performance dependence of the deep learning network according to the heterogeneous learning dataset with different resolutions. The DeepLabV3+ network was improved so that channel features of cloud detection were extracted and learned with two published heterogeneous datasets and mixed data respectively. As a result of the experiment, clouds' Jaccard index was low in a network that learned with different kind of images from test images. However, clouds' Jaccard index was high in a network learned with mixed data that added some of the same kind of test data. Clouds are not structured in a shape, so reflecting channel features in learning is more effective in cloud detection than spatial features. It is necessary to learn channel features of each satellite sensors for cloud detection. Therefore, cloud detection of heterogeneous sensors with different resolutions is very dependent on the learning dataset.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.42 no.5
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• pp.639-650
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• 2022

In areas where large-scale sediment transport occurs, it is important to apply appropriate countermeasure method because the phenomenon tends to accelerate by time duration. Among the various countermeasure methods applied so far, beach nourishment needs to be reviewed as an erosion prevention measure because the erosion pattern is mitigated and environmentally friendly depending on the particle size. In the case of beach nourishment. a detailed review is required to determine the size, range, etc., of an appropriate particle diameter. In this study, we investigated the characteristics of the related topographic change using the change in the particle size of nourishment materials, the application of partial area, and the condition under the coexistence of waves and wind as variables because those factors are hard to be analyzed and interpreted within results and limitation of that the existing numerical models are not able to calculate and result out so that it is required that phenomenon or efforts are reviewed at the same time through physical model experiments, field monitoring and etc. So we attempt to reproduce the tendency of beach erosion and deposition and predict possible phenomena in the future using machine learning techniques for phenomena that it is not able to be interpreted by numerical models. we used the hydraulic experiment results for the training data, and the accuracy of the prediction results according to the change in the training method was simultaneously analyzed. As a result of the study it was found that topographic changes using machine learning tended to be similar to those of previous studies in short-term predictions, but we also found differences in the formation of scour and sandbars.

• Journal of Internet of Things and Convergence
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• v.8 no.5
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• pp.119-125
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• 2022

With the recent increase in dementia patients due to aging, measures to prevent their wandering behavior and disappearance are urgently needed. To solve this problem, various authentication methods and location detection techniques have been introduced, but the security problem of personal authentication and a system that can check indoor and outdoor overall was lacking. In order to solve this problem, various authentication methods and location detection techniques have been introduced, but it was difficult to find a system that can check the security problem of personal authentication and indoor/outdoor overall. In this study, we intend to propose a system that can identify personal authentication, basic health status, and overall location indoors and outdoors by using wristband-type wearable devices in a private blockchain environment. In this system, personal authentication uses ECG, which is difficult to forge and highly personally identifiable, Bluetooth beacon that is easy to use with low power, non-contact and automatic transmission and reception indoors, and DGPS that corrects the pseudorange error of GPS satellites outdoors. It is intended to detect wandering behavior and abnormal signs by locating the patient. Through this, it is intended to contribute to the prompt response and prevention of disappearance in case of wandering behavior and abnormal symptoms of dementia patients living at home or in nursing homes.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.10
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• pp.409-418
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• 2022

In this paper, we propose SAAnnot-C3Pap, a semi-automatic annotation method for obtaining ground truth of a player's posture. In order to obtain ground truth about the two-dimensional joint position in the existing music domain, openpose, a two-dimensional posture estimation method, was used or manually labeled. However, automatic annotation methods such as the existing openpose have the disadvantages of showing inaccurate results even though they are fast. Therefore, this paper proposes SAAnnot-C3Pap, a semi-automated annotation method that is a compromise between the two. The proposed approach consists of three main steps: extracting postures using openpose, correcting the parts with errors among the extracted parts using supervisely, and then analyzing the results of openpose and supervisely. Perform the synchronization process. Through the proposed method, it was possible to correct the incorrect 2D joint position detection result that occurred in the openpose, solve the problem of detecting two or more people, and obtain the ground truth in the playing posture. In the experiment, we compare and analyze the results of the semi-automated annotation method openpose and the SAAnnot-C3Pap proposed in this paper. As a result of comparison, the proposed method showed improvement of posture information incorrectly collected through openpose.

• Journal of the Society of Disaster Information
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• v.17 no.2
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• pp.306-318
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• 2021

The dispatch law has a negative impact on safety management at construction sites as the command and command relationship to safety assistants of the original contractor are applied to the dispatch law. Purpose: The purpose is to study the importance and impact of safety management according to the dispatch law, and to propose a direction for safety management so that safety assistants can actively and proactively prevent accidents. In this study, we used AHP analysis techniques for experts to achieve the final goal and verified the suitability through logistic regression. Method: AHP analysis technique is used for experts and workers and logistic regression analysis is conducted. Result: The result of analyzing scenario data where the dispatch method can be applied showed the importance in the order of education (SkillUp education), management (work-time management) and direct instructions (feedback instruction). In logistic regression analysis, feedback is the factor that affects direct instruction, and in education management, the ratio of education management is 3.42 times lower than that of other groups when only the team leader of the company gives work instructions. Conclusion: The management of feedback and education is more important than anything else within the range in which the dispatch method is not applied, and the expansion of non-face-to-face online education is judged to avoid the violation of dispatch method because the expansion of non-face-to-face online education due to covid 19 recently has brought more various target for safety education.