• Journal of Internet Computing and Services
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• v.23 no.3
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• pp.21-33
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• 2022

This paper is to suggest the secure secret sharing system in order to outstandingly reduce the damage caused by the leakage of the corporate secret. This research system is suggested as efficient P2P distributed system kept from the centrally controlled server scheme. Even the bitcoin circulation system is also based on P2P distribution scheme recenly. This research has designed the secure circulation of the secret shares produced by Threshold Shamir Secret Sharing scheme instead of the shares specified in the torrent file using the simple, highly scalable and fast transferring torrent P2P distribution structure and its protocol. In addition, this research has studied to apply both Shamir Threshold Secret Sharing scheme and the securely strong multiple user authentication based on Collaborative Threshold Autentication scheme. The secure transmission of secret data is protected as using the efficient symmetric encryption with the session secret key which is safely exchanged by the public key encryption. Also it is safer against the leakage because the secret key is effectively alive only for short lifetime like a session. Especially the characteristics of this proposed system is effectively to apply the threshold secret sharing scheme into efficient torrent P2P distributed system without modifying its architecture of the torrent system. In addition, this system guaranttes the confidentiality in distributing the secret file using the efficient symmetric encryption scheme, which the session key is securely exchanged using the public key encryption scheme. In this system, the devices to be taken out can be dynamically registered as an user. This scalability allows to apply the confidentiality and the authentication even to dynamically registerred users.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.3
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• pp.227-237
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• 2022

The most critical factors for detecting changes in very high-resolution satellite images are building positional inconsistencies and relief displacements caused by satellite side-view. To resolve the above problems, additional processing using a digital elevation model and deep learning approach have been proposed. Unfortunately, these approaches are not sufficiently effective in solving these problems. This study proposed a change detection method that considers both positional and topology information of buildings. Mask R-CNN (Region-based Convolutional Neural Network) was trained on a SpaceNet building detection v2 dataset, and the central points of each building were extracted as building nodes. Then, triangulated irregular network graphs were created on building nodes from temporal images. To extract the area, where there is a structural difference between two graphs, a change index reflecting the similarity of the graphs and differences in the location of building nodes was proposed. Finally, newly changed or deleted buildings were detected by comparing the two graphs. Three pairs of test sites were selected to evaluate the proposed method's effectiveness, and the results showed that changed buildings were detected in the case of side-view satellite images with building positional inconsistencies.

• Korean Journal of Remote Sensing
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• v.38 no.6_4
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• pp.1889-1900
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• 2022

Roads are an essential factor in the physical functioning of modern society. The spatial information of the road has much longer update cycle than the traffic situation information, and it is necessary to generate the information faster and more accurately than now. In this study, as a way to achieve that goal, the Pan-sharpening technique was applied to satellite images of Kompsat 3 and 3A to improve spatial resolution. Then, the data were used for road extraction using the semantic segmentation technique, which has been actively researched recently. The acquired Kompsat 3/3A pan-sharpened images were trained by putting it into a U-Net based segmentation model along with Massachusetts road data, and the applicability of the images were evaluated. As a result of training and verification, it was found that the model prediction performance was maintained as long as certain conditions were maintained for the input image. Therefore, it is expected that the possibility of utilizing satellite images such as Kompsat satellite will be even higher if rich training data are constructed by applying a method that minimizes the impact of surrounding environmental conditions affecting models such as shadows and surface conditions.

• Journal of the Korea Society for Simulation
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• v.31 no.3
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• pp.1-10
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• 2022

Defense fields widely operate unmanned systems to lower vulnerability and enhance combat effectiveness. In the navy, swarm unmanned surface vehicles(USVs) form a cluster within communication range, share situational awareness information among the USVs, and cooperate with them to conduct military missions. This paper proposes an interface system, i.e., Interface Adapter System(IAS), to achieve inter-USV and intra-USV interoperability. We focus on the mission planning subsystem(MPS) for interoperability, which is the core subsystem of the USV to decide courses of action such as automatic path generation and weapon assignments. The central role of the proposed system is to exchange interface data between MPSs and other subsystems in real-time. To this end, we analyzed the operational requirements of the MPS and identified interface messages. Then we developed the IAS using the distributed real-time middleware. As experiments, we conducted several integration tests at swarm USVs simulation environment and measured delay time and loss ratio of interface messages. We expect that the proposed IAS successfully provides bridge roles between the mission planning system and other subsystems.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.27 no.2
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• pp.49-70
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• 2022

Sea Surface Temperature (SST), one of the ocean features, has a significant impact on climate, marine ecosystem and human activities. Therefore, SST prediction has been always an important issue. Recently, deep learning has drawn much attentions, since it can predict SST by training past SST patterns. Compared to the numerical simulations, deep learning model is highly efficient, since it can estimate nonlinear relationships between input data. With the recent development of Graphics Processing Unit (GPU) in computer, large amounts of data can be calculated repeatedly and rapidly. In this study, Short-term SST will be predicted through Convolutional Neural Network (CNN)-based U-Net that can handle spatiotemporal data concurrently and overcome the drawbacks of previously existing deep learning-based models. The SST prediction performance depends on the seasonal and interannual SST variabilities around the southern coast of Korea. The predicted SST has a wide range of variance during spring and summer, while it has small range of variance during fall and winter. A wide range of variance also has a significant correlation with the change of the Pacific Decadal Oscillation (PDO) index. These results are found to be affected by the intensity of the seasonal and PDO-related interannual SST fronts and their intensity variations along the southern Korean seas. This study implies that the SST prediction performance using the developed deep learning model can be significantly varied by seasonal and interannual variabilities in SST.

• Journal of Naturopathy
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• v.10 no.2
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• pp.77-85
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• 2021

Background: It is necessary to verify the resilience of the newly improved Kneipp psychotherapy. Purpose: This study assesses whether Kneipp Lebensordnung psychotherapy; KnLP program improves resilience. Methods: The KnLP program, including salutogenesis, logotherapy, meditation, and resilience training, is reorganized in consideration of Korean culture and sentiments. It was conducted 4 times for 25 adults (once a week, 3 hours a time), and data, KRQ-53 (Korean Resilience Quotient-53) measured intervention, before, and after was compared and analyzed. Results: The data for 9 adults were selected to draw reliable analysis, and it concluded that participants' KRQ-53 mean score increased by 14.66 from 191.56 to 206.22 during and after the program. The score increase by factor in resilience training is 5.89 points for self-regulation skills, 4.89 points for in interpersonal skills, and 3.89 points for positive capacity. Conclusions: KnL program improves participants' resilience skill (p<.05), and especially in self-regulation skill. Subsequent studies with more participants are required to achieve statistically significant and generalized results in the future.

• Journal of Broadcast Engineering
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• v.27 no.5
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• pp.794-807
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• 2022

Although registration of dogs is mandatory according to the revision of the Animal Protection Act, the registration rate is low due to the inconvenience of the current registration method. In this paper, a performance improvement study was conducted on the dog face recognition technology, which is being reviewed as a new registration method. Through deep learning learning, an embedding vector for facial recognition of a dog was created and a method for identifying each dog individual was experimented. We built a dog image dataset for deep learning learning and experimented with InceptionNet and ResNet-50 as backbone networks. It was learned by the triplet loss method, and the experiments were divided into face verification and face recognition. In the ResNet-50-based model, it was possible to obtain the best facial verification performance of 93.46%, and in the face recognition test, the highest performance of 91.44% was obtained in rank-5, respectively. The experimental methods and results presented in this paper can be used in various fields, such as checking whether a dog is registered or not, and checking an object at a dog access facility.

• Journal of the Korean Recycled Construction Resources Institute
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• v.10 no.3
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• pp.293-299
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• 2022

Recently, various stakeholder are interested in microplastic to cause pollution of the marine's ecosystem and effort to conduct study of product's life cycle to reduce pollution of marine's ecosystem. The micorplastic refer to materials of the nano- to micro- sized units and it can be classified into primary and secondary. The primary microplastic mean the manufactured for use in the specific field such as the microbead of the cosmetic or cleanser. also, secondary mean the unintentionally generated during use of the product such as the textile crumb by the doing the laundry. Tire and Road Wear Particles(TRWPs) are also defined as secondary microplastic. Typically, TRWPs are created by friction between the tread compound's rubber of the tire and the surface of the road du ring the driving cars. Most of the generated TRWPs exist on the roadside and some of them were carried to marine by the rainwater. In this study, we perform the quantitative analysis of the TRWPs existed in fine dust at the roadside. So, we collected the dust from the roadside in Chungcheongnam-do's C site with a movement of 1,300 cars per the hour. The collected samples were separated according to size and density. And shape analysis was performed using the Scanning Electron Microscope(SEM). We were possible to discover a lot of TRWPs at the fine dust of the 100 ± 20 ㎛. And we analysis it u sing the Thermo Gravimetric Analysis(TGA) and Gas Chromatography/Mass Spectrometer(GC/MS) for the quantitative components from the tire. As a result, it was confirmed that TRWPs generated from the roadside fine dust were included the 0.21 %, and the tire and road components in the generated TRWPs consisted of the 3:7 ratio.

• The Journal of the Convergence on Culture Technology
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• v.7 no.2
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• pp.85-95
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• 2021

Currently, we stand between computers as creative tools and computers as creators. A new genre of movies, which can be called a post-cinema situation, is emerging. This paper aims to diagnose the possibility of the emergence of AI cinema. To confirm the possibility of AI cinema, it was examined through a case study whether the creation of a story, narrative, image, and sound, which are necessary conditions for film creation, is possible by artificial intelligence. First, we checked the visual creation of AI painting algorithms Obvious, GAN, and CAN. Second, AI music has already entered the distribution stage in the market in cooperation with humans. Third, AI can already complete drama scripts, and automatic scenario creation programs using big data are also gaining popularity. That said, we confirmed that the filmmaking requirements could be met with AI algorithms. From the perspective of Manovich's 'AI Genre Convention', web documentaries and desktop documentaries, typical trends post-cinema, can be said to be representative genres that can be expected as AI cinemas. The conditions for AI, web documentaries and desktop documentaries to exist are the same. This article suggests a new path for the media of the 4th Industrial Revolution era through research on AI as a creator of post-cinema.

• Journal of Space Technology and Applications
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• v.2 no.1
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• pp.41-51
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• 2022

CubeSat is a satellite platform that is widely used not only for earth observation but also for space exploration. CubeSat is also used in magnetic field investigation missions to observe space physics phenomena with various shape configurations of magnetometer instrument unit. In case of magnetic field measurement, the magnetometer instrument should be far away from the satellite body to minimize the magnetic disturbances from satellites. But the accommodation setting of the magnetometer instrument is limited due to the volume constraint of small satellites like a CubeSat. In this paper, we investigated that the magnetic field interference generated by the cube satellite was analyzed how much it can affect the reliability of magnetic field measurement. For this analysis, we used a reaction wheel and Torque rods which have relatively high-power consumption as major noise sources. The magnetic dipole moment of these parts was derived by the data sheet of the manufacturer. We have been confirmed that the effect of the residual moment of the magnetic torque located in the middle of the 3U cube satellite can reach 36,000 nT from the outermost end of the body of the CubeSat in a space without an external magnetic field. In the case of accurate magnetic field measurements of less than 1 nT, we found that the magnetometer should be at least 0.6 m away from the CubeSat body. We expect that this analysis method will be an important role of a magnetic cleanliness analysis when designing a CubeSat to carry out a magnetic field measurement.