• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.12
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• pp.475-487
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• 2020

The current water-management paradigm is changing from the expansion of reservoirs and facilities for simple outflows and non-point source management to the building of a sound water circulation system throughout the watershed. Based on this, water management for the watershed as a whole is establishing standards through local ordinances. The purpose of this study is to establish water cycle targets that are resilient to water management even after the development of cities in watersheds where water management is highly needed. This was done by referring to research and ordinances related to water circulation by local governments. A method is proposed based on a storage and infiltration method for rainfall. Through a comparison of percentiles, it was found that the water circulation target of a planned waterside city can be treated with 52% of total rainfall and 80% of rainfall of 17 mm per day. To quantitatively improve the quality results of these calculation procedures, it is estimated that the calculation of water cycle targets will be more reliable if other various variables such as the safety of low impact development factors or the selection of appropriate specifications are considered later.

• Smart Media Journal
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• v.10 no.1
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• pp.88-98
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• 2021

As blockchain technology has emerged as a security issue for IoT, technology which integrates block chain into IoT is being studied. In this paper is a research concerning token-based IoT service access control technology for data sharing, which propose a possessor focused data sharing technic by using the permissioned blockchain. To share IoT health data, a Hyperledger Fabric Network consisting of three organizations was designed to provide a way to share data by applying different access control policies centered on device owners for different services. In the proposed system, the device owner issues access control tokens with different security levels applied to the participants in the organization, and the token issue information is shared through the distributed ledger of the HFN. In IoT, it is possible to lightweight the access control processing of IoT devices by granting tokens to service requesters who request access to data. Furthmore, by sharing token issuance information among network participants using HFN, the integrity of the token is guaranteed and all network participants can trust the token. The device owners can trust that their data is being used within their authorized rights, and control the collection and use of data.

• Journal of Wetlands Research
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• v.23 no.1
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• pp.67-73
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• 2021

As the impermeable area increases due to urbanization and industrialization, the influence of non-point pollutants caused by rainfall runoff on the water system is increasing. In the past, the best management practices(BMP) were used a lot to manage non-point pollutants, but recently, technology that naturally treats them through LID (Low Impact Development) technology is widely used. In this study, various rainfall events were simulated through the SWMM model based on the data of rainfall monitoring in bioretention among natural facilities. The characteristic of LID modeling research is that it is difficult to build accurate modeling data with short-term data because real data is the result obtained through natural facilities, and it is difficult to implement an accurate model. In this study, the data monitored for 3 years It is significant in that it has built a precise model. The actual data monitored a total of 18 times was simulated, and the inflow and outflow and the removal efficiency of five pollutants were simulated. As a result of performing the performance evaluation, most of the 7 items showed excellent indicators, and the TN and TP showed relatively low simulation performance. In the future, it is expected that Korea will introduce an integrated water management system in which the water supply system and the sewage system are substantially integrated and operated. Therefore, the results of this study are considered to play an important role in the initial stage of rainfall management in the future integrated water management system, and the extent of rainfall runoff reduction and pollutant reduction in the expected installation area can be predicted in advance. This is expected to prevent overdesign of bioretention.

• Journal of Broadcast Engineering
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• v.27 no.3
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• pp.378-390
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• 2022

With the recent development of deep composite multiplication neural network learning, deep learning techniques applied to single-image super-resolution have shown good results, and the strong expression ability of deep networks has enabled complex nonlinear mapping between low-resolution and high-resolution images. However, there are limitations in applying it to real-time or low-power devices with increasing parameters and computational amounts due to excessive use of composite multiplication neural networks. This paper uses blocks that extract hierarchical characteristics little by little using information distillation and suggests the Recursive Distillation Super Resolution Network (RDSRN), a lightweight network that improves performance by making more accurate high frequency components through high frequency residual purification blocks. It was confirmed that the proposed network restores images of similar quality compared to RDN, restores images 3.5 times faster with about 32 times fewer parameters and about 10 times less computation, and produces 0.16 dB better performance with about 2.2 times less parameters and 1.8 times faster processing time than the existing lightweight network CARN.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.4
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• pp.105-114
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• 2022

As the national income level is improving, housing urbanization and economic speed are progressing rapidly, household waste and food waste are rapidly increasing. The "Waste Management Act" (founded in 1991) introduced the volume-based household waste system in 1995, and even after implementation, the odor of food waste and the prompt disposal process continue to be a social problem.For this reason, the food waste recycling business is attracting attention. In this paper, regarding the role of resource recycling such as feed, compost, and other resources of food waste, this thesis aims to reduce the inefficiency of the recycling process. Data Envelopment Analysis (DEA) of the relationship between inputs and outputs of 33 facilities nationwide, excluding facility data (insufficient) among 394, (238 public, and 156 private ones), as of the end of 2020, which is running a domestic resource recycling project This study was conducted to investigate the important role in the relative management performance of food waste recycling facilities.It was hypothesized that the influence of business history, facility capacity, capital, and machinery of a company running a food waste recycling business on sales was tested.

• Journal of Korea Port Economic Association
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• v.38 no.3
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• pp.69-86
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• 2022

This study analyses the necessity of the large-size shipyard and explores competitiveness factors of it. Furthermore, the competitiveness is evaluated and the economic feasibility of building and operation of shipyard is examined. As a result of AHP analysis of the determining factors of the competitiveness of the repairing shipyard, the importance of the factors was found in the order of arrival and departure safety, repair technology, dock and wharf facilities, repair cost, repair period (on time delivery), and repair parts supply. Moving distance, repair service quality, repair parts supply, arrival and departure safety, repair technology, dock and quay wall facilities, and repair period (on time delivery) were identified as key factors in the AHP analysis for competitiveness of the Busan Port repair shipyard to be built in the future. As a result of the analysing economic feasibility, the net present value of the Busan Port repair shipyard construction and operation investment project was KRW 435.6 billion, and the internal rate of return was 9.8%, higher than the social discount rate (4.5%), and the cost-benefit ratio (B/C) was high at 1.167. As a result of the study, the necessity and economic feasibility of the Busan Port repair shipyard are sufficiently ensured, and the competitiveness assessment was highly positive.

• Journal of the Korea Society of Computer and Information
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• v.26 no.4
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• pp.11-19
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• 2021

An abnormal object refers to a person, an object, or a mechanical device that performs abnormal and unusual behavior and needs observation or supervision. In order to detect this through artificial intelligence algorithm without continuous human intervention, a method of observing the specificity of temporal features using optical flow technique is widely used. In this study, an abnormal situation is identified by learning an algorithm that translates an input image frame to an optical flow image using a Generative Adversarial Network (GAN). In particular, we propose a technique that improves the pre-processing process to exclude unnecessary outliers and the post-processing process to increase the accuracy of identification in the test dataset after learning to improve the performance of the model's abnormal behavior identification. UCSD Pedestrian and UMN Unusual Crowd Activity were used as training datasets to detect abnormal behavior. For the proposed method, the frame-level AUC 0.9450 and EER 0.1317 were shown in the UCSD Ped2 dataset, which shows performance improvement compared to the models in the previous studies.

• Journal of Broadcast Engineering
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• v.26 no.6
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• pp.692-703
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• 2021

Recently, with the development of computer graphics technology, research on technology for expressing real objects as more realistic virtual graphics is being actively conducted. Point cloud is a technology that uses numerous points, including 2D spatial coordinates and color information, to represent 3D objects, and they require huge data storage and high-performance computing devices to provide various services. Video-based Point Cloud Compression (V-PCC) technology is currently being studied by the international standard organization MPEG, which is a projection based method that projects point cloud into 2D plane, and then compresses them using 2D video codecs. V-PCC technology compresses point cloud objects using 2D images such as Occupancy map, Geometry image, Attribute image, and other auxiliary information that includes the relationship between 2D plane and 3D space. When increasing the density of point cloud or expanding an object, 3D calculation is generally used, but there are limitations in that the calculation method is complicated, requires a lot of time, and it is difficult to determine the correct location of a new point. This paper proposes a method to generate additional points at more accurate locations with less computation by applying 2D interpolation to the image on which the point cloud is projected, in the V-PCC technology.

• Journal of the Korea institute for structural maintenance and inspection
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• v.26 no.3
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• pp.11-20
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• 2022

The main purpose of this study is to investigate the applicability of the shear wave tomography technology as a non-destructive testing method to evaluate the debonding between the track concrete layer (TCL) and the hydraulically stabilized based course (HSB) of concrete slab tracks for the Korea high-speed railway system. A commercially available multi-channel shear wave measurement device (MIRA) is used to evaluate debonding defects in full-scaled mock-up test specimen that was designed and constructed according to the Rheda 200 system. A part of the mock-up specimen includes two artificial debonding defects with a length and a width of 400mm and thicknesses of 5mm and 10mm, respectively. The tomography images obtained by a MIRA on the surface of the concrete specimens are effective for visualizing the debonding defects in concrete. In this study, a simple image processing method is proposed to suppress the noisy signals reflected from the embedded items (reinforcing steel, precast sleeper, insert, etc.) in TCL, which significantly improves the readability of debonding defects in shear wave tomography images. Results show that debonding maps constructed in this study are effective for visualizing the spatial distribution and the depths of the debondiing defects in the railway concrete slab specimen.

• Journal of the Korea Society of Computer and Information
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• v.27 no.12
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• pp.29-40
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• 2022

In this paper, we studied a system that detects and analyzes the pathological features of diabetic retinopathy using Mask R-CNN and a Random Forest classifier. Those are one of the deep learning techniques and automatically diagnoses diabetic retinopathy. Diabetic retinopathy can be diagnosed through fundus images taken with special equipment. Brightness, color tone, and contrast may vary depending on the device. Research and development of an automatic diagnosis system using artificial intelligence to help ophthalmologists make medical judgments possible. This system detects pathological features such as microvascular perfusion and retinal hemorrhage using the Mask R-CNN technique. It also diagnoses normal and abnormal conditions of the eye by using a Random Forest classifier after pre-processing. In order to improve the detection performance of the Mask R-CNN algorithm, image augmentation was performed and learning procedure was conducted. Dice similarity coefficients and mean accuracy were used as evaluation indicators to measure detection accuracy. The Faster R-CNN method was used as a control group, and the detection performance of the Mask R-CNN method through this study showed an average of 90% accuracy through Dice coefficients. In the case of mean accuracy it showed 91% accuracy. When diabetic retinopathy was diagnosed by learning a Random Forest classifier based on the detected pathological symptoms, the accuracy was 99%.