• Journal of Internet Computing and Services
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• v.23 no.3
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• pp.35-45
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• 2022

Even in recent years, treatment of first-aid patients is still often delayed due to a shortage of medical resources in marginalized areas. Research on automating the analysis of medical data to solve the problems of inaccessibility for medical services and shortage of medical personnel is ongoing. Computer vision-based medical inspection automation requires a lot of cost in data collection and labeling for training purposes. These problems stand out in the works of classifying lesion that are rare, or pathological features and pathogenesis that are difficult to clearly define visually. Anomaly detection is attracting as a method that can significantly reduce the cost of data collection by adopting an unsupervised learning strategy. In this paper, we propose methods for detecting abnormal images on chest X-RAY images as follows based on existing anomaly detection techniques. (1) Normalize the brightness range of medical images resampled as optimal resolution. (2) Some feature vectors with high representative power are selected in set of patch features extracted as intermediate-level from lesion-free images. (3) Measure the difference from the feature vectors of lesion-free data selected based on the nearest neighbor search algorithm. The proposed system can simultaneously perform anomaly classification and localization for each image. In this paper, the anomaly detection performance of the proposed system for chest X-RAY images of PA projection is measured and presented by detailed conditions. We demonstrate effect of anomaly detection for medical images by showing 0.705 classification AUROC for random subset extracted from the PadChest dataset. The proposed system can be usefully used to improve the clinical diagnosis workflow of medical institutions, and can effectively support early diagnosis in medically poor area.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.10
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• pp.373-380
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• 2022

This paper presents a deep learning based group synchronization that supports networked immersive interactions between remote users. The goal of group synchronization is to enable all participants to synchronously interact with others for increasing user presence Most previous methods focus on NTP-based clock synchronization to enhance time accuracy. Moving average filters are used to control media playout time on the synchronization server. As an example, the exponentially weighted moving average(EWMA) would be able to track and estimate accurate playout time if the changes in input data are not significant. However it needs more time to be stable for any given change over time due to codec and system loads or fluctuations in network status. To tackle this problem, this work proposes the Deep Group Synchronization(DeepGroupSync), a group synchronization based on deep learning that models important features from the data. This model consists of two Gated Recurrent Unit(GRU) layers and one fully-connected layer, which predicts an optimal playout time by utilizing the sequential playout delays. The experiments are conducted with an existing method that uses the EWMA and the proposed method that uses the DeepGroupSync. The results show that the proposed method are more robust against unpredictable or rapid network condition changes than the existing method.

• Journal of the Korea institute for structural maintenance and inspection
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• v.26 no.4
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• pp.20-29
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• 2022

The operational modal analysis (OMA) technique, which extracts the modal parameters of a structural system using ambient vibrations, has been actively developed as a field of structural health monitoring of cable-supported bridges. In this paper, the short and long-term modal parameters of a cable-stayed bridge were evaluated using the acceleration data obtained from the two ambient vibration tests (AVTs) and three years of continuous measurements. A total of 27 vertical modes and 1 lateral mode in the range 0.1 ~ 2.5 Hz were extracted from the high-resolution AVTs which were conducted in the 6th and 19th years after its completion. Existing OMA methods such as Peak-Picking (PP), Eigensystem Realization Algorithm with Data Correlation (ERADC), Frequency Domain Decomposition (FDD) and Time Domain Decomposition (TDD) were applied for modal parameters extraction, and it was confirmed that there was no significant difference between the applied methods. From the correlation analysis between long-term natural frequencies and environmental factors, it was confirmed that temperature change is the dominant factor influencing natural frequency fluctuations. It was revealed that the decreased natural frequencies of the bridge were not due to changes in structural performance and integrity, but to the environmental effects caused by the temperature difference between the two AVTs. In addition, when the TDD technique is applied, the accuracy of extracted mode shapes is improved by adding a proposed algorithm that normalizes the sequence so that the autocorrelations at zero lag equal 1.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.6
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• pp.41-49
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• 2022

The Internet Engineering Task Force (IETF) has standardized RPL (IPv6 Routing Protocol for Low-power Lossy Network) as a routing protocol for Low Power and Lossy Networks (LLNs), a low power loss network environment. RPL creates a route through an Objective Function (OF) suitable for the service required by LLNs and builds a Destination Oriented Directed Acyclic Graph (DODAG). Existing studies check the residual energy of each node and select a parent with the highest residual energy to build a DODAG, but the energy exhaustion of the parent can not avoid the network disconnection of the children nodes. Therefore, this paper proposes EC-RPL (Enhanced Connectivity-RPL), in which ta node leaves DODAG in advance when the remaining energy of the node falls below the specified energy threshold. The proposed protocol is implemented in Contiki, an open-source IoT operating system, and its performance is evaluated in Cooja simulator, and the number of control messages is compared using Foren6. Experimental results show that EC-RPL has 6.9% lower latency and 5.8% fewer control messages than the existing RPL, and the packet delivery rate is 1.7% higher.

• Economic and Environmental Geology
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• v.55 no.1
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• pp.1-17
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• 2022

The mechanical and thermal properties of the rock masses can affect the performance associated with both the isolating and retarding capacities of radioactive materials within the deep geological disposal system for High-Level Radioactive Waste (HLW). In this study, the essential parameters for the site descriptive model (SDM) related to the rock mechanics and thermal properties of the HLW disposal facilities site were reviewed, and the technical background was explored through the cases of the preceding site descriptive models developed by SKB (Swedish Nuclear and Fuel Management Company), Sweden and Posiva, Finland. SKB and Posiva studied parameters essential for the investigation and evaluation of mechanical and thermal properties, and derived a rock mechanics site descriptive model for safety evaluation and construction of the HLW disposal facilities. The rock mechanics SDM includes the results obtained from investigation and evaluation of the strength and deformability of intact rocks, fractures, and fractured rock masses, as well as the geometry of large-scaled deformation zones, the small-scaled fracture network system, thermal properties of rocks, and the in situ stress distribution of the disposal site. In addition, the site descriptive model should provide the sensitivity analysis results for the input parameters, and present the results obtained from evaluation of uncertainty.

• Smart Media Journal
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• v.12 no.2
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• pp.66-75
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• 2023

Recently, the number of cloud web applications is increasing owing to the accelerated migration of enterprises and public sector information systems to the cloud. Traditional network attacks on cloud web applications are characterized by Denial of Service (DoS) attacks, which consume network resources with a large number of packets. However, HTTP DoS attacks, which consume application resources, are also increasing recently; as such, developing security technologies to prevent them is necessary. In particular, since low-bandwidth HTTP DoS attacks do not consume network resources, they are difficult to identify using traditional security solutions that monitor network metrics. In this paper, we propose a new detection model for detecting HTTP DoS attacks on cloud web applications by collecting the application metrics of web servers and learning them using machine learning. We collected 18 types of application metrics from an Apache web server and used five machine learning and two deep learning models to train the collected data. Further, we confirmed the superiority of the application metrics-based machine learning model by collecting and training 6 additional network metrics and comparing their performance with the proposed models. Among HTTP DoS attacks, we injected the RUDY and HULK attacks, which are low- and high-bandwidth attacks, respectively. As a result of detecting these two attacks using the proposed model, we found out that the F1 scores of the application metrics-based machine learning model were about 0.3 and 0.1 higher than that of the network metrics-based model, respectively.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.35 no.1
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• pp.1-12
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• 2023

Along with the growing interest in renewable energy development, Korea's west coast is one of the favorable regions for tidal power. Tidal power using tidal barrages that work like hydroelectric dams is a representative method of tidal power through long-term operation, but the promotion of tidal power projects is being delayed or stopped due to impacts on ecological changes, reproduction, water column processes and hydrology. In order to reduce the high construction cost and environmental cost problems caused by tidal power using tidal barrages, in this study, field experiments were conducted to develop and verify the performance of tidal power generation devices applicable to sea areas where dykes are already installed. As a result of conducting five cases of experiments using two water tanks, pipe lines, open channels, and water turbine and generator, the possibility of developing a power generation system capable of generating more than 10 kW output and more than 60% efficiency were confirmed. The results of this study can be used for small-scale tidal power by utilizing the existing dykes of the west coast.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.35 no.1
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• pp.13-21
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• 2023

As problems such as difficulties in securing stable energy resources and global warming due to the emission of greenhouse gases due to the use of fossil fuels have emerged, interest in the development of renewable energy is increasing. Since the tidal phenomenon has a regularity that occurs regularly with a certain period, it is possible to predict accurately in advance, which has a advantage in terms of energy recovery. Therefore, various methods have been devised to utilize the tide as an energy source. Tidal power using barrages is a representative method that is widely operated, but the promotion of tidal power generation projects is being delayed or stopped due to the decrease in the level of water in the tidal basin, changes in water quality and in the ecosystem. In this study, a field experiment was conducted to develop and verify the performance of a tidal power device applicable to sea areas where dykes are already installed. As a result of carrying out four cases of experiments using two water tanks, pipe lines, open channels, weirs, and water turbine and generator, the possibility of developing a power generation system capable of 10 kW output or more and 60% efficiency or more was confirmed. These research results can be used for small-scale tidal power by utilizing the existing dykes.

• Applied Chemistry for Engineering
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• v.34 no.3
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• pp.213-220
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• 2023

Mixed-matrix membranes (MMMs), which are composed of a polymer matrix filled with high-performance fillers as a dispersed phase, have been intensively studied for gas separations for the past 30 years. It has been demonstrated that MMMs exhibit superior gas separation performance compared to polymer membranes and are more scalable than polycrystalline membranes. Despite their potential, the commercialization of MMMs has yet to be reported due to several challenging issues. One of the major challenges of MMMs is the non-ideal interface between the continuous polymer phase and dispersed phase, which can result in defect formation (i.e., interfacial voids, etc.). With respect, many MMM studies have focused on addressing the issues through scientific approaches. The engineering approaches for facile and effective large-scale fabrication of MMMs, however, have been relatively underestimated. In this review paper, a novel strategy for fabricating MMMs in a facile and scalable manner using in situ metal-organic framework (MOF) formation is introduced. This new MMM fabrication methodology can effectively address the issues facing current MMMs, likely facilitating the commercialization of MMMs.

• The Journal of the Acoustical Society of Korea
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• v.42 no.3
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• pp.243-249
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• 2023

The occurrence of cavitation on the propeller is directly linked to the naval ship's survivability, and it is necessary to design a propeller shape that delays the cavitation inception. However, the propeller cavitation can occur under various operating conditions, thus it is important to identify whether the propeller cavitation exists during operation as well as in the design phase. To this end, it is necessary to use noise or vibration signals on board to monitor the cavitation inception. In this study, a hydrophone and an accelerometer were installed on the ship hull right above the propeller to compare the performance of analyzing cavitation inception between acoustic and vibration signals. Also, a high speed camera was used to visually observe the occurrence of cavitation through an observation window. The measured results showed that the spectral shapes between acoustic and vibration signals were different, but the level increases at each frequency band and the overall level of the frequency band from 1 kHz to 10 kHz showed a similar tendency. The Detection of Envelope Modulation On Noise (DEMON) analysis also showed similar results for both acoustic and vibration signals, confirming that both hydrophones and accelerometers can be utilized in the analysis of cavitation inception.