• Journal of Biomedical Engineering Research
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• v.43 no.6
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• pp.390-398
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• 2022

Ultrasound examination using ultrasound equipment is an ultrasound device that images human organs using sound waves and is used in various areas such as diagnosis, follow-up, and treatment of diseases. However, if the quality of ultrasound equipment is not guaranteed, the possibility of misdiagnosis increases, and the diagnosis rate decreases. Accordingly, The Korean Society of Radiology and Korea society of Ultrasound in Medicine presented guidelines for quality management of ultrasound equipment using ATS-539 phantom. The DenseNet201 classification algorithm shows 99.25% accuracy and 5.17% loss in the Dead Zone, 97.52% loss in Axial/Lateral Resolution, 96.98% accuracy and 20.64% loss in Sensitivity, 93.44% accuracy and 22.07% loss in the Gray scale and Dynamic Range. As a result, it is the best and is judged to be an algorithm that can be used for quantitative evaluation. Through this study, it can be seen that if quantitative evaluation using artificial intelligence is conducted in the qualitative evaluation item of ultrasonic equipment, the reliability of ultrasonic equipment can be increased with high accuracy.

• Journal of Digital Convergence
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• v.20 no.2
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• pp.217-223
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• 2022

Subspace clustering for online data streams requires a large amount of memory resources as all subsets of data dimensions must be examined. In order to track the continuous change of clusters for a data stream in a finite memory space, in this paper, we propose a grid-based subspace clustering algorithm that effectively uses memory resources. Given an n-dimensional data stream, the distribution information of data items in data space is monitored by a grid-cell list. When the frequency of data items in the grid-cell list of the first level is high and it becomes a unit grid-cell, the grid-cell list of the next level is created as a child node in order to find clusters of all possible subspaces from the grid-cell. In this way, a maximum n-level grid-cell subspace tree is constructed, and a k-dimensional subspace cluster can be found at the k^th level of the subspace grid-cell tree. Through experiments, it was confirmed that the proposed method uses computing resources more efficiently by expanding only the dense space while maintaining the same accuracy as the existing method.

• Journal of Digital Convergence
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• v.20 no.5
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• pp.375-381
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• 2022

The DEA-BCC model. And the Malmquist index have been used, from static and dynamic perspectives, to measure the eco-efficiency of 30 cities and provinces in China from 2011 to 2020. The results shows that: the average static eco-efficiency of 30 cities and provinces in China is 0.643. Differences exists all over China. While Shanghai and Beijing are ecologically efficient, other 28 cities and provinces are faced with different extents of inefficiency. There are also differences among regions, which generally show the spatial distribution pattern with high efficiency in the eastern regions while low in the western regions. The Malmquist index of eco-efficiency in total 30 cities and provinces shows a healthy growth trend, and the technological progress. Acts as its main driving force. Therefore, eastern regions should enhance the. radiation capacity, strengthen the synergy among regions, give full play to. The advantages of each regions. It is sensible to improve the eco-efficiency by means of optimizing the industrial structure, enhancing the technological level and improving eco-efficiency of China and realizing green development.

• KNOM Review
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• v.23 no.2
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• pp.1-10
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• 2020

Network Function Virtualization (NFV) is a key technology of 5G networks that has the advantage of enabling building and operating networks flexibly. However, NFV can complicate network management because it creates numerous virtual resources that should be managed. In NFV environments, service function chaining (SFC) composed of virtual network functions (VNFs) is widely used to apply a series of network functions to traffic. Therefore, it is required to dynamically allocate the right amount of computing resources or instances to SFC for meeting service requirements. In this paper, we propose Deep Q-Networks (DQN)-based auto-scaling to operate the appropriate number of VNF instances in SFC. The proposed approach not only resizes the number of VNF instances in SFC composed of multi-tier architecture but also selects a tier to be scaled in response to dynamic traffic forwarding through SFC.

• Journal of Convergence for Information Technology
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• v.12 no.5
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• pp.92-99
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• 2022

This study show the empirical results and the models that explain the content creator's personal brand value in the user-generated content platform. Producer's brand value performance could have enhancement and dilution by their activities for the long-term and repetitive change. Therefore, for the measure and analysis, the models have to catch the effect from producer's the diverse activities. This study would find the guideline by competitive analysis between (1) the impact of in-group user's self-motivated participation and (2) the impact of the social links from the outside platform. Based on the analysis results, producer's creation activity as focused on the specific and professional category increase their brand value for the long-term. However, producers would have to upload diverse category, after users are bored to their similar videos' as before. These empirical results would be a guidelines to the content management strategies for producers and the platform.

• International Journal of Computer Science & Network Security
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• v.22 no.11
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• pp.11-18
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• 2022

Linking Ukraine's education system with the trends of global digitalization is mandatory to ensure the sustainable, long-term development of the country, as well as to increase the sustainability of the education system and the economy as a whole during the crisis period. Now the main problems of the education system in Ukraine are manifested in a complex context caused by Russian armed aggression. In the context of war, problems include differences in adaptation to online learning among educational institutions, limited access to education for vulnerable groups in the zone of active hostilities, the lack of digital educational resources suitable for online learning, and the lack of basic digital skills and competencies among students and teachers necessary to properly conduct online classes. Some of the problems of online learning were solved in the pandemic, but in the context of war Ukrainian society needs a new vision of education and continuous efforts of all social structures in the public and private environment. In the context of war, concerted action is needed to keep education on track and restore it in active zones, adapting to the needs of a dynamic society and an increasingly digitized economy. Among the urgent needs of the education system are a change in the teaching-learning paradigm, which is based on content presentation, memorization, and reproduction, and the adoption of a new, hybrid educational model that will encourage the development of necessary skills and abilities for students and learners in a digitized society and enable citizens close to war zones to learn.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.59 no.2
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• pp.164-171
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• 2023

This study analyzed the flow inside floating seedling equipment for Scapharca subcrenata. Due to the aging society of fishing villages, it is impossible to continuously input the labor force. Therefore, it is necessary to improve efficiency. Scapharca subcrenata has high per capita consumption. It serves as an important aquatic food resource. Scapharca subcrenata culture tends to be highly dependent on the natural environment. Production of Scapharca subcrenata is difficult to predict with low stability. In the past, manpower directly installed bamboo nets in mudflats. The seedling equipment devised in this study is a floating type and can be freely moved on the sea according to the prediction of Scapharca subcrenata generation. The flow around the floating seedling equipment was analyzed by numerical analysis. The physical phenomena of the flow around the net inside the floating seedling equipment were visualized. As a result, the space between the floating seedling equipment and the bottom net and the space between the net groups showed a lower flow rate than the inlet flow rate. It is expected that the low flow rate of the floating seedling equipment will have a positive effect on the attachment of Scapharca subcrenata.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.1
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• pp.216-238
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• 2023

In intelligent transportation systems, traffic management is an important task. The accurate forecasting of traffic characteristics like flow, congestion, and density is still active research because of the non-linear nature and uncertainty of the spatiotemporal data. Inclement weather, such as rain and snow, and other special events such as holidays, accidents, and road closures have a significant impact on driving and the average speed of vehicles on the road, which lowers traffic capacity and causes congestion in a widespread manner. This work designs a model for multivariate short-term traffic congestion prediction using SLSTM_AE-BiLSTM. The proposed design consists of a Bidirectional Long Short Term Memory(BiLSTM) network to predict traffic flow value and a Convolutional Neural network (CNN) model for detecting the congestion status. This model uses spatial static temporal dynamic data. The stacked Long Short Term Memory Autoencoder (SLSTM AE) is used to encode the weather features into a reduced and more informative feature space. BiLSTM model is used to capture the features from the past and present traffic data simultaneously and also to identify the long-term dependencies. It uses the traffic data and encoded weather data to perform the traffic flow prediction. The CNN model is used to predict the recurring congestion status based on the predicted traffic flow value at a particular urban traffic network. In this work, a publicly available Caltrans PEMS dataset with traffic parameters is used. The proposed model generates the congestion prediction with an accuracy rate of 92.74% which is slightly better when compared with other deep learning models for congestion prediction.

• Journal of Korean Port Research
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• v.3 no.1
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• pp.35-55
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• 1989

Today, about 99% of total import and export cargo in Korea is being transported through the port. The general trends of cargo handling show increases in capacity and speed, In order to cope with these trends, it is not only required to raise the efficiencies of port operation and function but also necessary to decide the optimal amount of the skilled labor force for cargo handling in the port. Cargo handling in the port is basically relied on the cargo handling facilities. Therefore, it is very important to reserve the amount of labor force for cargo handling system has been developed up to a certain level but the personnel management system which is the superior structure has not been followed well. In this study, therefore, we show a method to determine the required amount of labor force for cargo handling considering the amount of cargo and type of cargo handling work per each cargo, and the optimal amount labor force in cope with the fluctuation of the basic cargo handling labor force with respect to the time of in and out cargo flow in the viewpoint of minimizing the expences due to reservation of extra labor force than needed and firing employment of labor force using the Dynamic Programming. The derived algorithm is introduced into the computer simulation for Pusan port with the analyzed real data such as amount of cargo handling in the port with respect to working hour, cargo capacity, working step, the ratio of cargo handling facility and actual number of workers and we estimated the required labor force. As a result of analysis the labor force of Pusan port showed the over-employment such as maximum 21.4%, minimum 8.2% when we assumed that the averages of actual working hours and days were 8 hours in a day and 20 day in a month.

• Journal of Digital Convergence
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• v.20 no.3
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• pp.389-397
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• 2022

As increasing the growth of the Internet in medical area, a new technology to transmit effectively massive medical data is required. In optical internet, all OBS nodes have fiber delay lines, hardware components. These components are calculated under some optimal traffic conditions, and this means that if the conditions change, then the components should be altered. Therefore, in this article a new service differentiation algorithm using the previously installed components is proposed, which is used although the conditions vary. When traffic conditions change, the algorithm dynamically recalculates the threshold value used to decide the length of data bursts. By doing so, irrelevant to changes, the algorithm can maintain the service differentiation between classes without replacing any fiber delay lines. With the algorithm, loss sensitive medical data can be transferred well.