• Journal of Internet of Things and Convergence
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• v.8 no.2
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• pp.79-84
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• 2022

This paper presents an enhancement method of document restoration capability which is robust for security, loss, and contamination, It is based on two methods, that is, encryption and DnCNN(DeNoise Convolution Neural Network). In order to implement this encryption method, a mathematical model is applied as a spatial frequency transfer function used in optics of 2D image information. Then a method is proposed with optical interference patterns as encryption using spatial frequency transfer functions and using mathematical variables of spatial frequency transfer functions as ciphers. In addition, by applying the DnCNN method which is bsed on deep learning technique, the restoration capability is enhanced by removing noise. With an experimental evaluation, with 65% information loss, by applying Pre-Training DnCNN Deep Learning, the peak signal-to-noise ratio (PSNR) shows 11% or more superior in compared to that of the spatial frequency transfer function only. In addition, it is confirmed that the characteristic of CC(Correlation Coefficient) is enhanced by 16% or more.

• Journal of the Institute of Convergence Signal Processing
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• v.22 no.4
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• pp.156-163
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• 2021

Attention mechanisms have been used in deep learning-based computer vision systems, including single image super-resolution (SISR) networks. However, existing SISR networks with attention mechanism focused on real image super-resolution, so it is hard to know whether they are available for CG or VR images. In this paper, we attempt to apply a recent attention module, called balanced attention mechanism (BAM) module, to 12 state-of-the-art SISR networks, and then check whether the BAM module can achieve performance improvement in CG or VR image super-resolution. In our experiments, it has been confirmed that the performance improvement in CG or VR image super-resolution is limited and depends on data characteristics, size, and network type.

• Journal of Internet of Things and Convergence
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• v.8 no.5
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• pp.1-9
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• 2022

As various services are linked to IoT(Internet of Things) and portable communication terminals, cyber attacks that exploit security vulnerabilities of the devices are rapidly increasing. In particular, cyber attacks targeting heterogeneous devices in large-scale network environments through advanced persistent threat (APT) attacks are on the rise. Therefore, in order to improve the effectiveness of the response system in the event of a breach, it is necessary to apply a data enrichment mechanism for the collected artifact data to improve threat analysis and detection performance. Therefore, in this study, by analyzing the data supplementation common elements performed in the existing incident management framework for the artifacts collected for the analysis of intrusion accidents, characteristic elements applicable to the actual system were derived, and based on this, an improved accident analysis framework The prototype structure was presented and the suitability of the derived data supplementary extension elements was verified. Through this, it is expected to improve the detection performance when analyzing cyber incidents targeting artifacts collected from heterogeneous devices.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.1
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• pp.29-34
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• 2022

We propose a novel network architecture and build dataset for recognizing clickable objects on mobile device screens. The data was collected based on clickable objects on the mobile device screen that have numerous resolution, and a total of 24,937 annotation data were subdivided into seven categories: text, edit text, image, button, region, status bar, and navigation bar. We use the Deconvolution Single Shot Detector as a baseline, the backbone network with Squeeze-and-Excitation blocks, the Single Shot Detector layer structure to derive inference results and the Feature pyramid networks structure. Also we efficiently extract features by changing the input resolution of the existing 1:1 ratio of the network to a 1:2 ratio similar to the mobile device screen. As a result of experimenting with the dataset we have built, the mean average precision was improved by up to 101% compared to baseline.

• Journal of the Institute of Convergence Signal Processing
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• v.22 no.3
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• pp.99-103
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• 2021

Recently, convolutional neural networks (CNNs) have been widely utilized due to their outstanding performance in various computer vision fields. However, due to their computational-intensive and high memory requirements, it is difficult to deploy CNNs on hardware platforms that have limited resources, such as mobile devices and IoT devices. To address these limitations, a neural network compression research is underway to reduce the size of neural networks while maintaining their performance. This paper proposes a CNN compression technique that dynamically adjusts the thresholds of pruning, one of the neural network compression techniques. Unlike the conventional pruning that experimentally or heuristically sets the thresholds that determine the weights to be pruned, the proposed technique can dynamically find the optimal thresholds that prevent accuracy degradation and output the light-weight neural network in less time. To validate the performance of the proposed technique, the LeNet was trained using the MNIST dataset and the light-weight LeNet could be automatically obtained 1.3 to 3 times faster without loss of accuracy.

• Journal of Internet of Things and Convergence
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• v.8 no.4
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• pp.41-47
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• 2022

Smoothing is a transmission plan that converts video data stored at a variable bit rate into a fixed bit rate. Algorithms for smoothing include CBA, which aims to minimize the number of transmission rate increases, MCBA, which minimizes the number of transmission rate changes, and MVBA algorithms that minimize the amount of transmission rate change. This paper compares the proposed algorithm with the CBA algorithm with various video data, buffer size, and performance evaluation factors as a follow-up to the proposed smoothing algorithm that minimizes (maximizes) the transmission rate increase (decrease) when the server requires more bandwidth The evaluation factors used were compared with the number of changes in the fps rate, the minimum fps, the average fps, fps variability, and the number of frames to be discarded. As a result of the comparison, the proposed algorithm showed superiority in comparing the number of fps rate changes and the number of frames discarded.

• Journal of Software Assessment and Valuation
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• v.16 no.2
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• pp.127-133
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• 2020

As the usages of artificial intelligence is increased, the demand to algorithms for small portable devices increases. Also as the embedded system becomes high-performance, it is possible to implement algorithms for high-speed computation and machine learning as well as operating systems. As the machine learning algorithms process repetitive calculations, it depend on the cloud environment by network connection. For an stand alone system, low power consumption and fast execution by optimized algorithm are required. In this study, for the purpose of smart control, an energy measurement sensor is connected to an embedded system, and a real-time monitoring system is implemented to store measurement information as a database. Continuously measured and stored data is applied to a learning algorithm, which can be utilized for optimal power control, and a system interfacing various sensors required for energy measurement was constructed.

• Journal of The Korean Association of Information Education
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• v.26 no.5
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• pp.467-480
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• 2022

This paper aimed to explore research trends for the conceptualization of IT humanities. Reflecting domestic and international references which focused on the possibility of the integration of digital technology and humanities, the authors examined the beginning, background, and relevant concepts of IT humanities to figure out the meaning and the research trends. In addition, using the search word "IT humanities," the authors analyzed network topics of the keywords retrieved from 1,566 KCI and 64 SCI journal articles published since 2001. The concept of IT humanities in the previous studies has tended to associate with competencies that allow considering various fields of IT based on the lens of humanities perspectives. The result of the topic modeling revealed four groups as fields to be integrated with IT humanities, methods of implementation, connections of literature or culture, and creations of IT humanities. Instead of instrumentalization or merger by one stance of IT or humanities, it is imperative to collaboratively work for the generation of a new viewpoint through mutual respect of disciplines.

• KIPS Transactions on Computer and Communication Systems
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• v.12 no.6
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• pp.189-196
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• 2023

Today, in the era of the 4th industrial revolution based on the paradigm of hyper-connectivity, super-intelligence, and superconvergence, the remote work environment is becoming central based on technologies such as mobile, cloud, and big data. This remote work environment has been accelerated by the demand for non-face-to-face due to COVID-19. Since the remote work environment can perform various tasks by accessing services and resources anytime and anywhere, it has increased work efficiency, but has caused a problem of incapacitating the traditional boundary-based network security model by making the internal and external boundaries ambiguous. In this paper, we propse a method to improve the limitations of the traditional boundary-oriented security strategy by building a security model centered on core components and their relationships based on the zero trust idea that all actions that occur in the network beyond the concept of the boundary are not trusted.

• Journal of the Korea Society for Simulation
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• v.18 no.3
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• pp.61-71
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• 2009

The server redirection system replicates contents on replica servers in the distributed network environment. When users make a request for contents, the system can provide the contents much faster than the client-server network system. In the server redirection system, it is the most important to select the optimal replica server with user's information because many replica servers are geographically distributed. And, the system can serve contents to users flexibly if a redirector is well constructed. In this paper, the redirector makes a list of candidates suitable for user conditions by the decision tree method and calculates the availability of each replica server. And, the redirector selects the replica server with the highest availability. This paper also proposes an availability-based server redirection system that predicts the availability of each replica server in order to reduce the workload of replica servers and the repetitive communication messages between a redirector and replica servers. If a user accesses the network path for the replica server selected and noticed by the proposed system, the user can get contents effectively and efficiently.