• 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 Korea Institute of Information and Communication Engineering
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• v.26 no.8
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• pp.1222-1230
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• 2022

As the number of signal sources with low detectability by using various modulation techniques increases, research to classify signal modulation methods is steadily progressing. Recently, a Convolutional Neural Network (CNN) deep learning technique using FFT as a preprocessing process has been proposed to improve the performance of received signal classification in signal interference or noise environments. However, due to the characteristics of the FFT in which the window is fixed, it is not possible to accurately classify the change over time of the detection signal. Therefore, in this paper, we propose a CNN model that has high resolution in the time domain and frequency domain and uses wavelet transform as a preprocessing process that can express various types of signals simultaneously in time and frequency domains. It has been demonstrated that the proposed wavelet transform method through simulation shows superior performance regardless of the SNR change in terms of accuracy and learning speed compared to the FFT transform method, and shows a greater difference, especially when the SNR is low.

• Proceedings of the National Institute of Ecology of the Republic of Korea
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• v.2 no.3
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• pp.210-218
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• 2021

Recently, pest-resistant living modified (LM) crops developed using RNA interference (RNAi) technology have been imported into South Korea. However, the potential adverse effects of unintentionally released RNAi-based LM crops on non-target species have not yet been reported. Coccinella septempunctata, which feeds on aphids, is an important natural enemy insect which can be exposed to the double-stranded RNA (dsRNA) produced by RNAi-based LM plants. To assess the risk of ingestion of Snf7 dsRNA by C. septempunctata, we first identified the species through morphological analysis of collected insects. A method for species identification at the gene level was developed using a specific C. septempunctata 12S rRNA. Furthermore, an experimental model was devised to assess the risk of Snf7 dsRNA ingestion in C. septempunctata. Snf7 dsRNA was mass-purified using an effective dsRNA synthesis method and its presence in C. septempunctata was confirmed after treatment with purified Snf7 dsRNA. Finally, the survival rate, development time, and dry weight of Snf7 dsRNA-treated C. septempunctata were compared with those of GFP and vATPase A dsRNA control treatments, and no risk was found. This study illustrates an effective Snf7 dsRNA synthesis method, as well as a high-concentration domestic insect risk assessment method which uses dsRNA to assess the risk of unintentional released of LM organisms against non-target species.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.12
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• pp.4292-4307
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• 2021

For the research field of night infrared surveillance video, the target imaging in the video is easily affected by the light due to the characteristics of the active infrared camera and the classical ViBe algorithm has some problems for moving target extraction because of background misjudgment, noise interference, ghost shadow and so on. Therefore, an improved ViBe algorithm (I-ViBe) for moving target extraction in night infrared surveillance video is proposed in this paper. Firstly, the video frames are sampled and judged by the degree of light influence, and the video frame is divided into three situations: no light change, small light change, and severe light change. Secondly, the ViBe algorithm is extracted the moving target when there is no light change. The segmentation factor of the ViBe algorithm is adaptively changed to reduce the impact of the light on the ViBe algorithm when the light change is small. The moving target is extracted using the region growing algorithm improved by the image entropy in the differential image of the current frame and the background model when the illumination changes drastically. Based on the results of the simulation, the I-ViBe algorithm proposed has better robustness to the influence of illumination. When extracting moving targets at night the I-ViBe algorithm can make target extraction more accurate and provide more effective data for further night behavior recognition and target tracking.

• International Journal of Advanced Culture Technology
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• v.11 no.3
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• pp.277-296
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• 2023

The South Korean ethnic diaspora US lobby shows efficacy as an interest group in generating influence in American foreign and domestic public policy making. The persuasive portrayal of South Korea as a critical Cold War US ally reinforced US amenability to pro-South Korea lobbying. Also, the South Korean US diaspora is a comparatively recent immigrant group, thus its lingering resistance to assimilation facilitates its political mobilization to lobby the US government. One source of this influence includes the foundational legacy of proselytizing Western and particularly American religious social movement representatives in Korean religiosity and society. US protestant Christianity acquired a strong public association with emerging Korean nationalism in response to Japanese imperialism and occupation. Hostility towards Japanese colonialism followed by the threat from Soviet-sponsored, North Korean Communism meant Christianity did not readily become a cultural symbol of excessive external, US interference in South Korean society by South Korean public opinion. The post-Cold War shift in US foreign policy towards targeting so-called rogue state vestiges of the Cold War including North Korea enhanced further South Korea's influence in Washington. Due to essential differences in the perceived historical role of American influence, extrapolation of the South Korean development model is problematic. US hegemony in South Korea indicates that perceived alliance with national self-determination constitutes the core of soft power appeal. Civilizational appeal per se in the form of religious beliefs are not critically significant in promoting American polity influence in target polities in South Korea or, comparatively, in the Middle East. The United States is a perceived opponent of pan-Arab nationalism which has trended towards populist Islamic religious symbolism with the failure of secular nationalism. The pronounced component of evangelical Christianity in American core community nationalism which the Trump campaign exploited is a reflection of this orientation in the US.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.3
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• pp.549-559
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• 2023

As mobile edge computing (MEC) is gaining attention as a core technology of 5G networks, edge AI technology of 5G network environment based on mobile user data is recently being used in various fields. However, as in traditional AI security, there is a possibility of adversarial interference of standard 5G network functions within the core network responsible for edge AI core functions. In addition, research on data poisoning attacks that can occur in the MEC environment of standalone mode defined in 5G standards by 3GPP is currently insufficient compared to existing LTE networks. In this study, we explore the threat model for the MEC environment using NWDAF, a network function that is responsible for the core function of edge AI in 5G, and propose a feature selection method to improve the performance of detecting data poisoning attacks for Leaf NWDAF as some proof of concept. Through the proposed methodology, we achieved a maximum detection rate of 94.9% for Slowloris attack-based data poisoning attacks in NWDAF.

• Journal of the Korea Society of Computer and Information
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• v.28 no.4
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• pp.113-120
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• 2023

Smart farms are steadily increasing in research to minimize labor, energy, and quantity put into crops as IoT technology and artificial intelligence technology are combined. However, research on efficiently managing crop growth information in smart farms has been insufficient to date. In this paper, we propose a management technique that can efficiently monitor crop growth information by applying autonomous sensors to smart farms. The proposed technique focuses on collecting crop growth information through autonomous sensors and then recycling the growth information to crop cultivation. In particular, the proposed technique allocates crop growth information to one slot and then weights each crop to perform load balancing, minimizing interference between crop growth information. In addition, when processing crop growth information in four stages (sensing detection stage, sensing transmission stage, application processing stage, data management stage, etc.), the proposed technique computerizes important crop management points in real time, so an immediate warning system works outside of the management criteria. As a result of the performance evaluation, the accuracy of the autonomous sensor was improved by 22.9% on average compared to the existing technique, and the efficiency was improved by 16.4% on average compared to the existing technique.

• The korean journal of orthodontics
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• v.53 no.4
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• pp.254-263
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• 2023

Objective: The evidence on the accuracy of bite registration using intraoral scanners is sparse. This study aimed to develop a new method for evaluating bite registration accuracy using intraoral scanners. Methods: Two different types of models were used; 10 stone models and 10 with acrylic resin teeth. A triangular frame with cylindrical posts at each apex (one anterior and two posteriors) was digitally designed and manufactured using three-dimensional (3D) printing. Such a structure was fitted in the lingual space of each maxillary and mandibular model so that, in occlusion, the posts would contact their opposing counterparts, enforcing a small interocclusal gap between the two arches. This ensured no tooth interference and full contact between opposing posts. Bite registration accuracy was evaluated by measuring the distance between opposing posts, with small values indicating high-accuracy. Three intraoral scanners were used: Medit i500, Primescan, and Trios 4. Viewbox software was used to measure the distance between opposing posts and compute roll and pitch. Results: The average maximum error in interocclusal registration exceeded 50 ㎛. Roll and pitch orientation errors ranged above 0.1 degrees, implying an additional interocclusal error of around 40 ㎛ or more. The models with acrylic teeth exhibited higher errors. Conclusions: A method that avoids the need for reference hardware and the imprecision of locating reference points on tooth surfaces, and offers simplicity in the assessment of bite registration with an intraoral scanner, was developed. These results suggest that intraoral scanners may exhibit clinically significant errors in reproducing the interocclusal relationships.

• International Journal of Computer Science & Network Security
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• v.24 no.6
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• pp.207-215
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• 2024

Cloud computing is now used by most companies, business centres and academic institutions to embrace new computer technology. Cloud Service Providers (CSPs) are limited to certain services, missing some of the assets requested by their customers, it means that different clouds need to interconnect to share resources and interoperate between them. The clouds may be interconnected in different characteristics and systems, and the network may be vulnerable to volatility or interference. While information technology and cloud computing are also advancing to accommodate the growing worldwide application, criminals use cyberspace to perform cybercrimes. Cloud services deployment is becoming highly prone to threats and intrusions. The unauthorised access or destruction of records yields significant catastrophic losses to organisations or agencies. Human intervention and Physical devices are not enough for protection and monitoring of cloud services; therefore, there is a need for more efficient design for cyber defence that is adaptable, flexible, robust and able to detect dangerous cybercrime such as a Denial of Service (DOS) and Distributed Denial of Service (DDOS) in heterogeneous cloud computing platforms and make essential real-time decisions for forensic investigation. This paper aims to develop a framework for digital forensic for the detection of cybercrime in a joined heterogeneous cloud setup. We developed a Digital Forensics model in this paper that can function in heterogeneous joint clouds. We used Unified Modeling Language (UML) specifically activity diagram in designing the proposed framework, then for deployment, we used an architectural modelling system in developing a framework. We developed an activity diagram that can accommodate the variability and complexities of the clouds when handling inter-cloud resources.

• BMB Reports
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• v.56 no.12
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• pp.657-662
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• 2023

Neurodegenerative diseases are characterized by distinct protein aggregates, such as those of α-synuclein and tau. Lysosomal defect is a key contributor to the accumulation and propagation of aberrant protein aggregates in these diseases. The discoveries of common proteinopathies in multiple forms of lysosomal storage diseases (LSDs) and the identification of some LSD genes as susceptible genes for those proteinopathies suggest causative links between LSDs and the proteinopathies. The present study hypothesized that defects in lysosomal genes will differentially affect the propagation of α-synuclein and tau proteins, thereby determining the progression of a specific proteinopathy. We established an imaging-based high-contents screening (HCS) system in Caenorhabditis elegans (C. elegans) model, by which the propagation of α-synuclein or tau is measured by fluorescence intensity. Using this system, we performed RNA interference (RNAi) screening to induce a wide range of lysosomal malfunction through knock down of 79 LSD genes, and to obtain the candidate genes with significant change in protein propagation. While some LSD genes commonly affected both α-synuclein and tau propagation, our study identified the distinct sets of LSD genes that differentially regulate the propagation of either α-synuclein or tau. The specificity and efficacy of these LSD genes were retained in the disease-related phenotypes, such as pharyngeal pumping behavior and life span. This study suggests that distinct lysosomal genes differentially regulate the propagation of α-synuclein and tau, and offer a steppingstone to understanding disease specificity.