• Plant Breeding and Biotechnology
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• v.2 no.3
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• pp.224-230
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• 2014

Amplified fragment length polymorphism (AFLP) is a molecular marker technique based on DNA and is extremely useful in detection of high polymorphism between closely related genotypes like Korean wheat cultivars. Six sequence characterized amplified regions (SCARs) have been developed from inter simple sequence repeat (ISSR) analysis which enabled the identification and differentiation of 13 Korean wheat cultivars from the other cultivars. We used six combinations of primer sets in our AFLP analysis for developing additional cultivar-specific markers in Korean wheat. Fifty-eight of the AFLP bands were isolated from EA-ACG/MA-CAC, EA-AGC/MA-CTG and EA-AGG/MA-CTA primer combinations. Of which 40 bands were selected to design SCAR primer pairs for Korean wheat cultivar identification. Three of 58 amplified primer pairs, KWSM006, KWSM007 and JkSP, enabled wheat cultivar identification. Consequently, 23 of 32 Korean wheat cultivars were classified by eight SCAR marker sets.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.150-153
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• 2022

This paper reports a technique that automatically extracts object shapes through Dense-Net, and subsequently, detects the objects using Mask R-CNN in a manufacturing site, in which workers and objects are mixed. It is based on the customized factory dataset by targeting workers, machines, tools, control boxes, and products as the objects. Mask R-CNN supports multi-object recognition as a well-known object recognition method, while Dense-Net effectively extracts a feature from multiple and overlapping objects. After immediate implementation using the two technologies, the object is naturally extracted from a still image of the manufacturing site to describe image. Afterwards, the result is planned to be used to detect workers' abnormal behavior by adding a label on the objects.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.256-257
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• 2022

In this paper, we introduce a method of extracting three-dimensional feature points through the movement of a single mobile device. Using a monocular camera, a 2D image is acquired according to the camera movement and a baseline is estimated. Perform stereo matching based on feature points. A feature point and a descriptor are acquired, and the feature point is matched. Using the matched feature points, the disparity is calculated and a depth value is generated. The 3D feature point is updated according to the camera movement. Finally, the feature point is reset at the time of scene change by using scene change detection. Through the above process, an average of 73.5% of additional storage space can be secured in the key point database. By applying the algorithm proposed to the depth ground truth value of the TUM Dataset and the RGB image, it was confirmed that the\re was an average distance difference of 26.88mm compared with the 3D feature point result.

• Journal of Advanced Navigation Technology
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• v.27 no.5
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• pp.540-545
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• 2023

AESA radar is able to instantaneously and adaptively position and control the beam, and this enables to have interleaved mode in modern airborne AESA radar which can maximize situational awareness capability. Interleaved mode provides two or more modes simultaneously, such as Air to Air mode and Sea Surface mode by time sharing technique. In this interleaved mode, performance degradation is inevitable, compared with single mode operation, and effective resource allocation is the key component for the success of interleaved mode. In this paper, we identified performance evaluation items for each mode to analyze interleaved mode performance and proposed effective resource allocation methodology to achieve graceful performance degradation of each mode, focusing on detection range. We also proposed beam scheduling techniques for interleaved mode.

• International Journal of Computer Science & Network Security
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• v.23 no.11
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• pp.99-109
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• 2023

The darknet is frequently referred to as the hub of illicit online activity. In order to keep track of real-time applications and activities taking place on Darknet, traffic on that network must be analysed. It is without a doubt important to recognise network traffic tied to an unused Internet address in order to spot and investigate malicious online activity. Any observed network traffic is the result of mis-configuration from faked source addresses and another methods that monitor the unused space address because there are no genuine devices or hosts in an unused address block. Digital systems can now detect and identify darknet activity on their own thanks to recent advances in artificial intelligence. In this paper, offer a generalised method for deep learning-based detection and classification of darknet traffic. Furthermore, analyse a cutting-edge complicated dataset that contains a lot of information about darknet traffic. Next, examine various feature selection strategies to choose a best attribute for detecting and classifying darknet traffic. For the purpose of identifying threats using network properties acquired from darknet traffic, devised a hybrid deep learning (DL) approach that combines Recurrent Neural Network (RNN) and Bidirectional LSTM (BiLSTM). This probing technique can tell malicious traffic from legitimate traffic. The results show that the suggested strategy works better than the existing ways by producing the highest level of accuracy for categorising darknet traffic using the Black widow optimization algorithm as a feature selection approach and RNN-BiLSTM as a recognition model.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.6
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• pp.1073-1080
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• 2023

The development and speed improvement of the Internet network, and the development of many platforms based on it, have brought about a rapid expansion of production and consumption of various contents. However, the existing IP-based Internet system cannot efficiently cope with such an urgent increase in data. Accordingly, an alternative called the CCN(Contents Centric Network) has emerged, enabling more efficient data transmission and reception centered on content rather than host. In this paper, we will deal with the mobility of intermediate nodes in CCN real-time streaming service, which is one of the major research fields of CCN, and minimize network overload through more efficient path switching through RSSI detection. In other words, by improving the method of selecting and switching a spare path when an intermediate node located between the requester(consumer) and the provider moves, a mechanism for managing data transmission is not interrupted and unnecessary load due to route switching does not occur in the network.

• Journal of the Korea Society of Computer and Information
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• v.29 no.6
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• pp.39-51
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• 2024

This paper proposes a novel approach to enhance the security of container-based systems by analyzing system calls to dynamically detect race conditions without modifying the kernel. Container escape attacks allow attackers to break out of a container's isolation and access other systems, utilizing vulnerabilities such as race conditions that can occur in parallel computing environments. To effectively detect and defend against such attacks, this study utilizes eBPF to observe system call patterns during attack attempts and employs a AdaBoost model to detect them. For this purpose, system calls invoked during the attacks such as Dirty COW and Dirty Cred from popular applications such as MongoDB, PostgreSQL, and Redis, were used as training data. The experimental results show that this method achieved a precision of 99.55%, a recall of 99.68%, and an F1-score of 99.62%, with the system overhead of 8%.

• Journal of the Korea Society of Computer and Information
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• v.29 no.1
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• pp.69-75
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• 2024

This study focuses on enhancing the understanding of the form and characteristics of traditional towers, or 'pagodas,' by utilizing WebXR technology to enable users to explore 3D models and experience them in virtual reality on the web. Traditional towers in Korea pose challenges for direct on-site verification due to their size, making it difficult to examine the structure and features of each level. To address these issues, this research aims to provide users with a WebXR service that allows them to remotely explore and analyze towers without geographical or temporal constraints. The research methodology involves utilizing WebAR to offer a web-based service where users can directly view the original form of the tower's 3D model using smart devices both online and on-site. However, outdoor conditions may affect performance, and to address this, a tower-outline detection and matching technique was employed. Consequently, we propose a remote support service for traditional towers, allowing users to remotely access information and features of various towers nationwide on the web. Meanwhile, on-site visits can involve experiencing augmented reality representations of towers using smart devices.

• Journal of Practical Engineering Education
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• v.16 no.1_spc
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• pp.59-64
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• 2024

As the aging population grows, the incidence of cancer is increasing. Skin cancer appears externally, but people often don't notice it or simply overlook it. As a result, if the early detection period is missed, the survival rate in the case of late stage cancer is only 7.5-11%. However, the disadvantage of diagnosing, serious skin cancer is that it requires a lot of time and money, such as a detailed examination and cell tests, rather than simple visual diagnosis. To overcome these challenges, we propose an Attention-based CNN model skin cancer classification system. If skin cancer can be detected early, it can be treated quickly, and the proposed system can greatly help the work of a specialist. To mitigate the problem of image data imbalance according to skin cancer type, this skin cancer classification model applies the Over Sampling, technique to data with a high distribution ratio, and adds a pre-learning model without an Attention layer. This model is then compared to the model without the Attention layer. We also plan to solve the data imbalance problem by strengthening data augmentation techniques for specific classes.

• The Korean Journal of Physiology and Pharmacology
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• v.28 no.4
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• pp.335-344
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• 2024

Diphenyleneiodonium (DPI) has been widely used as an inhibitor of NADPH oxidase (Nox) to discover its function in cardiac myocytes under various stimuli. However, the effects of DPI itself on Ca²⁺ signaling and contraction in cardiac myocytes under control conditions have not been understood. We investigated the effects of DPI on contraction and Ca²⁺ signaling and their underlying mechanisms using video edge detection, confocal imaging, and whole-cell patch clamp technique in isolated rat cardiac myocytes. Application of DPI suppressed cell shortenings in a concentration-dependent manner (IC₅₀ of ≅0.17 µM) with a maximal inhibition of ~70% at ~100 µM. DPI decreased the magnitude of Ca²⁺ transient and sarcoplasmic reticulum Ca²⁺ content by 20%-30% at 3 µM that is usually used to remove the Nox activity, with no effect on fractional release. There was no significant change in the half-decay time of Ca²⁺ transients by DPI. The L-type Ca²⁺ current (I_Ca) was decreased concentration-dependently by DPI (IC₅₀ of ≅40.3 µM) with ≅13.1%-inhibition at 3 µM. The frequency of Ca²⁺ sparks was reduced by 3 µM DPI (by ~25%), which was resistant to a brief removal of external Ca²⁺ and Na⁺. Mitochondrial superoxide level was reduced by DPI at 3-100 µM. Our data suggest that DPI may suppress L-type Ca²⁺ channel and RyR, thereby attenuating Ca²⁺-induced Ca²⁺ release and contractility in cardiac myocytes, and that such DPI effects may be related to mitochondrial metabolic suppression.