• Journal of Platform Technology
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• v.12 no.3
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• pp.62-70
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• 2024

Recent surveillance systems employ multiple sensors, such as cameras and radars, to enhance the accuracy of intrusion detection. However, object recognition through camera (RGB, Thermal) sensors may not always be accurate during nighttime, in adverse weather conditions, or when the intruder is camouflaged. In such situations, it is possible to detect intruders by utilizing the trajectories of objects extracted from camera or radar sensors. This paper proposes a method to detect intruders using only trajectory information in environments where object recognition is challenging. The proposed method involves training an LSTM-Attention based trajectory classification model using normal and abnormal (intrusion, loitering) trajectory data of animals and humans. This model is then used to identify abnormal human trajectories and perform intrusion detection. Finally, the validity of the proposed method is demonstrated through experiments using real data.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.5
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• pp.875-884
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• 2024

The proliferation of IoT networks has led to an increase in cyber attacks, highlighting the importance of Network Intrusion Detection Systems (NIDS). To overcome the limitations of traditional NIDS and cope with more sophisticated cyber attacks, there is a trend towards integrating artificial intelligence models into NIDS. However, AI-based NIDS are vulnerable to adversarial attacks, which exploit the weaknesses of algorithm. Model Type Inference Attack is one of the types of attacks that infer information inside the model. This paper proposes an optimized framework for Model Type Inference attacks against NIDS models, applying more realistic assumptions. The proposed method successfully trained an attack model to infer the type of NIDS models with an accuracy of approximately 0.92, presenting a new security threat to AI-based NIDS and emphasizing the importance of developing defence method against such attacks.

• Journal of KIISE:Databases
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• v.32 no.3
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• pp.297-303
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• 2005

When confronted with a query, question answering systems endeavor to extract the most exact answers possible by determining the answer type that fits with the key terms used in the query. However, the efficacy of such systems is limited by the fact that the terms used in a query may be in a syntactic form different to that of the same words in a document. In this paper, we present an efficient semantic query expansion methodology based on a question category concept list comprised of terms that are semantically close to terms used in a query. The semantically close terms of a term in a query may be hypernyms, synonyms, or terms in a different syntactic category. The proposed system constructs a concept list for each question type and then builds the concept list for each question category using a learning algorithm. In the question answering experiments on 42,654 Wall Street Journal documents of the TREC collection, the traditional system showed in 0.223 in MRR and the proposed system showed 0.50 superior to the traditional question answering system. The results of the present experiments suggest the promise of the proposed method.

• International Journal of Contents
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• v.12 no.2
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• pp.12-23
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• 2016

This paper presents a novel concept of Disaster Prevention Design (DPD) and its derived subjects and topics for the safety of folk villages in both Korea and Japan. Nowadays, design concepts are focused on 'human-oriented nature' as a whole and this tendency fits to be appropriate for disaster prevention against real dangers of a future society, which is expected to have far more complicated features. On the other hand, convergences have performed with other areas in the field of Information Communication Technology (ICT) so that we can easily find examples like 'the strategy of ICT-based convergence' of the Korean Government in 2014. Modern content designs including UI (user interface) and USN (ubiquitous sensor network) have been developed as one of the representative areas of ICT & UD (universal design) convergences. These days this novel concept of convergence is overcoming the existing limitations of the conventional design concept focused on product and/or service. First of all, from that point our deduced topic or subject would naturally be a monitoring system design of constructional structures in folk villages for safety. We offer an integrated model of maintenance and a management-monitoring scheme. Another important point of view in the research is a safety sign or sign system installed in folk villages or traditional towns and their standardization. We would draw up and submit a plan that aims to upgrade signs and sign systems applied to folk villages in Korea and Japan. According to our investigations, floods in Korea and earthquakes in Japan are the most harmful disasters of folk villages. Therefore, focusing on floods in the area of traditional towns in Korea would be natural. We present a water-level expectation model using deep learning simulation. We also apply this method to the area of 'Andong Hahoe' village which has been registered with the World Cultural Heritage of UNESCO. Folk village sites include 'Asan Oeam', 'Andong Hahoe' and 'Chonju Hanok' villages in Korea and 'Beppu Onsen' village in Japan. Traditional Streets and Markets and Safe Schools and Parks are also chosen as nearby test-beds for DPD based on ICT. Our final goal of the research is to propose and realize an integrated disaster prevention and/or safety system based on big data for both Korea and Japan.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.37-55
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• 2023

With the development of electronic journals and the emergence of various interdisciplinary studies, the selection of journals for publication has become a new challenge for researchers. Even if a paper is of high quality, it may face rejection due to a mismatch between the paper's topic and the scope of the journal. While research on assisting researchers in journal selection has been actively conducted in English, the same cannot be said for Korean journals. In this study, we propose a system that recommends Korean journals for submission. Firstly, we utilize SBERT (Sentence BERT) to embed abstracts of previously published papers at the document level, compare the similarity between new documents and published papers, and recommend journals accordingly. Next, the order of recommended journals is determined by considering the similarity of abstracts, keywords, and title. Subsequently, journals that are similar to the top recommended journal from previous stage are added by using a dictionary of words constructed for each journal, thereby enhancing recommendation diversity. The recommendation system, built using this approach, achieved a Top-10 accuracy level of 76.6%, and the validity of the recommendation results was confirmed through user feedback. Furthermore, it was found that each step of the proposed framework contributes to improving recommendation accuracy. This study provides a new approach to recommending academic journals in the Korean language, which has not been actively studied before, and it has also practical implications as the proposed framework can be easily applied to services.

• The Transactions of the Korea Information Processing Society
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• v.13 no.2
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• pp.48-59
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• 2024

As functions that support virtualization on their own in hardware are developed, user applications having various workloads are operating efficiently in the virtualization system. SR-IOV is a virtualization support function that takes direct access to PCI devices, thus giving a high I/O performance by minimizing the need for hypervisor or operating system interventions. With SR-IOV, network I/O acceleration can be realized in virtualization systems that have relatively long I/O paths compared to bare-metal systems and frequent context switches between the user area and kernel area. To take performance advantages of SR-IOV, network resource management policies that can derive optimal network performance when SR-IOV is applied to an instance such as a virtual machine(VM) or container are being actively studied.This paper evaluates and analyzes the network performance of SR-IOV implementing I/O acceleration is compared with Virtio in terms of 1) network delay, 2) network throughput, 3) network fairness, 4) performance interference, and 5) multi-network. The contributions of this paper are as follows. First, the network I/O process of Virtio and SR-IOV was clearly explained in the virtualization system, and second, the evaluation results of the network performance of Virtio and SR-IOV were analyzed based on various performance metrics. Third, the system overhead and the possibility of optimization for the SR-IOV network in a virtualization system with high VM density were experimentally confirmed. The experimental results and analysis of the paper are expected to be referenced in the network resource management policy for virtualization systems that operate network-intensive services such as smart factories, connected cars, deep learning inference models, and crowdsourcing.

• Journal of the Society of Cosmetic Scientists of Korea
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• v.50 no.2
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• pp.119-129
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• 2024

The cutaneous lymphatic system in humans plays a crucial role in draining interstitial fluid and activating the immune system. Environmental factors, such as ultraviolet light and natural aging, often affect structural changes of such lymphatic vessels, causing skin dysfunction. However, some limitations still exist because of no alternatives to animal testing. To better understand the skin lymphatic system, a biomimetic microfluidic platform, skin-lymph-on-a-chip, was fabricated to develop a novel in vitro skin lymphatic model of humans and to investigate the molecular and physiological changes involved in lymphangiogenesis, the formation of lymphatic vessels. Briefly, the platform involved co-culturing differentiated primary normal human epidermal keratinocytes (NHEKs) and dermal lymphatic endothelial cells (HDLECs) in vitro. Based on our system, Lymphanax^TM, which is a condensed Panax ginseng root extract obtained through thermal conversion for 21 days, was applied to evaluate the lymphangiogenic effect, and the changes in molecular factors were analyzed using a deep-learning-based algorithm. Lymphanax^TM promoted healthy lymphangiogenesis in skin-lymphon-a-chip and indirectly affected HDELCs as its components rarely penetrated differentiated NHEKs in the chip. Overall, this study provides a new perspective on Lymphanax^TM and its effects using an innovative in vitro system.

• Journal of The Korean Institute of Defense Technology
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• v.5 no.3
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• pp.8-17
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• 2023

The current weapon system is operated as a complex weapon system with various standards and protocols applied, so there is a risk of failure in smooth information exchange during combined and joint operations on the battlefield. The interoperability of weapon systems to carry out precise strikes on key targets through rapid situational judgment between weapon systems is a key element in the conduct of war. Since the Korean military went into service, there has been a need to change the configuration and improve performance of a large number of software and hardware, but there is no verification system for the impact on interoperability, and there are no related test tools and facilities. In addition, during combined and joint training, errors frequently occur during use after arbitrarily changing the detailed operation method and software of the weapon/power support system. Therefore, periodic verification of interoperability between weapon systems is necessary. To solve this problem, rather than having people schedule an evaluation period and conduct the evaluation once, AI should continuously evaluate the interoperability between weapons and power support systems 24 hours a day to advance warfighting capabilities. To solve these problems, To this end, preliminary research was conducted to improve defense interoperability capabilities by applying natural language processing techniques (①Word2Vec model, ②FastText model, ③Swivel model) (using published algorithms and source code). Based on the results of this experiment, we would like to present a methodology (automated evaluation of interoperability requirements evaluation / level measurement through natural language processing model) to implement an automated defense interoperability evaluation tool without relying on humans.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.1-25
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• 2020

In this paper, we suggest an application system architecture which provides accurate, fast and efficient automatic gasometer reading function. The system captures gasometer image using mobile device camera, transmits the image to a cloud server on top of private LTE network, and analyzes the image to extract character information of device ID and gas usage amount by selective optical character recognition based on deep learning technology. In general, there are many types of character in an image and optical character recognition technology extracts all character information in an image. But some applications need to ignore non-of-interest types of character and only have to focus on some specific types of characters. For an example of the application, automatic gasometer reading system only need to extract device ID and gas usage amount character information from gasometer images to send bill to users. Non-of-interest character strings, such as device type, manufacturer, manufacturing date, specification and etc., are not valuable information to the application. Thus, the application have to analyze point of interest region and specific types of characters to extract valuable information only. We adopted CNN (Convolutional Neural Network) based object detection and CRNN (Convolutional Recurrent Neural Network) technology for selective optical character recognition which only analyze point of interest region for selective character information extraction. We build up 3 neural networks for the application system. The first is a convolutional neural network which detects point of interest region of gas usage amount and device ID information character strings, the second is another convolutional neural network which transforms spatial information of point of interest region to spatial sequential feature vectors, and the third is bi-directional long short term memory network which converts spatial sequential information to character strings using time-series analysis mapping from feature vectors to character strings. In this research, point of interest character strings are device ID and gas usage amount. Device ID consists of 12 arabic character strings and gas usage amount consists of 4 ~ 5 arabic character strings. All system components are implemented in Amazon Web Service Cloud with Intel Zeon E5-2686 v4 CPU and NVidia TESLA V100 GPU. The system architecture adopts master-lave processing structure for efficient and fast parallel processing coping with about 700,000 requests per day. Mobile device captures gasometer image and transmits to master process in AWS cloud. Master process runs on Intel Zeon CPU and pushes reading request from mobile device to an input queue with FIFO (First In First Out) structure. Slave process consists of 3 types of deep neural networks which conduct character recognition process and runs on NVidia GPU module. Slave process is always polling the input queue to get recognition request. If there are some requests from master process in the input queue, slave process converts the image in the input queue to device ID character string, gas usage amount character string and position information of the strings, returns the information to output queue, and switch to idle mode to poll the input queue. Master process gets final information form the output queue and delivers the information to the mobile device. We used total 27,120 gasometer images for training, validation and testing of 3 types of deep neural network. 22,985 images were used for training and validation, 4,135 images were used for testing. We randomly splitted 22,985 images with 8:2 ratio for training and validation respectively for each training epoch. 4,135 test image were categorized into 5 types (Normal, noise, reflex, scale and slant). Normal data is clean image data, noise means image with noise signal, relfex means image with light reflection in gasometer region, scale means images with small object size due to long-distance capturing and slant means images which is not horizontally flat. Final character string recognition accuracies for device ID and gas usage amount of normal data are 0.960 and 0.864 respectively.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.1
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• pp.169-177
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• 2020

In South Korea, the results of R&D in science and technology are submitted to the National Science and Technology Information Service (NTIS) in reports that have Korea national science and technology standard classification codes (K-NSCC). However, considering there are more than 2000 sub-categories, it is non-trivial to choose correct classification codes without a clear understanding of the K-NSCC. In addition, there are few cases of automatic document classification research based on the K-NSCC, and there are no training data in the public domain. To the best of our knowledge, this study is the first attempt to build a highly performing K-NSCC classification system based on NTIS report meta-information from the last five years (2013-2017). To this end, about 210 mid-level categories were selected, and we conducted preprocessing considering the characteristics of research report metadata. More specifically, we propose a convolutional neural network (CNN) technique using only task names and keywords, which are the most influential fields. The proposed model is compared with several machine learning methods (e.g., the linear support vector classifier, CNN, gated recurrent unit, etc.) that show good performance in text classification, and that have a performance advantage of 1% to 7% based on a top-three F1 score.