• Journal of the Korea Convergence Society
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• v.12 no.5
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• pp.127-138
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• 2021

The aim of this study is to demonstrate interaction as a describable field and tries to understand interaction from the perspective of attributes, thus building a theoretical to help interactive designer understand this field by common rule, rather than waste huge time and labor cost on iteration. Since the concept of interaction language has been brought out in 2000, there are varies of related academical studies, but all with defect such as proposed theoretical models are built on a non-uniform scale, or the analyzing perspective are mainly based on researcher's personal experience and being too unobjective. The value of this study is the clustered resource of research which mainly based on academical review. It collected 21 papers researched on interaction paradigm or interaction attributes published since 2000, extracting 19 interaction attribute models which contains 174 interaction attributes. Furthermore, these 174 attributes were re-clustered based on a more unified standard scale, and the two theoretical models summarized from it are respectively focuses on interaction control and interaction experience, both of which covered 6 independent attributes. The propose of this theoretical models and the analyzation of the cluster static will contribute on further revealing of the importance of interaction attribute, or the attention interaction attribute has been paid on. Also, in this regard, the interactive designer could reasonably allocate their energy during design process, and the future potential on various direction of interaction design could be discussed.

• Journal of Broadcast Engineering
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• v.24 no.4
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• pp.592-601
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• 2019

For coronary procedures, X-ray angiogram images are useful for diagnosing and assisting procedures. It is challenging to accurately segment a coronary artery using only a single segmentation model in 2D X-ray images due to a complex structure of three-dimensional coronary artery, especially from phenomenon of vessels being broken in the middle or end of coronary artery. In order to solve these problems, the initial segmentation is performed using an existing single model, and the candidate regions for the sophisticate correction is estimated based on the initial segment, and the local patch-based correction is performed in the candidate regions. Through this research, not only the broken coronary arteries are re-connected, but also the distal part of coronary artery that is very thin is additionally correctly found. Further, the performance can be much improved by combining the proposed correction method with any existing coronary artery segmentation method. In this paper, the U-net, a fully convolutional network was chosen as a segmentation method and the proposed correction method was combined with U-net to demonstrate a significant improvement in performance through X-ray images from several patients.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.6
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• pp.40-53
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• 2018

In smart card data, movement of railway passengers appears in order of smart card terminal ID. The initial terminal ID holds information on the entering station's tag-in railway line, the final terminal ID the exit station tag-out railway line, and the middle terminal ID the transfer station tag subway line. During the past, when the metropolitan city rail consisted of three public corporations (Seoul Metro, Incheon Transit Corporation, and Korail), OD data was expressed in two metrics of initial and final smart card terminal ID. Recently, with the entrance of private corporations like Shinbundang Railroad Corporation, and UI Corporation, inclusion of entering transfer line terminal ID and exiting transfer line terminal ID as part of Chain OD has become standard. Exact route construction using Chain OD has thus become integral as basic data for revenue allocation amongst metropolitan railway transport corporations. Accordingly, path detection in railway networks has evolved to an optimal path detection problem using Chain OD, hence calling for a renewed solution method. This research proposes an optimal path detection method between the initial terminal ID and final terminal ID of Chain OD terminal IDs within the railway network. Here, private line transfer TagIn/Out must be reflected in optimal path detection using Chain OD. To achieve this, three types of link-based optimum path detection methods are applied in order of 1. node-link, 2. link-link, 3. link-node. The method proposed based on additional path costs is shown to satisfy the optimal conditions.

• Journal of Internet Computing and Services
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• v.22 no.4
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• pp.1-11
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• 2021

As the Internet developed, various and complex cyber attacks began to emerge. Various detection systems were used outside the network to defend against attacks, but systems and studies to detect attackers inside were remarkably rare, causing great problems because they could not detect attackers inside. To solve this problem, studies on the lateral movement detection system that tracks and detects the attacker's movements have begun to emerge. Especially, the method of using the Remote Desktop Protocol (RDP) is simple but shows very good results. Nevertheless, previous studies did not consider the effects and relationships of each logon host itself, and the features presented also provided very low results in some models. There was also a problem that the model could not explain why it predicts that way, which resulted in reliability and robustness problems of the model. To address this problem, this study proposes an interpretable RDP-based lateral movement detection system using page rank algorithm and SHAP(Shapley Additive Explanations). Using page rank algorithms and various statistical techniques, we create features that can be used in various models and we provide explanations for model prediction using SHAP. In this study, we generated features that show higher performance in most models than previous studies and explained them using SHAP.

• The Southeast Asian review
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• v.20 no.3
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• pp.183-222
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• 2010

본 연구의 목적은 2008년 후반에 발생한 국제금융위기에 대응하여 조직 개편을 선택한 기업과 그 기업 내의 근로자들 실태 파악을 하는데 있다. 특히 기업구조조정 명목으로 단행된 해고를 모면한 근로자들 (생존자)의 상태를 그들이 지각하는 각 개인의 관리자에 대한 만족도와 그들의 직무관련태도인 직무만족, 조직적 몰입 그리고 이직 의도 간의 관계 관점에서 살피고자 한다. 관리자에 대한 만족도는 근로자들의 직무 관련 태도 변수와 관련되어 사회적 선행변수로 선택되었으며 직무관련태도는 결과변수로 선택하였다. 본 연구를 통해 생존자들의 조직 내 직무관련태도에 관한 이해를 높이는데 필요한 정보를 표출해내어 그들의 실태를 파악하는데 하나의 목적을 두었으며 그들의 관리자와의 사회적 관계가 그들에게 미치는 영향을 파악하는 것이 또 하나의 목적이라 하겠다. 이상과 같은 관계 파악을 통해 얻은 정보로 관련기업이 단기적으로 성공적인 자립을 하여 장기적으로 경쟁력 있는 조직으로 성장할 수 있는 인적자원전략을 제공하는데 2차적인 목적을 두고 있다. 본 연구는 이상과 같은 목적을 달성하고자 그 유의성이 증명된 현존하는 설문을 활용한 조사기법을 채택하여 실증연구를 하였다. 수집한 자료는 SPSS 17.0과 LISREL 8.5을 사용하여 연구모형과 연구의 가설들을 검증하였다. 연구목적을 달성하고자 채택된 가설들은 기존의 문헌연구를 토대로 세워졌다. 연구대상은 태국 북부지역의 대표도시인 치앙마이 내 관광호텔 종사자들로서 외부적으로는 2008년 국제금융위기와 그에 따른 경제침체, 내부적으로는 극심한 정치적 사회적 불안에 의한 관광객의 급감소와 조직의 자구책으로 행해진 조직재구조 차원에서의 해고를 이겨낸 근로자들, 즉 생존자들이었다. 가설검증을 통해 입증된 연구결과에 의하면 1) 근로자들의 관리자에 대한 만족은 그들의 직무만족, 정서적 조직몰입과 지속적 조직몰입에 영향을 미치는 것으로 검증됐다. 2) 근로자들의 관리자에 대한 만족은 그들의 이직의도에는 영향을 미치지 못하는 것으로 검증됐다. 3) 근로자들의 직무만족은 그들의 이직의도에 영향을 미치는 것으로 검증되었다. 4) 근로자들의 조직적 몰입과 이직의도 간의 관계에 있어서는 오직 경제적 성격의 지속적 조직몰입만 영향을 미치는 것으로 검증되었다. 이러한 연구 결과는 기존의 타 연구 결과와 기본적인 맥락은 같게 나왔으나 근로자들의 정서적인 면의 부재는 2008년 후반 당시의 독특한 태국의 사회적, 경제적 그리고 문화적 실태를 보여주는 것이 아닌 가 사료된다. 특히 근로자들이 그들의 직장과의 관계와 관련된 의사결정을 가치가 아닌 경제적인 요소에 의존한다는 결과 그리고 상관인 관리자가 이러한 태도에 전혀 영향력을 미치지 못한다는 결과는 전통적으로 감성적이며 집단주의적인 동양적 문화가 지배하리라고 예상했던 태국에서는 이례적인 결과였다. 그러나 관리자들이 그들의 직무만족 그리고 조직적 몰입에 영향을 주며 직무만족이 이직 의도에 영향을 미친다는 결과와 지속적 조직몰입이 이직의도에 영향을 미친다는 결과는 장기적으로 적극적인 관리자 교육을 통해 근로자들을 조직에 정서적으로도 올바르게 안착시키고 긍정적인 구성원으로 존재하면서 조직에 기여할 수 있는 그들의 역할을 기대할 수 있다고 볼 수도 있다. 이 점은 매우 중요한데 그 이유는 해고를 이겨낸 근로자들은 그 조직이 미래를 짊어지는 마지막 기대변수이기 때문이다.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.21 no.4
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• pp.61-75
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• 2011

Recently, there is significant increasing of massive attacks, which try to infect PCs that visit websites containing pre-implanted malicious code. When visiting the websites, these hidden malicious codes can gain monetary profit or can send various cyber attacks such as BOTNET for DDoS attacks, personal information theft and, etc. Also, this kind of malicious activities is continuously increasing, and their evasion techniques become professional and intellectual. So far, the current signature-based detection to detect websites, which contain malicious codes has a limitation to prevent internet users from being exposed to malicious codes. Since, it is impossible to detect with only blacklist when an attacker changes the string in the malicious codes proactively. In this paper, we propose a novel approach that can detect unknown malicious code, which is not well detected by a signature-based detection. Our method can detect new malicious codes even though the codes' signatures are not in the pattern database of Anti-Virus program. Moreover, our method can overcome various obfuscation techniques such as the frequent change of the included redirection URL in the malicious codes. Finally, we confirm that our proposed system shows better detection performance rather than MC-Finder, which adopts pattern matching, Google's crawling based malware site detection, and McAfee.

• Journal of Broadcast Engineering
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• v.27 no.2
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• pp.185-197
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• 2022

Recently, deep learning networks with high performance for object recognition are emerging. In the case of object recognition using deep learning, it is important to build a training data set to improve performance. To build a data set, we need to collect and label the images. This process requires a lot of time and manpower. For this reason, open data sets are used. However, there are objects that do not have large open data sets. One of them is data required for license plate detection and recognition. Therefore, in this paper, we propose an artificial license plate generator system that can create large data sets by minimizing images. In addition, the detection rate according to the artificial license plate arrangement structure was analyzed. As a result of the analysis, the best layout structure was FVC_III and B, and the most suitable network was D2Det. Although the artificial data set performance was 2-3% lower than that of the actual data set, the time to build the artificial data was about 11 times faster than the time to build the actual data set, proving that it is a time-efficient data set building system.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.5
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• pp.179-186
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• 2021

Manipulation of complex objects with an anthropomorphic robot hand like a human hand is a challenge in the human-centric environment. In order to train the anthropomorphic robot hand which has a high degree of freedom (DoF), human demonstration augmented deep reinforcement learning policy optimization methods have been proposed. In this work, we first demonstrate augmentation of human demonstration in deep reinforcement learning (DRL) is effective for object manipulation by comparing the performance of the augmentation-free Natural Policy Gradient (NPG) and Demonstration Augmented NPG (DA-NPG). Then three DRL policy optimization methods, namely NPG, Trust Region Policy Optimization (TRPO), and Proximal Policy Optimization (PPO), have been evaluated with DA (i.e., DA-NPG, DA-TRPO, and DA-PPO) and without DA by manipulating six objects such as apple, banana, bottle, light bulb, camera, and hammer. The results show that DA-NPG achieved the average success rate of 99.33% whereas NPG only achieved 60%. In addition, DA-NPG succeeded grasping all six objects while DA-TRPO and DA-PPO failed to grasp some objects and showed unstable performances.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.1
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• pp.67-75
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• 2022

When acquiring a product having an OS, it is very important to identify the exact kernel version of the OS. This is because the product's administrator needs to keep checking whether a new vulnerability is found in the kernel version. Also, if there is an acquisition requirement for exclusion or inclusion of a specific kernel version, the kernel identification becomes critical to the acquisition decision. In the case of the Linux kernel used in various equipment, sometimes it becomes difficult to pinpoint the device's exact version. The reason is that many manufacturers often modify the kernel to produce their own firmware optimized for their device. Furthermore, if a kernel patch is applied to the modified kernel, it will be very different from its base kernel. Therefore, it is hard to identify the Linux kernel accurately by simple methods such as a specific file existence test. In this paper, we propose a static method to classify a specific kernel version by analyzing function names stored in the symbol table. In an experiment with 100 Linux devices, we correctly identified the Linux kernel version with 99% accuracy.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.11
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• pp.403-410
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• 2022

Non-Fungible Tokens (NFTs) created on the basis of blockchain have their own unique value, so they cannot be forged or exchanged with other tokens or coins. Using these characteristics, NFTs can be issued to digital assets such as images, videos, artworks, game characters, and items to claim ownership of digital assets among many users and objects in cyberspace, as well as proving the original. However, interest in NFTs exploded from the beginning of 2020, causing a lot of load on the blockchain network, and as a result, users are experiencing problems such as delays in computational processing or very large fees in the mining process. Additionally, all actions of users are stored in the blockchain, and digital assets are stored in a blockchain-based distributed file storage system, which may unnecessarily expose the personal information of users who do not want to identify themselves on the Internet. In this paper, we propose an NFT platform using cloud computing, access gate, conversion table, and cloud ID to improve usability and privacy problems that occur in existing system. For performance comparison between local and cloud systems, we measured the gas used for smart contract deployment and NFT-issued transaction. As a result, even though the cloud system used the same experimental environment and parameters, it saved about 3.75% of gas for smart contract deployment and about 4.6% for NFT-generated transaction, confirming that the cloud system can handle computations more efficiently than the local system.