• Journal of the Korea Institute of Information Security & Cryptology
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• v.22 no.3
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• pp.585-600
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• 2012

Security issues in online games are increasing as the online game industry grows. Real money trading (RMT) by online game users has become a security issue in several countries including Korea because RMT is related to criminal activities such as money laundering or tax evasion. RMT-related activities are done by professional work forces, namely gold-farmers, and many of them employ the automated program, bot, to gain cyber asset in a quick and efficient way. Online game companies try to prevent the activities of gold-farmers using game bots detection algorithm and block their accounts or IP addresses. However, game bot detection algorithm can detect a part of gold-farmer's network and IP address blocking also can be detoured easily by using the virtual private server or IP spoofing. In this paper, we propose a method to detect gold-farmer groups by analyzing their connection patterns to the online game servers, particularly information on their routing and source locations. We verified that the proposed method can reveal gold-farmers' group effectively by analyzing real data from the famous MMORPG.

• International Journal of Advanced Culture Technology
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• v.8 no.3
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• pp.54-60
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• 2020

Online cybercrime has various causes. The criminal guilty language, Criminal lingo is active in the shaded area with the bilateral aspect of the word on cyber. It has been continuously producing massive risk factors in cyberspace. Criminals are shared and disseminated online. It has been linked with fake news and aids to suicide that has recently become an issue. Thus the criminal lingo has become a real danger factor on cyber interface. Recently, Criminal lingo is shared and distributed as cyber hazard information. It is transformed that damaging to the youth and ordinary people through the internet and social networks. In order to take action, it is necessary to construct an expert system based on AI to implement a smart management architecture with block-chain technology. In this paper, we study technically a new smart management architecture which uses artificial intelligence based decision algorithm and block-chain tracking technology to prevent the spread of criminal lingo factors in the evolving cyber world. In addition, through the off-line regular patrol program of police units, we proposed the conversion of online regular patrol program for "cyber harem area".

• Korean Journal of Computational Design and Engineering
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• v.15 no.6
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• pp.460-466
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• 2010

These days, it is getting important to have more chances to visit natural environment for the wellbeing life. The advancement of digital technologies allows us to get useful information regardless of time and location. In this paper, we investigate a way of improving user experience in the natural green environment using the digital technologies. This paper proposes a system called Green-UX-Story which supports users to create and share their story through the online and offline green experience. The system consists of two main systems: the "Story-Field" and the "Story-Tree". The Story-Field consists of a certain number of the Story-Trees allocated to a certain green site. In the Story-Field, users can see the Story-Trees that are owned by other users and share their stories. The Story-Tree contains the user-story in each branch and green information that are captured by the user on the web. The Story-Tree is generated using the L-system algorithm as the user adds stories. The main purpose of the system is to enable users to write their green stories from the both online and offline experiences. This paper describes the system configuration and the user scenario of the Green-UX-story.

• Journal of Drive and Control
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• v.14 no.3
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• pp.40-49
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• 2017

This study presents an online estimation of an excavator's rotational inertia by using recursive least square with forgetting. It is difficult to measure rotational inertia in real systems. Against this background, online estimation of rotational inertia is essential for improving safety and automation of construction equipment such as excavators because changes in inertial parameter impact dynamic characteristics. Regarding an excavator, rotational inertia for swing motion may change significantly according to working posture and digging conditions. Hence, rotational inertia estimation by predicting swing motion is critical for enhancing working safety and automation. Swing velocity and damping coefficient were used for rotational inertia estimation in this study. Updating rules are proposed for enhancing convergence performance by using the damping coefficient and forgetting factors. The proposed estimation algorithm uses three forgetting factors to estimate time-varying rotational inertia, damping coefficient, and torque with different variation rates. Rotational inertia in a typical working scenario was considered for reasonable performance evaluation. Three simulations were conducted by considering several digging conditions. Presented estimation results reveal the proposed estimation scheme is effective for estimating varying rotational inertia of the excavator.

• Journal of Power Electronics
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• v.14 no.5
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• pp.980-988
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• 2014

This paper proposes an online gain tuning algorithm for a robust sliding mode speed controller of surface-mounted permanent magnet synchronous motor (SPMSM) drives. The proposed controller is constructed by a fuzzy neural network control (FNNC) term and a sliding mode control (SMC) term. Based on a fuzzy neural network, the first term is designed to approximate the nonlinear factors while the second term is used to stabilize the system dynamics by employing an online tuning rule. Therefore, unlike conventional speed controllers, the proposed control scheme does not require any knowledge of the system parameters. As a result, it is very robust to system parameter variations. The stability evaluation of the proposed control system is fully described based on the Lyapunov theory and related lemmas. For comparison purposes, a conventional sliding mode control (SMC) scheme is also tested under the same conditions as the proposed control method. It can be seen from the experimental results that the proposed SMC scheme exhibits better control performance (i.e., faster and more robust dynamic behavior, and a smaller steady-state error) than the conventional SMC method.

• Journal of Information Processing Systems
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• v.8 no.4
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• pp.653-668
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• 2012

In this study, an improved HMM based recognition model is proposed for online English and Korean handwritten characters. The pattern elements of the handwriting model are sub character strokes and ligatures. To deal with the problem of handwriting style variations, a modified Hierarchical Clustering approach is introduced to partition different writing styles into several classes. For each of the English letters and each primitive grapheme in Korean characters, one HMM that models the temporal and spatial variability of the handwriting is constructed based on each class. Then the HMMs of Korean graphemes are concatenated to form the Korean character models. The recognition of handwritten characters is implemented by a modified level building algorithm, which incorporates the Korean character combination rules within the efficient network search procedure. Due to the limitation of the HMM based method, a post-processing procedure that takes the global and structural features into account is proposed. Experiments showed that the proposed recognition system achieved a high writer independent recognition rate on unconstrained samples of both English and Korean characters. The comparison with other schemes of HMM-based recognition was also performed to evaluate the system.

• Physical Therapy Korea
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• v.26 no.3
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• pp.106-114
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• 2019

Background: It is difficult to assess whether community-dwelling frail older adults may remain pre-frail status or improve into a robust state without being directly checked by health care professionals. The health information perceived by older adults is considered to be one of best sources of potential concerns in older adult population. An online measurement system combined with item response theory (IRT) and computer adaptive testing (CAT) methods is likely to become a realistic approach to remotely monitor physical activity status of frail older adults. Objects: This article suggests an approach to provide a precise and efficient means of measuring physical activity levels of community-dwelling frail older adults. Methods: Article reviews were reviewed and summarized. Results: In comparison to the classical test theory (CTT), the IRT method is empirically aimed to focus on the psychometric properties of individual test items in lieu of the test as a whole. These properties allow creating a large item pool that can capture the broad range of physical activity levels. The CAT method administers test items by an algorithm that select items matched to the physical activity levels of the older adults. Conclusion: An online measurement system combined with these two methods would allow adequate physical activity measurement that may be useful to remotely monitor the activity level of community-dwelling frail older adults.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.05a
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• pp.89-93
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• 2014

Reviews are very important for potential consumer' making choices. They are also used by manufacturers to find problems of their products and to collect competitors' business information. But someone write fake reviews to mislead readers to make wrong choices. Therefore detecting fake reviews is an important problem for the E-commerce sites. Support Vector Machines (SVMs) are very important text classification algorithms with excellent performance. In this paper, we propose a new incremental algorithm based on weight and the extension of Karush-Kuhn-Tucker(KKT) conditions and Convex Hull for online Review Spam Detection. Finally, we analyze its performance in theory.

• Journal of Information Technology Services
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• v.18 no.1
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• pp.219-234
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• 2019

Fake news threaten democratic elections and causes social conflicts, resulting in major damage. However, the concept of fake news is hard to define, as there is a saying, "News is not fake, fake is not news." Fake news, however, has irreversible characteristics that can not be recovered or reversed completely through post-punishment of economic and political benefits. It is also rapidly spreading in the early days. Therefore, it is very important to preemptively detect these types of articles and prevent their blind proliferation. The existing countermeasures are focused on reporting fake news, raising the level of punishment, and the media & academia to determine the authenticity of the news. Researchers are also trying to determine the authenticity by analyzing its contents. Apart from the contents of fake news, determining the behavioral characteristics of the promoters and its qualities can help identify the possibility of having fake news in advance. The online community has a fake news interception and response tradition through its long-standing community-based activities. As a result, I attempted to model the fake news by analyzing the affirmation-denial analysis and posting behavior by securing the web board crawl of the 'M community' bulletin board during the 2017 Korean presidential election period. Random forest algorithm deemed significant. The results of this research will help counteract fake news and focus on preemptive blocking through behavioral analysis rather than post-judgment after semantic analysis.

• International conference on construction engineering and project management
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• 2020.12a
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• pp.387-396
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• 2020

Wearable biosensors have the potential to non-invasively and continuously monitor seniors' stress in their daily interaction with the urban environment, thereby enabling to address the stress and ultimately advance their outdoor mobility. However, current wearable biosensor-based stress detection methods have several drawbacks in field application due to their dependence on batch-learning algorithms. First, these methods train a single classifier, which might not account for multiple subjects' different physiological reactivity to stress. Second, they require a great deal of computational power to store and reuse all previous data for updating the signle classifier. To address this issue, we tested the feasibility of online multi-task learning (OMTL) algorithms to identify multiple seniors' stress from electrodermal activity (EDA) collected by a wristband-type biosensor in a daily trip setting. As a result, OMTL algorithms showed the higher test accuracy (75.7%, 76.2%, and 71.2%) than a batch-learning algorithm (64.8%). This finding demonstrates that the OMTL algorithms can strengthen the field applicability of the wearable biosensor-based stress detection, thereby contributing to better understanding the seniors' stress in the urban environment and ultimately advancing their mobility.