• Journal of Power Electronics
• /
• v.18 no.2
• /
• pp.558-572
• /
• 2018

In addition to its modular nature, a Hybrid Modular Multilevel Converter (HMMC) assembled from half-bridge and full-bridge sub-modules, is able to block DC faults with a minimum number of switching devices, which makes it attractive for high power applications. This paper introduces a control strategy based on the Root-Least Square (RLS) algorithm to estimate the capacitor voltages instead of using direct measurements. This action eliminates the need for voltage transducers in the HMMC sub-modules and the associated communication link with the central controller. In addition to capacitor voltage balancing and suppression of circulating currents, a fault tolerant control unit (FTCU) is integrated into the proposed strategy to modify the parameters of the HMMC controller. On advantage of the proposed FTCU is that it does not need extra components. Furthermore, a fault detection unit is adapted by utilizing a hybrid estimation scheme to detect sub-module faults. The behavior of the suggested technique is assessed using PSCAD offline simulations. In addition, it is validated using a real-time digital simulator connected to a real time controller under various normal and fault conditions. The proposed strategy shows robust performance in terms of accuracy and time response since it succeeds in stabilizing the HMMC under faults.

• Journal of Korea Game Society
• /
• v.9 no.5
• /
• pp.53-61
• /
• 2009

Game industry, especially MMORPG (Massively Multiplayer Online Role Playing Game) has rapidly been expanding in these days. In this background, lots of online game security incidents have been increasing and getting more diversity. One of the most critical security incidents is 'Bots', mimics human player's playing behaviors. Bots performs the task without any manual works, it is considered unfair with other players. So most game companies try to block Bots by analyzing the packets between clients and servers. However this method can be easily attacked, because the packets are changeable when it is send to server. In this paper, we propose a Bots detection method by observing the playing patterns of game characters with data on server. In this method, Bots developers cannot handle the data, because it is working on server. Therefore Bots cannot avoid it and we can find Bots users more completely.

• Journal of Internet Computing and Services
• /
• v.21 no.2
• /
• pp.19-26
• /
• 2020

This paper analyses the feasibility of using implantable antennas to detect and monitor tumors. We analyze this setting according to the wireless propagation loss and signal fading produced by human bodies and their environment in an indoor scenario. The study is based on the ITU-R propagation recommendations and prediction models for the planning of indoor radio communication systems and radio local area networks in the frequency range of 300 MHz to 100 GHz. We conduct primary estimations on 915 MHz and 2.4 GHz operating frequencies. The path loss presented in most short-range wireless implant devices does not take into account the human body as a channel itself, which causes additional losses to wireless designs. In this paper, we examine the propagation through the human body, including losses taken from bones, muscles, fat, and clothes, which results in a more accurate characterization and estimation of the channel. The results obtained from our simulation indicates a variation of the return loss of the spiral antenna when a tumor is located near the implant. This knowledge can be applied in medical detection, and monitoring of early tumors, by analyzing the electromagnetic field behavior of the implant. The tumor was modeled under CST Microwave Studio, using Wisconsin Diagnosis Breast Cancer Dataset. Features like the radius, texture, perimeter, area, and smoothness of the tumor are included along with their label data to determine whether the external shape has malignant or benign physiognomies. An explanation of the feasibility of the system deployment and technical recommendations to avoid interference is also described.

• Proceedings of the Korean Magnestics Society Conference
• /
• 2010.06a
• /
• pp.96-96
• /
• 2010

We report the proper resistance-area products in the single crystalline bcc CoFe/MgO tunnel contact on nondegenerate n-Ge desirable for efficient spin injection and detection at room temperature. The electric properties of the crystalline CoFe(5 nm)/MgO(1.5,2.0,2.5 nm)/n-Ge(001) tunnel contacts have been investigated by I-V-T and C-V measurements. Interestingly, the tunnel contact with the 2-nm MgO exhibits the ohmic behavior with low resistance-area products, satisfying the theoretical conditions required for significant spin injection and detection. This result is ascribed to the presence of MgO layer between CoFe and n-Ge, enhancing the Schottky pinning parameter as well as shifting the charge neutrality level.

• Journal of the Korea Society of Computer and Information
• /
• v.25 no.11
• /
• pp.131-138
• /
• 2020

Malware is generally recognized as a computer program that penetrates another computer system and causes malicious behavior intended by the developer. In cyberspace, it is also used as a cyber weapon to attack adversary. The most important factor that a malware must have as a cyber weapon is that it must achieve its intended purpose before being detected by the other's detection system. It requires a lot of time and expertise to create a single malware to avoid the other's detection system. We propose the framework that automatically generates variant malware when a binary code type malware is input using the DCM technique. In this framework, the sample malware was automatically converted into variant malware, and it was confirmed that this variant malware was not detected in the signature-based malware detection system.

• Journal of the Korea Society for Simulation
• /
• v.17 no.3
• /
• pp.9-18
• /
• 2008

Unlike conventional researches, we are able to i) compare the fuzzy logic based BBA with non-fuzzy BBA for verifying the effective performance of the proposed fuzzy logic application ii) dynamically respond to the intrusion using BBA whereas the previous IDS was responding statically and iii) expect that this would be a cornerstone for more practical application researches (analyzing vulnerability and examining countermeasures, etc.) of security simulation. Several simulation tests performed on the targer network will illustrate our techniques. And this paper applies fuzzy logic to reduce the false negative that is one of the main problems of IDS. Intrusion detection is complicated decision-making process, which generally involves enormous factors about the monitored system. Fuzzy evaluation component model, which is a decision agent in the distributed IDS, can consider various factors based on fuzzy logic when an intrusion behavior is detected. The performance obtained from the coordination of intrusion detection agent with fuzzy logic is compared against the corresponding non fuzzy type intrusion detection agent. The results of these comparisons allow us to evaluate a relevant improvement on the fuzzy logic based BBA.

• Journal of Convergence for Information Technology
• /
• v.10 no.6
• /
• pp.140-146
• /
• 2020

Since the current crime prevention systems have a standard mechanism that victims request for help by themselves or ask for help from a third party nearby, it is difficult to obtain appropriate help in situations where a prompt response is not possible. In this study, we proposed and developed an automatic rescue request model and system using Deep Learning and OpenCV. This study is based on the prerequisite that immediate and precise threat detection is essential to ensure the user's safety. We validated and verified that the system identified by more than 99% of the object's accuracy to ensure the user's safety, and it took only three seconds to complete all necessary algorithms. We plan to collect various types of threats and a large amount of data to reinforce the system's capabilities so that the system can recognize and deal with all dangerous situations, including various threats and unpredictable cases.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.9 no.8
• /
• pp.3005-3027
• /
• 2015

Spam has become one of the top threats of micro-blogging networks as the representations of rumor spreading, advertisement abusing and malware distribution. With the increasing popularity of micro-blogging, the problems will exacerbate. Prior detection tools are either designed for specific types of spams or not robust enough. Spammers may escape easily from being detected by adjusting their behaviors. In this paper, we present a novel model to quantitatively evaluate information diffusion in micro-blogging networks. Under this model, we found that spam posts differ wildly from the non-spam ones. First, the propagations of non-spam posts mostly result from their followers, but those of spam posts are mainly from strangers. Second, the non-spam posts relatively last longer than the spam posts. Besides, the non-spam posts always get their first reposts/comments much sooner than the spam posts. With the features defined in our model, we propose an RBF-based approach to detect spams. Different from the previous works, in which the features are extracted from individual profiles or contents, the diffusion features are not determined by any single user but the crowd. Thus, our method is more robust because any single user's behavior changes will not affect the effectiveness. Besides, although the spams vary in types and forms, they're propagated in the same way, so our method is effective for all types of spams. With the real data crawled from the leading micro-blogging services of China, we are able to evaluate the effectiveness of our model. The experiment results show that our model can achieve high accuracy both in precision and recall.

• Smart Media Journal
• /
• v.1 no.2
• /
• pp.47-53
• /
• 2012

Wind energy is currently the fastest growing source of renewable energy used for electrical generation around world. Wind farms are adding a significant amount of electrical generation capacity. The increase in the number of wind farms has led to the need for more effective operation and maintenance procedures. Condition Monitoring System(CMS) can be used to aid plant owners in achieving these goals. In this work, systematic design procedure for artificial neural network based normal behavior model which can be applied for fault detection of various devices is proposed. Furthermore, to verify the design method SCADA(Supervisor Control and Data Acquisition) data from 850kW wind turbine system installed in Beaung port were utilized.

• Journal of the Korea Society of Computer and Information
• /
• v.18 no.3
• /
• pp.35-45
• /
• 2013

MANET has a weak point because it allows access from not only legal nodes but also illegal nodes. Most of the MANET researches had been focused on attack on routing path or packet forwarding. Nevertheless, there are insuffcient studies on a comprehensive approach to detect various attacks on malicious nodes at packet forwarding processes. In this paper, we propose a technique, named DTecBC (detection technique of malicious node behaviors based on collaboration), which can handle more effciently various types of malicious node attacks on MANET environment. The DTecBC is designed to detect malicious nodes by communication between neighboring nodes, and manage malicious nodes using a maintain table. OPNET tool was used to compare with Watchdog, CONFIDANT, SRRPPnT for verifying effectiveness of our approach. As a result, DTecBC detects various behaviors of malicious nodes more effectively than other techniques.