• Smart Structures and Systems
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• v.15 no.4
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• pp.997-1018
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• 2015

Structural health monitoring along with damage detection and assessment of its severity level in non-accessible reinforced concrete members using piezoelectric materials becomes essential since engineers often face the problem of detecting hidden damage. In this study, the potential of the detection of flexural damage state in the lower part of the mid-span area of a simply supported reinforced concrete beam using piezoelectric sensors is analytically investigated. Two common severity levels of flexural damage are examined: (i) cracking of concrete that extends from the external lower fiber of concrete up to the steel reinforcement and (ii) yielding of reinforcing bars that occurs for higher levels of bending moment and after the flexural cracking. The purpose of this investigation is to apply finite element modeling using admittance based signature data to analyze its accuracy and to check the potential use of this technique to monitor structural damage in real-time. It has been indicated that damage detection capability greatly depends on the frequency selection rather than on the level of the harmonic excitation loading. This way, the excitation loading sequence can have a level low enough that the technique may be considered as applicable and effective for real structures. Further, it is concluded that the closest applied piezoelectric sensor to the flexural damage demonstrates higher overall sensitivity to structural damage in the entire frequency band for both damage states with respect to the other used sensors. However, the observed sensitivity of the other sensors becomes comparatively high in the peak values of the root mean square deviation index.

• Journal of the Korea Convergence Society
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• v.9 no.6
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• pp.33-38
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• 2018

In this study, we try to detect new attacks for vehicle by learning only one class. We use Car-Hacking dataset, an intrusion detection dataset, which is used to evaluate classification performance. The dataset are created by logging CAN (Controller Area Network) traffic through OBD-II port from a real vehicle. The dataset have four attack types. One class classification is one of unsupervised learning methods that classifies attack class by learning only normal class. When using unsupervised learning, it difficult to achieve high efficiency because it does not use negative instances for learning. However, unsupervised learning has the advantage for classifying unlabeled data, which are new attacks. In this study, we use one class classifier to detect new attacks that are difficult to detect using signature-based rules on network intrusion detection system. The proposed method suggests a combination of parameters that detect all new attacks and show efficient classification performance for normal dataset.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.2
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• pp.39-44
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• 2020

Malicious codes cause damage to equipments while avoiding detection programs(vaccines). The reason why it is difficult to detect such these new malwares using the existing vaccines is that they use "signature-based" detection techniques. these techniques effectively detect already known malicious codes, however, they have problems about detecting new malicious codes. Therefore, most of vaccines have recognized these drawbacks and additionally make use of "heuristic" techniques. This paper proposes a technology to detecting unknown malicious code using deep learning. In addition, detecting malware skill using Supervisor Learning approach has a clear limitation. This is because, there are countless files that can be run on the devices. Thus, this paper utilizes Stacked Convolution AutoEncoder(SCAE) known as Semi-Supervisor Learning. To be specific, byte information of file was extracted, imaging was carried out, and these images were learned to model. Finally, Accuracy of 98.84% was achieved as a result of inferring unlearned malicious and non-malicious codes to the model.

• Smart Media Journal
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• v.4 no.4
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• pp.80-85
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• 2015

As the number of applications using the internet the rapidly increasing incidence of cyber attacks made on the internet has been increasing. In the equipment of L3 DDoS attack detection equipment in the world and incomplete detection of application layer based intelligent. Next-generation networks domestic product in high-performance wired and wireless network threat response techniques to meet the diverse requirements of the security solution is to close one performance is insufficient compared to the situation in terms of functionality foreign products, malicious code detection and signature generation research primarily related to has progressed malware detection and analysis of the research center operating in Window OS. In this paper, we describe the current status survey and analysis of the latest variety of new attack techniques and analytical skills with the latest cyber-attack analysis prejudice the security situation.

• Journal of Digital Convergence
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• v.13 no.1
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• pp.313-319
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• 2015

As the number of applications using the internet the rapidly increasing incidence of cyber attacks made on the internet has been increasing. In the equipment of L3 DDoS attack detection equipment in the world and incomplete detection of application layer based intelligent. Next-generation networks domestic product in high-performance wired and wireless network threat response techniques to meet the diverse requirements of the security solution is to close one performance is insufficient compared to the situation in terms of functionality foreign products, malicious code detection and signature generation research primarily related to has progressed malware detection and analysis of the research center operating in Window OS. In this paper, we describe the current status survey and analysis of the latest variety of new attack techniques and analytical skills with the latest cyber-attack analysis prejudice the security situation.

• Journal of Korea Multimedia Society
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• v.13 no.5
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• pp.754-762
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• 2010

Nowadays, email-based targeted attacks using malcode-bearing documents have been steadily increased. To improve the success rate of the attack and avoid anti-viruses, attackers mainly employ zero-day exploits and relevant social engineering techniques. In this paper, we propose an architecture of the email vaccine cloud system to prevent targeted attacks using malcode-bearing documents. The system extracts attached document files from email messages, performs behavior analysis as well as signature-based detection in the virtual machine environment, and completely removes malicious documents from the messages. In the process of behavior analysis, the documents are regarded as malicious ones in cases of creating executable files, launching new processes, accessing critical registry entries, connecting to the Internet. The email vaccine cloud system will help prevent various cyber terrors such as information leakages by preventing email based targeted attacks.

• Smart Media Journal
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• v.5 no.2
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• pp.77-83
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• 2016

Today, the great revolution in all financial industrial such as bank have been progressing through the utilization of IT technology actively, it is called the fintech. In this paper, we draw the draft design of NFC-based electronic payment and coupon system using FIDO framework to apply the 2 factor authentication technique for strength security. In detailed, we will study that the terminal device in front-end will be applied the 2 factor authentication and electric signature, and cloud-based payment gateway in back-end will be applied malicious code detection technique of distributed avoidance type.

• Journal of The Korean Astronomical Society
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• v.54 no.4
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• pp.129-137
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• 2021

It has been established that the acoustic mode parameters of the Sun and Sun-like stars vary over activity cycles. Since the observed variations are not consistent with an activity-related origin, even Sun-like stars showing out-of-phase changes of mode frequencies and amplitudes need to be carefully studied using other observational quantities. In order to test whether the presumed relations between the global seismic parameters are a signature of the stellar activity cycle, we analyze the photometric light curve of HD 49933 for which the first direct detection of an asteroseismic signature for activity-induced variations in a Sun-like star was made, using observations by the CoRoT space telescope. We find that the amplitude of the envelope significantly anti-correlates with both the maximum frequency of the envelope and the width of the envelope unless superflare-like events completely contaminate the light curve. However, even though the photometric proxy for stellar magnetic activity appears to show relations with the global asteroseismic parameters, they are statistically insignificant. Therefore, we conclude that the global asteroseismic parameters can be utilized in cross-checking asteroseismic detections of activity-related variations in Sun-like stars, and that it is probably less secure and effective to construct a photometric magnetic activity proxy to indirectly correlate the global asteroseismic parameters. Finally, we seismically estimate the mass of HD 49933 based on our determination of the large separation of HD 49933 with evolutionary tracks computed by the MESA code and find a value of about 1.2M_☉ and a sub-solar metallicity of Z = 0.008, which agrees with the current consensus and with asteroseismic and non-asteroseismic data.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2004.11a
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• pp.371-374
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• 2004

Induction motors are a critical component of many industrial processes and are frequently integrated in commercially available equipment. Safety, reliability, efficiency, and performance are some of the major concerns of induction motor applications. Preventive maintenance of induction motors has been a topic great interest to industry because of their wide range application of industry. Since the use of mechanical sensors, such as vibration probes, strain gauges, and accelerometers is often impractical, the motor current signature analysis (MACA) techniques have gained murk popularity as diagnostic tool. Fault tolerant control (FTC) strives to make the system stable and retain acceptable performance under the system faults. All present FTC method can be classified into two groups. The first group is based on fault detection and diagnostics (FDD). The second group is independent of FDD and includes methods such as integrity control, reliable stabilization and simultaneous stabilization. This paper presents the fundamental FDD-based FTC methods, which are capable of on-line detection and diagnose of the induction motors. Therefore, our group has developed the embedded distributed fault tolerant and fault diagnosis system for industrial motor. This paper presents its architecture. These mechanisms are based on two 32-bit DSPs and each TMS320F2407 DSP module is checking stator current, voltage, temperatures, vibration and speed of the motor. The DSPs share information from each sensor or DSP through DPRAM with hardware implemented semaphore. And it communicates the motor status through field bus (CAN, RS485). From the designed system, we get primitive sensors data for the case of normal condition and two abnormal conditions of 3 phase induction motor control system is implemented. This paper is the first step to drive multi-motors with serial communication which can satisfy the real time operation using CAN protocol.

• Journal of Korean Society for Atmospheric Environment
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• v.31 no.5
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• pp.482-492
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• 2015

Ground-based measurements were conducted from January 6 to 12 of 2015 for understanding characteristics of nitrogen containing carbonaceous aerosols as 16 amino acids at the Mokpo National University, Korea. The detailed amino acid components such as Cystine ($(SCH_2CH(NH_2)CO_2H)_2$) and Methionine ($C_5H_{11}NO_2S$) and their sources were analyzed by High-Performance Liquid Chromatography with Fluorescence Detection (HPLC-FLD) for behavior of secondary products in particulate matter. In addition, organic carbon (OC) and elemental carbon (EC) based on the carbonaceous thermal distribution (CTD), which provides detailed carbon signature characteristics relative to analytical temperature, and water soluble organic carbon (WSOC) by total organic carbon (TOC) analyzer were used to understand the carbon compound behaviors. The backward trajectories were discussed for originations of carbonaceous aerosols as well. Different airmasses were classified with the amino acids and OC thermal signatures. The results can provide to understand the aging process influenced by the long-range transport from East Sea area.