• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.7
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• pp.963-971
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• 2022

Water pipeline network in local and metropolitan area is buried underground, by which it is hard to know the degree of pipe aging and leakage. In this study, assuming various sensor combinations installed in the water pipeline network, the optimal algorithm was derived by predicting the water flow rate and pressure through artificial intelligence algorithms such as linear regression and neuro fuzzy analysis to examine the possibility of detecting pipe leakage according to the data combination. In the case of leakage detection through water supply pressure prediction, Neuro fuzzy algorithm was superior to linear regression analysis. In case of leakage detection through water supply flow prediction, flow rate prediction using neuro fuzzy algorithm should be considered first. If flow meter for prediction don't exists, linear regression algorithm should be considered instead for pressure estimation.

• The Journal of the Korea Contents Association
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• v.9 no.4
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• pp.131-141
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• 2009

An increasing variety of malware, such as worms, spyware and adware, threatens both personal and business computing. Remotely controlled bot networks of compromised systems are growing quickly. This paper proposes an intrusion detection system based outflow traffic analysis. Many research efforts and commercial products have focused on preventing intrusion by filtering known exploits or unknown ones exploiting known vulnerabilities. Complementary to these solutions, the proposed IDS can detect intrusion of unknown new mal ware before their signatures are widely distributed. The proposed IDS is consists of a outflow detector, user monitor, process monitor and network monitor. To infer user intent, the proposed IDS correlates outbound connections with user-driven input at the process level under the assumption that user intent is implied by user-driven input. As a complement to existing prevention system, proposed IDS decreases the danger of information leak and protects computers and networks from more severe damage.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.11
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• pp.4662-4679
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• 2015

Currently, the leakage of internal information has emerged as one of the most significant security concerns in enterprise computing environments. Especially, damage due to internal information leakage by insiders is more serious than that by outsiders because insiders have considerable knowledge of the system's identification and password (ID&P/W), the security system, and the main location of sensitive data. Therefore, many security companies are developing internal data leakage prevention techniques such as data leakage protection (DLP), digital right management (DRM), and system access control, etc. However, these techniques cannot effectively block the leakage of internal information by insiders who have a legitimate access authorization. The security system does not easily detect cases which a legitimate insider changes, deletes, and leaks data stored on the server. Therefore, we focused on the insider as the detection target to address this security weakness. In other words, we switched the detection target from objects (internal information) to subjects (insiders). We concentrated on biometrics signals change when an insider conducts abnormal behavior. When insiders attempt to leak internal information, they appear to display abnormal emotional conditions due to tension, agitation, and anxiety, etc. These conditions can be detected by the changes of biometrics signals such as pulse, temperature, and skin conductivity, etc. We carried out experiments in two ways in order to verify the effectiveness of the emotional recognition technology based on biometrics signals. We analyzed the possibility of internal information leakage detection using an emotional recognition technology based on biometrics signals through experiments.

• Smart Structures and Systems
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• v.5 no.2
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• pp.153-171
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• 2009

In this paper, the analytical fault detection and diagnosis (FDD) is presented using model-based and signal-based methodology with wavelet analysis on signals obtained from sensors and sensor networks. In the model-based FDD, we present the modeling of contact interface found in soft materials, including the biomedical contacts. Fingerprint analysis and signal-based FDD are also presented with an experimental framework consisting of a mechanical pneumatic system typically found in manufacturing automation. This diagnosis system focuses on the signal-based approach which employs multi-resolution wavelet decomposition of various sensor signals such as pressure, flow rate, etc., to determine leak configuration. Pattern recognition technique and analytical vectorized maps are developed to diagnose an unknown leakage based on the established FDD information using the affine mapping. Experimental studies and analysis are presented to illustrate the FDD methodology. Both model-based and wavelet-based FDD applied in contact interface and manufacturing automation have implication towards better quality of life by applying theory and practice to understand how effective diagnosis can be made using intelligent FDD. As an illustration, a model-based contact surface technology an benefit the diabetes with the detection of abnormal contact patterns that may result in ulceration if not detected and treated in time, thus, improving the quality of life of the patients. Ultimately, effective diagnosis using FDD with wavelet analysis, whether it is employed in biomedical applications or manufacturing automation, can have impacts on improving our quality of life.

• Journal of the Korea Safety Management & Science
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• v.17 no.2
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• pp.129-137
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• 2015

This study is in regard to the gas detection system and gas detection method utilizing smart phone. This study includes; 1) the sensor module attached to the smart phone to detect and measure flammable gas or toxic gas; and 2) gas detection APP which is installed inside the smart phone and recognizes the user information and location information automatically by reading RFID tag indicating the user or the location to detect gas through the contact area where RFID and blue tooth reader is installed inside of the above mentioned smart phone, and then measures the combustible gas or toxic gas by operating above mentioned sensor module and obtains the data thus measured, and above mentioned smart phone is characterized by its transmission of the above mentioned user information, location information and measured data which are obtained by above mentioned gas detecting APP to operation server via communication network. With this, reliability for the location detecting gas by the user, the result of the measurement, etc. can be secured. Furthermore, this provides the effect of preventing artificial manipulation at the time of input which is associated with the identification of the user to be measured by utilizing removable sensor module and application or the mistake resulted from wrong input by the user. In addition, by transmitting the measured data from the sensor module carrying out gas detection to operation server, this provides the effect of making it possible to process the data thus collected to a specialized data for combustible gas or toxic gas.

• KIPS Transactions on Computer and Communication Systems
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• v.8 no.6
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• pp.151-158
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• 2019

FLUSH+RELOAD attack exposes the most serious security threat among cache side channel attacks due to its high resolution and low noise. This attack is exploited by a variety of malicious programs that attempt to leak sensitive information. In order to prevent such information leakage, it is necessary to detect FLUSH+RELOAD attack in real time. In this paper, we propose a novel run-time detection technique for FLUSH+RELOAD attack by utilizing PCM (Performance Counter Monitor) of processors. For this, we conducted four kinds of experiments to observe the variation of each counter value of PCM during the execution of the attack. As a result, we found that it is possible to detect the attack by exploiting three kinds of important factors. Then, we constructed a detection algorithm based on the experimental results. Our algorithm utilizes machine learning techniques including a logistic regression and ANN(Artificial Neural Network) to learn from different execution environments. Evaluation shows that the algorithm successfully detects all kinds of attacks with relatively low false rate.

• Journal of Radiation Protection and Research
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• v.43 no.4
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• pp.154-159
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• 2018

Background: To prevent small leakage accidents, a real-time and direct detection system for small leaks with a detection limit below that of existing systems, e.g. $0.5gpm{\cdot}hr^{-1}$, is required. In this study, a small-size beta detector, which can be installed inside the reactor containment (CT) building and detect small leaks directly, was suggested and its feasibility was evaluated using MCNPX simulation. Materials and Methods: A target nuclide was selected through analysis of radiation from radionuclides in the reactor coolant system (RCS) and the spectrum was obtained via a silicon detector simulated in MCNPX. A window was designed to reduce the background signal caused by other nuclides. The sensitivity of the detector was also estimated, and its shielding designed for installation inside the reactor CT. Results and Discussion: The beta and gamma spectrum of the silicon detector showed a negligible gamma signal but it also contained an undesired peak at 0.22 MeV due to other nuclides, not the $^{16}N$ target nuclide. Window to remove the peak was derived as 0.4 mm for beryllium. The sensitivity of silicon beta detector with a beryllium window of 1.7 mm thickness was derived as $5.172{\times}10^{-6}{\mu}Ci{\cdot}cc^{-1}$. In addition, the specification of the shielding was evaluated through simulations, and the results showed that the integrity of the silicon detector can be maintained with lead shielding of 3 cm (<15 kg). This is a very small amount compared to the specifications of the lead shielding (600 kg) required for installation of $^{16}N$ gamma detector in inside reactor CT, it was determined that beta detector would have a distinct advantage in terms of miniaturization. Conclusion: The feasibility of the beta detector was evaluated for installation inside the reactor CT to detect small leaks below $0.5gpm{\cdot}hr^{-1}$. In future, the design will be optimized on specific data.

• Analytical Science and Technology
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• v.35 no.2
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• pp.69-81
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• 2022

The method of measuring and classifying the energy category of neutrons directly using raw data acquired through a CZT detector is not satisfactory, in terms of accuracy and efficiency, because of its poor energy resolution and low measurement efficiency. Moreover, this method of measuring and analyzing the characteristics of low-energy or low-activity gamma-ray sources might be not accurate and efficient in the case of neutrons because of various factors, such as the noise of the CZT detector itself and the influence of environmental radiation. We have therefore developed an efficient method of analyzing radiation characteristics using a neutron and gamma-ray analysis algorithm for the rapid and clear identification of the type, energy, and radioactivity of gamma-ray sources as well as the detection and classification of the energy category (fast or thermal neutrons) of neutron sources, employing raw data acquired through a CZT detector. The neutron analysis algorithm is based on the fact that in the energy-spectrum channel of 558.6 keV emitted in the nuclear reaction ¹¹³Cd + ¹n → ¹¹⁴Cd + in the CZT detector, there is a notable difference in detection information between a CZT detector without a PE modulator and a CZT detector with a PE modulator, but there is no significant difference between the two detectors in other energy-spectrum channels. In addition, the gamma-ray analysis algorithm uses the difference in the detection information of the CZT detector between the unique characteristic energy-spectrum channel of a gamma-ray source and other channels. This efficient method of analyzing radiation characteristics is expected to be useful for the rapid radiation detection and accurate information collection on radiation sources, which are required to minimize radiation damage and manage accidents in national disaster situations, such as large-scale radioactivity leak accidents at nuclear power plants or nuclear material handling facilities.

• Journal of Sensor Science and Technology
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• v.33 no.5
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• pp.259-264
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• 2024

The early detection of hydrogen gas leaks is crucial because of their high explosion risk. Current oxide-semiconductor-based hydrogen sensors are reliant on electrical circuits that may fail during accidents and require high temperatures, thereby raising safety concerns. Thus, there is an urgent need for the development of simpler and more intuitive sensors that can operate at room temperature. This study proposed a hydrogen sensor based on Pd-MoO₃ nanowires. The sensor exhibited a visible color change upon exposure to hydrogen at room temperature. The Pd-MoO₃ nanowires were synthesized by decorating the surface of hydrothermally produced MoO₃ nanowires with 1-5 wt.% Pd. Upon exposure to 5% hydrogen gas at room temperature, all Pd-MoO₃ nanowires exhibited distinct color changes (∆E). In particular, the MoO₃ nanowires with 3 wt.% Pd (3Pd-MoO₃) yielded an exceptionally high ∆E value of over 15 within 10 min. Further, the 3Pd-MoO₃ nanowires exhibited a noticeable color change (∆E > 1.6) within 2 min, demonstrating their potential for highly sensitive and rapid hydrogen detection. The outstanding color change of the 3Pd-MoO₃ nanowires was attributed to valence changes in both Mo (Mo⁶⁺ and Mo⁵⁺) and Pd (Pd²⁺ and Pd⁰) upon exposure to hydrogen.

• 한국신재생에너지학회:학술대회논문집
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• 2010.11a
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• pp.81.1-81.1
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• 2010

SOFC 발전시스템의 상용화를 위해 선행되어야 할 것은 스택의 안정적 출력 및 신뢰성 확보이다. 이를 이루기 위해서는 스택을 구성하는 구성요소의 신뢰성 있는 규격 및 검수가 필요하다. 즉, 셀, 밀봉재, 분리판 및 집전체로 대변되는 구성요소들이 스택에 장착되었을 때 그 기능을 최대한 발휘하면서도 점진적 또는 급격한 품질저하가 발생되지 말아야 한다. 특히, 셀의 경우 스택의 성능에 직접적인 영향을 미치는 구성요소로서 품질에 대한 명확한 검수기준이 필요하다. SOFC용 셀은 다공성 anode, 치밀한 전해질, 그리고 다공성 cathode로 구성된 세라믹 소결체이다. 이 때 치밀한 전해질에 결함이 내재되어 있거나 물리적 힘에 의해 신규로 발생할 경우, 연료로 사용되는 수소와 공기가 만나는 cross-over가 발생하게 된다. Cross-over는 연료가 소모되는 문제도 있지만 발열로 인한 Hot spot을 형성시켜서 주변과의 온도구배를 유발하고, 이로 인해 고체 전해질의 균열전파를 일으킬 수 있고 나아가 급격한 셀의 파괴를 야기할 수 있다. 본 연구에서는 SOFC에 사용되는 셀의 형상측정, 물리적 강도 및 결함 검출을 위한 검수기법을 개발하여 스택의 신뢰성 향상과 향후 규격표준화를 위한 기반을 제공하고자, 평판형 셀의 3차원 형상을 정밀하게 측정하는 장치와 일정 면압을 인가하여 특정 형상을 갖고 있는 셀의 물리적 파괴여부를 판단할 수 있는 장치, 그리고 셀의 전해질에 내재된 결함을 검출할 수 있는 장치를 제작하였다. 본 장치들은 $1,000cm^2$급 평판형 셀까지 검수할 수 있도록 고안하여 양산시스템에 접목시킬 수 있도록 고안된 것이다. 본 장치들을 이용한 검수결과, 현재 $700cm^2$급 평판형 셀의 경우 최대 camber가 4mm 이하, 전해질의 He leak rate는 $5{\times}10^{-5}mbar.l/s.cm^2$ 이하라는 검수규격을 본 연구소에서 운전하는 스택에 1차적으로 적용하였으며 현재 검수규격의 신뢰성 및 강화를 위한 연구를 수행 중에 있다.