• KEPCO Journal on Electric Power and Energy
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• v.4 no.2
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• pp.101-105
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• 2018

In this paper, the kernel regression model is applied for the case study of gas turbine abnormal state analysis. In addition to vibration analysis at the remote site, the kernel regression model technique can is useful for analyzing abnormal state of rotor vibration signals of gas turbine in power plant. In monitoring based on data-driven techniques correlated measurements, the fault free training data of shaft vibration obtained during normal operations of gas turbine are used to develop a empirical model based on auto-associative kernel regression. This data-driven model can be used to predict virtual measurements, which are compared with real-time data, generating residuals. Any faults in the system may cause statistically abnormal changes in these residuals and could be detected. As the result, the kernel regression model provides information that can distinguish anomalies such as sensor failure in a shaft vibration signal.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.10a
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• pp.273-275
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• 2009

In recent years, many home machines have been developed in order to design and control comfortable interior inside houses. For such task, important factors are temperature, natural and interior lights, and space and control of relative factors each other. This paper presents novel sensor module inside houses, which is designed based on these factors through the PMV (Predicted Mean Vote) standard using information about home state. Moreover, LonWorks based power line communication technique is utilized for control interior lights and air-conditioners by means of wireless remote controllers. This mechanism is systemically operated via intelligent control framework.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.6
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• pp.2148-2167
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• 2021

The proliferation of the Internet of Things (IoT) technologies and applications, especially the rapid rise in the use of mobile devices, from individuals to organizations, has led to the fundamental role of secure wireless networks in all aspects of services that presented with many opportunities and challenges. To ensure the CIA (confidentiality, integrity and accessibility) security model of the networks security and high efficiency of performance results in various resource-constrained applications and environments of the IoT platform, DDO-(data-driven operation) based constructions have been introduced as a primitive design that meet the demand of high speed encryption systems. Among of them, the TMN-family ciphers which were proposed by Tuan P.M., Do Thi B., etc., in 2016, are entirely suitable approaches for various communication applications of wireless mobile networks (WMNs) and advanced wireless sensor networks (WSNs) with high flexibility, applicability and mobility shown in two different algorithm selections, TMN64 and TMN128. The two ciphers provide strong security against known cryptanalysis, such as linear attacks and differential attacks. In this study, we demonstrate new probability results on the security of the two TMN construction versions - TMN64 and TMN128, by proposing efficient related-key recovery attacks. The high probability characteristics (DCs) are constructed under the related-key differential properties on a full number of function rounds of TMN64 and TMN128, as 10-rounds and 12-rounds, respectively. Hence, the amplified boomerang attacks can be applied to break these two ciphers with appropriate complexity of data and time consumptions. The work is expected to be extended and improved with the latest BCT technique for better cryptanalytic results in further research.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.22 no.2
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• pp.347-355
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• 2012

A biometric hardware security module is a physical device that comes in the form of smartcard or some other USB type security token is composed with biometric sensor and microcontroller unit (MCU). These modules are designed to process key generation and electronic signature generation inside of the device (so that the security token can safely save and store confidential information, like the electronic signature generation key and the biometric sensing information). However, the existing model is not consistent that can be caused by the disclosure of an ID and password, which is used by the existing personal authentication technique based on the security token, and provide a high level of security and personal authentication techniques that can prevent any intentional misuse of a digital certificate. So, this paper presents a model that can provide high level of security by utilizing the biometric security token and Public Key Infrastructure efficiently, presenting a model for privacy preserving personal authentication that links the biometric security token and the digital certificate.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.2
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• pp.165-170
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• 2021

Machine learning has recently been applied to research in most areas. This is because the results of machine learning are not determined, but the learning of input data creates the objective function, which enables the determination of new data. In addition, the increase in accumulated data affects the accuracy of machine learning results. The data collected here is an important factor in machine learning. The proposed system is a convergence system of cloud systems and local fog systems for service delivery. Thus, the cloud system provides machine learning and infrastructure for services, while the fog system is located in the middle of the cloud and the user to collect and refine data. The data for this application shall be based on the Sensitive data generated by smart devices. The machine learning technique applied to this system uses SVM algorithm for classification and RNN algorithm for status recognition.

• Nuclear Engineering and Technology
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• v.54 no.7
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• pp.2614-2624
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• 2022

Monte Carlo simulations were used to model a portable Neutron backscattering (NBT) sensor suitable for detecting plastic anti-personnel mines (APMs) buried in dry and moist soils. The model consists of a 100 MBq ²⁵²Cf source encapsulated in a neutron reflector/shield assembly and centered between two ³He detectors. Multi-parameter optimization was performed to investigate the efficiency of Be/Zr(BH₄)₄ and Be/Be(BH₄)₂ assemblies in terms of increasing the signal-to-background (S/B) ratio and reducing the total dose equivalent rate. The MCNP results showed that 2 cm Be/3 cm Zr(BH₄)₄ and 2 cm Be/3 cm Be(BH₄)₂ are the optimal configurations. However, due to portability requirements and abundance of Be, the ²⁵²Cf-2 cm Be/3 cm Be(BH₄)₂ NBT model was selected to scan the center of APM buried 3 cm deep in dry and moist soils. The selected NBT model has positively identified the APM with a S/B ratio of 886 for dry soils of 1 wt% hydrogen content and with S/B ratios of 615, 398, 86, and 12 for the moist soils containing 4, 6, 10, and 14 wt% hydrogen, respectively. The total dose equivalent rate reached 0.0031 mSv/h, suggesting a work load of 8 h/day for 806 days within the permissible annual dose limit of 20 mSv.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.25 no.5
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• pp.29-41
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• 2022

Alien species cause problems in various ecosystems, reduce biodiversity, and destroy ecosystems. Due to these problems, the problem of a management plan is increasing, and it is difficult to accurately identify each individual and calculate the number of individuals, especially when researching alien turtle species such as GPS and PIT based on capture. this study intends to conduct an individual recognition study using a UAV. Recently, UAVs can take various sensor-based photos and easily obtain high-definition image data at low altitudes. Therefore, based on previous studies, this study investigated five variables to be considered in UAV flights and produced a test paper using them. OCR was used to monitor the displayed turtles using the manufactured test paper, and this confirmed the recognition rate. As a result, the use of yellow numbers showed the highest recognition rate. In addition, the minimum threat distance was confirmed to be 3 to 6m, and turtles with a shell size of 6 to 8cm were also identified during the flight. Therefore, we tried to propose an object recognition methodology for turtle display text using OCR, and it is expected to be used as a new turtle monitoring technique.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.6
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• pp.1275-1282
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• 2022

In this study, real-time character navigation using AR lens developed by Nreal is developed. Real-time character navigation is not possible with general marker-based AR because NPC characters must guide while moving in an unspecified space. To replace this, a markerless AR system was developed using Digital Twin technology. Existing markerless AR is operated based on hardware such as GPS, gyroscope, and magnetic sensor, so location accuracy is low and processing time in the system is long, resulting in low reliability in real-time AR environment. In order to solve this problem, using the SLAM technique to construct a space into a 3D object and to construct a markerless AR based on point location, AR can be implemented without any hardware intervention in a real-time AR environment. This real-time AR environment configuration made it possible to implement a navigation system using characters in tourist attractions such as Suncheon Bay Garden and Suncheon Drama Filming Site.

• Smart Structures and Systems
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• v.29 no.1
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• pp.181-193
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• 2022

Data-driven structural health monitoring (SHM) of civil infrastructure can be used to continuously assess the state of a structure, allowing preemptive safety measures to be carried out. Long-term monitoring of large-scale civil infrastructure often involves data-collection using a network of numerous sensors of various types. Malfunctioning sensors in the network are common, which can disrupt the condition assessment and even lead to false-negative indications of damage. The overwhelming size of the data collected renders manual approaches to ensure data quality intractable. The task of detecting and classifying an anomaly in the raw data is non-trivial. We propose an approach to automate this task, improving upon the previously developed technique of image-based pre-processing on one-dimensional (1D) data by enriching the features of the neural network input data with multiple channels. In particular, feature engineering is employed to convert the measured time histories into a 3-channel image comprised of (i) the time history, (ii) the spectrogram, and (iii) the probability density function representation of the signal. To demonstrate this approach, a CNN model is designed and trained on a dataset consisting of acceleration records of sensors installed on a long-span bridge, with the goal of fault detection and classification. The effect of imbalance in anomaly patterns observed is studied to better account for unseen test cases. The proposed framework achieves high overall accuracy and recall even when tested on an unseen dataset that is much larger than the samples used for training, offering a viable solution for implementation on full-scale structures where limited labeled-training data is available.

• The Korean Journal of Physiology and Pharmacology
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• v.26 no.5
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• pp.397-404
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• 2022

Fesoterodine, an antimuscarinic drug, is widely used to treat overactive bladder syndrome. However, there is little information about its effects on vascular K⁺ channels. In this study, voltage-dependent K⁺ (Kv) channel inhibition by fesoterodine was investigated using the patch-clamp technique in rabbit coronary artery. In whole-cell patches, the addition of fesoterodine to the bath inhibited the Kv currents in a concentration-dependent manner, with an IC₅₀ value of 3.19 ± 0.91 μM and a Hill coefficient of 0.56 ± 0.03. Although the drug did not alter the voltage-dependence of steady-state activation, it shifted the steady-state inactivation curve to a more negative potential, suggesting that fesoterodine affects the voltage-sensor of the Kv channel. Inhibition by fesoterodine was significantly enhanced by repetitive train pulses (1 or 2 Hz). Furthermore, it significantly increased the recovery time constant from inactivation, suggesting that the Kv channel inhibition by fesoterodine is use (state)-dependent. Its inhibitory effect disappeared by pretreatment with a Kv 1.5 inhibitor. However, pretreatment with Kv2.1 or Kv7 inhibitors did not affect the inhibitory effects on Kv channels. Based on these results, we conclude that fesoterodine inhibits vascular Kv channels (mainly the Kv1.5 subtype) in a concentration- and use (state)-dependent manner, independent of muscarinic receptor antagonism.