• Convergence Security Journal
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• v.12 no.4
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• pp.77-84
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• 2012

The control network has been operated in a closed. But it changes to open to external for business convenience and cooperation with several organizations. As the way of connecting with user extends, the risk of control network gets high. Thus, in this paper, proposed the technique of an anomalous event detection using white-list for control network security and minimizing the cyber threats. The proposed method can be collected and cataloged of only normal data from traffic of internal network, control network and field devices. Through way to check the this situation, we can separate normal and abnormal behavior.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.12
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• pp.4909-4926
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• 2020

Cyberattacks penetrate the server and perform various malicious acts such as stealing confidential information, destroying systems, and exposing personal information. To achieve this, attackers perform various malicious actions by infecting endpoints and accessing the internal network. However, the current countermeasures are only anti-viruses that operate in a signature or pattern manner, allowing initial unknown attacks. Endpoint Detection and Response (EDR) technology is focused on providing visibility, and strong countermeasures are lacking. If you fail to respond to the initial attack, it is difficult to respond additionally because malicious behavior like Advanced Persistent Threat (APT) attack does not occur immediately, but occurs over a long period of time. In this paper, we propose a technique that detects an unknown attack using an event log without prior knowledge, although the initial response failed with anti-virus. The proposed technology uses a combination of AutoEncoder and 1D CNN (1-Dimention Convolutional Neural Network) based on semi-supervised learning. The experiment trained a dataset collected over a month in a real-world commercial endpoint environment, and tested the data collected over the next month. As a result of the experiment, 37 unknown attacks were detected in the event log collected for one month in the actual commercial endpoint environment, and 26 of them were verified as malicious through VirusTotal (VT). In the future, it is expected that the proposed model will be applied to EDR technology to form a secure endpoint environment and reduce time and labor costs to effectively detect unknown attacks.

• Horticultural Science & Technology
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• v.29 no.2
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• pp.123-129
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• 2011

To identify unintended vertical gene-transfer rates from the developed transgenic plants, rapid and unequivocal techniques are needed to identify event-specific markers based on flanking sequences around the transgene and to distinguish zygosity such as homo- and hetero-zygosity. To facilitate evaluation of zygosity, a polymerase chain reaction technique was used to analyze a transgenic pepper line B20 (homozygote), P915 wild type (null zygote), and their F1 hybrids, which were used as transgene contaminated plants. First, we sequenced the 3'-flanking region of the T-DNA (1,277 bp) in the transgenic pepper event B20. Based on sequence information for the 3'- and 5'-flanking region of T-DNA provided in a previous study, a primer pair was designed to amplify full length T-DNA in B20. We successfully amplified the full length T-DNA containing 986 bp from the flanking regions of B20. In addition, a 1,040 bp PCR product, which was where the T-DNA was inserted, was amplified from P915. Finally, both full length T-DNA and the 1,040 bp fragment were simultaneously amplified in the F1 hybrids; P915 ${\times}$ B20, Pungchon ${\times}$ B20, Gumtap ${\times}$ B20. In the present study, we were able to identify zygosity among homozygous transgenic event B20, its wild type P915, and hemizygous F1 hybrids. Therefore, this novel zygosity identification technique, which is based on PCR, can be effectively used to examine gene flow for transgenic pepper event B20.

• Journal of the Korean Society of Safety
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• v.27 no.1
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• pp.7-13
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• 2012

This paper describes the diagnosis techniques using UV images taken in the field under energized condition of power equipments in order to figure out and analyze the abnormal states on the terminals of power equipments. To classify the features of the terminals, the counted No. of the corona generated at the terminals is defined. According to the result detected, the No. of corona detected on the power equipments installed inside a building is less than that installed outside a building, and it strongly depends on the environment and installed condition. Thus, the environmental condition needs enhanced, and stable operation by the periodic inspection under energized condition of the power equipments is required. Especially, the event counting technique using UV camera is useful for the power equipments apart more than 20 m to apply, and there can be an error due to the features of the sensing techniques when the distance between the user and the objects is close less than 15 m. Therefore, the experimental result shows that event counting technique is employed in the case of the distance more than 15 m. The electrical safety can be ensured by using the UV detection technique and the criteria.

• Geophysics and Geophysical Exploration
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• v.25 no.4
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• pp.189-200
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• 2022

Ground subsidence on urban roads is a social issue that can lead to human and property damages. Therefore, it is crucial to detect underground cavities in advance and repair them. Underground cavity detection is mainly performed using ground penetrating radar (GPR) surveys. This process is time-consuming, as a massive amount of GPR data needs to be interpreted, and the results vary depending on the skills and subjectivity of experts. To address these problems, researchers have studied automation and quantification techniques for GPR data interpretation, and recent studies have focused on deep learning-based interpretation techniques. In this study, we described a hyperbolic event detection process based on deep learning for GPR data interpretation. To demonstrate this process, we implemented a series of algorithms introduced in the preexisting research step by step. First, a deep learning-based YOLOv3 object detection model was applied to automatically detect hyperbolic signals. Subsequently, only hyperbolic signals were extracted using the column-connection clustering (C3) algorithm. Finally, the horizontal locations of the underground cavities were determined using regression analysis. The hyperbolic event detection using the YOLOv3 object detection technique achieved 84% precision and a recall score of 92% based on AP50. The predicted horizontal locations of the four underground cavities were approximately 0.12 ~ 0.36 m away from their actual locations. Thus, we confirmed that the existing deep learning-based interpretation technique is reliable with regard to detecting the hyperbolic patterns indicating underground cavities.

• Journal of Korea Water Resources Association
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• v.51 no.8
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• pp.703-711
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• 2018

Water distribution system (WDS) pipe bursts are caused from excessive pressure, pipe aging, and ground shift from temperature change and earthquake. Prompt detection of and response to the failure event help prevent large-scale service interruption and catastrophic sinkhole generation. To that end, this study proposes a improved Western Electric Company (WECO) method to improve the detection effectiveness and efficiency of the original WECO method. The original WECO method is an univariate Statistical Process Control (SPC) technique used for identifying any non-random patterns in system output data. The improved WECO method multiples a threshold modifier (w) to each threshold of WECO sub-rules in order to control the sensitivity of anomaly detection in a water distribution network of interest. The Austin network was used to demonstrated the proposed method in which normal random and abnormal pipe flow data were generated. The best w value was identified from a sensitivity analysis, and the impact of measurement frequency (dt = 5, 10, 15 min etc.) was also investigated. The proposed method was compared to the original WECO method with respect to detection probability, false alarm rate, and averaged detection time. Finally, this study provides a set of guidelines on the use of the WECO method for real-life WDS pipe burst detection.

• IEIE Transactions on Smart Processing and Computing
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• v.4 no.5
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• pp.330-339
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• 2015

Power quality (PQ) problems are becoming a big issue, since delicate complex electronic devices are widely used. We present a new denoising technique using discrete wavelet transform (DWT), where a modified correlation thresholding is used in order to reliably detect the PQ disturbances. We consider various PQ disturbances on the basis of IEEE-1159 standard over noisy environments, including voltage swell, voltage sag, transient, harmonics, interrupt, and their combinations. These event signals are decomposed using DWT for the detection of disturbances. We then evaluate the PQ disturbance detection ratio of the proposed denoising scheme over Gaussian noise channels. Simulation results also show that the proposed scheme has an improved signal-to-noise ratio (SNR) over existing scheme.

• Journal of Sensor Science and Technology
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• v.22 no.1
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• pp.28-37
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• 2013

This paper presents a fault-tolerant control technique for wind turbine systems with sensor and actuator faults. The control objective is to maximize power production and minimize turbine loads by calculating a desired pitch angle within their limits. Any fault with a sensor and actuator can cause significant error in the pitch position of the corresponding blade. This problem may result in insufficient torque such that the power reference cannot be achieved. In this paper, a fault-tolerant control technique using a robust dynamic inversion observer and control allocation is employed to achieve successful pitch control despite these faults in the sensor and actuator. The observer based detection method is used to detect and isolate sensor faults by checking whether errors are larger than threshold values. In addition, the control allocation technique is adopted to tolerate actuator fault. Control allocation is one of the most commonly used fault-tolerant control techniques, especially for over-actuated systems. Further, the control allocation method can be used to achieve the power reference even in the event of blade actuator fault by redistributing the lost torque due to erroneous pitch position into non-faulty blade actuators. The effectiveness of the proposed method is demonstrated through simulations with a benchmark model of the wind turbine.

• IEMEK Journal of Embedded Systems and Applications
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• v.14 no.2
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• pp.87-96
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• 2019

Low power issue is one of the most critical problems in the Internet of Things (IoT), which are powered by battery. To solve this problem, various approaches have been presented so far. In this paper, we propose a method to reduce the power consumption by reducing the numbers of accesses into the flash memory consuming a large amount of power for on-chip software execution. Our approach is based on using cooperative logging structure to distribute the sampling overhead in single sensor node to adjacent nodes in case of rare-event applications. The proposed algorithm to identify event occurrence is newly introduced with negative feedback method by observing difference between past data and recent data coming from the sensor. When an event with need of flash access is determined, the proposed approach only allows access to write the sampled data in flash memory. The proposed event detection algorithm (EDA) result in 30% reduction of power consumption compared to the conventional flash write scheme for all cases of event. The sampled data from the sensor is first traced into the random access memory (RAM), and write access to the flash memory is delayed until the page buffer of the on-chip flash memory controller in the micro controller unit (MCU) is full of the numbers of the traced data, thereby reducing the frequency of accessing flash memory. This technique additionally reduces power consumption by 40% compared to flash-write all data. By sharing the sampling information via LoRa channel, the overhead in sampling data is distributed, to reduce the sampling load on each node, so that the 66% reduction of total power consumption is achieved in several IoT edge nodes by removing the sampling operation of duplicated data.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.882-885
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• 2003

In this paper, we describe a haptic jacket and wheel as a haptic interface to enhance VR game realism. Building upon the VR game system using this devices, our haptic interface technique allows the user to intuitive interact on game contents, and then to sense the game event properties such as walking, attacking, driving and fire in a natural way. In addition, we extended the initial haptic model to support haptic decoration and dynamic interactions due to the added game event in a real time display. An application example presented here is a VR Dino-Attack game. This game supports interactions among dynamic and our intuitive haptic interface. Modeling physic interactions involves precise collision detection, real-time force computation, and high control-loop bandwidth.