• Journal of the Institute of Electronics Engineers of Korea CI
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• v.46 no.4
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• pp.102-110
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• 2009

In this paper, we propose an object detection method for real-time in infrared (IR) images and PowerPC (PPC) and H/W design based on field programmable gate array (FPGA). An open H/W architecture has the advantages, such as easy transplantation of HW and S/W, support of compatibility and scalability for specification of current and previous versions, common module design using standardized design, and convenience of management and maintenance. Proposed background modeling for an open H/W architecture design decreases size of search area to construct a sparse block template of search area in IR images. We also apply to compensate for motion compensation when image moves in previous and current frames of IR sensor. Separation method of background and objects apply to adaptive values through time analysis of pixel intensity. Method of clutter reduction to appear near separated objects applies to median filter. Methods of background modeling, object detection, median filter, labeling, merge in the design embedded system execute in PFC processor. Based on experimental results, proposed method showed real-time object detection through global motion compensation and background modeling in the proposed embedded system.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.48 no.10
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• pp.67-71
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• 2011

Existing face detection systems, which are used for digital door locks, digital cameras, video surveillance systems, and so on, are software-based implementation for relatively low level resolution images. Therefore, in this case, there are difficulties in detecting faces in a real-time fashion due to the increasing amount of operational processing as well as in allowing the requirements of face detections for HD(High Definition) resolutions. A hardware approach is necessary to efficiently find faces for HD images in real-time embedded systems. This paper proposes and implements a hardware architecture for an LBP(Local Binary Pattern) operation which is a time-consuming part as one of preprocessing steps for face detection. The hardware architecture proposed in this research has been implemented and tested with a FPGA(Field Programmable Gate Array) chip, and shown that the approach guarantees efficient face detection for HD images.

• Proceedings of the Korean Vacuum Society Conference
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• 2012.02a
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• pp.195-195
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• 2012

Recently, graphene based solution-gated field-effect transistors (SGFETs) have been received a great attention in biochemical sensing applications. Graphene and reduced graphene oxide (RGO) possess various advantages such as high sensitivity, low detection limit, label-free electrical detection, and ease of fabrication due to their 2D nature and large sensing area compared to 1D nanomaterials- based nanobiosensors. Therefore, graphene or RGO -based SGFET is a good potential candidate for sensitive detection of protons (H+ ions) which can be applied as the transducer in various enzymatic or cell-based biosensing applications. However, reports on detection of H+ ions using graphene or RGO based SGFETs have been still limited. According to recent reports, clean graphene grown by CVD or exfoliation is electrochemically insensitive to changes of H+ concentration in solution because its surface does not have terminal functional groups that can sense the chemical potential change induced by varying surface charges of H+ on CVD graphene surface. In this work, we used RGO -SGFETs having oxygen-containing functional groups such as hydroxyl (OH) groups that effectively interact with H+ ions for expectation of increasing pH sensitivity. Additionally, we also investigate RGO based SGFETs for bio-sensing applications. Hydroloytic enzymes were introduced for sensing of biomolecular interaction on the surface of RGO -SGFET in which enzyme and substrate are acetylcholinesterase (AchE) and acetylcholine (Ach), respectively. The increase in H+ generated through enzymatic reaction of hydrolysis of Ach by AchE immobilized on RGO channel in SGFET could be monitored by the change in the drain-source current (Ids).

• Smart Media Journal
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• v.6 no.2
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• pp.33-41
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• 2017

Railway point machines act as actuators that provide different routes to trains by driving switchblades from the current position to the opposite one. Since point failure caused by the aging effect can significantly affect railway operations with potentially disastrous consequences, replacement detection of point machine at an appropriate time is critical. In this paper, we propose a replacement condition detection method of point machine in railway condition monitoring systems using electrical current signals, after analyzing and relabeling domestic in-field replacement data by means of OLAP(On-Line Analytical Processing) operations in the multidimensional data cube into "does-not-need-to-be replaced" and "needs-to-be-replaced" data. The system enables extracting suitable feature vectors from the incoming electrical current signals by DWT(Discrete Wavelet Transform) with reduced feature dimensions using PCA(Principal Components Analysis), and employs SVM(Support Vector Machine) for the real-time replacement detection of point machine. Experimental results with in-field replacement data including points anomalies show that the system could detect the replacement conditions of railway point machines with accuracy exceeding 98%.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.5
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• pp.997-1008
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• 2022

Today, AI(Artificial Intelligence) technology is being extensively researched in various fields, including the field of malware detection. To introduce AI systems into roles that protect important decisions and resources, it must be a reliable AI model. AI model that dependent on training dataset should be verified to be robust against new attacks. Rather than generating new malware detection, attackers find malware detection that succeed in attacking by mass-producing strains of previously detected malware detection. Most of the attacks, such as adversarial attacks, that lead to misclassification of AI models, are made by slightly modifying past attacks. Robust models that can be defended against these variants is needed, and the Robustness level of the model cannot be evaluated with accuracy and recall, which are widely used as AI evaluation indicators. In this paper, we experiment a framework to evaluate robustness level by generating an adversarial sample based on one of the adversarial attacks, C&W attack, and to improve robustness level through adversarial training. Through experiments based on malware dataset in this study, the limitations and possibilities of the proposed method in the field of malware detection were confirmed.

• Atmosphere
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• v.33 no.4
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• pp.399-411
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• 2023

This study compares the three detection algorithms of East Asian summer atmospheric rivers (ARs). The algorithms developed by Guan and Waliser (GW15), Park et al. (P21), and Tian et al. (T23) are particularly compared in terms of the AR frequency, the number of AR events, and the AR duration for the period of 2016-2020. All three algorithms show similar spatio-temporal distributions of AR frequency, centered along the edge of the North Pacific high. The maximum AR frequency gradually shifts northward in early summer as the edge of the North Pacific High expands, and retreats in late summer. However, the detailed pattern and the maximum value differ among the algorithms. When the AR frequency is decomposed into the number of AR events and the AR duration, the AR frequencies detected by GW15 and P21 are equally explained by both factors. However, the number of AR events primarily determine the AR frequency in T23. This difference occurs as T23 utilizes the machine learning algorithm applied to moisture field while GW15 and P21 apply the threshold value to moisture transport field. When evaluating AR-related precipitation, the ARs detected by P21 show the closest relationship with total precipitation in East Asia by up to 60%. These results indicate that AR detection in the East Asian summer is sensitive to the choice of the detection algorithm and can be optimized for the target region.

• The Plant Pathology Journal
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• v.39 no.5
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• pp.486-493
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• 2023

Cowpea mild mottle virus (CPMMV) is a global plant virus that poses a threat to the production and quality of legume crops. Early and accurate diagnosis is essential for effective managing CPMMV outbreaks. With the advancement in isothermal recombinase polymerase amplification and lateral flow strips technologies, more rapid and sensitive methods have become available for detecting this pathogen. In this study, we have developed a reverse transcription recombinase polymerase amplification combined with lateral flow strips (RT-RPA-LFS) method for the detection of CPMMV, specifically targeting the CPMMV coat protein (CP) gene. The RT-RPA-LFS assay only requires 20 min at 40℃ and demonstrates high specificity. Its detection limit was 10 copies/µl, which is approximately up to 100 times more sensitive than RT-PCR on agarose gel electrophoresis. The developed RT-RPA-LFS method offers a rapid, convenient, and sensitive approach for field detection of CPMMV, which contribute to controlling the spread of the virus.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.31 no.5
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• pp.351-356
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• 2018

Commonly, a live-line alarm can be used to measure the electric field strength of a high-voltage system to calculate its current, but it is hard to detect the electric field of shielded cables or concealed structures, such as underground distribution cables. Current sensors can detect the magnetic field in a single core wire, but they cannot determine the magnetic field about a double-core wire because the currents flow in opposite directions. Therefore, it is very difficult to detect certain current problems, such as a fault current in an extension line comprised of a double line. In this paper, to ultimately develop a sensor that can detect the current regardless of line conditions, we used a simulation to determine the concentration of the magnetic field dependent on the distribution of the external magnetic field and the path of each line's core.

• Journal of Magnetics
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• v.15 no.4
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• pp.221-224
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• 2010

A laboratory sensor model was designed, constructed, and tested based on a newly proposed working principle of magnetic field detection. The principle of sensing employed a time-coded method in correlation with exploiting the advantageous features of the GMI effect. The sensor demonstrated a sensitivity of $10\;{\mu}s/{\mu}T$ in the field range of ${\pm}100\;{\mu}T$. The sensing element in the form of an amorphous thin wire, $100\;{\mu}m$ in diameter ${\times}50\;mm$ long, was fit into a small field modulation coil of 60 mm length. At a magnetic field modulation in the range of hundreds of Hz, the change in time interval of two adjacent GMI voltage peaks was linearly related to the external magnetic field to be measured. This mechanism improved the sensor linearity of the GMI sensor to better than 0.2% in the measuring range of ${\pm}100\;{\mu}T$.

• Tunnel and Underground Space
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• v.19 no.1
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• pp.1-10
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• 2009

Site characterization for structural stability in rock masses mainly involves the collection of joint property data, and in the current practice, much of this data is collected by hand directly at exposed slopes and outcrops. There are many issues with the collection of this data in the field, including issues of safety, slope access, field time, lack of data quantity, reusability of data and human bias. It is shown that information on joint orientation, spacing and roughness in rock masses, can be automatically extracted from LIDAR (light detection and ranging) point floods using the currently available Split-FX point cloud processing software, thereby reducing processing time, safety and human bias issues.