• Journal of Information Processing Systems
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• v.7 no.1
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• pp.173-186
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• 2011

Due to the disadvantages of signature-based computer virus detection techniques, behavior-based detection methods have developed rapidly in recent years. However, current popular behavior-based detection methods only take API call sequences as program behavior features and the difference between API calls in the detection is not taken into consideration. This paper divides virus behaviors into separate function modules by introducing DLLs into detection. APIs in different modules have different importance. DLLs and APIs are both considered program calling resources. Based on the calling relationships between DLLs and APIs, program calling resources can be pictured as a tree named program behavior resource tree. Important block structures are selected from the tree as program behavior features. Finally, a virus detection model based on behavior the resource tree is proposed and verified by experiment which provides a helpful reference to virus detection.

• IEMEK Journal of Embedded Systems and Applications
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• v.13 no.6
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• pp.289-296
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• 2018

Drone detection in FMCW radar system needs complex techniques because a drone beat frequency is highly dynamic and unpredictable. Therefore, the current static signal processing algorithms cannot show appropriate detection accuracy. With dynamic signal fluctuation and environmental clutters, it can fail to detect a drone or make false detection. It affects to the radar system integrity and safety. Constant false alarm rate (CFAR), one of famous static signal process algorithm is effective for static environment. But for drone detection, it shows low detection accuracy. In this paper, we suggest neural network based FMCW radar system for detecting a drone. We use recurrent neural network (RNN) because it is the effective neural network for signal processing. In our FMCW radar system, one transmitter emits FMCW signal and four-way fixed receivers detect reflected drone beat frequency. The coordinate of the drone can be calculated with four receivers information by triangulation. Therefore, RNN only learns and inferences reflected drone beat frequency. It helps higher learning and detection accuracy. With several drone flight experiments, RNN shows false detection rate and detection accuracy as 21.1% and 96.4%, respectively.

• Journal of Power Electronics
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• v.19 no.1
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• pp.265-277
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• 2019

The application of shunt active power filters (S-APFs) is considered to be the most popular approach for harmonic compensation due to its high simplicity, ease of installation and efficient control. Its functionality mainly depends upon the rapidness and precision of its internally built control algorithms. A S-APF is generally operated in the current controlled mode (CCM) with the detection of harmonic load current. Its operation may not be appropriate for the distributed power generation system (DPGS) due to the wide dispersion of nonlinear loads. Despite the fact that the voltage detection based resistive-APF (R-APF) appears to be more appropriate for use in the DPGS, the R-APF experiences poor performance in terms of mitigating harmonics and parameter tuning. Therefore, this paper introduces a direct harmonic voltage detection based control approach for the S-APF that does not need a remote harmonic load current since it only requires a local point of common coupling (PCC) voltage for the detection of harmonics. The complete design procedure of the proposed control approach is presented. In addition, experimental results are given in detail to validate the performance and superiority of the proposed method over the conventional R-APF control. Thus, the outcomes of this study approve the predominance of the discussed strategy.

• Journal of Internet Computing and Services
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• v.24 no.2
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• pp.37-45
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• 2023

In line with the 4th Industrial Revolution, facility maintenance technologies for building smart factories are receiving attention and are being advanced. In addition, technology is being applied to smart farms and smart fisheries following smart factories. Among them, in the case of a recirculating aquaculture system, there is a motor pump that circulates water for a stable quality environment in the tank. Motor pump maintenance activities for recirculating aquaculture system are carried out based on preventive maintenance and data obtained from vibration sensor. Preventive maintenance cannot cope with abnormalities that occur before prior planning, and vibration sensors are affected by the external environment. This paper proposes an anomaly detection algorithm that utilizes ADTK, a Python open source, for motor pump anomaly detection based on data collected through current sensors that are less affected by the external environment than noise, temperature and vibration sensors.

• Progress in Superconductivity and Cryogenics
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• v.19 no.1
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• pp.26-29
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• 2017

We studied the distribution of the current density and its magnetic-field dependence in GdBCO coated conductors with AC bias currents using low temperature scanning Hall probe microscopy. We selectively measured magnetic field profiles from AC signal obtained by Lock-in technique and calculated current distributions by inversion calculation. In order to confirm the AC measurement results, we applied DC current corresponding to RMS value of AC current and compared distribution of AC and DC transport current. We carried out the same measurements at various external DC magnetic fields, and investigated field dependence of AC current distribution. We notice that the AC current distribution unaffected by external magnetic fields and preserved their own path on the contrary to DC current.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.55 no.6
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• pp.255-260
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• 2006

Arc Fault Current is an which occurrs in two opposite electrode. In this paper, arc current control algorithm is designed for the interruption of arc fault current which is occurred in the low voltage network. This arc is one of the main causes of electric fire. General arc current sensor has troubles for detecting arc currents, thus we would like to propose the arc current detection method without current sensor. In this parer, arc discharge currents within power lines are being detected through the arc current control algorithm.

• Microbiology and Biotechnology Letters
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• v.43 no.3
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• pp.177-186
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• 2015

With a view to supporting the provisions of the current Korean food code for the detection of Benzo[a]pyrene, various analytical methods of detection in foods were evaluated and established in terms of linearity, limits of detection/quantitation, efficiency, and accuracy, amongst others. It was observed that to improve the technologies involved in the application of these methods, complicated and combined preparation processes of foods, including extraction, separation and purification, have been the main focus of efforts at optimization. Recently, on-site quick reaction for the detection of hazardous substances in the environment and food materials aims at developing simplified examination processes, such as lable-free and non-invasive technological analysis, to reduce the costs and time involved in the examination. Herein, current benzo[a]pyrene detection methods are reviewed in addition to new Raman spectroscopy-based trials established to pursue improve the speed, simplicity and suitability of testing.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.23 no.4
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• pp.204-209
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• 1987

• Proceedings of the Korean Society of Crop Science Conference
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• 2001.05a
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• pp.24-29
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• 2001

• Journal of The Institute of Information and Telecommunication Facilities Engineering
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• v.11 no.1
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• pp.23-28
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• 2012

In this paper, we proposed and developed a fast and efficient real time face detection and recognition which can be run on embedded system instead of high performance desktop. In the face detection process, we detect a face by finding eye part which is one of the most salient facial features after applying various image processing methods, then in the face recognition, we finally recognize the face by comparing the current face with the prepared face database using a template matching algorithm. Also we optimized the algorithm in our system to be successfully used in the embedded system, and performed the face detection and recognition experiments on the embedded board to verify the performance. The developed method can be applied to automatic door, mobile computing environment and various robot.