• The KIPS Transactions:PartC
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• v.9C no.5
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• pp.765-774
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• 2002

Analyzing the code using static heuristics is a widely used technique for detecting unknown malicious codes. It decides the maliciousness of a code by searching for some fragments that had been frequently found in known malicious codes. However, in script codes, it tries to search for sequences of method calls, not code fragments, because finding such fragments is much difficult. This technique makes many false alarms because such method calls can be also used in normal scripts. Thus, static heuristics for scripts are used only to detect malicious behavior consisting of specific method calls which is seldom used in normal scripts. In this paper. we suggest a static analysis that can detect malicious behavior more accurately, by concerning not only the method calls but also parameters and return values. The result of experiments show that malicious behaviors, which were difficult to detect by previous works, due to high false positive, will be detected by our method.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.1
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• pp.197-202
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• 2019

A small millimeter-wave tracking radar power supply must provide stable power with minimal ripple noise and the switching frequency noise of the DC-DC converter must have a real-time self-test capability through on-the-fly monitoring without causing false alarms and ghost In this study, we developed a multi-output switching power supply with output power of more than 80% (@ 100% load) and 10 output power by adopting + 28VDC input for application to small millimeter wave tracking radar, DC-DC converter is applied for large power output and multi-output flyback method is applied for the remaining small power output. The test results show that 85% efficiency efficiency is achieved under 100% load condition.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.4
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• pp.119-125
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• 2022

The power supply for the anti-aircraft radar homing sensor should allow the system to receive power quickly and stably without the influence of noise. For this purpose, DC-DC converters are widely used for reliable power conversion. Also, switching of DC-DC converters Frequency noise should not cause false alarms and ghosts that may affect the detection and tracking performance of the system, and it should have a check function that can monitor power in real time while the homing sensor is operating. In order to apply to anti-aircraft radar homing sensor, we developed a multi-output switching power supply with maximum output 𐩒𐩒𐩒 W, efficiency 80% or more (@100% load), output power by receiving 28VDC input, and power supply to achieve more than 80% efficiency. A DC-DC converter was applied to this large output, and the multi-output flyback method was applied to the rest of the low-power output.

• The Bulletin of The Korean Astronomical Society
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• v.40 no.1
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• pp.56.2-56.2
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• 2015

The KMTNet Supernovae Project utilizes the large $2^{\circ}{\times}2^{\circ}$ field of view of the three KMTNet telescopes to search and monitor supernovae, especially early ones, and other optical transients. A key component of the project is to build a data pipeline with a descent latency and an early alerting system that can handle the large volume of the data in an efficient and a prompt way, while minimizing false alarms, which casts a significant challenge to the software development. Here we present the current status of their development. The pipeline utilizes a difference image analysis technique to discover candidate transient sources after making correction of image distortion. In the early phase of the program, final selection of transient sources from candidates will mainly rely on multi-filter, multi-epoch and multi-site screening as well as human inspection, and an interactive web-based system is being developed for this purpose. Eventually, machine learning algorithms, based on the training set collected in the early phase, will be used to select true transient sources from candidates.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.11
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• pp.2720-2736
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• 2013

Multi-view face detection has become an active area for research in the last few years. In this paper, a novel multi-view human face detection algorithm based on improved real Adaboost is presented. Real Adaboost algorithm is improved by weighted combination of weak classifiers and the approximately best combination coefficients are obtained. After that, we proved that the function of sample weight adjusting method and weak classifier training method is to guarantee the independence of weak classifiers. A coarse-to-fine hierarchical face detector combining the high efficiency of Haar feature with pose estimation phase based on our real Adaboost algorithm is proposed. This algorithm reduces training time cost greatly compared with classical real Adaboost algorithm. In addition, it speeds up strong classifier converging and reduces the number of weak classifiers. For frontal face detection, the experiments on MIT+CMU frontal face test set result a 96.4% correct rate with 528 false alarms; for multi-view face in real time test set result a 94.7 % correct rate. The experimental results verified the effectiveness of the proposed approach.

• Journal of KIISE:Software and Applications
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• v.37 no.8
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• pp.647-652
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• 2010

Esterel is an imperative synchronous language well-adapted to control-intensive systems. When an Esterel program is translated to a circuit, the synchronizer of a parallel statement may be executed more than once in a clock; the synchronizer is called schizophrenic. Existing compilers cure the problems of schizophrenic parallel synchronizers using logic duplications. This paper proposes the conditions under which a synchronizer causes no problem in circuits when it is executed more than once in a clock. In addition we design a detection algorithm based on those conditions. Our algorithm detects schizophrenic parallel synchronizers that have to be duplicated in Esterel source codes so that compilers can save the size of synthesized circuits

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.27 no.2
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• pp.148-154
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• 2017

This paper discussed design for resonance avoidance of sensor plates of loose-parts monitoring systems (LPMS) in nuclear power plants (NPP). An LPMS monitors impact of loose parts in primary loop of NPP by using accelerometers, which is mounted on sensor plates. Resonance of the plates may cause false alarms at frequencies over 10 kHz, which can be misunderstood as impact signals of loose parts with small mass and cause unnecessary response of NPP operators. Modal analysis was carried out for the existing sensor plate and design parameters affecting natural frequencies were chosen. Frequency response functions of plates were analyzed by changing the parameters and the optimized plate design for avoiding resonance was determined. Experiments was carried out for the plate specimen with improved design and verified the proposed approach and design.

• Structural Monitoring and Maintenance
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• v.5 no.1
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• pp.51-65
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• 2018

A number of sensing techniques have been implemented for detecting defects in civil infrastructures instead of onsite human inspections in structural health monitoring. However, the issue of faults in sensors has not received much attention. This issue may lead to incorrect interpretation of data and false alarms. To overcome these challenges, this article presents a deep learning-based method with a new architecture of Stateful Long Short Term Memory Neural Networks (S-LSTM NN) for detecting sensor fault without going into details of the fault features. As LSTMs are capable of learning data features automatically, and the proposed method works without an accurate mathematical model. The detection of four types of sensor faults are studied in this paper. Non-stationary acceleration responses of a three-span continuous bridge when under operational conditions are studied. A deep network model is applied to the measured bridge data with estimation to detect the sensor fault. Another set of sensor output data is used to supervise the network parameters and backpropagation algorithm to fine tune the parameters to establish a deep self-coding network model. The response residuals between the true value and the predicted value of the deep S-LSTM network was statistically analyzed to determine the fault threshold of sensor. Experimental study with a cable-stayed bridge further indicated that the proposed method is robust in the detection of the sensor fault.

• Journal of Satellite, Information and Communications
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• v.10 no.2
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• pp.70-74
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• 2015

In this paper, satellite communication based video surveillance system that consisted of ultra-small aperture terminals with small-size smart vision sensor is proposed. The events such as forest fire, smoke, intruder movement are detected automatically in field and false alarms are minimized by using intelligent and high-reliable video analysis algorithms. The smart vision sensor is necessary to achieve high-confidence, high hardware endurance, seamless communication and easy maintenance requirements. To satisfy these requirements, real-time digital signal processor, camera module and satellite transceiver are integrated as a smart vision sensor-based ultra-small aperture terminal. Also, high-performance video analysis and image coding algorithms are embedded. The video analysis functions and performances were verified and confirmed practicality through computer simulation and vision sensor prototype test.

• Journal of The Korean Astronomical Society
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• v.47 no.6
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• pp.209-214
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• 2014

In this study we develop a set of solar proton event (SPE) forecast models with NOAA scales by Multi Layer Perceptron (MLP), one of neural network methods, using GOES solar X-ray flare data from 1976 to 2011. Our MLP models are the first attempt to forecast the SPE scales by the neural network method. The combinations of X-ray flare class, impulsive time, and location are used for input data. For this study we make a number of trials by changing the number of layers and nodes as well as combinations of the input data. To find the best model, we use the summation of F-scores weighted by SPE scales, where F-score is the harmonic mean of PODy (recall) and precision (positive predictive value), in order to minimize both misses and false alarms. We find that the MLP models are much better than the multiple linear regression model and one layer MLP model gives the best result.