• Journal of the Institute of Electronics Engineers of Korea CI
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• v.48 no.5
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• pp.55-63
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• 2011

This paper describes new moving target detection technique combining two algorithms to detect targets and reject clutters in video frame images for surveillance system: One obtains the region of moving target using phase correlation method using $N{\times}M$ sub-block images in frequency domain. The other uses adaptive threshold using learning weight for extracting target candidates in subtracted image. The block region with moving target can be obtained using the characteristics that the highest value of phase correlation depends on the movement of largest image in block. This technique can be used in camera motion environment calculating and compensating camera movement using FFT phase correlation between input video frame images. The experimental results show that the proposed algorithm accurately detects target(s) with a low false alarm rate in variety environment using the receiver operating characteristics (ROC) curve.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.12 no.2
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• pp.137-142
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• 2012

The temporal subtraction technique as one of computer aided diagnosis has been introduced in medical fields to enhance the interval changes such as formation of new lesions and changes in existing abnormalities on deference image. With the temporal subtraction technique radiologists can easily detect lung nodules on visual screening. Until now, two-dimensional temporal subtraction imaging technique has been introduced for the clinical test. We have developed new temporal subtraction method to remove the subtraction artifacts which is caused by mis-registration on temporal subtraction images of lungs on MDCT images. In this paper, we propose a new computer aided diagnosis scheme for automatic enhancing the lung nodules from the temporal subtraction of thoracic MDCT images. At first, the candidates regions included nodules are detected by the multiple threshold technique in terms of the pixel value on the temporal subtraction images. Then, a rule-base method and artificial neural networks is utilized to remove the false positives of nodule candidates which is obtained temporal subtraction images. We have applied our detection of lung nodules to 30 thoracic MDCT image sets including lung nodules. With the detection method, satisfactory experimental results are obtained. Some experimental results are shown with discussion.

• Korean Journal of Remote Sensing
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• v.24 no.1
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• pp.1-16
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• 2008

Steam and advection fogs are frequently observed in the Yellow Sea from March to July except for May. This study uses remote sensing (RS) data for the monitoring of sea fog. Meteorological data obtained from the Ieodo Ocean Research Station provided a valuable information for the occurrence of steam and advection fogs as a ground truth. The RS data used in this study were GOES-9, MTSAT-1R images and QuikSCAT wind data. A dual channel difference (DCD) approach using IR and shortwave IR channel of GOES-9 and MTSAT-1R satellites was applied to detect sea fog. The results showed that DCD, texture-related measurement and the weak wind condition are required to separate the sea fog from the low cloud. The QuikSCAT wind data was used to provide the wind speed criteria for a fog event. The laplacian computation was designed for a measurement of the homogeneity. A new combined method, which includes DCD, QuikSCAT wind speed and laplacian computation, was applied to the twelve cases with GOES-9 and MTSAT-1R. The threshold values for DCD, QuikSCAT wind speed and laplacian are -2.0 K, $8m\;s^{-1}$ and 0.1, respectively. The validation results showed that the new combined method slightly improves the detection of sea fog compared to DCD method: improvements of the new combined method are $5{\sim}6%$ increases in the Heidke skill score, 10% decreases in the probability of false detection, and $30{\sim}40%$ increases in the odd ratio.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.3
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• pp.531-539
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• 2019

This paper proposes the training features for malware family analysis and analyzes the multi-classification performance of ensemble models. We construct training data by extracting API and DLL information from malware executables and use Random Forest and XGBoost algorithms which are based on decision tree. API, API-DLL, and DLL-CM features for malware detection and family classification are proposed by analyzing frequently used API and DLL information from malware and converting high-dimensional features to low-dimensional features. The proposed feature selection method provides the advantages of data dimension reduction and fast learning. In performance comparison, the malware detection rate is 93.0% for Random Forest, the accuracy of malware family dataset is 92.0% for XGBoost, and the false positive rate of malware family dataset including benign is about 3.5% for Random Forest and XGBoost.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.21 no.1
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• pp.39-52
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• 2011

An application-layer attack can effectively achieve its objective with a small amount of traffic, and detection is difficult because the traffic type is very similar to that of legitimate users. We have discovered a unique characteristic that is produced by a difference in client intention: Both a legitimate user and DDoS attacker establish a session through a 3-way handshake over the TCP/IP layer. After a connection is established, they request at least one HTTP service by a Get request packet. The legitimate HTTP user waits for the server's response. However, an attacker tries to terminate the existing session right after the Get request. These different actions can be interpreted as a difference in client intention. In this paper, we propose a detection algorithm for application layer DDoS attacks based on this difference. The proposed algorithm was simulated using traffic dump files that were taken from normal user networks and Botnet-based attack tools. The test results showed that the algorithm can detect an HTTP-Get flooding attack with almost zero false alarms.

• 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.

• International Journal of Computer Science & Network Security
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• v.24 no.5
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• pp.11-20
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• 2024

While artificial neural networks are adept at identifying patterns, they can struggle to distinguish between actual correlations and false associations between extracted facial features and criminal behavior within the training data. These associations may not indicate causal connections. Socioeconomic factors, ethnicity, or even chance occurrences in the data can influence both facial features and criminal activity. Consequently, the artificial neural network might identify linked features without understanding the underlying cause. This raises concerns about incorrect linkages and potential misclassification of individuals based on features unrelated to criminal tendencies. To address this challenge, we propose a novel region-based training approach for artificial neural networks focused on criminal propensity detection. Instead of solely relying on overall facial recognition, the network would systematically analyze each facial feature in isolation. This fine-grained approach would enable the network to identify which specific features hold the strongest correlations with criminal activity within the training data. By focusing on these key features, the network can be optimized for more accurate and reliable criminal propensity prediction. This study examines the effectiveness of various algorithms for criminal propensity classification. We evaluate YOLO versions YOLOv5 and YOLOv8 alongside VGG-16. Our findings indicate that YOLO achieved the highest accuracy 0.93 in classifying criminal and non-criminal facial features. While these results are promising, we acknowledge the need for further research on bias and misclassification in criminal justice applications

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.12
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• pp.67-75
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• 2016

DNA computing technology makes the interests on DNA storage and DNA watermarking / steganography that use the DNA information as a newly medium. DNA watermarking that embeds the external watermark into DNA information without the biological mutation needs the reversibility for the perfect recovery of host DNA, the continuous embedding and detecting processing, and the mutation analysis by the watermark. In this paper, we propose a reversible DNA watermarking based on circular histogram shifting of DNA code values with the prevention of false start codon, the preservation of DNA sequence length, and the high watermark capacity, and the blind detection. Our method has the following features. The first is to encode nucleotide bases of 4-character variable to integer code values by code order. It makes the signal processing of DNA sequence easy. The second is to embed the multiple bits of watermark into -order coded value by using circular histogram shifting. The third is to check the possibility of false start codon in the inter or intra code values. Experimental results verified the our method has higher watermark capacity 0.11~0.50 bpn than conventional methods and also the false start codon has not happened in our method.

• Pediatric Infection and Vaccine
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• v.7 no.1
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• pp.113-119
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• 2000

Purpose : Rotavirus is a most common etiologic agent of pediatric gastroenteritis. The standard method to diagnose rotavirus infection was the detection of viral particles in specimens through electron microscopy. But it was complex. Enzyme immunoassay and latex agglutinin are preferred because they are relatively handy, inexpensive and take a short time, in comparison with electron microscopy. However, several reports have shown that the use of ELISA to diagnose rotavirus infection in neonates can result in false positive reactions. The main purpose of this study is to compare ELISA and RT-PCR in the diagnosis of neonatal rotavirus infection. Methods : Data presented in this study were obtained form 123 newborn babies in the nursery of the Fatima Hospital, Masan, Korea, form Jury to December, 1997. We obtained two samples of stool from each of the newborn babies and then performed the Rotazyme test and the RT-PCR. In the Rotazyme test, the results were interpreted according to visual findings. The samples were used for the RT-PCR test after at stock $-30^{\circ}C$ to identify rotavirus group A. The result of the two tests were compared. Results : The informations are divided into 73 males and females. Out of the total informations 15 were transferred from other hospitals. Their average gestational age was $38.5{\pm}1.6$ weeks. The average birth weight was $3134.8{\pm}539gm$. In the Rotazyme test, 75 samples turned out to be positive. Out of them, 55 samples(75.3%) were positive and 18 samples(24.7%) were negative in the RT-PCR. On the other hand, in the Rotazyme test, 50 samples turned out be negative. Out of them, 27 samples(54%) were positive and 23 samples(46%) were negative in the RT-PCR. Conclusion : Rotavirus infection in uncommon in neonates. The diagnosis based on visual findings using Rotazyme test has a disadvantage in the sense that it can result in false positive reactions and false negative reactions in the diagnosis of neonatal rotavirus infection.

• Journal of Radiation Protection and Research
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• v.45 no.3
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• pp.117-129
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• 2020

Background: Radiation portal monitors (RPMs) involving plastic scintillators installed at the border inspection sites can detect illicit trafficking of radioactive sources in cargo containers within seconds. However, RPMs may generate false alarms because of the naturally occurring radioactive materials. To manage these false alarms, we previously suggested an energy-weighted algorithm that emphasizes the Compton-edge area as an outstanding peak. This study intends to evaluate the identification of radioactive sources using an improved energy-weighted algorithm. Materials and Methods: The algorithm was modified by increasing the energy weighting factor, and different peak combinations of the energy-weighted spectra were tested for source identification. A commercialized RPM system was used to measure the energy-weighted spectra. The RPM comprised two large plastic scintillators with dimensions of 174 × 29 × 7 ㎤ facing each other at a distance of 4.6 m. In addition, the in-house-fabricated signal processing boards were connected to collect the signal converted into a spectrum. Further, the spectra from eight radioactive sources, including special nuclear materials (SNMs), which were set in motion using a linear motion system (LMS) and a cargo truck, were estimated to identify the source identification rate. Results and Discussion: Each energy-weighted spectrum exhibited a specific peak location, although high statistical fluctuation errors could be observed in the spectrum with the increasing source speed. In particular, ¹³⁷Cs and ⁶⁰Co in motion were identified completely (100%) at speeds of 5 and 10 km/hr. Further, SNMs, which trigger the RPM alarm, were identified approximately 80% of the time at both the aforementioned speeds. Conclusion: Using the modified energy-weighted algorithm, several characteristics of the energy weighted spectra could be observed when the used sources were in motion and when the geometric efficiency was low. In particular, the discrimination between ⁶⁰Co and ⁴⁰K, which triggers false alarms at the primary inspection sites, can be improved using the proposed algorithm.