• JSTS:Journal of Semiconductor Technology and Science
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• v.12 no.2
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• pp.150-161
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• 2012

Eye detection is widely used in applications, such as face recognition, driver behavior analysis, and human-computer interaction. However, it is difficult to achieve real-time performance with software-based eye detection in an embedded environment. In this paper, we propose a parallel hardware architecture for real-time eye detection. We use the AdaBoost algorithm with modified census transform(MCT) to detect eyes on a face image. We parallelize part of the algorithm to speed up processing. Several downscaled pyramid images of the eye candidate region are generated in parallel using the input face image. We can detect the left and the right eye simultaneously using these downscaled images. The sequential data processing bottleneck caused by repetitive operation is removed by employing a pipelined parallel architecture. The proposed architecture is designed using Verilog HDL and implemented on a Virtex-5 FPGA for prototyping and evaluation. The proposed system can detect eyes within 0.15 ms in a VGA image.

• Journal of Advanced Navigation Technology
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• v.12 no.3
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• pp.280-285
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• 2008

The traditional face detection method is to use difference picture method are used to detect movement. However, most do not consider this mathematical approach using real-time or real-time implementation of the algorithm is complicated, not easy. This paper, the first to detect real-time facial image is converted YCbCr and RGB video input. Next, you convert the difference between video images of two adjacent to obtain and then to conduct Glassfire Labeling. Labeling value compared to the threshold behavior Area recognizes and converts video extracts. Actions to convert video to conduct face detection, and detection of facial characteristics required for the extraction and use of AdaBoost algorithm.

• Korean Journal of Biological Psychiatry
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• v.22 no.3
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• pp.109-112
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• 2015

Since the early 2000s, there has been a continued interest in lie detection using functional magnetic resonance imaging (fMRI) in neuroscience and forensic sciences, as well as in newly emerging fields including neuroethics and neurolaw. Related fMRI studies have revealed converging evidence that brain regions including the prefrontal cortex, anterior cingulate cortex, parietal cortex, and anterior insula are associated with deceptive behavior. However, fMRI-based lie detection has thus far not been generally accepted as evidence in court, as methodological shortcomings, generalizability issues, and ethical and legal concerns are yet to be resolved. In the present review, we aim to illustrate these achievements and limitations of fMRI-based lie detection.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.11
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• pp.1044-1052
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• 2010

In this paper, a new realtime algorithm called the RTPBTD-HPDAF (Recursive Temporal Profile Base Target Detection with Highest Probability Data Association Filter) is presented for tracking fast moving small targets with IIR (Imaging Infrared) sensor systems. Spatial filter algorithms are mainly used for target in IIR sensor system detection and tracking however they often generate high density clutter due to various shapes of cloud. The TPBTD (Temporal Profile Base Target Detection) algorithm based on the analysis of temporal behavior of individual pixels is known to have good performance for detection and tracking of fast moving target with suppressing clutter. However it is not suitable to detect stationary and abruptly maneuvering targets. Moreover its computational load may not be negligible. The PTPBTD-HPDAF algorithm proposed in this paper for real-time target detection and tracking is shown to be computationally cheap while it has benefit of tracking targets with abrupt maneuvers. The performance of the proposed RTPBTD-HPDAF algorithm is tested and compared with the spatial filter with HPDAF algorithm for run-time and track initiation at real IIR video.

• The Journal of Korea Robotics Society
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• v.13 no.1
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• pp.31-37
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• 2018

Recently, the safety in vehicle also has become a hot topic as self-driving car is developed. In passive safety systems such as airbags and seat belts, the system is being changed into an active system that actively grasps the status and behavior of the passengers including the driver to mitigate the risk. Furthermore, it is expected that it will be possible to provide customized services such as seat deformation, air conditioning operation and D.W.D (Distraction While Driving) warning suitable for the passenger by using occupant information. In this paper, we propose robust vehicle occupant detection algorithm based on RGB-Depth-Thermal camera for obtaining the passengers information. The RGB-Depth-Thermal camera sensor system was configured to be robust against various environment. Also, one of the deep learning algorithms, OpenPose, was used for occupant detection. This algorithm is advantageous not only for RGB image but also for thermal image even using existing learned model. The algorithm will be supplemented to acquire high level information such as passenger attitude detection and face recognition mentioned in the introduction and provide customized active convenience service.

• Journal of Satellite, Information and Communications
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• v.9 no.4
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• pp.91-96
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• 2014

This paper proposed an adaptive threshold method for detecting flame candidate regions in a infrared image and it adapts according to the contrast and intensity changes in the image. Conventional flame detection systems uses fixed threshold method since surveillance environment does not change, once the system installed. But it needs a adaptive threshold method as requirements of surveillance system has changed. The proposed adaptive threshold algorithm uses the dynamic behavior of flame as featured parameter. The test result is analysed by comparing test result of proposed adaptive threshold algorithm and conventional fixed threshold method. The analysed data shows, the proposed method has 91.42% of correct detection rate and false detection is reduced by 20% comparing to the conventional method.

• Molecules and Cells
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• v.23 no.3
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• pp.340-348
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• 2007

Although considerable effort has been devoted in the mass spectrometric analysis of phosphorylated peptides, successful identification of multi-phosphorylated peptides in enzymatically digested protein samples still remains challenging. The ionization behavior of multi-phosphorylated peptides appears to be somewhat different from that of mono- or di-phosphorylated peptides. In this study, we demonstrate increased sensitivity of detection of multi-phosphorylated peptides of beta casein without using phosphopeptide enrichment techniques. Proteinase K digestion alone increased the detection limit of beta casein multi-phosphorylated peptides in the LC-MS analysis almost 500 fold, compared to conventional trypsin digestion (~50 pmol). In order to understand this effect, various factors affecting the ionization of phosphopeptides were investigated. Unlike ionizations of phosphopeptides with minor modifications, those of multi-phosphorylated peptides appeared to be subject to effects such as selectively suppressed ionization by more ionizable peptides and decreased ionization efficiency by multi-phosphorylation. The enhanced detection limit of multi-phosphorylated peptides resulting from proteinase K digestion was validated using a complex protein sample, namely a lysate of HEK 293 cells. Compared to trypsin digestion, the numbers of phosphopeptides identified and modification sites per peptide were noticeably increased by proteinase K digestion. Non-specific proteases such as proteinase K and elastase have been used in the past to increase detection of phosphorylation sites but the effectiveness of proteinase K digestion for multi-phosphorylated peptides has not been reported.

• Journal of Sensor Science and Technology
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• v.28 no.1
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• pp.47-51
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• 2019

Sweat-based glucose sensors are being widely investigated and researched as they facilitate painless and continuous measurement. However, because the concentration of sweat glucose is almost a hundred times lower than that of blood glucose, it is important to develop electrochemical sensing electrode materials that are highly sensitive to glucose molecules for the detection of low concentrations of glucose. The preparation of a flexible and ultra-sensitive sensor for detection of sweat glucose is presented in this study. Oxygen and nitrogen are removed from the surface of a polyimide film by exposure to a CO2 laser; hence, laser-induced graphene (LIG) is formed. The fabricated LIG electrode showed favorable properties of high roughness and good stability, flexibility, and conductivity. After the laser scanning, Pt nanoparticles (PtNP) with good catalytic behavior were electrodeposited and the glucose sensor thus developed, with a LIG/PtNP hybrid electrode, exhibited a high order of sensitivity and detection limit for sweat glucose.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.12
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• pp.5819-5840
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• 2018

Considering the topology of hierarchical tree structure, each cluster in WSNs is faced with various attacks launched by malicious nodes, which include network eavesdropping, channel interference and data tampering. The existing intrusion detection algorithm does not take into consideration the resource constraints of cluster heads and sensor nodes. Due to application requirements, sensor nodes in WSNs are deployed with approximately uncorrelated security weights. In our study, a novel and versatile intrusion detection system (IDS) for the optimal defense strategy is primarily introduced. Given the flexibility that wireless communication provides, it is unreasonable to expect malicious nodes will demonstrate a fixed behavior over time. Instead, malicious nodes can dynamically update the attack strategy in response to the IDS in each game stage. Thus, a multi-stage intrusion detection game (MIDG) based on Bayesian rules is proposed. In order to formulate the solution of MIDG, an in-depth analysis on the Bayesian equilibrium is performed iteratively. Depending on the MIDG theoretical analysis, the optimal behaviors of rational attackers and defenders are derived and calculated accurately. The numerical experimental results validate the effectiveness and robustness of the proposed scheme.

• Journal of Korean Society for Quality Management
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• v.50 no.3
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• pp.459-471
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• 2022

Purpose: The purpose of this study was to increase prediction accuracy for an anomaly interval identified using an artificial intelligence-based time series anomaly detection technique by establishing a pre-processing process. Methods: Significant variables were extracted by applying feature selection techniques, and anomalies were derived using the TadGAN time series anomaly detection algorithm. After applying machine learning and deep learning methodologies using normal section data (excluding anomaly sections), the explanatory power of the anomaly sections was demonstrated through performance comparison. Results: The results of the machine learning methodology, the performance was the best when SHAP and TadGAN were applied, and the results in the deep learning, the performance was excellent when Chi-square Test and TadGAN were applied. Comparing each performance with the papers applied with a Conventional methodology using the same data, it can be seen that the performance of the MLR was significantly improved to 15%, Random Forest to 24%, XGBoost to 30%, Lasso Regression to 73%, LSTM to 17% and GRU to 19%. Conclusion: Based on the proposed process, when detecting unsupervised learning anomalies of data that are not actually labeled in various fields such as cyber security, financial sector, behavior pattern field, SNS. It is expected to prove the accuracy and explanation of the anomaly detection section and improve the performance of the model.