• ETRI Journal
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• v.40 no.2
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• pp.218-226
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• 2018

Multiuser detection (MUD) and channel estimation techniques in space-division multiple-access aided orthogonal frequency-division multiplexing systems recently has received intensive interest in receiver design technologies. The maximum likelihood (ML) MUD that provides optimal performance has the cost of a dramatically increased computational complexity. The minimum mean-squared error (MMSE) MUD exhibits poor performance, although it achieves lower computational complexity. With almost the same complexity, an MMSE with successive interference cancellation (SIC) scheme achieves a better bit error rate performance than a linear MMSE multiuser detector. In this paper, hybrid ML-MMSE with SIC adaptive multiuser detection based on the joint channel estimation method is suggested for signal detection. The simulation results show that the proposed method achieves good performance close to the optimal ML performance at low SNR values and a low computational complexity at high SNR values.

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.17-18
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• 2006

In this paper, we propose a robust detection scheme of OSTBCs with channel estimation errors over time-selective fading channels. Channel estimation errors are inevitable over time-selective fading channels and even small channel estimation errors dramatically degrade the performance of space-time block coding schemes. Therefore, it is desired to investigate the effect of channel estimation errors on the performance of the proposed detection scheme compared with the existing detection scheme. The proposed detection scheme minimizes noise enhancement and impact of channel estimation errors which occur in an existing detection scheme. It is shown by simulations that the proposed detection scheme performs better than the existing detection scheme over time-selective fading channels.

• Journal of Information Processing Systems
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• v.1 no.1 s.1
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• pp.9-13
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• 2005

Computer security has become a critical issue with the rapid development of business and other transaction systems over the Internet. The application of artificial intelligence, machine learning and data mining techniques to intrusion detection systems has been increasing recently. But most research is focused on improving the classification performance of a classifier. Selecting important features from input data leads to simplification of the problem, and faster and more accurate detection rates. Thus selecting important features is an important issue in intrusion detection. Another issue in intrusion detection is that most of the intrusion detection systems are performed by off-line and it is not a suitable method for a real-time intrusion detection system. In this paper, we develop the real-time intrusion detection system, which combines an on-line feature extraction method with the Least Squares Support Vector Machine classifier. Applying the proposed system to KDD CUP 99 data, experimental results show that it has a remarkable feature extraction and classification performance compared to existing off-line intrusion detection systems.

• Journal of Information Processing Systems
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• v.17 no.2
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• pp.227-241
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• 2021

Object detection techniques based on deep learning such as YOLO have high detection performance and precision in a single channel video stream. In order to expand to multiple channel object detection in real-time, however, high-performance hardware is required. In this paper, we propose a novel back-end server framework, a real-time AI vision platform (RAVIP), which can extend the object detection function from single channel to simultaneous multi-channels, which can work well even in low-end server hardware. RAVIP assembles appropriate component modules from the RODEM (real-time object detection module) Base to create per-channel instances for each channel, enabling efficient parallelization of object detection instances on limited hardware resources through continuous monitoring with respect to resource utilization. Through practical experiments, RAVIP shows that it is possible to optimize CPU, GPU, and memory utilization while performing object detection service in a multi-channel situation. In addition, it has been proven that RAVIP can provide object detection services with 25 FPS for all 16 channels at the same time.

• Journal of Digital Convergence
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• v.14 no.10
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• pp.271-276
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• 2016

In this paper, we propose robust face and eye detection algorithm under changing environmental condition such as lighting and pose variations. Generally, the eye detection process is performed followed by face detection and variations in pose and lighting affects the detection performance. Therefore, we have explored face detection based on Modified Census Transform algorithm. The eye has dominant features in face area and is sensitive to lighting condition and eye glasses, etc. To address these issues, we propose a robust eye detection method based on Gabor transformation and Features from Accelerated Segment Test algorithms. Proposed algorithm presents 27.4ms in detection speed with 98.4% correct detection rate, and 36.3ms face detection speed with 96.4% correct detection rate for eye detection performance.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.2
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• pp.201-211
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• 2022

According to the recent change in the cybersecurity paradigm, research on anomaly detection methods using machine learning and deep learning techniques, which are AI implementation technologies, is increasing. In this study, a comparative study on data preprocessing techniques that can improve the anomaly detection performance of a GRU (Gated Recurrent Unit) neural network-based intrusion detection model using NGIDS-DS (Next Generation IDS Dataset), an open dataset, was conducted. In addition, in order to solve the class imbalance problem according to the ratio of normal data and attack data, the detection performance according to the oversampling ratio was compared and analyzed using the oversampling technique applied with DCGAN (Deep Convolutional Generative Adversarial Networks). As a result of the experiment, the method preprocessed using the Doc2Vec algorithm for system call feature and process execution path feature showed good performance, and in the case of oversampling performance, when DCGAN was used, improved detection performance was shown.

• KIPS Transactions on Software and Data Engineering
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• v.9 no.10
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• pp.323-328
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• 2020

Recently, new approaches that significantly improve performance in object detection and recognition using deep learning technology have been proposed quickly. Of the various techniques for object detection, especially facial object detection (Faster R-CNN, R-CNN, YOLO, SSD, etc), SSD is superior in accuracy and speed to other techniques. At the same time, multiple object detection networks are also readily available. In this paper, among object detection networks, Mobilenet v2 network is used, models combined with SSDs are trained, and methods for detecting objects at a rate of four times or more than conventional performance are proposed using TensorRT engine, and the performance is verified through experiments. Facial object detector was created as an application to verify the performance of the proposed method, and its behavior and performance were tested in various situations.

• Journal of Digital Contents Society
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• v.19 no.4
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• pp.711-717
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• 2018

The breakthrough in computer and network has facilitated a variety of information exchange. However, at the same time, malicious users and groups are attacking vulnerable systems. Intrusion Detection System(IDS) detects malicious behaviors through network packet analysis. However, it has a burden of processing a large amount of packets in a short time. Therefore, in order to solve these problem, we propose a network intrusion detection system that operates at kernel level to improve detection performance at user level. In fact, we confirmed that the network intrusion detection system implemented at kernel level improves packet analysis and detection performance.

• Journal of the Korea Society of Computer and Information
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• v.25 no.3
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• pp.19-26
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• 2020

Steel production requires high-quality surfaces with minimal defects. Therefore, the detection algorithms for the surface defects of steel strip should have good generalization performance. To meet the growing demand for high-quality products, the use of intelligent visual inspection systems is becoming essential in production lines. In this paper, we proposed a ShuffleDefectNet defect detection system based on deep learning. The proposed defect detection system exceeds state-of-the-art performance for defect detection on the Northeastern University (NEU) dataset obtaining a mean average accuracy of 99.75%. We train the best performing detection with different amounts of training data and observe the performance of detection. We notice that accuracy and speed improve significantly when use the overall architecture of ShuffleDefectNet.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.2C
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• pp.229-237
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• 2010

The conventional detection methods developed for spatially-multiplexed MIMO systems such as OSIC and QRD-M show performance difference for each user depending on the order of detection when they are applied to detection of multi-user signals in uplink multiuser systems based on collaborative spatial multiplexing. In this paper, a signal detection method for uplink multiuser systems based on collaborative spatial multiplexing is proposed to provide similar performance for each user while its performance is close to the case of ML detection. Compared with QRD-M method, computational complexity of the proposed signal detection method is similar in the case of QPSK, and significantly lower in the case of high modulation order with 16-QAM and 64-QAM.