• 한국통신학회논문지
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• 제40권9호
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• pp.1722-1724
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• 2015

본 논문은 MIMO-OFDM 시스템에서 복잡도와 검출 성능의 관점에서 효율성을 위하여, QRD-M과 DFE 및 반복 검출을 통한 분할 검출 기법을 제안한다. 제안된 기법은 공간 스트림에 따라 다른 검출 방법을 사용하여 신호들을 검출한다. 제안된 기법에서 낮은 복잡도를 요구하는 공간 스트림에서는 높은 복잡도와 높은 검출 성능을 가지는 QRD-M을 사용하고 높은 복잡도를 요구하는 공간 스트림에서는 낮은 복잡도와 낮은 검출 성능을 가지는 DFE를 사용한다. 또한 DFE가 사용된 공간 스트림에 대해서는 신뢰성을 보장하기 위해 반복 검출을 수행한다. 시뮬레이션을 통하여, 제안된 기법은 비록 기존의 기법보다 증가된 복잡도를 가지지만, 검출 성능을 월등히 개선시키는 것을 확인하였다.

• 대한임베디드공학회논문지
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• 제15권5호
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• pp.243-249
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• 2020

Event detection in wireless sensor networks is a key requirement in many applications. Acoustic sensors are one of the most frequently used sensors for event detection in sensor networks, but they are sensitive and difficult to handle because they vary greatly depending on the environment and target characteristics of the sensor field. In this paper, we propose a learning-based improvement of CFAR algorithm for increasing node-level event detection performance in acoustic sensor networks, and verify the effectiveness of the designed algorithm by comparing and evaluating the event detection performance with other algorithms. Our experimental results demonstrate the superiority of the proposed algorithm by increasing the detection accuracy by more than 45.16% by significantly reducing false positives by 7.97 times while slightly increasing the false negative compared to the existing algorithm.

• International Journal of Advanced Culture Technology
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• 제11권1호
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• pp.370-375
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• 2023

As we enter a super-aged society, studies are being conducted to reduce complications and deaths caused by falls in elderly adults. Research is being conducted on interventions for preventing falls in the elderly, wearable devices for detecting falls, and methods for improving the performance of fall detection algorithms. Wearable devices for detecting falls of the elderly generally use gyro sensors. In addition, to improve the performance of the fall detection algorithm, an artificial intelligence algorithm is applied to the x, y, z coordinate data collected from the gyro sensor. In this paper, we develop a wearable device that uses a gyro sensor, body temperature, and heart rate sensor for health management as well as fall detection for the elderly. In addition, we develop a fall detection and health management system that works with wearable devices and a guardian's mobile app to improve the performance of the fall detection algorithm and provide health information to guardians.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권8호
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• pp.2688-2703
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• 2022

Research on the advanced detection of harmful objects in airport cargo for passenger safety against terrorism has increased recently. However, because associated studies are primarily focused on the detection of relatively large objects, research on the detection of small objects is lacking, and the detection performance for small objects has remained considerably low. Here, we verified the limitations of existing research on object detection and developed a new model called the Small Hazardous Object detection enhanced and reconstructed Model based on the You Only Look Once version 5 (YOLOv5) algorithm to overcome these limitations. We also examined the performance of the proposed model through different experiments based on YOLOv5, a recently launched object detection model. The detection performance of our model was found to be enhanced by 0.3 in terms of the mean average precision (mAP) index and 1.1 in terms of mAP (.5:.95) with respect to the YOLOv5 model. The proposed model is especially useful for the detection of small objects of different types in overlapping environments where objects of different sizes are densely packed. The contributions of the study are reconstructed layers for the Small Hazardous Object detection enhanced and reconstructed Model based on YOLOv5 and the non-requirement of data preprocessing for immediate industrial application without any performance degradation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권4호
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• pp.1504-1526
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• 2018

For uplink multi-user massive MIMO systems, conventional minimum mean square error (MMSE) linear detection method achieves near-optimal performance when the number of antennas at base station is much larger than that of the single-antenna users. However, MMSE detection involves complicated matrix inversion, thus making it cumbersome to be implemented cost-effectively and rapidly. In this paper, we first summarize in detail the state-of-the-art simplified MMSE detection algorithms that circumvent the complicated matrix inversion and hence reduce the computation complexity from ${\mathcal{O}}(K^3)$ to ${\mathcal{O}}(K^2)$ or ${\mathcal{O}}(NK)$ with some certain performance sacrifice. Meanwhile, we divide the simplified algorithms into two categories, namely the matrix inversion approximation and the classical iterative linear equation solving methods, and make comparisons between them in terms of detection performance and computation complexity. In order to further optimize the detection performance of the existing detection algorithms, we propose more proper solutions to set the initial values and relaxation parameters, and present a new way of reconstructing the exact effective noise variance to accelerate the convergence speed. Analysis and simulation results verify that with the help of proper initial values and parameters, the simplified matrix inversion based detection algorithms can achieve detection performance quite close to that of the ideal matrix inversion based MMSE algorithm with only a small number of series expansions or iterations.

• 디지털융복합연구
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• 제15권6호
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• pp.391-398
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• 2017

본 논문에서는 "퍼지 컨트롤 언어를 이용한 공격 특징 선택기반 네트워크 침입탐지 시스템"[1]과 "RNN을 이용한 공격 분류를 위한 지능형 침입탐지 시스템 모델"[2]의 성능을 비교 하였다. 이 논문에서는 KDD CUP 99 데이터 셋[3]을 이용하여 두 기법의 침입 탐지 성능을 비교하였다. KDD CUP 99 데이터 셋에는 훈련을 위한 데이터 셋과 훈련을 통해 기존의 침입을 탐지 할 수 있는 테스트 데이터 셋이 있다. 또한 훈련 데이터 및 테스트 데이터에 존재 하지 않는 침입의 유형을 탐지할 수 있는가를 테스트 할 수 있는 데이터도 존재한다. 훈련 및 테스트 데이터에서 좋은 침입탐지 성능을 보이는 두 개의 논문을 비교하였다. 비교한 결과 존재하는 침입을 탐지 하는 성능은 우수하지만 기존에 존재하지 않는 침입을 탐지 하는 성능은 부족한 부분이 있다. 공격 유형 중 DoS, Probe, R2L는 퍼지를 이용하는 것이 탐지율이 높았고, U2L은 RNN을 이용하는 것이 탐지율이 높았다.

• 한국군사과학기술학회지
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• 제13권3호
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• pp.461-469
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• 2010

In this paper, we have proposed an improved fusion method of detection features which can enhance the detection probability under the given false alarm rate in the prescreening stage of SAR ATR(Synthetic Aperture Radar Automatic Target Recognition) system. Since the detection features have the positive correlation, the detection performance can be improved if the joint probability distribution of detection features is considered in the fusion process. The detection region is designed as a simple piecewise linear function which can be represented by few parameters. The parameters for the detection region can be derived by training the sample SAR images to maximize the detection probability with the given false alarm rate. Simulation result shows that the detection performance of the proposed method is improved for all combinations of detection features.

• International Journal of Internet, Broadcasting and Communication
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• 제11권4호
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• pp.76-85
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• 2019

We introduce a vision-based object detection method for real-time video surveillance system in low-end edge computing environments. Recently, the accuracy of object detection has been improved due to the performance of approaches based on deep learning algorithm such as Region Convolutional Neural Network(R-CNN) which has two stage for inferencing. On the other hand, one stage detection algorithms such as single-shot detection (SSD) and you only look once (YOLO) have been developed at the expense of some accuracy and can be used for real-time systems. However, high-performance hardware such as General-Purpose computing on Graphics Processing Unit(GPGPU) is required to still achieve excellent object detection performance and speed. To address hardware requirement that is burdensome to low-end edge computing environments, We propose sub-frame analysis method for the object detection. In specific, We divide a whole image frame into smaller ones then inference them on Convolutional Neural Network (CNN) based image detection network, which is much faster than conventional network designed forfull frame image. We reduced its computationalrequirementsignificantly without losing throughput and object detection accuracy with the proposed method.

• 한국정보기술응용학회:학술대회논문집
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• 한국정보기술응용학회 2005년도 6th 2005 International Conference on Computers, Communications and System
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• pp.153-156
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• 2005

Skin region detection in images is an important process in many computer vision applications targeting humans such as hand gesture recognition and face identification. It usually starts at a pixel-level, and involves a pre-process of color spae transformation followed by a classification process. A color space transformation is assumed to increase separability between skin classes and other classes, to increase similarity among different skin tones, and to bring a robust performance under varying imaging conditions, without any complicated analysis. In this paper, we examine if the color space transformation actually brings those benefits to the problem of skin region detection on a set of human hand images with different postures, backgrounds, people, and illuminations. Our experimental results indicate that color space transfomation affects the skin detection performance. Although the performance depends on camera and surround conditions, normalized [R, G, B] color space may be a good choice in general.

• 한국정보전자통신기술학회논문지
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• 제4권3호
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• pp.189-196
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• 2011

본 논문에서는 다중입출력 (MIMO : multiple-input multiple-output) 시스템에서 순차적 간섭 제거 기반 (SIC : successive interference cancellation) 신호 검출 기법의 성능을 분석한다. 고려되는 신호검출 기법들은 SIC 기법와 LR-SIC 기법이며, 이러한 신호 검출 기법들의 블록오류확률 (BLER; block error ratio) 성능을 나타내는 식을 유도 하고, 모의실험 결과를 통해 유도된 식과 성공적으로 일치함을 확인한다.