• 한국지반공학회:학술대회논문집
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• 한국지반공학회 2007년 가을학술발표회
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• pp.558-569
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• 2007

It is important to obtain real-time data from long-term monitoring of landfills and develop leachate leakage detection system for the integrated management of landfills. A novel real time monitoring system and early leakage detection system was suggested in this study. The suggested monitoring system is composed of two parts; (1) a set of moisture sensors which monitor the areas surrounding the landfill, and (2) a set of moisture and temperature sensors which monitor the landfill inside. For the assessment for landfills stabilization, real-time monitoring system was evaluated in dry and wet cell of pilot-site. In addition, the grid-net electrical conductivity measurement system was also suggested as early leakage detection system. In this study, the field applicability of suggested systems was evaluated through pilot-scale field tests. The results of pilot-scale field model tests indicate that the grid-net electrical conductivity measurement method can be applicable to the detection of landfill leachate at the initial stage of intrusion, and thus has a potential for monitoring leachate leakage at waste landfills.

• 대한임베디드공학회논문지
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• 제12권4호
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• pp.231-238
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• 2017

Detecting small infrared targets from the low-SCR images at a long distance is very hard. The previous Local Contrast Method (LCM) algorithm based on the human visual system shows a superior performance of detecting small targets by a background suppression technique through local contrast measure. However, its slow processing speed due to the heavy multi-scale processing overhead is not suitable to a variety of real-time applications. This paper presents a lightweight real-time small target detection algorithm, called by the Improved Selective Local Contrast Method (ISLCM), to reduce the scale-invariant computational overhead. The proposed ISLCM applies the improved local contrast measure to the predicted selective region so that it may have a comparable detection performance as the previous LCM while guaranteeing low scale-invariant computational load by exploiting both adaptive scale estimation and small target feature feasibility. Experimental results show that the proposed algorithm can reduce its computational overhead considerably while maintaining its detection performance compared with the previous LCM.

• ETRI Journal
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• 제29권2호
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• pp.143-152
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• 2007

In this paper, we propose a time-varying modified minimum mean-squared error (MMSE) detector for the detection of higher data rate signals in a multirate asynchronous code-division multiple-access (CDMA) system which is signaled in a fast Rayleigh fading channel. The interference viewed by a higher data rate symbol will be periodic due to the presence of a lower data rate symbol which spans multiple higher data rate symbols. The detection is carried out on the basis of a modified MMSE criterion which incorporates differential detection and the ratio of channel coefficients in two consecutive observation intervals inherently compensating the fast variation of the channel due to fading. The numerical results obtained by the MMSE detector with time-varying detection show around 3 dB (M=2) and 6 dB (M=4) performance improvement at a BER of $10^{-3}$ in the AWGN channel, while introducing more computational complexity than the MMSE detector without time-varying detection. At a higher $E_b/N_0$, the proposed scheme can achieve a BER of approximately $10^{-3}$ in the presence of fast channel variation which is an improvement over other schemes.

• 제어로봇시스템학회논문지
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• 제13권11호
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• pp.1074-1081
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• 2007

This paper presents a method for real-time face detection and tracking which combined Adaboost and Camshift algorithm. Adaboost algorithm is a method which selects an important feature called weak classifier among many possible image features by tuning weight of each feature from learning candidates. Even though excellent performance extracting the object, computing time of the algorithm is very high with window size of multi-scale to search image region. So direct application of the method is not easy for real-time tasks such as multi-task OS, robot, and mobile environment. But CAMshift method is an improvement of Mean-shift algorithm for the video streaming environment and track the interesting object at high speed based on hue value of the target region. The detection efficiency of the method is not good for environment of dynamic illumination. We propose a combined method of Adaboost and CAMshift to improve the computing speed with good face detection performance. The method was proved for real image sequences including single and more faces.

• 한국HCI학회:학술대회논문집
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• 한국HCI학회 2009년도 학술대회
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• pp.178-185
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• 2009

가속도계 신호를 대상으로 패턴 인식을 행하는 연구에서 공통적으로 사용될 수 있는 끝점 검출 방법을 제안한다. 기존의 연구 결과물은 추가적인 단추 등을 부착하여 수동으로 구분하거나, 고성능 고주파 대역 필터 등의 사용으로 알고리즘 상에서 필히 시간 지연이 발생하며 또한 알고리즘 구현상 여러 매개 변수 및 이를 위한 문턱값이 존재하였다. 본 논문에서는 가속도의 일계도 미분의 시퀀스를 입력 벡터로 사용하여, 시계열 데이터 예측과 유사한 형태로 focused Time Lagged Feedforward Network(TLFN)을 설계, 이를 학습시키는 방법을 제안 하였다. 제안한 방법을 글자 궤적에 대해 적용하여 신뢰도 있는 끝점 검출 성능과 실시간 응답 특성을 확인하였다.

• International journal of advanced smart convergence
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• 제10권4호
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• pp.110-116
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• 2021

This paper proposed a real-time earlobe detection system using deep learning on the web. Existing deep learning-based detection methods often find independent objects such as cars, mugs, cats, and people. We proposed a way to receive an image through the camera of the user device in a web environment and detect the earlobe on the server. First, we took a picture of the user's face with the user's device camera on the web so that the user's ears were visible. After that, we sent the photographed user's face to the server to find the earlobe. Based on the detected results, we printed an earring model on the user's earlobe on the web. We trained an existing YOLO v5 model using a dataset of about 200 that created a bounding box on the earlobe. We estimated the position of the earlobe through a trained deep learning model. Through this process, we proposed a real-time earlobe detection system on the web. The proposed method showed the performance of detecting earlobes in real-time and loading 3D models from the web in real-time.

• Journal of information and communication convergence engineering
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• 제4권4호
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• pp.151-154
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• 2006

In this work, a novel optimal multiuser detection (MUD) approach, which not only achieves the optimal maximum-likelihood (ML)-like performance but also has reasonably low computational complexity, for Space-time coded OFDM (ST-OFDM) systems is presented. In the proposed detection scheme, the signal model is firstly re-expressed into linearly equivalent one. Then, with the linearly equivalent signal model, a new jointly MUD algorithm is proposed to detect signals. The ML-like bit-error-rate (BER) performance and reasonably low complexity of the proposed detection are verified by computer simulations.

• 정보통신설비학회논문지
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• 제11권1호
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• pp.23-28
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• 2012

In this paper, we proposed and developed a fast and efficient real time face detection and recognition which can be run on embedded system instead of high performance desktop. In the face detection process, we detect a face by finding eye part which is one of the most salient facial features after applying various image processing methods, then in the face recognition, we finally recognize the face by comparing the current face with the prepared face database using a template matching algorithm. Also we optimized the algorithm in our system to be successfully used in the embedded system, and performed the face detection and recognition experiments on the embedded board to verify the performance. The developed method can be applied to automatic door, mobile computing environment and various robot.

• ETRI Journal
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• 제37권2호
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• pp.251-261
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• 2015

We present a novel method for real-time automatic license plate detection in high-resolution videos. Although there have been extensive studies of license plate detection since the 1970s, the suggested approaches resulting from such studies have difficulties in processing high-resolution imagery in real-time. Herein, we propose a novel cascade structure, the fastest classifier available, by rejecting false positives most efficiently. Furthermore, we train the classifier using the core patterns of various types of license plates, improving both the computation load and the accuracy of license plate detection. To show its superiority, our approach is compared with other state-of-the-art approaches. In addition, we collected 20,000 images including license plates from real traffic scenes for comprehensive experiments. The results show that our proposed approach significantly reduces the computational load in comparison to the other state-of-the-art approaches, with comparable performance accuracy.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2003년도 ICCAS
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• pp.974-977
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• 2003

The GPS/INS integration system navigation can provide improved navigation performance and has been widely used as a main navigation system for military and commercial vehicles. When two navigation systems are tightly coupled and the structure is complicated, a fault in either the GPS or the INS can lead to a disastrous failure of the whole integration system. This paper proposes a real-time fault detection method for an AGPS/INS integration system. The proposed fault detection method comprises a BIT and a fault detection algorithm based on chi-square test. It is implemented by real-time software modules to apply the AGPS/INS integration system and van test is carried out to evaluate its performance.