• 센서학회지
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• 제23권4호
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• pp.251-258
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• 2014

While the feasibility of vertical velocity as a threshold parameter for pre-impact fall detection has been verified, effects of sensor attachment locations and methods calculating vertical acceleration and velocity on the detection performance have not been studied yet. Regarding the vertical velocity-based pre-impact fall detection, this paper investigates detection accuracies of eight different cases depending on sensor locations (waist vs. sternum), vertical accelerations (accurate acceleration based on both accelerometer and gyroscope vs. approximated acceleration based on only accelerometer), and vertical velocities (velocity with attenuation vs. velocity difference). Test results show that the selection of waist-attached sensor, accurate acceleration, and velocity with attenuation based on accelerometer and gyroscope signals is the best in overall in terms of sensitivity and specificity of the detection as well as lead time.

• 한국정보통신학회논문지
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• 제8권2호
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• pp.281-288
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• 2004

본 논문에서는 저속 페이딩 환경 하에서 다중 심볼 차동 복조기의 다이버시티 수신 방법에 대하여 연구한다. MSDD(Multiple Symbol Differential Detection)를 이용하여 다이버시티 수신을 할 경우 복조 블럭의 길이를 크게 할수록 차동 부호화된 MPSK의 Maxim -Ratio-Combining(MRC) 다이버시티 수신기 성능에 수렴하지만 복잡도가 지수적으로 증가하여 현실적으로 구현이 불가능하다. 본 논문에서는 MSDD 수신기에 입력하기 전에 수신 신호들을 정렬 시켜서 결합하는 pre-combining 방식을 제안하였다. 여기서 제안된 pre-combined MSDD 다이버시티 수신기는 준최적 수신기로서 수신기의 복잡도가 복조 블록의 길이에 선형적으로 증가하는 효율적인 MSDD 복조를 가능케 한다. 따라서 고속의 버스트 모뎀과 같이 동기 복조의 어려움이 있을 경우, 채널에 대한 정보에 의존치 않고도 다이버시티 수신을 할 수 있으며 기존의 차동 복조 방식에 비하여 큰 성능 향상을 보여준다.

• Journal of Multimedia Information System
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• 제8권2호
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• pp.79-84
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• 2021

Detecting brain tumors of different sizes is a challenging task. This study aimed to identify brain tumors using detection algorithms. Most studies in this area use segmentation; however, we utilized detection owing to its advantages. Data were obtained from 64 patients and 11,200 MR images. The deep learning model used was RetinaNet, which is based on ResNet152. The model learned three different types of pre-processing images: normal, general histogram equalization, and contrast-limited adaptive histogram equalization (CLAHE). The three types of images were compared to determine the pre-processing technique that exhibits the best performance in the deep learning algorithms. During pre-processing, we converted the MR images from DICOM to JPG format. Additionally, we regulated the window level and width. The model compared the pre-processed images to determine which images showed adequate performance; CLAHE showed the best performance, with a sensitivity of 81.79%. The RetinaNet model for detecting brain tumors through deep learning algorithms demonstrated satisfactory performance in finding lesions. In future, we plan to develop a new model for improving the detection performance using well-processed data. This study lays the groundwork for future detection technologies that can help doctors find lesions more easily in clinical tasks.

• 한국군사과학기술학회지
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• 제21권6호
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• pp.744-751
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• 2018

In this paper, we propose a fast detection and identification algorithm of chemical gases with a passive FTIR spectrometer. We use a pre-detection algorithm that can reduce the spatial region effectively for gas detection and the candidates of the target. It is possible to remove background spectra effectively from measured spectra with the least-squares method. The CC(Correlation Coefficients) and the SNR(Signal-to-Noise Ratio) methods are used for the detection of target gases. The proposed pre-detection algorithm allows the total process of chemical gas detection to be performed with lower complexity compared with the conventional algorithms. This paper can help developing real-time chemical detection instruments and various applications of FTIR spectrometers.

• International Journal of Computer Science & Network Security
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• 제21권3호
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• pp.177-184
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• 2021

All over the world, people are affected by many chronic diseases and medical practitioners are working hard to find out the symptoms and remedies for the diseases. Many researchers focus on the feature detection of the disease and trying to get a better health recommendation system. It is necessary to detect the features automatically to provide the most relevant solution for the disease. This research gives the framework of Health Recommendation System (HRS) for identification of relevant and non-redundant features in the dataset for prediction and recommendation of diseases. This system consists of three phases such as Pre-processing, Feature Selection and Performance evaluation. It supports for handling of missing and noisy data using the proposed Imputation of missing data and noise detection based Pre-processing algorithm (IMDNDP). The selection of features from the pre-processed dataset is performed by proposed ensemble-based feature selection using an expert's knowledge (EFS-EK). It is very difficult to detect and monitor the diseases manually and also needs the expertise in the field so that process becomes time consuming. Finally, the prediction and recommendation can be done using Support Vector Machine (SVM) and rule-based approaches.

• 대한기계학회논문집
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• 제14권3호
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• pp.603-609
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• 1990

본 연구에서는 미소균열의 정의로서 균열의 크기가 재료의 조직의 크기와 order적으로 같은 균열의 특성이라는 것과 균열의 크기가 소성역 크기와 order적으로 같은 균열의 특성에 착안해서 탄소강 평활재와 예균열재(pre-cracked specimen)에 대 해서 응력비 R=-1 및 R=0의 피로한도 특성과 평활재의 미소균열의 검출 및 정류균열의 생성기구를 균열 열림 닫힘에 주목해서 검토하였다.

• JSTS:Journal of Semiconductor Technology and Science
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• 제12권4호
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• pp.418-425
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• 2012

In this paper, efficient implementation of error correction code (ECC) processing circuits based on single error correction and double error detection (SEC-DED) code with check bit pre-computation is proposed for memories. During the write operation of memory, check bit pre-computation eliminates the overall bits computation required to detect a double error, thereby reducing the complexity of the ECC processing circuits. In order to implement the ECC processing circuits using the check bit pre-computation more efficiently, the proper SEC-DED codes are proposed. The H-matrix of the proposed SEC-DED code is the same as that of the odd-weight-column code during the write operation and is designed by replacing 0's with 1's at the last row of the H-matrix of the odd-weight-column code during the read operation. When compared with a conventional implementation utilizing the odd-weight- column code, the implementation based on the proposed SEC-DED code with check bit pre-computation achieves reductions in the number of gates, latency, and power consumption of the ECC processing circuits by up to 9.3%, 18.4%, and 14.1% for 64 data bits in a word.

• 대한전자공학회논문지SP
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• 제49권4호
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• pp.120-128
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• 2012

본 논문에서는 불균일한 클러터 환경에서 다중 표적탐지 성능을 향상시키기 위한 변형된 반복 백색화 투영 통계(modified iterative pre-whitening projection statistics: MIPPS) 기법을 제안하였다. MIPPS 기법은 항공기용 레이더에서 사용하는 시공간 적응 처리(space-time adaptive processing) 알고리듬의 불균일성 검출(non-homogeneity detection: NHD) 기법으로 반사신호 세기가 서로 다른 다수의 표적이 근접거리에 혼재되어 있는 환경에서 우수한 표적탐지 성능을 나타낸다. 모의실험을 통해 기존의 다양한 NHD 기법들의 성능을 분석하고, 본 논문에서 제안하는 MIPPS 기법이 강한 표적신호에 의해 야기되는 마스킹 효과(masking effect)를 최소화하면서 반사신호 세기가 약한 표적에 대한 평균 탐지 확률을 향상시킬 수 있음을 확인하였다.

• 한국컴퓨터정보학회논문지
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• 제17권12호
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• pp.179-185
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• 2012

본 논문에서는 RST(Rough Set Theory)과 SVM(Support Vector Machine) 알고리즘을 이용한 RSIDS (RST and SVM based Intrusion Detection System)를 설계하였다. RSIDS는 PrePro(Preprocessing) 모듈, RRG(RST based Rule Generation) 모듈, 그리고 SAD(SVM based Attack Detection) 모듈로 구성된다. PrePro 모듈은 수집한 정보를 RSIDS의 데이터 형식에 맞게 변경한다. RRG 모듈은 공격 자료를 분석하여 공격 규칙을 생성하고, 그 규칙을 이용하여 대량화된 데이터에서 공격정보를 추출하고, 그리고 추출한 공격정보를 SAD 모듈에 전달한다. SAD 모듈은 추출된 공격 정보를 이용하여 공격을 탐지하여 관리자에게 통보한다. 그 결과, 기존의 SVM과 비교해볼 때, RSIDS는 평균 공격 탐지율 77.71%에서 85.28%로 향상되었으며, 평균 FPR은 13.25%에서 9.87%로 감소하였다. 따라서 RSIDS는 기존의 SVM을 이용한 공격 탐지 기법보다 향상되었다고 할 수 있다.

• Journal of Microbiology and Biotechnology
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• 제27권3호
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• pp.471-479
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• 2017

White spot syndrome virus (WSSV) is a major threat to the shrimp farming industry and so far there is no effective therapy for it, and thus early diagnostic of WSSV is of great importance. However, at the early stage of infection, the extremely low-abundance of WSSV DNA challenges the detection sensitivity and accuracy of PCR. To effectively detect low-abundance WSSV, here we developed a pre-amplification PCR (pre-amp PCR) method to amplify trace amounts of WSSV DNA from massive background genomic DNA. Combining with normal specific PCR, 10 copies of target WSSV genes were detected from ${\sim}10^{10}$ magnitude of backgrounds. In particular, multiple target genes were able to be balanced amplified with similar efficiency due to the usage of the universal primer. The efficiency of the pre-amp PCR was validated by nested-PCR and quantitative PCR, and pre-amp PCR showed higher efficiency than nested-PCR when multiple targets were detected. The developed method is particularly suitable for the super early diagnosis of WSSV, and has potential to be applied in other low-abundance sample detection cases.