• Asian Pacific Journal of Cancer Prevention
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• 제13권12호
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• pp.6527-6531
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• 2012

Purpose: Complement receptor 1 (CR1) is induced by Epstein-Barr virus (EBV) and may be a potential biomarker of nasopharyngeal carcinoma (NPC). We conducted the present study to evaluate the association of CR1 expression with clinicopathological features and prognosis of NPC. Methods: We enrolled 145 NPC patients and 110 controls. Expression levels of CR1 in peripheral blood mononuclear cells (PBMCs) were detected using quantitative real-time PCR and associations with clinicopathological features and prognosis were examined. Results: CR1 levels in the NPC group [3.54 (3.34, 3.79)] were slightly higher than those in the controls [3.33 (3.20, 3.47)] (P<0.001). Increased CR1 expression was associated with histology classification (type III vs. type II, P=0.002), advanced clinical stage (P=0.003), high T stage (P=0.017), and poor overall survival (HR, 4.89; 95% CI, 1.23-19.42; P=0.024). However, there were no statistically significant differences in CR1 expression among N or M stages. Conclusion: These findings indicate that CR1 expression in PBMCs may be a new biomarker for prognosis of NPC and a potential therapeutic target.

• Journal of Microbiology and Biotechnology
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• 제33권3호
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• pp.299-309
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• 2023

Glutathione peroxidases (Gpx) are a group of antioxidant enzymes that protect cells or tissues against damage from reactive oxygen species (ROS). The Gpx proteins identified in mammals exhibit high catalytic activity toward glutathione (GSH). In contrast, a variety of non-mammalian Gpx proteins from diverse organisms, including fungi, plants, insects, and rodent parasites, show specificity for thioredoxin (TRX) rather than GSH and are designated as TRX-dependent peroxiredoxins. However, the study of the properties of Gpx in the environmental microbiome or isolated bacteria is limited. In this study, we analyzed the Gpx sequences, identified the characteristics of sequences and structures, and found that the environmental microbiome Gpx proteins should be classified as TRX-dependent, Gpx-like peroxiredoxins. This classification is based on the following three items of evidence: i) the conservation of the peroxidatic Cys residue; ii) the existence and conservation of the resolving Cys residue that forms the disulfide bond with the peroxidatic cysteine; and iii) the absence of dimeric and tetrameric interface domains. The conservation/divergence pattern of all known bacterial Gpx-like proteins in public databases shows that they share common characteristics with that from the environmental microbiome and are also TRX-dependent. Moreover, phylogenetic analysis shows that the bacterial Gpx-like proteins exhibit a star-like radiating phylogenetic structure forming a highly diverse genetic pool of TRX-dependent, Gpx-like peroxidases.

• 전자공학회논문지CI
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• 제45권5호
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• pp.102-109
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• 2008

본 논문은 다중경로 페이딩 채널 조건에서 사전 정보없이 입사하는 디지털 신호 10종의 변조형태를 고정확도로 인식할 수 있도록 고차 통계량(HOS)과 웨이브릿 변환(WT)에서 선정된 특징(key features)을 이용한 견실한 하이브리드 분류기를 제안하였다. 제안된 분류기는 실제 시나리오를 고려하여 다양한 다중경로 환경(즉, 농촌, 소도시, 도심지역)에서 측정된 채널 데이터를 이용하였다. 실제 측정된 다중경로 페이딩 채널 데이터를 이용하여 Holdout-like 방식으로 총 15개 채널 중 9개 채널은 트레이닝용으로 사용하고, 나머지 6개 채널은 테스트용으로 사용하였다. 제안된 분류기는 다중경로 환경에서 높은 변별력을 유지하는 HOS 특징을 기반으로 구현되었고, AMA(Alphabet Matched Algorithm) 또는 MMA(Multi-modulus Algerian)와 같은 등화기법의 적용없이 분류가 어렵다고 알려진 MQAM신호(M=16, 64, 256)들에 대해서만 WT 특징을 적용하였다. 선정된 특징들을 이용한 변조인식은 입력공간에서 최대 마진을 갖는 하이퍼 공간으로 매핑시킴으로서 분류 능력이 우수하다고 알려진 SVM 메소드를 적용하여 시뮬레이션을 실시하였다. 제안된 분류기의 성능은 트레이닝 채널과 테스트 채널에서 WT 또는 HOS 특징만을 단독으로 사용하는 분류기에 비해 현저한 성능 향상을 보였고, 특히, MQAM 신호의 인식률은 낮은 SNR레벨에서도 거의 완전하게 분류되었다.

• Nuclear Engineering and Technology
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• 제55권1호
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• pp.339-352
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• 2023

Prediction of the time-related traits of pebble flow inside pebble-bed HTGRs is of great significance for reactor operation and design. In this work, an image-driven approach with the aid of a convolutional neural network (CNN) is proposed to predict the remaining time of initially loaded pebbles and the time interval of paired flow images of the pebble bed. Two types of strategies are put forward: one is adding FC layers to the classic classification CNN models and using regression training, and the other is CNN-based deep expectation (DEX) by regarding the time prediction as a deep classification task followed by softmax expected value refinements. The current dataset is obtained from the discrete element method (DEM) simulations. Results show that the CNN-aided models generally make satisfactory predictions on the remaining time with the determination coefficient larger than 0.99. Among these models, the VGG19+DEX performs the best and its CumScore (proportion of test set with prediction error within 0.5s) can reach 0.939. Besides, the remaining time of additional test sets and new cases can also be well predicted, indicating good generalization ability of the model. In the task of predicting the time interval of image pairs, the VGG19+DEX model has also generated satisfactory results. Particularly, the trained model, with promising generalization ability, has demonstrated great potential in accurately and instantaneously predicting the traits of interest, without the need for additional computational intensive DEM simulations. Nevertheless, the issues of data diversity and model optimization need to be improved to achieve the full potential of the CNN-aided prediction tool.

• 컴퓨터교육학회논문지
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• 제7권5호
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• pp.83-91
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• 2004

본 논문에서 청소년들의 정신 건강을 해치는 유해 정보 사이트를 차단하기 위해 기존 방식이 아닌 내용 기반을 중심으로 하여 중요도가 가장 높은 한 개의 복합 키워드와 정보통신윤리 위원회에서 제시한 유해단어의 가중치를 이용하여 가중치 평균을 더해 유해도를 판단하여 유해 사이트와 일반 사이트를 구별하는 시스템을 구현하였다. 예비 실험을 통해 구해진 유해도의 값 3.5를 유해정보 사이트를 판단하는 기준으로 정한 다음 유해 정보 차단 시스템의 성능 실험을 위해 유해 정보 사이트와 일반 사이트를 각각 무작위로 100개씩 추출해 접속해 본 결과 유해 사이트를 유해 정보 사이트로 판명한 비율이 78%를 보였고 일반 사이트를 일반 사이트로 판명한 비율이 96%가 되어 본 시스템의 유효성을 확인 할 수가 있었다.

• 한국지능시스템학회논문지
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• 제16권6호
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• pp.736-741
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• 2006

분류 시스템은 데이터 전처리 모듈, 학습모듈, 의사결정모듈로 구성되어 있으며 지능형시스템의 중요한 구성요소로 활용되어왔다. 특히 학습모듈은 사전정보를 제공하므로 분류를 위한 핵심 역할을 수행하여 왔다. 기존의 학습을 위한 기법은 주로 승자독점방식으로 데이터를 처리하므로 경계가 불명확한 대부분의 실세계 응용에 적합하지 못하다. 또한 학습 알고리즘에 필요한 데이터를 한꺼번에 준비해야 하지만 이는 일반적으로 가능하지 않은 경우가 많다. 이를 위하여 본 논문에서는 점증적 학습 퍼지신경망, FNN-I,를 이용한 적응 분류모델을 설계한다. 이 모델에서는 유용하게 정보를 표현하기 위하여 퍼지이론을 도입하고 계속적으로 모여지는 데이터를 가지고 점증적으로 학습할 수 있는 알고리즘을 제시한다. 제안된 모델을 컴퓨터 바이러스 분류를 위한 실제 데이터에 적용하여 점증적으로 학습할 수 있고 효과적으로, 새로운 바이러스 데이터를 분류할 수 있음을 보인다.

• International Journal of Internet, Broadcasting and Communication
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• 제12권4호
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• pp.83-92
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• 2020

A recent study to determine the fall is focused on analyzing fall motions using a recurrent neural network (RNN), and uses a deep learning approach to get good results for detecting human poses in 2D from a mono color image. In this paper, we investigated the improved detection method to estimate the position of the head and shoulder key points and the acceleration of position change using the skeletal key points information extracted using PoseNet from the image obtained from the 2D RGB low-cost camera, and to increase the accuracy of the fall judgment. In particular, we propose a fall detection method based on the characteristics of post-fall posture in the fall motion analysis method and on the velocity of human body skeleton key points change as well as the ratio change of body bounding box's width and height. The public data set was used to extract human skeletal features and to train deep learning, GRU, and as a result of an experiment to find a feature extraction method that can achieve high classification accuracy, the proposed method showed a 99.8% success rate in detecting falls more effectively than the conventional primitive skeletal data use method.

• 정보관리학회지
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• 제25권4호
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• pp.131-148
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• 2008

본 연구에서는 비디오의 의미를 잘 표현하고 있는 키프레임들을 추출하는 알고리즘을 설계하고 평가하였다. 구체적으로 영상 초록의 키프레임 선정을 위한 이론 체계를 수립하기 위해서 선행 연구와 이용자들의 키프레임 인식 패턴을 조사하여 분석해 보았다. 그런 다음 이러한 이론 체계를 기초로 하여 하이브리드 방식으로 비디오에서 키프레임을 추출하는 알고리즘을 설계한 후 실험을 통해서 그 효율성을 평가해 보았다. 끝으로 이러한 실험 결과를 디지털 도서관과 인터넷 환경의 비디오 검색과 브라우징에 활용할 수 있는 방안을 제안하였다.

• 한국통신학회논문지
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• 제34권10B호
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• pp.1086-1092
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• 2009

Stereoscopic conversion has been an important and challenging issue for many 3-D video applications. Usually, there are two different stereoscopic conversion approaches, i.e., image motion-based conversion that uses motion information and object-based conversion that partitions an image into moving or static foreground object(s) and background and then converts the foreground in a stereoscopic object. As well, since the input sequence is MPEG-1/2 compressed video, motion data stored in compressed bitstream are often unreliable and thus the image motion-based conversion might fail. To solve this problem, we present the utilization of key-motion that has the better accuracy of estimated or extracted motion information. To deal with diverse motion types, a transform space produced from motion vectors and color differences is introduced. A key-motion is determined from the transform space and its associated stereoscopic image is generated. Experimental results validate effectiveness and robustness of the proposed method.

• Smart Structures and Systems
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• 제29권3호
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• pp.475-484
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• 2022

The loss of cable tension for civil infrastructure reduces structural bearing capacity and causes harmful deformation of structures. Currently, most of the structural health monitoring (SHM) approaches for cables rely on contact transducers. This paper proposes a cable tension identification technology using percussion sound, which provides a fast determination of steel cable tension without physical contact between cables and sensors. Notably, inspired by the concept of tensioning strings for piano tuning, this proposed technology predicts cable tension value by deep learning assisted classification of "percussion" sound from tapping a steel cable. To simulate the non-linear mapping of human ears to sound and to better quantify the minor changes in the high-frequency bands of the sound spectrum generated by percussions, Mel-frequency cepstral coefficients (MFCCs) were extracted as acoustic features to train the deep learning network. A convolutional neural network (CNN) with four convolutional layers and two global pooling layers was employed to identify the cable tension in a certain designed range. Moreover, theoretical and finite element methods (FEM) were conducted to prove the feasibility of the proposed technology. Finally, the identification performance of the proposed technology was experimentally investigated. Overall, results show that the proposed percussion-based technology has great potentials for estimating cable tension for in-situ structural safety assessment.