• 대한임베디드공학회논문지
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• 제18권5호
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• pp.203-208
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• 2023

Recently, with the growing interest in the field of autonomous driving, many researchers have been focusing on developing autonomous driving software platforms. In particular, we have concentrated on developing 3D object detection models that can improve real-time performance. In this paper, we introduce a self-constructed 3D LiDAR dataset specific to domestic environments and propose a VariFocal-based CenterPoint for the 3D object detection model, with improved performance over the previous models. Furthermore, we present experimental results comparing the performance of the 3D object detection modules using our self-built and public dataset. As the results show, our model, which was trained on a large amount of self-constructed dataset, successfully solves the issue of failing to detect large vehicles and small objects such as motorcycles and pedestrians, which the previous models had difficulty detecting. Consequently, the proposed model shows a performance improvement of about 1.0 mAP over the previous model.

• 융합신호처리학회논문지
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• 제7권2호
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• pp.81-86
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• 2006

본 논문에서는 새로운 개념의 공정 검출 회로를 제안하였다. 제안된 공정 검출 회로는 장채널 트랜지스터와 최소의 배선폭을 갖는 단채널 트랜지스터 사이의 공정변수의 차이를 비교한다. 이 회로는 공정 변이에 따라 발생하는 캐리어 이동도의 차이를 이용하여 이에 비례하는 차동 전류를 생성해 낸다. 이 방법에서는 고 이득 연산증폭기를 사용한 궤환 회로를 구현함으로써 두 개의 트랜지스터의 드레인 전압이 같아지도록 유지한다. 또한, 본 논문은 제안한 자기-바이어스 슈퍼 MOS 복합회로를 이용하여 고 이득 자기-바이어스 rail-to-rail 연산증폭기를 설계하는 새로운 방법을 소개한다. 설계된 연산증폭기의 이득은 단상의 $0.2V{\sim}1.6V$ 공통모드 범위에서 100dB 이상으로 측정되었다 최종적으로, 제안한 공정 검출 회로는 차동 VCO 회로에 직접 적용하였으며, 설계된 VCO 회로를 통해서 공정 검출 회로가 공정 코너들을 성공적으로 보상하고 광범위한 동작 영역에서 안정된 동작을 수행함을 확인할 수 있었다.

• Journal of Microbiology and Biotechnology
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• 제12권5호
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• pp.780-786
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• 2002

An immunosensor based on surface plasmon resonance (SPR) with a self-assembled protein G layer was developed for the detection of Escherichia coli O157:H7. A self-assembled protein C layer on a gold (Au) surface was fabricated by adsorbing the mixture of 11-mercaptoundecanoic acid (MUA) and hexanethiol at various molar ratios and by activating chemical binding between free amine (-$NH_2$) of protein G and 11-(MUA) using 1-ethyl-3-(3-dimethylaminopropyl) carbodiimide hydrochloride (EDAC) in series. The formation of a self-assembled protein G layer on an Au substrate and the binding of the antibody and antigen in series were confirmed by SPR spectroscopy. The surface morphology analyses of the self-assembled protein G layer on the Au substrate, monoclonal antibody (Mab) against E. coli O157:H7 which was immobilized on protein G, and bound E. coli O157:H7 extracts on Immobilized Mab against E. coii O157:H7 were performed by atomic force microscopy (AFM). The detection limit of the SPR-based immunosensor for E. coli O157:H7 was found to be about $10^4$ cells/ml.

• 디지털산업정보학회논문지
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• 제13권3호
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• pp.19-25
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• 2017

Nowadays, networked computer systems play an increasingly important role in our society and its economy. They have become the targets of a wide array of malicious attacks that invariably turn into actual intrusions. This is the reason computer security has become an essential concern for network administrators. Recently, a number of Detection/Prevention System schemes have been proposed based on various technologies. However, the techniques, which have been applied in many systems, are useful only for the existing patterns of intrusion. Therefore, probe detection has become a major security protection technology to detection potential attacks. Probe detection needs to take into account a variety of factors ant the relationship between the various factors to reduce false negative & positive error. It is necessary to develop new technology of probe detection that can find new pattern of probe. In this paper, we propose an hybrid probe detection using Fuzzy Cognitive Map(FCM) and Self Adaptive Module(SAM) in dynamic environment such as Cloud and IoT. Also, in order to verify the proposed method, experiments about measuring detection rate in dynamic environments and possibility of countermeasure against intrusion were performed. From experimental results, decrease of false detection and the possibilities of countermeasures against intrusions were confirmed.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2002년도 ITC-CSCC -2
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• pp.928-931
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• 2002

Network based intrusion detection system is a computer network security tool. In this paper, we present an intrusion detection system based on Self-Organizing Maps (SOM) and Resilient Propagation Neural Network (RPROP) for visualizing and classifying intrusion and normal patterns. We introduce a cluster matching equation for finding principal associated components in component planes. We apply data from The Third International Knowledge Discovery and Data Mining Tools Competition (KDD cup'99) for training and testing our prototype. From our experimental results with different network data, our scheme archives more than 90 percent detection rate, and less than 5 percent false alarm rate in one SYN flooding and two port scanning attack types.

• 한국통신학회논문지
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• 제35권4B호
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• pp.566-575
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• 2010

동적인 네트워크 공격에 대응하기 위하여 인공 신경망, 유전 알고리즘, 면역 알고리즘과 같은 지능적 기술들이 공격 탐지에 적용되어 왔으며 최근에는 인공 면역 체계를 이용한 공격 탐지가 활발히 연구되고 있다. 기존의 인공면역체계 기반의 공격 탐지 기법들은 주로 자기 세포 집합과 비교를 통하여 항원을 인지하고 제거하는 부정 선택 원리만을 이용하였다. 그러나 실제 네트워크에서는 정상 상태와 비정상 상태가 거의 유사한 상태를 보이는 경우가 발생하므로 오탐지가 빈번히 발생하는 문제점이 있다. 이러한 문제점을 해결하기 위하여 본 논문에서는 새로운 인공면역체계 기반의 공격 탐지 및 대응 기법을 제안하고 그 성능을 평가한다. 제안하는 기법에서는 인간면역 체계에서 발생하는 수지상 세포와 T 세포의 면역 상호 작용을 적용하여 버퍼 점유율 변화를 이용한 검출기 집합을 발생시키고 공격 탐지 모듈과 대응 모듈을 다음과 같이 설계하였다. 첫째, self/non-self 구별을 위한 부정 선택 원리를 이용하여 검출기 집합을 발생시킨다. 둘째, 공격 탐지 모듈에서는 발생된 검출기 집합을 이용하여 네트워크 이상 상태를 탐지하고 경고 신호를 발생시킨다. 이때 오탐지를 줄이기 위하여 위험이론을 적용하며 위험도를 추측하기 위해 퍼지 이론을 이용한다. 마지막으로 공격 대응 모듈에서는 역추적된 공격 노드에 제어 신호를 전송 하여 공격 트래픽을 제한하도록 한다.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 2001년도 The Pacific Aisan Confrence On Intelligent Systems 2001
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• pp.97-108
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• 2001

Frauds detection is a difficult problem, requiring huge computer resources and complicated search activities Researchers have struggled with the problem. Even though a fee research approaches have claimed that their solution is much better than others, research community has not found 'the best solution'well fitting every fraud. Because of the evolving nature of the frauds. a novel and self-adapting method should be devised. In this research a new approach is suggested to solving frauds in insurance claims credit card transaction. Based on evolutionary computing approach, the method is itself self-adjusting and evolving enough to generate a new self of decision-makin rules. We believe that this new approach will provide a promising alternative to conventional ones, in terms of computation performance and classification accuracy.

• 제어로봇시스템학회논문지
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• 제5권2호
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• pp.228-236
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• 1999

In this paper, we propose visual-based self-localization and obstacle detection algorithms for indoor mobile robots. The algorithms do not require calibration, and can be worked with only single image by using the projective invariant relationship between natural landmarks. We predefine a risk zone without obstacles for a robot, and update the image of the risk zone, which will be used to detect obstacles inside the zone by comparing the averaging image with the current image of a new risk zone. The positions of the robot and the obstacles are determined by relative positioning. The method does not require the prior information for positioning robot. The robustness and feasibility of our algorithms have been demonstrated through experiments in hallway environments.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2001년도 추계학술대회 학술발표 논문집
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• pp.185-188
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• 2001

According as many people use a computer newly, damage of computer virus and hacking is rapidly increasing by the crucial users. To block hacking that is intrusion of a person's computer and the computer virus that destroys data, a study for intrusion-detection of system and virus detection using a biological immune system is in progress. In this paper, we make a model of positive selection and negative selection of self-recognition process that is ability of T-cytotoxic cell that plays an important part in biological immune system. So we embody a self-nonself distinction algorithm in computer. To prove the efficacy of self-recognition algorithm, we use simulations by a cell change and a string change of self file.

• Asian Pacific Journal of Cancer Prevention
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• 제17권3호
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• pp.1277-1284
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• 2016

Background: Early detection is a critical part of reducing the burden of breast cancer and breast self-examination (BSE) has been found to be an especially important early detection strategy in low and middle income countries such as Malaysia. Although reports indicate that Malaysian women report an increase in BSE activity in recent years, additional research is needed to explore factors that may help to increase this behavior among Southeastern Asian women. Objective: This study is the first of its kind to explore how the predicting variables of self-efficacy, perceived barriers, and body image factors correlate with self-reports of past BSE, and intention to conduct future breast self-exams among female students in Malaysia. Materials and Methods: Through the analysis of data collected from a prior study of female students from nine Malaysian universities (n=842), this study found that self-efficacy, perceived barriers and specific body image sub-constructs (MBSRQ-Appearance Scales) were correlated with, and at times predicted, both the likelihood of past BSE and the intention to conduct breast self-exams in the future. Results: Self-efficacy (SE) positively predicted the likelihood of past self-exam behavior, and intention to conduct future breast self-exams. Perceived barriers (BR) negatively predicted past behavior and future intention of breast self-exams. The body image sub-constructs of appearance evaluation (AE) and overweight preoccupation (OWP) predicted the likelihood of past behavior but did not predict intention for future behavior. Appearance orientation (AO) had a somewhat opposite effect: AO did not correlate with or predict past behavior but did correlate with intention to conduct breast self-exams in the future. The body image sub-constructs of body area satisfaction (BASS) and self-classified weight (SCW) showed no correlation with the subjects' past breast self-exam behavior nor with their intention to conduct breast self-exams in the future. Conclusions: Findings from this study indicate that both self-efficacy and perceived barriers to BSE are significant psychosocial factors that influence BSE behavior. These results suggest that health promotion interventions that help enhance self-efficacy and reduce perceived barriers have the potential to increase the intentions of Malaysian women to perform breast self-exams, which can promote early detection of breast cancers. Future research should evaluate targeted communication interventions for addressing self-efficacy and perceived barriers to breast self-exams with at-risk Malaysian women. and further explore the relationship between BSE and body image.