• Smart Structures and Systems
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• 제9권2호
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• pp.145-164
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• 2012

In this paper, solar-powered, multi-scale, vibration-impedance sensor node on Imote2 platform is presented for hybrid structural health monitoring (SHM) in cable-stayed bridge. In order to achieve the objective, the following approaches are proposed. Firstly, vibration- and impedance-based hybrid SHM methods are briefly described. Secondly, the multi-scale vibration and impedance sensor node on Imote2-platform is presented on the design of hardware components and embedded software for vibration- and impedance-based SHM. In this approach, a solar-powered energy harvesting is implemented for autonomous operation of the smart sensor nodes. Finally, the feasibility and practicality of the smart sensor-based SHM system is evaluated on a full-scale cable-stayed bridge, Hwamyung Bridge in Korea. Successful level of wireless communication and solar-power supply for smart sensor nodes are verified. Also, vibration and impedance responses measured from the target bridge which experiences various weather conditions are examined for the robust long-term monitoring capability of the smart sensor system.

• 대한임베디드공학회논문지
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• 제9권3호
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• pp.165-171
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• 2014

In this paper, we propose the hardware and software of a test-bed of a hovering AUV (autonomous underwater vehicle). Test-bed to develop as the underwater robot for the hovering -type is planning to apply for marine resource development and exploration for deep sea. The RTU that controls a azimuth thruster and a vertical thruster of test-bed is a intergrated-type thruster. The main control unit that collects sensor's data and performs high-speed processing and controls a movement of test-bed is a underwater hybrid navigation system. Also it transfers position, posture, state information of test-bed to the host PC of user using a wireless communication. The host PC checks a test-bed in real time by using a realtime monitoring system that is implemented by LabVIEW.

• 제어로봇시스템학회논문지
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• 제14권2호
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• pp.141-147
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• 2008

In this paper, we present one approach to achieve safe navigation in indoor dynamic environment. So far, there have been various useful collision avoidance algorithms and path planning schemes. However, those algorithms have a fundamental limitation that the robot can avoid only "visible" obstacles. In real environment, it is not possible to detect all the dynamic obstacles around the robot. There exist a lot of "occluded" regions due to the limitation of field of view. In order to avoid possible collisions, it is desirable to consider visibility information. Then, a robot can reduce the speed or modify a path. This paper proposes a safe navigation scheme to reduce the risk of collision due to unexpected dynamic obstacles. The robot's motion is controlled according to a hybrid control scheme. The possibility of collision is dually reflected to a path planning and a speed control. The proposed scheme clearly indicates the structural procedure on how to model and to exploit the risk of navigation. The proposed scheme is experimentally tested in a real office building. The presented result shows that the robot moves along the safe path to obtain sufficient field of view, while appropriate speed control is carried out.

• 한국지능시스템학회논문지
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• 제13권2호
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• pp.169-174
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• 2003

인터넷 보급의 확산과 전자상거래의 활성화 그리고 유ㆍ무선 인터넷의 보급에 따른 악의적인 사이버 공격의 시도가 점점 증가하고 있다. 이로 인해 점차 더 많은 문제가 야기될 것으로 예상된다. 현재 일반적인 인터넷상의 시스템은 악의적인 공격에 적절하게 대응해오지 못하고 있으며, 다른 범용의 시스템들도 기존의 백신 프로그램에 의존하며 그 공격에 대응해오고 있다. 따라서 새로운 침입에 대하여는 대처하기 힘든 단점을 가지고 있다. 본 논문에서는 생체 자율분산시스템의 일부분인 T세포의 positive selection과 negative selection을 이용한 자기/비자기 인식 알고리즘을 제안한다 제안한 알고리즘은 네트워크 환경에서 침입탐지 시스템에 적용하여 기존에 알려진 침입뿐만 아니라 새로운 침입에 대해서도 대처할 수 있다.

• 한국통신학회논문지
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• 제28권3B호
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• pp.224-236
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• 2003

MANET(Mobile Ad hoc Network)에서는 이동 단말들이 기존에 설치된 구조물이나 운영자의 노력없이 스스로 임시망을 구성하여 통신을 하게 된다. ad hoc 망에서의 라우팅은 이동성이 많은 단말들이 임시로 망을 구성하기 때문에 망 자체가 유기적으로 자주 변하며, 이로 인해 잦은 연결실패로 인한 불안정한 환경이 조성되어 기존의 유선환경보다 좀 더 어려우며 유선망에서 쓰이던 라우팅 프로토콜들은 ad hoc 망에 적합하지 않게 된다. 본 논문에서는 ad hoc 망에 대한 전반적인 이해와 더불어 ad hoc 망에서 사용되는 여러 라우팅 프로토콜에 대해 알아보고, 그 중 hybrid 기법을 사용하는 ZRP(Zone Routing Protocol)의 성능을 분석하였다. ZRP 프로토콜의 경우 효율적인 사용을 위해선 최적의 존 반경을 사용하는 것이 필수적이다. 그렇지 않을 경우 IARP 또는 IERP 트래픽의 급증으로 인하여 패킷의 전송을 위해 필요로 하는 ZRP 트래픽의 오버헤드를 크게 증가시켜 전체적인 망의 성능이 저하됨을 모의실험을 통하여 확인하였다. 또한 ad hoc 망에서의 QoS 라우팅을 위한 in-band 시그널을 이용한 QoS 경로를 찾는 과정을 제시하였으며 QoS를 보장한 경로에 한하여 실시간 멀티미디어 서비스 등을 가능하게 하는 경로를 보장할 수 있었다.

• 한국정보통신학회논문지
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• 제23권8호
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• pp.923-929
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• 2019

자율운항선박의 기반은 추진시스템의 안정성이 중요하며, 추진체계의 안정성을 위하여 다중 발전 체계 및 추진체계를 갖추어야한다. 기존 선박에서는 안정성을 위하여 높은 발전 용량을 산정하며, 그 결과 저부하 운전으로 인한 경제성 하락을 야기한다. 이를 해결하기 위해서는 전력체계의 최적화를 통하여 발전 체계의 경량화와 효율의 증가가 필요하다. 본 논문에서는 전기추진선박용 OPMS(Optimization Power Management System)를 구축한다. OPMS는 하이브리드형 발전시스템, 에너지저장시스템, 부하제어시스템으로 구성된다. 발전시스템은 이중연료엔진, 에너지저장시스템은 배터리, 부하제어시스템은 추진 부하, 상용 부하, 불규칙 부하, 화물 기기 관련 부하, 갑판 부하로 구성된다. 각 시스템별 기기들의 특성에 대하여 모델링하여 전력체계를 구축하였다. 실험을 위하여 선박 운용에 따른 시나리오를 작성하고 안정성 및 경제성을 기존의 전기추진선박과 비교하였다. 실험의 결과 발전기의 비교적 적은 시간 투입으로 같은 전력량을 공급함으로써 선박의 LNG 1.3%, Main Fuel 0.3%, Pilot Fuel 35.1%의 연료소모량 감소를 통하여 경제성 및 안정성을 확인하였다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2003년도 ICCAS
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• pp.2033-2038
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• 2003

This paper describes an integrated navigation strategy for the autonomous service robot PSR. The PSR is under development at the KIST for service tasks in indoor public environments. The PSR is a multi-functional mobile-manipulator typed agent, which works in daily life. Major advantages of proposed navigation are as follows: 1) Structured control architecture for a systematic integration of various software modules. A Petri net based configuration design enables stable control flow of a robot. 2) A range sensor based generalized scheme of navigation. Any range sensor can be selectively applied using the proposed navigation scheme. 3) No need for modification of environments. (No use of artificial landmarks.) 4) Hybrid approaches combining reactive behavior as well as deliberative planner, and local grid maps as well as global topological maps. A presented experimental result shows that the proposed navigation scheme is useful for mobile service robot in practical applications.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권8호
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• pp.1996-2015
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• 2023

High-definition (HD) maps can provide precise road information that enables an autonomous driving system to effectively navigate a vehicle. Recent research has focused on leveraging semantic segmentation to achieve automatic annotation of HD maps. However, the existing methods suffer from low recognition accuracy in automatic driving scenarios, leading to inefficient annotation processes. In this paper, we propose a novel semantic segmentation method for automatic HD map annotation. Our approach introduces a new encoder, known as the convolutional transformer hybrid encoder, to enhance the model's feature extraction capabilities. Additionally, we propose a multi-level fusion module that enables the model to aggregate different levels of detail and semantic information. Furthermore, we present a novel decoupled boundary joint decoder to improve the model's ability to handle the boundary between categories. To evaluate our method, we conducted experiments using the Bird's Eye View point cloud images dataset and Cityscapes dataset. Comparative analysis against stateof-the-art methods demonstrates that our model achieves the highest performance. Specifically, our model achieves an mIoU of 56.26%, surpassing the results of SegFormer with an mIoU of 1.47%. This innovative promises to significantly enhance the efficiency of HD map automatic annotation.

• Smart Structures and Systems
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• 제29권1호
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• pp.77-91
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• 2022

Various monitoring systems have been implemented in civil infrastructure to ensure structural safety and integrity. In long-term monitoring, these systems generate a large amount of data, where anomalies are not unusual and can pose unique challenges for structural health monitoring applications, such as system identification and damage detection. Therefore, developing efficient techniques is quite essential to recognize the anomalies in monitoring data. In this study, several machine learning techniques are explored and implemented to detect and classify various types of data anomalies. A field dataset, which consists of one month long acceleration data obtained from a long-span cable-stayed bridge in China, is employed to examine the machine learning techniques for automated data anomaly detection. These techniques include the statistic-based pattern recognition network, spectrogram-based convolutional neural network, image-based time history convolutional neural network, image-based time-frequency hybrid convolution neural network (GoogLeNet), and proposed ensemble neural network model. The ensemble model deliberately combines different machine learning models to enhance anomaly classification performance. The results show that all these techniques can successfully detect and classify six types of data anomalies (i.e., missing, minor, outlier, square, trend, drift). Moreover, both image-based time history convolutional neural network and GoogLeNet are further investigated for the capability of autonomous online anomaly classification and found to effectively classify anomalies with decent performance. As seen in comparison with accuracy, the proposed ensemble neural network model outperforms the other three machine learning techniques. This study also evaluates the proposed ensemble neural network model to a blind test dataset. As found in the results, this ensemble model is effective for data anomaly detection and applicable for the signal characteristics changing over time.

• 한국멀티미디어학회논문지
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• 제19권2호
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• pp.252-263
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• 2016

Road sign is an important street furniture that gives some information such as road conditions, driving direction and condition for a driver. Thus, road sign is a major target of image recognition for self-driving car, ADAS(autonomous vehicle and intelligent driver assistance systems), and ITS(intelligent transport systems). In this paper, an enhanced road sign recognition system is proposed for MMS(Mobile Mapping System) using the single camera and GPS. For the proposed system, first, a road sign recognition scheme is proposed. this scheme is composed of detection and classification step. In the detection step, object candidate regions are extracted in image frames using hybrid road sign detection scheme that is based on color and shape features of road signs. And, in the classification step, the area of candidate regions and road sign template are compared. Second, a Geo-marking scheme for geo-content that is consist of road sign image and coordinate value is proposed. If the serious situation such as car accident is happened, this scheme can protect geographical information of road sign against illegal users. By experiments with test video set, in the three parts that are road sign recognition, coordinate value estimation and geo-marking, it is confirmed that proposed schemes can be used for MMS in commercial area.