• KSII Transactions on Internet and Information Systems (TIIS)
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• 제3권4호
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• pp.344-365
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• 2009

The performance of a tracking system is greatly increased if multiple types of sensors are combined to achieve the objective of the tracking instead of relying on single type of sensor. To conduct the multi-modal tracking, we have previously developed a multi-modal sensor-based tracking model where acoustic sensors mainly track the objects and visual sensors compensate the tracking errors [1]. In this paper, we find a network synchronization problem appearing in the developed tracking system. The problem is caused by the different location and traffic characteristics of multi-modal sensors and non-synchronized arrival of the captured sensor data at a processing server. To effectively deliver the sensor data, we propose a time-based packet aggregation algorithm where the acoustic sensor data are aggregated based on the sampling time and sent to the server. The delivered acoustic sensor data is then compensated by visual images to correct the tracking errors and such a compensation process improves the tracking accuracy in ideal case. However, in real situations, the tracking improvement from visual compensation can be severely degraded due to the aforementioned network synchronization problem, the impact of which is analyzed by simulations in this paper. To resolve the network synchronization problem, we differentiate the service level of sensor traffic based on Weight Round Robin (WRR) scheduling at the routers. The weighting factor allocated to each queue is calculated by a proposed Delay-based Weight Allocation (DWA) algorithm. From the simulations, we show the traffic differentiation model can mitigate the non-synchronization of sensor data. Finally, we analyze expected traffic behaviors of the tracking system in terms of acoustic sampling interval and visual image size.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권9호
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• pp.3357-3376
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• 2015

Wireless sensor network is an important research topic that has attracted a lot of attention in recent years. However, most of the interest has focused on wireless sensor network to gather scalar data such as temperature, humidity and vibration. Scalar data are insufficient for diverse applications such as video surveillance, target recognition and traffic monitoring. However, if we use camera sensors in wireless sensor network to collect video data which are vast in information, they can provide important visual information. Video sensor networks continue to gain interest due to their ability to collect video information for a wide range of applications in the past few years. However, how to efficiently store the massive data that reflect environmental state of different times in video sensor network and how to quickly search interested information from them are challenging issues in current research, especially when the sensor network environment is complicated. Therefore, in this paper, we propose a fast algorithm for extracting key frames from video and describe the design and implementation of key frame extraction and sharing in Android for wireless video sensor network.

• Journal of Advanced Marine Engineering and Technology
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• 제37권4호
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• pp.418-424
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• 2013

센서네트워크 라우팅 프로토콜에 있어서 시뮬레이션과 물리적 구현은 모두가 중요한 작업이다. 본 연구에서는 무선센서네트워크를 이용하여 여러 제약조건 내에서 효과적인 지하매설물 감시 방법을 제안한다. 먼저 적용 대상 네트워크의 프로토콜을 물리적으로 구현하기 위해서 센서 네트워크 라우팅 프로토콜의 기존 nesC 코드를 적용하는 분산 이벤트기반 시뮬레이션을 구현하고 분석하였다. 또한, TOSSIM[2]과 분산이벤트 기반의 Ptolemy II를 연동한 VIPTOS(Visual Ptolemy and TinyOS)환경에서 지하 매설물 감시를 위한 라우팅프로토콜 응용모델의 시뮬레이션을 수행하고 실행결과를 분석하였다.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2001년도 추계학술대회 학술발표 논문집
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• pp.263-266
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• 2001

It has been integrated into several navigation systems. This paper shows that system recognizes difficult indoor roads and open area without any specific mark such as painted guide line or tape. In this method, Robot navigates with visual sensors, which uses visual information to navigate itself along the road. An Artificial Neural Network System was used to decide where to move. It is designed with USB web camera as visual sensor.

• 한국전자통신학회논문지
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• 제11권11호
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• pp.1069-1076
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• 2016

최근 비쥬얼 센서 기술의 발달로 센서 네트워크에 영상을 추가하기 위한 다양한 연구가 진행되고 있다. 비쥬얼 센서는 다른 센서 정보에 비해 데이터가 크기 때문에 데이터의 크기를 효율적으로 관리하는 것이 무엇보다 중요하다. 본 논문에서는 효과적인 데이터 관리에 필요한 비디오 트래픽 예측 방법을 제안한다. 제안하는 방법은 비디오 센서에서 획득되는 영상의 특성을 반영하여 1차 AR 모델로 비디오 트래픽을 모델링하고 칼만필터 알고리즘을 적용하여 트래픽을 예측한다. 제안하는 방법은 계산량이 많지 않아 센서 노드에 적용되기 적합하다. 실험 결과는 제안하는 방법이 비교적 간단한 형태이지만 전체 평균 트래픽의 1% 이내로 오차로 정확하게 트래픽을 예측하는 것을 보여준다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제6권2호
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• pp.161-166
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• 2006

Localization of mobile agent within a sensing network is a fundamental requirement for many applications, using networked navigating systems such as the sonar-sensing system or the visual-sensing system. To fully utilize the strengths of both the sonar and visual sensing systems, This paper describes a networked sensor-based navigation method in an indoor environment for an autonomous mobile robot which can navigate and avoid obstacle. In this method, the self-localization of the robot is done with a model-based vision system using networked sensors, and nonstop navigation is realized by a Kalman filter-based STSF(Space and Time Sensor Fusion) method. Stationary obstacles and moving obstacles are avoided with networked sensor data such as CCD camera and sonar ring. We will report on experiments in a hallway using the Pioneer-DX robot. In addition to that, the localization has inevitable uncertainties in the features and in the robot position estimation. Kalman filter scheme is used for the estimation of the mobile robot localization. And Extensive experiments with a robot and a sensor network confirm the validity of the approach.

• 대한전기학회논문지:시스템및제어부문D
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• 제54권3호
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• pp.156-165
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• 2005

Human being recognizes the physical world by integrating a great variety of sensory inputs, the information acquired by their own action, and their knowledge of the world using hierarchically parallel-distributed mechanism. In this paper, authors propose the sensor fusion system that can recognize multiple 3D objects from 2D projection images and tactile informations. The proposed system focuses on improving recognition performance of 3D objects. Unlike the conventional object recognition system that uses image sensor alone, the proposed method uses tactual sensors in addition to visual sensor. Neural network is used to fuse the two sensory signals. Tactual signals are obtained from the reaction force of the pressure sensors at the fingertips when unknown objects are grasped by four-fingered robot hand. The experiment evaluates the recognition rate and the number of learning iterations of various objects. The merits of the proposed systems are not only the high performance of the learning ability but also the reliability of the system with tactual information for recognizing various objects even though the visual sensory signals get defects. The experimental results show that the proposed system can improve recognition rate and reduce teeming time. These results verify the effectiveness of the proposed sensor fusion system as recognition scheme for 3D objects.

• 한국전자통신학회논문지
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• 제13권3호
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• pp.525-532
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• 2018

최근 센서 네트워크 기술의 발달과 카메라 기술의 발달로 이 두 기술을 접목한 비주얼 센서 기술을 이용하여 사람이 접근하기 어려운 지역의 환경을 효과적으로 모니터링하고자 하는 수요가 증가하고 있다. 비주얼 센서에서 획득된 영상은 자연 현상을 그대로 반영하기 때문에 날씨나 시간에 따라 영상의 품질이 나빠질 수 있다. 이 논문에서는 비주얼 센서에서 획득되는 영상의 특성을 이용하여 영상의 대비를 개선하는 알고리즘을 제안한다. 제안하는 방법에서는 먼저 비주얼 센서가 촬영하는 대상에 대해서 관심 영역을 설정하고 관심 영역의 컬러 값의 변화를 영상의 밝기 값에 따라 분석한다. 그리고 분석한 결과와 동일한 대상의 고대비 영상을 이용하여 저대비 영상의 대비를 개선한다. 실험 결과는 제안하는 방법이 간단하면서도 정확하게 저대비 영상의 컬러 성분들을 복원하여 영상의 대비를 개선하는 것을 보여준다.

• 제어로봇시스템학회논문지
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• 제5권5호
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• pp.550-557
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• 1999

This paper describes a road-following controller using the proposed neural network for autonomous vehicle. Road-following with visual sensor like camera requires intelligent control algorithm because analysis of relation from road image to steering control is complex. The proposed neural network, relative similarity modular network(RSMN), is composed of some learning networks and a partitioniing network. The partitioning network divides input space into multiple sections by similarity of input data. Because divided section has simlar input patterns, RSMN can learn nonlinear relation such as road-following with visual control easily. Visual control uses two criteria on road image from camera; one is position of vanishing point of road, the other is slope of vanishing line of road. The controller using neural network has input of two criteria and output of steering angle. To confirm performance of the proposed neural network controller, a software is developed to simulate vehicle dynamics, camera image generation, visual control, and road-following. Also, prototype autonomous electric vehicle is developed, and usefulness of the controller is verified by physical driving test.

• 대기
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• 제20권2호
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• pp.153-160
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• 2010

In this paper, an attempt has been made to build and test self-configuring weather sensor networks and internet based observation system to gather atmospheric data. The aim is to provide integrated or real-time weather information in standard form using network data access protocol. This system was successfully developed to record weather information both digital as well as visual using sensor network and web-enabled surveillance cameras. These data were transformed by network based data access protocol to access and utilize for public domain. The competed system has been successfully utilized to monitor different types of weather. The results show that this is one of the most useful weather monitoring system.