• 제어로봇시스템학회논문지
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• 제22권9호
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• pp.745-752
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• 2016

We suggest an efficient Simultaneous Localization and 3D Polygon Map Building (SLAM) method with Kinect depth sensor for mobile robots in indoor environments. In this method, Kinect depth data is separated into row planes so that scan line segments are on each row plane. After grouping all scan line segments from all row planes into line groups, a set of 3D Scan polygons are fitted from each line group. A map matching algorithm then figures out pairs of scan polygons and existing map polygons in 3D, and localization is performed to record correct pose of the mobile robot. For 3D map-building, each 3D map polygon is created or updated by merging each matched 3D scan polygon, which considers scan and map edges efficiently. The validity of the proposed 3D SLAM algorithm is revealed via experiments.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권8호
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• pp.3136-3150
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• 2015

A vision-based 3D tracking of articulated human hand is one of the major issues in the applications of human computer interactions and understanding the control of robot hand. This paper presents an improved approach for tracking and recovering the 3D position and orientation of a human hand using the Kinect sensor. The basic idea of the proposed method is to solve an optimization problem that minimizes the discrepancy in 3D shape between an actual hand observed by Kinect and a hypothesized 3D hand model. Since each of the 3D hand pose has 23 degrees of freedom, the hand articulation tracking needs computational excessive burden in minimizing the 3D shape discrepancy between an observed hand and a 3D hand model. For this, we first created a 3D hand model which represents the hand with 17 different parts. Secondly, Random Forest classifier was trained on the synthetic depth images generated by animating the developed 3D hand model, which was then used for Haar-like feature-based classification rather than performing per-pixel classification. Classification results were used for estimating the joint positions for the hand skeleton. Through the experiment, we were able to prove that the proposed method showed improvement rates in hand part recognition and a performance of 20-30 fps. The results confirmed its practical use in classifying hand area and successfully tracked and recovered the 3D hand pose in a real time fashion.

• Journal of Ship and Ocean Technology
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• 제11권1호
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• pp.36-46
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• 2007

The maintenance of a ship is essential for safe navigation and hence regular surveys are prescribed according to the rule of classification societies. A hull inspection is generally performed by professional divers, but it takes a long time and the efficiency is low in terms of time and cost. In this research, a ROV(Remotely Operated Vehicle) named as SNU-ROV(Seoul National University-ROV) is developed to replace the conventional inspection method. In this system, the ROV is intended to be used for inspecting ship and harbor because harbor inspection is merging as a safety measure against any possible terror actions. In order to increase the efficiency of inspection, the ROV must be able to measure the exact position of damages. SNU-ROV has a positioning system based on LBL(Long Base Line). In shallow water such as harbor, however, LBL has bad DOP(Dilution of Precision) in the depth direction due to the limited depth. Thus LBL only can not locate the exact depth position. To solve the DOP problem, a pressure sensor is introduced to LBL and a complementary filter is attached by using indirect feedback Kalman filter. Thus developed positioning system is verified by simulation and experiment in towing tank.

• ETRI Journal
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• 제42권1호
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• pp.78-89
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• 2020

Human activity recognition (HAR) has become effective as a computer vision tool for video surveillance systems. In this paper, a novel biometric system that can detect human activities in 3D space is proposed. In order to implement HAR, joint angles obtained using an RGB-depth sensor are used as features. Because HAR is operated in the time domain, angle information is stored using the sliding kernel method. Haar-wavelet transform (HWT) is applied to preserve the information of the features before reducing the data dimension. Dimension reduction using an averaging algorithm is also applied to decrease the computational cost, which provides faster performance while maintaining high accuracy. Before the classification, a proposed thresholding method with inverse HWT is conducted to extract the final feature set. Finally, the K-nearest neighbor (k-NN) algorithm is used to recognize the activity with respect to the given data. The method compares favorably with the results using other machine learning algorithms.

• 센서학회지
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• 제6권2호
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• pp.123-130
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• 1997

이 논문은 다음과 같은 두가지 요소로 구성되는 독창적인 stereo vision system을 논술한다. declivity라는 새로운 개념을 도입한 자동선택 영상 분할처리 (self-adaptive image segmentation process) 와 자동선택 결정변수 (self-adaptive decision parameters) 를 응용하여 설계된 신속한 stereo matching algorithm. 현재, 실내 image의 depth map을 완성하는데 SUN-IPX 에서 3sec가 소요되나 연구중인 DSP Chip의 조합은 이 시간을 1초 이하로 단축시킬 수 있을 것이다.

• 한국군사과학기술학회지
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• 제19권1호
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• pp.66-75
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• 2016

Stereoscopic image generated by depth image-based rendering(DIBR) for surveillance robot and camera is appropriate in a low bandwidth network. The image is very important data for the decision-making of a commander and thus its integrity has to be guaranteed. One of the methods used to detect manipulation is to check if the stereoscopic image is taken from the original camera. Sensor pattern noise(SPN) used widely for camera identification cannot be directly applied to a stereoscopic image due to the stereo warping in DIBR. To solve this problem, we find out a shifted object in the stereoscopic image and relocate the object to its orignal location in the center image. Then the similarity between SPNs extracted from the stereoscopic image and the original camera is measured only for the object area. Thus we can determine the source of the camera that was used.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권6호
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• pp.1706-1727
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• 2023

Under Water Sensor Networks (UWSN) has gained attraction among various communities for its potential applications like acoustic monitoring, 3D mapping, tsunami detection, oil spill monitoring, and target tracking. Unlike terrestrial sensor networks, it performs an acoustic mode of communication to carry out collaborative tasks. Typically, surface sink nodes are deployed for aggregating acoustic phenomena collected from the underwater sensors through the multi-hop path. In this context, UWSN is constrained by factors such as lower bandwidth, high propagation delay, and limited battery power. Also, the vulnerabilities to compromise the aquatic environment are in growing numbers. The paper proposes an Energy-Efficient standalone Intrusion Detection System (EEIDS) to entail the acoustic environment against malicious attacks and improve the network lifetime. In EEIDS, attributes such as node ID, residual energy, and depth value are verified for forwarding the data packets in a secured path and stabilizing the nodes' energy levels. Initially, for each node, three agents are modeled to perform the assigned responsibilities. For instance, ID agent verifies the node's authentication of the node, EN agent checks for the residual energy of the node, and D agent substantiates the depth value of each node. Next, the classification of normal and malevolent nodes is performed by determining the score for each node. Furthermore, the proposed system utilizes the sheep-flock heredity algorithm to validate the input attributes using the optimized probability values stored in the training dataset. This assists in finding out the best-fit motes in the UWSN. Significantly, the proposed system detects and isolates the malicious nodes with tampered credentials and nodes with lower residual energy in minimal time. The parameters such as the time taken for malicious node detection, network lifetime, energy consumption, and delivery ratio are investigated using simulation tools. Comparison results show that the proposed EEIDS outperforms the existing acoustic security systems.

• 한국구조물진단유지관리공학회 논문집
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• 제18권3호
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• pp.143-152
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• 2014

본 연구에서는 콘크리트내로 침투하는 염소이온을 모니터링하기 위하여, 스크린프리트 기법으로 염소이온 반응형 부식센서를 개발하고, 센서의 세선 수가 부식반응도 및 민감도에 미치는 영향을 실험을 통하여 정량적으로 분석하였다. 개발된 부식센서를 이용하여 염소이온량에 따라 부식 반응도을 확인하였으며, 센서의 파괴정도에 따른 저항변화에서는 단선형 센서보다 다선형 센서에서 큰 저항 변화를 나타내었다. 또한, 부식센서는 NaCl 수용액의 농도가 높은 만큼 센서의 저항변화가 크고, 콘크리트 내에서 센서 종류에 따른 부식저항은 단선형보다 다선형에서 민감도가 높게 나타났으며, 센서의 매설깊이가 클수록 저항변화 사이클 (cycle)은 증가하였다. 이상의 결과로, 본 연구에서 개발된 부식센서는 염분에 대한 부식반응과 민감도, 저항의 변화를 감지할 수 있었으며, 특히 7세선이 우수한 결과를 나타내어, 염분의 침투정도를 모니터링 하는데 가장 적합하다고 판단된다.

• 한국전자통신학회논문지
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• 제9권2호
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• pp.155-160
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• 2014

본 논문에서 RGB-Depth 카메라를 이용하여 실내에서의 연기를 검출하는 알고리즘을 제안한다. RGB-Depth 카메라는 RGB 색영상과 깊이 정보를 제공한다. 키넥트(Kinect)는 특정한 패턴의 적외선을 출력하고 이를 적외선 카메라로 수집하고 분석하여 깊이 정보를 획득한다. 특정한 패턴을 구성하는 점들 각각에 대하여 거리를 측정하고 객체면의 깊이를 추정한다. 따라서, 이웃하는 점들의 깊이 변화가 많은 객체인 경우에는 객체면의 깊이를 결정하지 못한다. 연기의 농도가 일정 주파수로 변화하고, 적외선 영상의 이웃하는 화소간의 변화가 많기 때문에 키넥트가 깊이를 결정하지 못한다. 본 논문에서는 연기에 대한 키넥트의 특성을 이용하여 연기를 검출한다. 키넥트가 깊이를 결정하지 못한 영역을 후보영역으로 설정하고, 색영상의 밝기가 임계값보다 큰 경우 연기영역으로 결정한다. 본 논문에서는 시뮬레이션을 통하여 실내에서의 연기 검출에 RGB-Depth 카메라가 효과적임을 확인할 수 있다.

• 한국농공학회논문집
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• 제63권2호
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• pp.75-84
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• 2021

As the weather changes become frequent, weather disasters are increasing, causing more damage to plastic greenhouses. Among the damage caused by various disasters, damage by snow to the greenhouse takes a relatively long time, so if an alarm system is properly prepared, the damage can be reduced. Existing greenhouse design standards and snow warning systems are based on snow depth. However, even in the same depth, the load on the greenhouse varies depending on meteorological characteristics and snow density. Therefore, this study aims to secure the structural safety of greenhouses by developing sensors that can directly measure snow loads, and analysing the warning criteria for load using a stochastic model. Markov chain was applied to estimate the failure probability of various types of greenhouses in various regions, which let users actively cope with heavy snowfall by selecting an appropriate time to respond. Although it was hard to predict the precise snow depth or amounts, it could successfully assess the risk of structures by directly detecting the snow load using the developed sensor.