• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.5
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• pp.1014-1035
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• 2013

With recent advances in wireless communication and low-power miniaturized biomedical sensor and semiconductor technologies, wireless body area networks (WBAN) has become an integral part of the disaster aid system. Wearable vital sign sensors can track patients' status and location, thus enhancing disaster rescue efficiency. In the past few years, most of the literatures in the area of disaster aid system based on WBAN have focused on issues concerning wireless sensor design, sensor miniaturization, energy efficiency and communication protocols. In this paper, we will give an overview of disaster aid, discuss about the types of network communication as well as outline related issues. We will emphasize on analyzing six key issues in employing the disaster aid system. Finally, we will also highlight some of the challenges that still need to be addressed in the future in order to help the disaster aid system be truly and widely accepted by the public.

• The Journal of Korea Robotics Society
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• v.15 no.1
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• pp.70-76
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• 2020

Multi-floor navigation of a mobile robot requires a technology that allows the robot to safely get on and off the elevator. Therefore, in this study, we propose a method of recognizing the elevator from the current position of the robot and estimating the location of the elevator locally so that the robot can safely get on the elevator regardless of the accumulated position error during autonomous navigation. The proposed method uses a deep learning-based image classifier to identify the elevator from the image information obtained from the RGB-D sensor and extract the boundary points between the elevator and the surrounding wall from the point cloud. This enables the robot to estimate the reliable position in real time and boarding direction for general elevators. Various experiments exhibit the effectiveness and accuracy of the proposed method.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2004.10a
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• pp.201-206
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• 2004

Laser scanner are getting used for inspection and reverse engineering in industry such as motors, electronic products, dies and molds. However, due to the lack of efficient scanning technique, the tasks become limited to the low accuracy purpose. The main reasons for this limitation for usefulness are caused from the optical drawback, such as irregular reflection, scanning direction normal to measuring surface, the influence of surface integrity, and other optical disturbances. To overcome these drawback of laser scanner, this study propose the mechanism to reduce the optical trouble by using the 2 kinds of rotational movement axis and by composing the spherical coordinate to scanning the surface keeping normal direction consistently. So, it could be designed and interfaced the measuring device to realize that mechanism, and then it could acquisite the accurate 3D form cloud data. Also, these data are compared with the standard master ball and the data acquisited from the touch point sensor, to evaluate the accuracy and stability of measurement and to demonstrate the implementation of an dental tooth purpose system

• Korean Journal of Remote Sensing
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• v.34 no.6_1
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• pp.941-951
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• 2018

Utilization and demand of UAV (unmanned aerial vehicle) for the generation of 3D city model are increasing. In this study, we performed an experiment to adjustment position/orientation of UAV with incomplete attitude information and to extract point cloud data. In order to correct the attitude of the UAV, the rotation angle was calculated by using the continuous position information of UAV movements. Based on this, the corrected position/orientation information was obtained by applying IBA (Incremental Bundle Adjustment) based on photogrammetry. Each pair was transformed into an epipolar image, and the MDR (Multi-Dimensional Relaxation) technique was applied to obtain high precision DSM. Each extracted pair is aggregated and output in the form of a single point cloud or DSM. Using the DJI inspire1 and Phantom4 images, we can confirm that the point cloud can be extracted which expresses the railing of the building clearly. In the future, research will be conducted on improving the matching performance and establishing sensor models of oblique images. After that, we will continue the image processing technology for the generation of the 3D city model through the study of the extraction of 3D cloud It should be developed.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.29 no.4
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• pp.429-438
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• 2011

LiDAR technology is a combination of laser ranging, satellite positioning technology and digital image technology for study and determination with high accuracy of the true earth surface features in 3 D. Laser scanning data is typically a points cloud on the ground, including coordinates, altitude and intensity of laser from the object on the ground to the sensor (Wehr & Lohr, 1999). Data from laser scanning can produce products such as digital elevation model (DEM), digital surface model (DSM) and the intensity data. In Vietnam, the LiDAR technology has been applied since 2005. However, the application of LiDAR in Vietnam is mostly for topological mapping and DEM establishment using point cloud 3D coordinate. In this study, another application of LiDAR data are present. The study use the intensity image combine with some other data sets (elevation data, Panchromatic image, RGB image) in Bacgiang City to perform land cover classification using neural network method. The results show that it is possible to obtain land cover classes from LiDAR data. However, the highest accurate classification can be obtained using LiDAR data with other data set and the neural network classification is more appropriate approach to conventional method such as maximum likelyhood classification.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.5
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• pp.436-443
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• 2011

Recognition of structures in urban environments is a fundamental ability for unmanned ground vehicles. In this paper we propose the geometrical featured voxel which has not only 3-D coordinates but also the type of geometrical properties of point cloud. Instead of dealing with a huge amount of point cloud collected by range sensors in urban, the proposed voxel can efficiently represent and save 3-D urban structures without loss of geometrical properties. We also provide an urban structure classification algorithm by using the proposed voxel and machine learning techniques. The proposed method enables to recognize urban environments around unmanned ground vehicles quickly. In order to evaluate an ability of the proposed map representation and the urban structure classification algorithm, our vehicle equipped with the sensor system collected range data and pose data in campus and experimental results have been shown in this paper.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.4
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• pp.1146-1165
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• 2022

A huge amount of data in the form of videos and images is being produced owning to advancements in sensor technology. Use of low performance commodity hardware coupled with resource heavy image processing and analyzing approaches to infer and extract actionable insights from this data poses a bottleneck for timely decision making. Current approach of GPU assisted and cloud-based architecture video analysis techniques give significant performance gain, but its usage is constrained by financial considerations and extremely complex architecture level details. In this paper we propose a data pipeline system that uses open-source tools such as Apache Spark, Kafka and OpenCV running over commodity hardware for video stream processing and image processing in a distributed environment. Experimental results show that our proposed approach eliminates the need of GPU based hardware and cloud computing infrastructure to achieve efficient video steam processing for face detection with increased throughput, scalability and better performance.

• Journal of Platform Technology
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• v.8 no.4
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• pp.11-19
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• 2020

In an autonomous driving environment, dynamic recognition of objects is essential as the situation changes in real time. In addition, as the number of sensors and control modules built into an autonomous vehicle increases, the amount of data the central control unit has to process also rapidly increases. By minimizing the output data from the sensor, the load on the central control unit can be reduced. This study proposes a dynamic object recognition algorithm solely using the embedded processor on a LiDAR sensor. While there are open source algorithms to process the point cloud output from LiDAR sensors, most require a separate high-performance processor. Since the embedded processors installed in LiDAR sensors often have resource constraints, it is essential to optimize the algorithm for efficiency. In this study, an embedded processor based object recognition algorithm was developed for autonomous vehicles, and the correlation between the size of the point clouds and processing time was analyzed. The proposed object recognition algorithm evaluated that the processing time directly increased with the size of the point cloud, with the processor stalling at a specific point if the point cloud size is beyond the threshold

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.05a
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• pp.573-574
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• 2016

Before the Machine Socialization System used to connected between server and router. However, the data flow increases due to the poor performance of the router increased traffic, as a result, the loss of data when the problem occurred Collaboration between devices increases that have been interrupted. This action moves the server connected to the router is required to solve these problems. In this paper, by utilizing the cloud server to reduce bottlenecks proposed a system that can reduce the loss of data during cooperation between devices. In addition, by dividing the management unit and the sensor using the virtualization technology, we designed a system that can efficiently make use of the resource.

• Journal of the Korean Institute of Intelligent Systems
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• v.27 no.1
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• pp.65-71
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• 2017

Since existing Internet of Things(IoT) is vulnerable, it may cause property damage due to the information leakage. Especially, the smart door lock system built on the IoT can cause more damage. To solve these problems, this paper classify the data generated by the sensor according to the condition and send an alarm message to the user's smartphone through Google Cloud Message (GCM). We made it possible to check the images in real time through the smartphone application and control the door lock using the TCP / IP protocol. Also, we applied OTP-Based Matrix SEED algorithm to door lock system to improve security.