• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.177-178
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• 2006

The real-time tracking system is an essential factor for the development of low cost sensor networks for use in pervasive computing and ubiquitous networking. In this paper, we address the interference problems of the sensor network platform with ultrasonic for location tracking system. Ubiquitous indoor environments often contain substantial amounts of metal and other such reflective materials that affect the propagation of radio frequency signals in non-trivial ways, causing severe multi-path effects, dead-spots, noise, and interference. Especially we present a novel reducing interference location system that is particularly well suited to support context-aware computing. The system called Pharos, aims to combine the advantages of real-time tracking systems that implement distributed environment with regardless of infrastructure or infrastructure-less wireless sensor networks.

• Journal of Platform Technology
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• v.6 no.4
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• pp.18-25
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• 2018

This paper describes a real-time human and robot tracking method in Intelligent Space with multi-camera networks. The proposed method detects candidates for humans and robots by using the histogram of oriented gradients (HOG) feature in an image. To classify humans and robots from the candidates in real time, we apply cascaded structure to constructing a strong classifier which consists of many weak classifiers as follows: a linear support vector machine (SVM) and a radial-basis function (RBF) SVM. By using the multiple view geometry, the method estimates the 3D position of humans and robots from their 2D coordinates on image coordinate system, and tracks their positions by using stochastic approach. To test the performance of the method, humans and robots are asked to move according to given rectangular and circular paths. Experimental results show that the proposed method is able to reduce the localization error and be good for a practical application of human-centered services in the Intelligent Space.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.12
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• pp.996-1001
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• 2005

Recently, with the development of service robots and with the new concept of ubiquitous world, the position estimation of mobile objects has been raised to an important problem. As pre-liminary research results, some of the localization schemes are introduced, which provide the relative location of the moving objects subjected to accumulated errors. To implement a real time localization system, a new absolute position estimation method for a mobile robot in indoor environment is proposed in this paper. Design and implementation of the localization system comes from the usage of active beacon systems (based upon RFID technology). The active beacon system is composed of an RFID receiver and an ultra-sonic transmitter: 1. The RFID receiver gets the synchronization signal from the mobile robot and 2. The ultra-sonic transmitter sends out the traveling signal to be used for measuring the distance. Position of a mobile robot in a three dimensional space can be calculated basically from the distance information from. Three beacons and the absolute position information of the beacons themselves. In some case, the mobile robot can get the ultrasonic signals from only one or two beacons, because of the obstacles located along the moving path. Therefore, in this paper, as one of our dedicated contribution, the position estimation scheme with less than three sensors has been developed. Also, the extended Kalman filter algorithm is applied for the improvement of position estimation accuracy of the mobile robot.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.6A
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• pp.481-487
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• 2005

In order to increase the autonomous navigation capability of a mobile robot, it is very crucial to develop a method for recognizing a priori known environmental characteristics. This paper proposes an ultrasonic sensor based real-time method for recognizing a priori known indoor environmental characteristics like a wall and corner. The ultrasonic sensor consists of an ultrasonic transmitter and two ultrasonic receivers placed symmetrically about the transmitter. Unlike previous methods the information obtained from the sensor is processed in real-time by extended Kalman filter to be able to correct the position and orientation of robot with respect to known environmental characteristics.

• Nuclear Engineering and Technology
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• v.49 no.4
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• pp.781-786
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• 2017

The presence of crust on the inner walls of metallic ducts impairs transportation because crust completely or partially hinders the passage of fluid to the processing unit and causes damage to equipment connected to the production line. Its localization is crucial. With the development of the electronic imaging system installed at the Argonauta/Nuclear Engineering Institute (IEN)/National Nuclear Energy Commission (CNEN) reactor, it became possible to visualize crust in the interior of metallic piping of small diameter using real-time neutron radiography images obtained with a low neutron flux. The obtained images showed the resistance offered by crust on the passage of water inside the pipe. No discrepancy of the flow profile at the bottom of the pipe, before the crust region, was registered. However, after the passage of liquid through the pipe, images of the disturbances of the flow were clear and discrepancies in the flow profile were steep. This shows that this technique added the assembled apparatus was efficient for the visualization of the crust and of the two-phase flows.

• Smart Structures and Systems
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• v.23 no.4
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• pp.359-371
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• 2019

As part of a structural health monitoring system, the relative geometric relationship between a ship and bridge has been recognized as important for bridge authorities and ship owners to avoid ship-bridge collision. This study proposes a novel computer vision method for the real-time geometric parameter identification of moving ships based on a single shot multibox detector (SSD) by using transfer learning techniques and monocular vision. The identification framework consists of ship detection (coarse scale) and geometric parameter calculation (fine scale) modules. For the ship detection, the SSD, which is a deep learning algorithm, was employed and fine-tuned by ship image samples downloaded from the Internet to obtain the rectangle regions of interest in the coarse scale. Subsequently, for the geometric parameter calculation, an accurate ship contour is created using morphological operations within the saturation channel in hue, saturation, and value color space. Furthermore, a local coordinate system was constructed using projective geometry transformation to calculate the geometric parameters of ships, such as width, length, height, localization, and velocity. The application of the proposed method to in situ video images, obtained from cameras set on the girder of the Wuhan Yangtze River Bridge above the shipping channel, confirmed the efficiency, accuracy, and effectiveness of the proposed method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.4
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• pp.1603-1623
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• 2020

Multiple Mobile Robots System (MMRS) has shown many attractive features in lots of real-world applications that motivate their rapid and wide diffusion. In MMRS, the Cooperative Localization (CL) is the basis and premise of its high-performance task. However, the statistical characteristics of the system noise should be already known in traditional CL algorithms, which is difficult to satisfy in actual MMRS because of the numerous of disturbances form the complex external environment. So the CL accuracy will be reduced. To solve this problem, an improved Adaptive Active Cooperative Localization (A²CL) algorithm based on information optimization strategy for MMRS is proposed in this manuscript. In this manuscript, an adaptive information fusion algorithm based on the variance component estimation under Extended Kalman filter (VCEKF) method for MMRS is introduced firstly to enhance the robustness and accuracy of information fusion by estimating the covariance matrix of the system noise or observation noise in real time. Besides, to decrease the effect of observation uncertainty on CL accuracy further, an observation optimization strategy based on information theory, the Model Predictive Control (MPC) strategy, is used here to maximize the information amount from observations. And semi-physical simulation experiments were carried out to verity the A²CL algorithm's performance finally. Results proved that the presented A²CL algorithm based on information optimization strategy for MMRS cannot only enhance the CL accuracy effectively but also have good robustness.

• The Journal of the Acoustical Society of Korea
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• v.26 no.8
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• pp.415-425
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• 2007

This paper proposes a method of underwater source localization using the wideband interference patterns matching. By matching two interference patterns in the spectrogram, it is estimated a ratio of the range from source to sensor5, and then this ratio is applied to the Apollonius circle. The Apollonius circle is defined as the locus of all points whose distances from two fixed points are in a constant value so that it is possible to represent the locus of potential source location. The Apollonius circle alone, however still keeps the ambiguity against the correct source location. Therefore another equation is necessary to estimate the unique locus of the source location. By estimating time differences of signal arrivals between source and sensors, the hyperbola equation is used to get the cross point of the two equations, where the point being assumed to be the source position. Simulations are performed to get performances of the proposed algorithm. Also, comparisons with real sea experiment data are made to prove applicability of the algorithm in real environment. The results show that the proposed algorithm successfully estimates the source position within an error bound of 10%.

• Smart Structures and Systems
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• v.30 no.6
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• pp.673-686
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• 2022

Deep learning algorithms for Structural Health Monitoring (SHM) have been extracting the interest of researchers and engineers. These algorithms commonly used loss functions and evaluation indices like the mean square error (MSE) which were not originally designed for SHM problems. An updated loss function which was specifically constructed for deep-learning-based structural damage detection problems has been proposed in this study. By tuning the coefficients of the loss function, the weights for damage localization and quantification can be adapted to the real situation and the deep learning network can avoid unnecessary iterations on damage localization and focus on the damage severity identification. To prove efficiency of the proposed method, structural damage detection using convolutional neural networks (CNNs) was conducted on a truss bridge model. Results showed that the validation curve with the updated loss function converged faster than the traditional MSE. Data augmentation was conducted to improve the anti-noise ability of the proposed method. For reducing the training time, the normalized modal strain energy change (NMSEC) was extracted, and the principal component analysis (PCA) was adopted for dimension reduction. The results showed that the training time was reduced by 90% and the damage identification accuracy could also have a slight increase. Furthermore, the effect of different modes and elements on the training dataset was also analyzed. The proposed method could greatly improve the performance for structural damage detection on both the training time and detection accuracy.

• Journal of applied mathematics & informatics
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• v.26 no.5_6
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• pp.1247-1256
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• 2008

Based on two real and invertible $d{\times}d$ matrices Band C such that the norm $||C^T\;B||$ is sufficiently small, we provide a construction of tight Gabor frames $\{E_{Bm}T_{Cn}g\}_{m,n{\in}{\mathbb{Z}^d}$ with explicitly given and compactly supported generators. The generators can be chosen with arbitrary polynomial decay in the frequency domain.