• Journal of Advanced Navigation Technology
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• v.25 no.4
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• pp.299-304
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• 2021

Existing human activity recognition systems detect activities through devices such as wearable sensors and cameras. However, these methods require additional devices and costs, especially for cameras, which cause privacy issue. Using WiFi signals that are already installed can solve this problem. In this paper, we propose a CNN-based human activity recognition system using channel state information of WiFi signals, and present results of designing and implementing accelerated hardware structures. The system defined four possible behaviors during studying in indoor environments, and classified the channel state information of WiFi using convolutional neural network (CNN), showing and average accuracy of 91.86%. In addition, for acceleration, we present the results of an accelerated hardware structure design for fully connected layer with the highest computation volume on CNN classifiers. As a result of performance evaluation on FPGA device, it showed 4.28 times faster calculation time than software-based system.

• Journal of Positioning, Navigation, and Timing
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• v.11 no.3
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• pp.217-227
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• 2022

In order to provide a location-based services regardless of indoor or outdoor space, it is important to provide position information of the terminal regardless of location. Among the wireless/mobile communication resources used for this purpose, Long Term Evolution (LTE) signal is a representative infrastructure that can overcome spatial limitations, but the positioning method based on the location of the base station has a disadvantage in that the accuracy is low. Therefore, a fingerprinting technique, which is a pattern recognition technology, has been widely used. The simplest yet widely applied algorithm among Fingerprint positioning technologies is k-Nearest Neighbors (kNN). However, in the kNN algorithm, it is difficult to find the optimal K value with the lowest positioning error for each location to be estimated, so it is generally fixed to an appropriate K value and used. Since the optimal K value cannot be applied to each estimated location, therefore, there is a problem in that the accuracy of the overall estimated location information is lowered. Considering this problem, this paper proposes a technique for adaptively varying the K value by using a Convolutional Neural Network (CNN) model among Artificial Neural Network (ANN) techniques. First, by using the signal information of the measured values obtained in the service area, an image is created according to the Physical Cell Identity (PCI) and Band combination, and an answer label for supervised learning is created. Then, the structure of the CNN is modeled to classify K values through the image information of the measurements. The performance of the proposed technique is verified based on actual data measured in the testbed. As a result, it can be seen that the proposed technique improves the positioning performance compared to using a fixed K value.

• KIPS Transactions on Computer and Communication Systems
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• v.5 no.7
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• pp.159-172
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• 2016

This paper presents a novel algorithm for searching evacuation paths in indoor disaster environments. The proposed method significantly improves the time complexity to find the paths to the evacuation exit by introducing a light-weight Disaster Evacuation Graph (DEG) for a building in terms of the size of the graph. With the DEG, the method also considers load balancing and bottleneck capacity of the paths to the evacuation exit simultaneously. The behavior of the algorithm consists of two phases: horizontal tiering (HT) and vertical tiering (VT). The HT phase finds a possible optimal path from anywhere of a specific floor to the evacuation stairs of the floor. Thus, after finishing the HT phases of all floors in parallel the VT phase begins to integrate all results from the previous HT phases to determine a evacuation path from anywhere of a floor to the safety zone of the building that could be the entrance or the roof of the building. It should be noted that the path produced by the algorithm. And, in order to define the range of graph to process, tiering scheme is used. In order to test the performance of the method, computing times and evacuation times are compared to the existing path searching algorithms. The result shows the proposed method is better than the existing algorithms in terms of the computing time and evacuation time. It is useful in a large-scale building to find the evacuation routes for evacuees quickly.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.11
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• pp.1092-1098
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• 2007

A mobile robot localization problem can be classified into following three sub-problems as an observation likelihood model, a motion model and a filtering technique. So far, we have developed the range sensor based, integrated localization scheme, which can be used in human-coexisting real environment such as a science museum and office buildings. From those experiences, we found out that there are several significant issues to be solved. In this paper, we focus on three key issues, and then illustrate our solutions to the presented problems. Three issues are listed as follows: (1) Investigation of design requirements of a desirable observation likelihood model, and performance analysis of our design (2) Performance evaluation of the localization result by computing the matching error (3) The semi-global localization scheme to deal with localization failure due to abrupt wheel slippage In this paper, we show the significance of each concept, developed solutions and the experimental results. Experiments were carried out in a typical modern building environment, and the results clearly show that the proposed solutions are useful to develop practical and integrated localization schemes.

• 한국HCI학회:학술대회논문집
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• 2008.02a
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• pp.510-516
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• 2008

In ubiquitous environment, there are no limits to utilize networks whenever and wherever you are. That pervasive networks are combined with the information devices and then create various services such as car navigation of LBS(location based service) and DMB(Digital Multimedia Broadcasting). As these kinds of services are getting more various, the complexity is also getting higher and ultimately the convergence will make people feel frustrated. One of the solutions is Context-Awareness[1] technology. User interface with context-awareness filters unnecessary information and prevents users from being blocked due to the massive information. In this paper, we describe the seamless service system based on location-awareness, which is a type of context-awareness. We developed the system based on UPnP AV Framework. This system provides the automatic terminal device selection for the nomadic user. The system establishes new connections for the ongoing streaming playback session with the new AV devices without substantial loss of playback so that the user can enjoy the seamless service. The AV device selection based on the user's location needs no user's intervention or notification so it achieves the improvement of usability and complexity.

• International Journal of Aeronautical and Space Sciences
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• v.11 no.2
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• pp.69-79
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• 2010

This paper describes the experimental framework for the control system design and validation of a rotorcraft unmanned aerial vehicle (UAV). Our approach follows the general procedure of nonlinear modeling, linear controller design, nonlinear simulation and flight test but uses an indoor-installed multi-camera system, which can provide full 6-degree of freedom (DOF) navigation information with high accuracy, to overcome the limitation of an outdoor flight experiment. In addition, a 3-DOF flying mill is used for the performance validation of the attitude control, which considers the characteristics of the multi-rotor type rotorcraft UAV. Our framework is applied to the design and mathematical modeling of the control system for a quad-rotor UAV, which was selected as the test-bed vehicle, and the controller design using the classical proportional-integral-derivative control method is explained. The experimental results showed that the proposed approach can be viewed as a successful tool in developing the controller of new rotorcraft UAVs with reduced cost and time.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.12
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• pp.1226-1232
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• 2010

A door can serve as a feature for place classification and localization for navigation of a mobile robot in indoor environments. This paper proposes a door detection method based on the recognition of various door handles using the general Hough transform (GHT) and support vector machine (SVM). The contour and color histogram of a door handle extracted from the database are used in GHT and SVM, respectively. The door recognition scheme consists of four steps. The first step determines the region of interest (ROI) images defined by the color information and the environment around the door handle for stable recognition. In the second step, the door handle is recognized using the GHT method from the ROI image and the image patches are extracted from the position of the recognized door handle. In the third step, the extracted patch is classified whether it is the image patch of a door handle or not using the SVM classifier. The door position is probabilistically determined by the recognized door handle. Experimental results show that the proposed method can recognize various door handles and detect doors in a robust manner.

• Journal of Electrical Engineering and Technology
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• v.8 no.3
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• pp.658-665
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• 2013

While the chirp signal is extensively used in radar and sonar systems for target decision in wireless communication systems, it has not been widely used for positioning in indoor environments. Recently, the IEEE 802.15.4a standard has adopted the chirp spread spectrum (CSS) as an underlying technique for low-power and low-complexity precise localization. Chirp signal based ranging solutions have been established and deployed but their ranging performance has not been analyzed in multipath environments. This paper presents a ranging performance analysis of a chirp signal and suggests a method to suppress multipath error by using a type of non-linear chirp signal. Multipath ranging performance is evaluated using a conventional linear chirp signal and the proposed non-linear chirp signal. We verify the feasibility of both methods using two-ray multipath model simulation. Our results demonstrate that the proposed non-linear chirp signal can successfully suppress the multipath error.

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.923-924
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• 2006

In this paper, we propose the sensor fusing method for the obstacle avoidance of guiding robot for the visually impaired In our system, we acquire obstacles distances information using ultrasonic sensors, and its width is acquired by image sensor. We also compute avoidance angle using are distance and width information gained by sensor. After the robot avoid the obstacle by computed angle, the robot returns to its original path using odometry. The robot consists of the SA1110-based controller, sensory part using sonar array and image sensor, and motion part using differential drive for climbing stairs. This system use the embedded linux for OS, and also is developed by the QT/Embedded for GUI.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.2
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• pp.415-421
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• 2009

TOA(time-of-arrival) and TDOA(time-difference-of-arrival) positioning techniques are commonly used in many radio-navigation systems. From the literature, it is known that the position estimate and error covariance matrix of TDOA obtained by GN(Gauss-Newton) method is exactly the same as that of TOA when the error source of the range measurement is only an IID white Gaussian noise. In case of geo-location and indoor positioning, however, multi-path or NLOS(non-line-of-sight) error is frequently appeared in range measurements. Though its occurrence is random, the multipath acts like a bias for a stationary user if it occurs. This paper presents the comparisons of error characteristics between TOA and TDOA positioning in presence of multi-path or NLOS error. It is analytically shown that the position estimate of TDOA is exactly the same as that of TOA even when bias errors are included in range measurements with different magnitudes. By computer simulation, position estimation error and error distribution are analyzed in presence of range bias errors.