• Smart Structures and Systems
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• v.7 no.3
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• pp.229-240
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• 2011

Recent developments in Smart Structures with very large scale embedded sensors and actuators have introduced new challenges in terms of data processing and sensor fusion. These smart structures are dynamically classified as a large-scale system with thousands of sensors and actuators that form the musculoskeletal of the structure, analogous to human body. In order to develop structural health monitoring and diagnostics with data provided by thousands of sensors, new sensor informatics has to be developed. The focus of our on-going research is to develop techniques and algorithms that would utilize this musculoskeletal system effectively; thus creating the intelligence for such a large-scale autonomous structure. To achieve this level of intelligence, three major research tasks are being conducted: development of a Bio-Inspired data analysis and information extraction from thousands of sensors; development of an analytical technique for Optimal Sensory System using Structural Observability; and creation of a bio-inspired decision-making and control system. This paper is focused on the results of our effort on the first task, namely development of a Neuro-Morphic Engineering approach, using a neuro-symbolic data manipulation, inspired by the understanding of human information processing architecture, for sensor fusion and structural diagnostics.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2008.06a
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• pp.661-662
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• 2008

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.2317-2319
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• 2001

This paper presents a sensor fusion algorithm for the RC helicopter which uses a complementary filter. To measure the attitude angle of the helicopter, 3rate gyroscopes and a 3-axis accelerometer are mounted on the helicopter. The signals from them are passed though a complementary filter to produce estimation outputs. Experiments show that designed system is effective for the attitude estimation.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.51 no.9
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• pp.389-401
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• 2002

To complement the conventional fusion methodologies of state fusion and measurement fusion, a time-propagated measurement fusion methodology is proposed. Various aspects of common process noise are investigated regarding information preservation. Based on time-propagated measurement fusion methodology, four compression filters are derived. The derived compression filters are efficient in asynchronous sensor fusion and fault detection since they maintain correct statistical information. A new batch Kalman recursion is proposed to show the optimality under the time-propagated measurement fusion methodology. A simple simulation result evaluates estimation efficiency and characteristic.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.7
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• pp.637-648
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• 2007

This paper presents a sensor fusion method based on indirect Kalman filter(IKF) for error compensation of low-cost inertial sensors and its application to the determination of attitude and position of small flying robots. First, the analysis of the measurement error characteristics to zero input is performed, focusing on the bias due to the temperature variation, to derive a simple nonlinear bias model of low-cost inertial sensors. Moreover, from the experimental results that the coefficients of this bias model possess non-deterministic (stochastic) uncertainties, the bias of low-cost inertial sensors is characterized as consisting of both deterministic and stochastic bias terms. Then, IKF is derived to improve long term stability dominated by the stochastic bias error, fusing low-cost inertial sensor measurements compensated by the deterministic bias model with non-inertial sensor measurement. In addition, in case of using intermittent non-inertial sensor measurements due to the unreliable data link, the upper and lower bounds of the state estimation error covariance matrix of discrete-time IKF are analyzed by solving stochastic algebraic Riccati equation and it is shown that they are dependant on the throughput of the data link and sampling period. To evaluate the performance of proposed method, experimental results of IKF for the attitude determination of a small flying robot are presented in comparison with that of extended Kaman filter which compensates only deterministic bias error model.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.4
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• pp.41-51
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• 1996

This paper presents a fuzzy data fusion scheme which can analyze the sensor condition, the strength and location of a force applied to a test material. These can be realized by the modelling and fusioning of sensor signals and sensor properties. The technique uses, as the inference variables, relative magnitude of data (RMD), absolute magnitude of data (AMD) initial state (IS), synchronized relational function (SRF) and asynchronized relational function (ARF). To show the usefulness of this scheme, an experiment on the cantilever bar and six strain gages is carried out. The location of the force is inferred from SRF and ARF and the strength from RMD and AMD. In particular, the strength is compared with the measurement data of the force sensor.

• Journal of Sensor Science and Technology
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• v.20 no.6
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• pp.393-399
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• 2011

In this paper, we designed and fabricated the novel air flow sensor using air drag force, which can be applied to the low air flow detection. To measure the low air flow, we should enlarge the air drag force and the output signal at the given air flow. The paddle structure is applied to the device, and the device is vertically located against the air flow to magnify the air drag force. We also adapt the corrugation structure to improve the output signals on the given air velocity. The device structure is made up of the silicon nitride layer and the output signal is measured with the piezoresistive layer. The output signals from the corrugated device show the better measurement sensitivity and the response time than that of flat one. The repeated measurement also shows the stabilized signals.

• Journal of Advanced Marine Engineering and Technology
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• v.30 no.8
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• pp.919-926
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• 2006

To move in unknown or uncertain environment, a mobile robot must collect informations from various sensors and use it to construct a representation of the external world. Ultrasonic sensor can provide range data for this purpose in a simple cost-effective way. However conventional ultrasonic sensor system for a mobile robot are not sufficient for environment recognition because of their large beam opening angle, specular reflection. This paper describe on environmental perception algorithm which can solve these problems in case using ultrasonic sensor. The algorithm consist of two parts. One is to solve beam opening angle problem by fusion from multiple ultrasonic sensors. The other is to cope with specular reflection problem in wall line extract, which is based on Hough Transform. Experiments to verify the validity of the proposed algorithm are carried out, and the results are provided at last part in this paper.

• The Journal of Korea Robotics Society
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• v.9 no.2
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• pp.96-103
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• 2014

Global localization is one of the essential issues for mobile robot navigation. In this study, an indoor global localization method is proposed which uses a Kinect sensor and a monocular upward-looking camera. The proposed method generates an environment map which consists of a grid map, a ceiling feature map from the upward-looking camera, and a spatial feature map obtained from the Kinect sensor. The method selects robot pose candidates using the spatial feature map and updates sample poses by particle filter based on the grid map. Localization success is determined by calculating the matching error from the ceiling feature map. In various experiments, the proposed method achieved a position accuracy of 0.12m and a position update speed of 10.4s, which is robust enough for real-world applications.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.7 no.3
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• pp.14-21
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• 1998

The in-process detection of the state of cutting tool is one of the most important technical problem in Intelligent Machining System. This paper presents a method of detecting the state of cutting tool in turning process, by using Artificial Neural Network. In order to sense the state of cutting tool. the sensor fusion of an acoustic emission sensor and a force sensor is applied in this paper. It is shown that AErms and three directional dynamic mean cutting forces are sensitive to the tool wear. Therefore the six pattern features that is, the four sensory signal features and two cutting conditions are selected for the monitoring system with Artificial Neural Network. The proposed monitoring system shows a good recogniton rate for the different cutting conditions.