• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.12
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• pp.6040-6045
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• 2012

In this study, by utilizing the information fusion of multi sensor data and time within the crosswalk safety Assistive gait secondary to the safety of pedestrians on the system design and system performance verification through support to. Environmental awareness, and time information in addition to leveraging the default behavior for pedestrian safety design of the secondary system performed a study on the scenario and the behavior of a system for fuzzy control was performed for each sensor data processing, median filtering, including filters processing leveraging, and was attached by the time we complete the final algorithm, the system behavior. In addition, taking advantage of the sensor measurements, so basically uncertainties and sensor results, and you want to give at least the reliability of the data fusion experiment equipment using this simple verification.

• International Journal of Control, Automation, and Systems
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• v.4 no.2
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• pp.165-171
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• 2006

This paper proposes a data fusion algorithm of nonlinear multi sensor dynamic systems of synchronous sampling based on filtering step by step. Firstly, the object state variable at the next time index can be predicted by the previous global information with the systems, then the predicted estimation can be updated in turn by use of the extended Kalman filter when all of the observations aiming at the target state variable arrive. Finally a fusion estimation of the object state variable is obtained based on the system global information. Synchronously, we formulate the new algorithm and compare its performances with those of the traditional nonlinear centralized and distributed data fusion algorithms by the indexes that include the computational complexity, data communicational burden, time delay and estimation accuracy, etc.. These compared results indicate that the performance from the new algorithm is superior to the performances from the two traditional nonlinear data fusion algorithms.

• Tribology and Lubricants
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• v.28 no.3
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• pp.130-135
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• 2012

This study presents a new methodology for realtime tool breakage detection by sensor fusion concept of two hall sensor and an acoustic emission (AE) sensor. Spindle induction motor torque of CNC Lathe during machining is estimated by two hall sensor. Estimated motor torque instead of a tool dynamometer was used to measure the cutting torque and tool breakage detection. A burst of AE signal was used as a triggering signal to inspect the cutting torque. A significant drop of cutting torque was utilized to detect tool breakage. The algorithm was implemented on a NI DAQ (Data Acquisition) board for in-process tool breakage detection. The result of experiment showed an excellent monitoring capability of the proposed tool breakage detection system. This system is available tool breakage monitoring through internet also provides this system's user with current cutting torque of induction motor.

• The Journal of Korea Robotics Society
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• v.4 no.4
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• pp.312-319
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• 2009

This paper proposes a low-complexity indoor localization method of mobile robot under the dynamic environment by fusing the landmark image information from an ordinary camera and the distance information from sensor nodes in an indoor environment, which is based on sensor network. Basically, the sensor network provides an effective method for the mobile robot to adapt to environmental changes and guides it across a geographical network area. To enhance the performance of localization, we used an ordinary CCD camera and the artificial landmarks, which are devised for self-localization. Experimental results show that the real-time localization of mobile robot can be achieved with robustness and accurateness using the proposed localization method.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.12
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• pp.1232-1237
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• 2008

This paper introduces an application of wavelet analysis to the sensor fusion of GPS/INS/baroaltimeter. Using wavelet analysis the baro-inertial altitude is decomposed into the low frequency content and the high frequency content. The high frequency components, 'details', represent the perturbed altitude change from the long time trend. GPS altitude is also broken down by a wavelet decomposition. The low frequency components, 'approximations', of the decomposed signal address the long-term trend of altitude. It is proposed that the final altitude be determined as the sum of both the details of the baro-inertial altitude and the approximations of GPS altitude. Then the final altitude exclude long-term baro-inertial errors and short-term GPS errors. Finally, it is shown from the test results that the proposed method produces continuous and sensitive altitude successfully.

• Nuclear Engineering and Technology
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• v.52 no.11
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• pp.2535-2542
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• 2020

To understand the fundamental parameters of Alvand tokamak, A Rogowski coil with an active integrator was designed and constructed. Considering the characteristics of the Alvand tokamak, the structural and electrical parameters affecting the sensor function, were designed. Calibration was performed directly in the presence of plasma. The sensor has a high resistance against interference of external magnetic fields. Plasma current was measured in various experiments. Based on the plasma current profile and loop voltage signal, the time evolution of plasma discharge was investigated and plasma behavior was analyzed. Alvand tokamak discharge was divided into several regions that represents different physical phenomena in the plasma. During the plasma discharge time, plasma had significant changes and its characteristic was not uniform. To understand the plasma behavior in each of the phases, the Rogowski sensor should have sufficient time resolution. The Rogowski sensor with a frequency up to 15 kHz was appropriate for this purpose.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.4
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• pp.365-371
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• 2009

A intelligent mobile hexapod robot with various types of sensors and wireless camera is introduced. We show this mobile robot can detect objects well by combining the results of active sensors and image processing algorithm. First, to detect objects, active sensors such as infrared rays sensors and supersonic waves sensors are employed together and calculates the distance in real time between the object and the robot using sensor's output. The difference between the measured value and calculated value is less than 5%. This paper suggests effective visual detecting system for moving objects with specified color and motion information. The proposed method includes the object extraction and definition process which uses color transformation and AWUPC computation to decide the existence of moving object. We add weighing values to each results from sensors and the camera. Final results are combined to only one value which represents the probability of an object in the limited distance. Sensor fusion technique improves the detection rate at least 7% higher than the technique using individual sensor.

• Transactions of the Korean Society of Automotive Engineers
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• v.22 no.7
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• pp.107-116
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• 2014

This paper presents camera-based road surface marking detection methods suited to sensor fusion-based positioning system that consists of low-cost GPS (Global Positioning System), INS (Inertial Navigation System), EDM (Extended Digital Map), and vision system. The proposed vision system consists of two parts: lane marking detection and RSM (Road Surface Marking) detection. The lane marking detection provides ROIs (Region of Interest) that are highly likely to contain RSM. The RSM detection generates candidates in the regions and classifies their types. The proposed system focuses on detecting RSM without false detections and performing real time operation. In order to ensure real time operation, the gating varies for lane marking detection and changes detection methods according to the FSM (Finite State Machine) about the driving situation. Also, a single template matching is used to extract features for both lane marking detection and RSM detection, and it is efficiently implemented by horizontal integral image. Further, multiple step verification is performed to minimize false detections.

• 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 the Korea Institute of Information and Communication Engineering
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• v.24 no.9
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• pp.1224-1230
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• 2020

Since the spread of smart phones, interest in wearable devices has increased and diversified, and is closely related to the lives of users, and has been used as a method for providing personalized services. In this paper, we propose a method to detect the user's behavior by applying information from a 3-axis acceleration sensor and a 3-axis gyro sensor embedded in a smartphone to a convolutional neural network. Human behavior differs according to the size and range of motion, starting and ending time, including the duration of the signal data constituting the motion. Therefore, there is a performance problem for accuracy when applied to a convolutional neural network as it is. Therefore, we proposed a Time-Division Feature Fusion Convolutional Neural Network (TDFFCNN) that learns the characteristics of the sensor data segmented over time. The proposed method outperformed other classifiers such as SVM, IBk, convolutional neural network, and long-term memory circulatory neural network.