• Journal of the Semiconductor & Display Technology
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• v.18 no.2
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• pp.87-91
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• 2019

In this paper, we have development of horizontal attitude monitoring system for agricultural robots. A two-axis gyro sensor and a two-axis accelerometer sensor are used to measure the horizontal attitude angle. The roll angle and pitch angle were measured through the fusion of the gyro sensor signal and the acceleration sensor signal for the horizontal attitude monitoring of the robot. This attitude monitoring system includes GPS and Bluetooth communication module for remote monitoring. The roll angle and pitch angle can be measured by the error of less than 1 degree and the linearity and the reproducibility of the output signal are excellent.

• Journal of Sensor Science and Technology
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• v.31 no.4
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• pp.194-213
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• 2022

This study comprehensively reviewed four types of integrated-optic electric-field sensors based on titanium diffused lithium-niobate waveguides: symmetric and asymmetric Mach-Zehnder interferometers, 1×2 directional couplers, and Y-fed balanced-bridge Mach-Zehnder interferometers. First, we briefly explain the crystal properties and electro-optic effect of lithium niobate and the waveguide fabrication process. We theoretically analyzed the key parameters and operating principles of each sensor and antennas. We also describe and compare the design, simulation, implementation, and performance tests: dc and ac characteristics, frequency response, dynamic range, and sensitivity. The experimental results revealed that the sensitivity of the sensor based on the Y-fed balanced bridge Mach-Zehnder interferometer (YBB-MZI) was higher than that of the other types of sensors.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.2
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• pp.121-129
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• 2015

This paper presents a multiple vehicle recognition algorithm based on radar and vision sensor fusion for lane change assistance. To determine whether the lane change is possible, it is necessary to recognize not only a primary vehicle which is located in-lane, but also other adjacent vehicles in the left and/or right lanes. With the given sensor configuration, two challenging problems are considered. One is that the guardrail detected by the front radar might be recognized as a left or right vehicle due to its genetic characteristics. This problem can be solved by a guardrail recognition algorithm based on motion and shape attributes. The other problem is that the recognition of rear vehicles in the left or right lanes might be wrong, especially on curved roads due to the low accuracy of the lateral position measured by rear radars, as well as due to a lack of knowledge of road curvature in the backward direction. In order to solve this problem, it is proposed that the road curvature measured by the front vision sensor is used to derive the road curvature toward the rear direction. Finally, the proposed algorithm for multiple vehicle recognition is validated via field test data on real roads.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.39 no.5
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• pp.517-526
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• 2015

This paper presents the results of study for improving the reliability of quadrotor attitude control by applying a multi-sensor along with a data fusion algorithm. First, a mathematical model of the quadrotor dynamics was developed. Then, using the quadrotor mathematical model, simulations were performed using the improved reliability multi-sensor data as the inputs. From the simulation results, we designed a Gimbal-equipped quadrotor system. With the quadrotor in a hover state, we performed experiments according to the angle change of the user's specifications. We then calculated the attitude control data from the actual experimental data. Furthermore, with additional simulations, we verified the performance of the designed quadrotor attitude control system with multiple sensors.

• Korean Journal of Metals and Materials
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• v.56 no.11
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• pp.835-843
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• 2018

In this work, we report a delta rosette strain sensor based on highly stretchable silver nanowire (AgNW) percolation piezoresistors. The proposed rosette strain sensors were easily prepared by a facile two-step fabrication route. First, three identical AgNW piezoresistive electrodes were patterned in a simple and precise manner on a donor film using a solution-processed drop-coating of the AgNWs in conjunction with a tape-type shadow mask. The patterned AgNW electrodes were then entirely transferred to an elastomeric substrate while embedding them in the polymer matrix. The fabricated stretchable AgNW piezoresistors could be operated at up to 20% strain without electrical or mechanical failure, showing a maximum gauge factor as high as 5.3, low hysteresis, and high linearity ($r^2{\approx}0.996$). Moreover, the sensor responses were also found to be highly stable and reversible even under repeated strain loading/unloading for up to 1000 cycles at a maximum tensile strain of 20%, mainly due to the mechanical stability of the AgNW/elastomer composites. In addition, both the magnitude and direction of the principal strain could be precisely characterized by configuring three identical AgNW piezoresistors in a delta rosette form, representing the potential for employing the devices as a multidimensional strain sensor in various practical applications.

• Smart Structures and Systems
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• v.22 no.5
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• pp.631-641
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• 2018

This paper outlines a computational procedure for the effective merging of diverse sensor measurements, displacement and acceleration signals in particular, in order to successfully monitor and simulate the current health condition of civil structures under dynamic loadings. In particular, it investigates a Kalman Filter implementation for the Heterogeneous Data Fusion of displacement and acceleration response signals of a structural system toward dynamic identification purposes. The procedure is perspectively aimed at enhancing extensive remote displacement measurements (commonly affected by high noise), by possibly integrating them with a few standard acceleration measurements (considered instead as noise-free or corrupted by slight noise only). Within the data fusion analysis, a Kalman Filter algorithm is implemented and its effectiveness in improving noise-corrupted displacement measurements is investigated. The performance of the filter is assessed based on the RMS error between the original (noise-free, numerically-determined) displacement signal and the Kalman Filter displacement estimate, and on the structural modal parameters (natural frequencies) that can be extracted from displacement signals, refined through the combined use of displacement and acceleration recordings, through inverse analysis algorithms for output-only modal dynamics identification, based on displacements.

• Korean Journal of Remote Sensing
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• v.28 no.6
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• pp.611-622
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• 2012

This study proposed two multisensor fusion methods for segment-based image classification utilizing a region-growing segmentation. The proposed algorithms employ a Gaussian-PDF measure and an evidential measure respectively. In remote sensing application, segment-based approaches are used to extract more explicit information on spatial structure compared to pixel-based methods. Data from a single sensor may be insufficient to provide accurate description of a ground scene in image classification. Due to the redundant and complementary nature of multisensor data, a combination of information from multiple sensors can make reduce classification error rate. The Gaussian-PDF method defines a regional measure as the PDF average of pixels belonging to the region, and assigns a region into a class associated with the maximum of regional measure. The evidential fusion method uses two measures of plausibility and belief, which are derived from a mass function of the Beta distribution for the basic probability assignment of every hypothesis about region classes. The proposed methods were applied to the SPOT XS and ENVISAT data, which were acquired over Iksan area of of Korean peninsula. The experiment results showed that the segment-based method of evidential measure is greatly effective on improving the classification via multisensor fusion.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.8 no.2
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• pp.105-110
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• 2008

Human being recognizes emotion fusing information of the other speech signal, expression, gesture and bio-signal. Computer needs technologies that being recognized as human do using combined information. In this paper, we recognized five emotions (normal, happiness, anger, surprise, sadness) through speech signal and facial image, and we propose to method that fusing into emotion for emotion recognition result is applying to multimodal method. Speech signal and facial image does emotion recognition using Principal Component Analysis (PCA) method. And multimodal is fusing into emotion result applying fuzzy membership function. With our experiments, our average emotion recognition rate was 63% by using speech signals, and was 53.4% by using facial images. That is, we know that speech signal offers a better emotion recognition rate than the facial image. We proposed decision fusion method using S-type membership function to heighten the emotion recognition rate. Result of emotion recognition through proposed method, average recognized rate is 70.4%. We could know that decision fusion method offers a better emotion recognition rate than the facial image or speech signal.

• The Journal of Korea Robotics Society
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• v.3 no.3
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• pp.176-185
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• 2008

In this paper, new filtering method for sensor registration is provided to estimate and correct error of registration parameters in multiple sensor environments. Sensor registration is based on filtering method to estimate registration parameters in multiple sensor environments. Accuracy of sensor registration can increase performance of data fusion method selected. Due to various error sources, the sensor registration has registration errors recognized as multiple objects even though multiple sensors are tracking one object. In order to estimate the error parameter, new nonlinear information filtering method is developed using minimum mean square error estimation. Instead of linearization of nonlinear function like an extended Kalman filter, information estimation through unscented prediction is used. The proposed method enables to reduce estimation error without a computation of the Jacobian matrix in case that measurement dimension is large. A computer simulation is carried out to evaluate the proposed filtering method with an extended Kalman filter.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.400-406
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• 2002

Recently, the enormous increase in the volume of remotely sensed data is being acquired by an ever-growing number of earth observation satellites. The combining of diversely sourced imagery together is an important requirement in many applications such as data fusion, city modeling and object recognition. Aerial triangulation is a procedure to reconstruct object space from imagery. However, since the different kinds of imagery have their own sensor model, characteristics, and resolution, the previous approach in aerial triangulation (or georeferencing) is performed on a sensor model separately. This study evaluated the advantages of aerial triangulation of large number of images from multi-sensors simultaneously. The incorporated multi-sensors are frame, push broom, and whisky broom cameras. The limits and problems of push-broom or whisky broom sensor models can be compensated by combined triangulation with frame imagery and vise versa. The reconstructed object space from multi-sensor triangulation is more accurate than that from a single model. Experiments conducted in this study show the more accurately reconstructed object space from multi-sensor triangulation.