• Smart Structures and Systems
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• v.31 no.3
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• pp.301-309
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• 2023

Tilt is a key indicator of structural safety. Real-time monitoring of tilt responses helps to evaluate structural condition, enable cost-effective maintenance, and enhance lifetime resilience. This paper presents a prototype wireless sensing system for structural tilt measurement. Long range (LoRa) technology is adopted by the sensing system to offer long-range wireless communication with low power consumption. The sensor integrates a gyroscope and an accelerometer as the sensing module. Although tilt can be estimated from the gyroscope or the accelerometer measurements, these estimates suffer from either drift issue or high noise. To address this challenging issue and obtain more reliable tilt results, two sensor fusion algorithms, the complementary filter and the Kalman filter, are investigated to fully exploit the advantages of both gyroscope and accelerometer measurements. Numerical simulation is carried out to validate and compare the sensor fusion algorithms. Laboratory experiment is conducted on a simply supported beam under moving vehicle load to further investigate the performance of the proposed wireless tilt sensing system.

• Journal of the Korea Institute of Military Science and Technology
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• v.11 no.6
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• pp.38-46
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• 2008

A distributed system for tracking multiple targets with a pair of multifunction radars is proposed and implemented. The system performs track-to-track association and track-to-track fusion at the fusion center to form fused tracks. The association and fusion are performed using target state information linked via communication nodes from a radar at a remote location. Many factors can affect the track-to-track association and fusion performances. They include delays in data transmission buffer of the remote radar, the error in estimating time-stamp of the remote radar, and the gating in track-to-track association. The effects on association and fusion performances due to these factors are investigated through extensive numerical simulations.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.8A
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• pp.633-639
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• 2009

For operation of IEEE 802.22 WRAN system, spectrum sensing is a essential function. However, due to strict sensing requirement of WRAN system, spectrum sensing process of CR nodes require long quiet period. In addition, CR nodes sometimes fail to detect licensed users due to shadowing effect of wireless communication environment. To overcome this problem, CR nodes collaborate with each other for increasing the sensing reliability or mitigating the sensitivity requirement. A general approach for decision fusion, the "k out of N" rule is often taken as the decision fusion rule for its simplicity. However, since k out of N rules can not achieve better performance than the highest SNR node when SNR is largely different among CR nodes, the local SNR of each node should be considered to achieve better performance. In this paper, we propose two novel data fusion methods by utilizing confidence vector which represents the confidence level of individual sensing result. The simulation results show that the proposed schemes improve the signal detection performance than the conventional data fusion algorithms.

• Smart Structures and Systems
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• v.23 no.3
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• pp.279-293
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• 2019

To ensure high quality data being used for data mining or feature extraction in the bridge structural health monitoring (SHM) system, a practical sensor fault diagnosis methodology has been developed based on the similarity of symmetric structure responses. First, the similarity of symmetric response is discussed using field monitoring data from different sensor types. All the sensors are initially paired and sensor faults are then detected pair by pair to achieve the multi-fault diagnosis of sensor systems. To resolve the coupling response issue between structural damage and sensor fault, the similarity for the target zone (where the studied sensor pair is located) is assessed to determine whether the localized structural damage or sensor fault results in the dissimilarity of the studied sensor pair. If the suspected sensor pair is detected with at least one sensor being faulty, field test could be implemented to support the regression analysis based on the monitoring and field test data for sensor fault isolation and reconstruction. Finally, a case study is adopted to demonstrate the effectiveness of the proposed methodology. As a result, Dasarathy's information fusion model is adopted for multi-sensor information fusion. Euclidean distance is selected as the index to assess the similarity. In conclusion, the proposed method is practical for actual engineering which ensures the reliability of further analysis based on monitoring data.

• Transactions of the Korean Society of Mechanical Engineers
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• v.16 no.11
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• pp.2021-2032
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• 1992

A redundant sensor system, which consists of two incremental encoders and a gyro sensor, has been proposed for the estimation of the posture of mobile robots. A hardware system was built for estimating the heading angle change of the mobile robot from outputs of the gyro sensor. The proposed hardware system of the gyro sensor produced an accurate estimate for the heading angle change of the robot. A sensor data fusion algorithm has been developed to find the optimal estimates of the heading angle change based on the stochastic measurement equations of our readundant sensor system. The maximum likelihood estimation method is applied to combine the noisy measurement data from both encoders and gyro sensor. The proposed fusion algorithm demonstrated a satisfactory performance, showing significantly reduced estimation error compared to the conventional method, in various navigation experiments.

• Nuclear Engineering and Technology
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• v.55 no.1
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• pp.100-108
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• 2023

In this study, a neural network model inspired by a one-dimensional convolution U-net is developed to automatically accelerate edge localized mode (ELM) detection from big diagnostic data of fusion devices and increase the detection accuracy regardless of the hyperparameter setting. This model recognizes the input signal patterns and overcomes the problems of existing detection algorithms, such as the prominence algorithm and those of differential methods with high sensitivity for the threshold and signal intensity. To train the model, 10 sets of discharge radiation data from the KSTAR are used and sliced into 11091 inputs of length 12 ms, of which 20% are used for validation. According to the receiver operating characteristic curves, our model shows a positive prediction rate and a true prediction rate of approximately 90% each, which is comparable to the best detection performance afforded by other algorithms using their optimized hyperparameters. The accurate and automatic ELM-burst detection methodology used in our model can be beneficial for determining plasma properties, such as the ELM frequency from big data measured in multiple experiments using machines from the KSTAR device and ITER. Additionally, it is applicable to feature detection in the time-series data of other engineering fields.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.5
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• pp.706-711
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• 2008

This paper is presented an accurate localization scheme for mobile robots based on the fusion of ultrasonic satellite (U-SAT) with inertial navigation system (INS), i.e., sensor fusion. Our aim is to achieve enough accuracy less than 100 mm. The INS consist of a yaw gyro, two wheel-encoders. And the U-SAT consist of four transmitters, a receiver. Besides the localization method in this paper fuse these in an extended Kalman filter. The performance of the localization is verified by simulation and two actual data(straight, curve) gathered from about 0.5 m/s of driving actual driving data. localization methods used are general sensor fusion and sensor fusion through Kalman filter using data from INS. Through the simulation and actual data studies, the experiment show the effectiveness of the proposed method for autonomous mobile robots.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.104.2-104
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• 2002

This paper proposes a sensor-fusion technique where the data sets for the previous moments are properly transformed and fused into the current data sets to enable accurate measurement, such as, distance to an obstacle and location of the service robot itself. In the conventional fusion schemes, the measurement is dependent on the current data sets. As the results, more of sensors are required to measure a certain physical parameter or to improve the accuracy of the measurement. However, in this approach, instead of adding more sensors to the system , the temporal sequence of the data sets are stored and utilized for the measurement improvement. Theoretical basis is il lustrated by examples and...

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.248-250
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• 2003

An automatic approach and strategy for extracting building information from aerial images using combined image analysis and interpretation techniques is described in this paper. A dense DSM is obtained by stereo image matching. Multi-band classification, DSM, texture segmentation and Normalised Difference Vegetation Index (NDVI) are used to reveal building interest areas. Then, based on the derived approximate building areas, a shape modelling algorithm based on the level set formulation of curve and surface motion has been used to precisely delineate the building boundaries. Data fusion, based on the Dempster-Shafer technique, is used to interpret simultaneously knowledge from several data sources of the same region, to find the intersection of propositions on extracted information derived from several datasets, together with their associated probabilities. A number of test areas, which include buildings with different sizes, shape and roof colour have been investigated. The tests are encouraging and demonstrate that the system is effective for building extraction, and the determination of more accurate elevations of the terrain surface.

• Proceedings of the KOR-KST Conference
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• 1999.10a
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• pp.446-451
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• 1999