• Proceedings of the KWS Conference
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• 2002.10a
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• pp.336-341
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• 2002

Recent developments in sensor hardware and in advanced software have made it feasible to consider automating some of the most difficult welding operations. This paper describes some techniques used to automate successfully multipass submerged arc welding operations typically used in pressure vessel manufacture, shipbuilding, production of offshore structures and in pipe mills.

• Computers and Concrete
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• v.20 no.5
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• pp.521-527
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• 2017

This paper proposes using the multi-type sensor vibration measurements, such as from a relative displacement sensors and a traditional accelerometer for the damage detection of shear connectors in composite bridge under moving loads. Hilbert-Huang Transform (HHT) spectra of these responses will be fused with a data fusion approach i.e., Dempster-Shafer method, to detect the damage of shear connectors. Experimental studies on a composite bridge model in the laboratory are conducted to demonstrate the effectiveness and performance of using the proposed approach in detecting the damage of shear connectors in composite bridges. Both undamaged and damaged scenarios are considered. The detection results with the data fusion of multi-type sensor measurements show a more reliable and robust performance and accuracy, avoiding the false identifications.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.283-285
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• 2005

To account for the importance of variable in multi-sensor data fusion, random forests are applied to supervised land-cover classification. The random forests approach is a non-parametric ensemble classifier based on CART-like trees. Its distinguished feature is that the importance of variable can be estimated by randomly permuting the variable of interest in all the out-of-bag samples for each classifier. Supervised classification with a multi-sensor remote sensing data set including optical and polarimetric SAR data was carried out to illustrate the applicability of random forests. From the experimental result, the random forests approach could extract important variables or bands for land-cover discrimination and showed good performance, as compared with other non-parametric data fusion algorithms.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.12
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• pp.142-149
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• 2015

Most of the mono-camera based lane detection systems are fragile on poor illumination conditions. In order to compensate limitations of single sensor utilization, lane information fusion system using multiple lane sensors is an alternative to stabilize performance and guarantee high precision. However, conventional fusion schemes, which only concerns object detection, are inappropriate to apply to the lane information fusion. Even few studies considering lane information fusion have dealt with limited aids on back-up sensor or omitted cases of asynchronous multi-rate and coverage. In this paper, we propose a lane information fusion scheme utilizing multiple lane sensors with different coverage and cycle. The precise lane information fusion is achieved by the proposed fusion framework which considers individual ranging capability and processing time of diverse types of lane sensors. In addition, a novel lane estimation model is proposed to synchronize multi-rate sensors precisely by up-sampling spare lane information signals. Through quantitative vehicle-level experiments with around view monitoring system and frontal camera system, we demonstrate the robustness of the proposed lane fusion scheme.

• Journal of the Korea Institute of Military Science and Technology
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• v.15 no.3
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• pp.250-256
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• 2012

Unmanned ground vehicles may be operated by remote control unit through the wireless communication or autonomously. However, the autonomous technology is still challenging and not perfectly developed. For some reason or other, the wireless communication is not always available. If wireless communication is abruptly disconnected, the UGV will be nothing but a lump of junk. What was worse, the UGV can be captured by enemy. This paper suggests a method, autonomous return technology with which the UGV can autonomously go back to a safer position along the reverse path. The suggested autonomous return technology for UGV is based on multi-correlated information based DB creation and matching. While SUGV moves by remote-control, the multi-correlated information based DB is created with the multi-sensor information; the absolute position of the trajectory is stored in DB if GPS is available and the hybrid MAP based on the fusion of VISION and LADAR is stored with the corresponding relative position if GPS is unavailable. In multi-correlated information based autonomous return, SUGV returns autonomously based on DB; SUGV returns along the trajectory based on GPS-based absolute position if GPS is available. Otherwise, the current position of SUGV is first estimated by the relative position using multi-sensor fusion followed by the matching between the query and DB. Then, the return path is created in MAP and SUGV returns automatically based on the MAP. Experimental results on the pre-built trajectory show the possibility of the successful autonomous return.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.17 no.1
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• pp.77-85
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• 2008

The objective of the study is to construct a sensor fusion system for tool-condition monitoring (TCM) that will lead to a more efficient and economical drill usage. Drill-wear monitoring has an important attribute in the automatic machining processes as it can help preventing the damage of tools and workpieces, and optimizing the drill usage. In this study, we present the architectures of a multi-layer feed-forward neural network with Levenberg-Marquardt training algorithm based on sensor fusion for the monitoring of drill-wear condition. The input features to the neural networks were extracted from AE, vibration and current signals using the wavelet packet transform (WPT) analysis. Training and testing were performed at a moderate range of cutting conditions in the dry drilling of steel plates. The results show good performance in drill- wear monitoring by the proposed method of sensor fusion and neural network analysis.

• International Journal of Control, Automation, and Systems
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• v.1 no.4
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• pp.444-452
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• 2003

An integration system with multi-measurement sets can be realized via combined application of a centralized and federated Kalman filter. It is difficult for the centralized Kalman filter to remove a failed sensor in comparison with the federated Kalman filter. All varieties of Kalman filters monitor innovation sequence (residual) for detection and isolation of a failed sensor. The innovation sequence, which is selected as an indicator of real time estimation error plays an important role in adaptive mechanism design. In this study, the centralized Kalman filter with adaptive measurement fusion is introduced by means of innovation sequence. The objectives of adaptive measurement fusion are automatic isolation and recovery of some sensor failures as well as inherent monitoring capability. The proposed adaptive filter is applied to the GPS/SDINS integration system with an additional sensor. Simulation studies attest that the proposed adaptive scheme is effective for isolation and recovery of immediate sensor failures.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.7 no.3
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• pp.198-204
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• 2007

In mobile robotics, ultrasonic sensors became standard devices for collision avoiding. Moreover, their applicability for map building and navigation has exploited in recent years. In this paper, as the preliminary step for developing a multi-purpose autonomous carrier mobile robot to transport trolleys or heavy goods and serve as robotic nursing assistant in hospital wards. The aim of this paper is to present the use of multi-sensor data fusion such as ultrasonic sensor, IR sensor for mobile robot to navigate, and presents an experimental mobile robot designed to operate autonomously within both indoor and outdoor environments. The global map building based on multi-sensor data fusion is applied for recognition an obstacle free path from a starting position to a known goal region, and simultaneously build a map of straight line segment geometric primitives based on the application of the Hough transform from the actual and noisy sonar data. We will give an explanation for the robot system architecture designed and implemented in this study and a short review of existing techniques, Hough transform, since there exist several recent thorough books and review paper on this paper. Experimental results with a real Pioneer DX2 mobile robot will demonstrate the effectiveness of the discussed methods.

• Proceedings of the IEEK Conference
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• 2003.11c
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• pp.135-139
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• 2003

Sensor networks consist of sensor nodes with small-size, low-cost, low-power, and multi-functions to sense, to process and to communicate. Minimizing power consumption of sensors is an important issue in sensor networks due to limited power in sensor networks. Clustering is an efficient way to reduce data flow in sensor networks and to maintain less routing information. In this paper, we propose a multi-hop clustering mechanism using global and local ID to reduce transmission power consumption and an efficient routing method for improved data fusion and transmission.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.780-785
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• 1992

We present a multi-sensor fusion method in positioning control of a robot by using fuzzy logic. In general, the vision sensor is used in the gross motion control and the force/torque sensor is used in the fine motion control. We construct a fuzzy logic controller to combine the vision sensor data and the force/torque sensor data. Also, we apply the fuzzy logic controller to the peg-in-hole process. Simulation results uphold the theoretical results.