• Journal of Institute of Control, Robotics and Systems
• /
• v.17 no.10
• /
• pp.995-1005
• /
• 2011

An efficient outdoor local localization method is suggested using multi-sensor fusion with MU-EKF (Multi-Update Extended Kalman Filter) for car-like mobile robots. In outdoor environments, where mobile robots are used for explorations or military services, accurate localization with multiple sensors is indispensable. In this paper, multi-sensor fusion outdoor local localization algorithm is proposed, which fuses sensor data from LRF (Laser Range Finder), Encoder, and GPS. First, encoder data is used for the prediction stage of MU-EKF. Then the LRF data obtained by scanning the environment is used to extract objects, and estimates the robot position and orientation by mapping with map objects, as the first update stage of MU-EKF. This estimation is finally fused with GPS as the second update stage of MU-EKF. This MU-EKF algorithm can also fuse more than three sensor data efficiently even with different sensor data sampling periods, and ensures high accuracy in localization. The validity of the proposed algorithm is revealed via experiments.

• Journal of Sensor Science and Technology
• /
• v.30 no.2
• /
• pp.88-93
• /
• 2021

In this paper, a folding sensor based on capacitance is proposed. The sensor was developed to sense the length and angle data for the milli-scale actuators without causing any interference to the actuating joints. For the sensing and testing the robotic joint with reducing the cost and complexity aspects of manufacturing, a simple composition was adopted. The sensor comprises a pair of copper tapes, papers, and wires. The complete sensing unit is constructed by bonding the tapes with the papers and soldering the wire to the copper parts. For accuracy, a teensy 4.0 board, which has a 12-bit ADC resolution, is employed. Furthermore, the sensed analog data is not translated into the unit of capacitance for accuracy; however, it is filtered using a low-pass filter and subsequently, a Butter-worth filter. The data obtained demonstrate a periodic waveform, which implies that the data are in good agreement with the hypothesis set prior to the experiments. Compared to other milli-scale sensors, this could be a better option for sensing the length and angle data for milliscale actuators.

• The Journal of the Korea Contents Association
• /
• v.10 no.8
• /
• pp.51-58
• /
• 2010

In data collection using a mobile sink, the time that sensor nodes are included in its communication radius is not uniform. The data collection schedule in non-uniform time is needed between a mobile sink and sensor nodes for efficient data collection. The existing data collection schemes using a mobile sink considered staying time in its communication range and data collected by the mobile sink. However, they did not consider the characteristics of data collected in sensor networks. In this paper, we propose a TDMA based schedule scheme that consists of the data collection period by each sensor nodes and the data collection period between a mobile sink and sensor nodes. Moreover, we propose a data collection scheme considering the variability of data in sensor networks. The proposed data collection scheme collects only data that changed larger than the threshold set by the user. In order to show the superiority of the proposed scheme, we compare it with DWEDF that aims to collect data uniformly. As a result, our experimental results show that the proposed scheme reduces about 23% energy consumption and the data collection failure of sensor nodes over the DWEDF.

• Journal of the Institute of Convergence Signal Processing
• /
• v.22 no.4
• /
• pp.185-192
• /
• 2021

Various methods have been proposed to determine the defect detection of welding bead, and recently sensor data and image data inspection have been steadily announced. There are advantages that sensor data inspection is highly accurate, and two-dimensional-based image data inspection is able to determine the position of the welding bead. However, when analyzing only with sensor data, it is difficult to determine whether the welding has been performed at the correct position. On the other hand, the image data inspection does not have high accuracy due to noise and measurement errors. In this paper, we propose a method that can complement the shortcomings of each inspection method and increase its advantages to improve accuracy and speed up inspection by fusing sensor data inspection which are average current, average volt, and mixed gas data, and image data inspection methods and is implemented as software. In addition, it is intended to allow users to conveniently and intuitively analyze and grasp the results by performing analysis using a graphical user interface(GUI) and checking the data and inspection results used for the inspection. Sensor inspection is performed using the characteristics of each sensor data, and image data is inspected by applying a morphology geodesic active contour algorithm. The experimental results showed 98% accuracy, and when performing the inspection on the four image data, and sensor data the inspection time was about 1.9 seconds, indicating the performance of software that can be used as a real-time inspector in the welding process.

• Journal of KIISE:Information Networking
• /
• v.37 no.3
• /
• pp.204-216
• /
• 2010

Since Sensor nodes around a fixed sink have huge concentrated network traffic, the battery consumption of them is increased extremely. Therefore the lifetime of sensor networks is limited because of huge battery consumption. To address this problem, a mobile sink has been studied for load distribution among sensor nodes. Since a mobile sink changes its location in sensor networks continuously, the mobile sink has time limits to communicate with each sensor node and unstable signal strength from each sensor node. Therefore, a fair and stable data collection method between a mobile sink and sensor nodes is necessary in this environment. When some sensor nodes are not able to send data to the mobile sink, a real-time application in sensor networks cannot be provided. In this paper, the new scheduling method, FQMS (Fair Queuing for Mobile Sink), is proposed for fair and stable data collection for mobile sinks in sensor networks. The FQMS guarantees balanced data collecting between sensor nodes for a mobile sink. In out experiments, the FQMS receives more packets from sensor nodes than legacy scheduling methods and provides fair data collection, because moving speed of a mobile sink, distance between a mobile sink and sensor nodes and the number of sensor nodes are considered.

• Journal of Institute of Control, Robotics and Systems
• /
• v.7 no.8
• /
• pp.656-663
• /
• 2001

This paper presents a sensor fusion method to recognize a cylindrical object a CCD camera, a laser slit beam and ultrasonic sensors on a pan/tilt device. For object recognition with a vision sensor, an active light source projects a stripe pattern of light on the object surface. The 2D image data are transformed into 3D data using the geometry between the camera and the laser slit beam. The ultrasonic sensor uses an ultrasonic transducer array mounted in horizontal direction on the pan/tilt device. The time of flight is estimated by finding the maximum correlation between the received ultrasonic pulse and a set of stored templates - also called a matched filter. The distance of flight is calculated by simply multiplying the time of flight by the speed of sound and the maximum amplitude of the filtered signal is used to determine the face angle to the object. To determine the position and the radius of cylindrical objects, we use a statistical sensor fusion. Experimental results show that the fused data increase the reliability for the object recognition.

• Journal of Korea Multimedia Society
• /
• v.13 no.6
• /
• pp.911-923
• /
• 2010

In wireless sensor networks (WSNs), cluster based data routing protocols have the advantages of reducing energy consumption and link maintenance cost. Unfortunately, most of clustering protocols have been designed for uniformly distributed sensor networks. However, some urgent situations do not allow thousands of sensor nodes being deployed uniformly. For example, air vehicles or balloons may take the responsibility for deploying sensor nodes hence leading a normally distributed topology. In order to improve energy efficiency in such sensor networks, in this paper, we propose a new cluster formation algorithm named DAEEC (Density Aware Energy-Efficient Clustering). In this algorithm, we define two kinds of clusters: Low Density (LD) clusters and High Density (HD) clusters. They are determined by the number of nodes participated in one cluster. During the data routing period, the HD clusters help the neighbor LD clusters to forward the sensed data to the central base station. Thus, DAEEC can distribute the energy dissipation evenly among all sensor nodes by considering the deployment density to improve network lifetime and average energy savings. Moreover, because the HD clusters are densely deployed they can work in a manner of our former algorithm EEVAR (Energy Efficient Variable Area Routing Protocol) to save energy. According to the performance analysis result, DAEEC outperforms the conventional data routing schemes in terms of energy consumption and network lifetime.

• Structural Monitoring and Maintenance
• /
• v.5 no.1
• /
• pp.51-65
• /
• 2018

A number of sensing techniques have been implemented for detecting defects in civil infrastructures instead of onsite human inspections in structural health monitoring. However, the issue of faults in sensors has not received much attention. This issue may lead to incorrect interpretation of data and false alarms. To overcome these challenges, this article presents a deep learning-based method with a new architecture of Stateful Long Short Term Memory Neural Networks (S-LSTM NN) for detecting sensor fault without going into details of the fault features. As LSTMs are capable of learning data features automatically, and the proposed method works without an accurate mathematical model. The detection of four types of sensor faults are studied in this paper. Non-stationary acceleration responses of a three-span continuous bridge when under operational conditions are studied. A deep network model is applied to the measured bridge data with estimation to detect the sensor fault. Another set of sensor output data is used to supervise the network parameters and backpropagation algorithm to fine tune the parameters to establish a deep self-coding network model. The response residuals between the true value and the predicted value of the deep S-LSTM network was statistically analyzed to determine the fault threshold of sensor. Experimental study with a cable-stayed bridge further indicated that the proposed method is robust in the detection of the sensor fault.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.16 no.2
• /
• pp.378-383
• /
• 2012

Nowadays, the study is going on develop USN(Ubiquitous Sensor Network) with diffusion of the internet and development of computer network technology. USN sensor nodes equipped with various types of sensors provide sensor information to each individual sensors. To do this there needs standardized data description language for allowing many people to use based on XML in web services environment. In this paper, USN sensor information required for application services in a standardized form to describe the sensor data representation language was designed. USN-based technology utilized in the field, and will be utilized for service activation.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
• /
• 2002.10a
• /
• pp.158-163
• /
• 2002

This paper presents a multiple sensor system for rapid and high-precision coordinate data acquisition in the OMM (On-machine measurement) process. In this research, three sensors (touch probe, laser, and vision sensor) are integrated to obtain more accurate measuring results. The touch-type probe has high accuracy, but is time-consuming. Vision sensor can acquire many point data rapidly over a spatial range but its accuracy is less than other sensors. Also, it is not possible to acquire data for invisible areas. Laser sensor has medium accuracy and measuring speed among the sensors, and can acquire data for sharp or rounded edge and the features with very small holes and/or grooves. However, it has range- constraints to use because of its system structure. In this research, a new optimum sensor integration method for OMM is proposed by integrating the multiple-sensor to accomplish mote effective inspection planning. To verify the effectiveness of the proposed method, simulation and experimental works are performed, and the results are analyzed.