• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2594-2599
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• 2003

We consider some problems of the Modified SPRT(Sequential Probability Ratio Test) method for fault detection and isolation of inertial redundant sensor systems and propose an Advanced SPRT method to solve the problems of the Modified SPRT method. One problem of the Modified SPRT method to apply to inertial sensor system comes from the effect of inertial sensor errors such as misalignment, scale factor error and sensor bias in the parity vector, which make the Modified SPRT method hard to be applicable. The other problem is due to the correlation of parity vector components which may induce false alarm. We use a two-stage Kalman filter to remove effects of the inertial sensor errors and propose the modified parity vector and the controlled parity vector which removes the effect of correlation of parity vector components. The Advanced SPRT method is derived form the modified parity vector and the controlled parity vector. Some simulation results are presented to show the usefulness of the Advanced SPRT method to redundant inertial sensor systems.

• Journal of Information Processing Systems
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• v.16 no.5
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• pp.1158-1168
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• 2020

The distance vector-hop wireless sensor node location method is one of typical range-free location methods. In distance vector-hop location method, if a wireless node A can directly communicate with wireless sensor network nodes B and C at its communication range, the hop count from wireless sensor nodes A to B is considered to be the same as that form wireless sensor nodes A to C. However, the real distance between wireless sensor nodes A and B may be dissimilar to that between wireless sensor nodes A and C. Therefore, there may be a discrepancy between the real distance and the estimated hop count distance, and this will affect wireless sensor node location error of distance vector-hop method. To overcome this problem, it proposes a wireless sensor network node location method by modifying the method of distance estimation in the distance vector-hop method. Firstly, we set three different communication powers for each node. Different hop counts correspond to different communication powers; and so this makes the corresponding relationship between the real distance and hop count more accurate, and also reduces the distance error between the real and estimated distance in wireless sensor network. Secondly, distance difference between the estimated distance between wireless sensor network anchor nodes and their corresponding real distance is computed. The average value of distance errors that is computed in the second step is used to modify the estimated distance from the wireless sensor network anchor node to the unknown sensor node. The improved node location method has smaller node location error than the distance vector-hop algorithm and other improved location methods, which is proved by simulations.

• ETRI Journal
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• v.33 no.6
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• pp.924-934
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• 2011

Sensor networks play an important role in making the dream of ubiquitous computing a reality. With a variety of applications, sensor networks have the potential to influence everyone's life in the near future. However, there are a number of issues in deployment and exploitation of these networks that must be dealt with for sensor network applications to realize such potential. Localization of the sensor nodes, which is the subject of this paper, is one of the basic problems that must be solved for sensor networks to be effectively used. This paper proposes a probabilistic support vector machine (SVM)-based method to gain a fairly accurate localization of sensor nodes. As opposed to many existing methods, our method assumes almost no extra equipment on the sensor nodes. Our experiments demonstrate that the probabilistic SVM method (PSVM) provides a significant improvement over existing localization methods, particularly in sparse networks and rough environments. In addition, a post processing step for PSVM, called attractive/repulsive potential field localization, is proposed, which provides even more improvement on the accuracy of the sensor node locations.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.2
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• pp.10-19
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• 1996

This paper presents a finger-shaped multisensor system which can measure the tyep and position of a target surface by contactl. The multi-sensor system consists of a sphere-shpaed optical tactile sensor located at the finger tip and a force/torque sensor located at the joint of a finger. The optial tactile sensor determines the type and position of the target surface using the shape and position of the CCD image of the touching area generated by a contact between the sensor and the taget surface. The force/torque sensor also determines the position and surface normal vector by applying the distributionof forces and torques t the contact point to the equations of finger shape. The measurements on the position and surface normal vector at a contact point obtined by two individual sensors are fused using a statistical method. The integrated sensor system has 0.8mm error in position measurement and 1.31$^{\circ}$ error in normal vector measurement. The developed sensor system has many applications, such as autonomous compliance control, automatic grasping and recognition, etc.

• The Journal of the Acoustical Society of Korea
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• v.35 no.5
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• pp.403-409
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• 2016

Traditional towed line arrays using omni-directional sensor suffer from the well known port-starboard ambiguity, because the direction of arrival is determined by conic angle. The operational method and structure of the sensor arrays method have been proposed to solve this problem. Recently, a lot of research relating to the acoustic vector sensor are studied. In this paper, we study port-starboard discrimination for roll of acoustic vector sensor array. With one omni-directional sensor and three orthogonally-placed directional sensors, an acoustic vector sensor is able to measure both the acoustic pressure and the three directional velocities at the point of the sensor. The wrong axis due to the roll at directional sensors can degrade performance of beamforming. We investigate port-starboard discrimination for roll of sensor array and confirm the validity of performance of beamforming with compensated the roll.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.6
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• pp.398-404
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• 2021

This paper proposes a method of using a magnetic position sensor to detect accurately the rotor position required to perform vector control of a permanent-magnet synchronous motor, particularly the initial rotor position at startup. In the existing vector control systems, the initial rotor position was determined using the output signals of the Hall sensors, or the control was performed in a sensorless method without using such a sensor. On the other hand, the accuracy is degraded due to the occurrence of a position detection error, and the practicality was not satisfactory. This paper attempts to detect the initial rotor position using a magnetic position sensor to solve this problem. This method is used to solve the deteriorating starting characteristics of the motor in the vector control system. In addition, to lower the price of a low-power vector control inverter, this paper proposes a method of integrating the existing sensors and reducing the price to less than half using a magnetic position sensor for speed and position detection.

• Proceedings of the KIEE Conference
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• 2002.07d
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• pp.2067-2069
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• 2002

The study of the vector control of the induction machine without speed sensor is going on and there are the adaptive performance method to use the flux observer. This study is to make the vector control without the speed sensor based on the flux oriented reference vector control theory. This paper proposes the new speed follow-up method to deduce the current value in the current sensor and the 2 order flux observer based on the observer theory and examine the possibility to realize the flux oriented vector control system using the simulation in this proposed method of this study.

• The Journal of the Acoustical Society of Korea
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• v.34 no.2
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• pp.108-116
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• 2015

An acoustic vector sensor is a device that is capable of measuring the direction of wave propagation and the acoustic pressure. In this paper, the modeling of micro-cantilever sensor for the vector sensor are proposed by consideration of acoustic phenomenon in water. Two models based on unimorph structure are proposed in this paper and corresponding transfer function which describes the relation between input pressure wave and output voltage depending on incidence angle and frequency of pressure wave is derived based on lumped model. It has been shown that very thin and flexible micro-cantilever can be used to measure directly the particle velocity component in water.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.944-946
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• 2014

Localization of a sensor network node using machine learning has been recently studied. It is easy for Support vector machines algorithm to implement in high level language enabling parallelism. In this paper, we realized Support vector machine using python language and built a sensor network cluster with 5 Pi's. We also established a Hadoop software framework to employ MapReduce mechanism. We modified the existing Support vector machine algorithm to fit into the distributed hadoop architecture system for localization of a sensor node. In our experiment, we implemented the test sensor network with a variety of parameters and examined based on proficiency, resource evaluation, and processing time.

• Aerospace Engineering and Technology
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• v.3 no.1
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• pp.179-187
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• 2004

We consider some problems of the Modified SPRT(Sequential Probability Ratio Test) method for fault detection and isolation of inertial redundant sensor systems and propose an Advanced SPRT method which solves the problems of the Modified SPRT method. The problems of the Modified SPRT method to apply to inertial sensor system come from the effect of inertial sensor errors and the correlation of parity vector components. We use a two-stage Kalman filter to remove effects of the inertial sensor errors and propose the modified parity vector and the controlled parity vector which reduces the effect of correlation of parity vector components. The Advanced SPRT method is derived form the modified parity vector and the controlled party vector. Some simulation results are presented to show the usefulness of the Advanced SPRT method to redundant inertial sensor systems.