• Journal of IKEEE
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• v.22 no.4
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• pp.907-915
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• 2018

This paper proposes a method to improve the performance of low cost infrared distance sensor. Infrared distance sensor measures the intensity of reflected light and converts it into distance. The proposed method improves the sensing distance of the sensor and makes it operate robustly in various lighting environments. This is achieved by extracting the characteristic curves of the sensor and applying the HDR (High Dynamic Range) technique. The output value of the sensor was obtained by varying the intensity of the infrared input and the exposure time, and the characteristic curve of the sensor was extracted from it.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.12
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• pp.4492-4507
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• 2021

In camera sensor networks (CSNs), in order to better identify the point, full-view problem requires capture any facing direction of target (point or intruder), and its coverage prediction and sensor density issues are more complicated. At present, a lot of research supposes that a large number of homogeneous camera sensors are randomly distributed in a bounded square monitoring region to obtain full-view rate which is close to 1. In this paper, we deduce the sensor density prediction model in heterogeneous deployed CSNs with arbitrary full-view rate. Aiming to reduce the influence of boundary effect, we introduce the concepts of expanded monitoring region and maximum detection area. Besides, in order to verify the performance of the proposed sensor density model, we carried out different scenarios in simulation experiments to verify the theoretical results. The simulation results indicate that the proposed model can effectively predict the sensor density with arbitrary full-view rate.

• Journal of Sensor Science and Technology
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• v.32 no.6
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• pp.475-480
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• 2023

When modeling a sensor system mathematically, we assume that the sensor noise is Gaussian and white to simplify the model. If this assumption fails, the performance of the sensor model-based controller or estimator degrades due to incorrect modeling. In practice, non-Gaussian or non-white noise sources often arise in many digital sensor systems. Additionally, the noise parameters of the sensor model are not known in advance without additional noise statistical information. Moreover, disturbances or high nonlinearities often cause unknown sensor modeling errors. To estimate the uncertain noise and model parameters of a sensor system, this paper proposes an iterative batch calibration method using data-driven machine learning. Our simulation results validate the calibration performance of the proposed approach.

• Smart Structures and Systems
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• v.8 no.1
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• pp.93-105
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• 2011

This paper presents a flexible low-profile antenna sensor for fatigue crack detection and monitoring. The sensor was inspired by the sense of pain in bio-systems as a protection mechanism. Because the antenna sensor does not need wiring for power supply or data transmission, it is an ideal candidate as sensing elements for the implementation of engineering sensor skins with a dense sensor distribution. Based on the principle of microstrip patch antenna, the antenna sensor is essentially an electromagnetic cavity that radiates at certain resonant frequencies. By implementing a metallic structure as the ground plane of the antenna sensor, crack development in the metallic structure due to fatigue loading can be detected from the resonant frequency shift of the antenna sensor. A monostatic microwave radar system was developed to interrogate the antenna sensor remotely. Fabrication and characterization of the antenna sensor for crack monitoring as well as the implementation of the remote interrogation system are presented.

• Journal of Communications and Networks
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• v.7 no.4
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• pp.438-449
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• 2005

In this paper, we describes the information-theoretic approaches to sensor selection and sensor placement in sensor net­works for target localization and tracking. We have developed a sensor selection heuristic to activate the most informative candidate sensor for collaborative target localization and tracking. The fusion of the observation by the selected sensor with the prior target location distribution yields nearly the greatest reduction of the entropy of the expected posterior target location distribution. Our sensor selection heuristic is computationally less complex and thus more suitable to sensor networks with moderate computing power than the mutual information sensor selection criteria. We have also developed a method to compute the posterior target location distribution with the minimum entropy that could be achieved by the fusion of observations of the sensor network with a given deployment geometry. We have found that the covariance matrix of the posterior target location distribution with the minimum entropy is consistent with the Cramer-Rao lower bound (CRB) of the target location estimate. Using the minimum entropy of the posterior target location distribution, we have characterized the effect of the sensor placement geometry on the localization accuracy.

• Journal of Sensor Science and Technology
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• v.25 no.2
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• pp.148-154
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• 2016

This paper describes the design and fabrication of a two-axis force/torque sensor and an one-axis force sensor with parallel plate beams(PPSs) for measuring forces and torque in an ankle rehabilitation exercise using by a lower rehabilitation robot. The two-axis force/torque sensor is composed of a Fy force sensor and Tz torque sensor and the force sensor detects x direction force. The two-axis force/torque sensor and one-axis force sensor were designed using by FEM(Finite Element Method), and manufactured using strain-gages. The characteristics experiment of the two-axis force/torque sensor and one-axis force sensor were carried out respectively. As a test results, the interference error of the two-axis force/torque sensor was less than 1.56%, the repeatability error and the non-linearity of the two-axis force/torque sensor were less than 0.03% respectively, and the repeatability error and the non-linearity of the one-axis force sensor were less than 0.03% and 0.02% respectively.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.6
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• pp.546-551
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• 2011

Tilt sensor is required to control the attitude of the biped robot when it walks on an uneven terrain. Vision sensor, which is used for recognizing human or detecting obstacles, can be used as a tilt angle sensor by comparing current image and reference image. However, vision sensor alone has a lot of technological limitations to control biped robot such as low sampling frequency and estimation time delay. In order to verify limitations of vision sensor, experimental setup of an inverted pendulum, which represents pitch motion of the walking or running robot, is used and it is proved that only vision sensor cannot control an inverted pendulum mainly because of the time delay. In this paper, to overcome limitations of vision sensor, Kalman filter for the multi-rate sensor fusion algorithm is applied with low-quality gyro sensor. It solves limitations of the vision sensor as well as eliminates drift of gyro sensor. Through the experiment of an inverted pendulum control, it is found that the tilt estimation performance of fusion sensor is greatly improved enough to control the attitude of an inverted pendulum.

• Proceedings of the Korean Information Science Society Conference
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• 2007.06d
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• pp.105-108
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• 2007

Bayesian learning network is employed for diverse applications. This paper discusses the Bayesian learning network algorithm structure which can be applied in the wireless sensor network environment for various online applications. First, this paper discusses Bayesian parameter learning, Bayesian DAG structure learning, characteristics of wireless sensor network, and data gathering in the wireless sensor network. Second, this paper discusses the important considerations about the online Bayesian learning network and the conceptual structure of the learning network algorithm.

• International journal of advanced smart convergence
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• v.6 no.3
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• pp.38-44
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• 2017

Currently, wired gables have been installed or portable storage devices have been installed for data acquisition of flying drone. In this paper, we propose a technology to transmit data wirelessly by sensing information such as battery discharge value, acceleration, and temperature by attaching RF sensor to a drone. The purpose of this paper is to design and develop the monitoring technology of agriculture drone battery usage in real time using RF sensor. In this paper, we propose a monitoring system that can check real time data of battery changed value, temperature, and acceleration during pesticide control activity of agricultural drone.

• Journal of KSNVE
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• v.10 no.6
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• pp.941-948
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• 2000

Active noise control is an approach to reduce the noise by utilizing a secondary noise source that destructively interferes with the unwanted noise. In general, active noise control systems rely on multiple sensors to measure the unwanted noise field and the effect of the cancellation. This paper develops an approach that utilizes a single sensor. The noise field is modeled as a stochastic process, and an adaptive algorithm is used to adaptively estimate the parameters of the process. Based on these parameter estimates, a canceling signal is generated. Oppenheim assumed that transfer function characteristics from the canceling source to the error sensor is only a propagation delay. This paper proposes a modified Oppenheim algorithm by considering transfer characteristics of speaker-path-sensor This transfer characteristics is adaptively cancelled by the proposed adaptive modeling technique. Feasibility of the proposed method is proved by computer simulations with artificially generated random noises and sine wave noise. The details of the proposed architecture. and theoretical simulation of the noise cancellation system for three dimension enclosure are presented in the Paper.