• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1254-1258
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• 2003

One of the major factors that effect sensor performance is analog noise that added in a sensor signal such as voltage. In multi-axis force sensors, error sources may be classified mainly in two groups. One is structural error due to inaccuracy of sensor body. The other error source is noise signals existing in the sensed information. This paper presents a brief review about the principle of multi-axis force sensors, and then proposes a method that can reduce the effect of noise signal to sensor performance. The method is to convert analog voltage signal to digital numbers near sensor body and then to read these digital signals and conduct signal processing in the computer. By this way, we can eliminate a bad effect of electromagnetic wave emitted from computer and of 60 Hz noise emitted from AC source. The proposed method is investigated through experimental demonstration. The experimental results show that it improves S/N ratio of the sensor about 40 times in our experimental setup.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.44 no.5
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• pp.49-56
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• 2007

In the sensor networks, the first step in the signal processing is noise reduction. It is obvious that the noise in the sensing data by sensor nodes have a bad effect on the collaborative signal processing results. In this paper we propose noise reduction filters for the wireless sensor networks. The proposed filters use only relative distance information between sensor nodes thus it is very simple in complexity and easy to implement. Also it will be able to extend the sensor network life time by adjusting communication radius of sensors within proper limit to reduce consuming power. Various simulation results are presented to verify our approaches, we observed that the proposed filters can reduce the effect of the noise on the sensing data.

• Journal of Korea Multimedia Society
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• v.6 no.7
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• pp.1131-1138
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• 2003

Active noise control(ANC) is an approach to noise reduction in which a secondary noise source destructively interferes with the unwanted noise is introduced. Generally, the performance of ANC is determined how well a secondary noise tracks noises. A secondary noise is generated from the cancelling speaker and a error sensor pick up error signal. The transfer function between the cancelling speaker and the error sensor is not flat and distorts secondary noises. Consequently, the performance of ANC is degraded by the transfer function. In this paper, a single sensor ANC which considers the characteristics of the speaker and the error sensor is proposed. To reduce distortion of secondary noises, the transfer function is estimated by adaptive inverse modelling and the primary noises are estimated by Kalman filter. Experimental results show that the proposed single sensor ANC effectively attenuates noises.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2002.05a
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• pp.283-286
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• 2002

In fiber optic sensor, the error of the measurement is influenced by the surface roughness of the target and surroundings, especially the outside light. To reduce or modify this error, the sensitivity of the fiber optic sensor and the noise change by the surface roughness of the target should be known. The purpose of this paper is to observe the sensitivity of the fiber optic sensor and the noise according to the surface roughness of the target.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.5
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• pp.1166-1179
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• 2013

In wireless sensor network (WSN) environments, environmental noises are generated by, for example, small passing animals, crickets chirping or foliage blowing and will interfere target detection if the noises are higher than the sensor threshold value. For accurate tracking by acoustic WSNs, these environmental noises should be filtered out before initiating track. This paper presents the effect of environmental noises on target tracking and proposes a new algorithm for the noise mitigation in acoustic WSNs. We find that our noise mitigation algorithm works well even for targets with sensing range shorter than the sensor separation as well as with longer sensing ranges. It is also found that noise duration at each sensor affects the performance of the algorithm. A detection algorithm is also presented to account for the Doppler effect which is an important consideration for tracking higher-speed ground targets. For tracking, we use the weighted sensor position centroid to represent the target position measurement and use the Kalman filter (KF) for tracking.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 1996.10a
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• pp.266-271
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• 1996

Optimal design of disk shaped piezoelectric actuator and sensor mounted on the plate structure is studied for the control of noise radiated fro the structure. The sensor signal is returned to the actuator through negative gain. Finite element modelling is used for the plate structure and the disk shaped piezoelectric sensor and actuator. The objective function is the total radiated sound power and the design variables are the locations and sizes of the piezoelectric actuator and sensor. The optimal is performed at the resonance and the off resonance frequency and the results show good noise reduction.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.23 no.12
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• pp.1082-1089
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• 2013

3-Axis sensor measurement system is needed for measuring ride quality of elevator. But because 3-Axis piezoelectric accelerometer is expensive. We developed 3-Axis sensor system which is suitable for measuring ride quality of elevator using cheap MEMS sensor. There are two types of MEMS sensor that are piezoresistive and capacitive type. The excellence of piezoresistive type in characteristic of frequency response and noise is confirmed compare to capacitive type as a result of this paper's experiment and reference. 3-Axis system using MEMS sensor needs MEMS's proper frequency response characteristic. Additionally noise characteristic of sensor and circuit, stiffness of assembly are needed for deciding frequency range and accuracy of amplitude.

• Journal of information and communication convergence engineering
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• v.5 no.4
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• pp.300-304
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• 2007

Wireless sensor networks represent a new and exciting communication paradigm which could have multiple applications in future wireless communication. Therefore, performance analysis of such a wireless sensor network paradigm is needed in complex wireless channel. Wireless networks could be an important means of providing ubiquitous communication in the future. In this paper, the BER performance of uniform distributed wireless sensor networks is evaluated in non-Gaussian noise channel. Using an analytical approach, the impact of Av. BER performance relating the coherent BPSK system at the end of a multi-hop route versus the spatial density of sensor nodes and impulsive noise parameters A and $\Gamma$ is evaluated.

• International Journal of Control, Automation, and Systems
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• v.3 no.3
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• pp.469-476
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• 2005

Performance of a force-torque sensor is affected significantly by an error signal that is included in the sensor signal. The error sources may be classified mainly into two categories: one is a structural error due to inaccuracy of sensor body, and the other is a noise signal existing in sensed information. This paper presents a principle of 6-axis force-torque sensor briefly, a double-hole structure to be able to improve a structural error, and then a signal conditioning to reduce the effect of a noise signal. The validity of the proposed method is investigated through experimental study, which shows that SIN ratio is improved significantly in our experimental setup, and the sensor can be implemented cheaply with reasonable performance.

• Transactions of the Society of Information Storage Systems
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• v.5 no.2
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• pp.89-95
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• 2009

We setup the system to measure the noise characteristics of the 5M complementary metal-oxide semiconductor (CMOS) image sensor by generic measurement indicator of Standard mobile imaging architecture (SMIA) which is one of internal standard of mobile imaging architecture. To evaluate the effect of environment and setting parameters, such as temperature and integration time, we measure the variation of the dark signal, dynamic range and fixed pattern noise of image sensor. We also detect the number of defective pixels and cluster defects defined as adjacent single defect pixels at 5M CMOS image sensor. Then, we find the existence of some cluster defects in experiment, which are not expected in calculation.