• 한국ITS학회:학술대회논문집
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• 2010.05a
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• pp.33-36
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• 2010

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.8
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• pp.849-853
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• 2014

In micro-scale electromechanical systems, the power to perform accurate position sensing often greatly exceeds the power needed to generate motion. This paper explores the implications of sampling rate and amplifier noise density selection on the performance of a system identification algorithm using a capacitive sensing circuit. Specific performance objectives are to minimize or limit convergence rate and power consumption to identify the dynamics of a rotary micro-stage. A rearrangement of the conventional recursive least-squares identification algorithm is performed to make operating cost an explicit function of sensor design parameters. It is observed that there is a strong dependence of convergence rate and error on the sampling rate, while energy dependence is driven by error that may be tolerated in the final identified parameters.

• The KSFM Journal of Fluid Machinery
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• v.3 no.4 s.9
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• pp.15-21
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• 2000

An anemometer employing the bulk PTC thermistor as the sensing element is investigated in this study. The numerical and experimental works are carried out to improve the sensitivity problem of the element by focusing fluid dynamics point of view. The typical shape of the sensing element has been used as a rectangular type, but this shape has a sensitivity problem because of flow separations on the sharp edge when the flow direction is different from that of the sensing element. In order to reduce the reading error, the installer has to be very careful about the flow direction. The reading error fluctuation by time as well as the sensitivity problem can be improved considerably through this study. It can be concluded that the small change of the sensor shape can improve the performance of the flow sensor.

• Spatial Information Research
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• v.2 no.2
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• pp.175-188
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• 1994

A competitive learning network was proposed as unsupervised training method of remote sensing data, Its performance and computational re¬quirements were compared with conventional clustering techniques such as Se¬quential and K - Means. An airborne remote sensing data set was used to study the performance of these classifiers. The proposed algorithm required a little more computational time than the conventional techniques. However, the perform¬ance of competitive learning network algorithm was found to be slightly more than those of Sequential and K - Means clustering techniques.

• Journal of Communications and Networks
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• v.14 no.3
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• pp.225-229
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• 2012

We examine spectrum sensing in a situation of uncertain channel model. In particular, we assume that, besides additive noise, the observed signal contains an interference term whose probability distribution is unknown, and only its range and maximum power are known. We discuss the evaluation of the detector performance and its design in this situation. Although this paper specifically deals with the design of spectrum sensors, its scope is wider, as the applicability of its results extends to a general class of problems that may arise in the design of receivers whenever there is uncertainty about how to model the environment in which one is expected to operate. The theory expounded here allows one to determine the performance of a receiver, by combining the available (objective) probabilistic information with (subjective) information describing the designer's attitude.

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.89-90
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• 2006

In this paper, the performances of the IEEE 802.15.4 under the IEEE 802.11b interference are compared under two kinds of channel sensing mechanisms: carrier sense (CS) and energy detection (ED). For each channel sensing mechanism, the average transmission delay, and the throughput are used as performance measures.

• Journal of the Optical Society of Korea
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• v.17 no.5
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• pp.405-409
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• 2013

In this paper, a unifying framework to evaluate the depth resolution of axially distributed image sensing (ADS) systems under fixed resource constraints is proposed. The proposed framework enables one to evaluate the system performance as a function of system parameters such as the number of cameras, the number of pixels, pixel size, and so on. The Monte Carlo simulations are carried out to evaluate ADS system performance as a function of system parameters. To the best of our knowledge, this is the first report on quantitative analysis of ADS systems under fixed resource constraints.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.645-647
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• 2003

In this work a new method for on-line scene segmentation is developed. In remote sensing a scene is represented by the pixel-oriented features. It is possible to reduce data redundancy by an unsupervised segment-feature extraction process, where the segment-features, rather than the pixelfeatures, are used for multispectral scene representation. The algorithm partitions the observation space into exhaustive set of disjoint segments. Then, pixels belonging to each segment are characterized by segment features. Illustrative examples are presented, and the performance of features is investigated. Results show an average compression more than 25, the classification performance is improved for all classes, and the CPU time required for classification is reduced by the same factor.

• Journal of IKEEE
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• v.23 no.3
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• pp.1033-1037
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• 2019

GaN-based sensors have been widely investigated thanks to its potential in detecting the presence of hydrogen. In this study, we fabricated hydrogen gas sensors with AlGaN/GaN heterojunction and investigated how the sensing performance to be affected by SiN surface passivation. The gas sensor employed a high electron mobility transistors (HEMTs) with 30 nm platinum catalyst as a gate to detect the hydrogen presence. SiN layer was deposited by inductively-coupled chemical vapor deposition as post-passivation. The sensors with SiN passivation exhibited hydrogen sensing characteristics with various gas flow rates and concentrations of hydrogen in inert background gas at $200^{\circ}C$ similar to the ones without passivation. Aside from quick response time for both sensors, there are differences in sensitivity and recovery time because of the existence of the passivation layer. The results also confirmed the dependence of sensing performance on gas flow rate and gas concentration.

• Journal of Communications and Networks
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• v.11 no.2
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• pp.122-133
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• 2009

As wireless spectrum resources become more scarce while some portions of frequency bands suffer from low utilization, the design of cognitive radio (CR) has recently been urged, which allows opportunistic usage of licensed bands for secondary users without interference with primary users. Spectrum sensing is fundamental for a secondary user to find a specific available spectrum hole. Cooperative spectrum sensing is more accurate and more widely used since it obtains helpful reports from nodes in different locations. However, if some nodes are compromised and report false sensing data to the fusion center on purpose, the accuracy of decisions made by the fusion center can be heavily impaired. Weighted sequential probability ratio test (WSPRT), based on a credit evaluation system to restrict damage caused by malicious nodes, was proposed to address such a spectrum sensing data falsification (SSDF) attack at the price of introducing four times more sampling numbers. In this paper, we propose two new schemes, named enhanced weighted sequential probability ratio test (EWSPRT) and enhanced weighted sequential zero/one test (EWSZOT), which are robust against SSDF attack. By incorporating a new weight module and a new test module, both schemes have much less sampling numbers than WSPRT. Simulation results show that when holding comparable error rates, the numbers of EWSPRT and EWSZOT are 40% and 75% lower than WSPRT, respectively. We also provide theoretical analysis models to support the performance improvement estimates of the new schemes.