• Nuclear Engineering and Technology
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• v.48 no.1
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• pp.98-113
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• 2016

A reactor vessel level monitoring system measures the water level in a reactor during normal operation and abnormal conditions. A drop in the water level can expose nuclear fuel, which may lead to fuel meltdown and radiation spread in accident conditions. A level monitoring system mainly consists of a sensing line and pressure transmitter. Over a period of time boron sediments or other impurities can clog the line which may degrade the accuracy of the monitoring system. The aim of this study is to determine blockage in a sensing line using the energy of the composite signal. An equivalent Pi circuit model is used to simulate blockages in the sensing line and the system's response is examined under different blockage levels. Composite signals obtained from the model and plant's unblocked and blocked channels are decomposed into six levels of details and approximations using a wavelet filter bank. The percentage of energy is calculated at each level for approximations. It is observed that the percentage of energy reduces as the blockage level in the sensing line increases. The results of the model and operational data are well correlated. Thus, in our opinion variation in the energy levels of approximations can be used as an index to determine the presence and degree of blockage in a sensing line.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.4
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• pp.1208-1222
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• 2014

Due to the low utilization and scarcity of frequency spectrum in current spectrum allocation methodology, cognitive radio networks (CRNs) have been proposed as a promising method to solve the problem, of which spectrum sensing is an important technology to utilize the precious spectrum resources. In order to protect the primary user from being interfered, most of the related works focus only on the restriction of the missed detection probability, which may causes over-protection of the primary user. Thus the interference probability is defined and the interference-aware sensing model is introduced in this paper. The interference-aware sensing model takes the spatial conditions into consideration, and can further improve the network performance with good spectrum reuse opportunity. Meanwhile, as so many fading factors affect the spectrum channel, errors are inevitably exist in the reporting channel in cooperative sensing, which is improper to be ignored. Motivated by the above, in this paper, we study the throughput tradeoff for interference-aware cognitive radio networks over imperfect reporting channel. For the cooperative spectrum sensing, the K-out-of-N fusion rule is used. By jointly optimizing the sensing time and the parameter K value, the maximum throughput can be achieved. Theoretical analysis is given to prove the feasibility of the optimization and computer simulations also shows that the maximum throughput can be achieved when the sensing time and the parameter of K value are both optimized.

• Journal of Computing Science and Engineering
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• v.8 no.1
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• pp.17-24
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• 2014

In this paper, we propose a new implementation of a failure detector. The implementation uses a dual model of heartbeat and interaction. First, the heartbeat model is adopted to shorten the detection time, if the detection process does not receive the heartbeat message in the expected time. The interaction model is then used to check the process further. The expected time is calculated using the exponential smoothing method. Exponential smoothing can be used to estimate the next arrival time not only in the random data, but also in the data of linear trends. It is proven that the new detector in the paper can eventually be a perfect detector.

• Journal of Information Processing Systems
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• v.15 no.3
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• pp.604-615
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• 2019

Single-user spectrum sensing is susceptible to multipath effects, shadow effects, hidden terminals and other unfavorable factors, leading to misjudgment of perceived results. In order to increase the detection accuracy and reduce spectrum sensing cost, we propose an adaptive cooperative sensing strategy based on an estimated signal-to-noise ratio (SNR). Which can adaptive select different sensing strategy during the local sensing phase. When the estimated SNR is higher than the selection threshold, adaptive double threshold energy detector (ED) is implemented, otherwise cyclostationary feature detector is performed. Due to the fact that only a better sensing strategy is implemented in a period, the detection accuracy is improved under the condition of low SNR with low complexity. The local sensing node transmits the perceived results through the control channel to the fusion center (FC), and uses voting rule to make the hard decision. Thus the transmission bandwidth is effectively saved. Simulation results show that the proposed scheme can effectively improve the system detection probability, shorten the average sensing time, and has better robustness without largely increasing the costs of sensing system.

• ETRI Journal
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• v.37 no.3
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• pp.491-501
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• 2015

Spectrum sensing is an elementary function in cognitive radio designed to monitor the existence of a primary user (PU). To achieve a high rate of detection, most techniques rely on knowledge of prior spectrum patterns, with a trade-off between high computational complexity and long sensing time. On the other hand, blind techniques ignore pattern matching processes to reduce processing time, but their accuracy degrades greatly at low signal-to-noise ratios. To achieve both a high rate of detection and short sensing time, we propose fast spectrum sensing with coordinate system (FSC) - a novel technique that decomposes a spectrum with high complexity into a new coordinate system of salient features and that uses these features in its PU detection process. Not only is the space of a buffer that is used to store information about a PU reduced, but also the sensing process is fast. The performance of FSC is evaluated according to its accuracy and sensing time against six other well-known conventional techniques through a wireless microphone signal based on the IEEE 802.22 standard. FSC gives the best performance overall.

• Molecules and Cells
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• v.46 no.7
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• pp.451-460
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• 2023

Animals generally prefer nutrients and avoid toxic and harmful chemicals. Recent behavioral and physiological studies have identified that sweet-sensing gustatory receptor neurons (GRNs) in Drosophila melanogaster mediate appetitive behaviors toward fatty acids. Sweet-sensing GRN activation requires the function of the ionotropic receptors IR25a, IR56d, and IR76b, as well as the gustatory receptor GR64e. However, we reveal that hexanoic acid (HA) is toxic rather than nutritious to D. melanogaster. HA is one of the major components of the fruit Morinda citrifolia (noni). Thus, we analyzed the gustatory responses to one of major noni fatty acids, HA, via electrophysiology and proboscis extension response (PER) assay. Electrophysiological tests show this is reminiscent of arginine-mediated neuronal responses. Here, we determined that a low concentration of HA induced attraction, which was mediated by sweet-sensing GRNs, and a high concentration of HA induced aversion, which was mediated by bitter-sensing GRNs. We also demonstrated that a low concentration of HA elicits attraction mainly mediated by GR64d and IR56d expressed by sweet-sensing GRNs, but a high concentration of HA activates three gustatory receptors (GR32a, GR33a, and GR66a) expressed by bitter-sensing GRNs. The mechanism of sensing HA is biphasic in a dose dependent manner. Furthermore, HA inhibit sugar-mediated activation like other bitter compounds. Taken together, we discovered a binary HA-sensing mechanism that may be evolutionarily meaningful in the foraging niche of insects.

• Geophysics and Geophysical Exploration
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• v.14 no.4
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• pp.335-341
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• 2011

Most sensing and instrumentation systems should have very higher sampling rate than required data rate not to miss important information. This means that the system can be inefficient in some cases. This paper introduces two new research areas about information acquisition with high accuracy from less number of sampled data. One is Compressed Sensing technology (which obtains original information with as little samples as possible) and the other is Super-Resolution technology (which gains very high-resolution information from restrictively sampled data). This paper explains fundamental theories and reconstruction algorithms of compressed sensing technology and describes several applications to geophysical exploration. In addition, this paper explains the fundamentals of super-resolution technology and introduces recent research results and its applications, e.g. FRI (Finite Rate of Innovation) and LIMS (Least-squares based Iterative Multipath Super-resolution). In conclusion, this paper discusses how these technologies can be used in geophysical exploration systems.

• Journal of Sensor Science and Technology
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• v.31 no.3
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• pp.139-144
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• 2022

Advanced tactile sensors are receiving significant attention in various industries such as extended reality, electronic skin, organic user interfaces, and robotics. The capabilities of advanced tactile sensors require a variety of functions, including position sensing, pressure sensing, and material recognition. Moreover, they should comsume less power and be bio-friendly with human contact. Recently, a tactile sensor based on the triboelectrification effect was developed. Triboelectric tactile sensors have the advantages of wide material availability, simple structure, and low manufacturing cost. Because they generate electricity by contact, they have low power consumption compared to conventional tactile sensors such as capacitive and piezoresistive. Furthermore, they have the ability to recognize the contact material as well as execute position and pressure sensing functions using the triboelectrification effect. The aim of this study is to introduce the progress of research on triboelectrification-based tactile sensors with various functions such as position sensing, pressure sensing and contact material recognition.

• Proceedings of the KSRS Conference
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• v.1
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• pp.38-38
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• 2006

• Journal of Korea Multimedia Society
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• v.24 no.3
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• pp.355-372
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• 2021

Recently, high-performance HMDs (Head-Mounted Display) are becoming wireless due to the growth of virtual reality technology. Accordingly, environmental constraints on the hardware usage are reduced, enabling multiple users to experience virtual reality within a single space simultaneously. Existing multi-user virtual reality platforms use the user's location tracking and motion sensing technology based on vision sensors and active markers. However, there is a decrease in immersion due to the problem of overlapping markers or frequent matching errors due to the reflected light. Goal of this study is to develop a multi-user virtual reality moving platform in a single space that can resolve sensing errors and user immersion decrease. In order to achieve this goal hybrid sensing technology was developed, which is the convergence of vision sensor technology for position tracking, IMU (Inertial Measurement Unit) sensor motion capture technology and gesture recognition technology based on smart gloves. In addition, integrated safety operation system was developed which does not decrease the immersion but ensures the safety of the users and supports multimodal feedback. A 6 m×6 m×2.4 m test bed was configured to verify the effectiveness of the multi-user virtual reality moving platform for four users.