• Journal of Biosystems Engineering
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• v.18 no.4
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• pp.358-370
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• 1993

In Korea, fruit grading has been mainly done manually, and manual grading depends on human sense. Thus it is subjected to human error and is not always as consistent as would be desired. Therefore, a study on the development of fruit grader was initiated to improve the consistency of fruit grading. The sensitivity for fruit weight of the conventional spring type weight grader has a tendency to decrease by physical characteristics of spring which is used as a weight sensing unit. This study was carried out to develop weight measuring device for establishing the base of weight sensing unit of electronic weight grader. This device consists of a weight sensor using load cell, data acquisition system, and a microcomputer containing program to calculate fruit weight. The weight measuring device using load cell was developed to increase sensitivity of fruit weight. The result of this study showed that the weight sensing unit of electronic weight grader contributed to the improvement of performance of weight measuring device.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.11
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• pp.5654-5673
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• 2016

Participatory sensing applications rely on recruiting appropriate participants to share their surrounding conditions with others, and have been widely used in many areas like environmental monitoring, health care, and traffic congestion monitoring, etc. In such applications, how to ensure the privacy of a participant is important, since incentive mechanisms are used to maintain their enthusiasm for sustainable participation by offering certain amount of reward. In this paper, we propose a pseudonym-based privacy protection scheme, that takes both privacy protection and user incentives into consideration. The proposed scheme uses the pseudonym mechanism and one-way hash function to achieve user incentives, while protecting their identity. We also show extensive analysis of the proposed scheme to demonstrate that it can meet the security and performance the requirement of a participatory sensing application.

• Journal of Sensor Science and Technology
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• v.26 no.4
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• pp.228-234
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• 2017

Gas sensors based on metal-oxide-semiconductors are predominantly used in numerous applications including monitoring indoor air quality and detecting harmful substances such as volatile organic compounds. Nanostructures, e.g., nanoparticles, nanotubes, nanodomes, or nanofibers, have been widely utilized to improve the gas sensing properties of metal-oxide-semiconductors by increasing the effective surface area participating in the surface reaction with target gas molecules. Recently, 1-dimensional (1D) metal oxide nanostructures fabricated using glancing angle deposition (GAD) method with e-beam evaporation have been widely employed to increase the surface-to-volume ratio significantly with large-area uniformity and reproducibility, leading to promising gas sensing properties. Herein, we provide a brief overview of 1D metal oxide nanostructures fabricated using GAD and their gas sensing properties in terms of fabrication methods, morphologies, and additives. Moreover, the gas sensing mechanisms and perspectives are presented.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.11
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• pp.3731-3750
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• 2014

This paper considers a wideband cognitive radio network (WCRN) which can simultaneously sense multiple narrowband channels and thus aggregate the detected available channels for transmission and studies the ergodic throughput of the WCRN that operated under: the wideband sensing-based spectrum sharing (WSSS) scheme and the wideband opportunistic spectrum access (WOSA) scheme. In our analysis, besides the average interference power constraint at PU, the average transmit power constraint of SU is also considered for the two schemes and a novel cognitive radio sensing frame that allows data transmission and spectrum sensing at the same time is utilized, and then the maximization throughput problem is solved by developing a gradient projection method. Finally, numerical simulations are presented to verify the performance of the two proposed schemes.

• Ceramist
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• v.22 no.1
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• pp.96-106
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• 2019

Semiconducting carbon-based nanomaterials including single-walled carbon nanotubes(SWCNTs), multi-walled CNT(MWCNTs), graphene(GR), graphene oxide(GO), and reduced graphene oxide(RGO), are very promising sensing materials due to their large surface area, high conductivity, and ability to operate at room temperature. Despite of these advantages, the semiconducting carbon-based nanomaterials intrinsically possess crucial disadvantages compared with semiconducting metal oxide nanomaterials, such as relatively low gas response, irreversible recovery, and poor selectivity. Therefore, in this paper, we introduce a variety of strategies to overcome these disadvantages and investigate principle parameters to improve gas sensing performances.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.1
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• pp.108-122
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• 2014

Massive antenna array is an attractive candidate technique for future broadband wireless communications to acquire high spectrum and energy efficiency. However, such benefits can be realized only when proper channel information is available at the transmitter. Since the amount of the channel information required by the transmitter is large for massive antennas, the feedback is burdensome in practice, especially for frequency division duplex (FDD) systems, and needs normally to be reduced. In this paper a novel channel feedback reduction scheme based on the theory of distributed compressive sensing (DCS) is proposed to apply to massive antenna arrays with spatial correlation, which brings substantially reduced feedback load. Simulation results prove that the novel scheme is better than the channel feedback technique based on traditional compressive sensing (CS) in the aspects of mean square error (MSE), cumulative distributed function (CDF) performance and feedback resources saving.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.10
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• pp.4625-4641
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• 2016

The performances of two commonly used feature detectors for wireless energy-harvesting cognitive radio systems are compared with the energy detector under energy causality and collision constraints. The optimal sensing duration is obtained by analyzing the effect of the detection threshold on the average throughput and collision probability. Numerical examples show that the covariance detector has the optimal sensing duration depending on an appropriate choice of the detection threshold, but no optimal sensing duration exists for the ratio of average energy to minimum eigenvalue detector.

• Journal of Korea Multimedia Society
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• v.7 no.6
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• pp.767-780
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• 2004

Interaction requires dynamic relationship between objects. In ubiquitous computing environment, interaction between human and the environment is implied. Tactile interaction has so far been less addressed, while tactile sensation should be an important topic in the field of multimedia study. This paper describes development of a novel PVDF (Polyvinylidene Fluoride) dynamic tactile sensor and associated experiments. PVDF dynamic tactile sensors detect touch events applied to the sensor skin by low frequency components of the signal. Rubber skin-covered sensing material was mounted on the bones. Robust performance with low noise was figured out in our robotic experiment. Whereas most conventional sensors are interested in measurement, our dynamic tactile sensor is sensitive to change of state, which could be a key for economic understanding of happenings in the dynamic world. We note that dynamic sensing uses motion as a part of sensing modality We suggest that dynamic sensing be understood in technological terms in the perspective of interactive media and ubiquitous computing.

• Journal of Korea Multimedia Society
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• v.19 no.8
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• pp.1505-1515
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• 2016

The age of autonomous vehicles has come according to development of high performance sensing and artificial intelligence technologies. And importance of the vehicle's surrounding environment sensing and observation is increasing accordingly because of its stability and control efficiency. In this paper we propose an integrated platform for efficient networking, analysis and monitoring of multiple sensing data on the vehicle that are equiped with various automotive sensors such as GPS, weather radar, automotive radar, temperature and humidity sensors. From simulation results, we could see that the proposed platform could perform realtime analysis and monitoring of various sensing data that were observed from the vehicle sensors. And we expect that our system can support drivers or autonomous vehicles to recognize optimally various sudden or danger driving environments on the road.

• Journal of Sensor Science and Technology
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• v.30 no.2
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• pp.76-81
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• 2021

In this study, we propose a machine vision system with a high object recognition rate. By utilizing a multiple-exposure image sensing technique, the proposed deep learning-based machine vision system can cover a wide light intensity range without further learning processes on the various light intensity range. If the proposed machine vision system fails to recognize object features, the system operates in a multiple-exposure sensing mode and detects the target object that is blocked in the near dark or bright region. Furthermore, short- and long-exposure images from the multiple-exposure sensing mode are synthesized to obtain accurate object feature information. That results in the generation of a wide dynamic range of image information. Even with the object recognition resources for the deep learning process with a light intensity range of only 23 dB, the prototype machine vision system with the multiple-exposure imaging method demonstrated an object recognition performance with a light intensity range of up to 96 dB.