• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2379-2384
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• 2005

We propose a novel method for object recognition using the smart tag system with a stereo vision on a pan-tilt mechanism. We developed a smart tag which included IRED device. The smart tag is attached onto the object. We also developed a stereo vision system which pans and tilts for the object image to be the centered on each whole image view. A Stereo vision system on the pan-tilt mechanism can map the position of IRED to the robot coordinate system by using pan-tilt angles. And then, to map the size and pose of the object for the robot to coordinate the system, we used a simple model-based vision algorithm. To increase the possibility of tag-based object recognition, we implemented our approach by using as easy and simple techniques as possible.

• Korean Journal of Remote Sensing
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• v.19 no.3
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• pp.191-200
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• 2003

Remote sensing division of satellite technology research center (SaTReC) , Korea advanced institute of science and technology (KAIST) has developed a ground receiving and processing system for high resolution satellite images. The developed system will be adapted and operated to receive, process and distributes images acquired from of the second Korean Multi-purpose Satellite (KOMPSAT-2), which will be launched in 2004. This project had initiated to develop and Koreanize the state-of-the-art technologies for the ground receiving system for high resolution remote sensing images, which range from direct ingestion of image data to the distribution of products through precise image correction. During four years development from Dec. 1998 until Aug. 2002, the system had been verified in various ways including real operation of custom-made systems such as a prototype system for SPOT and a commercialized system for KOMPSAT-1. Currently the system is under customization for installation at KOMPSAT-2 ground station. In this paper, we present accomplished work and future work.

• Korean Journal of Materials Research
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• v.18 no.12
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• pp.655-659
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• 2008

The NO gas sensing properties of ZnO-carbon nanotube (ZnO-CNT) composites fabricated by the coaxial coating of single-walled CNTs with ZnO were investigated using pulsed laser deposition. Upon examination, the morphology and crystallinity of the ZnO-CNT composites showed that CNTs were uniformly coated with polycrystalline ZnO with a grain size as small as 5-10 nm. Gas sensing measurements clearly indicated a remarkable enhancement of the sensitivity of ZnO-CNT composites for NO gas compared to that of ZnO films while maintaining the strong sensing stability of the composites, properties that CNT-based sensing materials do not have. The enhanced gas sensing properties of the ZnO-CNT composites are attributed to an increase in the surface adsorption area of the ZnO layer via the coating by CNTs of a high surface-to-volume ratio structure. These results suggest that the ZnO-CNT composite is a promising template for novel solid-state semiconducting gas sensors.

• Journal of Sensor Science and Technology
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• v.32 no.6
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• pp.340-351
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• 2023

Wearable sensors designed for strain, pressure, and temperature measurements are essential for monitoring human movements, health status, physiological data, and responses to external stimuli. Notably, recent research has led to the development of high-performance wearable sensors using innovative materials and device structures that exhibit ultra-high sensitivity compared with their commercial counterparts. However, the quest for accurate sensing has identified a critical challenge. Specifically, the mechanical flexibility of the substrates in wearable sensors can introduce interference signals, particularly when subjected to varying external stimuli and environmental conditions, potentially resulting in signal crosstalk and compromised data fidelity. Consequently, the pursuit of non-interference sensing technology is pivotal for enabling independent measurements of concurrent input signals related to strain, pressure, and temperature, ensuring precise signal acquisition. In this comprehensive review, we present an overview of the recent advances in noninterference sensing strategies. We explore various fabrication methods for sensing strain, pressure, and temperature, emphasizing the use of hybrid composite materials with distinct mechanical properties. This review contributes to the understanding of critical developments in wearable sensor technology that are vital for their ongoing application and evolution in numerous fields.

• Journal of Industrial Technology
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• v.2
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• pp.77-81
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• 1982

• Proceedings of the KSRS Conference
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• 1999.11a
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• pp.165-170
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• 1999

Previous technology of remote sensing was focused on analyzing raster image and gaining information through image processing. But now it has extended to diverse fields like automatic map generation, material exploitation or monitoring environmental changes with effort to utilizing practical usage. And with rapid expansion of information exchange on Internet and high-speed network, the demand of public which want to utilize remotely sensed image has been increased. This makes growth of service on acquisition and processing remotely sensed image. GeoNet is a Java-based remotely sensed image processing system. It is based on Java object-oriented paradigm and features cross-platform, web-based execution and extensibility to client/server remotely sensed image processing model. Remotely sensed image processing software made by Java programming language can suggest alternatives to meet readily demand on remotely sensed image processing in proportion to increase of remotely sensed data. In this paper, we introduce GeoNet and explain its architecture.

• Journal of information and communication convergence engineering
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• v.13 no.4
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• pp.241-247
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• 2015

Cognitive radio has been attracting increased attention as an effective approach to improving spectrum efficiency. One component of cognitive radio, spectrum sensing, has an important relationship with the performance of cognitive radio. In this paper, after a summary and analysis of the existing spectrum-sensing algorithms, we report that the existing eigenvalue-based semi-blind detection algorithm and blind detection algorithm have not made full use of the eigenvalues of the received signals. Applying multi-antenna systems to cognitive users, we design a variety of spectrum-sensing algorithms based on the joint distribution of the eigenvalues of the received signal. Simulation results validate that the proposed algorithms in this paper are able to detect whether the signal of the primary user exists or not with high probability of detection in an environment with a low signal-to-noise ratio. Compared with traditional algorithms, the new algorithms have the advantages of high detection performance and strong robustness

• Journal of Communications and Networks
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• v.17 no.4
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• pp.394-405
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• 2015

This paper considers a wideband cognitive radio network which can simultaneously sense multiple narrowband channels and thus aggregate the detected available channels for transmission and proposes a novel cognitive radio system that exhibits improved sensing throughput and can save power consumption of secondary user (SU) compared to the conventional cognitive radio system studied so far. More specifically, under the proposed cognitive radio system, we study the problem of designing the optimal sensing time and power allocation strategy, in order to maximize the ergodic throughput of the proposed cognitive radio system under two different schemes, namely the wideband sensing-based spectrum sharing scheme and the wideband opportunistic spectrum access scheme. In our analysis, besides the average interference power constraint at primary user, the average transmit power constraint of SU is also considered for the two schemes and then a subgradient algorithm is developed to obtain the optimal sensing time and the corresponding power allocation strategy. Finally, numerical simulations are presented to verify the performance of the two proposed schemes.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.9
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• pp.3432-3448
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• 2015

Interference can cause serious problems in our daily life. Traditional ways in localizing a target can't work well when it comes to the source of interference for it may take an uncooperative or even resistant attitude towards localization. To tackle this issue, we take the BPSN (Binary Proximity Sensor Networks) and consider a passive way in this paper. No cooperation is needed and it is based on simple sensor node suitable for large-scale deployment. By dividing the sensing field into different patches, when enough patches are formed, good localization accuracy can be achieved with high resolution. Then we analyze the relationship between sensing radius and localization error, we find that in a finite region where edge effect can't be ignored, the trend between sensing radius and localization error is not always consistent. Through theoretical analysis and simulation, we explore to determine the best sensing radius to achieve high localization accuracy.

• Journal of Communications and Networks
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• v.14 no.5
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• pp.471-480
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• 2012

Reliable detection of primary user activity increases the opportunity to access temporarily unused bands and prevents harmful interference to the primary system. By extracting a global decision from local sensing results, cooperative sensing achieves high reliability against multipath fading. For the effective combining of sensing results, which is generalized by a likelihood ratio test, the fusion center should learn some parameters, such as the probabilities of primary transmission, false alarm, and detection at the local sensors. During the training period in supervised learning, the on/off log of primary transmission serves as the output label of decision statistics from the local sensor. In this paper, we extend unsupervised learning techniques with an expectation maximization algorithm for cooperative spectrum sensing, which does not require an external primary transmission log. Local sensors report binary hard decisions to the fusion center and adjust their operating points to enhance learning performance. Increasing the number of sensors, the joint-expectation step makes a confident classification on the primary transmission as in the supervised learning. Thereby, the proposed scheme provides accurate parameter estimates and a fast convergence rate even in low signal-to-noise ratio regimes, where the primary signal is dominated by the noise at the local sensors.