• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.10a
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• pp.527-530
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• 2010

WSN is composed of a lot of small sensors with the limited hardware resources. In WSN, at the initial stage, sensor nodes are randomly deployed over the region of interest, and self-configure the clustered networks by grouping a bunch of sensor nodes and selecting a cluster header among them. Specially, in WSN environment, in which the administrator's intervention is restricted, the self-configuration capability is essential to establish a power-conservative WSN which provides broad sensing coverage and communication coverage. In this paper, we propose a communication coverage-aware cluster head election algorithm for Herearchical WSNs which consists of communication coverage-aware of the Base station is the cluster head node is elected and a clustering.

• Journal of Sensor Science and Technology
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• v.31 no.5
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• pp.301-306
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• 2022

With the recent widespread implementation of Internet of Things (IoT) technology driven by Industry 4.0, self-powered sensors for wearable and implantable systems are increasingly gaining attention. Piezoelectric nanogenerators (PENGs) and triboelectric nanogenerators (TENGs), which convert biomechanical energy into electrical energy, can be considered as efficient self-powered sensor platforms. These are energy harvesters that are used as low-power energy sources. However, they can also be used as sensors when an output signal is used to sense any mechanical stimuli. For sensors, collecting high-quality data is important. However, the accuracy of sensing for practical applications is equally important. This paper provides a brief review of the performance advanced by the materials and structures of the latest PENG/TENG-based wearable sensors and intelligent applications applied using artificial intelligence (AI)

• Journal of the Korea Concrete Institute
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• v.23 no.5
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• pp.603-608
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• 2011

In recent years, numerous mega-sized and complex civil infrastructures are being constructed all over the world. Therefore, more precise construction and maintenance technologies are required for these complicated construction projects. Especially, exact strength measurement and curing process monitoring of the concrete structures are very crucial to confirm the safety and effectiveness of these complicated structures. In this paper, a new Quick Response (QR) code-based concrete strength labeling technique using embedded self-sensing monitoring system is introduced. It is important to note that the QR code-based concrete labeling technique enables easy access of the databases related to the concrete strength at anytime, anywhere, and any smart PC devices. Finally, by integrating the proposed QR code-based concrete labeling with the concrete strength databases already prepared at a designated web-server, a feasibility of the current system is investigated for a next generation concrete life-cycle quality maintenance.

• Korean Journal of Remote Sensing
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• v.34 no.5
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• pp.739-747
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• 2018

Integration analysis of multi-sensor satellite images is becoming increasingly important. The first step in integration analysis is image registration between multi-sensor. SIFT (Scale Invariant Feature Transform) is a representative image registration method. However, optical image and SAR (Synthetic Aperture Radar) images are different from sensor attitude and radiation characteristics during acquisition, making it difficult to apply the conventional method, such as SIFT, because the radiometric characteristics between images are nonlinear. To overcome this limitation, we proposed a modified method that combines the SAR-SIFT method and shape descriptor vector DLSS(Dense Local Self-Similarity). We conducted an experiment using two pairs of Cosmo-SkyMed and KOMPSAT-2 images collected over Daejeon, Korea, an area with a high density of buildings. The proposed method extracted the correct matching points when compared to conventional methods, such as SIFT and SAR-SIFT. The method also gave quantitatively reasonable results for RMSE of 1.66m and 2.45m over the two pairs of images.

• Smart Structures and Systems
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• v.8 no.1
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• pp.17-37
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• 2011

The microcantilever (MCL) sensor is one of the most promising platforms for next-generation label-free biosensing applications. It outperforms conventional label-free detection methods in terms of portability and parallelization. In this paper, an overview of recent advances in our understanding of the coupling between biomolecular interactions and MCL responses is given. A dual compact optical MCL sensing platform was built to enable biosensing experiments both in gas-phase environments and in solutions. The thermal bimorph effect was found to be an effective nanomanipulator for the MCL platform calibration. The study of the alkanethiol self-assembly monolayer (SAM) chain length effect revealed that 1-octanethiol ($C_8H_{17}SH$) induced a larger deflection than that from 1-dodecanethiol ($C_{12}H_{25}SH$) in solutions. Using the clinically relevant biomarker C-reactive protein (CRP), we revealed that the analytical sensitivity of the MCL reached a diagnostic level of $1{\sim}500{\mu}g/ml$ within a 7% coefficient of variation. Using grazing incident x-ray diffractometer (GIXRD) analysis, we found that the gold surface was dominated by the (111) crystalline plane. Moreover, using X-ray photoelectron spectroscopy (XPS) analysis, we confirmed that the Au-S covalent bonds occurred in SAM adsorption whereas CRP molecular bindings occurred in protein analysis. First principles density functional theory (DFT) simulations were also used to examine biomolecular adsorption mechanisms. Multiscale modeling was then developed to connect the interactions at the molecular level with the MCL mechanical response. The alkanethiol SAM chain length effect in air was successfully predicted using the multiscale scheme.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.2
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• pp.378-384
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• 2008

The sensor network for the health care application service has the man or movable object as the main sensing object. In order to support inter-node interaction by the movement of such sensing objects, the node's dynamic function modification, dynamic self-configuration and energy efficiency must be considered. In this paper, the Agilla model which supports the dynamic function modification through the agent migration between nodes and LEACH protocol which guarantees the dynamic self-configuration and energy efficiency through the configuration of inter-node hierarchical cluster configuration are analyzed. Based on the results of the analysis, the Mobility Agent Middleware which supports the dynamic function modification between nodes is designed, and LEACH_Mobile protocol which guarantees the node nobility as the weakness of the existing LEACH protocol is suggested. Also, the routing module which supports the LEACH_Mobile protocol is designed and the interface for conjunction with Mobility Agent Middleware is designed. Then, it is definitely increase performance which un mobility node of transfer data rate through LEACH_Mobile protocol of simulation result.

• Korean Journal of Remote Sensing
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• v.37 no.1
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• pp.85-95
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• 2021

Recently, as data mining and artificial neural network techniques are developed, analyzing large amounts of data is proposed to reduce the dimension of the data. In general, empirical orthogonal function (EOF) used to reduce the dimension in the ocean data and recently, Self-organizing maps (SOM) algorithm have been investigated to apply to the ocean field. In this study, both algorithms used the monthly Sea level anomaly (SLA) data from 1993 to 2018 around the East Asia Coasts. There was dominated by the influence of the Kuroshio Extension and eddy kinetic energy. It was able to find the maximum amount of variance of EOF modes. SOM algorithm summarized the characteristic of spatial distributions and periods in EOF mode 1 and 2. It was useful to find the change of SLA variable through the movement of nodes. Node 1 and 5 appeared in the early 2000s and the early 2010s when the sea level was high. On the other hand, node 2 and 6 appeared in the late 1990s and the late 2000s, when the sea level was relatively low. Therefore, it is considered that the application of the SOM algorithm around the East Asia Coasts is well distinguished. In addition, SOM results processed by SLA data, it is able to apply the other climate data to explain more clearly SLA variation mechanisms.

• Journal of Adhesion and Interface
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• v.23 no.3
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• pp.59-69
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• 2022

In composite materials, the adhesion and interfacial properties were the most important factors to obtain high performance of mechanical properties. This review paper had been focused on the micromechanical evaluation methods for the interfacial property historically. The interfacial property of fiber-reinforced composites (FRC) could be evaluated using only a single fiber and matrix via various micromechanical testing methods. Self-sensing due to the fracture behavior of FRC could be determined and discussed more critically and clearly using electro-micromechanical evaluation. In this paper, the research trends for micro-mechanical evaluation of composites was summarized, and their practical applications would be suggested in the future.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.65-65
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• 2002

In remote sensing, images are acquired over the same area by sensors of different spectral ranges (from the visible to the microwave) and/or with different number, position, and width of spectral bands. These images are generally partially redundant, as they represent the same scene, and partially complementary. For many applications of image classification, the information provided by a single sensor is often incomplete or imprecise resulting in misclassification. Fusion with redundant data can draw more consistent inferences for the interpretation of the scene, and can then improve classification accuracy. The common approach to the classification of multisensor data as a data fusion scheme at pixel level is to concatenate the data into one vector as if they were measurements from a single sensor. The multiband data acquired by a single multispectral sensor or by two or more different sensors are not completely independent, and a certain degree of informative overlap may exist between the observation spaces of the different bands. This dependence may make the data less informative and should be properly modeled in the analysis so that its effect can be eliminated. For modeling and eliminating the effect of such dependence, this study employs a strategy using self and conditional information variation measures. The self information variation reflects the self certainty of the individual bands, while the conditional information variation reflects the degree of dependence of the different bands. One data set might be very less reliable than others in the analysis and even exacerbate the classification results. The unreliable data set should be excluded in the analysis. To account for this, the self information variation is utilized to measure the degrees of reliability. The team of positively dependent bands can gather more information jointly than the team of independent ones. But, when bands are negatively dependent, the combined analysis of these bands may give worse information. Using the conditional information variation measure, the multiband data are split into two or more subsets according the dependence between the bands. Each subsets are classified separately, and a data fusion scheme at decision level is applied to integrate the individual classification results. In this study. a two-level algorithm using hierarchical clustering procedure is used for unsupervised image classification. Hierarchical clustering algorithm is based on similarity measures between all pairs of candidates being considered for merging. In the first level, the image is partitioned as any number of regions which are sets of spatially contiguous pixels so that no union of adjacent regions is statistically uniform. The regions resulted from the low level are clustered into a parsimonious number of groups according to their statistical characteristics. The algorithm has been applied to satellite multispectral data and airbone SAR data.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.19 no.6
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• pp.1182-1189
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• 1994

In this paper, we evaluate the performance of a fuzzy controller with a self-learning structure. The fuzzy controller is based on a fuzzy logic that approximates and effectively represents the uncertain phenomena of the real world. The fuzzy controller has control of a plant with a fuzzy inference logic. However, it is not easy to decide the membership function of a fuzzy controller and its controlrule. This problem can be solved by designing a self-learning controller that improves its own contropllaw to its goal with a performance table. The fuzzy controller is implemented with a 386PC, an interface board, a D/A converter, a PWM(Pulse Width Modulation) motor drive-circuit, and a sensing circuit, for error and differential of error. Since a Ball and Beam System is used in the experiment, the validity of the fuzzy controller with the self-learning structure can be evaluated through the actual experiment and the computer simulation of the real plant. The self-learning fuzzy controller reduces settling time by just under 10%.