• KIPS Transactions on Computer and Communication Systems
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• v.2 no.1
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• pp.1-6
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• 2013

Variety of smart devices including smart phone have become and essential item in user's daily life. This means that smart devices are good mediators to get collecting user's behavior by sensors mounted on the devices. The information from smart devices is important clues to identify by analyzing the user's preferences and needs. Through this, the intelligent service which is fitted to the user is possible. This paper propose a smart service recommendation model based on user scenario using fusion context-awareness. The information for recommendation services is collected to make the scenario depending on time, location, action based on the Fusion process. The scenarios can help predict a user's situation and provide the services in advance. Also, content categories as well as the content types are determined depending on the scenario. The scenario is a method for providing the best service as well as a basis for the user's situation. Using this method, proposing a smart service model with the fusion context-awareness based on the hybrid sensing is the goal of this paper.

• Korean Journal of Remote Sensing
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• v.37 no.4
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• pp.719-731
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• 2021

This study proposes a two-stage hybrid classification model for crop classification using multi-temporal remote sensing images; the model combines feature embedding by using an autoencoder (AE) with a convolutional neural network (CNN) classifier to fully utilize features including informative temporal and spatial signatures. Long short-term memory (LSTM)-based AE (LAE) is fine-tuned using class label information to extract latent features that contain less noise and useful temporal signatures. The CNN classifier is then applied to effectively account for the spatial characteristics of the extracted latent features. A crop classification experiment with multi-temporal unmanned aerial vehicle images is conducted to illustrate the potential application of the proposed hybrid model. The classification performance of the proposed model is compared with various combinations of conventional deep learning models (CNN, LSTM, and convolutional LSTM) and different inputs (original multi-temporal images and features from stacked AE). From the crop classification experiment, the best classification accuracy was achieved by the proposed model that utilized the latent features by fine-tuned LAE as input for the CNN classifier. The latent features that contain useful temporal signatures and are less noisy could increase the class separability between crops with similar spectral signatures, thereby leading to superior classification accuracy. The experimental results demonstrate the importance of effective feature extraction and the potential of the proposed classification model for crop classification using multi-temporal remote sensing images.

• Korean Journal of Remote Sensing
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• v.18 no.2
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• pp.107-116
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• 2002

A hybrid image segmentation algorithm is proposed which integrates edge-based and region-based techniques through the watershed algorithm. First, by using mean curvature diffusion coupled to min/max flow, noise is eliminated and thin edges are preserved. After images are segmented by watershed algorithm, the segmented regions are combined with neighbor regions. Region adjacency graph (RAG) is employed to analyze the relationship among the segmented regions. The graph nodes and edge costs in RAG correspond to segmented regions and dissimilarities between two adjacent regions respectively. After the most similar pair of regions is determined by searching minimum cost RAG edge, regions are merged and the RAG is updated. The proposed method efficiently reduces noise and provides one-pixel wide, closed contours.

• Journal of the Society of Naval Architects of Korea
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• v.57 no.6
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• pp.373-380
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• 2020

We have developed the hybrid prototype load cell structures. These developed load cell structures may increase the reliability of the load sensing by deriving the load values through the double sensing method through the vertical maximum deflection and bending stress of the simple beams. For this purpose, the structure design was performed so that the load value, the deflection and stress value could be output to the same value through the optimal structure design. The structurally designed dimensions reaffirmed the accuracy of the design through the structural analysis program and the matching of the load value and the deflection value. Based on the designed structural dimension, the prototype form was constructed through laser cutting and production using hot rolled steel materials. The developed prototype load cell structure can be used as good educational material in various subjects such as material mechanics, steel structure design, measurement engineering, and mechatronics engineering. It is also believed that the measurement system ideas can inform the occurrence of errors in the event of a problem, and if a major accident caused by a sensing error is predicted, it will show good utilization to prevent accidents.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.27 no.5
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• pp.454-462
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• 2016

portable white space device(WSD) obeying the Korean regulations of TV white space(TVWS) can cause harmful interference to a digital TV receiver residing at the same pixel around the edge of the digital TV service coverage for the case with a changed propagation environment. In order to solve this problem, we propose a method to allocate the resource of a hybrid WSD based on TVWS geo-location DB with spectrum sensing. Using the received power of digital TV signal through the spectrum sensing, a hybrid WSD can calculate the maximum permitted EIRP based on location probability. Based on the accurate allocation method proposed in this paper, it is possible to satisfy the Korean TVWS regulations and to eliminate the harmful interference to TV receivers nearby the hybrid WSD.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38C no.2
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• pp.155-164
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• 2013

Two design techniques for more accurate and more convenient hybrid positioning system with visible light communication (VLC) and ad-hoc wireless network infrastructure are proposed, in order to overcome the problems of high estimation error, high cost, and limited service range of the conventional positioning techniques. First method is based on a non-carrier VLC based hybrid positioning technique for applications involving of low data rate optical sensing and narrow-range visible light reception from transmitter, and long-range positioning. The second method uses a 4 MHz carrier VLC-based hybrid positioning technique for a high data rate optical sensing and wide-range visible light receiving from transmitter, and mid-range positioning applications. In indoor environments with obstacles where there are long-range 7731.4cm and mid-range 2368cm distances between an observer and a target respectively, the hybrid positioning developed with two design techniques are tested, and the proposed system is verified and analyzed in this paper.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.4 no.6
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• pp.1023-1041
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• 2010

In cognitive radio networks, cooperative spectrum sensing schemes are proposed to improve the performance of detecting licensees by secondary users. Commonly, the cooperative sensing can be realized by means of hard decision fusion (HDF) or soft decision fusion (SDF) schemes. The SDF schemes are superior to the HDF ones in terms of the detection performance whereas the HDF schemes are outperforming the SDF ones when the traffic overhead is taken into account. In this paper, a hybrid SFD-HDF cluster-based approach is developed to jointly exploit the advantages of SFD and HDF schemes. Different SDF schemes have been proposed and compared within a given cluster whereas the OR-rule base HDF scheme is applied to combine the decisions reported by cluster headers to a common receiver or base station. The computer simulations show promising results as the performance of the proposed scenario of hybridizing soft and hard fusion schemes is significantly outperforming other different combinations of conventional SDF and HDF schemes while it noticeably reduces the network traffic overhead.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.12
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• pp.3364-3382
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• 2023

Remote sensing image segmentation plays an important role in realizing intelligent city construction. The current mainstream segmentation networks effectively improve the segmentation effect of remote sensing images by deeply mining the rich texture and semantic features of images. But there are still some problems such as rough results of small target region segmentation and poor edge contour segmentation. To overcome these three challenges, we propose an improved semantic segmentation model, referred to as MRU-Net, which adopts the U-Net architecture as its backbone. Firstly, the convolutional layer is replaced by BasicBlock structure in U-Net network to extract features, then the activation function is replaced to reduce the computational load of model in the network. Secondly, a hybrid multi-scale recognition module is added in the encoder to improve the accuracy of image segmentation of small targets and edge parts. Finally, test on Massachusetts Buildings Dataset and WHU Dataset the experimental results show that compared with the original network the ACC, mIoU and F1 value are improved, and the imposed network shows good robustness and portability in different datasets.

• Proceedings of the Korean Vacuum Society Conference
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• 2014.02a
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• pp.434.2-434.2
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• 2014

We observe enhanced pH response of solution-gated field-effect transistors (SG-FET) having 1D-2D hybrid channel of vertical grown ZnO nanorods grown on CVD graphene (Gr). In recent years, SG-FET based on Gr has received a lot of attention for biochemical sensing applications, because Gr has outstanding properties such as high sensitivity, low detection limit, label-free electrical detection, and so on. However, low-defect CVD Gr has hardly pH responsive due to lack of hydroxyl group on Gr surface. On the other hand, ZnO, consists of stable wurtzite structure, has attracted much interest due to its unique properties and wide range of applications in optoelectronics, biosensors, medical sciences, etc. Especially, ZnO were easily grown as vertical nanorods by hydrothermal method and ZnO nanostructures have higher sensitivity to environments than planar structures due to plentiful hydroxyl group on their surface. We prepared for ZnO nanorods vertically grown on CVD Gr (ZnO nanorods/Gr hybrid channel) and to fabricate SG-FET subsequently. We have analyzed hybrid channel FETs showing transfer characteristics similar to that of pristine Gr FETs and charge neutrality point (CNP) shifts along proton concentration in solution, which can determine pH level of solution. Hybrid channel SG-FET sensors led to increase in pH sensitivity up to 500%, compared to pristine Gr SG-FET sensors. We confirmed plentiful hydroxyl groups on ZnO nanorod surface interact with protons in solution, which causes shifts of CNP. The morphology and electrical characteristics of hybrid channel SG-FET were characterized by FE-SEM and semiconductor parameter analyzer, respectively. Sensitivity and sensing mechanism of ZnO nanorods/Gr hybrid channel FET will be discussed in detail.

• Proceedings of the Polymer Society of Korea Conference
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• 2006.10a
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• pp.309-309
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• 2006

Latest developments on hybrid nanostructured materials fabricated by applying self-assembly strategies on organic/inorganic nanotemplates are discussed. Within this frame, numerous functional nanomaterials including arrays of composite metal/semiconductor nanoparticles, planar waveguides and functional multilayer thin films are generated using self-assembled polymers as templates or building blocks. In particular, surface plasmon resonance based optical sensing is employed to investigate nanofabrication processes occurring in nanoscale dimention. We also suggest unprecedented pathways to hybrid supramolecular multilayer nanoarchitectures in 1D or 2D geometry via layer-by-layer self-assembly.