• Proceedings of the Korean Vacuum Society Conference
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• 2012.08a
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• pp.222-222
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• 2012

Metal oxide gas sensors based on semiconductor type have attracted a great deal of attention due to their low cost, flexible production and simple usability. However, most works have been focused on n-type oxides, while the characteristics of p-type oxide gas sensors have been barely studied. An investigation on p-type oxides is very important in that the use of them makes possible the novel sensors such as p-n diode and tandem devices. Monoclinic cupric oxide (CuO) is p-type semiconductor with narrow band gap (~1.2 eV). This is composed of abundant, nontoxic elements on earth, and thus low-cost, environment-friendly devices can be realized. However, gas sensing properties of neat CuO were rarely explored and the mechanism still remains unclear. In this work, the neat CuO layers with highly ordered mesoporous structures were prepared by a template-free, one-pot solution-based method using novel ink solutions, formulated with copper formate tetrahydrate, hexylamine and ethyl cellulose. The shear viscosity of the formulated solutions was 5.79 Pa s at a shear rate of 1 s-1. The solutions were coated on SiO2/Si substrates by spin-coating (ink) and calcined for 1 h at the temperature of $200{\sim}600^{\circ}C$ in air. The surface and cross-sectional morphologies of the formed CuO layers were observed by a focused ion beam scanning electron microscopy (FIB-SEM) and porosity was determined by image analysis using simple computer-programming. XRD analysis showed phase evolutions of the layers, depending on the calcination temperature, and thermal decompositions of the neat precursor and the formulated ink were investigated by TGA and DSC. As a result, the formation of the porous structures was attributed to the vaporization of ethyl cellulose contained in the solutions. Mesoporous CuO, formed with the ink solution, consisted of grains and pores with nano-meter size. All of them were strongly dependent on calcination temperature. Sensing properties toward H2 and C2H5OH gases were examined as a function of operating temperature. High and fast responses toward H2 and C2H5OH gases were discussed in terms of crystallinity, nonstoichiometry and morphological factors such as porosity, grain size and surface-to-volume ratio. To our knowledge, the responses toward H2 and C2H5OH gases of these CuO gas sensors are comparable to previously reported values.

• 한국HCI학회:학술대회논문집
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• 2006.02a
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• pp.355-361
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• 2006

In large scale environments like airport, museum, large warehouse and department store, autonomous mobile robots will play an important role in security and surveillance tasks. Robotic security guards will give the surveyed information of large scale environments and communicate with human operator with that kind of data such as if there is an object or not and a window is open. Both for visualization of information and as human machine interface for remote control, a 3D model can give much more useful information than the typical 2D maps used in many robotic applications today. It is easier to understandable and makes user feel like being in a location of robot so that user could interact with robot more naturally in a remote circumstance and see structures such as windows and doors that cannot be seen in a 2D model. In this paper we present our simple and easy to use method to obtain a 3D textured model. For expression of reality, we need to integrate the 3D models and real scenes. Most of other cases of 3D modeling method consist of two data acquisition devices. One for getting a 3D model and another for obtaining realistic textures. In this case, the former device would be 2D laser range-finder and the latter device would be common camera. Our algorithm consists of building a measurement-based 2D metric map which is acquired by laser range-finder, texture acquisition/stitching and texture-mapping to corresponding 3D model. The algorithm is implemented with laser sensor for obtaining 2D/3D metric map and two cameras for gathering texture. Our geometric 3D model consists of planes that model the floor and walls. The geometry of the planes is extracted from the 2D metric map data. Textures for the floor and walls are generated from the images captured by two 1394 cameras which have wide Field of View angle. Image stitching and image cutting process is used to generate textured images for corresponding with a 3D model. The algorithm is applied to 2 cases which are corridor and space that has the four wall like room of building. The generated 3D map model of indoor environment is shown with VRML format and can be viewed in a web browser with a VRML plug-in. The proposed algorithm can be applied to 3D model-based remote surveillance system through WWW.

• Journal of IKEEE
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• v.15 no.1
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• pp.76-85
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• 2011

In this paper, we analysed RSS characteristics by external factors and presented an efficient algorithm for real-time location tracking and its hardware system. The proposed algorithm enhanced the ranging accuracy using Kalman Filter based on the RSS DB. The location tracking system that consists of the tag, AP(Access Point), a data collector(Data Receiver) with IEEE 802.15.4(ZigBee) network environment, and location tracking application that reveal locations of each tag is implemented for the test environment. The location tracking system presented in this paper is implemented with MSP430 microprocessor manufactured by TI(Texas Instrument), CC2420 RF chipset and the location tracking application. With the results of the experiment, the proposed algorithm and the system can achieve the efficiency and the accuracy of location tracking with the average error of 19.12cm, and its standard deviation of 5.31cm in outdoor circumstance. Also, the experimental result shows that exact tracking of position in indoor circumstance cannot achieve because of vulnerable RSS with external circumstance.

• Journal of the Korea Institute of Building Construction
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• v.17 no.6
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• pp.545-557
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• 2017

The study is about the efficient alternative to concrete surface in the field of visual inspection technology for deteriorated infrastructure. By combining industrial drones and deep learning based image analysis techniques with traditional visual inspection and research, we tried to reduce manpowers, time requirements and costs, and to overcome the height and dome structures. On board device mounted on drones is consisting of a high resolution camera for detecting cracks of more than 0.3 mm, a lidar sensor and a embeded image processor module. It was mounted on an industrial drones, took sample images of damage from the site specimen through automatic flight navigation. In addition, the damege parts of the site specimen was used to measure not only the width and length of cracks but white rust also, and tried up compare them with the final image analysis detected results. Using the image analysis techniques, the damages of 54ea sample images were analyzed by the segmentation - feature extraction - decision making process, and extracted the analysis parameters using supervised mode of the deep learning platform. The image analysis of newly added non-supervised 60ea image samples was performed based on the extracted parameters. The result presented in 90.5 % of the damage detection rate.

• Journal of the Korean earth science society
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• v.38 no.4
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• pp.251-262
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• 2017

The system of magnetic exploration with a drone flight was constructed and applied to the iron mine site. The magnetic probe system installed on the drone used a sensor as Bartington's fluxgate type magnetometer, Mag639 and the A/D converter to collect magnetic intensity values on the tablet PC. The drone flight control module is a highly expandable Pixhawk with allowing 15 minutes of flight by loading 3kg. Experiments on the magnetic field interference range were performed to remove the erroneous effect from the drone with applying RTK GPS to obtain the magnetic intensity value at the accurate position. The accurate location information enabled to obtain the gradient measurement of magnetic field by measuring twice at different altitudes. Also, by using the terrain information, we could eliminate the terrain effect by setting the flight path to fly along the terrain. These results are in line with the field experiments using the nuclear proton magnetometer G-858 of Geometrics Co., Ltd, which adds to the reliability of the drone based aeromagnetic survey system we constructed.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.11 no.1
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• pp.45-52
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• 2017

EEG signals that are the same as those that have the same disabled people. So, the EEG signals are becoming the next generation. In this paper, we propose an internet of things system that controls the indoor environment using EEG signal. The proposed system consists EEG measurement device, EEG simulation software and indoor environment control device. We use data as EEG signal data on emotional imagination condition in a comfortable state and logical imagination condition in concentrated state. The noise of measured signal is removed by the ICA algorithm and beta waves are extracted from it. then, it goes through learning and test process using SVM. The subjects were trained to improve the EEG signal accuracy through the EEG simulation software and the average accuracy were 87.69%. The EEG signal from the EEG measurement device is transmitted to the EEG simulation software through the serial communication. then the control command is generated by classifying emotional imagination condition and logical imagination condition. The generated control command is transmitted to the indoor environment control device through the Zigbee communication. In case of the emotional imagination condition, the soft lighting and classical music are outputted. In the logical imagination condition, the learning white noise and bright lighting are outputted. The proposed system can be applied to software and device control based BCI.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.9
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• pp.1704-1709
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• 2016

In this paper, we propose efficient uplink data transmission method in ultra dense wireless networks as in intra-aircraft, where large-scale APs and wireless sensors are deployed. In the ultra dense wireless networks, a performance degradation is inevitable due to the inter-AP interference. However, the performance degradation can be avoided if a scheduling algorithm can estimate the amount of interference caused by each wireless sensor and reflects it. SGIR (Signal-to-Generating Interference Ratio) based scheduling algorithms is a typical example. Unfortunately, the scheduling algorithms based on the interference caused by wireless sensors necessarily yield large scale exchange of information through backhaul which connects APs. Therefore, we, in this paper, propose a novel scheme which can dramatically reduce the amount of information which are exchanged through backhaul connection. Monte-Carlo simulation results show that the proposed scheme can reduce the amount of backhaul traffic by 27% without loss of data transmission rate.

• Journal of Sensor Science and Technology
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• v.14 no.1
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• pp.33-41
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• 2005

For more convenient electrode-electrolyte interface impedance analysis in biosensor, a stand-alone impedance measurement system is required. In our study, we developed a PC-based portable system to analyze impedance of the electrochemical cell using microprocessor. The devised system consists of signal generator, programmable amplifiers, A/D converter, low pass filter, potentiostat, I/V converter, microprocessor, and PC interface. As a microprocessor, PIC16F877 which has the processing speed of 5 MIPS was used. For data acquisition, the sampling rate at 40 k samples/sec, resolution of 12 bit is used. RS-232 with 115.2 kbps speed is used for the PC communication. The square wave was used as stimuli signal for impedance analysis and voltage-controlled current measurement method of three-electrode-method were adopted. Acquired voltage and current data are calculated to multifrequency impedance signal after Fourier transform. To evaluate the implemented system, we set up the dummy cell as equivalent circuit of which was composed of resistor, parallel circuit of capacitor and resistor connected in parallel and measured the impedance of the dummy cell; the result showed that there exist accuracy within 5 % errors and reproduction within 1 % errors compared to output of Hioki LCR tester and HP impedance analyzer as a standard product. These results imply that it is possible to analyze electrode-electrolyte interface impedance quantitatively in biosensor and to implement the more portable high speed impedance analysis system compared to existing systems.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.44 no.2 s.314
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• pp.63-73
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• 2007

This paper proposes an illuminant-adaptive reproduction method using light adaptation and flare conditions for a mobile display. Displayed images in daylight are perceived as quite dark due to the light adaptation of the human visual system, as the luminance of a mobile display is considerably lower than that of an outdoor environment. In addition, flare phenomena decrease the color gamut of a mobile display and de-saturating the chroma. Therefore, this paper presents an enhancement method composed of lightness enhancement and chroma compensation. First, the ambient light intensity is measured using a lux-sensor, then the flare is calculated based on the reflection ratio of the display device and the ambient light intensity. To improve the perceived image, the image's luminance is transformed by linearization of the response to the input luminance according to the ambient light intensity. Next, the displayed image is compensated according to the physically reduced chroma, resulting from flare phenomena. This study presents a color reproduction method based on an inverse cone response curve and flare condition. Consequently, the proposed algorithm improves the quality of the perceived image adaptive to an outdoor environment.

• Journal of Internet Computing and Services
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• v.21 no.3
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• pp.133-144
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• 2020

The number of elderly people with dementia is increasing as fast as the proportion of older people due to aging, which creates a social and economic burden. In particular, dementia care costs, including indirect costs such as increased care costs due to lost caregiver hours and caregivers, have grown exponentially over the years. In order to reduce these costs, it is urgent to introduce a management system to care for dementia patients. Therefore, this study proposes a sensor-based abnormal behavior detection system to manage dementia patients who live alone or in an environment where they cannot always take care of dementia patients. Existing studies were merely evaluating behavior or evaluating normal behavior, and there were studies that perceived behavior by processing images, not data from sensors. In this study, we recognized the limitation of real data collection and used both the auto-encoder, the unsupervised learning model, and the LSTM, the supervised learning model. Autoencoder, an unsupervised learning model, trained normal behavioral data to learn patterns for normal behavior, and LSTM further refined classification by learning behaviors that could be perceived by sensors. The test results show that each model has about 96% and 98% accuracy and is designed to pass the LSTM model when the autoencoder outlier has more than 3%. The system is expected to effectively manage the elderly and dementia patients who live alone and reduce the cost of caring.