• Electronics and Telecommunications Trends
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• v.33 no.5
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• pp.103-110
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• 2018

Studies on artificial intelligence have been developed for the past couple of decades. After a few periods of prosperity and recession, a new machine learning method, so-called Deep Learning, has been introduced. This is the result of high-quality big- data, an increase in computing power, and the development of new algorithms. The main targets for deep learning are 1D audio and 2D images. The application domain is being extended from a discriminative model, such as classification/segmentation, to a generative model. Currently, deep learning is used for processing 3D data. However, unlike 2D, it is not easy to acquire 3D learning data. Although low-cost 3D data acquisition sensors have become more popular owing to advances in 3D vision technology, the generation/acquisition of 3D data remains a very difficult problem. Moreover, it is not easy to directly apply an existing network model, such as a convolution network, owing to the variety of 3D data representations. In this paper, we summarize the 3D deep learning technology that have started to be developed within the last 2 years.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.05a
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• pp.258-259
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• 2017

Recently, as science and technology is very growing, wire and wireless communication is merged and interconnected. Therefore, advanced internet technology allow all kinds of communication to integrate with heterogeneous device and sensors. The representative example is smart home network based on internet of things. Communication surroundings under IoT services are more complex. Conventional encryption techniques can't provide to IoT application because of its limited resources such as small memory capacity and low computing power. In this paper, we analyzed authentication procedure between home gateway and node in sensor under smart home network.

• ETRI Journal
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• v.38 no.1
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• pp.112-122
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• 2016

This paper presents a set of candidate regulatory requirements for TV band devices (TVBDs) in the Rep. of Korea. To guarantee the protection of incumbent services, especially digital TV (DTV) and wireless microphones, in TV frequency bands, we suggest minimum separation distances of TVBDs from the noise-limited contour according to incumbent users and TVBD types. This paper also deals with multiple sets of separation distances of a co-channel TVBD network from a DTV protected contour on the basis of the radio propagation characteristics of different geographic areas to make good use of TV white space (TVWS) and safely protect the DTV service. We present a low-power transmission mode of TVBDs and the relevant separation distances for small-cell deployment. The service coverage reduction ratio of a TVBD network is investigated in the presence of DTV interference in four geographic areas. The TVWS field verification results, conducted on the island of Jeju (Rep. of Korea), show that incumbent services operate well without harmful interference from neighboring TVBDs with the proposed separation distances.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.8
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• pp.713-718
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• 2012

Lumber spine range of motion has been used to measure of physical and functional impairment by various tools from a ruler to 3D kinematic devices. However, pre-existing tools have problems in either movement or accuracy and reliability limitations. Accurate devices are limited by fixed space whereas simple devices are limited in measuring complex movements with less accuracy. In order to solve the location, movement and accuracy limitations at once, we have developed a novice measurement device equipped with accelerometer sensor and gyroscope sensor for getting three-dimensional information of motion. Furthermore, Kalman filter was applied to the algorithm to improve accuracy. In addition, RF wireless communication was added for the user to conveniently check measured data in real time. Finally, the measurement method was improved by considering the movement by a reference point. An experiment was conducted to test the accuracy and reliability of the device by conducting a test-retest reliability test. Further modification will be conducted to used the device in various joints range of motion in clinical settings in the future.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.12a
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• pp.427-430
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• 2002

Recently the subject of "wavelet analysis" has be drawn by both mathematical and engineering application fields such as Signal Processing, Compression/Decomposition, Wavelet-Neural Network, Statistics and etc. Even though its similar to Fourier analysis, wavelet is a versatile tool with much mathematical content and great potential for applications. Especially, wavelet transform uses localizable various mother wavelet functions in time-frequency domain. Therefore, wavelet transform has good time-analysis ability for high frequency component, and has good frequency-analysis ability for low frequency component. Using the discriminative ability is more easy method than other conventional techniques. In this paper, Morlet wavelet transform was applied to discriminate the kind of line fault by acquired data from real power transformation network. The experimental result presented that Morlet wavelet transform is easier,and more useful method than the FFT (Fast Fourier Transform).

• The Journal of the Korea institute of electronic communication sciences
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• v.7 no.1
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• pp.21-26
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• 2012

This paper presents a real-time image transferring and localization system utilizing HSDPA, a commercial wireless network system. A novel image compression algorithm is developed based on MPEG4 to comply with uploading bandwidth of 130 kbps and QVGA image transmission of 30 fps. Aiming at being embedded in moving vehicles, the proposed system has a small size, low power consumption, and robustness to disturbances. We validate the performance of the system by presenting captured images of transferring video and localization data. Our system can be applied to real-time surround monitoring in moving vehicles or real-time ecology observation in remote places.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.2
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• pp.252-256
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• 2011

We analyze the channel capacity with considering the effect of the loading conditions of indoor PLC networks on channel state information. We consider various numbers of load for two kinds of the networks with regular length branches and a deployed network of indoor PLC. For calculating the channel capacity degradation, two noise scenarios and impedances are considered. From the simulation results, we suggest the robust regression lines for modeling the channel capacity degradation. In the cases of 0 $\Omega$ and $Z_0$ loads, natural log and linear function curve show the best goodness of fit, respectively. For the deployed indoor PLC network with 0 $\Omega$ loads, compared with the networks with regular length branches, the goodness of fit decreases by the amount of 0.12 and 0.15 for low noise and high noise scenarios, respectively. Using the regression lines, we can estimate the channel capacity degradation without measurement.

• Journal of Electrical Engineering and Technology
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• v.13 no.1
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• pp.521-531
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• 2018

This paper presents a procedure for field measurements which provides a generalized Narrowband Power Line Communications (NB-PLC) channel model for low voltage (LV) access network in order to deploy advanced metering infrastructure (AMI) within Lahore, Pakistan. The measurements of allocated sites were performed in the residential (urban and rural), industrial and commercial electricity consumers for the NB-PLC channel modeling of overhead transmission lines (TLs). On the basis of extensive field measurement results, the average attenuation profile and transfer functions are presented. The results obtained from field measurements are validated by comparing them with a proposed Simulink model. A close agreement in the measured and simulated transfer function (TF) results is observed. The proposed Simulink model is an effort to model the NB-PLC channels in an effective way, especially in South Asian countries.

• International Journal of Aeronautical and Space Sciences
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• v.18 no.3
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• pp.474-484
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• 2017

This paper studies the applicability of an efficient numerical model based on artificial neural networks (ANNs) to predict the dynamic responses of the wing structure of an airplane due to atmospheric turbulence in the time domain. The turbulence velocity is given in the form of a stationary Gaussian random process with the von Karman power spectral density. The wing structure is modeled by a classical beam considering bending and torsional deformations. An unsteady vortex-lattice method is applied to estimate the aerodynamic pressure distribution on the wing surface. Initially, the trim condition is obtained, then structural dynamic responses are computed. The numerical solution of the wing structure's responses to a random turbulence profile is used as a training data for the ANN. The current ANN is a three-layer network with the output fed back to the input layer through delays. The results from this study have validated the proposed low-cost ANN model for the predictions of dynamic responses of wing structures due to atmospheric turbulence. The accuracy of the predicted results by the ANN was discussed. The paper indicated that predictions for the bending moments are more accurate than those for the torsional moments of the wing structure.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.27 no.5
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• pp.1117-1127
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• 2017

With the advent of Internet of Things (IoT), the number of devices connected to Internet has rapidly increased, but the security for IoT is still vulnerable. It is difficult to integrate existing security technologies due to generating a large amount of traffic by using different protocols to use various IoT devices according to purposes and to operate in a low power environment. Therefore, in this paper, we propose a normal network behavior profiling method based on big data analysis techniques. The proposed method utilizes a Hadoop/Hive for Big Data analytics and an R for statistical computing. Also we verify the effectiveness of the proposed method through a simulation.