• Smart Media Journal
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• v.8 no.1
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• pp.51-58
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• 2019

This paper describes a gesture application to controlling the special effects of physical devices for 4D contents using the PWM (Pulse Width Modulation) method. The user operation recognized by the infrared sensor is interpreted as a command for 3D content control, several of which manipulate the device that generates the special effect to display the physical stimulus to the user. With the content controlled under the NUI (Natural User Interface) technique, the user can be directly put into an immersion experience, which leads to provision of the higher degree of interest and attention. In order to measure the efficiency of the proposed method, we implemented a PWM-based real-time linear control system that manages the parameters of the motion recognition and animation controller using the infrared sensor and transmits the event.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.9
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• pp.1799-1807
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• 2016

In this paper, we propose the automatic demand response systems which reduce the electric power consumption for the period automatically distinct from the existing passive demand response that a subscriber directly controls the energy consumption. The proposed systems are based on SEP 2.0 and consist of the demand response management program, the demand response server, and the demand response client. The demand response program shows the current status of the electric power use to a subscriber and supports the function which the administrator enables to creates or cancels a demand response event. The demand response server transmits the demand response event received from the demand response management program to the demand response client through SEP 2.0 protocol, and it stores the metering data from the demand response client in a database. After extracting the data, such as the demand response the start time, the duration, the reduction level, the demand response client reduces the electric power consumption for the period.

• Journal of Korean Society of Water and Wastewater
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• v.29 no.4
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• pp.511-517
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• 2015

Korea's modern waterworks began with construction of DDukdo water treatment plant in 1908 and has been growing rapidly along with the country's economic development. As a result, water supply rates have reached 98.5% based on 2013. Despite multilateral efforts for high-quality water supply, such as introduction of advanced water treatment process, expansion of waterworks infrastructure and so on, distrust for drinking tap water has been continuing and domestic consumption rate of tap water is in around 5% level and extremely poor comparing to advanced countries such as the United States(56%), Japan(52%), etc. Recently, the water management has been facing the new phase due to water environmental degradation caused by climate change, aging facilities, etc. Therefore, K-water has converted water management paradigm from the "clean and safe water" to the "healthy water" and been pushing the Smart Water City(SWC) Pilot Project in order to develop and spread new water supply models for consumers to believe and drink tap water through systematic water quality and quantity management combining ICT in the whole water supply process. The SWC pilot projects in Pa-ju city and Go-ryeong county were an opportunity to check the likelihood of the "smart water management" as the answer to future water management. It is needed to examine the necessity of smart water management introduction and nationwide SWC expansion in order to improve water welfare for people and resolve domestic & foreign water problems.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2006.05a
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• pp.213-217
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• 2006

An intelligent performance diagnostic program using the Neural Network was proposed for PW206C turboshaft engine. It was selected as a power plant for the tilt rotor type Smart UAV (Unmanned Aerial Vehicle) which has been developed by KARI (Korea Aerospace Research Institute). For teaming the NN, a BPN with one hidden, one input and one output layer was used. The input layer had seven neurons of variations of measurement parameters such as SHP, MF, P2, T2, P4, T4 and T5, and the output layer used 6 neurons of degradation ratios of flow capacities and efficiencies for compressor, compressor turbine and power turbine. Database for network teaming and test was constructed using a gas turbine performance simulation program. From application results for diagnostics of the PW206C turboshaft engine using the learned networks, it was confirmed that the proposed diagnostics algorithm could detect well the single fault types such as compressor fouling and compressor turbine erosion.

• The Journal of Society for e-Business Studies
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• v.18 no.2
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• pp.81-93
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• 2013

Remote user authentication is a method, in which remote server verifies the legitimacy of a user over an common communication channel. Currently, smart card based remote user authentication schemes have been widely adopted due to their low computational cost and convenient portability for the mutual authentication. 2009 years, Wang et al.'s proposed a dynamic ID-based remote user authentication schemes using smart cards. They presented that their scheme preserves anonymity of user, has the feature of storing password chosen by the server, and protected from several attacks. However, in this paper, I point out that Wang et al.'s scheme has practical vulnerability. I found that their scheme does not provide anonymity of a user during authentication. In addition, the user does not have the right to choose a password. And his scheme is vulnerable to limited replay attacks. In particular, the parameter y to be delivered to the user is ambiguous. To overcome these security faults, I propose an enhanced authentication scheme, which covers all the identified weakness of Wang et al.'s scheme and an efficient user authentication scheme that preserve perfect anonymity to both the outsider and remote server.

• Journal of the Korean Society of Propulsion Engineers
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• v.10 no.2
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• pp.15-22
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• 2006

An intelligent performance diagnostic program using the Neural Network was proposed for PW206C turboshaft engine. It was selected as a power plant for the tilt rotor type Smart UAV(Unmanned Aerial Vehicle) which is being developed by KARI (Korea Aerospace Research Institute). For teeming the NN(Neural Network), a BPN(Back Propagation Network) with one hidden, one input and one output layer was used. The input layer has seven neurons: variations of measurement parameters such as SHP, MF, P2, T2, P4, T4 and T5, and the output layer uses 6 neurons: degradation ratios of flow capacities and efficiencies for compressor, compressor turbine and power turbine, respectively, Database for network teaming and test was constructed using a gas turbine performance simulation program. From application of the learned networks to diagnostics of the PW206C turboshaft engine, it was confirmed that the proposed diagnostics algorithm could detect well the single fault types such as compressor fouling and compressor turbine erosion.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.6 no.4
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• pp.31-40
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• 2016

Recently, technology of Virtual Reality(VR) based on HMD among various kinds of VR implemented products has received widespread attention. Major IT-related companies around the world participated in VR HMD research and development. Therefore, the possibility of the spread of VR HMD has been highly praised. Demands of VR HMD products using Smart Phone has been especially increased so that it is required to create a high quality of VR contents. The purpose of study in this paper is to apply the depth value of stereoscopic to VR HMD. To implement it, we analyzed VR HMD optical system and converted an experimental image to virtual depth caused by binocular disparity based on the result of calculating NPP(Native Pixel Parallax). We produced the image of stereoscopic applied with the value converted and applied to VR HMD. This study is expected to be utilized as a VR content creation field of quantitative data.

• KIPS Transactions on Computer and Communication Systems
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• v.10 no.7
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• pp.191-198
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• 2021

Since the size of training dataset become large and the model is getting deeper to achieve high accuracy in deep learning, the deep neural network training requires a lot of computation and it takes too much time with a single node. Therefore, distributed deep learning is proposed to reduce the training time by distributing computation across multiple nodes. In this study, we propose hybrid allreduce strategy that considers the characteristics of each layer and communication and computational overlapping technique for synchronization of distributed deep learning. Since the convolution layer has fewer parameters than the fully-connected layer as well as it is located at the upper, only short overlapping time is allowed. Thus, butterfly allreduce is used to synchronize the convolution layer. On the other hand, fully-connecter layer is synchronized using ring all-reduce. The empirical experiment results on PyTorch with our proposed scheme shows that the proposed method reduced the training time by up to 33% compared to the baseline PyTorch.

• The Journal of the Korea Contents Association
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• v.21 no.3
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• pp.616-625
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• 2021

Deep neural networks are an approximation method that approximates an arbitrary function to a linear model and then repeats additional approximation using a nonlinear active function. In this process, the method of evaluating the performance of approximation uses the loss function. Existing in-depth learning methods implement approximation that takes into account loss functions in the linear approximation process, but non-linear approximation phases that use active functions use non-linear transformation that is not related to reduction of loss functions of loss. This study proposes parametric activation functions that introduce scale parameters that can change the scale of activation functions and location parameters that can change the location of activation functions. By introducing parametric activation functions based on scale and location parameters, the performance of nonlinear approximation using activation functions can be improved. The scale and location parameters in each hidden layer can improve the performance of the deep neural network by determining parameters that minimize the loss function value through the learning process using the primary differential coefficient of the loss function for the parameters in the backpropagation. Through MNIST classification problems and XOR problems, parametric activation functions have been found to have superior performance over existing activation functions.

• Composites Research
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• v.36 no.6
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• pp.429-434
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• 2023

In this study, aimed at improving the existing acoustic emission sensor for real time monitoring, a macro-fiber composite (MFC) transducer was employed as the acoustic emission sensor in the gas leak detection system. Prior to implementation, structural analysis was conducted to optimize the MFC's design. Consequently, the flexibility of the MFC facilitated excellent adherence to curved pipes, enabling the reception of acoustic emission (AE) signals without complications. Analysis of AE signals revealed substantial variations in parameter values for both high-pressure and low-pressure leaks. Notably, in the parameters of the Fast Fourier Transform (FFT) graph, the change amounted to 120% to 626% for high-pressure leaks compared to the case without leaks, and approximately 9% to 22% for low-pressure leaks. Furthermore, depending on the distance from the leak site, the magnitude of change in parameters tended to decrease as the distance increased. As the results, in the future, not only will it be possible to detect a leak by detecting the amount of parameter change in the future, but it will also be possible to identify the location of the leak from the amount of change.