• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2019.06a
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• pp.163-165
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• 2019

The task of image denoising is gaining popularity in the computer vision research field. Its main objective of restoring the sharp image from given noisy input is demanded in all image processing procedure. In this work, we treat the process of residual hair removal on faces images similar to the task of image denoising. In particular, our method removes the residual hair that presents on the frontal or profile face images and in-paints it with the relevant skin color. To achieve this objective, we employ a deep neural network that able to perform both tasks in one time. Furthermore, simple technic of residual hair color augmentation is introduced to increase the number of training data. This approach is beneficial for improving the robustness of the network. Finally, we show that the experimental results demonstrate the superiority of our network in both quantitative and qualitative performances.

• Journal of Korea Multimedia Society
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• v.24 no.5
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• pp.701-708
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• 2021

As one of the most widely used technology in the world right now, Face recognition has already received widespread attention by all the researcher and institutes. It has been used in many fields such as safety protection, surveillance system, crime control and even in our ordinary life such as home security and so on. This technology with today's technology has advantages such as high connectivity and real time transformation. But we still need to improve its recognition rate, reaction time and also reduce impact of different environmental status to the whole system. So in this paper we proposed a face recognition system model with improved CNN which combining the characteristics of flat network and residual network, integrated learning, simplify network structure and enhance portability and also improve the recognition accuracy. We also used AR and ORL database to do the experiment and result shows higher recognition rate, efficiency and robustness for different image conditions.

• Proceedings of the KIPE Conference
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• 2001.10a
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• pp.656-659
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• 2001

In this paper a neural network controller for achieving maximum power tracking as well as output voltage regulation, for a wind energy conversion system(WECS) employing a permanent magnet synchronous generator, is proposed. The permanent magnet generator (PMG) supplies a dc load via a bridge rectifier and two buck-boost converters. Adjusting the switching frequency of the first buck-boost converter achieves maximum power tracking. Adjusting the switching frequency of the second buck-boost converter allows output voltage regulation. The on-times of the switching devices of the two converters are supplied by the developed neural network(NN). The effect of sudden changes in wind speed ,and/or in reference voltage on the performance of the NN controller are explored. Simulation results showed the possibility of achieving maximum power tracking and output voltage regulation simultaneously with the developed neural network controller. The results proved also the fast response and robustness of the proposed control system.

• Smart Structures and Systems
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• v.11 no.5
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• pp.453-476
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• 2013

DuraMote is a remote sensing system developed for the "NIST TIP project: next generation SCADA for prevention and mitigation of water system infrastructure disaster". It is designed for supervisory control and data acquisition (SCADA) of ruptures in water pipes. Micro-electro mechanical (MEMS) accelerometers, which record the vibration of the pipe wall, are used detect the ruptures. However, the performance of Duramote cannot be verified directly on a water distribution system because it lacks an acceptable recordable level of ambient vibration. Instead, a long-span cable-stayed bridge is an ideal test-bed to validate the accuracy, the reliability, and the robustness of DuraMote because the bridge has an acceptable level of ambient vibration. The acceleration data recorded on the bridge were used to identify the modal properties of the structure and to verify the performance of DuraMote. During the test period, the bridge was subjected to heavy rain, wind, and a typhoon but the system demonstrates its robustness and durability.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10b
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• pp.378-384
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• 1993

In this paper, an intelligent adaptive controller is proposed for the process with unmodelled dynamics. The intelligent adaptive controller consists of the numeric adaptive controller and the intelligent tuning part. The continuous scheme is used for the numeric adaptive controller to avoid the problems occurred in the discrete time schemes. The adaptive controller is adopted to the process with time delay. It is an implicit adaptive algorithm based on GMV using the emulator. The tuning part changes the design parameters in the control algorithm. It is a multilayer neural network trained by robustness analysis data. The proposed method can improve the robustness of the adaptive control system because the design parameters are tuned according to the operating points of the process. Through the simulation, robustnesses are shown for intelligent adaptive controller. Finally, the proposed algorithms are implemented on the electric furnace temperature control system. The effectiveness of the proposed algorithm is shown from experiments.

• Journal of Mechanical Science and Technology
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• v.20 no.8
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• pp.1195-1208
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• 2006

This paper aims at presenting robustness analysis under the uncertainties of the time delay and plant parameters in symmetrically coupled dynamic systems connected through network having time delay. The delay-involved closed loop characteristic function is mathematically formulated, incorporated with active synchronization control. And the robust stability of the corresponding system is analyzed by investigating the formation of characteristic equation containing second- order terms of uncertainty variables representing delay and plant dynamics mismatches. For the two individual types of uncertainties, we elucidate details of how to compute the bounds and what they imply physically. To support the validity of the mathematical claims, numerical examples and simulations are presented.

• Journal of Communications and Networks
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• v.17 no.2
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• pp.145-156
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• 2015

In recent years, electronic commerce and online social networks (OSNs) have experienced fast growth, and as a result, recommendation systems (RSs) have become extremely common. Accuracy and robustness are important performance indexes that characterize customized information or suggestions provided by RSs. However, nefarious users may be present, and they can distort information within the RSs by creating fake identities (Sybils). Although prior research has attempted to mitigate the negative impact of Sybils, the presence of these fake identities remains an unsolved problem. In this paper, we introduce a new weighted link analysis and influence level for RSs resistant to Sybil attacks. Our approach is validated through simulations of a broad range of attacks, and it is found to outperform other state-of-the-art recommendation methods in terms of both accuracy and robustness.

• Journal of Korea Society of Industrial Information Systems
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• v.12 no.5
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• pp.1-13
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• 2007

In this research, we found out that bridging nodes have great effect on the robustness of protein-protein interaction networks. Until now, many researchers have focused on node's degree as node's essentiality. Hub nodes in the scale-free network are very essential in the network robustness. Some researchers have tried to relate node's essentiality with node's betweenness centrality. These approaches with betweenness centrality are reasonable but there is a positive relation between node's degree and betweenness centrality value. So, there are no differences between two approaches. We first define a bridging node as the node with low connectivity and high betweenness value, we then verify that such a bridging node is a primary factor in the network robustness. For a biological network database from Internet, we demonstrate that the removal of bridging nodes defragment an entire network severally and the importance of the bridging nodes in the network robustness.

• Journal of Korea Multimedia Society
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• v.23 no.12
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• pp.1540-1551
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• 2020

Aiming at the problem that the existing human behavior recognition algorithm cannot fully utilize the multi-level spatio-temporal information of the network, a human behavior recognition algorithm based on a dense three-dimensional residual network is proposed. First, the proposed algorithm uses a dense block of three-dimensional residuals as the basic module of the network. The module extracts the hierarchical features of human behavior through densely connected convolutional layers; Secondly, the local feature aggregation adaptive method is used to learn the local dense features of human behavior; Then, the residual connection module is applied to promote the flow of feature information and reduced the difficulty of training; Finally, the multi-layer local feature extraction of the network is realized by cascading multiple three-dimensional residual dense blocks, and use the global feature aggregation adaptive method to learn the features of all network layers to realize human behavior recognition. A large number of experimental results on benchmark datasets KTH show that the recognition rate (top-l accuracy) of the proposed algorithm reaches 93.52%. Compared with the three-dimensional convolutional neural network (C3D) algorithm, it has improved by 3.93 percentage points. The proposed algorithm framework has good robustness and transfer learning ability, and can effectively handle a variety of video behavior recognition tasks.

• Journal of Electrical Engineering and Technology
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• v.12 no.1
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• pp.363-372
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• 2017

This paper presents a novel face recognition algorithm based on the deep convolution neural network and key point detection jointed local binary pattern methodology to enhance the accuracy of face recognition. We firstly propose the modified face key feature point location detection method to enhance the traditional localization algorithm to better pre-process the original face images. We put forward the grey information and the color information with combination of a composite model of local information. Then, we optimize the multi-layer network structure deep learning algorithm using the Fisher criterion as reference to adjust the network structure more accurately. Furthermore, we modify the local binary pattern texture description operator and combine it with the neural network to overcome drawbacks that deep neural network could not learn to face image and the local characteristics. Simulation results demonstrate that the proposed algorithm obtains stronger robustness and feasibility compared with the other state-of-the-art algorithms. The proposed algorithm also provides the novel paradigm for the application of deep learning in the field of face recognition which sets the milestone for further research.