• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.4
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• pp.1661-1678
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• 2016

Clustering is one of the key technologies in vehicular networks. Constructing and maintaining stable clusters is a challenging task in high mobility environments. DWCM (Distributed and Weighted Clustering based on Mobility Metrics) is proposed in this paper based on the d-hop dominating set of the network. Each vehicle is assigned a priority that describes the cluster relationship. The cluster structure is determined according to the d-hop dominating set, where the vehicles in the d-hop dominating set act as the cluster head nodes. In addition, cluster maintenance handles the cluster structure changes caused by node mobility. The rationality of the proposed algorithm is proven. Simulation results in the NS-2 and VanetMobiSim integrated environment demonstrate the performance advantages.

• Journal of Contemporary Eastern Asia
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• v.15 no.2
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• pp.191-210
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• 2016

While general belief is that the military alliances are stable and rigid, the authors argue that the states are far more flexible in their behavior and often act against their alliances. This paper looks at states' behavior in the UN GA and looks how it is reflected in participation in military alliances during three periods of history since 1976 to this day. The authors illustrate the need to consider the network representation of co-sponsoring groups in General Assembly votes. They find significant support for their argument, indicating that social aspects can be extended beyond alliances. An application of social network analysis shows some unexpected affiliations in UN GA. If the UN GA is the "true" nature of these countries' alliance strategies, then it might suggest some significant defections and interesting association.

• Proceedings of the IEEK Conference
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• 2003.11c
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• pp.223-226
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• 2003

In this paper, we propose new agent discovery and route discovery schemes to support Mobile IP (MIP) in Ad Hoc networks with wireless backbone. The wireless backbone consisting of stationary wireless routers and Internet gateways (IGs) is a kind of wireless access network of IP-based core network. The proposed scheme utilizes favorable features of wireless backbone such as stable links and no energy constraints. In the agent discovery scheme, backbone-limited periodic Agent Advertisement (AA) and proxy-AA messages are used, which reduce network-wide broadcasting overhead caused by AA and Agent Solicitation messages and decentralize MIP processing overhead in IGs. In order to reduce delay time and control message overhead during route discovery far the destination outside Ad Hoc network, we propose a cache-based scheme which can be easily added to the conventional on-demand routing protocols. The proposed schemes can reduce control overhead during agent discovery and route discovery, and efficiently support MIP in Ad Hoc network with wireless backbone.

• Proceedings of the Optical Society of Korea Conference
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• 2000.08a
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• pp.124-125
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• 2000

Since Nye and Berry$^{(1)}$ showed that in free space the electromagnetic field could contain stable, propagating phase singularities termed "dislocations", optical dislocations have been extensively investigated in nonlinear optics and laser physics. As the wave propagates, the lines of constant phase surrounding a dislocation trace out a spiral in space or in time. So these phase singularities are now usually referred to as optical vortices. Baranova and her co-workers$^{(2)}$ have shown that in fully developed speckle patterns, there is, one optical vortex accompanying each speckle spot on average. Among these vortices there are networks in phasemap because only one phase is to be assigned in one point except optical dislocations having zero amplitude. Freund et al.$^{(3)}$ have been studied optical dislocation networks and simulations are compared with experimental results. (omitted)

• Journal of Industrial Technology
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• v.18
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• pp.303-308
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• 1998

In this paper a neural network controller called "Feedback-State Learning" for control of the attitude of a wheeled inverted pendulum is presented. For the controller the design of a stable feedback controller is necessary, so the LQR is used for the feedback controller because the LQR has good performance on controlling nonlinear systems. And the neural networks are used for a feed forward controller. The designed controller is applied to the stabilization of a wheeled inverted pendulum. Because of its nonlinear characteristics such as friction and parameter variations in the linearization, the wheeled inverted pendulum is used for demonstration of the effectiveness of the proposed controller.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.11
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• pp.1464-1471
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• 1999

This paper introduces complex-valued competitive learning neural network for spatio-temporal pattern recognition. There have been quite a few neural networks for spatio-temporal pattern recognition. Among them, recurrent neural network, TDNN, and avalanche model are acknowledged as standard neural network paradigms for spatio-temporal pattern recognition. Recurrent neural network has complicated learning rules and does not guarantee convergence to global minima. TDNN requires too many neurons, and can not be regarded to deal with spatio-temporal pattern basically. Grossberg's avalanche model is not able to distinguish long patterns, and has to be indicated which layer is to be used in learning. In order to remedy drawbacks of the above networks, unsupervised competitive learning using complex umber is proposed. Suggested neural network also features simultaneous recognition, time-shift invariant recognition, stable categorizing, and learning rate modulation. The network is evaluated by computer simulation with randomly generated patterns.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2020.07a
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• pp.717-720
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• 2020

An indoor localization system that uses Wi-Fi RSSI signals for localization gives accurate user position results. The conventional Wi-Fi RSSI signal based localization system uses raw RSSI signals from access points (APs) to estimate the user position. However, the RSSI values of a particular location are usually not stable due to the signal propagation in the indoor environments. To reduce the RSSI signal fluctuations, shadow fading, multipath effects and the blockage of Wi-Fi RSSI signals, we propose a Wi-Fi localization system that utilizes the advantages of Wi-Fi RSSI heat maps. The proposed localization system uses a regression model with deep convolutional neural networks (DCNNs) and gives accurate user position results for indoor localization. The experiment results demonstrate the superior performance of the proposed localization system for indoor localization.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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• pp.653-655
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• 2022

Face aging aims to synthesize future face images by reflecting the age factor on given faces. In recent years, deep learning-based approaches have made outstanding progress in simulating the aging process of the human face. However, generating accurate and high-quality aging faces is still intrinsically difficult. We propose a new method that incorporates gender information into the model, which achieves comparable and stable performance. Experimental results demonstrate that our method can preserve the identity well and generate diverse aged faces.

• International journal of advanced smart convergence
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• v.12 no.4
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• pp.134-141
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• 2023

We present a novel method aimed at refining ground truth data through regularization and modification, particularly applicable when working with the original ground truth set. Enhancing the performance of deep neural networks is achieved by applying regularization techniques to the existing ground truth data. In many machine learning tasks requiring pixel-level segmentation sets, accurately delineating objects is vital. However, it proves challenging for thin and elongated objects such as blood vessels in X-ray coronary angiography, often resulting in inconsistent generation of ground truth data. This method involves an analysis of the quality of training set pairs - comprising images and ground truth data - to automatically regulate and modify the boundaries of ground truth segmentation. Employing the active contour model and a recursive ground truth generation approach results in stable and precisely defined boundary contours. Following the regularization and adjustment of the ground truth set, there is a substantial improvement in the performance of deep neural networks.

• Journal of Sensor Science and Technology
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• v.33 no.3
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• pp.147-151
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• 2024

The importance of Wireless Sensor Networks is becoming more evident owing to their practical applications in various areas. However, the energy problem remains a critical barrier to the progress of WSNs. By reducing the energy consumed by the sensor nodes that constitute WSNs, the performance and lifespan of WSNs will be enhanced. In this study, we introduce an energy-efficient ternary modulator that employs multi-threshold CMOS for logic conversion. We optimized the design with a low-power ternary gate structure based on a pass transistor using the MTCMOS process. Our design uses 71.69% fewer transistors compared to the previous design. To demonstrate the improvements in our design, we conducted the HSPICE simulation using a CMOS 180 nm process with a 1.8V supply voltage. The simulation results show that the proposed ternary modulator is more energy-efficient than the previous modulator. Power-delay product, a benchmark for energy efficiency, is reduced by 97.19%. Furthermore, corner simulations demonstrate that our modulator is stable against PVT variations.