• Journal of the Institute of Electronics Engineers of Korea TC
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• v.47 no.12
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• pp.78-83
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• 2010

In this paper, a novel dual-band power amplifier using metamaterials has been realized with one RF GaN HEMT diffusion metal-oxide-semiconductor field effect transistor. The CRLH TL can lead to metamaterial transmission line with the dual-band tuning capability. The dual-band operation of the CRLH TL is achieved by the frequency offset and the nonlinear phase slope of the CRLH TL for the matching network of the power amplifier. We have managed only the second- and third-harmonics to obtain the high efficiency with the CRLH TL in dual-band. Also, the proposed power amplifier has been realized by using the harmonic control circuit for not only the output matching network, but also the input matching network for better efficiency. Two operating frequencies are chosen at 900 MHz and 2140 MHz in this work. The measured results show that the output power of 39.83 dBm and 35.17 dBm was obtained at 900 MHz and 2140 MHz, respectively. At this point, we have obtained the power-added efficiency (PAE) and IMD of 60.2 %, -23.17dBc and 67.3 %, -25.67dBc at two operation frequencies, respectively.

• The KIPS Transactions:PartC
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• v.17C no.1
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• pp.119-128
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• 2010

In the shipbuilding industry, due to the global trends where the number of IT (Information Technology) devices of a smart ship have been increased rapidly, the need to develop a new shipboard backbone network has recently emerged for integrating and managing the IT devices of a smart ship efficiently. A shipboard backbone network requires high survivability because it is constructed in automatic and unmanned smart ships where a failure of the backbone network can cause critical problems. The purpose of this paper thus is to study SAN (Ship Area Network) as a efficient shipboard backbone network, considering particularity of shipboard environment and requirement of high survivability. In order to do so, we designed a dual network topology that all network nodes, including the IT devices installed in a smart ship, are connected each other through dual paths, and reuding tht IT devices pnstalles supporices network survivability as well as t Iffic efficiency for the dual network topology. And then, we verified the performance of the suggested SAN by theoretical and practical analysis including the graph theory, the probability theory, implemental specifications, and computer simulations.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.25 no.4
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• pp.401-410
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• 2014

This paper presents a high-efficiency concurrent dual-band Class-E power amplifier(PA) that is based on a multi-harmonic matching network(MHMN). The proposed MHMN controls the impedance at 1.3 GHz, 2.1 GHz, and their second and third harmonics, respectively, by using transmission lines only rather than switches or lumped components. The dual-band Class-E PA is implemented using Avago ATF-50189 GaAs p-HEMT. The PA exhibits a measured output power of 27.1 dBm and 25.7 dBm, a power gain of 6.1 dB and 4.7 dB, and a drain efficiency of 71.2 % and 60.1 % at 1.3 GHz and 2.1 GHz, respectively.

• Journal of IKEEE
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• v.27 no.3
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• pp.251-257
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• 2023

In this paper, we propose a dual-structured self-attention method that improves the lack of regional features of the vision transformer's self-attention. Vision Transformers, which are more computationally efficient than convolutional neural networks in object classification, object segmentation, and video image recognition, lack the ability to extract regional features relatively. To solve this problem, many studies are conducted based on Windows or Shift Windows, but these methods weaken the advantages of self-attention-based transformers by increasing computational complexity using multiple levels of encoders. This paper proposes a dual-structure self-attention using self-attention and neighborhood network to improve locality inductive bias compared to the existing method. The neighborhood network for extracting local context information provides a much simpler computational complexity than the window structure. CIFAR-10 and CIFAR-100 were used to compare the performance of the proposed dual-structure self-attention transformer and the existing transformer, and the experiment showed improvements of 0.63% and 1.57% in Top-1 accuracy, respectively.

• Journal of Korea Multimedia Society
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• v.10 no.6
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• pp.760-769
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• 2007

In this paper, we consider a group mobility model to formulate a clustering mechanism called Dual Coalescent Energy-Efficient Algorithm (DCEE) which is scalable, distributed and energy-efficient for wireless mesh network. The differences of the network nodes will be distinguished to exploit heterogeneity of the network. Furthermore, a topology control, that is, adjusting the transmission range to further reduce power consumption will be integrated with the cluster formation to improve network lifetime and connectivity. Along with network lifetime and power consumption, clusterhead changes will be measured as a performance metric to evaluate the. effectiveness and robustness of the algorithm.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.9 no.6
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• pp.117-123
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• 2009

There are two types of survivability. We find the characteristics of them for networks. And using the dual homing, we analysis the routing cost and link cost. Also we propose the cost-efficient heuristic design method of network topology in order to use survivability. By design samples, we analysis the cost efficiency and show that the new design method can be used to design network topology for survivability easily.

• The Korean Journal of Physiology and Pharmacology
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• v.23 no.2
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• pp.131-139
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• 2019

Manually reviewing electroencephalograms (EEGs) is labor-intensive and demands automated seizure detection systems. To construct an efficient and robust event detector for experimental seizures from continuous EEG monitoring, we combined spectral analysis and deep neural networks. A deep neural network was trained to discriminate periodograms of 5-sec EEG segments from annotated convulsive seizures and the pre- and post-EEG segments. To use the entire EEG for training, a second network was trained with non-seizure EEGs that were misclassified as seizures by the first network. By sequentially applying the dual deep neural networks and simple pre- and post-processing, our autodetector identified all seizure events in 4,272 h of test EEG traces, with only 6 false positive events, corresponding to 100% sensitivity and 98% positive predictive value. Moreover, with pre-processing to reduce the computational burden, scanning and classifying 8,977 h of training and test EEG datasets took only 2.28 h with a personal computer. These results demonstrate that combining a basic feature extractor with dual deep neural networks and rule-based pre- and post-processing can detect convulsive seizures with great accuracy and low computational burden, highlighting the feasibility of our automated seizure detection algorithm.

• Journal of Korean Institute of Industrial Engineers
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• v.10 no.2
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• pp.37-43
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• 1984

For the polymatroidal network, which has set-constraints on arcs, solution procedures to get the weighted maximal flows are investigated. These procedures are composed of the transformation of the polymatroidal network flow problem into a polymatroid intersection problem and a polymatroid intersection algorithm. A greedy polymatroid intersection algorithm is presented, and an example problem is solved. The greedy polymatroid intersection algorithm is a variation of Hassin's. According to these procedures, there is no need to convert the primal problem concerned into dual one. This differs from the procedures of Hassin, in which the dual restricted problem is used.

• Journal of Communications and Networks
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• v.12 no.3
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• pp.246-252
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• 2010

A binary linear integer program is formulated for the problem of expanding the capacity of a fiber optic network and routing the traffic to deliver new interactive multimedia services. A two-phase Lagrangian dual search procedure and a Lagrangian heuristic are developed. Computational results show superior performance of the two-phase subgradient optimization compared with the conventional one-phase approach.

• Journal of Korea Society of Industrial Information Systems
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• v.22 no.1
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• pp.1-9
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• 2017

With the Development of Wireless Network Technology and Wireless Link Technology, Wireless Mesh Network (WMN) is Attracting Attention as a Key Technology to Construct the Wireless Transit Network. The WMN has been Studied for a Long Time in Various Fields, however there are still many Problems that have not been solved yet. One of them is the Routing Problem to find an Optimal path in a Multi-hop Network Composed of Wireless Links. In the Hybrid-WMN, which is one of the Three Types of WMN, Optimal Path Selection Requires Research on Path Search Protocols that Effectively use the Infrastructure Mesh as a Transit Network, Together with Research for a Routing Metric with Excellent Performance. Therefore, this Paper Proposes a Dual Mode-AODV(Ad hoc On-demand Distance Vector) for Hybrid-WMN. Simulation result shows that the Path Selection Delay was Reduced by 52% than AODV when the Proposed Dual Mode-AODV was applied.