• 제어로봇시스템학회:학술대회논문집
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• 1998.10a
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• pp.250-253
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• 1998

This paper presents a multi-modal neural network composed of a preprocessing module and a multi-layer neural network module in order to enhance the nonlinear characteristics of neural network. The former module is based on spectral method using Chebyschev polynomials and transforms input data into spectra. The latter module identifies the system using the spectra generated by the preprocessing module. The omnibus numerical experiments show that the method is applicable to many a nonlinear dynamic system in the real world, and that preprocessing using Chebyschev polynomials reduces the number of neurons required for the multi-layer neural network.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.4
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• pp.1437-1459
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• 2020

Wireless Sensor Networks (WSN) is a distributed Sensor network whose terminals are sensors that can sense and check the environment. Sensors are typically battery-powered and deployed in where the batteries are difficult to replace. Therefore, maximize the consumption of node energy and extend the network's life cycle are the problems that must to face. Low-energy adaptive clustering hierarchy (LEACH) protocol is an adaptive clustering topology algorithm, which can make the nodes in the network consume energy in a relatively balanced way and prolong the network lifetime. In this paper, the novel multi-objective LEACH protocol is proposed, in order to solve the proposed protocol, we design a multi-objective coupling algorithm based on bat algorithm (BA), glowworm swarm optimization algorithm (GSO) and bacterial foraging optimization algorithm (BFO). The advantages of BA, GSO and BFO are inherited in the multi-objective coupling algorithm (MBGF), which is tested on ZDT and SCH benchmarks, the results are shown the MBGF is superior. Then the multi-objective coupling algorithm is applied in the multi-objective LEACH protocol, experimental results show that the multi-objective LEACH protocol can greatly reduce the energy consumption of the node and prolong the network life cycle.

• Journal of Information Processing Systems
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• v.13 no.6
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• pp.1544-1553
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• 2017

The network coding mechanism has attracted much attention because of its advantage of enhanced network throughput which is a desirable characteristic especially in a multi-hop wireless network with limited link capacity such as the device-to-device (D2D) communication network of 5G. COPE proposes to use the XOR-based network coding in the two-hop wireless network topology. For multi-hop wireless networks, the Distributed Coding-Aware Routing (DCAR) mechanism was proposed, in which the coding conditions for two flows intersecting at an intermediate node are defined and the routing metric to improve the coding opportunity by preferring those routes with longer queues is designed. Because the routes with longer queues may increase the delay, DCAR is inefficient in delivering real-time multimedia traffic flows. In this paper, we propose a network coding-aware routing protocol for multi-hop wireless networks that enhances DCAR by considering traffic load distribution and link quality. From this, we can achieve higher network throughput and lower end-to-end delay at the same time for the proper delivery of time-sensitive data flow. The Qualnet-based simulation results show that our proposed scheme outperforms DCAR in terms of throughput and delay.

• Proceedings of the Korean Information Science Society Conference
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• 2008.06d
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• pp.170-175
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• 2008

모든 Network는 대역폭의 한계성을 갖고 있다. Overlay 구조의 Network 또한 예외는 아니다. 이러한 Network 대역폭 자원의 한계성은 Network를 이용한 많은 통신에 있어서 심한 Traffic 을 형성하고 그로 인한 낮은 대역폭을 제공해준다. 이에 대한 해결책으로 Multi-Hour Ability 를 적용할 수 있다. 우리가 살아가는 환경은 서로 다른 시간대가 존재하며, 그로 인한 생활패턴에 따라 Network를 이용하는 정도 또한 시간대별로 다르다. 이 논문에서는 라우팅이 자유로운 Overlay Network를 통해 Multi-Hour Ability를 적용하여 Network에 발생한 Traffic 을 완화시키고, 잉여 대역폭을 효과적으로 사용하며, 보다 안정적인 대역폭 확보를 통해 성능 향상을 할 수 있다는 것을 보인다.

• IEMEK Journal of Embedded Systems and Applications
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• v.6 no.4
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• pp.231-241
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• 2011

Recently, system virtualization has been applied to consumer electronics (CE) such as smart mobile phones. Although multi-core processors have become a viable solution for complex applications of consumer electronics, the issue of utilizing multi-core resources in the virtualization layer has not been researched sufficiently. In this paper, we present a new hypervisor design and implementation for multi-core CE devices. We concretely describe virtualization methods for a multi-core processor and multi-core-related subsystems. We also analyze bottlenecks of network performance in a virtualization environment that supports multimedia applications and propose an efficient virtual interrupt distributor. Our new multi-core hypervisor improves network performance by 5.5 times as compared to a hypervisor without the virtual interrupt distributor.

• Journal of the Korea Society of Computer and Information
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• v.24 no.3
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• pp.19-27
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• 2019

This paper proposes a new method using multi-layer overlapped windows to improve the performance of YOLO network which is vulnerable to detect small dense objects. In particular, the proposed method uses the YOLO Network based on the multi-layer overlapped windows to track small dense vehicles that approach from long distances. The method improves the detection performance for location and size of small vehicles. It allows crossing area of two multi-layer overlapped windows to track moving vehicles from a long distance to a short distance. And the YOLO network is optimized so that GPU computation time due to multi-layer overlapped windows should be reduced. The superiority of the proposed algorithm has been proved through various experiments using captured images from road surveillance cameras.

• Journal of Information Technology Services
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• v.11 no.2
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• pp.291-306
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• 2012

Recently, Wireless Mesh Network (WMN) technology recently has been used in various industries. Especially, a number of multi-channel assignment schemes have been presented to improve the throughput of IEEE 802.11-based multi-hop WMN. However, performance of the conventional multi-channel assignment schemes is not enough to satisfy the industry requirements. We, thus, should study more about the multi-channel assignment scheme in order to enhance the performance. This paper proposes a novel channel assignment scheme that employs Multi-channel and Multi-Interface in the WMN. The proposed scheme can obtain the traffic information of the network and the efficient channel assignment result without any message exchanges. We verify the efficiency of the proposed scheme through the mathematical modeling and the real-world experiments. The results show that the proposed scheme improves the throughput of the network compared with the conventional schemes.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.9
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• pp.1679-1686
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• 2007

Clustering is a topology control approach often used in wireless ad hoc networks to improve scalability and prolong network lifetime. Furthermore, it is also employed to provide semi-management functionalities and capacity enhancement. The usage of clustering topology control technique can also be applied to multi-radio wireless mesh network. This would utilize the advantages of the multi-radio implementation in the network. The aggregation would result to a more stable, connected, scalable and energy-efficient network. On this paper, we design a clustering algorithm for multi-radio wireless mesh network that would use these advantages and would take into consideration both mobility and heterogeneity of the network entities. We also show that the algorithm terminates at a definite time t and the message control overhead complexity is of constant order of O(1) per node.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.11
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• pp.5546-5559
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• 2019

Facial action units (AUs) have recently drawn increased attention because they can be used to recognize facial expressions. A variety of methods have been designed for frontal-view AU detection, but few have been able to handle multi-view face images. In this paper we propose a method for multi-view facial AU detection using a fused multilayer, multi-task, and multi-label deep learning network. The network can complete two tasks: AU detection and facial view detection. AU detection is a multi-label problem and facial view detection is a single-label problem. A residual network and multilayer fusion are applied to obtain more representative features. Our method is effective and performs well. The F1 score on FERA 2017 is 13.1% higher than the baseline. The facial view recognition accuracy is 0.991. This shows that our multi-task, multi-label model could achieve good performance on the two tasks.

• The KIPS Transactions:PartC
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• v.18C no.5
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• pp.317-326
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• 2011

This study is meaningful in that it standardizes various security and defense policies and devices, newly defines characteristics of defense policies and defense techniques, and specify and report various kinds of security polities and devices in order for administrators or users to add and apply the policies when introducing new security policies including the implementation of existing network infra and applying additionally. Therefore, this study aims to divide the policies into ONSU-MF(One Network Security Unit-Multi Function) that classifies one network security device-based policies and OSD-MD(One Security Device-Multi Defense), which implements various security methods by using one security device, and suggest network security infra improvement mechanism through the standardization implementation technique integrating the two methods.