• Title/Summary/Keyword: active network

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Active Network for IP Traceback (IP 역추적을 위한 액티브 네트워크 기법 적용 방안)

  • 최병선;이성현;이재광
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2004.05b
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    • pp.420-423
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    • 2004
  • Advance of computer technique becomes efficient of business in recent years. It has become high-speed data transmission and large data transmission. Network and computer system need to increasingly security because advance of computer technique. So this paper analyzes IP Traceback system that prevent cyber attack as hacking and security vulnerability of network. And this paper design IP Traceback system that based on active network.

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Adaptive Queue Management Based On the Change Trend of Queue Size

  • Tang, Liangrui;Tan, Yaomu
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.13 no.3
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    • pp.1345-1362
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    • 2019
  • Most active queue management algorithms manage network congestion based on the size of the queue but ignore the network environment which makes queue size change. It seriously affects the response speed of the algorithm. In this paper, a new AQM algorithm named CT-AQM (Change Trend-Adaptive Queue Management) is proposed. CT-AQM predicts the change trend of queue size in the soon future based on the change rate of queue size and the network environment, and optimizes its dropping function. Simulation results indicate that CT-AQM scheme has a significant improvement in loss-rate and throughput.

Analysis of Research Trends of Lifelong Education through Social Network (사회연결망을 통한 평생교육 연구동향 분석)

  • KIM, Taeyeon;KANG, Beodeul
    • Journal of Fisheries and Marine Sciences Education
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    • v.29 no.1
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    • pp.224-233
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    • 2017
  • This study aims to analyze the research trend of lifelong education in Korea over the last 10 years based on social network analysis. To do this, a dataset has been collected from KCI (Korea Citation Index) database. According to the results of the study, firstly, the current status of lifelong education research by the year in the last 10 years showed a relatively high ratio between 2008 and 2009 and 2014 ~ 2015. Secondly, the most active networks between authors and journals constitute a key group in the order of 'Lifelong Education Study' and 'Lifelong Learning Society'. Thirdly, the research institutes with the largest number of lifelong education research papers are Soongsil University, Dong-Eui University, and Korea National Open University. In the network with the authors' network, the only authors were K8 working at Chonbuk National University, and the co-authors, H4, who works at Kyungpook National University, showed the most active network. Finally, the core keyword network based on the thesis topic was analyzed as having higher connection centrality in the order of 'lifelong education', 'lifelong educator', and 'university lifelong education'.

ZnO nanoparticles with different concentrations inside organic solar cell active layer

  • Saravanan, Shanmugam;Ismail, Yasser A.M.;Silambarasan, Murugesan;Kishi, Naoki;Soga, Tetsuo
    • Advances in Energy Research
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    • v.4 no.4
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    • pp.275-284
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    • 2016
  • In the present work, ZnO nanoparticles (NPs) have been dispersed alone in the same solvent of the active layer for improving performance parameters of the organic solar cells. Different concentrations of the ZnO NPs have been blended inside active layer of the solar cell based on poly(3-hexylthiophene) (P3HT), which forms the hole-transport network, and [6,6]-phenyl-C61-butyric acid methyl ester (PCBM), which forms the electron-transport network. In the present investigations, the ZnO NPs may represent an efficient tool for improving light harvesting through light scattering inside active layer, electron mobility, and electron acceptance strength which tend to improve photocurrent and performance parameters of the investigated solar cell. The fill factor (FF) of the ZnO-doped solar cell increases nearly 14% compared to the non-doped solar cell when the doping is 50%. The present investigations show that ZnO NPs improve power conversion efficiency of the solar cell from 1.23% to 1.64% with increment around 25% that takes place after incorporation of 40% as a volume ratio of the ZnO NPs inside P3HT:PCBM active layer.

Congestion Control Using Active Network and Active Queue Management (액티브 네트워크 기술과 액티브 큐 매니지먼트를 이용한 혼잡제어)

  • Choi, Kee-Hyun;Jang, Kyung-Soo;Shin, Ho-Jin;Shin, Dong-Ryeol
    • Proceedings of the Korea Information Processing Society Conference
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    • 2002.04b
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    • pp.1345-1348
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    • 2002
  • 기존의 TCP/IP 네트워크 상에서 혼잡을 제어하는 일반적인 방법은 end-to-end 피드백 방식이다. 혼잡이 발생했는지의 여부는 수신측의 ACK 신호로 예측할 수밖에 없으므로 RTT(round trip time)가 길어질 경우 혼잡처리 시간이 길어지게 된다. Active Network 기술을 이용한 ACC(Active Congestion Control)의 경우 혼잡이 발생한 라우터에서 전송자에게 직접적으로 정보를 전송하기 때문에 보다 빠른 혼잡처리가 가능하다. 비록 코어 라우터에서 패킷을 누락시키는 방법에 있어 상태정보를 필요로 하는 단점이 있지만 모의 실험 결과 향상된 처리량을 보여주었다. 혼잡처리에 있어 새로운 처리 방식은 AQM(Active Queue Management)이다. 이 방식은 RED 를 기본 모델로 변형된 것이 대부분이며 RED 가 갖는 단점을 개선하고 혼잡처리와 공평성(fairness)을 향상 시키는데 목적이 있다. 본 논문에서는 ACC 의 피드백 메커니즘과 AQM 의 융통성 있는 큐 관리 기법을 이용하여 보다 향상된 혼잡제어 기법을 보이겠다.

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QoS controlled handoff by SNMP information-based routing in active network (액티브 네트워크에서 SNMP 정보기반 라우팅을 이용한 핸드오프 QoS 향상 기법)

  • Jang, Hani;Park, Soo-Hyun;Baik, Doo-Kwon
    • The KIPS Transactions:PartC
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    • v.9C no.6
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    • pp.883-892
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    • 2002
  • Mobile IP has been designed only to maintain communications as they move from place to place, so it doesn't guarantee Quality of Service (QoS). QoS in mobile IP is important to provide multimedia and real-time applications services in mobile environment, and it is closely related to handoff delay. Therefore, handoff delay problem is actively studied to guarantee QoS as a main issue in mobile IP research area. In this paper, we suggest SNMP Information-based routing that adds keyword management method to Information-based routing in active network in order to resolve such a problem, and then suggest QoS controlled handoff based on SNMP Information-based routing. After modeling of suggested method and existing handoff method, simulations are carried out with NS -2 for performance evaluation. The results of simulations show the some improvement on handoff delay, and therefore on QoS improvement.

A Packet Dropping Algorithm based on Queue Management for Congestion Avoidance (폭주회피를 위한 큐 관리 기반의 패킷 탈락 알고리즘)

  • 이팔진;양진영
    • Journal of Internet Computing and Services
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    • v.3 no.6
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    • pp.43-51
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    • 2002
  • In this paper, we study the new packet dropping scheme using an active queue management algorithm. Active queue management mechanisms differ from the traditional drop tail mechanism in that in a drop tail queue packets are dropped when the buffer overflows, while in active queue management mechanisms, packets may be dropped early before congestion occurs, However, it still incurs high packet loss ratio when the buffer size is not large enough, By detecting congestion and notifying only a randomly selected fraction of connection, RED causes to the global synchronization and fairness problem. And also, it is the biggest problem that the network traffic characteristics need to be known in order to find the optimum average queue length, We propose a new efficient packet dropping method based on the active queue management for congestion control. The proposed scheme uses the per-flow rate and fair share rate estimates. To this end, we present the estimation algorithm to compute the flow arrival rate and the link fair rate, We shows the proposed method improves the network performance because the traffic generated can not cause rapid fluctuations in queue lengths which result in packet loss

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Active pulse classification algorithm using convolutional neural networks (콘볼루션 신경회로망을 이용한 능동펄스 식별 알고리즘)

  • Kim, Geunhwan;Choi, Seung-Ryul;Yoon, Kyung-Sik;Lee, Kyun-Kyung;Lee, Donghwa
    • The Journal of the Acoustical Society of Korea
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    • v.38 no.1
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    • pp.106-113
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    • 2019
  • In this paper, we propose an algorithm to classify the received active pulse when the active sonar system is operated as a non-cooperative mode. The proposed algorithm uses CNN (Convolutional Neural Networks) which shows good performance in various fields. As an input of CNN, time frequency analysis data which performs STFT (Short Time Fourier Transform) of the received signal is used. The CNN used in this paper consists of two convolution and pulling layers. We designed a database based neural network and a pulse feature based neural network according to the output layer design. To verify the performance of the algorithm, the data of 3110 CW (Continuous Wave) pulses and LFM (Linear Frequency Modulated) pulses received from the actual ocean were processed to construct training data and test data. As a result of simulation, the database based neural network showed 99.9 % accuracy and the feature based neural network showed about 96 % accuracy when allowing 2 pixel error.

Distributed Control Framework based on Mobile Agent Middleware

  • Lee, Yon-Sik
    • Journal of the Korea Society of Computer and Information
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    • v.25 no.12
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    • pp.195-202
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    • 2020
  • The control system for the efficiency of resource utilization in sensor network environment based on object detection and environmental sensor requires active control function which based on sensor data acquisition and transmission functions and server's data analysis. Using active rule-based mobile agent middleware, this paper proposes a new distributed control framework that reduces the load of central sensor data server in sensor network environment by implementing remote data sensing and Zigbee-based communication with server and data analysis method of server. In addition, we implemented a power-saving system prototype using active rule-based distributed control methods that applied consumer's demand and environmental variables, and verified the validity of the proposed system through experiments and evaluations in the mobile agent middleware environment. The proposed system is a system framework that can efficiently autonomously control distributed objects in the sensor network environment, and it can be applied effectively to the development of demand response service based on optimal power control for the smart power system in the future.

Mechanism of Wenshen Xuanbi Decoction in the treatment of osteoarthritis based on network pharmacology and experimental verification

  • Hankun You;Siyuan Song;Deren Liu;Tongsen Ren;Song Jiang Yin;Peng Wu;Jun Mao
    • The Korean Journal of Physiology and Pharmacology
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    • v.28 no.1
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    • pp.59-72
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    • 2024
  • To investigate the mechanism of Wenshen Xuanbi Decoction (WSXB) in treating osteoarthritis (OA) via network pharmacology, bioinformatics analysis, and experimental verification. The active components and prediction targets of WSXB were obtained from the TCMSP database and Swiss Target Prediction website, respectively. OA-related genes were retrieved from GeneCards and OMIM databases. Protein-protein interaction and functional enrichment analyses were performed, resulting in the construction of the Herb-Component-Target network. In addition, differential genes of OA were obtained from the GEO database to verify the potential mechanism of WSXB in OA treatment. Subsequently, potential active components were subjected to molecular verification with the hub targets. Finally, we selected the most crucial hub targets and pathways for experimental verification in vitro. The active components in the study included quercetin, linolenic acid, methyl linoleate, isobergapten, and beta-sitosterol. AKT1, tumor necrosis factor (TNF), interleukin (IL)-6, GAPDH, and CTNNB1 were identified as the most crucial hub targets. Molecular docking revealed that the active components and hub targets exhibited strong binding energy. Experimental verification demonstrated that the mRNA and protein expression levels of IL-6, IL-17, and TNF in the WSXB group were lower than those in the KOA group (p < 0.05). WSXB exhibits a chondroprotective effect on OA and delays disease progression. The mechanism is potentially related to the suppression of IL-17 and TNF signaling pathways and the down-regulation of IL-6.