• 제목/요약/키워드: Long-Reach Transmission

검색결과 13건 처리시간 0.018초

Handover based on Maximum Cell Residence Time and Adaptive TTT for LTE-R High-Speed Railways

  • Cho, Hanbyeog;Han, Donghyuk;Shin, Sungjin;Cho, Hyoungjun;Lee, Changsung;Lim, Goeun;Kang, Mingoo;Chung, Jong-Moon
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • 제11권8호
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    • pp.4061-4076
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    • 2017
  • With the development of high-speed railway technologies, train velocities can now reach speeds up to 350 km/h, and higher in the future. In high-speed railway systems (HSRs), loss of communication can result in serious accidents, especially when the train is controlled through wireless communications. For to this reason, operators of Long Term Evolution for Railway (LTE-R) communication systems install eNodeBs (eNBs) with high density to achieve highly reliable communications. However, densely located eNBs can result in unnecessary frequent handovers (HOs) resulting in instability because, during every HO process, there is a period of time in which the communication link is disconnected. To solve this problem, in this paper, an HO scheme based on the maximum cell residence time (CRT) and adaptive time to trigger (aTTT), which are collectively called CaT, is proposed to reduce unnecessary HOs (using CRT estimations) and decrease HO failures by improving the handover command transmission point (HCTP) in LTE-R HSR communications.

접시꽃 (Althaea rosea) 엽육표피에서의 모용의 분화 발달 (Trichome Type and Development in Leaves of Althaea rosea)

  • 김인선;이승희
    • Applied Microscopy
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    • 제35권2호
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    • pp.97-104
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    • 2005
  • 표피조직은 비교적 분화되지 않은 기본 표피세포와 식물에 따라 특수하게 분화되어 형태와 기능이 매우 다른 여러 이형세포로 발달할 수 있다. 본 연구에서는 약용으로 활용되는 접시꽃 (Althaea rosea)의 엽육 표피조직에 발달하는 모용에 대하여 이들 모용 유형에 따른 분화 발달 양상을 주사전자현미경적으로 연구하였다. 접시꽃 엽육 표피상에 발달하는 모용은 크게 단모(simple hairs), 총생모 (non-grandular tufted hairs)와 분비모 (peltate glandular hairs)로 대별되는데, 총생모는 상, 하피 및 하피의 엽맥을 따라 특히 더 발달하는 장상의 총생모 (longtufted hairs, ${\sim}1000{\mu}m,\;2{\sim}10$ branchlet)와 상, 하피 및 하피의 엽신에 더 밀생 분포하는 단상의 총생모 (shorttufted hairs, ${\sim}210{\mu}m,\;2{\sim}7$ branchlet)로 나뉘어진다. 장상의 총생모는 단순모로부터 시작하여 10개까지 분지되며, 분화 초기에는 비교적 일정한 방향으로 분지되는 양상을 보인다. 반면 분비모는 $1{\sim}2$개의 두정세포(head cell, 직경 $20{\sim}35{\mu}m$), 2개의 자루세포(stalk cell, 길이 $10{\sim}15{\mu}m$), 기저세포 (basal cell)로 이루어진다. 또한, 엽신 전체에 발달하는 총생모와는 달리 분비모는 특히 표피면에서 함몰된 망목극(areole)을 따라 분포한다. 이들 모용 중 총생모는 엽육 표피의 보호 및 방어기능을 주로 수행할 것으로 추측되며, 분비모는 두정세포 내에 함유되어 있는 성분이 유용한 이차대사 물질을 분비하는 기능과 보호기능에도 중요한 역할을 하는 것으로 추정된다. 일반적으로 모용은 분비기능이 활발한 식물에서 유용한 이차대사 화합물을 생성, 축적하고 분비하는데 중추적인 기능을 수행하는 것으로 알려져 있어 이들에 대한 세포수준에서의 연구 및 분비되는 유용한 물질에 대한 연구는 구조적 정보 및 적응을 위한 방어기작 규명 등에 이르기까지 매우 유용하게 쓰일 것이다.

A Best Effort Classification Model For Sars-Cov-2 Carriers Using Random Forest

  • Mallick, Shrabani;Verma, Ashish Kumar;Kushwaha, Dharmender Singh
    • International Journal of Computer Science & Network Security
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    • 제21권1호
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    • pp.27-33
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    • 2021
  • The whole world now is dealing with Coronavirus, and it has turned to be one of the most widespread and long-lived pandemics of our times. Reports reveal that the infectious disease has taken toll of the almost 80% of the world's population. Amidst a lot of research going on with regards to the prediction on growth and transmission through Symptomatic carriers of the virus, it can't be ignored that pre-symptomatic and asymptomatic carriers also play a crucial role in spreading the reach of the virus. Classification Algorithm has been widely used to classify different types of COVID-19 carriers ranging from simple feature-based classification to Convolutional Neural Networks (CNNs). This research paper aims to present a novel technique using a Random Forest Machine learning algorithm with hyper-parameter tuning to classify different types COVID-19-carriers such that these carriers can be accurately characterized and hence dealt timely to contain the spread of the virus. The main idea for selecting Random Forest is that it works on the powerful concept of "the wisdom of crowd" which produces ensemble prediction. The results are quite convincing and the model records an accuracy score of 99.72 %. The results have been compared with the same dataset being subjected to K-Nearest Neighbour, logistic regression, support vector machine (SVM), and Decision Tree algorithms where the accuracy score has been recorded as 78.58%, 70.11%, 70.385,99% respectively, thus establishing the concreteness and suitability of our approach.