• 제목/요약/키워드: Node Comparison

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Approximated Outage Probability for ADF Relay Systems with Burst MPSK and MQAM Symbol Transmission

  • Ko, Kyunbyoung;Lim, Sungmook
    • International Journal of Contents
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    • 제11권1호
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    • pp.7-14
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    • 2015
  • In this paper, we derive the outage probability for M-ary phase shifting keying (MPSK) and M-ary quadrature amplitude modulation (MQAM) burst transmission (BT) of adaptive decode-and-forward (ADF) cooperative relay systems over quasi-static Rayleigh fading channels. Within a burst, there are pilot symbols and data symbols. Pilot symbols are used for channel estimation schemes and each relay node's transmission mode selection schemes. At first, we focus on ADF relay systems in which the probability density function (PDF) is derived on the basis of error events at relay nodes corresponding to channel estimation errors. Next, the average outage probability is derived as an approximate expression for an arbitrary link signal-to-noise ratio (SNR) for different modulation orders. Its accuracy is demonstrated by comparison with simulation results. Further, it is confirmed that BT-ADF relay systems with pilot symbol based channel estimation schemes enables to select correctly decoded relay nodes without additional signaling between relay nodes and the destination node, and it is verified that the ideal performance is achieved with small SNR loss.

타부 서치 알고리즘 기반의 무선 센서 네트워크에서 센서 노드 배치 (Sensor Node Deployment in Wireless Sensor Networks Based on Tabu Search Algorithm)

  • 장길웅
    • 한국정보통신학회논문지
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    • 제19권5호
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    • pp.1084-1090
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    • 2015
  • 본 논문에서는 무선 센서 네트워크에서 네트워크의 감시영역을 최대화하기 위해 센서 노드를 효과적으로 배치하는 타부 서치 알고리즘을 제안한다. 무선 센서 네트워크에서 센서 노드의 수가 증가하게 되면 네트워크의 감시영역을 최대화하기 위한 계산량은 급격히 늘어나게 된다. 본 논문에서는 센서 배치 밀도가 높은 네트워크에서 적정한 실행 시간 내에 네트워크의 감시영역을 최대화하는 타부 서치 알고리즘을 제안하며, 효율적인 검색을 위해 타부 서치 알고리즘의 새로운 이웃해 생성 동작을 제안한다. 제안된 알고리즘은 네트워크의 최대 감시영역과 실행속도 관점에서 성능을 평가하며, 평가 결과에서 제안된 알고리즘이 기존의 알고리즘에 비해 성능이 우수함을 보인다.

tnet과 WNET의 가중 네트워크 중심성 지수 비교 연구 (A Comparison Study on the Weighted Network Centrality Measures of tnet and WNET)

  • 이재윤
    • 정보관리학회지
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    • 제30권4호
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    • pp.241-264
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    • 2013
  • 이 연구에서는 공개된 가중 네트워크 분석용 소프트웨어인 Opsahl의 tnet과 이재윤의 WNET에서 지원하는 가중 네트워크 중심성 지수를 비교 분석해보았다. tnet은 가중 연결정도중심성, 가중 근접중심성, 가중 매개중심성을 지원하고, WNET은 최근접이웃중심성, 평균연관성, 평균프로파일연관성, 삼각매개중심성을 지원한다. 가상 데이터를 대상으로 한 분석에서 tnet의 중심성 지수는 링크 가중치의 선형변화에 민감한 반면 WNET의 중심성 지수는 선형 변화에 영향을 받지 않았다. 실제 네트워크 6종을 대상으로 가중 네트워크 중심성을 측정하고 결과를 비교하여 두 소프트웨어의 가중 네트워크 중심성지수들의 특징을 파악하고 중심성 지수 간 관계를 살펴보았다.

K-Shortest Path 알고리즘에 기초한 새로운 대역폭 보장 라우팅 알고리즘 (New Bandwidth Guaranteed Routing Algorithms based on K-Shortest Path Algorithm)

  • 이준호;이성호
    • 한국통신학회논문지
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    • 제28권11B호
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    • pp.972-984
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    • 2003
  • 본 논문에서는 MPLS 네트워크에서 LSP 설정에 적용될 수 있는 새로운 대역폭 보장 온라인 라우팅 알고리즘들을 제안하고 기존의 알고리즘들과 함께 그 성능을 시뮬레이션을 통해서 평가한다. 제안된 방식은 기존의 WSP나 SWP 알고리즘을 K-shortest loopless path 알고리즘에 기초해서 확장시킨 형태를 가진다. 시뮬레이션을 통해서 accepted bandwidth, accepted request number 그리고 average path length라는 성능을 평가한 결과, 모든 노드들이 LSP 설정의 ingress나 egress 노드가 될 수 있는 상황에서 제안된 방식들이 전반적으로 우수한 성능을 보였는데 네트워크 부하가 큰 경우에는 특히, 최소 홉 경로에 기초한 방식들이 좋은 성능을 보임을 알 수 있다.

기억-탐험 방법을 이용한 단일-질의 확률 로드맵 계획 알고리즘 (Single-Query Probabilistic Roadmap Planning Algorithm using Remembering Exploration Method)

  • 김정태;김대진
    • 한국정보과학회논문지:컴퓨팅의 실제 및 레터
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    • 제16권4호
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    • pp.487-491
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    • 2010
  • 고차원의 구성 공간 상에서 빠르게 동작하는 경로 계획을 위하여, 본 논문에서는 단일-질의 알고리즘의 일종인 새로운 경로 계획 알고리즘을 제안한다. 단일-질의 알고리즘의 동작과 탐험 알고리즘의 유사성에 주목하여 탐험 알고리즘의 하나인 기억-탐험(Remembering Exploration) 방법을 응용하여, 로드맵의 한 노드를 선택하여 그 주위의 자유 공간상에 있는 노드들을 새로 로드맵에 추가하는 방법으로 로드맵을 키워나가는 것이 본 논문이 제안하는 알고리즘이다. 성능 평가를 위하여 2차원 공간상에서의 경로 계획 문제와 3차원 공간상의 움직임 계획 문제를 제안하는 알고리즘과 다른 잘 알려진 알고리즘을 이용하여 성능 비교 실험을 하였으며, 경로의 발견 유무와 발견하기까지의 시간 비교를 한 결과 제안하는 알고리즘의 성능 우위를 확인할 수 있었다.

공간 탐색 최적화 알고리즘을 이용한 K-Means 클러스터링 기반 다항식 방사형 기저 함수 신경회로망: 설계 및 비교 해석 (K-Means-Based Polynomial-Radial Basis Function Neural Network Using Space Search Algorithm: Design and Comparative Studies)

  • 김욱동;오성권
    • 제어로봇시스템학회논문지
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    • 제17권8호
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    • pp.731-738
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    • 2011
  • In this paper, we introduce an advanced architecture of K-Means clustering-based polynomial Radial Basis Function Neural Networks (p-RBFNNs) designed with the aid of SSOA (Space Search Optimization Algorithm) and develop a comprehensive design methodology supporting their construction. In order to design the optimized p-RBFNNs, a center value of each receptive field is determined by running the K-Means clustering algorithm and then the center value and the width of the corresponding receptive field are optimized through SSOA. The connections (weights) of the proposed p-RBFNNs are of functional character and are realized by considering three types of polynomials. In addition, a WLSE (Weighted Least Square Estimation) is used to estimate the coefficients of polynomials (serving as functional connections of the network) of each node from output node. Therefore, a local learning capability and an interpretability of the proposed model are improved. The proposed model is illustrated with the use of nonlinear function, NOx called Machine Learning dataset. A comparative analysis reveals that the proposed model exhibits higher accuracy and superb predictive capability in comparison to some previous models available in the literature.

The Comparison of the 3D graph for the energy-equal of LEACH-Mobile

  • Jang, Seong Pil;Jung, Kye-Dong;Lee, Jong-Yong
    • International journal of advanced smart convergence
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    • 제6권1호
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    • pp.57-67
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    • 2017
  • In this paper, propose an algorithm to improve network lifetime by equally consuming energy of LEACH - Mobile sensor nodes. LEACH is one of energy efficient protocols. However, we did not consider the mobility of nodes. Therefore, the transmission reception success rate of the moving data is reduced. LEACH-Mobile is a protocol that has improved the drawbacks of these LEACH. However, since LEACH-Mobile has a larger number of data packets and consumes more energy than LEACH, it has a disadvantage that the lifetime of the network is short. In order to improvement these disadvantage, Based on the average of the remaining energy of the node, cluster heads are elected with a number of nodes whose energies are larger than the average of the remaining energy from the member nodes. After that, by trying to increase the lifetime of the network by equalizing the remaining energy. In to confirm whether improve the lifetime of the network, In this paper, the number of nodes and the position of all nodes are varied for each specific round, the rest energy is equalized, and the algorithm which uniformly selected the cluster head is compared with LEACH.

Feasibility and Safety of Totally Laparoscopic Radical Gastrectomy for Advanced Gastric Cancer: Comparison with Early Gastric Cancer

  • Lee, Seungyeob;Lee, Hayemin;Lee, Junhyun
    • Journal of Gastric Cancer
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    • 제18권2호
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    • pp.152-160
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    • 2018
  • Purpose: Totally laparoscopic gastrectomy (TLG) for advanced gastric cancer (AGC) is a technically and oncologically challenging procedure for surgeons. This study aimed to compare the oncologic feasibility and technical safety of TLG for AGC versus early gastric cancer (EGC). Materials and Methods: Between 2011 and 2016, 535 patients (EGC, 375; AGC, 160) underwent curative TLG for gastric cancer. Clinicopathologic characteristics and surgical outcomes of both patient groups were analyzed and compared. Results: Patients with AGC required a longer operation time and experienced more intraoperative blood loss than those with EGC did. However, patients from both the AGC and EGC groups demonstrated similar short-term surgical outcomes such as postoperative morbidity (14.4% vs. 13.3%, P=0.626), mortality (0% vs. 0.5%, P=0.879), time-to-first oral intake (2.7 days for both groups, P=0.830), and postoperative hospital stay (10.2 days vs. 10.1 days, P=0.886). D2 lymph node dissection could be achieved in the AGC group (95%), with an adequate number of lymph nodes being dissected ($36.0{\pm}14.9$). In the AGC group, the 3-year overall and disease-free survival rates were 80.5% and 73.7%, respectively. Conclusions: TLG is as safe and effective for AGC as it is for EGC.

절제술이 시행되었던 폐암환자에서 종격동 림프절 크기와 암전이에 관한 상관 관계 (Inter Relationship between the Size of the Mediastinal Lymph Node 4 the Status of Metastases of Lung Carcinoma)

  • 이두연
    • Journal of Chest Surgery
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    • 제25권11호
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    • pp.1180-1184
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    • 1992
  • The use of computed tomography of the chest in mediastinal staging of lung carcinoma lies the premiss that malignant lymph nodes are larger than benign ones. We have studied the size of mediastinal lymph nodes & the malignancy rate in 55 lung carcinomas from March 1990 to July 1992 at the Department of Thoracic and Cardiovascular Surgery, Yongdong Severance Hospital, Yonsei University College of medicine. The lack of relationship between the size of mediastinal lymph node and the probability of malignancy helps to clarify the limitations of the use of computed tomography in the staging of the mediastinum in lung carcinoma. There was no tendency for all malignant lymph nodes to be larger than benign nodes. To allow comparison with our data, malignancy rates for all lymph nodes larger than 10mm are 24.8% in sensitivity & benign rates for all lymph nodes less than 10mm are 96% in specificity. But all mediastinal lymph nodes larger than 30mm are metastatic lymph nodes in our cases. We are going to try to have thoracotomy for complete resection of lung carcinoma as possible as we can if there no evidence of contralateral mediastinal metastases of lymph nodes, even though there are large mediastinal lymph nodes in lung carcinoma.

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Spark 기반에서 Python과 Scala API의 성능 비교 분석 (Performance Comparison of Python and Scala APIs in Spark Distributed Cluster Computing System)

  • 지경엽;권영미
    • 한국멀티미디어학회논문지
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    • 제23권2호
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    • pp.241-246
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    • 2020
  • Hadoop is a framework to process large data sets in a distributed way across clusters of nodes. It has been a popular platform to process big data, but in recent years, other platforms became competitive ones depending on the characteristics of the application. Spark is one of distributed platforms to enable real-time data processing and improve overall processing performance over Hadoop by introducing in-memory processing instead of disk I/O. Whereas Hadoop is designed to work on Java and data analysis is processed using Java API, Spark provides a variety of APIs with Scala, Python, Java and R. In this paper, the goal is to find out whether the APIs of different programming languages af ect the performances in Spark. We chose two popular APIs: Python and Scala. Python is easy to learn and is used in AI domain in a wide range. Scala is a programming language with advantages of parallelism. Our experiment shows much faster processing with Scala API than Python API. For the performance issues on AI-based analysis, further study is needed.