• Title/Summary/Keyword: Decision Algorithm

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Strategy for Task Offloading of Multi-user and Multi-server Based on Cost Optimization in Mobile Edge Computing Environment

  • He, Yanfei;Tang, Zhenhua
    • Journal of Information Processing Systems
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    • v.17 no.3
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    • pp.615-629
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    • 2021
  • With the development of mobile edge computing, how to utilize the computing power of edge computing to effectively and efficiently offload data and to compute offloading is of great research value. This paper studies the computation offloading problem of multi-user and multi-server in mobile edge computing. Firstly, in order to minimize system energy consumption, the problem is modeled by considering the joint optimization of the offloading strategy and the wireless and computing resource allocation in a multi-user and multi-server scenario. Additionally, this paper explores the computation offloading scheme to optimize the overall cost. As the centralized optimization method is an NP problem, the game method is used to achieve effective computation offloading in a distributed manner. The decision problem of distributed computation offloading between the mobile equipment is modeled as a multi-user computation offloading game. There is a Nash equilibrium in this game, and it can be achieved by a limited number of iterations. Then, we propose a distributed computation offloading algorithm, which first calculates offloading weights, and then distributedly iterates by the time slot to update the computation offloading decision. Finally, the algorithm is verified by simulation experiments. Simulation results show that our proposed algorithm can achieve the balance by a limited number of iterations. At the same time, the algorithm outperforms several other advanced computation offloading algorithms in terms of the number of users and overall overheads for beneficial decision-making.

A Study of Improving on Test Costs in Decision Trees (Decision Tree의 Test Cost 개선에 관한 연구)

  • 석현태
    • Proceedings of the Korean Information Science Society Conference
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    • 2002.10c
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    • pp.223-225
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    • 2002
  • Decision tree는 목표 데이터에 대한 계층적 관점을 보여준다는 의미에서 데이터를 보다 잘 이해하는데 많은 도움이 되나 탐욕법(greedy algorithm)에 의한 트리 생성법의 한계로 인해 최적의 예측자라고는 할 수가 없다. 이와 같은 약점을 보완하기 위하여 일반적 방법으로 생성한 decision tree에 대하여 다차원 연관규칙 알고리즘을 적용함으로써 짱은 길이의 최적 부분 규칙집합을 구하는 방법을 제시하였고 실험을 통해 그와 같은 사실을 확인하였다.

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Markov Decision Process-based Potential Field Technique for UAV Planning

  • MOON, CHAEHWAN;AHN, JAEMYUNG
    • Journal of the Korean Society for Industrial and Applied Mathematics
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    • v.25 no.4
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    • pp.149-161
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    • 2021
  • This study proposes a methodology for mission/path planning of an unmanned aerial vehicle (UAV) using an artificial potential field with the Markov Decision Process (MDP). The planning problem is formulated as an MDP. A low-resolution solution of the MDP is obtained and used to define an artificial potential field, which provides a continuous UAV mission plan. A numerical case study is conducted to demonstrate the validity of the proposed technique.

The Structure of Boundary Decision Using the Back Propagation Algorithms (역전파 알고리즘을 이용한 경계결정의 구성에 관한 연구)

  • Lee, Ji-Young
    • The Journal of Information Technology
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    • v.8 no.1
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    • pp.51-56
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    • 2005
  • The Back propagation algorithm is a very effective supervised training method for multi-layer feed forward neural networks. This paper studies the decision boundary formation based on the Back propagation algorithm. The discriminating powers of several neural network topology are also investigated against five manually created data sets. It is found that neural networks with multiple hidden layer perform better than single hidden layer.

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A Study on Hybrid Feature Selection in Intrusion Detection System (침입탐지시스템에서 하이브리드 특징 선택에 관한 연구)

  • Han Myeong-Muk
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 2006.05a
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    • pp.279-282
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    • 2006
  • 네트워크를 기반으로 한 컴퓨터 시스템이 현대 사회에 있어서 더욱 더 불가결한 역할을 하는 것에 따라, 네트워크 기반 컴퓨터 시스템은 침입자의 침입 목표가 되고 있다. 이를 보호하기 위한 침입탐지시스템(Intrusion Detection System : IDS)은 점차 중요한 기술이 되었다. 침입탐지시스템에서 패턴들을 분석한 후 정상/비정상을 판단 및 예측하기 위해서는 초기단계인 특징추출이나 선택이 매우 중요한 부분이 되고 있다. 본 논문에서는 IDS에서 중요한 부분인 feature selection을 Data Mining 기법인 Genetic Algorithm(GA)과 Decision Tree(DT)를 적용해서 구현했다.

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Trellis Defection of Tamed FM with the DLMS and Convergence

  • Kang, Min-Goo;Lee, Yang-Won;Cho, Hyung-Rae;Kang, Sung-Chul
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.1 no.2
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    • pp.199-207
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    • 1997
  • The Maximum Likelihood Sequence Estimation scheme is modified to improve the error performance of the correlative coding in the Tamed FM. To remove intersymbol interference, the Decision Feedback Equalization scheme with the delayed LMS algorithm and the Viterbi algorithm(10-symbol delay) in the delayed adaptive equalization are proposed for the performance of decision-directed adaptive equalization under the High Frequency channels, and the condition of convergence is analyzed.

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Knowledge Representation Using Decision Trees Constructed Based on Binary Splits

  • Azad, Mohammad
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.14 no.10
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    • pp.4007-4024
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    • 2020
  • It is tremendously important to construct decision trees to use as a tool for knowledge representation from a given decision table. However, the usual algorithms may split the decision table based on each value, which is not efficient for numerical attributes. The methodology of this paper is to split the given decision table into binary groups as like the CART algorithm, that uses binary split to work for both categorical and numerical attributes. The difference is that it uses split for each attribute established by the directed acyclic graph in a dynamic programming fashion whereas, the CART uses binary split among all considered attributes in a greedy fashion. The aim of this paper is to study the effect of binary splits in comparison with each value splits when building the decision trees. Such effect can be studied by comparing the number of nodes, local and global misclassification rate among the constructed decision trees based on three proposed algorithms.

Routing Decision with Link-weight Calculating Function in WDM Switching Networks

  • Charoenphetkul, Pongnatee;Thipchaksurat, Sakchai;Varakulsiripunth, Ruttikorn
    • 제어로봇시스템학회:학술대회논문집
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    • 2004.08a
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    • pp.1346-1349
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    • 2004
  • In this paper, we have proposed the new link-weight calculating function using for routing decision in WDM networks. The proposed link-weight calculating functions includes following factors those are available wavelengths per link, distance loss, total wavelengths, and limited wavelength conversion. The calculated link-weight is applied into the algorithm of routing decision in order to determine the available lightpath that qualifies user requests. The objective is to improve the performance of wavelengths assignment with fast determining the suitable lightpath by using the proposed link-weights calculating function. The analytical model of WDM switching networks is introduced for numerical analysis. The link-weight calculating function is performed. Finally, the performance of proposed algorithm is displayed with numerical results in term of the blocking probability, the probability that connection requests from users are rejected due to there are no available lightpath to be assigned for them. It is also shown that the blocking probability is varied in depending on the number of available wavelengths and the degree of wavelength conversion. The numerical results also show that the proposed link-weight calculating function is more cost-effective choice for the routing decision in WDM switching networks.

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Communication Equalizer Algorithms with Decision Feedback based on Error Probability (오류 확률에 근거한 결정 궤환 방식의 통신 등화 알고리듬)

  • Kim, Nam-Yong;Hwang, Young-Soo
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.12 no.5
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    • pp.2390-2395
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    • 2011
  • For intersymbol interference (ISI) compensation from communication channels with multi-path fading and impulsive noise, a decision feedback equalizer algorithm that minimizes Euclidean distance of error probability is proposed. The Euclidean distance of error probability is defined as the quadratic distance between the probability error signal and Dirac-delta function. By minimizing the distance with respect to equalizer weight based on decision feedback structures, the proposed decision feedback algorithm has shown to have significant effect of residual ISI cancellation on severe multipath channels as well as robustness against impulsive noise.

Complex-Channel Blind Equalization using Euclidean-Distance Algorithms with Decision-Directed Modes (Decision-Directed 모드와 유클리드 거리 알고리듬을 사용한 복소채널의 블라인드 등화)

  • Kim, Namyong
    • The Journal of Korea Institute of Information, Electronics, and Communication Technology
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    • v.3 no.3
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    • pp.73-80
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    • 2010
  • Complex-valued blind algorithms which are based on constant modulus error and Euclidian distance (ED) between two probability density functions show relatively poor performance in spite of the advantages of information theoretic learning since the inherent characteristics of the constant modulus error prevent the algorithm from coping with the symbol phase rotation caused by the complex channels. In this paper, we show that the symbol phase rotation problem can be avoided and the advantages of information theoretic learning can be preserved by introducing decision-directed mode to the blind algorithm whenever the equalizer output power lies in the neighborhood of multi-modulus levels. Simulation results through MSE convergence and constellation comparison for severely distorted complex channels show significantly enhanced performance of symbol-point concentration and no phase rotation problems caused by the complex channel models.

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