scholarly journals A Hierarchical Mapping System for Flat Identifier to Locator Resolution Based on Active Degree

2018 ◽  
Vol 10 (8) ◽  
pp. 75
Author(s):  
Jianqiang Liu ◽  
Shuai Huo ◽  
Yi Wang
Keyword(s):  
Decision Process ◽  
Network Architecture ◽  
Local Level ◽  
Global Level ◽  
Ip Address ◽  
Mapping System ◽  
Markov Decision ◽  
Simulation Results ◽  
Logical Topology ◽  
Different Levels

Overloading of IP address semantics appeals for a new network architecture based on Identifier (ID)/Locator separation. The challenge of Identifier (ID)/Locator separation is how to solve the scalability and efficiency challenges of identity-to-location resolution. By analyzing the requirements of the Identifier (ID)/Locator separation protocol, this paper proposes a hierarchical mapping architecture on active-degree (HMAA). This HMAA was divided into three levels: active local level, neutral transfer level, and inert global level. Each mapping item is dynamically allocated to different levels to ensure minimizing delay according to its activity characteristics. The top layer CHORD is constructed by the Markov Decision Process, which can keep consistency between the physical topology and the logical topology. The simulation results on delay time show that HMAA can satisfy the scalability and efficiency requirements of an Identifier (ID)/Locator separation network.

scholarly journals A New Decentralized Approach of Multiagent Cooperative Pursuit Based on the Iterated Elimination of Dominated Strategies Model

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Mohammed El Habib Souidi ◽  
Songhao Piao
Keyword(s):  
Game Theory ◽  
Coalition Formation ◽  
Decision Process ◽  
Pursuit Evasion ◽  
Dominated Strategies ◽  
Markov Decision ◽  
Simulation Results ◽  
The Given ◽  
Dynamic Formation ◽  
Motion Strategy

Game Theory is a promising approach to acquire coalition formations in multiagent systems. This paper is focused on the importance of the distributed computation and the dynamic formation and reformation of pursuit groups in pursuit-evasion problems. In order to address this task, we propose a decentralized coalition formation algorithm based on the Iterated Elimination of Dominated Strategies (IEDS). This Game Theory process is common to solve problems requiring the withdrawal of dominated strategies iteratively. Furthermore, we have used the Markov Decision Process (MDP) principles to control the motion strategy of the agents in the environment. The simulation results demonstrate the feasibility and the validity of the given approach in comparison with different decentralized methods.

scholarly journals A Smart Cache Content Update Policy Based on Deep Reinforcement Learning

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Lincan Li ◽  
Chiew Foong Kwong ◽  
Qianyu Liu ◽  
Jing Wang
Keyword(s):  
Decision Process ◽  
Training Data ◽  
Q Learning ◽  
Learning Network ◽  
The Neural Network ◽  
Markov Decision ◽  
Experience Replay ◽  
Average Latency ◽  
Simulation Results ◽  
Cache Hit Ratio

This paper proposes a DRL-based cache content update policy in the cache-enabled network to improve the cache hit ratio and reduce the average latency. In contrast to the existing policies, a more practical cache scenario is considered in this work, in which the content requests vary by both time and location. Considering the constraint of the limited cache capacity, the dynamic content update problem is modeled as a Markov decision process (MDP). Besides that, the deep Q-learning network (DQN) algorithm is utilised to solve the MDP problem. Specifically, the neural network is optimised to approximate the Q value where the training data are chosen from the experience replay memory. The DQN agent derives the optimal policy for the cache decision. Compared with the existing policies, the simulation results show that our proposed policy is 56%–64% improved in terms of the cache hit ratio and 56%–59% decreased in terms of the average latency.

Adaptive Cross-layer Optimization Based on Markov Decision Process

1970 ◽  
Vol 108 (2) ◽  
pp. 39-42
Author(s):  
Z. Velickovic ◽  
M. Jevtovic
Keyword(s):  
Markov Decision Process ◽  
Decision Process ◽  
Cross Layer ◽  
Cross Layer Design ◽  
Protocol Stack ◽  
Wireless Multimedia ◽  
Average Value ◽  
Cross Layer Optimization ◽  
Markov Decision ◽  
Simulation Results

In order to satisfy QoS demands of wireless multimedia application it is necessary to make an optimization on several ISO-OSI layers in the protocol stack. In this paper an optimization cross-layer algorithm has been applied based on Markov decision process (MDP). The wireless communication system with one user has been optimized by the transmitting policies in order to maximize the throughput along with the optimization of the average value of the engaged power, satisfying the demanded BER and the average value of rejected packets. Simulation results show that the application of cross-layer design based on MDP is justified. Ill. 2, bibl. 9 (in English; abstracts in English and Lithuanian).http://dx.doi.org/10.5755/j01.eee.108.2.141

Do You Want to See the Tree? Ignore the Forest

2014 ◽  
Vol 61 (3) ◽  
pp. 205-214 ◽  
Author(s):  
Nicolas Poirel ◽  
Claire Sara Krakowski ◽  
Sabrina Sayah ◽  
Arlette Pineau ◽  
Olivier Houdé ◽  
...  
Keyword(s):  
Visual Recognition ◽  
Local Level ◽  
Local Information ◽  
Visual Environment ◽  
Global Information ◽  
Focus Attention ◽  
Global Level ◽  
Hierarchical Stimuli ◽  
Priming Paradigm ◽  
The One

The visual environment consists of global structures (e.g., a forest) made up of local parts (e.g., trees). When compound stimuli are presented (e.g., large global letters composed of arrangements of small local letters), the global unattended information slows responses to local targets. Using a negative priming paradigm, we investigated whether inhibition is required to process hierarchical stimuli when information at the local level is in conflict with the one at the global level. The results show that when local and global information is in conflict, global information must be inhibited to process local information, but that the reverse is not true. This finding has potential direct implications for brain models of visual recognition, by suggesting that when local information is conflicting with global information, inhibitory control reduces feedback activity from global information (e.g., inhibits the forest) which allows the visual system to process local information (e.g., to focus attention on a particular tree).

A Densely Connected Network Based on U-Net for Medical Image Segmentation

2021 ◽  
Vol 17 (3) ◽  
pp. 1-14
Author(s):  
Zhenzhen Yang ◽  
Pengfei Xu ◽  
Yongpeng Yang ◽  
Bing-Kun Bao
Keyword(s):  
Feature Extraction ◽  
Image Segmentation ◽  
Loss Function ◽  
Network Architecture ◽  
Medical Image ◽  
Medical Image Segmentation ◽  
Cross Entropy ◽  
Loss Functions ◽  
Feature Maps ◽  
Different Levels

The U-Net has become the most popular structure in medical image segmentation in recent years. Although its performance for medical image segmentation is outstanding, a large number of experiments demonstrate that the classical U-Net network architecture seems to be insufficient when the size of segmentation targets changes and the imbalance happens between target and background in different forms of segmentation. To improve the U-Net network architecture, we develop a new architecture named densely connected U-Net (DenseUNet) network in this article. The proposed DenseUNet network adopts a dense block to improve the feature extraction capability and employs a multi-feature fuse block fusing feature maps of different levels to increase the accuracy of feature extraction. In addition, in view of the advantages of the cross entropy and the dice loss functions, a new loss function for the DenseUNet network is proposed to deal with the imbalance between target and background. Finally, we test the proposed DenseUNet network and compared it with the multi-resolutional U-Net (MultiResUNet) and the classic U-Net networks on three different datasets. The experimental results show that the DenseUNet network has significantly performances compared with the MultiResUNet and the classic U-Net networks.

scholarly journals Development of a Shipment Policy for Collection Centers

Mathematics ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 1385
Author(s):  
Irais Mora-Ochomogo ◽  
Marco Serrato ◽  
Jaime Mora-Vargas ◽  
Raha Akhavan-Tabatabaei
Keyword(s):  
Climate Change ◽  
Natural Disasters ◽  
Markov Decision Process ◽  
Decision Process ◽  
Necessary Conditions ◽  
Decision Makers ◽  
Humanitarian Organizations ◽  
The World ◽  
Markov Decision ◽  
Unsatisfied Demand

Natural disasters represent a latent threat for every country in the world. Due to climate change and other factors, statistics show that they continue to be on the rise. This situation presents a challenge for the communities and the humanitarian organizations to be better prepared and react faster to natural disasters. In some countries, in-kind donations represent a high percentage of the supply for the operations, which presents additional challenges. This research proposes a Markov Decision Process (MDP) model to resemble operations in collection centers, where in-kind donations are received, sorted, packed, and sent to the affected areas. The decision addressed is when to send a shipment considering the uncertainty of the donations’ supply and the demand, as well as the logistics costs and the penalty of unsatisfied demand. As a result of the MDP a Monotone Optimal Non-Decreasing Policy (MONDP) is proposed, which provides valuable insights for decision-makers within this field. Moreover, the necessary conditions to prove the existence of such MONDP are presented.

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