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检索条件"任意字段=2021 3rd International Conference on Computer Modeling, Simulation and Algorithm, CMSA 2021"

共 219 条记录，以下是151-160 订阅

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RobotDrlSim: A Real Time Robot simulation Platform for Reinforcement Learning and Human Interactive Demonstration Learning 3

RobotDrlSim: A Real Time Robot Simulation Platform for Reinf...

引用

2020 3rd international conference on modeling, simulation and Optimization Technologies and Applications, MSOTA 2020

作者： Sun, Te Gong, Liang Li, Xvdong Xie, Shenghan Chen, Zhaorun Hu, Qizi Filliat, David School of Mechanical Engineering Shanghai Jiao Tong University Shanghai China Computer Science and System Engineering Department and Inria Flowers Team Ensta Institut Polytechnique Paris Palaiseau France

Deep reinforcement learning (DRL) techniques give robotics research an AI boost in many applications. In order to simultaneously accommodate the complex robotic behaviour simulation and DRL algorithm verification, a new simulation platform, namely the RobotDrlSim, is proposed. First, we design a standardized API interfacing mechanism for coordinating diverse environments on RobotDrlSim platform, where PyBullet simulator is equipped with an API to form a physical engine for robotics simulation. Second, benchmark DRL models are included in the baseline library for evaluation. Third, real-Time human-robot interactions can be captured and imported to drive the RobotDrlSim tasks, which provide big data-stream for reinforcement learning. Experimentations show that cutting-edge DRL algorithms developed in python can be seamlessly deployed to the robots, and human interactions can be availed in training the robots. RobotDrlSim is valid for efficiently developing DRL algorithms for artificial intelligence models of robots, and it is especially suitable for the robot educational purposes. © Published under licence by IOP Publishing Ltd.

关键词： simulation platform

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UAV-enabled Edge Computing for Virtual Reality 3

UAV-enabled Edge Computing for Virtual Reality

引用

3rd international conference on Advanced Information Science and System, AISS 2021

作者： Ding, Shengjie Liu, Juan Xie, Lingfu School of Electrical Engineering and Computer Science Ningbo University China

ISBN: (纸本)9781450385862

5G communication promotes the development of VR (Virtual Reality) applications, providing users with immersive experiences. To accomplish VR tasks with large computation and low delay demands, an unmanned aerial vehicle (UAV)-enabled MEC (Mobile Edge Computing) method is proposed to assist VR devices in the rendering process. Under the constraints imposed by the VR characteristics and the device energy, the UAV flight trajectory and the VR rendering mode are jointly optimized to maximize the rendering completion rate of the VR tasks. This problem is modeled as a Markov decision process. To find the optimal policy, a UAV aided rendering algorithm is proposed in the framework of deep reinforcement learning. Specifically, the TD3 (Twin Delayed Deep Deterministic Policy Gradient) algorithm is applied to schedule the UAV trajectory and VR rendering mode to meet the requirements of the randomly arriving VR tasks as much as possible. simulation results show that the proposed method outperforms baseline strategies in both the rendering completion rate and the convergence speed. © 2021 ACM.

关键词： Reinforcement learning

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Energy and Spectrum-Aware Cluster-Based Routing in Cognitive Radio Sensor Networks 1

引用

3rd international conference on Futuristic Trends in Network and Communication Technologies, FTNCT 2020

作者： Gatate, Veeranna Agarkhed, Jayashree Computer Science and Engineering Department Poojya Doddappa Appa College of Engineering Kalaburagi Karnataka India

ISBN: (数字)9789811614835

ISBN: (纸本)9789811614828

Spectrum awareness with energy preservation is a primary requirement in Cognitive Radio Sensor Networks as the sensor nodes are energy constrained and operate in physically unattended areas. Cluster based routing coupled with channel allocation achieves energy efficient path selection as a countermeasure to conserve energy. The proposed protocol in this work implements reliable routing with cluster based channel allocation which contributes in enhancement of network performance. Shortest path is computed for source to destination using Dijkstra’s algorithm and simulation results show that proposed protocol achieves higher packet delivery with minimum delay, optimizing energy consumption than the existing approach. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Cognitive radio

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algorithm modeling of Smile Intensity Estimation Using a Spatial Attention Convolutional Neural Network 3

Algorithm Modeling of Smile Intensity Estimation Using a Spa...

引用

2020 3rd international conference on modeling, simulation and Optimization Technologies and Applications, MSOTA 2020

作者： Luo, Zhenzhen Sha, Zongyu Jin, Chen Xu, Ning School of Software Jiangxi Normal University Jiangxi China School of Computer Science and Technology Wuhan University of Technology Wuhan China School of Information Engineering Wuhan University of Technology Wuhan China

Smile intensity estimation is a challenging task as it required subtle feature extraction, self-Adapted weighted model and classifier. complexity of the problem domains, and problems on fine-grained image recognition are some of the issues related to intensity estimation. In this study, we designed a self-weighted deep convolutional neural network architecture for smiles intensity estimation using graphics processing unit. In the case of using only CK+ smile images, the accuracy of the model is also higher than that of the latest technology. Our model achieved better accuracy by just using CK+ smile images than state-of-The-Art techniques. Visualizations of learned features at various layers and their deconvolutions are also presented for understanding the learning process. © Published under licence by IOP Publishing Ltd.

关键词： Image recognition

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Causal discovery of cascaded data based on adversarial variational method 3

Causal discovery of cascaded data based on adversarial varia...

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3rd international Academic Exchange conference on Science and Technology Innovation, IAECST 2021

作者： Zhang, Xia-Peng Wan, Ya-Ping Yang, Li-Jun Ou Yang, Jing-Hua University of South China Hunan Medical Big Data International Technology Cooperation Base School of Computer Science Hengyang China

ISBN: (纸本)9781665402675

At present, causal reasoning mainly studies the direct causal effects between variables, and uses the observed data to identify the causal direction of variables. However, in real data, there may be intermediate variables between the cause and the result, which leads to indirect nonlinear causal effect between the initial cause and the final result, which makes the accuracy of traditional causal inference methods low. To solve this problem, based on the cascading nonlinear additive noise model, proposes a causal direction inference algorithm: adversarial variational cascade additive noise model (AVCANM). Based on adversarial variational method, which estimates the lower variational bound of the marginal log likelihood of the data through adversarial training, and then determine the causal direction. The feasibility of this method is proved in theory, and simulation experiments are carried out on simulated data and real data. The results of the experiment show that compared with the traditional causal inference algorithms, this method has better stability in identifying indirect and nonlinear causal relationship. © 2021 IEEE.

关键词： Additive noise

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Automatic Finite Deterministic algorithm and PSO Based Secured Energy Efficient Routing in Wireless Sensor Network 3

Automatic Finite Deterministic Algorithm and PSO Based Secur...

引用

3rd international conference on Advances in Computing, Communication Control and Networking, ICAC3N 2021

作者： Shah, Priyanka Kasbe, Tanmay Oriental University Department of Computer Science Madhya Pradesh Indore India

ISBN: (纸本)9781665438117

Economic sensor networks have always been a boon to disaster management applications due to their ease of setup. Communication is difficult for them due of their vulnerability to a variety of security dangers. This study presents a new routing strategy those guards against both external and internal threats such as hello flooding, eavesdropping, and wormhole attacks. To limit energy dissipation, this method employs a one-way hash chain. Event-driven clustering at the level also saves energy. The suggested technique extends network longevity even when the cluster-based wireless sensor network is under attack, according to simulated results. A 5G wireless sensor network (WSN) is a collection of self-contained nodes that monitor the environment. WSN design goals like as energy efficiency or secure data transmission are primarily measured. With the increasing difficulties of workstation networks, network-based attacks have drawn the attention of various academics from various domains. As a result, multiple intrusion detection systems (IDS) have been built to address various areas of complicated security, such as denial-of-service attacks, worms, viruses, malware, and so on. IDS automation has been designed to safely restore the realization of energy-efficient routing in wireless sensor networks. Intrusion detection and data transmission performed in a secure manner using automatic finite deterministic and particle swarm optimization (PSO) algorithms that determine and follow an optimum path. Routing along the optimal path can restore the overall appearance of a sensor network in wireless technology, and it can be managed using a variety of indicators (such as power expenditure, capacity, network life, active nodes and dead nodes). The threat model and safety objectives for secure routing in wireless networks are discussed in this paper. The simulation was carried out using the MATLAB simulation software. © 2021 IEEE.

关键词： Intrusion detection

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Optimizing the flight route of UAV using biology migration algorithm 3

Optimizing the flight route of UAV using biology migration a...

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2021 3rd international conference on Artificial Intelligence and computer Science, AICS 2021

作者： Zhang, Q.Y. He, X.Y. Dong, Y.Q. Jiang, S.Y. Song, H. School of Computer Science and Technology Jiangsu Normal University Xuzhou China School of Computer Science University of Lincoln Lincoln United Kingdom

Optimizing the flight trajectory unmanned aerial vehicles (UAVs) is always a popular optimization problem in computation intelligence field, which aims to search optimal flight path for avoiding detection and complementing some highly difficult missions in complex military environments. This paper mainly utilizes a recent proposed biology migration algorithm (BMA) inspired by the species migration mechanism for dealing with the UAV trajectory optimization problem. The main goal of the UAV model is to search feasible parameters including the flight angle, coordinates and distance for minimizing the flight price computed based on the threat and fuel costs. As one of swarm intelligence techniques, BMA has the characteristics of self-organization, fast convergence and self-adaption in the optimization process, So, it is able to find a safe flight route between the start point and target point while avoiding the dangerous regions and minimum cost. simulation experiments show that BMA can generate promising and better results with respective to other compared algorithms. © Journal of Physics: conference Series 2021.

关键词： Biology

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EERO: Energy Efficient Route Optimization Technique for IoT Network 1

引用

3rd international conference on Futuristic Trends in Network and Communication Technologies, FTNCT 2020

作者： Shreyas, J. Chouhan, Dharamendra Rao, Sowmya T. Udayaprasad, P.K. Srinidhi, N.N. Dilip Kumar, S.M. Department of Computer Science and Engineering University Visvesvaraya College of Engineering Bangalore India

ISBN: (数字)9789811614835

ISBN: (纸本)9789811614828

In the real world, it is essential to establish efficient routes in the Internet of Things (IoT) since sensor nodes operate mainly on battery and have limited energy. In this paper, an energy efficient routing technique is proposed to select an optimal routing path from source to destination for the transmission of data. Genetic algorithm (GA) is an optimization technique which is integrated into the proposed work to select an optimal path among the available paths. Since the proposed method selects an optimal path from source cluster head to the sink, this reduces the energy consumption and improves the lifetime of network. The validity of the proposed algorithm is evaluated in MATLAB simulation tool and results generate superiority while considering parameters such as energy consumption, delay and number of rounds. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Genetic algorithms

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Active Distribution Network Fault Recovery Based on Particle Swarm Optimization 3

Active Distribution Network Fault Recovery Based on Particle...

引用

3rd international conference on Green Energy and Sustainable Development, GESD 2020

作者： Jiao, Deqiang Wan, Lin Wang, Dinghao School of Electrical and Electronic Engineering Changchun University Beijing China

As a new mode of distribution network, it is necessary to study the minimum loss of active distribution network after line failure. Therefore, this paper proposes a method of active distribution network fault recovery based on particle swarm optimization (PSO). On the premise of satisfying the constraints of distribution network, this algorithm establishes an objective function model of balancing load based on reducing network loss. Using the active distribution network system of IEEE 33 node, the experimental simulation proves that the particle group algorithm can effectively solve the problem of active distribution network fault reconstruction by contrasting the particle group and ant group algorithm, and has the advantages of good stability and high efficiency. © Published under licence by IOP Publishing Ltd.

关键词： computer system recovery

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Prediction of Energy Consumption in Digital Twins of Intelligent Factory by Artificial Intelligence 3

Prediction of Energy Consumption in Digital Twins of Intelli...

引用

3rd international conference on Technology and Policy in Energy and Electric Power, ICT-PEP 2021

作者： Wu, Jingyi Dang, Yukun Jia, Hongxiang Liu, Xinyue Lv, Zhihan Qingdao University School of Computer Science and Technology Qingdao China

ISBN: (纸本)9781665416412

The goal is to explore the prediction performance of energy consumption in digital twins of the intelligent factory in the process of urbanization, so that it can provide good guidance for the green development of cities. The digital twin technology is introduced into the intelligent factory, in view of the influence of life cycle, data transmission delay and other factors of the equipment on product energy consumption of intelligent factory manufacturing in the real physical environment. Meanwhile, it is improved by DL (deep learning) algorithm to construct an improved AlexNet algorithm framework. Then, DPFS (Differential Privacy Frequent Subgraph) algorithm is adopted to build a digital twin energy consumption prediction model of intelligent factory based on DL and privacy algorithm. Finally, a simulation scenario is established to verify its performance. Through the comparative analysis of the digital twin energy consumption prediction model and other scholars' models in related fields, the feature recognition accuracy of the constructed model reaches 89.23%, at least 1.95% higher than that of other neural network models. From the perspective of energy consumption data transmission performance, the data message delivery rate of the proposed model is not less than 80%. Besides, the data message leakage rate and packet loss rate are less than 11%, and the average delay is basically stable. Therefore, the constructed model can achieve high energy consumption data transmission security performance under the premise of ensuring the prediction accuracy, which can provide experimental basis for the prediction of energy consumption and the improvement of security maintenance performance in intelligent factories. © 2021 IEEE.

关键词： Deep learning

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