2020 IEEE International Conference on Networking, Sensing and Control (ICNSC)最新文献_第4页

Bi-objective Intelligent Task Scheduling for Green Clouds with Deep Learning-based Prediction 基于深度学习预测的绿色云双目标智能任务调度

2020 IEEE International Conference on Networking, Sensing and Control (ICNSC) Pub Date : 2020-10-30 DOI: 10.1109/ICNSC48988.2020.9238050

Heng Liu, Xiaofen Zhang, J. Bi, Haitao Yuan, Mengchu Zhou

{"title":"Bi-objective Intelligent Task Scheduling for Green Clouds with Deep Learning-based Prediction","authors":"Heng Liu, Xiaofen Zhang, J. Bi, Haitao Yuan, Mengchu Zhou","doi":"10.1109/ICNSC48988.2020.9238050","DOIUrl":"https://doi.org/10.1109/ICNSC48988.2020.9238050","url":null,"abstract":"The ever-increasing deployment of cloud data centers causes high energy consumption, high cost, and harmful environmental pollution. To solve above problems, cloud service providers are actively exploring to use green cloud data centers (GCDCs) by using green energy. Yet it is challenging to accurately predict the future wind and solar energy before making intelligent task scheduling decisions. In addition, it is difficult to jointly optimize cost and revenue. In this work, to make optimal task scheduling, various types of applications, service level agreements, service rates, task loss probability, electricity prices and green energy in different GCDCs are considered. First, this work employs a long short-term memory network to predict wind and solar energy. Then, it adopts a bi-objective optimization algorithm to achieve a better trade-off between cost and revenue of GCDCs. Finally, it adopts real-world data including workload trace, wind energy, solar energy and electricity prices to demonstrate the effectiveness of the proposed energy prediction and task scheduling methods. It's shown that the proposed methods achieve higher performance than other neural network methods.","PeriodicalId":412290,"journal":{"name":"2020 IEEE International Conference on Networking, Sensing and Control (ICNSC)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127514833","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Remote Monitoring System Of Track Cleaning Vehicles Based On 4G-Network 基于4g网络的轨道清扫车远程监控系统

2020 IEEE International Conference on Networking, Sensing and Control (ICNSC) Pub Date : 2020-10-30 DOI: 10.1109/ICNSC48988.2020.9238083

Zenan Lin, Yanming Huang, Yong-Jun Xie, Qiantong Wu, Xiaojie Huang, Jingui Li, Xin Liu

引用次数: 3

K-9 Artificial Intelligence Education in Qingdao: Issues, Challenges and Suggestions 青岛市中小学人工智能教育:问题、挑战与建议

2020 IEEE International Conference on Networking, Sensing and Control (ICNSC) Pub Date : 2020-10-30 DOI: 10.1109/ICNSC48988.2020.9238087

Xiaoyan Gong, Ying Tang, Xiwei Liu, Sifeng Jing, Wei Cui, Joleen Liang, Feiyue Wang

引用次数: 7

An Overview of Robust Reinforcement Learning 鲁棒强化学习概述

2020 IEEE International Conference on Networking, Sensing and Control (ICNSC) Pub Date : 2020-10-30 DOI: 10.1109/ICNSC48988.2020.9238129

Shiyu Chen, Yanjie Li

{"title":"An Overview of Robust Reinforcement Learning","authors":"Shiyu Chen, Yanjie Li","doi":"10.1109/ICNSC48988.2020.9238129","DOIUrl":"https://doi.org/10.1109/ICNSC48988.2020.9238129","url":null,"abstract":"Reinforcement learning (RL) is one of the popular methods for intelligent control and decision making in the field of robotics recently. The goal of RL is to learn an optimal policy of the agent by interacting with the environment via trail and error. There are two main algorithms for RL problems, including model-free and model-based methods. Model-free RL is driven by historical trajectories and empirical data of the agent to optimize the policy, which needs to take actions in the environment to collect the trajectory data and may cause the damage of the robot during training in the real environment. The main different between model-based and model-free RL is that a model of the transition probability in the interaction environment is employed. Thus the agent can search the optimal policy through internal simulation. However, the model of the transition probability is usually estimated from historical data in a single environment with statistical errors. Therefore, an issue is faced by the agent is that the optimal policy is sensitive to perturbations in the model of the environment which can lead to serious degradation in performance. Robust RL aims to learn a robust optimal policy that accounts for model uncertainty of the transition probability to systematically mitigate the sensitivity of the optimal policy in perturbed environments. In this overview, we begin with an introduction to the algorithms in RL, then focus on the model uncertainty of the transition probability in robust RL. In parallel, we highlight the current research and challenges of robust RL for robot control. To conclude, we describe some research areas in robust RL and look ahead to the future work about robot control in complex environments.","PeriodicalId":412290,"journal":{"name":"2020 IEEE International Conference on Networking, Sensing and Control (ICNSC)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131162994","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Distributed Formation Control of Unicycle-Like Vehicles Without Direct Distance Measurements 无直接距离测量的类独轮车分布式编队控制

2020 IEEE International Conference on Networking, Sensing and Control (ICNSC) Pub Date : 2020-10-30 DOI: 10.1109/ICNSC48988.2020.9238078

Liang Liu, Xiaopeng Luo, Zhangqing Zhu

引用次数: 0

Accelerated Latent Factor Analysis for Recommender Systems via PID Controller 基于PID控制器的推荐系统加速潜在因素分析

2020 IEEE International Conference on Networking, Sensing and Control (ICNSC) Pub Date : 2020-10-30 DOI: 10.1109/ICNSC48988.2020.9238055

Jinli Li, Xuke Wu, Ye Yuan, Yajuan Wu, Kangkang Ma, Yue Zhou

引用次数: 1

Path Planning with Autonomous Obstacle Avoidance Using Reinforcement Learning for Six-axis Arms 基于强化学习的六轴臂自主避障路径规划

2020 IEEE International Conference on Networking, Sensing and Control (ICNSC) Pub Date : 2020-10-30 DOI: 10.1109/ICNSC48988.2020.9238112

Yinsen Jia, Yichen Li, Bo Xin, Chunlin Chen

引用次数: 1

Operator-based Nonlinear Modeling and Control for Microreactor 基于算子的微反应器非线性建模与控制

2020 IEEE International Conference on Networking, Sensing and Control (ICNSC) Pub Date : 2020-10-30 DOI: 10.1109/ICNSC48988.2020.9238111

Kosuke Nishizawa, M. Deng, Y. Noge

引用次数: 0

Time-varying unimodular function based robust right coprime factorization for nonlinear forced vibration control system 基于时变单模函数的非线性强迫振动控制系统鲁棒右互素分解

2020 IEEE International Conference on Networking, Sensing and Control (ICNSC) Pub Date : 2020-10-30 DOI: 10.1109/ICNSC48988.2020.9238113

Guang Jin, M. Deng

引用次数: 0

Root Cause Analysis of Concurrent Alarms Based on Random Walk over Anomaly Propagation Graph 基于异常传播图随机游走的并发报警根本原因分析

2020 IEEE International Conference on Networking, Sensing and Control (ICNSC) Pub Date : 2020-10-30 DOI: 10.1109/ICNSC48988.2020.9238084

Lingyu Zhang, Jiabao Zhao, Min Zhang

引用次数: 0