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Autonomous Energy Management by Applying Deep Q-Learning to Enhance Sustainability in Smart Tourism Cities

Pannee Suanpang, Pitchaya Jamjuntr, Kittisak Jermsittiparsert, Phuripoj Kaewyong
2022 Energies  
This resulted in this research paper, which aimed to apply deep reinforcement learning algorithms for an autonomous energy management system of a microgrid.  ...  Therefore, we proposed an autonomous energy management system for effective energy management.  ...  The high dimensionality of the variables in the microgrid components encouraged the employment of intelligent learning-based methods in autonomous energy management systems, such as deep reinforcement  ... 
doi:10.3390/en15051906 fatcat:ilpsnwsvgrcjfdlvdnjx3a42qm

Information Sharing Management System Based on Blockchain Using Deep Reinforcement Learning

Ms. Shruti Belsare, Dr. A. B. Raut
2022 International Journal of Scientific Research in Computer Science Engineering and Information Technology  
A Deep Reinforcement Learning based Supply Chain Management (DR-SCM) system is then offered to make effective judgments on the production and storage of agri-food commodities for profit optimization.  ...  Traditional traceability systems have issues with centralized administration, opaque information, untrustworthy data, and the ease with which information islands can be created.  ...  . • Deep Reinforcement Learning Model Deep reinforcement learning is a machine learning and artificial intelligence category in which intelligent robots can learn from their actions in the same way that  ... 
doi:10.32628/cseit228216 fatcat:74rid64yi5d55my3dpoykoorji

Risks of Deep Reinforcement Learning Applied to Fall Prevention Assist by Autonomous Mobile Robots in the Hospital

Takaaki Namba, Yoji Yamada
2018 Big Data and Cognitive Computing  
Second, we extract the risks linked to the use of autonomous mobile assistant robots based on deep reinforcement learning for patients in a hospital.  ...  However, there are risks associated with artificial intelligence (AI) in applications such as assistant mobile robots that use deep reinforcement learning.  ...  Our Objective In this paper, we extracted the risks and propose risk reduction measures when applying deep reinforcement learning to the control of an autonomous mobile robot.  ... 
doi:10.3390/bdcc2020013 fatcat:mkj423ijjjfx5fohamjm5nu7ze

An overview of machine learning applications for smart buildings

Kari Alanne, Seppo Sierla
2021 Sustainable cities and society  
This review article discusses the learning ability of buildings with a system-level perspective and presents an overview of autonomous machine learning applications that make independent decisions for  ...  Given the complexities related to the operational environment, the machine learning techniques 'reinforcement learning (RL)' and its derivative 'deep reinforcement learning (DRL)' have been experienced  ...  Here, we focused on autonomous AI agents that make independent decisions for building energy management and are based on (deep) reinforcement learning (RL).  ... 
doi:10.1016/j.scs.2021.103445 fatcat:mcyollihdfcnrmqp3mmz4bl3m4

Towards Autonomic Science Infrastructure

Rajkumar Kettimuthu, Alok Choudhary, Zhengchun Liu, Ian Foster, Peter H. Beckman, Alex Sim, Kesheng Wu, Wei-keng Liao, Qiao Kang, Ankit Agrawal
2018 Proceedings of the 1st International Workshop on Autonomous Infrastructure for Science - AI-Science'18  
Growing interest and recent developments in machine learning have spurred proposals to apply machine learning for goal-based optimization of computing systems in an autonomous fashion.  ...  We propose a hierarchical architecture that builds on the earlier proposals for autonomic computing systems to realize an autonomous science infrastructure.  ...  ACKNOWLEDGMENT This material is based upon work supported by the U.S.  ... 
doi:10.1145/3217197.3217205 dblp:conf/hpdc/KettimuthuLFBSW18 fatcat:q465b3cyibarnowssx4jny6jvu

A Review of Reinforcement Learning for Autonomous Building Energy Management [article]

Karl Mason, Santiago Grijalva
2019 arXiv   pre-print
This research gives a comprehensive review of the literature relating to the application of reinforcement learning to developing autonomous building energy management systems.  ...  Reinforcement learning is one of the most prominent machine learning algorithms used for control problems and has had many successful applications in the area of building energy management.  ...  Autonomous Building Energy Management via Reinforcement Learning An overview of both the problem of building energy management and RL was provided in Sections 2 and 3.  ... 
arXiv:1903.05196v2 fatcat:lihv4ftuovc3hhmofr7vpgn3mq

Autonomous Cyber Defense Introduces Risk: Can We Manage the Risk? [article]

Alexandre K. Ligo, Alexander Kott, Igor Linkov
2022 arXiv   pre-print
Autonomous agents have the potential to use ML with large amounts of data about known cyberattacks as input, in order to learn patterns and predict characteristics of future attacks.  ...  Here we focus on machine learning training, algorithmic feedback, and algorithmic constraints, with the aim of motivating a discussion on achieving trust in autonomous cyber defenses.  ...  Autonomous Cyberdefense Introduces Risk: Can We Manage the Risk?. Computer, 54 (10) , 106-110. ………………………….  ... 
arXiv:2201.11148v1 fatcat:77kbpj7tcna7ndsnveotw4m6nm

A survey of deep learning techniques for autonomous driving

Sorin Grigorescu, Bogdan Trasnea, Tiberiu Cocias, Gigel Macesanu
2019 Journal of Field Robotics  
The comparison presented in this survey helps to gain insight into the strengths and limitations of deep learning and AI approaches for autonomous driving and assist with design choices  ...  We start by presenting AI-based self-driving architectures, convolutional and recurrent neural networks, as well as the deep reinforcement learning paradigm.  ...  Deep Reinforcement Learning In the following, we review the Deep Reinforcement Learning (DRL) concept as an autonomous driving task, using the Partially Observable Markov Decision Process (POMDP) formalism  ... 
doi:10.1002/rob.21918 fatcat:pjyk4lwjavf63jz4pmc3mnuqe4

End-to-End Autonomous Driving Through Dueling Double Deep Q-Network

Baiyu Peng, Qi Sun, Shengbo Eben Li, Dongsuk Kum, Yuming Yin, Junqing Wei, Tianyu Gu
2021 Automotive Innovation  
This paper puts forward an end-to-end autonomous driving method through a deep reinforcement learning algorithm Dueling Double Deep Q-Network, making it possible for the vehicle to learn end-to-end driving  ...  AbstractRecent years have seen the rapid development of autonomous driving systems, which are typically designed in a hierarchical architecture or an end-to-end architecture.  ...  End-to-end driving can be easily combined with imitation learning or reinforcement learning, which achieves state-of-the-art autonomous driving [5] .  ... 
doi:10.1007/s42154-021-00151-3 fatcat:ssl63wjh4rf3bcpcloittl6yoe

Wireless 2.0: Towards an Intelligent Radio Environment Empowered by Reconfigurable Meta-Surfaces and Artificial Intelligence [article]

Haris Gacanin, Marco Di Renzo
2020 arXiv   pre-print
Later we elaborate on data management aspects, the requirements of supervised learning by examples, and the paradigm of reinforcement learning (RL) to learn by acting.  ...  This paper, in particular, puts the emphasis on AI-based computational methods and commence with an overview of the concept of intelligent radio environments based on reconfigurable meta-surfaces.  ...  Knowledge management: While machine learning techniques strictly devise learning mechanisms, an intelligent system is designed with broader disciplines of AI including decision-making, reasoning and knowledge  ... 
arXiv:2002.11040v1 fatcat:ffennzsbxjhtxhp2ye2dmrrz74

Towards Self-Regulated Learning in School Curriculum

Seifodin Rajabi
2012 Procedia - Social and Behavioral Sciences  
Such an educational system with its emphasis on individual learner moves towards humanizing the school curriculum and learner autonomy by considering student voices and interests.  ...  for self-regulation so that they accept the responsibility of their learning in the educational system.  ...  Moreover, as Holec (1981) points out, self-regulated learning is an umbrella concept which may lead a person to autonomously learn at different levels.  ... 
doi:10.1016/j.sbspro.2012.06.661 fatcat:ohm2oxnw3zfsdk4mdinyw6yzfe

Interactive Human–Robot Skill Transfer: A Review of Learning Methods and User Experience

Mehmet Ege Cansev, Honghu Xue, Nils Rottmann, Adna Bliek, Luke E. Miller, Elmar Rueckert, Philipp Beckerle
2021 Advanced Intelligent Systems  
through the project "Active transfer learning with neural networks through Figure 3. A broad view of LfD approaches considering user experience.  ...  autonomous systems.  ...  Recent deep reinforcement learning techniques even allow for training directly with the real robot, as shown by Gu et al.  ... 
doi:10.1002/aisy.202000247 fatcat:x7ljmoyi6zhx3a3lwsxiupeh4y

The importance of machine learning in autonomous actions for surgical decision making

Martin Wagner, Sebastian Bodenstedt, Marie Daum, Andre Schulze, Rayan Younis, Johanna Brandenburg, Fiona R. Kolbinger, Marius Distler, Lena Maier-Hein, Jürgen Weitz, Beat-Peter Müller-Stich, Stefanie Speidel
2022 Artificial Intelligence Surgery  
An integral part of surgical data science is to analyze the available data along the surgical treatment path and provide a context-aware autonomous action by means of ML methods.  ...  The emerging field of Surgical Data Science aims to improve the quality of surgery through acquisition, organization, analysis, and modeling of data, in particular using machine learning (ML).  ...  by the German Research Foundation (DFG, Deutsche Forschungsgemeinschaft) as part of Germany's Excellence Strategy -EXC 2050/1 -Project ID 390696704 -Cluster of Excellence "Centre for Tactile Internet with  ... 
doi:10.20517/ais.2022.02 fatcat:462tf4p4mbgvxlbvot43hadxwe


2020 2020 11th IEEE Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON)  
0540 SESSION 18: BODY AND PERSONAL AREA NETWORKS AND ROBOTICS AND AUTONOMOUS SYSTEMS 18.1 1570674090 Deep Learning Technique in Recognizing Hand Grasps Using FMG Signals 0546 18.2 1570677590  ...  TECHNOLOGY, SOFTWARE ENGINEERING AND OTHERS Design and Control of an Off-Grid Solar System for a Rural House in Pakistan 0786 Overcome IT Lab Challenge in COVID-19 with Windows-To-Go 0791 Smart and Interactive  ... 
doi:10.1109/iemcon51383.2020.9284848 fatcat:4ltdf33yerce3avsxsgjlcb7iy

A Survey on AI-Driven Digital Twins in Industry 4.0: Smart Manufacturing and Advanced Robotics

Ziqi Huang, Yang Shen, Jiayi Li, Marcel Fey, Christian Brecher
2021 Sensors 21(19)  
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY  ...  ., Fraunhofer IPT, as well as the Chair of Production Metrology and Quality Management, and Production Engineering of the Laboratory for Machine Tools and Production Engineering (WZL) for their permission  ...  Researchers have applied reinforcement learning algorithms, including Q-learning [96] , deep reinforcement learning [95, 97, 267] and deep deterministic policy gradient [199, 266, 275] to optimize  ... 
doi:10.18154/rwth-2021-09877 fatcat:yjhprcascvfutisat7olust3kq
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