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IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Borji, Ali Ahmadabadi, Majid Nili Araabi, Babak Nadjar Univ So Calif Dept Comp Sci Hedco Neurosci BldgRoom 93641 Watt Way Los Angeles CA 90089 USA Univ Tehran Sch Elect & Comp Engn Sch Cognit Sci Tehran Iran

ISBN: (纸本)9781424466757

This paper introduces a new method for learning top-down and task-driven visual attention control along with physical actions in interactive environments. Our method is based on the reinforcement learning of Visual Classes(RLVC) algorithm and adapts it for learning spatial visual selection in order to reduce computational complexity. Proposed algorithm also addresses aliasings due to not knowing previous actions and perceptions. Continuing learning shows our method is robust to perturbations in perceptual information. Our method also allows object recognition when class labels are used instead of physical actions. We have tried to gain maximum generalization while performing local processing. Experiments over visual navigation and object recognition tasks show that our method is more efficient in terms of computational complexity and is biologically more plausible.

关键词： computational complexity computational complexity generalisation (artificial intelligence) learning (artificial intelligence) navigation object recognition object recognition reinforcement learning algorithm spatial visual attention visual classes visual navigation

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Using the Online Cross-Entropy Method to Learn Relational Policies for Playing Different Games

Using the Online Cross-Entropy Method to Learn Relational Po...

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IEEE Conference on Computational Intelligence and Games (CIG)

作者： Sarjant, Samuel Pfahringer, Bernhard Driessens, Kurt Smith, Tony Univ Waikato Fac Comp & Math Sci Dunedin New Zealand Maastricht Univ Dept Knowledge Engn NL-6200 MD Maastricht Netherlands

ISBN: (纸本)9781457700095

By defining a video-game environment as a collection of objects, relations, actions and rewards, the relational reinforcement learning algorithm presented in this paper generates and optimises a set of concise, human-readable relational rules for achieving maximal reward. Rule learning is achieved using a combination of incremental specialisation of rules and a modified online cross-entropy method, which dynamically adjusts the rate of learning as the agent progresses. The algorithm is tested on the Ms. Pac-Man and Mario environments, with results indicating the agent learns an effective policy for acting within each environment.

关键词： Computational intelligence Conferences Games Heuristic algorithms Junctions learning learning systems Mario environments Ms. Pac-Man computer games human readable relational rules learning (artificial intelligence) online cross entropy method playing different games reinforcement learning algorithm relational policies video game environment video signal processing

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Agent-based Price Simulation of the Day-Ahead-Spot-Market and Markets for Control Reserve Power 13

Agent-based Price Simulation of the Day-Ahead-Spot-Market an...

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13th International Conference on the European Energy Market (EEM)

作者： Skiba, L. Maaz, A. Moser, A. Rhein Westfal TH Aachen Inst Power Syst & Power Econ IAEW Aachen Germany

ISBN: (纸本)9781509012985

The day-ahead spot market and the markets for control reserve power are among the most important markets for short-term trading of power plant capacities in Germany. Especially the prices for control reserve power have been varying severely in the last years. Considering the applied pay-as-bid auction design and market power of certain market participants, strategic bidding behavior must be assumed. Such strategic behavior cannot be modelled by fundamental simulation models alone. Thus, it is necessary to simulate individual bidding curves and trading decisions of market participants in order to simulate realistic market results and prices. Therefore, the aim of this paper is to model the bidding considerations on the day-ahead and control reserve power markets, to implement them in an agent based simulation model using a reinforcement learning algorithm and to investigate the resulting market prices.

关键词： Agent-based simulation electricity market modeling pay-as-bid pricing pricing of ancillary services reinforcement learning algorithm

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Securing 5G Network using low power wireless personal area network 4

Securing 5G Network using low power wireless personal area n...

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4th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2020

作者： Al-Sarray, Zaid ali Mahmood, Mohammed Thakir Ibrahim, Abdullahi Abdu Altinbas University Electrical and Computer Engineering Istanbul Turkey Karabuk University Computer Engineering Istanbul Turkey

ISBN: (纸本)9781728190907

The purpose of this work was to get acquainted with wireless communication technologies and the information security challenges they create from the viewpoint of 5G and its preceding technologies. 5th Generation (5G) is becoming a global phenomenon and it is currently being implemented in dozens of countries around the globe with it comes new information security challenges. Potential solutions for the challenges are also offered. The outcome of this research is an overview of information security challenges in 5G using Low-power wireless personal area Network (LPWAN) and in the technologies preceding 5G. Possible information security solutions are presented in this work for the new technologies coming with 5G. This work showed that the new technologies coming with 5G, such as the virtualization of hardware and services as well as the utilization of cloud computing, create completely new areas of attack for networks. With this knowledge, Labelled and Freely Available Dataset from Open-Source Repository will be used and it is possible to prevent attacks targeting networks by implementing necessary information security elements. For the training, testing and validation of our dataset which is an IoT and cyber-security based dataset, a well-known MATLAB R2019a software was used for this purpose. The proposed reinforcement learning algorithm for Securing 5G network is designed for mesh topology from the ground up by the model of the network itself using low power personal area networks. We model the network operating in a finite area with a finite number of nodes distributed inside the area randomly in this algorithm. Hence, we defined the service area of the target network by assuming the finiteness of the network in the model. © 2020 IEEE.

关键词： 5G network IOT LPWAN reinforcement learning algorithm security

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Mode-decomposition memory reinforcement network strategy for smart generation control in multi-area power systems containing renewable energy

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APPLIED ENERGY 2022年 307卷 118266-118266页

作者： Yin, Linfei Wu, Yunzhi Guangxi Univ Coll Elect Engn Nanning 530004 Guangxi Peoples R China Guangxi Univ Inst Artificial Intelligence Nanning 530004 Guangxi Peoples R China

The large-scale application of renewable energy can promote the global goal of carbon neutrality. However, the stochastic nature of wind and solar energy aggravates the active power imbalance and increases the frequency deviation. These obstacles hinder the load frequency control with the traditional proportional-integral-derivative as the primary approach for automatic generation control. Inspired by the "Divide and Conquer" strategy, a mode-decomposition memory reinforcement network strategy is proposed to reduce the impact of random fluctuations and uncertainties on power systems. The proposed strategy combines the traditional methods and intelligent algorithms for smart generation control. The proposed strategy includes empirical mode decomposition, proportional-integral-derivative, long short-term memory networks, and reinforcement learning algorithms. Firstly, the historical data that has been decomposed by the empirical mode decomposition is utilized to train long short-term memory networks. Then, the trained long short-term memory networks decompose and reorganize the frequency deviation into the high-frequency and low-frequency signals in real-time. Finally, reinforcement learning and proportional-integral-derivative respectively optimize the generation commands by the high-frequency and low-frequency signals to mitigate frequency deviation. Two cases results prove that the mode-decomposition memory reinforcement network has a higher control effect and lower generation cost than the other four strategies. Significantly, the frequency deviation and generation cost are respectively reduced by at least 9.77% and 4.39% in the four-area power system.

关键词： Smart generation control Empirical mode decomposition reinforcement learning algorithm Long short -term memory networks Proportional-integral-derivative

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Dynamic spectrum access-based augmenting coverage in narrow band Internet of Things

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INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2024年第1期37卷

作者： Gnanaselvam, R. Vasanthi, M. S. SRM Inst Sci & Technol Dept Elect & Commun Engn Kattankulathur Tamil Nadu India

The Third Generation Partnership Project launched the Narrowband Internet of Things (NB-IoT) as part of 5G technology under Release-13. These networks are authorized Low Power Wide Area Networks. It uses less power and sends message over long distances. The NB-IoT is can be used widely in industries, environment, and home for automation. While comparing NB-IoT to regular Long Term Evaluation Machine Type Communication, the repetition in uplink is 128. The extreme coverage by 164 dB in terms of Maximum Coupling Loss. The NB-IoT is employed in deep coverage for users. If we use many repetitions in NB-IoT technology, the results are shorter battery life and higher maintenance cost. Hence, we suggest abandoning the random spectrum access strategy in favor of a novel technique termed Dynamic Spectrum Access, which uses reinforcement learning algorithm to eliminate the shortcomings. This method is used to decrease the number of re-transmissions so that NB-IoT system power goes to less and augments coverage. The Third Generation Partnership Project launched the Narrowband Internet of Things (NB-IoT) as part of 5G technology under Release-13. These networks are authorized Low Power Wide Area Networks. It uses less power and sends message over long distances. The NB-IoT can be used widely in industries, environment, and home for ***

关键词： coverage improvement MAB model NB-IoT Proposed Dynamic Spectrum Access procedure reinforcement learning algorithm

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A comprehensive soft security model for Cognitive Internet of Things

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INTERNET OF THINGS 2023年 23卷 1-25页

作者： Abadi, Masoud Narimani Zaman Bidgoly, Amir Jalaly Farjami, Yaghoub Khani, Ebrahim Hossein Univ Qom Dept Comp & IT Amin Blvd Qom Iran Isfahan Univ Technol Dept Artificial Intelligence Esfahan Iran

The Cognitive Internet of Things (CIoT) is a rapidly evolving field that combines artificial intelligence (AI) with the Internet of Things (IoT) ecosystem. By augmenting IoT with AI, objects can sense, perceive, think, and make decisions independently with minimal initial knowledge. These cognitive objects form a society and work toward achieving their goals through cooperation. However, trust is a significant security challenge in such a society. To address this challenge, this paper proposes a soft security approach to model trust in CIoT using Collaborative Multi-Agent Systems (CMAS). Our model introduces an interactive, autonomous, and self-taught agent that can move toward a secure situation without needing a supervisor. We use a state machine to model an insecure ecosystem where agents can behave honestly, adversarially, neutrally, or hypocritically. We define inner trust as a combination of direct and indirect experiences and observations and global trust as the weighted average of inner trust and recommendations. Additionally, we employ the reinforcement learning algorithm to train agents. To evaluate our model, we developed a proprietary tool called ASSOCIATE and assessed our model using data from the Santander Smart City dataset and simulator-generated data. Our evaluations encompass three aspects: the success rate in achieving the goal, the recognition of agents' behavior, and the quality of the diagnosis.

关键词： Cognitive Internet of Things Soft security Trust model reinforcement learning algorithm Maclaurin series

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ROS-MUSIC toolchain for spiking neural network simulations in a robotic environment

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BMC Neuroscience 2015年第1期16卷 1-2页

作者： Philipp Weidel Renato Duarte Karolína Korvasová Jenia Jitsev Abigail Morrison Institute of Advanced Simulation (IAS-6) & Institute of Neuroscience and Medicine (INM-6) Forschungszentrum Juelich 52425 Juelich Germany Faculty of Psychology Institute of Cognitive Neuroscience Ruhr-University Bochum 44801 Bochum Germany Simulation Laboratory Neuroscience - Bernstein Facility for Simulation and Database Technology Institute for Advanced Simulation Jülich Aachen Research Alliance Jülich Research Center Jülich Germany

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P-103: Multi Path Control System for AGV Based on Digital Twin in Display Module Process

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SID Symposium Digest of Technical Papers 2023年第1期54卷

作者： Yonggu Kim Seungjoo Lee Cheollae Roh Samsung Display Co. Ltd. 1 Samsung-ro Giheung-gu Yongin-si Gyeonggi-do Korea 17113

Increasing display process complexity, Automated Guided Vehicle becomes more important and the delivery time constraint is getting shorter. The complicated process causes traffic congestion and deadlocks between AGVs. Preventing these issues, this paper suggests digital twin technology that allows AGV to choose the best route among various paths. This research validates the AGV simulation for the display plant. The result shows AGV average delivery time was shortened by 14.1% compared to the traditional algorithm. In addition, the AGV capacity rate can be reduced by 21.5% and the number of AGVs can be reduced by 10.1%.

关键词： Display Module Process Automated Guided Vehicle Multi Path Control System Digital Twin reinforcement learning algorithm

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