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Proposal of Self-generation of Reward for danger avoidance by disregarding specific situations

Proposal of Self-generation of Reward for danger avoidance b...

引用

ieee symposium Series on computational intelligence (SSCI)

作者： Yuya Ono Kentaro Kurashige Afiqe Anuar Bin Muhammad Nor Hakim Sosuke Kondo Kodai Fukuzawa Division of Information and Electronic Engineering Muroran Institute of Technology Japan ASTEC Co Ltd. Japan

ISBN: (纸本)9781728190495

Reward design in reinforcement learning should be less burdensome on the designer and be able to respond to changes in the environment and task. Therefore, we are studying the Self-generation of Reward, a method of reward design that does not depend on changes in the environment or task. Self-generation of Reward is a method in which an agent generates its own reward by evaluating sensor information from the outside world from multiple perspectives, using the sense of danger avoidance of biological life as an index. The only desired indicator of danger avoidance is a negative evaluation when the sensor information is dangerous. However, when the state is not dangerous, the evaluation is positive. Positive evaluation means that the agent is desired to achieve the goal, but the non-dangerous state has nothing to do with achieving the goal. Therefore, there is a problem that the measure of risk avoidance includes the evaluation of the achievement of undesired goals. To solve this problem, we propose a method to control whether sensor information is evaluated or not. Improve the indicator of risk avoidance to generate only negative evaluations by not including positive evaluations in the sensor evaluation if they are indicated. In this study, we conducted a simulation experiment using path finding on a grid map to verify whether the proposed method can be used for learning. As a result, we found that learning was possible by adjusting the reward given to each action.

关键词： Design methodology Reinforcement learning Biology Indexes Proposals Task analysis computational intelligence

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Application of Reinforcement Learning to Wind Farm Active Power control Design

Application of Reinforcement Learning to Wind Farm Active Po...

引用

ieee Canada Electric Power Conference, EPEC

作者： Xuanhe Zhang Hamed Badihi Ziquan Yu Mohamed Benbouzid Ningyun Lu Youmin Zhang College of Automation Engineering Nanjing University of Aeronautics and Astronautics Nanjing China Institut de Recherche Dupuy de Lome (IRDL) University of Brest Brest France Department of Mechanical Industrial and Aerospace Engineering Concordia University Montreal Canada

ISBN: (纸本)9781665463195

Wind power is becoming a key player of the world's energy landscape thanks to its cleanliness, abundance and huge potential. At the same time, it attracts an increasing attention when it comes to its efficient production and financial viability, which otherwise may restrict its development in the foreseeable future. Indeed, enhancing energy production efficiency to reduce costs and improve the ability of the system to resist faults are research areas of great interest. With the advancement of machine learning and artificial intelligence along with increasing computational power at hand, reinforcement learning enables achieving an optimal control solution in an application environment after continuous attempts and updates. In this paper, a novel solution based on reinforcement learning is applied to the control of wind farm. An intelligent agent is designed to explore the environment, and after training, it effectively maintains the necessary balance between power generation and load, which in turn regulates the wind farm grid frequency when enough wind is available. The trained agent is tested under different loads, realistic wind fields, as well as fault scenarios. All simulation results show that the agent accurately understands the environment and load requirements, mitigates the impact of faults, and thus, improves the stability of the grid frequency.

关键词： Training Wind energy Simulation Reinforcement learning Production Resists Wind farms

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Subject and Activity-wise Performance for Activity Independent Person Recognition

Subject and Activity-wise Performance for Activity Independe...

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ieee symposium Series on computational intelligence (SSCI)

作者： Seham Abd Elkader Michael Barlow Erandi Lakshika University of New South Wales Canberra Australia

ISBN: (纸本)9781728190495

Nowadays, We live in a digital age where novel technological improvements in processing units and sensors have resulted in the development of smart wearable devices. Indeed, the several merits of wearable devices have motivated different studies for using wearable devices for human recognition. Recently, several behavioural patterns have been captured through wearable devices for human recognition. However, most related work have used a small set of activities in their studies. Unlike the previous work, we have considered a comprehensive dataset of twenty daily activities for subject recognition. Our previous publication has exploited the behavioural patterns data of known activities captured from wearable devices for subject recognition. In this paper, the main objective is to investigate the recent idea of activity-independent subject identification. The study found that the recognition accuracy results significantly vary across all the subjects. Also, the activities with a larger movement of more than one body position showed promising recognition accuracy results in distinguishing subjects compared to activities with few body motions such as talking phone, texting phone, and watching TV.

关键词： TV Biometrics (access control) Wearable computers Information age Pattern recognition Object recognition Wearable sensors

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Adaptive Dynamic Programming Based on Multi-dimensional Taylor Network for Time-Delay Nonlinear System with Uncertainties 2

Adaptive Dynamic Programming Based on Multi-dimensional Tayl...

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2nd World symposium on Artificial intelligence, WSAI 2020

作者： Duan, Zheng-Yi Yan, Hong-Sen Southeast University School of Automation Nanjing Jiangsu210096 China

ISBN: (纸本)9781728167794

For the uncertain time-delay system, this paper investigates a novel robust adaptive dynamic programming (ADP) to guarantee the stability and performance of the system. By devising a novel cost function which integrates the effects of time delay and uncertainties, the uncertain time-delay system is transformed into a control problem of the nominal system through ADP. By applying the control policy designed for the nominal system, the uncertain system is guaranteed to be asymp-totically stable. Besides, in order to increase the computation efficiency, multi-dimensional Taylor network is utilized as the approximating architecture to estimate the optimal value function and optimal control. A simulation example is provided to verify the effectiveness of the presented control approach. © 2020 ieee.

关键词： Reinforcement learning

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Multi-robot Task Allocation Using Island Model Genetic Algorithm 17th

Multi-robot Task Allocation Using Island Model Genetic Algor...

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17th IFAC symposium on Information control Problems in Manufacturing (INCOM)

作者： Cechinel, Alan Kunz De Pieri, Edson Roberto Fernandes Perez, Anderson Luiz Della Mea Plentz, Patricia Fed Univ Santa Catarina UFSC Automat & Syst Dept Florianopolis SC Brazil Univ Fed Santa Catarina Automat & Syst Dept Florianopolis SC Brazil UFSC Comp Dept Ararangua Brazil Univ Fed Santa Catarina Informat & Stat Dept Florianopolis SC Brazil

Mobile robots have been used in several areas as agriculture, vigilance, and indoor transportation in factories and warehouses. The warehouses' efficiency is directly related to transportation in the order picking process. Using mobile robots can improve transportation efficiency when associated with optimized task allocation. This paper shows a method, using the island model genetic algorithm, to allocate tasks with deadlines in multi-robot systems where mobile robots have different payloads and speed capacities. The approach's performance was evaluated by varying parameters of the algorithm and the size of the task set, and the robot set involved in the allocation process. The performance criteria adopted were success rate in allocating all tasks, percentage of assigned tasks in non-complete solutions, and obtaining a complete solution. The results have shown that the method brings a more significant number of complete solutions in sets with a small and medium number of tasks. Besides that, the solutions obtained meets deadlines and show energy consumption reduction. Copyright (C) 2021 The Authors.

关键词： Engineering Applications of Artificial intelligence Scheduling Heuristics Robotic Systems Intelligent Robot Services in Manufacturing Factory and Industrial automation

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Design of Intelligent Remote control Car Based on MCU

Design of Intelligent Remote Control Car Based on MCU

引用

ieee International symposium on Information (IT) in Medicine and Education, ITME

作者： Pingyuan Liang Wei Yuan School of Information Hunan University of Humanities Science and Technology Loudi China

With the development of economy and society, intelligence has become more and more popular, and intelligent remote control car plays an increasingly important role in all walks of life. In order to promote the application of intelligent car and improve the automation level of unmanned transportation, this paper designs an intelligent remote control car based on STC89C52 micro-controller, which is composed of remote control module and car module. There are two selection modes in the remote control module: body mode (the purpose of controlling the movement of the car is achieved by tilting the remote control from front to back, left to right by the user) and remote sensing module (the purpose of controlling the movement of the car is achieved by the user by operating the two axis key rocker sensor on the remote control), which enables the car to make left turns, right turns, forward turns, backward turns, accelerates and decelerates according to the user’s requirements. Based on the technology of wireless communication and radio remote control, this paper focuses on the practical application design of wireless communication in life. Its design idea can be applied in transportation, production and other fields, and has a high market application value.

关键词： Wireless communication Transportation Production Interference Distance measurement Software Sensors

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Adaptive neural network control for a soft robotic manipulator 7

Adaptive neural network control for a soft robotic manipulat...

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7th International Conference on Information, Cybernetics, and computational Social Systems, ICCSS 2020

作者： Han, Jiangtao Liu, Zhijie He, Wei School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China Shunde Graduate School University of Science and Technology Beijing Foshan528300 China School of Automation and Electrical Engineering Institute of Artificial Intelligence University of Science and Technology Beijing Beijing China

ISBN: (纸本)9781728162461

This paper mainly introduces the modeling based on Cosserat rod theory and focuses on the adaptive neural network controller design based on model. In dealing with the external interference with the environment and the unmodeled dynamics of the system, a neural network (NN) is introduced to compensate it, and using Backstepping method to design the adaptive controller, finally the stability of the closed-loop system and the convergence of the signal in the system is proved with Lyapunov function theory, ensuring the end of the manipulator can track the given signal. In addition, the simulation experiment of soft manipulator swing and constant angle tracking control are carried out, and the simulation shows the rationality of the proposed controller. © 2020 ieee.

关键词： Closed loop systems

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Fast Calibration of Agent-Based Model using Mean-Field Approach

Fast Calibration of Agent-Based Model using Mean-Field Appro...

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Digital Twins and Parallel intelligence (DTPI), ieee International Conference on

作者： Wanze Lu Gang Xiong Xudong Liu Sheng Liu Peijun Ye Liguo Zhang Faculty of Information and Technology Beijing University of Technology Beijing China The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China The Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing China The Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing The Cloud Computing Center Chinese Academy of Sciences Beijing China

Calibration of simulation system is an important step to establish a reliable simulation system. In this paper, based on the existing mean field calibration theory, a new fast calibration method for complex urban traffic system based on mean field method is discussed by establishing the relationship between the macroscopic observation quantity and the proxy microscopic parameters. This method makes use of the machine learning method, and reduces the dimension of the state vector in the discrete time period, so that the dimension of the macroscopic state transition matrix is lower, and then reduces the computational complexity of the system calibration. Finally, the calibration results of the simulation system were verified by SUMO traffic simulation software.

关键词： Visualization computational modeling Microscopy Digital twin Machine learning Traffic control Reliability theory

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Chaldene: Towards Visual Programming Image Processing in Jupyter Notebooks

Chaldene: Towards Visual Programming Image Processing in Jup...

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ieee symposium on Visual Languages and Human Centric Computing (VL/HCC)

作者： Fei Chen Philipp Slusallek Martin Mü ller Tim Dahmen German Research Center for Artificial Intelligence (DFKI) Saarbr&#x00FC cken Germany Saarland University Saarbr&#x00FC

ISBN: (数字)9781665442145

ISBN: (纸本)9781665442152

Jupyter Notebook [1] is an open source, interactive computing platform widely used in the scientific computing and artificial intelligence community [2], [3], [4], [5]. The popularity of the platform is a consequence of the generated single notebook document combining source code, markdown, and visualizations (Fig. 1). This makes the platform ideal for tasks such as data analysis and scientific image processing, where repeatability and transparency of analysis tasks are just as important as functionality and performance. However, the obligatory use of code is an obstacle to acceptance of the platform in scientific communities where programming is not generally taught in the curriculum. Consequently, many experimental communities rely on manual image processing using graphical user interfaces [6], [7], [8]. The obvious disadvantages are the lack of repeatability, transparency, and precision in image processing and data analysis tasks. To solve these issues, we propose to extend Jupyter Notebook with visual programming cells. In each visual programming cell, users can create the program by assembling graphical nodes that represent computational instructions, and the textual program is automatically generated and executed by the environment. Cells will support version control aware serialization and deserialization. The core innovation of our proposed work lies in a change of workflow and the adaption of a jupyter-based workflow in experimental communities that have no culture of working with source code. The system can be adapted to multiple applications and domains by integrating new node types. We hereby present an early version of the system and provide one use case from microscopy image processing to demonstrate the integration of existing non-Python software.

关键词： Visualization Technological innovation Codes Data analysis Scientific computing Image processing Microscopy

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Zero-Reference Fractional-Order Low-Light Image Enhancement Based on Retinex Theory

Zero-Reference Fractional-Order Low-Light Image Enhancement ...

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ieee symposium Series on computational intelligence (SSCI)

作者： Qiang Zhang Feiqi Fu Kai Zhang Feng Lin Jian Wang College of Science China University of Petroleum (East China) Qingdao China College of Geosciences China University of Petroleum (East China) Qingdao China College of Petroleum Engineering China University of Petroleum (East China) Qingdao China College of Control Science and Engineering China University of Petroleum (East China) Qingdao China

ISBN: (纸本)9781728190495

The quality of images taken in an insufficiently lighting environment is degraded. These images limit the presentation of machine vision technology. To address the issue, many researchers have focused on enhancing low-light images. This paper presents a zero-reference learning method to enhance low-light images. A deep network is built for estimating the illumination component of the low-light image. We use the original image and the derivative graph to define a zero-reference loss function based on illumination constraints and priori conditions. Then the deep network is trained by minimizing the loss function. Final image is obtained according to the Retinex theory. In addition, we use fractional-order mask to preserve image details and naturalness. Experiments on several datasets demonstrate that the proposed algorithm can achieve low-light image enhancement. Experimental results indicate that the superiority of our algorithm over state-of-the-arts algorithms.

关键词： Learning systems Machine vision Brightness Lighting Visual effects Image enhancement computational intelligence

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