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IET Irish Signals and systems Conference (ISSC 2009)

作者： Swagat Kumar Laxmidhar Behera Martin McGinnity Department of Electrical Engineering Indian Institute of Technology Kanpur India Intelligent Systems Research Centre University of Ulster UK

The inverse kinematic (IK) relationship of a manipulator is a one-to-many map which can not be learnt using a feed-forward neural network (FFN). The usual method is to learn the forward kinematic relationship using a FFN and then obtaining the IK solution through inversion of the approximate forward model. The accuracy of the inverse kinematic solution thus obtained is limited by the accuracy of the forward model. However, it is not possible to learn the forward kinematic map accurately even with a large network size and a large training data set. Moreover, one needs to resolve redundancy while solving IK in order to make use of available dexterity effectively. These limitations of a conventional network inversion-based scheme are overcome by using an inverse-forward adaptive scheme where the idea is to learn a local solution around the current joint angle configuration rather than trying to learn the global solution through an accurate forward model. It is shown that the accuracy of the IK solution thus obtained, is almost independent of the accuracy of the forward model. The redundancy is resolved by using a KSOM-SC network architecture as a hint generator for initializing the inverse-forward adaptive scheme. The efficacy of the proposed scheme is demonstrated through simulations and real-time experiment on a 7 DOF PowerCube manipulator. A simple obstacle avoidance task is performed to demonstrate the redundancy resolution process.

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An Ontological Study of Data Purpose for Privacy Policy Enforcement

An Ontological Study of Data Purpose for Privacy Policy Enfo...

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IEEE International Conference on and IEEE International Confernece on Social Computing (SocialCom) Privacy, Security, Risk and Trust (PASSAT)

作者： Shan Chen Mary-Anne Williams Innovation and Enterprise Research Laboratory Centre of Quantum Computation and Intelligent Systems Faculty of Engineering and Information Technology University of Technology Sydney Australia

Data purpose is a central concept in modeling privacy requirements. Existing purpose-based approaches for privacy protection have mainly focused on access control. The problem of ensuring the consistency between data purpose and data usage has been under-addressed. In an attempt to bridge this research gap, we develop a grounded understanding of data purpose and relevant key concepts that is fundamental to address the problem. We propose a Minimum Action Permission Principle as a basic guideline to establish a path to solutions to the consistency problem for privacy management.

关键词： Privacy Data privacy Licenses Vehicles Equations Access control Data models

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Prediction of Gas Utilization Ratio Under Different Working Conditions of Blast Furnace Based on Extreme Learning Machine

Prediction of Gas Utilization Ratio Under Different Working ...

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International Conference on control and Robots (ICCR)

作者： Tianen Ma Jianqi An School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Ministry of Education of Engineering Research Centre for Earth Exploration Intelligent Technology Wuhan China

ISBN: (数字)9781728175621

ISBN: (纸本)9781728175638

Since the working condition affects the chemical reactions in a blast furnace (BF), the relationship between the blast-supply parameters and the gas utilization ratio (GUR) varies with different working conditions. This paper presents a GUR prediction model based on working condition classification. First, this paper analyzes the influence of working conditions on the state parameters of a BF. Then, a fuzzy clustering method is used to identify the working condition by using some state parameters. Next, the GUR prediction model was designed based on an extreme learning machine under different working conditions. Finally, the simulation results verified the effectiveness of this method.

关键词： Employee welfare Extreme learning machines Simulation Clustering methods Predictive models Blast furnaces Robots

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Finite-Time Synchronization of Flux-controlled Complex-Valued Memristive Neural Networks

Finite-Time Synchronization of Flux-Controlled Complex-Value...

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Neuromorphic Computing (ICNC), International Conference on

作者： Yonghuan Wang Leimin Wang Genping Wu School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China Wuhan Second Ship Design and Research Institute Wuhan China

In this paper, by using the flux-controlled memristor model, the finite-time synchronization problem of delayed complex-valued memristive neural networks (MCNNs) is studied. Firstly, according to the proposed memristor model, we model the MCNNs as continuous systems on voltage-flux-time $(\wp, \varpi, t)$ domain. Then, we design a class of approaches to realize state synchronization between the drive and response systems, and the corresponding synchronization conditions are achieved. Finally, the effectiveness of results is illustrated with the simulations.

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Video Driven Traffic Modelling

Video Driven Traffic Modelling

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IEEE/ASME (AIM) International Conference on Advanced intelligent Mechatronics

作者： Hailing Zhou Douglas Creighton Lei Wei David Yang Gao Saeid Nahavandi Centre for Intelligent Systems Research (CISR) Deakin University Australia School of Science Information Technology and Engineering University of Ballarat Australia

We propose Video Driven Traffic Modelling (VDTM) for accurate simulation of real-world traffic behaviours with detailed information and low-cost model development and maintenance. Computer vision techniques are employed to estimate traffic parameters. These parameters are used to build and update a traffic system model. The model is simulated using the Paramics traffic simulation platform. Based on the simulation techniques, effects of traffic interventions can be evaluated in order to achieve better decision makings for traffic management authorities. In this paper, traffic parameters such as vehicle types, times of starting trips and corresponding origin-destinations are extracted from a video. A road network is manually defined according to the traffic composition in the video, and individual vehicles associated with extracted properties are modelled and simulated within the defined road network using Paramics. VDTM has widespread potential applications in supporting traffic decision-makings. To demonstrate the effectiveness, we apply it in optimizing a traffic signal control system, which adaptively adjusts green times of signals at an intersection to reduce traffic congestion.

关键词： Vehicles Computational modeling Roads Cameras Delays Vehicle detection Biological system modeling

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An Aeromagnetic Compensation Algorithm Based on Broad Learning System

An Aeromagnetic Compensation Algorithm Based on Broad Learni...

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Robotics, Artificial Intelligence and intelligent control (RAIIC), International Conference on

作者： Qilong Wang Junlei Song Chao Zuo Kaifeng Dong Fang Jin School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo- Exploration Ministry of Education Wuhan China Wuhan Second Ship Design and Research Institute Wuhan China

ISBN: (数字)9798350352627

ISBN: (纸本)9798350352634

Aeromagnetic surveys, renowned for their operational flexibility and high efficiency, serve as a crucial technique for measuring the geomagnetic field. However, aeromagnetic surveys are easily affected by magnetic interference from navigation platforms, making the compensation of aeromagnetic interference a crucial step in the measurement process. To address the inadequate consideration of nonlinear magnetic field interference in traditional compensation algorithms, this paper introduces an aeromagnetic compensation approach based on broad learning system. The broad learning system employs an incremental learning mechanism aimed at enhancing the precision of the network alongside the increase in nodes. With each expansion of the network node, computation is streamlined to calculating the pseudo-inverse of the expansion node, eliminating the necessity for retraining the entire network structure. Leveraging the nonlinear fitting characteristics of the broad learning system, this paper improves the accuracy of aeromagnetic interference compensation. Through UAV flight experiments, the broad learning system is compared with methodologies using particle swarm optimization (PSO) and BP neural network. Compared with PSO, training time was reduced by $21.3 \%$ and magnetic interference by $33.6 \%$. Compared with BP neural networks, training time was reduced by $34.9 \%$ and magnetic interference by $28.6 \%$. This paper provides references and ideas for the selection of aeromagnetic interference compensation algorithms.

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A comprehensive review of electrochemical hybrid power supply systems and intelligent energy managements for unmanned aerial vehicles in public services

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Energy and AI 2022年第3期9卷 148-171页

作者： Caizhi Zhang Yuqi Qiu Jiawei Chen Yuehua Li Zhitao Liu Yang Liu Jiujun Zhang Chan Siew Hwa College of Mechanical and Vehicle Engineering The State Key Laboratory of Mechanical TransmissionsChongqing Automotive Collaborative Innovation CentreChongqing UniversityChongqing400044China College of Automation Chongqing UniversityKey Laboratory of Complex System Safety and ControlMinistry of EducationChongqing UniversityChongqing400044China School of Mechanical Engineering University of Science and Technology BeijingBeijing100083China State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and ControlZhejiang UniversityHangzhou310027China Institute for Sustainable Energy/College of Sciences Shanghai UniversityShanghai 200444China School of Mechanical and Aerospace Engineering Nanyang Technological University50 Nanyang Avenue639798Singapore Sichuan Energy Internet Research Institute Tsinghua UniversityChengdu 610213Sichuan ProvinceChina

The electric unmanned aerial vehicles (UAVs) are rapidly growing due to their abilities to perform some difficult or dangerous tasks as well as many public services including real-time monitoring, wireless coverage, search and rescue, wildlife surveys, and precision agriculture. However, the electrochemical power supply system of UAV is a critical issue in terms of its energy/power densities and lifetime for service endurance. In this paper, the current power supply systems used in UAVs are comprehensively reviewed and analyzed on the existing power configurations and the energy management systems. It is identified that a single type of electrochemical power source is not enough to support a UAV to achieve a long-haul flight;hence, a hybrid power system architecture is necessary. To make use of the advantages of each type of power source to increase the endurance and achieve good performance of the UAVs, the hybrid systems containing two or three types of power sources (fuel cell,battery, solar cell, and supercapacitor,) have to be developed. In this regard, the selection of an appropriate hybrid power structure with the optimized energy management system is critical for the efficient operation of a UAV. It is found that the data-driven models with artificial intelligence (AI) are promising in intelligent energy management. This paper can provide insights and guidelines for future research and development into the design and fabrication of the advanced UAV power systems.

关键词： Unmanned aerial vehicles(UAVs) Power supply system Fuel cell system Artificial intelligence(AI) Energy management systems

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Scenario Modeling Method for Collision Avoidance Testing in Inland Waterway

SSRN

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SSRN 2023年

作者： Zhou, Hai-Ming Zheng, Mao Chu, Xiu-Min Liu, Chen-Guang National Engineering Research Center for Water Transport Safety Hubei Wuhan430063 China School of Transportation and Logistics Engineering Wuhan University of technology Hubei Wuhan430063 China Intelligent Transportation Systems Research Center Wuhan University of Technology Hubei Wuhan430063 China Fujian Engineering Research Center of Safety Control for Ship Intelligent Navigation College of Physics and Electronic Information Engineering Minjiang University Fuzhou350108 China

To model encounter scenarios for autonomous collision avoidance of ships in inland waterways, a ship navigation data collection and fusion system is established by the utilization of AIS and radar data acquisition methods. Taking the waterway between Three Gorges dam and Gezhouba Dams in China as an example, the ship navigation data is collected and analyzed to identify key factors for collision avoidance scenarios. A parameterized modeling approach for generating encounter scenarios in inland waterway ship collision avoidance testing is proposed, which enables the automated generation of collision encounter scenarios by setting relevant parameters. By testing encounter scenarios between two or more ships, a series of test scenarios are generated. Simulation tests demonstrate the effectiveness of this parameterized scenario generation method in supporting the testing of autonomous collision avoidance algorithms for inland ships. © 2023, The Authors. All rights reserved.

关键词： Collision avoidance

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Optimal design of alloy steels using evolutionary computing

Optimal design of alloy steels using evolutionary computing

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International Conference on Knowledge-Based intelligent Information engineering systems (KES)

作者： M. Mahfouf J. Tenner D.A. Linkens M.F. Abbod Corus Research Development and Technology Swinden Technology Centre Rotherham UK Department of Automatic Control and Systems Engineering University of Sheffield Sheffield UK

Over the last four years efforts have been devoted towards the development and validation of mechanical test result models relating to a range of alloy steels. Several neural-network based models have been developed, two of which are related to the mechanical test results of Ultimate Tensile Strength (UTS) and Reduction of Area (ROA). The ultimate aim of developing these models is to pave the way to process optimisation through better predictions of mechanical properties. In this research we propose to exploit such neural network models in order to determine the optimal alloy composition and heat treatment temperatures required, given certain predefined mechanical properties such as the UTS and ROA. Generic Algorithms are used for this purpose. The results obtained are very encouraging.

关键词： Iron alloys Steel Mechanical factors Heat treatment Genetic algorithms systems engineering and theory System testing Predictive models Neural networks Temperature

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A Speech Cryptosystem Using the New Chaotic System with a Capsule-Shaped Equilibrium Curve

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Computers, Materials & Continua 2023年第6期75卷 5987-6006页

作者： Mohamad Afendee Mohamed Talal Bonny Aceng Sambas Sundarapandian Vaidyanathan Wafaa Al Nassan Sen Zhang Khaled Obaideen Mustafa Mamat Mohd Kamal Mohd Nawawi Faculty of Informatics and Computing Universiti Sultan Zainal AbidinMalaysia Department of Computer Engineering University of SharjahSharjah26666UAE Department of Mechanical Engineering Universitas Muhammadiyah TasikmalayaIndonesia Research and Development Centre Vel Tech UniversityAvadiChennaiIndia School of Artificial Intelligence and Automationand Key Laboratory of Image Processingand Intelligent Control of Education Ministry of ChinaHuazhong University of Scienceand TechnologyWuhan430074China Institute of Strategic Industrial Decision Modeling School of Quantitative SciencesUniversiti Utara Malaysia06010UUM SintokKedahMalaysia

In recent years,there are numerous studies on chaotic systems with special equilibrium curves having various shapes such as circle,butterfly,heart and *** paper describes a new 3-D chaotic dynamical system with a capsule-shaped equilibrium *** proposed chaotic system has two quadratic,two cubic and two quartic nonlinear *** is noted that the proposed chaotic system has a hidden attractor since it has an infinite number of equilibrium *** is also established that the proposed chaotic system exhibits multi-stability with two coexisting chaotic attractors for the same parameter values but differential initial states.A detailed bifurcation analysis with respect to variations in the system parameters is portrayed for the new chaotic system with capsule equilibrium *** have shown MATLAB plots to illustrate the capsule equilibrium curve,phase orbits of the new chaotic system,bifurcation diagrams and *** an engineering application,we have proposed a speech cryptosystem with a numerical algorithm,which is based on our novel 3-D chaotic system with a capsule-shaped equilibrium *** proposed speech cryptosystem follows its security evolution and implementation on Field Programmable Gate Array(FPGA)*** results show that the proposed encryption system utilizes 33%of the FPGA,while the maximum clock frequency is 178.28 MHz.

关键词： Chaos chaotic systems hidden attractors multi-stability bifurcation speech cryptosystem FPGA design

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