Volume 5, Number 2, 2008
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A new on-line fault detection and isolation(FDI)scheme proposed for engines using an adaptive neural network classifier is evaluated for a wide range of operational modes to check the robustness of the scheme in this paper.The neural classifier is adaptive to cope with the significant parameter uncertainty,disturbances,and environment changes.The developed scheme is capable of diagnosing faults in on-line mode and can be directly implemented in an on-board diagnosis system(hardware).The robustness of the FDI for the closed-loop system with crankshaft speed feedback is investigated by testing it for a wide range of operational modes including robustness against fixed and sinusoidal throttle angle inputs,change in load,change in an engine parameter,and all these changes occurring at the same time.The evaluations are performed using a mean value engine model(MVEM),which is a widely used benchmark model for engine control system and FDI system design.The simulation results confirm the robustness of the proposed method for various uncertainties and disturbances.
In this paper,a decentralized proportional-derivative(PD)controller design for non-uniform motion of a Hamiltonian hybrid system is considered.A Hamiltonian hybrid system with the capability of producing a non-uniform motion is developed. The structural properties of the system are investigated by means of the theory of Harniltonian systems.A relationship between the parameters of the system and the parameters of the proposed decentralized PD controller is shown to ensure local stability and tracking performance.Simulation results are included to show the obtained non-uniform motion.
In this paper,the normal Luenberger function observer design for second-order descriptor linear systems is considered. It is shown that the main procedure of the design is to solve a so-called second-order generalized Sylvester-observer matrix equation. Based on an explicit parametric solution to this equation,a parametric solution to the normal Luenberger function observer design problem is given.The design degrees of freedom presented by explicit parameters can be further utilized to achieve some additional design requirements.
Quantum neural network filters for signal processing have received a lot of interest in the recent past.The implementations of these filters had a number of design parameters that led to numerical inefficiencies.At the same time the solution procedures employed were explicit in that the evolution of the time-varying functions had to be controlled.This often led to numerical instabilities.This paper outlines a procedure for improving the stability,numerical efficiency,and the accuracy of quantum neural network filters.Two examples are used to illustrate the principles employed.
This paper considers the problems of practical stability analysis and synthesis of linear descriptor systems subject to time- varying and norm-bounded exogenous disturbances.A sufficient condition for the systems to be regular,impulsive-free and practically stable is derived.Then the synthesis problem is addressed and a state feedback controller is designed.To deal with the computational issue,the conditions of the main results are converted into linear matrix inequality(LMI)feasibility problems.Furthermore,two optimization algorithms are formulated to improve the system performances.Finally,numerical examples are given to illustrate the obtained results.
The performance of fuel cells and the vehicle applications they are embedded into depends on a delicate balance of the correct temperature,humidity,reactant pressure,purity and flow rate.This paper successfully investigates the problem related to flow control with implementation on a single cell membrane electrode assembly(MEA).This paper presents a systematic approach for performing system identification using recursive least squares identification to account for the non-linear parameters of the fuel cell. Then,it presents a fuzzy controller with a simplified rule base validated against real time results with the existing flow controller which calculates the flow required from the stoichiometry value.
This paper addresses a sensor-based simultaneous localization and mapping(SLAM)algorithm for camera tracking in a virtual studio environment.The traditional camera tracking methods in virtual studios are vision-based or sensor-based.However,the chroma keying process in virtual studios requires color cues,such as blue background,to segment foreground objects to be inserted into images and videos.Chroma keying limits the application of vision-based tracking methods in virtual studios since the background cannot provide enough feature information.Furthermore,the conventional sensor-based tracking approaches suffer from the jitter,drift or expensive computation due to the characteristics of individual sensor system.Therefore,the SLAM techniques from the mobile robot area are first investigated and adapted to the camera tracking area.Then,a sensor-based SLAM extension algorithm for two dimensional(2D)camera tracking in virtual studio is described.Also,a technique called map adjustment is proposed to increase the accuracy and efficiency of the algorithm.The feasibility and robustness of the algorithm is shown by experiments.The simulation results demonstrate that the sensor-based SLAM algorithm can satisfy the fundamental 2D camera tracking requirement in virtual studio environment.
The multi-modal information presentation,integrated into the virtual environment(VE),has potential for stimulating different senses,improving the users impression of immersion,and increasing the amount of information that is accepted and processed by the users perception system.The increase of the useful feedback information may reduce the users cognitive load,thus enhancing the users efficiency and performance while interacting with VEs.This paper presents our creation of a multi-seusory virtual assembly environment(VAE)and the evaluation of the effects of multi-sensory feedback on the usability.The VAE brings together complex technologies such as constraint-based assembly simulation,optical motion tracking technology,and real-time 3D sound generation tech- nology around a virtual reality workbench and a common software platform.The usability evaluation is in terms of its three attributes: efficiency of use,user satisfaction,and reliability.These are addressed by using task completion times(TCTs),questionnaires,and human performance error rates(HPERs),respectively.Two assembly tasks have been used to perform the experiments,using sixteen participants.The outcomes showed that the multi-sensory feedback could improve the usability.They also indicated that the integrated feedback offered better usability than either feedback used in isolation.Most participants preferred the integrated feedback to either feedback(visual or auditory)or no feedback.The participants comments demonstrated that nonrealistic or inappropriate feedback had negative effects on the usability,and easily made them feel frustrated.The possible reasons behind the outcomes are also analysed by using a unifying human computer interaction framework.The implications,concluded from the outcomes of this work,can serve as useful guidelines for improving VE system design and implementation.
This paper describes specific constraints of vision systems that are dedicated to be embedded in mobile robots.If PC- based hardware architecture is convenient in this field because of its versatility,flexibility,performance,and cost,current real-time operating systems are not completely adapted to long processing with varying duration,and it is often necessary to oversize the system to guarantee fall-safe functioning.Also,interactions with other robotic tasks having more priority are difficult to handle.To answer this problem,we have developed a dynamically reconfigurable vision processing system,based on the innovative features of Clopatre real-time applicative layer concerning scheduling and fault tolerance.This framework allows to define emergency and optional tasks to ensure a minimal quality of service for the other subsystems of the robot,while allowing to adapt dynamically vision processing chain to an exceptional overlasting vision process or processor overload.Thus,it allows a better cohabitation of several subsystems in a single hardware,and to develop less expensive but safe systems,as they will be designed for the regular case and not rare exceptional ones.Finally,it brings a new way to think and develop vision systems,with pairs of complementary operators.
Parameter identification is a key requirement in the field of automated control of unmanned excavators(UEs).Further- more,the UE operates in unstructured,often hazardous environments,and requires a robust parameter identification scheme for field applications.This paper presents the results of a research study on parameter identification for UE.Three identification methods, the Newton-Raphson method,the generalized Newton method,and the least squares method are used and compared for prediction accuracy,robustness to noise and computational speed.The techniques are used to identify the link parameters(mass,inertia,and length)and friction coefficients of the full-scale UE.Using experimental data from a full-scale field UE,the values of link parameters and the friction coefficient are identified.Some of the identified parameters are compared with measured physical values.Furthermore, the joint torques and positions computed by the proposed model using the identified parameters are validated against measured data. The comparison shows that both the Newton-Raphson method and the generalized Newton method are better in terms of prediction accuracy.The Newton-Raphson method is computationally efficient and has potential for real time application,but the generalized Newton method is slightly more robust to measurement noise.The experimental data were obtained in collaboration with QinetiQ Ltd.
In order to distinguish faces of various angles during face recognition,an algorithm of the combination of approximate dynamic programming(ADP)called action dependent heuristic dynamic programming(ADHDP)and particle swarm optimization (PSO)is presented.ADP is used for dynamically changing the values of the PSO parameters.During the process of face recognition, the discrete cosine transformation(DCT)is first introduced to reduce negative effects.Then,Karhunen-Love(K-L)transformation can be used to compress images and decrease data dimensions.According to principal component analysis(PCA),the main parts of vectors are extracted for data representation.Finally,radial basis function(RBF)neural network is trained to recognize various faces. The training of RBF neural network is exploited by ADP-PSO.In terms of ORL Face Database,the experimental result gives a clear view of its accurate efficiency.
This paper proposes a thorough scheme,by virtue of camera zooming descriptor with two-level threshold,to automatically retrieve close-ups directly from moving picture experts group(MPEG)compressed videos based on camera motion analysis.A new algorithm for fast camera motion estimation in compressed domain is presented.In the retrieval process,camera-motion-based semantic retrieval is built.To improve the coverage of the proposed scheme,close-up retrieval in all kinds of videos is investigated.Extensive experiments illustrate that the proposed scheme provides promising retrieval results under real-time and automatic application scenario.
According to the security requirement of the short message service(SMS)industry application,a secure short message communication protocol is proposed.This is an application level protocol constructed on the standard SMS communication protocol using public key authentication and key agreement without the need of wireless public key infrastructure(WPKI).Secure short message transmission and dynamic key agreement between mobile terminals and the accessing gateway are realized.The security of the proposed protocol is validated through the BAN logic.Compared with the standard SMS protocol,the effective payload rate of our protocol can reach 91.4%,and subscriber identity module(SIM)tool kit(STK)applications based on our protocol suit well for all kinds of mobile terminals in practical application.
The past decade witnessed rapid development of constraint satisfaction technologies,where algorithms are now able to cope with larger and harder problems.However,owing to the fact that constraints are inherently declarative,attention is quickly turning toward developing high-level programming languages within which such problems can be modeled and also solved.Along these lines,this paper presents DEPICT,the language.Its use is illustrated through modeling a number of benchmark examples.The paper continues with a description of a prototype system within which such models may be interpreted.The paper concludes with a description of a sample run of this interpreter showing how a problem modeled as such is typically solved.
IJAC CiteScore keeps raising in 2018
IJAC receives a CiteScore as high as 2.34 in 2018 which is 1.37 times higher than that in 2017. Being in the top 15%, it ranks #69 among 460 journals in respective categories.
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During the past two years, IJAC has published a series of high-quality papers by famous scholars around the world, including professor Tomaso Poggio from MIT, professor Brian Anderson from Australian National University, professor Yike Guo from Imperial College London, etc.. All the papers are open access, covering topics of Deep Learning, Artificial Intelligence, Neural Networks, and so on. You’ll never miss it!
2019 International Academic Conference List
International Journal of Automation and Computing (IJAC) maintains this list of conferences at the beginning of each year that are highly relevant to current hot research topics, including artificial intelligence, machine learning, computer vision, pattern recognition, robotics and automatic control.