Compensation of hysteresis in piezoelectric actuator with iterative learning control

This paper presents the application of iterative learning control (ILC) to compensate hysteresis in a piezoelectric actuator. The proposed controller is a hybrid of proportional-integral-differential (PID) control, whose main function is for trajectory tracking, and a chatter-based ILC, whose main function is for hysteresis compensation. Stability analysis of the proposed ILC is presented, with the PID included in the dynamic of the piezoelectric actuator. The performance of the proposed controller is analysed through simulation and verified with experiment with a piezoelectric actuator.     

Multi-goal Q-learning of cooperative teams

This paper studies a multi-goal Q-learning algorithm of cooperative teams. Member of the cooperative teams is simulated by an agent. In the virtual cooperative team, agents adapt its knowledge according to cooperative principles. The multi-goal Q-learning algorithm is approached to the multiple learning goals. In the virtual team, agents learn what knowledge to adopt and how much to learn (choosing learning radius). The learning radius is interpreted in Section 3.1. Five basic experiments are manipulated proving the validity of the multi-goal Q-learning algorithm. It is found that the learning algorithm causes agents to converge to optimal actions, based on agents’ continually updated cognitive maps of how actions influence learning goals. It is also proved that the learning algorithm is beneficial to the multiple goals. Furthermore, the paper analyzes how sensitive the learning performance is affected by the parameter values of the learning algorithm.     

A synergy of multi-objective optimization and data mining for the analysis of a flexible flow shop

A method for analyzing production systems by applying multi-objective optimization and data mining techniques on discrete-event simulation models, the so-called Simulation-based Innovization (SBI) is presented in this paper. The aim of the SBI analysis is to reveal insight on the parameters that affect the performance measures as well as to gain deeper understanding of the problem, through post-optimality analysis of the solutions acquired from multi-objective optimization. This paper provides empirical results from an industrial case study, carried out on an automotive machining line, in order to explain the SBI procedure. The SBI method has been found to be particularly suitable in this case study as the three objectives under study, namely total tardiness, makespan and average work-in-process, are in conflict with each other. Depending on the system load of the line, different decision variables have been found to be influencing. How the SBI method is used to find important patterns in the explored solution set and how it can be valuable to support decision making in order to improve the scheduling under different system loadings in the machining line are addressed.     

A simulation-based scheduling system for real-time optimization and decision making support

This paper presents an industrial application of simulation-based optimization (SBO) in the scheduling and real-time rescheduling of a complex machining line in an automotive manufacturer in Sweden. Apart from generating schedules that are robust and adaptive, the scheduler must be able to carry out rescheduling in real time in order to cope with the system uncertainty effectively. A real-time scheduling system is therefore needed to support not only the work of the production planner but also the operators on the shop floor by re-generating feasible schedules when required. This paper describes such a real-time scheduling system, which is in essence a SBO system integrated with the shop floor database system. The scheduling system, called OPTIMISE scheduling system (OSS), uses real-time data from the production line and sends back expert suggestions directly to the operators through Personal Digital Assistants (PDAs). The user interface helps in generating new schedules and enables the users to easily monitor the production progress through visualization of production status and allows them to forecast and display target performance measures. Initial results from this industrial application have shown that such a novel scheduling system can help both in improving the line throughput efficiently and simultaneously supporting real-time decision making.     

Simultaneous Localization and Mapping Using Rao-Blackwellized Particle Filters in Multi Robot Systems

In this paper we investigate the problem of Simultaneous Localization and Mapping (SLAM) for a multi robot system. Relaxing some assumptions that characterize related work we propose an application of Rao-Blackwellized Particle Filters (RBPF) for the purpose of cooperatively estimating SLAM posterior. We consider a realistic setup in which the robots start from unknown initial poses (relative locations are unknown too), and travel in the environment in order to build a shared representation of the latter. The robots are required to exchange a small amount of information only when a rendezvous event occurs and to measure relative poses during the meeting. As a consequence the approach also applies when using an unreliable wireless channel or short range communication technologies (bluetooth, RFId, etc.). Moreover it allows to take into account the uncertainty in relative pose measurements. The proposed technique, which constitutes a distributed solution to the multi robot SLAM problem, is further validated through simulations and experimental tests.     

Reliability analysis and optimal version-updating for open source software

Context

Although reliability is a major concern of most open source projects, research on this problem has attracted attention only recently. In addition, the optimal version-dating for open source software considering its special properties is not yet discussed.

Objective

In this paper, the reliability analysis and optimal version-updating for open source software are studied.

Method

A modified non-homogeneous Poisson process model is developed for open source software reliability modeling and analysis. Based on this model, optimal version-updating for open source software is investigated as well. In the decision process, the rapid release strategy and the level of reliability are the two most important factors. However, they are essentially contradicting with each other. In order to consider these two conflicting factors simultaneously, a new decision model based on multi-attribute utility theory is proposed.

Results

Our models are tested on the real world data sets from two famous open source projects: Apache and GNOME. It is found that traditional software reliability models provide overestimations of the reliability of open source software. In addition, the proposed decision model can help management to make a rational decision on the optimal version-updating for open source software.

Conclusion

Empirical results reveal that the proposed model for open source software reliability can describe the failure process more accurately. Furthermore, it can be seen that the proposed decision model can assist management to appropriately determine the optimal version-update time for open source software.     

A dynamic attribute-based data filtering and recovery scheme for web information processing

Web data being transmitted over a network channel on the Internet with excessive amount of data causes data processing problems, which include selectively choosing useful information to be retained for various data applications. In this paper, we present an approach for filtering less-informative attribute data from a source Website. A scheme for filtering attributes, instead of tuples (records), from a Website becomes imperative, since filtering a complete tuple would lead to filtering some informative, as well as less-informative, attribute data in the tuple. Since filtered data at the source Website may be of interest to the user at the destination Website, we design a data recovery approach that maintains the minimal amount of information for data recovery purpose while imposing minimal overhead for data recovery at the source Website. Our data filtering and recovery approach (1) handles a wide range of Web data in different application domains (such as weather, stock exchanges, Internet traffic, etc.), (2) is dynamic in nature, since each filtering scheme adjusts the amount of data to be filtered as needed, and (3) is adaptive, which is appealing in an ever-changing Internet environment.     

Sliding mode control for multi-agent systems under a time-varying topology

This paper addresses the tracking problem of a class of multi-agent systems under uncertain communication environments which has been modelled by a finite number of constant Laplacian matrices together with their corresponding scheduling functions. Sliding mode control method is applied to solve this nonlinear tracking problem under a time-varying topology. The controller of each tracking agent has been designed by using only its own and neighbours’ information. Sufficient conditions for the existence of a sliding mode control tracking strategy have been provided by the solvability of linear matrix inequalities. At the end of this work, numerical simulations are employed to demonstrate the effectiveness of the proposed sliding mode control tracking strategy.     

Retrieval of total suspended matter concentration from Gaofen-1 Wide Field Imager (WFI) multispectral imagery with the assistance of Terra MODIS in turbid water – case in Deep Bay

This study proposed a method for developing high spatial resolution Gaofen-1 satellite (GF-1) Wide Field Imager (WFI)-based total suspended matter concentration (C_TSM) retrieval model with the assistance of Moderate Resolution Imaging Spectroradiometer (MODIS) data, using the Deep Bay in China as a case. Based on long-term calibrated C_TSM measurements of optical backscatter (OBS) 3A turbidity and temperature monitoring system of two stationary stations from January 2007 through November 2008, 33 match-ups were selected to build an exponential retrieval model for MODIS atmospherically corrected remote-sensing reflectance (R_rs) ratio (R_rs,645/R_rs,555). Validation of the MODIS model showed well agreement with the seven in situ C_TSM measurements with a root mean squared error (RMSE) of 5.06 mg l^?1 and a coefficient of determination R² of 0.80. Aided with six MODIS retrieved C_TSM products, different band combinations (single band (R_rc,660), band subtraction (R_rc,660–R_rc,560), band ratio (R_rc,660/R_rc,560), and total suspended matter index at 660 nm band (TSMI₆₆₀) were evaluated for simultaneous GF-1 WFI Rayleigh-corrected reflectance (R_rc). The results showed that the exponential model based on the Rayleigh-corrected reflectance ratio (R_rc,660/R_rc,560) could achieve acceptable accuracy, with RMSE of 14.80 mg l^?1 and R² of 0.62. The proposed method would be helpful for dynamic monitoring in the Deep Bay, and more important could also provide an alternative approach for studies when in situ measurements are unreachable.