Event‐driven model predictive control of timed hybrid Petri nets

Hybrid Petri nets represent a powerful modeling formalism that offers the possibility of integrating, in a natural way, continuous and discrete dynamics in a single net model. Usual control approaches for hybrid nets can be divided into discrete‐time and continuous‐time approaches. Continuous‐time approaches are usually more precise, but can be computationally prohibitive. Discrete‐time approaches are less complex, but can entail mode‐mismatch errors due to fixed time discretization. This work proposes an optimization‐based event‐driven control approach that applies on continuous time models and where the control actions change when discrete events occur. Such an approach is computationally feasible for systems of interest in practice and avoids mode‐mismatch errors. In order to handle modelling errors and exogenous disturbances, the proposed approach is implemented in a closed‐loop strategy based on event‐driven model predictive control. Copyright © 2013 John Wiley & Sons, Ltd.     

Keep talking—Performance effectiveness with continuous voice recognition for spreadsheet users

The performance of spreadsheet users was compared for two modes of input to the computer—keyboard and continuous voice recognition (CVR)—for subjects classified by their decision style. In addition, the data for this experiment was compared to results of a similar experiment that used a discrete word recognition system. Dependent measures were task completion time, accuracy, keystroke count, correction count, and user confidence for spreadsheet tasks. Results, using a speaker-dependent continuous voice recognizer, showed that for both simple data input and more complex analytical problems, subjects did not perform more effectively using CVR compared to keyboard. In addition, a subject's decision style was found not to interact with CVR for an effect on performance. Compared to earlier discrete word recognition results, CVR tended to shorten the time to complete a spreadsheet analysis task.     

Keep talking—Performance effectiveness with continuous voice recognition for spreadsheet users

The performance of spreadsheet users was compared for two modes of input to the computer—keyboard and continuous voice recognition (CVR)—for subjects classified by their decision style. In addition, the data for this experiment was compared to results of a similar experiment that used a discrete word recognition system. Dependent measures were task completion time, accuracy, keystroke count, correction count, and user confidence for spreadsheet tasks. Results, using a speaker-dependent continuous voice recognizer, showed that for both simple data input and more complex analytical problems, subjects did not perform more effectively using CVR compared to keyboard. In addition, a subject's decision style was found not to interact with CVR for an effect on performance. Compared to earlier discrete word recognition results, CVR tended to shorten the time to complete a spreadsheet analysis task.     

Fast continuous collision detection and handling for desktop virtual prototyping

This paper presents an overview of our recent work on continuous collision detection methods and constraints handling for rigid polyhedral objects. We demonstrate that continuous collision detection algorithms are practical in interactive dynamics simulation of complex polyhedral rigid bodies and show how continuous collision detection and efficient constraint-based dynamics algorithms allow us to perform various virtual prototyping tasks intuitively, precisely and robustly on commodity desktop computers. Especially, we present two applications of our system to actual industrial cases. We note that both tasks are performed with a simple 2D mouse on a high-end computer.     

Online estimation of discrete,continuous, and conditional joint densities using classifier chains

We address the problem of estimating discrete, continuous, and conditional joint densities online, i.e., the algorithm is only provided the current example and its current estimate for its update. The family of proposed online density estimators, estimation of densities online (EDO), uses classifier chains to model dependencies among features, where each classifier in the chain estimates the probability of one particular feature. Because a single chain may not provide a reliable estimate, we also consider ensembles of classifier chains and ensembles of weighted classifier chains. For all density estimators, we provide consistency proofs and propose algorithms to perform certain inference tasks. The empirical evaluation of the estimators is conducted in several experiments and on datasets of up to several millions of instances. In the discrete case, we compare our estimators to density estimates computed by Bayesian structure learners. In the continuous case, we compare them to a state-of-the-art online density estimator. Our experiments demonstrate that, even though designed to work online, EDO delivers estimators of competitive accuracy compared to other density estimators (batch Bayesian structure learners on discrete datasets and the state-of-the-art online density estimator on continuous datasets). Besides achieving similar performance in these cases, EDO is also able to estimate densities with mixed types of variables, i.e., discrete and continuous random variables.     

Continuous scatterplots

Scatterplots are well established means of visualizing discrete data values with two data variables as a collection of discrete points. We aim at generalizing the concept of scatterplots to the visualization of spatially continuous input data by a continuous and dense plot. An example of a continuous input field is data defined on an n-D spatial grid with respective interpolation or reconstruction of in-between values. We propose a rigorous, accurate, and generic mathematical model of continuous scatterplots that considers an arbitrary density defined on an input field on an n-D domain and that maps this density to m-D scatterplots. Special cases are derived from this generic model and discussed in detail: scatterplots where the n-D spatial domain and the m-D data attribute domain have identical dimension, 1-D scatterplots as a way to define continuous histograms, and 2-D scatterplots of data on 3-D spatial grids. We show how continuous histograms are related to traditional discrete histograms and to the histograms of isosurface statistics. Based on the mathematical model of continuous scatterplots, respective visualization algorithms are derived, in particular for 2-D scatterplots of data from 3-D tetrahedral grids. For several visualization tasks, we show the applicability of continuous scatterplots. Since continuous scatterplots do not only sample data at grid points but interpolate data values within cells, a dense and complete visualization of the data set is achieved that scales well with increasing data set size. Especially for irregular grids with varying cell size, improved results are obtained when compared to conventional scatterplots. Therefore, continuous scatterplots are a suitable extension of a statistics visualization technique to be applied to typical data from scientific computation.     

Towards selective attention: generating image features by learning a visuo-motor map

Robots require a form of visual attention to perform a wide range of tasks effectively. Existing approaches specify in advance the image features and attention control scheme required for a given robot to perform a specific task. However, to cope with different tasks in a dynamic environment, a robot should be able to construct its own attentional mechanisms. This paper presents a method that a robot can use to generating image features by learning a visuo-motor map. The robot constructs the visuo-motor map from training data, and the map constrains both the generation of image features and the estimation of state vectors. The resulting image features and state vectors are highly task-oriented. The learned mechanism is attentional in the sense that it determines what information to select from the image to perform a task. We examine robot experiments using the proposed method for indoor navigation and scoring soccer goals.     

Derivative Estimation for Buffer Capacity of Continuous Transfer Lines Subject to Operation-Dependent Failures

We derive estimators of throughput sensitivity to changes in buffer capacity for continuous flow models of a transfer line comprising two machines separated by a buffer of finite capacity, where machines are subject to operation-dependent failures, i.e., a machine cannot fail when it is idle. Both repair times and failure times may be general, i.e., they need not be exponentially distributed. The system is hybrid in the sense that it has both continuous dynamics—as a result of continuous material flow—and discrete events: failures and repairs. The combination of operation-dependent failures and buffer capacity as the parameter of interest make the derivative estimation problem difficult, in that unlike previous work on continuous flow models, careful use of conditional expectation (i.e., smoothed perturbation analysis) is required to obtain unbiased estimators.     

A comparison of human and near-optimal task management behavior

OBJECTIVE: The primary contribution of this work is the development of an abstract framework to which a variety of multitasking scenarios can be mapped. The metaphor of a juggler spinning plates was introduced to represent an operator performing multiple concurrent tasks. BACKGROUND: This allowed seeking a quantitative model for management of multiple continuous tasks instead of a model for completing multiple discrete tasks, which was considered in previous studies. METHODS: The multitasking performance of 10 participants in five scenarios was measured in a low-fidelity simulator (named Tardast), which was developed based on the concept of the juggler metaphor. This performance was then compared with a normative model, which was a near-optimal solution to a mathematical programming problem found by tabu search heuristics. RESULTS: Tabu outperformed the participants overall, although the best individual performance nearly equaled that of tabu. It was also observed that participants initially tended to manage numerous tasks poorly but that they gradually learned to handle fewer tasks and excel in them. CONCLUSION: This suggests that they initially overreacted to the penalization associated with poor performance in the software. Participants' strategic task management (e.g., what tasks to handle) was more significant in obtaining a good score than their tactical task management (e.g., how often to switch between two tasks). APPLICATION: Potential applications include better design of equipment, procedures, and training of operators of complex systems.     

Verification of workflow nets with transition conditions

Workflow management is concerned with automated support for business processes.Workflow management systems are driven by process models specifying the tasks that need to be executed,the order in which they can be executed,which resources are authorised to perform which tasks,and data that is required for,and produced by,these tasks.As workflow instances may run over a sustained period of time,it is important that workflow specifications be checked before they are deployed.Workflow verification is usually concerned with control-flow dependencies only;however,transition conditions based on data may further restrict possible choices between tasks.In this paper we extend workflow nets where transitions have concrete conditions associated with them,called WTC-nets.We then demonstrate that we can determine which execution paths of a WTC-net that are possible according to the control-flow dependencies,are actually possible when considering the conditions based on data.Thus,we are able to more accurately determine at design time whether a workflow net with transition conditions is sound.     

Combined continuous-discrete system simulation with GASP IV

GASP IV provides an alternative to the several better known and often used simulation languages, such as GPSS, SIMSCRIPT, and DYNAMO[8, 10, 11, 13, 14]. Whereas GPSS and SIMSCRIPT are discrete change languages and DYNAMO is a continuous change simulator,⁴ GASP IV has the unique capability to perform a combination of both discrete and continuous change in the same simulation. Thus, one may perform the more traditional discrete simulation of business problems such as queueing, inventory, production, etc., or the “Forrester” (systems dynamics) type models may be simulated[2,3,4] with GASP IV. Systems that include a combination of both discrete events and continuous change variables can be readily analyzed with GASP IV.     

A networked transferable belief model approach for distributed data aggregation

This paper focuses on the extension of the transferable belief model (TBM) to a multiagent-distributed context where no central aggregation unit is available and the information can be exchanged only locally among agents. In this framework, agents are assumed to be independent reliable sources which collect data and collaborate to reach a common knowledge about an event of interest. Two different scenarios are considered: In the first one, agents are supposed to provide observations which do not change over time (static scenario), while in the second one agents are assumed to dynamically gather data over time (dynamic scenario). A protocol for distributed data aggregation, which is proved to converge to the basic belief assignment given by an equivalent centralized aggregation schema based on the TBM, is provided. Since multiagent systems represent an ideal abstraction of actual networks of mobile robots or sensor nodes, which are envisioned to perform the most various kind of tasks, we believe that the proposed protocol paves the way to the application of the TBM in important engineering fields such as multirobot systems or sensor networks, where the distributed collaboration among players is a critical and yet crucial aspect.     

Untangling euler diagrams

In many common data analysis scenarios the data elements are logically grouped into sets. Venn and Euler style diagrams are a common visual representation of such set membership where the data elements are represented by labels or glyphs and sets are indicated by boundaries surrounding their members. Generating such diagrams automatically such that set regions do not intersect unless the corresponding sets have a non-empty intersection is a difficult problem. Further, it may be impossible in some cases if regions are required to be continuous and convex. Several approaches exist to draw such set regions using more complex shapes, however, the resulting diagrams can be difficult to interpret. In this paper we present two novel approaches for simplifying a complex collection of intersecting sets into a strict hierarchy that can be more easily automatically arranged and drawn (Figure 1). In the first approach, we use compact rectangular shapes for drawing each set, attempting to improve the readability of the set intersections. In the second approach, we avoid drawing intersecting set regions by duplicating elements belonging to multiple sets. We compared both of our techniques to the traditional non-convex region technique using five readability tasks. Our results show that the compact rectangular shapes technique was often preferred by experimental subjects even though the use of duplications dramatically improves the accuracy and performance time for most of our tasks. In addition to general set representation our techniques are also applicable to visualization of networks with intersecting clusters of nodes.     

Cascaded model adaptation for dialog act segmentation and tagging

There are many speech and language processing problems which require cascaded classification tasks. While model adaptation has been shown to be useful in isolated speech and language processing tasks, it is not clear what constitutes system adaptation for such complex systems. This paper studies the following questions: In cases where a sequence of classification tasks is employed, how important is to adapt the earlier or latter systems? Is the performance improvement obtained in the earlier stages via adaptation carried on to later stages in cases where the later stages perform adaptation using similar data and/or methods? In this study, as part of a larger scale multiparty meeting understanding system, we analyze various methods for adapting dialog act segmentation and tagging models trained on conversational telephone speech (CTS) to meeting style conversations. We investigate the effect of using adapted and unadapted models for dialog act segmentation with those of tagging, showing the effect of model adaptation for cascaded classification tasks. Our results indicate that we can achieve significantly better dialog act segmentation and tagging by adapting the out-of-domain models, especially when the amount of in-domain data is limited. Experimental results show that it is more effective to adapt the models in the latter classification tasks, in our case dialog act tagging, when dealing with a sequence of cascaded classification tasks.     

Dynamic visual servo control of a 4-axis joint tool to track image trajectories during machining complex shapes

A large part of the new generation of computer numerical control systems has adopted an architecture based on robotic systems. This architecture improves the implementation of many manufacturing processes in terms of flexibility, efficiency, accuracy and velocity. This paper presents a 4-axis robot tool based on a joint structure whose primary use is to perform complex machining shapes in some non-contact processes. A new dynamic visual controller is proposed in order to control the 4-axis joint structure, where image information is used in the control loop to guide the robot tool in the machining task. In addition, this controller eliminates the chaotic joint behavior which appears during tracking of the quasi-repetitive trajectories required in machining processes. Moreover, this robot tool can be coupled to a manipulator robot in order to form a multi-robot platform for complex manufacturing tasks. Therefore, the robot tool could perform a machining task using a piece grasped from the workspace by a manipulator robot. This manipulator robot could be guided by using visual information given by the robot tool, thereby obtaining an intelligent multi-robot platform controlled by only one camera.     

Vision-based remote control of cellular robots

This paper describes the development and design of a vision-based remote controlled cellular robot. Cellular robots have numerous applications in industrial problems where simple inexpensive robots can be used to perform different tasks that involve covering a large working space. As a methodology, the robots are controlled based on the visual input from one or more cameras that monitor the working area. As a result, a robust control of the robot trajectory is achieved without depending on the camera calibration. The remote user simply specifies a target point in the image to indicate the robot final position.

We describe the complete system at various levels: the visual information processing, the robot characteristics and the closed loop control system design, including the stability analysis when the camera location is unknown. Results are presented and discussed.

In our opinion, such a system may have a wide spectrum of applications in industrial robotics and may also serve as an educational testbed for advanced students in the fields of vision, robotics and control.     

Integer approximation of 3D chamfer mask coefficients using a scaling factor in anisotropic grids

For discrete data represented by three variable scalar functions, the sampling step may be different according to the axes, leading to parallelepipedic sampling grids. This is the case for instance with medical or industrial computed tomography and confocal microscopy, as well as in grey level image analysis if images are modelized by means of their set representation (mathematical morphology). In this paper, 3D non-cubic chamfer masks are introduced. The problem of coefficient optimization is addressed for arbitrary mask size. Thank to this, first, the maximal normalized error with respect to Euclidean distance can be derived analytically, in any 3D anisotropic lattice, and second, optimal chamfer mask coefficients can be computed. We propose a method to calculate lower and upper bounds for integer scaling factors in order to obtain integer approximations for the coefficients. This approach helps the algorithm perform in scenarios where memory is limited.     

基于值分布的多智能体分布式深度强化学习算法

近年来深度强化学习在一系列顺序决策问题中取得了巨大的成功,使其为复杂高维的多智能体系统提供有效优化的决策策略成为可能.然而在复杂的多智能体场景中,现有的多智能体深度强化学习算法不仅收敛速度慢,而且算法的稳定性无法保证.本文提出了基于值分布的多智能体分布式深度确定性策略梯度算法(multi-agent distribut...     

Regression using hybrid Bayesian networks: Modelling landscape–socioeconomy relationships

Modelling environmental systems becomes a challenge when dealing directly with continuous and discrete data simultaneously. The aim in regression is to give a prediction of a response variable given the value of some feature variables. Multiple linear regression models, commonly used in environmental science, have a number of limitations: (1) all feature variables must be instantiated to obtain a prediction, and (2) the inclusion of categorical variables usually yields more complicated models. Hybrid Bayesian networks are an appropriate approach to solve regression problems without such limitations, and they also provide additional advantages. This methodology is applied to modelling landscape–socioeconomy relationships for different types of data (continuous, discrete or hybrid). Three models relating socioeconomy and landscape are proposed, and two scenarios of socioeconomic change are introduced in each one to obtain a prediction. This proposal can be easily applied to other areas in environmental modelling.     

Movement-Assisted Connectivity Restoration in Wireless Sensor and Actor Networks

Recent years have witnessed a growing interest in applications of wireless sensor and actor networks (WSANs). In these applications, a set of mobile actor nodes are deployed in addition to sensors in order to collect sensors' data and perform specific tasks in response to detected events/objects. In most scenarios, actors have to respond collectively, which requires interactor coordination. Therefore, maintaining a connected interactor network is critical to the effectiveness of WSANs. However, WSANs often operate unattended in harsh environments where actors can easily fail or get damaged. An actor failure may lead to partitioning the interactor network and thus hinder the fulfillment of the application requirements. In this paper, we present DARA, a Distributed Actor Recovery Algorithm, which opts to efficiently restore the connectivity of the interactor network that has been affected by the failure of an actor. Two variants of the algorithm are developed to address 1- and 2-connectivity requirements. The idea is to identify the least set of actors that should be repositioned in order to reestablish a particular level of connectivity. DARA strives to localize the scope of the recovery process and minimize the movement overhead imposed on the involved actors. The effectiveness of DARA is validated through simulation experiments.