Statistical properties of controlled fluid flows with applications to control of mixing

In this paper, we study statistical properties of fluid flows that are actively controlled. Statistical properties such as Lagrangian and Eulerian time-averages are important flow quantities in fluid flows, particularly during mixing processes. Due to the assumption of incompressibility, the transformations in the state space can be described by a sequence of measure preserving transformations on a measure space. The classical Birkhoff's pointwise ergodic theorem does not necessarily apply in the context of sequences of transformations. We call B-regular a sequence for which this theorem holds. Motivated by mixing control concepts, we define three notions of asymptotic equivalence for sequences of transformations. We show an example in which Birkhoff's pointwise ergodic theorem does not hold even when a ‘strong’ asymptotic equivalence to a B-regular sequence is assumed. Under a ‘very strong’ asymptotic equivalence condition, we prove B-regularity. In the context of optimize-then-stabilize strategy for mixing control, we also prove that very strong asymptotic equivalence to a mixing sequence implies mixing. The mean ergodic theorem and the Poincare’ recurrence theorem are also proven for sequences of transformations under suitable asymptotic equivalence assumptions.     

Discovering anomalous frequent patterns from partially ordered event logs

Conformance checking allows organizations to compare process executions recorded by the IT system against a process model representing the normative behavior. Most of the existing techniques, however, are only able to pinpoint where individual process executions deviate from the normative behavior, without considering neither possible correlations among occurred deviations nor their frequency. Moreover, the actual control-flow of the process is not taken into account in the analysis. Neglecting possible parallelisms among process activities can lead to inaccurate diagnostics; it also poses some challenges in interpreting the results, since deviations occurring in parallel behaviors are often instantiated in different sequential behaviors in different traces. In this work, we present an approach to extract anomalous frequent patterns from historical logging data. The extracted patterns can exhibit parallel behaviors and correlate recurrent deviations that have occurred in possibly different portions of the process, thus providing analysts with a valuable aid for investigating nonconforming behaviors. Our approach has been implemented as a plug-in of the ESub tool and evaluated using both synthetic and real-life logs.     

Codesign of controller,routing and scheduling in WirelessHART networked control systems

In this paper, we provide a novel methodology to co‐design controller, scheduling and routing in a wireless control network compliant with the WirelessHART protocol. We both provide a modeling framework and derive a novel suboptimal solution to the linear‐quadratic regulator problem for a class of systems that extends Markov jump linear system considering both continuous and discrete inputs. To allow that, our results can be directly implemented in a real WirelessHART network, we setup a receding horizon optimization problem that takes into account the constraint for compliance with WirelessHART and validate our solution on a batch reactor control loop.     

A distributed approach for real-time multi-camera multiple object tracking

Estimating the positions of a set of moving objects captured from a network of cameras is still an open problem in Computer Vision. In this paper, a distributed and real-time approach for tracking multiple objects on multiple cameras is presented. A quantitative comparison with six state-of-the-art methods has been carried out on the publicly available PETS 2009 data set, demonstrating the effectiveness of the algorithm. Moreover, the proposed method has been tested also on a multi-camera soccer data set, showing its data fusion capabilities.     

Programming models and systems for Big Data analysis

ABSTRACT

Big Data analysis refers to advanced and efficient data mining and machine learning techniques applied to large amount of data. Research work and results in the area of Big Data analysis are continuously rising, and more and more new and efficient architectures, programming models, systems, and data mining algorithms are proposed. Taking into account the most popular programming models for Big Data analysis (MapReduce, Directed Acyclic Graph, Message Passing, Bulk Synchronous Parallel, Workflow and SQL-like), we analysed the features of the main systems implementing them. Such systems are compared using four classification criteria (i.e. level of abstraction, type of parallelism, infrastructure scale and classes of applications) for helping developers and users to identify and select the best solution according to their skills, hardware availability, productivity and application needs.     

A quality assessment of the soil water index by the propagation of ASCAT soil moisture error estimates through an exponential filter

Soil moisture data from remote-sensing measurements are used extensively and error characterization is critical in assessing their utility for specific applications. In hydrological monitoring and forecast applications, an estimate of the root-zone soil moisture is often required, and the exponential filter constitutes a common approach to obtaining a soil water index (SWI), i.e. the profile soil moisture from the remotely-sensed surface states. Advanced Scatterometer (ASCAT) surface soil moisture (SSM) time series include error estimates as ancillary data, however their effects are rarely considered in SWI computations. In this study, we introduced a simple error propagation scheme in parallel to exponential filter computation, aimed at estimating a SWI noise, which implicitly takes into account both errors and availability of the input SSM data actually used for each single SWI estimation. Integrating a control on SWI noise within quality check procedures was analysed by comparing in situ measurements available at different depths, up to 60 cm, for the period 2007–2014 in 10 stations located in Italy. The capacity of ASCAT-derived SWI to capture the same processes observed in the root-zone by in situ sensors, was evaluated by computing standard statistics (the correlation coefficient r and the root mean square difference [RMSD]) for different ‘a priori’ data masking procedures on SSM data, based on several indicators (processing flag, orbit direction, SSM noise) available for the ASCAT product. The effects of using the information contained in SSM noise and in the proposed SWI noise were also investigated. Removing data according to ‘a posteriori’ control on SWI noise rather than a priori masking SSM data, with the exponential filter T value and the percentage of removed data being equal, was found to generally improve the statistics of the resulting SWI time series. In addition, evaluating T on the SWI time series screened according to the SWI noise, outperforms other approaches analysed in almost every test case.     

Robot Teams for Intervention in Confined and Structured Environments

Safety, security, and rescue robotics can be extremely useful in emergency scenarios such as mining accidents or tunnel collapses where robot teams can be used to carry out cooperative exploration, intervention, or logistic missions. Deploying a multirobot team in such confined environments poses multiple challenges that involve task planning, motion planning, localization and mapping, safe navigation, coordination, and communications among all the robots. To complete their mission, robots have to be able to move in the environment with full autonomy while at the same time maintaining communication among themselves and with their human operators to accomplish team collaboration. Guaranteeing connectivity enables robots to explicitly exchange information needed in the execution of collaborative tasks and allows operators to monitor and teleoperate the robots and receive information about the environment. In this work, we present a system that integrates several research aspects to achieve a real exploration exercise in a tunnel using a robot team. These aspects are as follows: deployment planning, semantic feature recognition, multirobot navigation, localization, map building, and real‐time communications. Two experimental scenarios have been used for the assessment of the system. The first is the Spanish Santa Marta mine, a large mazelike environment selected for its complexity for all the tasks involved. The second is the Spanish‐French Somport tunnel, an old railway between Spain and France through the Central Pyrenees, used to carry out the real‐world experiments. The latter is a simpler scenario, but it serves to highlight the real communication issues.     

On the investigation into the kinetics of the ultrasound-assisted atmospheric freeze drying of eggplant

Atmospheric freeze drying is a highly attractive process for the dehydration of thermosensitive products, like food, due to the fact that water is removed at low temperature by sublimation. Unfortunately, drying times can be very long because of the internal resistance of the product to vapor diffusion: power ultrasound can be an effective means of accelerating the process, thus reducing the operating cost. The aim of this study was to assess the effect of air temperature and velocity, ultrasound power and sample size on the drying kinetics of eggplant (Solanum melongena L.) samples and, afterward, to analyze in silico an industrial process. Experiments were performed under various conditions regarding air temperature (?5, ?7.5, ?10°C), velocity (2 and 5?m?s^?1), power ultrasound (0, 10.3, 20.5?kW?m^?3, 21.9?kHz), and sample size. Drying rate was measured experimentally. The air velocity showed no relevant effects on the drying kinetics, and the effect of air temperature was slight when compared to the marked reduction in the drying time obtained when ultrasound was applied. The uniformly retreating interface model was modified to account for the cubic shape of the samples and used to establish the kinetic parameters, in particular to evaluate water diffusivity in the dried product, searching for the best fit between measured and calculated moisture content. The model was finally used to optimize the process in silico, considering an industrial unit as test case. In this case, it appeared that power ultrasound can increase the productivity of a tunnel dryer up to four or five times, and it allows the operational and fixed costs of the plant to be reduced significantly.