Action Flow in Obsessive-Compulsive Disorder Rituals: a model based on Extended Synergetics and a Comment on the 4th Law

The flow of actions in rituals of obsessive individuals is discussed from a nonlinear physics perspective. An amplitude equation model based on extended synergetics is studied. The amplitude dynamics describes both the behavioral actions and the experienced emotions during obsessive-compulsive disorder rituals. The model suggests that in addition to the behavioral and emotional levels that are accessible to external observation and self-reports there are hidden levels captured by parameter dynamics that determine the action flow in obsessive-compulsive disorder rituals. The model can also be used to discuss on the mechanistic and behavioral levels differences between purposeful and purposeless rituals. While purposeful rituals involve a continuous control of the emotional level over the behavioral level, purposeless rituals do not exhibit such a continuous control mechanism. Finally, it is argued that the selection principle determining the action flow in obsessive-compulsive disorder rituals is consistent with the so-called 4th law of non-equilibrium phase transitions in animate and inanimate system

Gunrock: GPU Graph Analytics

For large-scale graph analytics on the GPU, the irregularity of data access and control flow, and the complexity of programming GPUs, have presented two significant challenges to developing a programmable high-performance graph library. "Gunrock", our graph-processing system designed specifically for the GPU, uses a high-level, bulk-synchronous, data-centric abstraction focused on operations on a vertex or edge frontier. Gunrock achieves a balance between performance and expressiveness by coupling high performance GPU computing primitives and optimization strategies with a high-level programming model that allows programmers to quickly develop new graph primitives with small code size and minimal GPU programming knowledge. We characterize the performance of various optimization strategies and evaluate Gunrock's overall performance on different GPU architectures on a wide range of graph primitives that span from traversal-based algorithms and ranking algorithms, to triangle counting and bipartite-graph-based algorithms. The results show that on a single GPU, Gunrock has on average at least an order of magnitude speedup over Boost and PowerGraph, comparable performance to the fastest GPU hardwired primitives and CPU shared-memory graph libraries such as Ligra and Galois, and better performance than any other GPU high-level graph library.Comment: 52 pages, invited paper to ACM Transactions on Parallel Computing (TOPC), an extended version of PPoPP'16 paper "Gunrock: A High-Performance Graph Processing Library on the GPU

Information flow audit for PaaS clouds

© 2016 IEEE. With the rapid increase in uptake of cloud services, issues of data management are becoming increasingly prominent. There is a clear, outstanding need for the ability for specified policy to control and track data as it flows throughout cloud infrastructure, to ensure that those responsible for data are meeting their obligations. This paper introduces Information Flow Audit, an approach for tracking information flows within cloud infrastructure. This builds upon CamFlow (Cambridge Flow Control Architecture), a prototype implementation of our model for data-centric security in PaaS clouds. CamFlow enforces Information Flow Control policy both intra-machine at the kernel-level, and inter-machine, on message exchange. Here we demonstrate how CamFlow can be extended to provide data-centric audit logs akin to provenance metadata in a format in which analyses can easily be automated through the use of standard graph processing tools. This allows detailed understanding of the overall system. Combining a continuously enforced data-centric security mechanism with meaningful audit empowers tenants and providers to both meet and demonstrate compliance with their data management obligations.This work was supported by UK Engineering and Physical Sciences Research Council grant EP/K011510 CloudSafetyNet: End-to-End Application Security in the Cloud. We acknowledge the support of Microsoft through the Microsoft Cloud Computing Research Centre

Integrating end-to-end threads of control into object-oriented analysis and design

Current object-oriented analysis and design methodologies fall short in their use of mechanisms for identifying threads of control for the system being developed. The scenarios which typically describe a system are more global than looking at the individual objects and representing their behavior. Unlike conventional methodologies that use data flow and process-dependency diagrams, object-oriented methodologies do not provide a model for representing these global threads end-to-end. Tracing through threads of control is key to ensuring that a system is complete and timing constraints are addressed. The existence of multiple threads of control in a system necessitates a partitioning of the system into processes. This paper describes the application and representation of end-to-end threads of control to the object-oriented analysis and design process using object-oriented constructs. The issue of representation is viewed as a grouping problem, that is, how to group classes/objects at a higher level of abstraction so that the system may be viewed as a whole with both classes/objects and their associated dynamic behavior. Existing object-oriented development methodology techniques are extended by adding design-level constructs termed logical composite classes and process composite classes. Logical composite classes are design-level classes which group classes/objects both logically and by thread of control information. Process composite classes further refine the logical composite class groupings by using process partitioning criteria to produce optimum concurrent execution results. The goal of these design-level constructs is to ultimately provide the basis for a mechanism that can support the creation of process composite classes in an automated way. Using an automated mechanism makes it easier to partition a system into concurrently executing elements that can be run in parallel on multiple processors

Microfluidic platform for multiple parameters readouts in a point-of-care

The research is motivated by real applications, such as pasteurization plant, water networks and autonomous system, which each of them require a specific control system to provide proper management able to take into account their particular features and operating limits in presence of uncertainties related to their operation and failures from component breakdowns. According to that most of the real systems have nonlinear behaviors, it can be approximated them by polytopic linear uncertain models such as Linear Parameter Varying (LPV) and Takagi-Sugeno (TS) models. Therefore, a new economic Model Predictive Control (MPC) approach based on LPV/TS models is proposed and the stability of the proposed approach is certified by using a region constraint on the terminal state. Besides, the MPC-LPV strategy is extended based on the system with varying delays affecting states and inputs. The control approach allows the controller to accommodate the scheduling parameters and delay change. By computing the prediction of the state variables and delay along a prediction time horizon, the system model can be modified according to the evaluation of the estimated state and delay at each time instant. To increase the system reliability, anticipate the appearance of faults and reduce the operational costs, actuator health monitoring should be considered. Regarding several types of system failures, different strategies are studied for obtaining system failures. First, the damage is assessed with the rainflow-counting algorithm that allows estimating the component’s fatigue and control objective is modified by adding an extra criterion that takes into account the accumulated damage. Besides, two different health-aware economic predictive control strategies that aim to minimize the damage of components are presented. Then, economic health-aware MPC controller is developed to compute the components and system reliability in the MPC model using an LPV modeling approach and maximizes the availability of the system by estimating system reliability. Additionally, another improvement considers chance-constraint programming to compute an optimal list replenishment policy based on a desired risk acceptability level, managing to dynamically designate safety stocks in flow-based networks to satisfy non-stationary flow demands. Finally, an innovative health-aware control approach for autonomous racing vehicles to simultaneously control it to the driving limits and to follow the desired path based on maximization of the battery RUL. The proposed approach is formulated as an optimal on-line robust LMI based MPC driven from Lyapunov stability and controller gain synthesis solved by LPV-LQR problem in LMI formulation with integral action for tracking the trajectory

Ouput-feedback control of combined sewer networks through receding horizon control with moving horizon estimation

An output-feedback control strategy for pollution mitigation in combined sewer networks is presented. The proposed strategy provides means to apply model-based predictive control to large-scale sewer networks, in-spite of the lack of measurements at most of the network sewers. In previous works, the authors presented a hybrid linear control-oriented model for sewer networks together with the formulation of Optimal Control Problems (OCP) and State Estimation Problems (SEP). By iteratively solving these problems, preliminary Receding Horizon Control with Moving Horizon Estimation (RHC/MHE) results, based on flow measurements, were also obtained. In this work, the RHC/MHE algorithm has been extended to take into account both flow and water level measurements and the resulting control loop has been extensively simulated to assess the system performance according to different measurement availability scenarios and rain events. All simulations have been carried out using a detailed physically-based model of a real case-study network as virtual reality.Peer ReviewedPostprint (author's final draft

Microfluidic platform for multiple parameters readouts in a point-of-care

Tesi amb una secció retallada per drets de l'editorThe research is motivated by real applications, such as pasteurization plant, water networks and autonomous system, which each of them require a specific control system to provide proper management able to take into account their particular features and operating limits in presence of uncertainties related to their operation and failures from component breakdowns. According to that most of the real systems have nonlinear behaviors, it can be approximated them by polytopic linear uncertain models such as Linear Parameter Varying (LPV) and Takagi-Sugeno (TS) models. Therefore, a new economic Model Predictive Control (MPC) approach based on LPV/TS models is proposed and the stability of the proposed approach is certified by using a region constraint on the terminal state. Besides, the MPC-LPV strategy is extended based on the system with varying delays affecting states and inputs. The control approach allows the controller to accommodate the scheduling parameters and delay change. By computing the prediction of the state variables and delay along a prediction time horizon, the system model can be modified according to the evaluation of the estimated state and delay at each time instant. To increase the system reliability, anticipate the appearance of faults and reduce the operational costs, actuator health monitoring should be considered. Regarding several types of system failures, different strategies are studied for obtaining system failures. First, the damage is assessed with the rainflow-counting algorithm that allows estimating the component’s fatigue and control objective is modified by adding an extra criterion that takes into account the accumulated damage. Besides, two different health-aware economic predictive control strategies that aim to minimize the damage of components are presented. Then, economic health-aware MPC controller is developed to compute the components and system reliability in the MPC model using an LPV modeling approach and maximizes the availability of the system by estimating system reliability. Additionally, another improvement considers chance-constraint programming to compute an optimal list replenishment policy based on a desired risk acceptability level, managing to dynamically designate safety stocks in flow-based networks to satisfy non-stationary flow demands. Finally, an innovative health-aware control approach for autonomous racing vehicles to simultaneously control it to the driving limits and to follow the desired path based on maximization of the battery RUL. The proposed approach is formulated as an optimal on-line robust LMI based MPC driven from Lyapunov stability and controller gain synthesis solved by LPV-LQR problem in LMI formulation with integral action for tracking the trajectory.Postprint (published version

Microfluidic platform for multiple parameters readouts in a point-of-care

The research is motivated by real applications, such as pasteurization plant, water networks and autonomous system, which each of them require a specific control system to provide proper management able to take into account their particular features and operating limits in presence of uncertainties related to their operation and failures from component breakdowns. According to that most of the real systems have nonlinear behaviors, it can be approximated them by polytopic linear uncertain models such as Linear Parameter Varying (LPV) and Takagi-Sugeno (TS) models. Therefore, a new economic Model Predictive Control (MPC) approach based on LPV/TS models is proposed and the stability of the proposed approach is certified by using a region constraint on the terminal state. Besides, the MPC-LPV strategy is extended based on the system with varying delays affecting states and inputs. The control approach allows the controller to accommodate the scheduling parameters and delay change. By computing the prediction of the state variables and delay along a prediction time horizon, the system model can be modified according to the evaluation of the estimated state and delay at each time instant. To increase the system reliability, anticipate the appearance of faults and reduce the operational costs, actuator health monitoring should be considered. Regarding several types of system failures, different strategies are studied for obtaining system failures. First, the damage is assessed with the rainflow-counting algorithm that allows estimating the component’s fatigue and control objective is modified by adding an extra criterion that takes into account the accumulated damage. Besides, two different health-aware economic predictive control strategies that aim to minimize the damage of components are presented. Then, economic health-aware MPC controller is developed to compute the components and system reliability in the MPC model using an LPV modeling approach and maximizes the availability of the system by estimating system reliability. Additionally, another improvement considers chance-constraint programming to compute an optimal list replenishment policy based on a desired risk acceptability level, managing to dynamically designate safety stocks in flow-based networks to satisfy non-stationary flow demands. Finally, an innovative health-aware control approach for autonomous racing vehicles to simultaneously control it to the driving limits and to follow the desired path based on maximization of the battery RUL. The proposed approach is formulated as an optimal on-line robust LMI based MPC driven from Lyapunov stability and controller gain synthesis solved by LPV-LQR problem in LMI formulation with integral action for tracking the trajectory

Denotational Translation Validation

In this dissertation we present a simple and scalable system for validating the correctness of low-level program transformations. Proving that program transformations are correct is crucial to the development of security critical software tools. We achieve a simple and scalable design by compiling sequential low-level programs to synchronous data-flow programs. Theses data-flow programs are a denotation of the original programs, representing all of the relevant aspects of the program semantics. We then check that the two denotations are equivalent, which implies that the program transformation is semantics preserving. Our denotations are computed by means of symbolic analysis. In order to achieve our design, we have extended symbolic analysis to arbitrary control-flow graphs. To this end, we have designed an intermediate language called Synchronous Value Graphs (SVG), which is capable of representing our denotations for arbitrary control-flow graphs, we have built an algorithm for computing SVG from normal assembly language, and we have given a formal model of SVG which allows us to simplify and compare denotations. Finally, we report on our experiments with LLVM M.D., a prototype denotational translation validator for the LLVM optimization framework.Engineering and Applied Science

Prediction of Exhaust Skin Temperature Integrating 1D Model with Vehicle Level CFD Model

Studies involving flow and heat transfer in automotive exhaust systems are regularly employed in the design and optimization phases. Both internal as well as external heat transfer are key to provide a better understanding of the underbody heat transfer, cold start warm-up and thermal aging of the catalytic converter for gasoline engines and adequate thermal protection for the underbody components. The internal flow in a typical automobile exhaust system can be simplified using a 1D model employing correctional factors to encompass the three-dimensional effects. However, the external flow and heat transfer underbody of a vehicle is highly complex as it involves the overall front-end design of the car as well as the packaging of components underhood and underbody. This would require the use of a full scale 3D model of a vehicle. The proposed research involves the prediction of exhaust skin (outer surface) temperature combining a 1D model with a full vehicle 3D model as well as investigating heat transfer characteristics of the exhaust system. The 1D model is developed using a commercial code, GT-Power and the 3D vehicle level model is simulated using STAR-CCM+. The 1D and the 3Dmodel will provide a real time closed loop control system based on the combustion requirements and exhaust system readings for internal flow and external flow. In the first stage, the gas side internal heat transfer is simulated using the 1D model by adding available heat transfer correlations considering entrance effects, engine induced pulsation, geometrical effects and surface conditions. Initially, the model is simulated for steady state wide open throttle (WOT) cases and validated with results available from bench test. In the second stage, the use of the model is extended further in transient heat transfer studies. In the third stage, the 3D vehicle level model is simulated using the commercial code STAR-CCM+ at various wind speeds based on a set of cluster points representing a transient drive cycle. A Reynold Averaged Navier-Stokes (RANS) based k-ε turbulence model is used for modeling flow and turbulence. Thermal models for free convection and thermal radiation, are used to account for external heat transfer. The initial thermal boundary condition of the exhaust for the simulation is obtained from the preliminary 1D simulation data. The predicted external heat transfer coefficients from the 3D model are then used as a boundary condition for the 1D model for heat transfer as a third phase of the study. The iterative of the process of using the 3D model as boundary condition for the 1D model and vice versa until convergence will ensure a more accurate prediction of the exhaust skin temperature. Further a parametric study involving the influence of external emissivity on exhaust system heat transfer was carried out. The results indicate that the effect of the external emissivity is significant on the skin temperature and external heat transfer. The variation in emissivity is seen to contribute to more than 50% in the overall heat transfer. A temperature difference of up to 200oC was seen on the heat shields of the exhaust at high loads. Similar results were seen for the other components underbody close to the exhaust system. This would potentially be higher at idling after a drive cycle where free convection and radiation are seen to be more dominant, indicating a strong influence of external radiation as a key parameter in the heat transfer from an exhaust. Further the study revealed that the variation in emissivity does not influence the convective heat transfer by more than 4%