On Robust Synchronization of Nonlinear Systems with Application to Grid Integration of Renewable Energy Sources

International audienceMany systems in the natural and physical world often work in unison with similar other systems. This process of simultaneous operation is known as synchronization. In the past few decades, owing to this phenomenon's importance, extensive research efforts have been made. However, many of the existing results consider the systems are identical and/or linear time-invariant, while practical systems are often nonlinear and nonidentical for various reasons. This observation motivated several recent studies on the synchronization of nonidentical (i.e., heterogeneous) nonlinear systems. This paper summarizes some recent results on the synchronization of heterogeneous nonlinear systems, as developed in the thesis [1]. First, the results on the synchronization of a particular class of robustly stable nonlinear systems are presented. Then, these results are applied to an example model known as Brockett oscillator. Finally, using the Brockett oscillator as a common dynamics, output oscillatory synchronization results are given for heterogeneous nonlinear systems of relative degree 2 or higher. An application example of Brockett oscillator for power-grid synchronization is also presented. Some outlooks are provided regarding future research directions

On Some Schützenberger Conjectures

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Decomposition and Descriptional Complexity of Shuffle on Words and Finite Languages

We investigate various questions related to the shuffle operation on words and finite languages. First we investigate a special variant of the shuffle decomposition problem for regular languages, namely, when the given regular language is the shuffle of finite languages. The shuffle decomposition into finite languages is, in general not unique. Thatis,therearelanguagesL^,L2,L3,L4withLiluL2= £3luT4but{L\,L2}^ {I/3, L4}. However, if all four languages are singletons (with at least two combined letters), it follows by a result of Berstel and Boasson [6], that the solution is unique; that is {L\,L2} = {L3,L4}. We extend this result to show that if L\ and L2 are arbitrary finite sets and Lz and Z-4 are singletons (with at least two letters in each), the solution is unique. This is as strong as it can be, since we provide examples showing that the solution can be non-unique already when (1) both L\ and L2 are singleton sets over different unary alphabets; or (2) L\ contains two words and L2 is singleton. We furthermore investigate the size of shuffle automata for words. It was shown by Campeanu, K. Salomaa and Yu in [11] that the minimal shuffle automaton of two regular languages requires 2mn states in the worst case (where the minimal automata of the two component languages had m and n states, respectively). It was also recently shown that there exist words u and v such that the minimal shuffle iii DFA for u and v requires an exponential number of states. We study the size of shuffle DFAs for restricted cases of words, namely when the words u and v are both periods of a common underlying word. We show that, when the underlying word obeys certain conditions, then the size of the minimal shuffle DFA for u and v is at most quadratic. Moreover we provide an efficient algorithm, which decides for a given DFA A and two words u and v, whether u lu u C L(A)

Partial aggregation for collective communication in distributed memory machines

High Performance Computing (HPC) systems interconnect a large number of Processing Elements (PEs) in high-bandwidth networks to simulate complex scientific problems. The increasing scale of HPC systems poses great challenges on algorithm designers. As the average distance between PEs increases, data movement across hierarchical memory subsystems introduces high latency. Minimizing latency is particularly challenging in collective communications, where many PEs may interact in complex communication patterns. Although collective communications can be optimized for network-level parallelism, occasional synchronization delays due to dependencies in the communication pattern degrade application performance. To reduce the performance impact of communication and synchronization costs, parallel algorithms are designed with sophisticated latency hiding techniques. The principle is to interleave computation with asynchronous communication, which increases the overall occupancy of compute cores. However, collective communication primitives abstract parallelism which limits the integration of latency hiding techniques. Approaches to work around these limitations either modify the algorithmic structure of application codes, or replace collective primitives with verbose low-level communication calls. While these approaches give fine-grained control for latency hiding, implementing collective communication algorithms is challenging and requires expertise knowledge about HPC network topologies. A collective communication pattern is commonly described as a Directed Acyclic Graph (DAG) where a set of PEs, represented as vertices, resolve data dependencies through communication along the edges. Our approach improves latency hiding in collective communication through partial aggregation. Based on mathematical rules of binary operations and homomorphism, we expose data parallelism in a respective DAG to overlap computation with communication. The proposed concepts are implemented and evaluated with a subset of collective primitives in the Message Passing Interface (MPI), an established communication standard in scientific computing. An experimental analysis with communication-bound microbenchmarks shows considerable performance benefits for the evaluated collective primitives. A detailed case study with a large-scale distributed sort algorithm demonstrates, how partial aggregation significantly improves performance in data-intensive scenarios. Besides better latency hiding capabilities with collective communication primitives, our approach enables further optimizations of their implementations within MPI libraries. The vast amount of asynchronous programming models, which are actively studied in the HPC community, benefit from partial aggregation in collective communication patterns. Future work can utilize partial aggregation to improve the interaction of MPI collectives with acclerator architectures, and to design more efficient communication algorithms

Avling og kvalitet hjå grønsaker gjødsla med materiale resirkulert frå organiske ressursar

Agriculture, aquaculture, fishery and households generate large amounts of organic wastes with high contents of nitrogen (N) and other nutrients. Concurrently, supply of off-farm N resources into horticultural production systems is essential to gain desirable yields, quality and economic outcome. Turning organic wastes into fertilizer resources can contribute to meeting the requirement of nutrients without consuming non-renewable resources will contribute to “closing the loop” and thus a more circular economy recycling nutrients from such locally available organic resources. However, recycling nutrients from organic materials is a complex task, and knowledge about nutrient dynamics is important for optimizing fertilizer effect without causing detrimental impacts on the environment. In particular, the N dynamics of organic materials requires substantial attention, due to the complexity of pathways in the N cycle and their potentially negative impacts on the environment. These processes depend upon the biochemical quality of the organic fertilizer materials and external factors such as temperature and moisture and soil texture and structure. There is a risk of loss of N through nitrate leaching, ammonia volatilization or fixation, and denitrification. Horticultural products are an important nutritional source for humans. Vegetables, fruit and berries are associated with a healthy diet. Fertilization strategy influences both internal and external product quality, and especially N fertilization is linked to yield and, hence, economic profit, as well as contents of nutritional value and taste. Knowledge about the N mineralization and immobilization from organic fertilizer resources is required to ensure a high degree of resource utilization and optimal quality of the horticultural produce. N models have been widely used to increase our understanding of how N dynamics influences the yield and environmental impact in both conventional and organic production systems. The overall aim of this thesis was to investigate the effect of fertilization with materials recycled from organic resources on yield and quality of selected vegetables. An incubation experiment with nine organic materials of different origin (anaerobically digested food wastes (AD), shrimp shell pellets (SSP), shrimp shell powder (SSM), meat bone meal (MBM), dried fish waste sludge (FW), sheep manure (SM), algal meal (AM) and meals of Laminaria digitata (LD) and Saccharina latissimi (SL)) was set up to determine the carbon (C) and N mineralization patterns. Broccoli, potato and lettuce were grown at two locations, Grimstad (58°N and 8°E) and Bodø (67°N and 14°E), with anaerobically digested food wastes, shrimp shell pellets, sheep manure and algal meal as fertilizers to investigate effects on yield, N use efficiency and selected quality parameters. The C and N mineralization data obtained during incubation and results from the field experiment in Bodø were used to calibrate and evaluate the EU-Rotate_N model. Based on net N mineralization, the organic materials were divided into three groups: N-rich industrial wastes which had a high initial N mineralization rate followed by a low rate (SSP, SSM, FW, MBM), materials with high initial mineral N content and further low rate of N mineralization (AD and SM), and seaweeds, which caused initial N immobilization followed by slow (SL and LD) or no (AM) N mineralization. Crop yield, N recovery efficiency and crop quality parameters could to a large extent be explained by the plant-available N from the different fertilizer materials as estimated from the mineralization data. However, sensory attributes of broccoli were affected by years. EU-Rotate_N was successfully calibrated for N-rich materials of industrial origin, whereas seaweeds, AD and SM proved to be difficult. The model’s ability to predict was evaluated with soil and crop data of broccoli and potato fertilized with AD, SSP, SM, AM, and mineral fertilizer (MF). The model satisfactorily predicted dry matter and N contents of the above-ground part of broccoli fertilized with AD, SSP and MF, but not AM, and of potato after adjusting critical %N for optimum growth. Prediction of soil inorganic N after harvest was poorer. In conclusion, the N-rich organic materials of industrial origin (SSP, SSM, MBM and FW) and AD have the potential to replace N from mineral fertilizer in conventional vegetable production systems or as complementary fertilizers in organic production systems. The decomposition of and N availability from seaweed species were not fully understood. The EU-Rotate_N model can be used as a learning tool for understanding the decomposition and N mineralization dynamics of organic materials and, thus, serve as a decision support tool for their use as fertilizers.This PhD Thesis was a part of the project Pre- and postharvest quality optimisation of organic vegetables that can stimulate an increased consumption (NFR 176767), which was led by researcher Dr. Randi Seljåsen, Norwegian Institute of Bioeconomy Research (NIBIO) and funded by the Research Council of Norway, The Council of Nordland and Troms, and NIBIO

ICASE

This report summarizes research conducted at the Institute for Computer Applications in Science and Engineering in the areas of (1) applied and numerical mathematics, including numerical analysis and algorithm development; (2) theoretical and computational research in fluid mechanics in selected areas of interest, including acoustics and combustion; (3) experimental research in transition and turbulence and aerodynamics involving Langley facilities and scientists; and (4) computer science

Analysis and simulation of emergent architectures for internet of things

The Internet of Things (IoT) promises a plethora of new services and applications supported by a wide range of devices that includes sensors and actuators. To reach its potential IoT must break down the silos that limit applications' interoperability and hinder their manageability. These silos' result from existing deployment techniques where each vendor set up its own infrastructure, duplicating the hardware and increasing the costs. Fog Computing can serve as the underlying platform to support IoT applications thus avoiding the silos'. Each application becomes a system formed by IoT devices (i.e. sensors, actuators), an edge infrastructure (i.e. Fog Computing) and the Cloud. In order to improve several aspects of human lives, different systems can interact to correlate data obtaining functionalities not achievable by any of the systems in isolation. Then, we can analyze the IoT as a whole system rather than a conjunction of isolated systems. Doing so leads to the building of Ultra-Large Scale Systems (ULSS), an extension of the concept of Systems of Systems (SoS), in several verticals including Autonomous Vehicles, Smart Cities, and Smart Grids. The scope of ULSS is large in the number of things and complex in the variety of applications, volume of data, and diversity of communication patterns. To handle this scale and complexity in this thesis we propose Hierarchical Emergent Behaviors (HEB), a paradigm that builds on the concepts of emergent behavior and hierarchical organization. Rather than explicitly program all possible situations in the vast space of ULSS scenarios, HEB relies on emergent behaviors induced by local rules that define the interactions of the "things" between themselves and also with their environment. We discuss the modifications to classical IoT architectures required by HEB, as well as the new challenges. Once these challenges such as scalability and manageability are addressed, we can illustrate HEB's usefulness dealing with an IoT-based ULSS through a case study based on Autonomous Vehicles (AVs). To this end we design and analyze well-though simulations that demonstrate its tremendous potential since small modifications to the basic set of rules induce different and interesting behaviors. Then we design a set of primitives to perform basic maneuver such as exiting a platoon formation and maneuvering in anticipation of obstacles beyond the range of on-board sensors. These simulations also evaluate the impact of a HEB deployment assisted by Fog nodes to enlarge the informational scope of vehicles. To conclude we develop a design methodology to build, evaluate, and run HEB-based solutions for AVs. We provide architectural foundations for the second level and its implications in major areas such as communications. These foundations are then validated through simulations that incorporate new rules, obtaining valuable experimental observations. The proposed architecture has a tremendous potential to solve the scalability issue found in ULSS, enabling IoT deployments to reach its true potential.El Internet de las Cosas (IoT) promete una plétora de nuevos servicios y aplicaciones habilitadas por una amplia gama de dispositivos que incluye sensores y actuadores. Para alcanzar su potencial, IoT debe superar los silos que limitan la interoperabilidad de las aplicaciones y dificultan su administración. Estos silos son el resultado de las técnicas de implementación existentes en las que cada proveedor instala su propia infraestructura y duplica el hardware, incrementando los costes. Fog Computing puede servir como la plataforma subyacente que soporte aplicaciones del IoT evitando así los silos. Cada aplicación se convierte en un sistema formado por dispositivos IoT (por ejemplo sensores y actuadores), una infraestructura (como Fog Computing) y la nube. Con el fin de mejorar varios aspectos de la vida humana, diferentes sistemas pueden interactuar para correlacionar datos obteniendo funcionalidades que no pueden lograrse por ninguno de los sistemas de forma aislada. Entonces, podemos analizar el IoT como un único sistema en lugar de una conjunción de sistemas aislados. Esta perspectiva conduce a la construcción de Ultra-Large Scale Systems (ULSS), una extensión del concepto de Systems of Systems (SoS), en varios verticales, incluidos los vehículos autónomos, Smart Cities y Smart Grids. El alcance de ULSS es vasto debido a la cantidad de dispositivos y complejo en la variedad de aplicaciones, volumen de datos y diversidad de patrones de comunicación. Para manejar esta escala y complejidad, en esta tesis proponemos Hierarchical Emergent Behaviors (HEB), un paradigma que se basa en los conceptos de comportamientos emergente y organización jerárquica. En lugar de programar explícitamente todas las situaciones posibles en el vasto espacio de escenarios presentes en los ULSS, HEB se basa en comportamientos emergentes inducidos por reglas locales que definen las interacciones de las "cosas" entre ellas y también con su entorno. Discutimos las modificaciones a las arquitecturas clásicas de IoT requeridas por HEB, así como los nuevos desafíos. Una vez que se abordan estos desafíos, como la escalabilidad y la capacidad de administración, podemos ilustrar la utilidad de HEB cuando se ocupa de un ULSS basado en IoT a través de un caso de estudio basado en Vehículos Autónomos (AV). Con este fin, diseñamos y analizamos simulaciones que demuestran su enorme potencial, ya que pequeñas modificaciones en el conjunto básico de reglas inducen comportamientos diferentes e interesantes. Luego, diseñamos un conjunto de primitivas para realizar una maniobra básica, como salir de un pelotón y maniobrar en anticipación de obstáculos más allá del alcance de los sensores de a bordo. Estas simulaciones también evalúan el impacto de una implementación de HEB asistida por nodos de Fog Computing para ampliar el alcance sensorial de los vehículos. Para concluir, desarrollamos una metodología de diseño para construir, evaluar y ejecutar soluciones basadas en HEB para AV. Brindamos fundamentos arquitectónicos para el segundo nivel de HEB y sus implicaciones en áreas importantes como las comunicaciones. Estas bases se validan a través de simulaciones que incorporan nuevas reglas, obteniendo valiosas observaciones experimentales. La arquitectura propuesta tiene un enorme potencial para resolver el problema de escalabilidad que presentan los ULSS, permitiendo que las implementaciones de IoT alcancen su verdadero potencial.Postprint (published version

Analysis and simulation of emergent architectures for internet of things

The Internet of Things (IoT) promises a plethora of new services and applications supported by a wide range of devices that includes sensors and actuators. To reach its potential IoT must break down the silos that limit applications' interoperability and hinder their manageability. These silos' result from existing deployment techniques where each vendor set up its own infrastructure, duplicating the hardware and increasing the costs. Fog Computing can serve as the underlying platform to support IoT applications thus avoiding the silos'. Each application becomes a system formed by IoT devices (i.e. sensors, actuators), an edge infrastructure (i.e. Fog Computing) and the Cloud. In order to improve several aspects of human lives, different systems can interact to correlate data obtaining functionalities not achievable by any of the systems in isolation. Then, we can analyze the IoT as a whole system rather than a conjunction of isolated systems. Doing so leads to the building of Ultra-Large Scale Systems (ULSS), an extension of the concept of Systems of Systems (SoS), in several verticals including Autonomous Vehicles, Smart Cities, and Smart Grids. The scope of ULSS is large in the number of things and complex in the variety of applications, volume of data, and diversity of communication patterns. To handle this scale and complexity in this thesis we propose Hierarchical Emergent Behaviors (HEB), a paradigm that builds on the concepts of emergent behavior and hierarchical organization. Rather than explicitly program all possible situations in the vast space of ULSS scenarios, HEB relies on emergent behaviors induced by local rules that define the interactions of the "things" between themselves and also with their environment. We discuss the modifications to classical IoT architectures required by HEB, as well as the new challenges. Once these challenges such as scalability and manageability are addressed, we can illustrate HEB's usefulness dealing with an IoT-based ULSS through a case study based on Autonomous Vehicles (AVs). To this end we design and analyze well-though simulations that demonstrate its tremendous potential since small modifications to the basic set of rules induce different and interesting behaviors. Then we design a set of primitives to perform basic maneuver such as exiting a platoon formation and maneuvering in anticipation of obstacles beyond the range of on-board sensors. These simulations also evaluate the impact of a HEB deployment assisted by Fog nodes to enlarge the informational scope of vehicles. To conclude we develop a design methodology to build, evaluate, and run HEB-based solutions for AVs. We provide architectural foundations for the second level and its implications in major areas such as communications. These foundations are then validated through simulations that incorporate new rules, obtaining valuable experimental observations. The proposed architecture has a tremendous potential to solve the scalability issue found in ULSS, enabling IoT deployments to reach its true potential.El Internet de las Cosas (IoT) promete una plétora de nuevos servicios y aplicaciones habilitadas por una amplia gama de dispositivos que incluye sensores y actuadores. Para alcanzar su potencial, IoT debe superar los silos que limitan la interoperabilidad de las aplicaciones y dificultan su administración. Estos silos son el resultado de las técnicas de implementación existentes en las que cada proveedor instala su propia infraestructura y duplica el hardware, incrementando los costes. Fog Computing puede servir como la plataforma subyacente que soporte aplicaciones del IoT evitando así los silos. Cada aplicación se convierte en un sistema formado por dispositivos IoT (por ejemplo sensores y actuadores), una infraestructura (como Fog Computing) y la nube. Con el fin de mejorar varios aspectos de la vida humana, diferentes sistemas pueden interactuar para correlacionar datos obteniendo funcionalidades que no pueden lograrse por ninguno de los sistemas de forma aislada. Entonces, podemos analizar el IoT como un único sistema en lugar de una conjunción de sistemas aislados. Esta perspectiva conduce a la construcción de Ultra-Large Scale Systems (ULSS), una extensión del concepto de Systems of Systems (SoS), en varios verticales, incluidos los vehículos autónomos, Smart Cities y Smart Grids. El alcance de ULSS es vasto debido a la cantidad de dispositivos y complejo en la variedad de aplicaciones, volumen de datos y diversidad de patrones de comunicación. Para manejar esta escala y complejidad, en esta tesis proponemos Hierarchical Emergent Behaviors (HEB), un paradigma que se basa en los conceptos de comportamientos emergente y organización jerárquica. En lugar de programar explícitamente todas las situaciones posibles en el vasto espacio de escenarios presentes en los ULSS, HEB se basa en comportamientos emergentes inducidos por reglas locales que definen las interacciones de las "cosas" entre ellas y también con su entorno. Discutimos las modificaciones a las arquitecturas clásicas de IoT requeridas por HEB, así como los nuevos desafíos. Una vez que se abordan estos desafíos, como la escalabilidad y la capacidad de administración, podemos ilustrar la utilidad de HEB cuando se ocupa de un ULSS basado en IoT a través de un caso de estudio basado en Vehículos Autónomos (AV). Con este fin, diseñamos y analizamos simulaciones que demuestran su enorme potencial, ya que pequeñas modificaciones en el conjunto básico de reglas inducen comportamientos diferentes e interesantes. Luego, diseñamos un conjunto de primitivas para realizar una maniobra básica, como salir de un pelotón y maniobrar en anticipación de obstáculos más allá del alcance de los sensores de a bordo. Estas simulaciones también evalúan el impacto de una implementación de HEB asistida por nodos de Fog Computing para ampliar el alcance sensorial de los vehículos. Para concluir, desarrollamos una metodología de diseño para construir, evaluar y ejecutar soluciones basadas en HEB para AV. Brindamos fundamentos arquitectónicos para el segundo nivel de HEB y sus implicaciones en áreas importantes como las comunicaciones. Estas bases se validan a través de simulaciones que incorporan nuevas reglas, obteniendo valiosas observaciones experimentales. La arquitectura propuesta tiene un enorme potencial para resolver el problema de escalabilidad que presentan los ULSS, permitiendo que las implementaciones de IoT alcancen su verdadero potencial