<p><a>Disruptive events during recent decades have highlighted the vulnerabilities of complex systems of systems to disruption propagation: </a>Disruptions that start in one part of a system and can propagate to other parts. Such examples include: Fire spreading in building complexes and forests; plant/crop diseases in agricultural production systems; propagating malware in computer networks and cyber-physical systems; and disruptions in supply networks. The impacts of disruption propagation are devastating, with fire causing annual US$23 billion loss in the US alone, plant diseases/crop reducing agricultural productivity 20% to 40% annually, and computer malware causing up to US$2.3 billion loss per event (as a conservative estimate). These problems, the response to disruption propagation (<a>RDP</a>) problems, are challenging due to the involvement of different problem aspects and their complex dynamics. To better design and control the responses to disruption propagation, a general framework and problem-solving guideline for the RDP problems is necessary.<br></p><p><br></p> <p> </p> <p>To address the aforementioned challenge, this research develops the Collaborative Response to Disruption Propagation (<a>CRDP</a>) unifying framework to classify, categorize, and characterize the different aspects of the RDP problems. The CRDP framework allows analogical reasoning across the different problem contexts, such as the examples mentioned above. Three main components applicable to the investigate RDP problems are identified and characterized: (1) The client system as the victims; (2) The response mechanisms as the rescuers/protectors; and (3) The disruption propagation as the aggressors/attackers. This allows further characterization of the complex interactions between the components, which augments the design and control decisions for the response mechanisms to better respond to the disruptions. The new Covering Lines of Collaboration (<a>CLOC</a>) principle, consisting of three guidelines, is developed to analyze the system state and guide the response decisions. The first CLOC guideline recommends the network modeling of potential disruption propagation directions, creating a complex network for better situation awareness and analysis. The second CLOC guideline recommends the analysis of the propagation-restraining effects due to the existence of the response mechanisms, and utilizing this interaction in optimizing response decisions. The third CLOC guideline recommends the development of collaboration protocols between the response decisions to maximize the coverage of response against disruption propagation.</p><p><br></p> <p> </p> <p>The CRDP framework and the CLOC principle are validated with three RDP case studies: (1) Detection of unknown disruptions; (2) Strategic prevention of unexpected disruptions; (3) Teaming and coordination of repair agents against recurring disruptions. Formulations, analytics, and protocols specific to each case are developed. TIE/CRDP, a new version of the Teamwork Integration Evaluator (<a>TIE</a>) software, is developed to simulate the complex interactions and dynamics of the CRDP components, the response decision protocols, and their performance. The evaluator is capable of simulating and evaluating the complex interactions and dynamics of the CRDP components and the response decision protocols. <a>Experiment results indicate that advanced CLOC-based decisions significantly outperform the baseline and less advanced protocols for all three cases, with performance superiority of 9.7-32.8% in case 1; 31.1%-56.6% in case 2; 2.1%-12.1% for teaming protocols, and at least 50% for team coordination protocols in case 3.</a></p>

PDE models are a major tool used in quantitative modeling of biological and scientific phenomena. Their major shortcoming is the high computational complexity of solving each model. When scaling up to millions of simulations needed to find their optimal parameters we frequently have to wait days or weeks for results to come back. To cope with that we propose a neural network approach that can produce comparable results to a PDE model while being about 1000x faster. We quantitatively and qualitatively show the neural network metamodels are accurate and demonstrate their potential for multi-objective optimization in biology. We hope this approach can speed up scientific research and discovery in biology and beyond.<br>

<p>Urban models are of growing importance today for urban and environmental planning, geographic information systems, urban simulations, and as content for entertainment applications. Various methods have addressed aerial or ground scale image-based and sensor-based reconstruction. However, few, if any, approaches have automatically produced urban models from satellite images due to difficulties of data noise, data sparsity, and data uncertainty. Our key observations are that many structures in urban areas exhibit regular properties, and a second or more satellite views for urban structures are usually available. Hence, we can overcome the aforementioned issues obtained from satellite imagery by synthesizing the underlying structure layout. In addition, recent advances in deep learning allow the development of novel algorithms that was not possible several years ago. We leverage relevant deep learning techniques for classifying/predicting urban structure parameters and modeling urban areas that address the problem of satellite data quality and uncertainty. In this dissertation, we present a machine learning-based procedural generation framework to automatically and quickly reconstruct urban areas by using regularities of urban structures (e.g., cities, buildings, facades, roofs, etc.) from satellite imagery, which can be applied to not only multiple resolutions ranging from low resolution (e.g., 3 meters) to high resolutions (e.g., WV3 0.3 meter) of satellite images but also the different scales (e.g., cities, blocks, parcels, buildings, facades) of urban environments. Our method is fully automatic and generates procedural structures in urban areas given satellite imagery. Experimental results show that our method outperforms previous state-of-the-art methods quantitatively and qualitatively for multiple datasets. Furthermore, by applying our framework to multiple urban structures, we demonstrate our approach can be generalized to various pattern types. We also have preliminary results applying this for flooding, archaeological sites, and more.</p>

No description available.

This research work is focused on understanding the effects of inhomogeneity on traffic flow by theoretical analysis and computer simulations. Traffic has been observed at almost all levels of natural and manmade systems (e.g., from microscopic protein motors to macroscopic objects like cars). For these various traffic, basic and emer- gent phenomena, modelling methods, theoretical analysis and physical meanings are normally concerned. Inhomogeneity like bottlenecks may cause traffic congestions or motor protein crowding. The crowded protein motors may lead to some human diseases. The congested traffic patterns have not been understood well so far. The modelling method in this research is based on totally asymmetric simple exclusion process (TASEP). The following TASEP models are developed: TASEP with single inhomogeneity, TASEP with zoned inhomogeneity, TASEP with junction, TASEP with site sharing and different boundary conditions. These models are motivated by vehicular traffic, pedestrian trafficc, ant traffic, protein motor traffic and/or Internet traffic. Theoretical solutions for the proposed models are obtained and verified by Monte Carlo simulations. These theoretical results can be used as a base for further developments. The emergent properties such as phase transitions, phase separations and spontaneous symmetry breaking are observed and discussed. This study has contributed to a deeper understanding of generic traffic dynamics, particularly, in the presence of inhomogeneity, and has important implications for explanation or guidance of future traffic studies.

The global greenhouse gas CO2 emission from the transportation sector is very significant. To reduce this gas emission, EU has set an average target of not more than 95 CO2/km for new passenger cars by the year 2020. A great reduction is still required to achieve the CO2 emission target in 2020, and many different approaches are being considered. This thesis focuses on the thermal management of the engine as an area that promise significant improvement of fuel efficiency with relatively small changes. The review of the literature shows that thermal management can improve engine efficiency through the friction reduction, improved air-fuel mixing, reduced heat loss, increased engine volumetric efficiency, suppressed knock, reduce radiator fan speed and reduction of other toxic emissions such as CO, HC and NOx. Like heat loss and friction, most emissions can be reduced in high temperature condition, but this may lead to poor volumetric efficiency and make the engine more prone to knock. The temperature trade-off study is conducted in simulation using a GT-SUITE engine model coupled with the FE in-cylinder wall structure and cooling system. The result is a map of the best operating temperature over engine speed and load. To quantify the benefit of this map, eight driving styles from the legislative and research test cycles are being compared using an immediate application of the optimal temperature, and significant improvements are found for urban style driving, while operation at higher load (motorway style driving) shows only small efficiency gains. The fuel consumption saving predicted in the urban style of driving is more than 4%. This assess the chance of following the temperature set point over a cycle, the temperature reference is analysed for all eight types of drive cycles using autocorrelation, lag plot and power spectral density. The analysis consistently shows that the highest volatility is recorded in the Artemis Urban Drive Cycle: the autocorrelation disappears after only 5.4 seconds, while the power spectral density shows a drop off around 0.09Hz. This means fast control action is required to implement the optimal temperature before it changes again. Model Predictive Control (MPC) is an optimal controller with a receding horizon, and it is well known for its ability to handle multivariable control problems for linear systems with input and state limits. The MPC controller can anticipate future events and can take control actions accordingly, especially if disturbances are known in advance. The main difficulty when applying MPC to thermal management is the non-linearity caused by changes in flow rate. Manipulating both the water pump and valve improves the control authority, but it also amplifies the nonlinearity of the system. Common linearization approaches like Jacobian Linearization around one or several operating points are tested, by found to be only moderately successful. Instead, a novel approach is pursued using feedback linearization of the plant model. This uses an algebraic transformation of the plant inputs to turn the nonlinear systems dynamics into a fully or predominantly linear system. The MPC controller can work with the linear model, while the actual control inputs are found using an inverse transformation. The Feedback Linearization MPC of the cooling system model is implemented and testing using MathWork Simulink®. The process includes the model transformation approach, model fitting, the transformation of the constraints and the tuning of the MPC controller. The simulation shows good temperature tracking performance, and this demonstrates that a MPC controller with feedback linearization is a suitable approach to thermal management. The controller strategy is then validated in a test rig replicating an actual engine cooling system. The new MPC controller is again evaluated over the eight driving cycles. The average water pump speed is reduced by 9.1% compared to the conventional cooling system, while maintaining good temperature tracking. The controller performance further improves with future disturbance anticipation by 20.5% for the temperature tracking (calculated by RMSE), 6.8% reduction of the average water pump speed, 47.3% reduction of the average valve movement and 34.0% reduction of the average radiator fan speed.

Orientador: Carlos Alberto Gasparetto / Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia de Alimentos / Made available in DSpace on 2018-08-08T22:28:21Z (GMT). No. of bitstreams: 1 Ohata_SueliMarie_D.pdf: 1133009 bytes, checksum: d9e555381242748bb00e413dc46b8c7d (MD5) Previous issue date: 2007 / Resumo: Os reatores de recirculação representam uma tecnologia alternativa muito atraente para o processo de hidrogenação, tecnologia esta ainda não totalmente desenvolvida para a hidrogenação de óleos vegetais. Os reatores convencionais utilizados no processo de hidrogenação possuem agitação mecânica, sistema de injeção de hidrogênio na base, e necessitam de condições mais severas de operação, como a temperatura e a pressão. Em um reator que opera num sistema de recirculação, um ejetor tipo Venturi e utilizado, o qual proporciona uma grande transferência de massa entre as fases presentes, dispensando a agitação mecânica, alem de requerer quantidades menores de catalisador, demandar menos hidrogênio e trabalhar com pressão, temperatura e tempo de reação menor. Foram encontradas poucas informações na literatura a respeito do processo de hidrogenação de óleos vegetais através do reator de recirculação, desta forma, o objetivo deste trabalho foi analisar o processo de hidrogenação de óleo de soja em um reator de recirculação através da modelagem e simulação. Para a formulação do modelo que descreve este sistema, foram considerados os fenômenos de transferência de massa, alem da cinética da reação. A partir das equações obtidas, propos-se uma metodologia para solucionar o sistema de equações diferenciais que descrevem o sistema. Foram propostos dois modelos para o estudo: reator em batelada, resolvido analiticamente e uma associação de um reator CSTR em serie com um PFR com dispersão axial, resolvido pelo método da colocação ortogonal. Ambos os modelos descreveram adequadamente o processo de hidrogenação nas condições estudadas / Abstract: Loop reactors represent a very attractive alternative technology to the hydrogenation process, but this technology was not totally developed for the hydrogenation of vegetable oils. The conventional reactors used for the hydrogenation process have mechanical agitation, system of injection of hydrogen in the basis and they need more severe conditions of operation, as the temperature and the pressure. In a reactor that operates in a system loop, an ejector type Venturi is used, which provides an high mass transfer rate between the present phases, without mechanical agitation, needs less catalyst, demands less hydrogen and works with lower values of pressure, temperature and time of reaction. There is very little information in the literature about the hydrogenation process of vegetable oils in a loop reactor, thus, the purpose of this study was to analyse of the process of hydrogenation of soybean oil in a loop reactor by modelling and simulation. For the formulation of the model that describes this system, the phenomena of mass transfer and kinetics of the reaction were considered. From the balance equations, a method was considered to solve the system of differential equations that describe the system. Two models of reactors were proposed for the study: batch reactor, solved analytically and an association of a reactor CSTR in series with a PFR with axial dispersion, solved by orthogonal collocation method. Both models described the process of hydrogenation appropriately in the studied conditions / Doutorado / Doutor em Engenharia de Alimentos

The aim of this research is to bridge the gap between CAD modeling and kinematic analysis packages by extracting kinematic information directly from part genometries. It will relieve the designers from the tedious task of specifying assembly constraints and specifying redundant information for creating kinematic models. Automatic generation of kinematic assembly models is achieved by characterizing the lower kinematic pairs: cylindrical, spherical, prismatic, planar and revolute; from the geometries point of view. Based on characterization, the algorithms are developed to recognize these kinematic pairs from a pair of part genometries. The combinations of primitive genometric entities: vertices, edges and faces; forming point, line, arc and surface contacts are studied. The signature geometry is found to be associated with each type of joint. The contacts are analysed for restraining the relative motion between a pair of parts. Based on this, the form closure conditions are derived for surface, line, arc and point contacts for each type of joint. The algorithms are developed to automatically recognize these joints and to assemble them into a kinematic assembly model represented as a graph. The strength and novelty of the present procedure is that kinematic pairs can be recognized for conforming as well as non conforming genometries. A Visual Basic for Application (VBA) for Solid Works has been developed using Application Programming Interface (API) for user interaction. The part genometries can be in any 3D solid modeling neutral file format (.sat, .igs, etc) or some of the native formats of CAD softwares supported by Solid Works. The regions of interest can be directly identified through mouse pick on parts using Solid Works Graphical User Interface (GUI). The transformation matrices are derived automatically to position the parts relative to each other. The local interference between part geometries is also considered for checking the validity of the kinematic pair in the assembly. Assembly model is created and represented as a directed graph. The present implementation, built on the ACIS geometry kernel, imports the parts into SolidWorks, specifies the mating regions using a visual Basic interfaces and finally generates the kinematic assembly model as an ADAMS input file complete with part genometries, their mass properties, kinematic joints and their locations.

Counter Unmanned Aerial System (CUAS) security systems have unrealistic performance expectations hyped on marketing and idealistic testing environments. By developing an agent-based model to simulate these systems, an average performance metric can be obtained, thereby providing better representative values of true system performance.<br><br>Due to high cost, excessive risk, and exponentially large parameter possibilities, it is unrealistic to test a CUAS system for optimal performance in the real world. Agent-based simulation can provide the necessary variability at a low cost point and allow for numerous parametric possibilities to provide actionable output from the CUAS system. <br><br>This study describes and documents the Simulation of CUAS Networks and Sensors (SCANS) Framework in a novel attempt at developing a flexible modeling framework for CUAS systems based on device parameters. The core of the framework rests on sensor and communication device agents. These sensors, including Acoustic, Radar, Passive Radio Frequency (RF), and Camera, use input parameters, sensor specifications, and UAS specifications to calculate such values as the sound pressure level, received signal strength, and maximum viewable distance. The communication devices employ a nearest-neighbor routing protocol to pass messages from the system which are then logged by a command and control agent. <br><br>This framework allows for the flexibility of modeling nearly any CUAS system and is designed to be easily adjusted. The framework is capable of reporting true positives, true negatives, and false negatives in terms of UAS detection. For testing purposes, the SCANS Framework was deployed in AnyLogic and models were developed based on existing, published, empirical studies of sensors and detection UAS.<br>

<div>Complex systems are often characterized by a large collection of components interacting in nontrivial ways. Self-organization among these individual components often leads to emergence of a macroscopic structure that is neither completely regular nor completely random. In order to understand what we observe at a macroscopic scale, conceptual, mathematical, and computational tools are required for modeling and analyzing these interactions. A principled approach to understand these complex systems (and the processes that give rise to them) is to formulate generative models and infer their parameters from given data that is typically stored in the form of networks (or graphs). The increasing availability of network data from a wide variety of sources, such as the Internet, online social networks, collaboration networks, biological networks, etc., has fueled the rapid development of network science. </div><div><br></div><div>A variety of generative models have been designed to synthesize networks having specific properties (such as power law degree distributions, small-worldness, etc.), but the structural richness of real-world network data calls for researchers to posit new models that are capable of keeping pace with the empirical observations about the topological properties of real networks. The mechanistic approach to modeling networks aims to identify putative mechanisms that can explain the dependence, diversity, and heterogeneity in the interactions responsible for creating the topology of an observed network. A successful mechanistic model can highlight the principles by which a network is organized and potentially uncover the mechanisms by which it grows and develops. While it is difficult to intuit appropriate mechanisms for network formation, machine learning and evolutionary algorithms can be used to automatically infer appropriate network generation mechanisms from the observed network structure.</div><div><br></div><div>Building on these philosophical foundations and a series of (not new) observations based on first principles, we extrapolate an action-based framework that creates a compact probabilistic model for synthesizing real-world networks. Our action-based perspective assumes that the generative process is composed of two main components: (1) a set of actions that expresses link formation potential using different strategies capturing the collective behavior of nodes, and (2) an algorithmic environment that provides opportunities for nodes to create links. Optimization and machine learning methods are used to learn an appropriate low-dimensional action-based representation for an observed network in the form of a row stochastic matrix, which can subsequently be used for simulating the system at various scales. We also show that in addition to being practically relevant, the proposed model is relatively exchangeable up to relabeling of the node-types. </div><div><br></div><div>Such a model can facilitate handling many of the challenges of understanding real data, including accounting for noise and missing values, and connecting theory with data by providing interpretable results. To demonstrate the practicality of the action-based model, we decided to utilize the model within domain-specific contexts. We used the model as a centralized approach for designing resilient supply chain networks while incorporating appropriate constraints, a rare feature of most network models. Similarly, a new variant of the action-based model was used for understanding the relationship between the structural organization of human brains and the cognitive ability of subjects. Finally, our analysis of the ability of state-of-the-art network models to replicate the expected topological variations in network populations highlighted the need for rethinking the way we evaluate the goodness-of-fit of new and existing network models, thus exposing significant gaps in the literature.</div>