Search

System architecting is concerned with exploring the tradespace of early, high-level, system design decisions with a holistic, value-centric view. In the last few years, several tools and methods have been developed to support the system architecting process, focusing on the representation of an architecture as a set of interrelated decisions. These tools are best suited for applications that focus on breadth - i.e., enumerating a large and representative part of the architectural tradespace -as opposed to depth - modeling fidelity. However, some problems in system architecting require good modeling depth in order to provide useful results. In some cases, a very large body of expert knowledge is required. Current tools are not designed to handle such large bodies of knowledge because they lack scalability and traceability. As the size of the knowledge base increases, it becomes harder: a) to modify existing knowledge or add new knowledge; b) to trace the results of the tool to the model assumptions or knowledge base. This thesis proposes a holistic framework for architecture tradespace exploration of large complex systems that require a large body of expert knowledge. It physically separates the different bodies of knowledge required to solve a system architecting problem (i.e., knowledge about the domain, knowledge about the class of optimization or search problem, knowledge about the particular instance of problem) by using a rule-based expert system. It provides a generic population-based heuristic algorithm for search, which can be augmented with rules that encode knowledge about the domain, or about the optimization problem or class of problems. It identifies five major classes of system architecting problems from the perspective of optimization and search, and provides rules to enumerate architectures and search through the architectural tradespace of each class. A methodology is also defined to assess the value of an architecture using a rule-based approach. This methodology is based on a decomposition of stakeholder needs into requirements and a systematic comparison between system requirements and system capabilities using the rules engine. The framework is applied to the domain of Earth observing satellite systems (EOSS). Three EOSS are studied in depth: the NASA Earth Observing System, the NRC Earth Science Decadal Survey, and the Iridium GEOscan program. The ability of the framework to produce useful results is shown, and specific insights and recommendations are drawn.

Architecture and Function of Complex Systems System architecture is the study of early decision making in complex systems. This text teaches how to capture experience and analysis about early system decisions, and how to choose architectures that meet stakeholder needs, integrate easily, and evolve flexibly. With case studies written by leading practitioners, from hybrid cars to communications networks to aircraft, this text showcases the science and art of system architecture.

One of the main problems of current system design and architecture tools is the communication between the tool and the user and vice-versa. This work describes an on-going effort to improve that communication by incorporating an interactive and transparent learning agent into a multi-agent tradespace exploration tool. This learning agent mines the current population of individuals, which we will name architectures, for driving features (combination of architectural variables) that appear to drive architectures towards a "good region'' or a "bad region'' of the tradespace, and shares that information with the user. This information is used to produce a surrogate model based on a decision tree. The information about driving features is also fed to an adaptive heuristic optimization agent. Information about driving features, surrogate models, and heuristics can help the user understand the results of the tool better and gain useful architectural insight.

[ANGLÈS] The space exploration endeavor started in 1957 with the launch and operation of the first manmade satellite, the URSS Sputnik 1. Since then, multiple space programs have been developed, pushing the limits of technology and science but foremost unveiling the mysteries of the universe. In all these cases, the need for flexible and reliable communication systems has been primordial, allowing the return of collected science data and, when necessary, ensuring the well-being and safety of astronauts. To that end, multiple space communication networks have been globally deployed, be it through geographically distributed ground assets or through space relay satellites. Until now most of these systems have relied upon mature technology standards that have been adapted to the specific needs of particular missions and customers. Nevertheless, current trends in the space programs suggest that a shift of paradigm is needed: an Internet-like space network would increase the capacity and reliability of an interplanetary network while dramatically reducing its overall costs. In this context, the System Architecting Paradigm can be a good starting point. Through its formal decomposition of the system, it can help determine the architecturally distinguishing decisions and identify potential areas of commonality and cost reduction. This thesis presents a general framework to evaluate space communication relay systems for the near Earth domain. It indicates the sources of complexity in the modeling process, and discusses the validity and appropriateness of past approaches to the problem. In particular, it proposes a discussion of current models vis-à-vis the System Architecting Paradigm and how they fit into tradespace exploration studies. Next, the thesis introduces a computational performance model for the analysis and fast simulation of space relay satellite systems. The tool takes advantage of a specifically built-in rule-based expert system for storing the constitutive elements of the architecture and perform logical interactions between them. Analogously, it uses numerical models to assess the network topology over a given timeframe, perform physical layer computations and calculate plausible schedules for the overall system. In particular, it presents a newly developed heuristic scheduler that guarantees prioritization of specific missions and services while ensuring manageable computational times.

This Bachelor Thesis aims to draw a first approach for building a highly versatile tool to simulate systems with adaptive modularity, that is, systems that are able to adapt their functional capabilities according to the type of mission or scanario they are involved into. The simulation framework follows an agent-based strategy, in which several agents with different roles (launcher, scenario, active agent) manage to accomplish missions. These missions consist of performing certain tasks of target observation in one or more spectral region (e.g, microwave, infrared, visible) with a certain set of temporal requirements and a score value. The goal of the system is to perform as many high-value observations as possible. If certain tasks require more requirements than a single agent can provide, it can build a coalition with other agents, which do carry enough resources for performing the task. These coalitions are temporal associations of agents that can be split or merged as considered by them, thus allowing the agents to adapt their modularity for better performing their mission. The main achievements attained in this thesis can be divided in two blocks: on the one hand, we have developed a support program using an open source Java-based platform that executes simulations upon a large set of possible scenarios (Turtlekit ), displays what agents are doing in real-time in a particular simulated case and stores their performance data for its posterior analysis; on the other hand, a widely known decentralized task allocation algorithm (Coupled Constraint Consensus Bundle-Based Algorithm, CCBBA) has been implemented and modiffied as necessary for handling speciffic constraints regarding adaptive modularity.

"We use forecast errors made by the Federal Reserve while preparing open market operations to identify a liquidity effect at a daily frequency in the federal funds market. Unlike Hamilton (1997), we find a liquidity effect on many days of the reserve maintenance period besides settlement day. The effect is non-linear; large changes in supply have a measurable effect, but small changes do not. In addition, a higher aggregate level of reserve balances in the banking system is associated with a smaller liquidity effect during the maintenance period but a larger liquidity effect on the last days of the period"--Abstract.

Ver desde adentro el efecto que causan estos sistema en los pueblos que lo padecen.

This is a study of the Miocene Carmel flora of California, an evergreen laurel–oak forest that grew in a mild temperate (mean annual temperature of 15 degrees C), frost-free climate, with annual precipitation of about 760 mm (30 in.). Collectively, the Carmel and other Miocene floras like the San Pablo and Temblor (broad-leafed deciduous trees, with few evergreen species), the Puente (evergreen oak forest with chaparral species), the Mint Canyon, Ricardo, and Tehachapi (numerous arid subtropical scrub associated with oak woodland and chaparral species) suggest they foreshadowed a similar distribution of the different California vegetation zones today.