Obtaining and Using Cumulative Bounds of Network Reliability

In this chapter, we study the task of obtaining and using the exact cumulative bounds of various network reliability indices. A network is modeled by a non-directed random graph with reliable nodes and unreliable edges that fail independently. The approach based on cumulative updating of the network reliability bounds was introduced by Won and Karray in 2010. Using this method, we can find out whether the network is reliable enough with respect to a given threshold. The cumulative updating continues until either the lower reliability bound becomes greater than the threshold or the threshold becomes greater than the upper reliability bound. In the first case, we decide that a network is reliable enough; in the second case, we decide that a network is unreliable. We show how to speed up cumulative bounds obtaining by using partial sums and how to update bounds when applying different methods of reduction and decomposition. Various reliability indices are considered: k-terminal probabilistic connectivity, diameter constrained reliability, average pairwise connectivity, and the expected size of a subnetwork that contains a special node. Expected values can be used for unambiguous decision-making about network reliability, development of evolutionary algorithms for network topology optimization, and obtaining approximate reliability values

Stochastic Nonlinear Model Predictive Control with Efficient Sample Approximation of Chance Constraints

This paper presents a stochastic model predictive control approach for nonlinear systems subject to time-invariant probabilistic uncertainties in model parameters and initial conditions. The stochastic optimal control problem entails a cost function in terms of expected values and higher moments of the states, and chance constraints that ensure probabilistic constraint satisfaction. The generalized polynomial chaos framework is used to propagate the time-invariant stochastic uncertainties through the nonlinear system dynamics, and to efficiently sample from the probability densities of the states to approximate the satisfaction probability of the chance constraints. To increase computational efficiency by avoiding excessive sampling, a statistical analysis is proposed to systematically determine a-priori the least conservative constraint tightening required at a given sample size to guarantee a desired feasibility probability of the sample-approximated chance constraint optimization problem. In addition, a method is presented for sample-based approximation of the analytic gradients of the chance constraints, which increases the optimization efficiency significantly. The proposed stochastic nonlinear model predictive control approach is applicable to a broad class of nonlinear systems with the sufficient condition that each term is analytic with respect to the states, and separable with respect to the inputs, states and parameters. The closed-loop performance of the proposed approach is evaluated using the Williams-Otto reactor with seven states, and ten uncertain parameters and initial conditions. The results demonstrate the efficiency of the approach for real-time stochastic model predictive control and its capability to systematically account for probabilistic uncertainties in contrast to a nonlinear model predictive control approaches.Comment: Submitted to Journal of Process Contro

Feasibility study of an Integrated Program for Aerospace-vehicle Design (IPAD) system. Volume 2: Characterization of the IPAD system, phase 1, task 1

The aircraft design process is discussed along with the degree of participation of the various engineering disciplines considered in this feasibility study

Equitable Energy Transition Planning in Holyoke Massachusetts: A Technical Analysis for Strategic Gas Decommissioning and Grid Resiliency

This report provides a framework for targeting geographic areas for electrification and strategically managing leak-prone gas pipe infrastructure. Section I evaluates alternatives for gas pipeline replacement, as aging gas infrastructure is a widespread issue and requires modernization to minimize methane leaks which have significant health, safety, and climate implications. We consider scenarios including business as usual, accelerated strategic electrification, and options including propane tanks. This analysis finds that avoided pipeline replacement can reduce methane leaks from the distribution system; reducing greenhouse gas emissions; and lead to overall costs savings for consumers. Section II of this report demonstrates how different data sets can be integrated to better inform site selection of infrastructure projects. It demonstrates a framework for identifying targeted geographic areas to prioritize and opportunities for coordinated efforts. The report shows how identifying sites where the rehabilitation of aging sewer or water assets can be coordinated with undergrounding electric distribution lines and gas pipeline decommissioning can save on trenching and utility relocation costs. Such costs often make up a significant portion of any capital planning project. Prioritizing specific street segments for decommissioning allows for cities to plan more efficiently, increasing reliability and resiliency

Model predictive emissions control of a diesel engine airpath: Design and experimental evaluation

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/163480/2/rnc5188.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/163480/1/rnc5188_am.pd

Location System solution in TErrestrial Trunked RAdio (TETRA) Professional Mobile Radio networks

TETRA-järjestelmään (TErrestrial Trunked RAdio) on toteutettu paikkatietojärjestelmä tarjoamaan palvelua yksiköiden maantieteellisen paikkatiedon välittämiseksi sitä tarvitseville tahoille. Tämän palvelun toteutus noudattaa TETRA:lle asetettuja standardeja muun muassa välitettävän tiedon sisällön sekä palvelun käytön rajoitusten osalta. Tässä työssä ollaan kiinnostuneita havaitusta tarpeesta poiketa standardoiduista palvelun käytön rajoitteista paikkatiedon lyhimmän päivitysvälin osalta. TETRA standardi rajoittaa pienimmän mahdollisen paikkatiedon päivitysvälin kymmeneen sekuntiin minkä ei ole todettu tyydyttävän nopeasti liikkuvien kohteiden seuraamiseen tarvittavaa tiedonkeruutarvetta. Niinpä tässä tutkimuksessa työnä on pyrkiä pienentämään tämä päivitysfrekvenssi alle standardien asettaman rajan ja teettämään mittauksia liikenteen myötä järjestelmään aiheutuvista vaikutuksista. Diplomityön tavoitteena on tutkia tämän poikkeuksellisesta paikkatietopalvelun käytöstä aiheutuvia seurauksia palvelun laadun ylläpidettävyyteen ja TETRA-järjestelmän resurssien kulutukseen. Diplomityön teoriaosuudessa käydään läpi taustaa ammattikäyttöön suunnattujen matkapuhelinjärjestelmien (Professional Mobile Radio) tarkoitusperästä sekä lisäksi erityisesti keskitytään näihin kuuluvan TETRA ratkaisun olennaisimpiin osiin liittyen paikkatietosovelluksen toteutukseen ja työn tavoitteisiin. Tämän analyysin lisäksi esitellään muunnellusta paikkatietojärjestelmästä testiympäristössä saavutettuja mittaustuloksia ja vertaillaan näitä teoreettiseen TETRA-järjestelmän tarjoamaan suorituskykyyn sekä tavoiteltuun loppukäyttäjän kokemaan palvelun laatuun. Lopuksi pohditaan mahdollisuuksia työn mittaustulosten sekä aineiston analyysin pohjalta kartoitettujen paikkatietopalveluun aiheutuneiden vaikutusten pienentämiseksi. Nämä poikkeavan toiminnallisuuden mahdollistavat järjestelmän muutostarpeet kohdistuisivat tiettyihin systeemin parametreihin ja standardien sanelemiin rajoituksiin joista voitaisiin mahdollisuuksien mukaan poiketa.For Terrestrial Trunked Radio (TETRA) systems, there is a location system designed for enabling service, that provides unit geographical positions for parties who need them. The implementation of such service follows various standards applied for TETRA, having influence on delivered information contents and service usage restrictions among others. The focus of this work is on a perceived need for differing from the service usage restrictions on location information minimum update interval. The TETRA standard sets a limitation for the minimum location update interval to ten seconds, which is not seen to satisfy the information gathering needs for tracking fast moving objects. Therefore, the work task is to try to decrease the update frequency below the limits set by the standard and to measure any influences caused by the resulting traffic to the system. The objective of this thesis is then to study the impacts caused by the exceptional usage of the location system to the perceived service quality and consumption of resources in TETRA systems. In the theoretical part of this thesis, the background and purpose of Professional Mobile Radio (PMR) systems is discussed while at the same time the focus is on the most relevant aspects for implementing the location system in the TETRA PMR solution and objectives of this work. In addition to this analysis, measurement results from a test environment of the altered location solution are introduced and compared to the theoretical performance offered by TETRA systems and service quality perceived by end-users. In the end, possibilities for mitigating the impacts to the location service found based on the measurements and data analysis is discussed. These system modifications required for improving the exceptional location system usage should be concentrated on certain system parameters and standard based restrictions which could possibly he deviated from

Deep Reinforcement Learning Algorithms for Hybrid V2X Communication: A Benchmarking Study

In today's era, autonomous vehicles demand a safety level on par with aircraft. Taking a cue from the aerospace industry, which relies on redundancy to achieve high reliability, the automotive sector can also leverage this concept by building redundancy in V2X (Vehicle-to-Everything) technologies. Given the current lack of reliable V2X technologies, this idea is particularly promising. By deploying multiple RATs (Radio Access Technologies) in parallel, the ongoing debate over the standard technology for future vehicles can be put to rest. However, coordinating multiple communication technologies is a complex task due to dynamic, time-varying channels and varying traffic conditions. This paper addresses the vertical handover problem in V2X using Deep Reinforcement Learning (DRL) algorithms. The goal is to assist vehicles in selecting the most appropriate V2X technology (DSRC/V-VLC) in a serpentine environment. The results show that the benchmarked algorithms outperform the current state-of-the-art approaches in terms of redundancy and usage rate of V-VLC headlights. This result is a significant reduction in communication costs while maintaining a high level of reliability. These results provide strong evidence for integrating advanced DRL decision mechanisms into the architecture as a promising approach to solving the vertical handover problem in V2X