9,413 research outputs found
Beam scanning by liquid-crystal biasing in a modified SIW structure
A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium
Application of Track Geometry Deterioration Modelling and Data Mining in Railway Asset Management
Modernin rautatiejärjestelmän hallinnassa rahankäyttö kohdistuu valtaosin nykyisen rataverkon korjauksiin ja parannuksiin ennemmin kuin uusien ratojen rakentamiseen. Nykyisen rataverkon kunnossapitotyöt aiheuttavat suurten kustannusten lisäksi myös usein liikennerajoitteita tai yhteyksien väliaikaisia sulkemisia, jotka heikentävät rataverkon käytettävyyttä Siispä oikea-aikainen ja pitkäaikaisia parannuksia aikaansaava kunnossapito ovat edellytyksiä kilpailukykyisille ja täsmällisille rautatiekuljetuksille. Tällainen kunnossapito vaatii vankan tietopohjan radan nykyisestä kunnosta päätöksenteon tueksi.
Ratainfran omistajat teettävät päätöksenteon tueksi useita erilaisia radan kuntoa kuvaavia mittauksia ja ylläpitävät kattavia omaisuustietorekistereitä. Kenties tärkein näistä datalähteistä on koneellisen radantarkastuksen tuottamat mittaustulokset, jotka kuvastavat radan geometrian kuntoa. Nämä mittaustulokset ovat tärkeitä, koska ne tuottavat luotettavaa kuntotietoa: mittaukset tehdään toistuvasti, 2–6 kertaa vuodessa Suomessa rataosasta riippuen, mittausvaunu pysyy useita vuosia samana, tulokset ovat hyvin toistettavia ja ne antavat hyvän yleiskuvan radan kunnosta. Vaikka laadukasta dataa on paljon saatavilla, käytännön omaisuudenhallinnassa on merkittäviä haasteita datan analysoinnissa, sillä vakiintuneita menetelmiä siihen on vähän. Käytännössä seurataan usein vain mittaustulosten raja-arvojen ylittymistä ja pyritään subjektiivisesti arvioimaan rakenteiden kunnon kehittymistä ja korjaustarpeita. Kehittyneen analytiikan puutteet estävät kuntotietojen laajamittaisen hyödyntämisen kunnossapidon suunnittelussa, mikä vaikeuttaa päätöksentekoa.
Tämän väitöskirjatutkimuksen päätavoitteita olivat kehittää ratageometrian heikkenemiseen mallintamismenetelmiä, soveltaa tiedonlouhintaa saatavilla olevan omaisuusdatan analysointiin sekä jalkauttaa kyseiset tutkimustulokset käytännön rataomaisuudenhallintaan. Ratageometrian heikkenemisen mallintamismenetelmien kehittämisessä keskityttiin tuottamaan nykyisin saatavilla olevasta datasta uutta tietoa radan kunnon kehityksestä, tehdyn kunnossapidon tehokkuudesta sekä tulevaisuuden kunnossapitotarpeista. Tiedonlouhintaa sovellettiin ratageometrian heikkenemisen juurisyiden selvittämiseen rataomaisuusdatan perusteella. Lopuksi hyödynnettiin kypsyysmalleja perustana ratageometrian heikkenemisen mallinnuksen ja rataomaisuusdatan analytiikan käytäntöön viennille.
Tutkimustulosten perusteella suomalainen radantarkastus- ja rataomaisuusdata olivat riittäviä tavoiteltuihin analyyseihin. Tulokset osoittivat, että robusti lineaarinen optimointi soveltuu hyvin suomalaisen rataverkon ratageometrian heikkenemisen mallinnukseen. Mallinnuksen avulla voidaan tuottaa tunnuslukuja, jotka kuvaavat rakenteen kuntoa, kunnossapidon tehokkuutta ja tulevaa kunnossapitotarvetta, sekä muodostaa havainnollistavia visualisointeja datasta. Rataomaisuusdatan eksploratiiviseen tiedonlouhintaan käytetyn GUHA-menetelmän avulla voitiin selvittää mielenkiintoisia ja vaikeasti havaittavia korrelaatioita datasta. Näiden tulosten avulla saatiin uusia havaintoja ongelmallisista ratarakennetyypeistä. Havaintojen avulla voitiin kohdentaa jatkotutkimuksia näihin rakenteisiin, mikä ei olisi ollut mahdollista, jollei tiedonlouhinnan avulla olisi ensin tunnistettu näitä rakennetyyppejä. Kypsyysmallin soveltamisen avulla luotiin puitteet ratageometrian heikkenemisen mallintamisen ja rataomaisuusdatan analytiikan kehitykselle Suomen rataomaisuuden hallinnassa. Kypsyysmalli tarjosi käytännöllisen tavan lähestyä tarvittavaa kehitystyötä, kun eteneminen voitiin jaotella neljään eri kypsyystasoon, jotka loivat selkeitä välitavoitteita. Kypsyysmallin ja asetettujen välitavoitteiden avulla kehitys on suunniteltua ja edistystä voidaan jaotella, mikä antaa edellytykset tämän laajamittaisen kehityksen onnistuneelle läpiviennille.
Tämän väitöskirjatutkimuksen tulokset osoittavat, miten nykyisin saatavilla olevasta datasta saadaan täysin uutta ja merkityksellistä tietoa, kun sitä käsitellään kehittyneen analytiikan avulla. Tämä väitöskirja tarjoaa datankäsittelyratkaisujen luomisen ja soveltamisen lisäksi myös keinoja niiden käytäntöönpanolle, sillä tietopohjaisen päätöksenteon todelliset hyödyt saavutetaan vasta käytännön radanpidossa.In the management of a modern European railway system, spending is predominantly allocated to maintaining and renewing the existing rail network rather than constructing completely new lines. In addition to major costs, the maintenance and renewals of the existing rail network often cause traffic restrictions or line closures, which decrease the usability of the rail network. Therefore, timely maintenance that achieves long-lasting improvements is imperative for achieving competitive and punctual rail traffic. This kind of maintenance requires a strong knowledge base for decision making regarding the current condition of track structures.
Track owners commission several different measurements that depict the condition of track structures and have comprehensive asset management data repositories. Perhaps one of the most important data sources is the track recording car measurement history, which depicts the condition of track geometry at different times. These measurement results are important because they offer a reliable condition database; the measurements are done recurrently, two to six times a year in Finland depending on the track section; the same recording car is used for many years; the results are repeatable; and they provide a good overall idea of the condition of track structures. However, although high-quality data is available, there are major challenges in analysing the data in practical asset management because there are few established methods for analytics. Practical asset management typically only monitors whether given threshold values are exceeded and subjectively assesses maintenance needs and development in the condition of track structures. The lack of advanced analytics prevents the full utilisation of the available data in maintenance planning which hinders decision making.
The main goals of this dissertation study were to develop track geometry deterioration modelling methods, apply data mining in analysing currently available railway asset data, and implement the results from these studies into practical railway asset management. The development of track geometry deterioration modelling methods focused on utilising currently available data for producing novel information on the development in the condition of track structures, past maintenance effectiveness, and future maintenance needs. Data mining was applied in investigating the root causes of track geometry deterioration based on asset data. Finally, maturity models were applied as the basis for implementing track geometry deterioration modelling and track asset data analytics into practice.
Based on the research findings, currently available Finnish measurement and asset data was sufficient for the desired analyses. For the Finnish track inspection data, robust linear optimisation was developed for track geometry deterioration modelling. The modelling provided key figures, which depict the condition of structures, maintenance effectiveness, and future maintenance needs. Moreover, visualisations were created from the modelling to enable the practical use of the modelling results. The applied exploratory data mining method, General Unary Hypotheses Automaton (GUHA), could find interesting and hard-to-detect correlations within asset data. With these correlations, novel observations on problematic track structure types were made. The observations could be utilised for allocating further research for problematic track structures, which would not have been possible without using data mining to identify these structures. The implementation of track geometry deterioration and asset data analytics into practice was approached by applying maturity models. The use of maturity models offered a practical way of approaching future development, as the development could be divided into four maturity levels, which created clear incremental goals for development. The maturity model and the incremental goals enabled wide-scale development planning, in which the progress can be segmented and monitored, which enhances successful project completion.
The results from these studies demonstrate how currently available data can be used to provide completely new and meaningful information, when advanced analytics are used. In addition to novel solutions for data analytics, this dissertation research also provided methods for implementing the solutions, as the true benefits of knowledge-based decision making are obtained in only practical railway asset management
Body-UAV Near-Ground LoRa Links through a Mediterranean Forest
LoRa low-power wide-area network protocol has recently gained attention for
deploying ad-hoc search and rescue (SaR) systems. They could be empowered by
exploiting body-UAV links that enable communications between a body-worn radio
and a UAV-mounted one. However, to employ UAVs effectively, knowledge of the
signal's propagation in the environment is required. Otherwise, communications
and localization could be hindered. The radio range, the packet delivery ratio
(PDR), and the large- and small-scale fading of body-UAV LoRa links at 868 MHz
when the radio wearer is in a Mediterranean forest are here characterized for
the first time with a near-ground UAV having a maximum flying height of 30 m. A
log-distance model accounting for the body shadowing and the wearer's movements
is derived. Over the full LoRa radio range of about 600 m, the new model
predicts the path loss (PL) better than the state-of-the-art ones, with a
reduction of the median error even by 10 dB. The observed small-scale fading is
severe and follows a Nakagami-m distribution. Extensions of the model for
similar scenarios can be drawn through appropriate corrective factors
Proceedings of FORM 2022. Construction The Formation of Living Environment
This study examines the integration of building information modelling (BIM) technologies in operation & maintenance stage in the system of managing real estate that helps to reduce transaction costs. The approach and method are based on Digital Twin technology and Model Based System Engineering (MBSE) approach.
The results of the development of a service for digital facility management and
digital expertise are presented. The connection between physical and digital objects is conceptualized
Reduction of Petri net maintenance modeling complexity via Approximate Bayesian Computation
This paper is part of the ENHAnCE ITN project (https://www.h2020-enhanceitn.eu/) funded by the European Union's Horizon 2020 research and innovation programme under the Marie SklodowskaCurie grant agreement No. 859957. The authors would like to thank the Lloyd's Register Foundation (LRF), a charitable foundation in the U.K. helping to protect life and property by supporting engineeringrelated education, public engagement, and the application of research. The authors gratefully acknowledge the support of these organizations which have enabled the research reported in this paper.The accurate modeling of engineering systems and processes using Petri nets often results in complex graph
representations that are computationally intensive, limiting the potential of this modeling tool in real life
applications. This paper presents a methodology to properly define the optimal structure and properties of
a reduced Petri net that mimic the output of a reference Petri net model. The methodology is based on
Approximate Bayesian Computation to infer the plausible values of the model parameters of the reduced model
in a rigorous probabilistic way. Also, the method provides a numerical measure of the level of approximation
of the reduced model structure, thus allowing the selection of the optimal reduced structure among a set
of potential candidates. The suitability of the proposed methodology is illustrated using a simple illustrative
example and a system reliability engineering case study, showing satisfactory results. The results also show
that the method allows flexible reduction of the structure of the complex Petri net model taken as reference,
and provides numerical justification for the choice of the reduced model structure.European Commission 859957Lloyd's Register Foundation (LRF), a charitable foundation in the U.K
Demand fulfillment in customer hierarchies with stochastic demand
Supply scarcity, due to demand or supply fluctuations, is a common issue in make-to-stock production systems. To increase profits when customers are heterogeneous, firms need to decide whether to accept a customer order or reject it in anticipation of more profitable orders, and if accepted, which supplies to use in order to fulfill the order. Such issues are addressed by solving demand fulfillment problems. In order to provide a solution, firms commonly divide their customers into different segments, based on their respective profitability. The available supply is first allocated to the customer segments based on their projected demand information. Then, as customer orders materialize, the allocated quotas are consumed. The customer segments commonly have a multilevel hierarchical structure, which reflects the structure of the sales organization. In this thesis, we study the demand fulfillment problem in make-to-stock production systems, considering such customer hierarchies with stochastic demand.
In the hierarchical setting, the available supply is allocated level by level from top to bottom of the hierarchy by multiple planners on different levels. The planners on higher levels of the hierarchy need to make their allocation decisions based on aggregated information, since transmitting all detailed demand information from the bottom to the top of the hierarchy is not generally feasible. In practice, simplistic rules of thumb are applied to deal with this decentralized problem, which lead to sub-optimal results. We aim to provide more effective approaches that result in near-optimal solutions to this decentralized problem.
We first consider the single-period problem with a single supply replenishment and focus on identifying critical information for good, decentralized allocation decisions. We propose two decentralized allocation methods, namely a stochastic Theil index approximation and a clustering approach, which provide near-optimal results even for large, complicated hierarchies. Both methods transmit aggregated information about profit heterogeneity and demand uncertainty in the hierarchy, which is missing in the current simplistic rules.
Subsequently, we expand our analysis to a multi-period setting, in which periodic supply replenishments are considered and periods are interconnected by inventory or backlog. We consider a periodic setting, meaning that in each period we allow multiple orders from multiple customer segments. We first formalize the centralized problem as a two-stage stochastic dynamic program. Due to the curse of dimensionality, the problem is computationally intractable. Therefore, we propose an approximate dynamic programming heuristic. For the decentralized case, we consider our proposed clustering method and modify it to fit the multi-period setting, relying on the approximate dynamic programming heuristic. Our results show that the proposed heuristics lead to profits very close to the ex-post optimal solution for both centralized and decentralized problems.
Finally, we look into the order promising stage and compare different consumption functions, namely partitioned, rule-based nested, and bid price methods. Our results show that nesting leads to performance improvements compared to partitioned consumption.
However, for decentralized problems, the improvement resulting from nesting cannot mitigate the profit loss from considerable mis-allocations made by simplistic rules, except for cases with high demand uncertainty or low profit heterogeneity. Moreover, among the nested consumption functions, the bid price approach, which integrates the allocation and consumption stages, leads to a higher performance than the rule-based consumption methods.
Altogether, our proposed decentralized methods lead to drastic profit improvements compared to the current simplistic rules for demand fulfillment in customer hierarchies, except for cases with very low shortage or for largely homogeneous customers, where simplistic rules perform similarly well. Applying our advanced methods is especially important when the shortage rate is high or customers are more heterogeneous. Regarding order promising, nesting is more crucial when demand uncertainty is high.
The research presented in this thesis was undertaken as part of the project “demand fulfillment in customer hierarchies”. It was funded by the German Research Foundation (DFG) under grant FL738/2-1
CITIES: Energetic Efficiency, Sustainability; Infrastructures, Energy and the Environment; Mobility and IoT; Governance and Citizenship
This book collects important contributions on smart cities. This book was created in collaboration with the ICSC-CITIES2020, held in San José (Costa Rica) in 2020. This book collects articles on: energetic efficiency and sustainability; infrastructures, energy and the environment; mobility and IoT; governance and citizenship
Ludotopia
Where do computer games »happen«? The articles collected in this pioneering volume explore the categories of »space«, »place« and »territory« featuring in most general theories of space to lay the groundwork for the study of spatiality in games. Shifting the focus away from earlier debates on, e.g., the narrative nature of games, this collection proposes, instead, that thorough attention be given to the tension between experienced spaces and narrated places as well as to the mapping of both of these
Paternalism to Partnership
A biographical sketch of each head of Indian affairs between 1786 and 2021, including each commissioner’s political philosophy
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