This thesis presents methods for simplifying the use of multi-agent systems in Plug & Produce. The demand for customized products and low volume production is constantly increasing. The industry has for many years used dedicated manufacturing systems where it is difficult and expensive to adapt to new product designs. Instead, factories are forced to use human workers for certain tasks that demand high flexibility and rapid adaption for new product designs. Several solutions have been proposed over the years to create highly flexible automation systems that automatically handles rapid adaption for new products. A concept called Plug & Produce aims at creating a system where resources and parts can be added in minutes rather than days in dedicated systems. One promising solution for implementing Plug & Produce is the distributed approach called multi-agent systems, where each resource and part get its own controller that communicates with each other to reach manufacturing goals. The idea is that the system automatically handles the adaption for new products. However, still today the use of such systems is extremely limited in the industry. One reason is the lack of mature multi-agent systems that are easy to use and that hides the complexity of the underlying agent system from the users. This is a huge problem since these systems tend to be more complex than traditional approaches. Thus, this thesis focuses on simplifying the use of multi-agent systems by proposing various methods for bringing the multi-agent technology for Plug & Produce closer to the industry. / Denna avhandling presenterar metoder för att förenkla användningen av multiagent-system för Plug & Produce. Efterfrågan på kundanpassade produkter och lågvolymproduktion ökar ständigt. Industrin har under många år använt sig avdedikerade tillverkningssystem som gör det både svårt och dyrt att anpassa sig till nya produktdesigner. Istället tvingas fabriker att antälla onödigt många operatörer för vissa arbetsuppgifter där det krävs hög flexibilitet och snabb anpassning till nya produktdesigner. Flera lösningar har föreslagits under åren för att skapa flexibla automatiseringssystem som automatiskt hanterar snabb omställning till nya produkter. Ett koncept som heter Plug & Produce handlar om att skapa system där nya typer av resurser och produkter kan kopplas in i systemet på ett fåtal minuter snarare än dagar i traditionella system. För att implementera Plug & Produce kan multi-agent-system användas, där varje resurs och produkt får sin egen styrning. Agenterna kan sedan kommunicera med varandra för att nå de mål som satts upp för tillverkningen av produkterna. Tanken är att systemet automatiskt hanterar anpassningen till nya produkter. Idag är dock användningen av sådana system extremt begränsad i industrin. En av anledningarna är avsaknaden av mogna multi-agent-system som är lätta att använda och där komplexiteten hos det underliggande agensystemet kan döljas från användaren. Detta är ett stort problem eftersom multi-agent-system tenderar att vara mer komplexa än traditionella system. Därför fokuserar denna avhandling på att förenkla användningen av multi-agent-system genom att föreslå olika metoder som kan underlätta användandet av multi-agent-tekniken för Plug & Produce i industrin.

The road transport sector is heavily dependent on fossil-fuel based technologies, and as a result, contribute a significant share towards climate change and other environmental problems. If the transport sector is to reduce its adverse impacts on climate change, then it requires a global shift towards low-carbon technologies. However, deploying these new technologies brings uncertainties regarding their environmental profile, hence, the need for applying a life cycle approach in evaluating their potential environmental impacts. This thesis aim to evaluate the potential life-cycle environmental impacts associated with travelling 1 km in a battery electric cars (BEV) and plug-in hybrid electric cars (PHEV) operated in the EU at present-day, and in the future up till 2050. The study applied the life cycle assessment (LCA) and ReCiPe Midpoint (H) methodologies to assess and calculate the potential life cycle environmental impacts of all vehicle scenarios. The datasets of the vehicles have been modelled with a modular approach by linking together various vehicle components. The future time perspective based on two future scenarios; the Mod-RES, representing the reference future scenario and the High-RES representing a future ambitious policy scenario.   The EU28 electricity production based on Fichtner, et al. was used to model the use phase all vehicle scenarios. The result showed BEV performed best in indicators for global warming (GWP), ozone depletion and fossil resource scarcity. The thesis best estimate for GWP is 5.61E-2 kgCO2 eq resulting from the BEV_High-RES scenario; representing a decrease in GWP of around 80% and 69% when compared to the ICEV and the baseline BEV respectively. On the other hand, the baseline BEV performed worst in impact categories related to human toxicity and damage to ecosystems; the conventional gasoline car showed the lowest estimate for indicators on human toxicity, acidification and eutrophication as defined in the baseline scenario. Nonetheless, the future scenarios showed promising results for all technologies; as projections for stringent environmental regulations, ‘cleaner’ energy systems and continuous advancement in vehicle technologies offered a significant reduction in all impact categories. Notably, the BEV reduced its impact on toxicity categories to around 38% of the initial values for the baseline scanario. Results are strongly dependent on assumptions regarding the vehicle and battery lifetime, the use phase electricity source and the vehicle consumption.  The findings establish the significance of carrying out a full LCA, including future time perspective and assessing impact categories beyond climate change. Also, it underlined the suggestion that production of electric cars raised more concern for EVs than conventional cars; thus, the tendency for environmental problem-shifting and the need for policy-makers to recognise existing trade-offs. / Vägtransportsektorn är starkt beroende av fossilbränslebaserad teknik och bidrar därmed till en betydande andel av klimatförändringen och andra miljöproblem. Om transportsektorn ska minska dess negativa inverkan på klimatförändringen, krävs det en global övergång till teknik med låga koldioxidutsläpp. Utnyttjandet av denna nya teknik medför dock osäkerhet om sin miljöprofil och därmed behovet av att tillämpa ett livscykelperspektiv vid utvärderingen av deras potentiella miljöpåverkan. Avhandlingen presenterar en livscykelanalys av nuvarande och framtida elfordon. Fokus ligger på batteri elbilar (BEV) och plug-in hybrid elbilar (PHEV) som drivs i EU. EU28-elproduktionen baserad på Fichtner, et al. användes för att beräkna de potentiella livscykelmiljöeffekterna av alla fordonsscenarier baserade på effektkategorier definierade i ReCiPe Midpoint (H) -metoden. Resultatet visade att BEV fungerade bäst i indikatorer för global uppvärmning (GWP), ozonförlust och fossila resurserbrist. Avhandlingens bästa uppskattning för GWP är 5,61E-2 kgCO2 ekv som härrör från BEV_High-RES-scenariot; vilket motsvarar en minskning av GWP på cirka 80% och 69% jämfört med ICEV respektive baseline BEV. Å andra sidan har baslinjens BEV den högsta andelen miljöindikatorer relaterade till human toxicitet och skador på ekosystemen. Den konventionella bensinbilen visade den lägsta uppskattningen av indikatorer för human toxicitet, försurning och eutrofiering enligt definitionen i baslinjen. De framtida scenarierna visade emellertid lovande resultat för all teknik, detta förutsätter strängre miljöregler, "renare" energisystem och kontinuerlig framsteg inom fordonsteknik som kommer att erbjuda en betydande minskning av alla påverkningskategorier. I synnerhet reducerade BEV: s påverkan på toxicitetskategorier till omkring 38% av de ursprungliga värdena för baslinjens scanario. Resultaten är starkt beroende av antaganden om fordonets och batteritiden, användningsfasens elkälla och fordonsförbrukningen. Resultaten visar betydelsen av att utföra en fullständig LCA, inklusive framtida tidsperspektiv och bedömning av påverkningskategorier utanför klimatförändringen. Det understryker också förslaget gällamde produktion av elbilar har en betydande ökade oro för elektriska motorer än konventionella bilar. Det finns en risk för miljöproblemförskjutning och ett behovet av att politiska beslutsfattare erkänner befintliga avvägningar. / REFLEX - Analysis of the European energy system under the aspects of flexibility and technological progress

No description available.

Adaptations to a fluctuating market and intensified customer demands for unique products are a challenge for manufacturers. Manual manufacturing is still the most flexible, nevertheless, automation ensures stable quality, minimizes wear and tear of the operators, and contributes to a safer and better working environment as the distance between the operator and the process can be increased and screened off. Hence, the manufacturing industry is searching for human-centric automation solutions that are flexible enough to handle these challenges. Conventional automation is tailored for one or a few similar variants of products, in addition, increased flexibility implies increased complexity to handle. This licentiate thesis demonstrates a flexible Plug &amp; Produce automated manufacturing concept where the complexity is redirected to focus on the products and manufacturing processes by utilizing artificial intelligence. Together with digital threads that connect the product design to automatic manufacturing that enables manufacturing companies to manage new production scenarios with their in-house knowledge. Data is picked directly from the computer-based design of the products and process knowledge that normally exists within the manufacturing company is added through graphical user interfaces. The graphical configuration tools visualize the flow of sequential and parallel manufacturing operations together with process-bound information. Plug &amp; Produce relies on pluggable process modules with re-cyclical manufacturing resources that can be plugged in and out as needed. As an example, a module with a robot can be plugged in to help an existing robot and thereby balance the production capacity. In Plug &amp; Produce resources start working and cooperate with other resources automatically when they are plugged in. To achieve this, the resources are provided with distributed artificial intelligence together with intelligent products that know how to be finalized. In this concept, everything is digitally configurable by the in-house knowledge of the manufacturing companies. A Plug &amp; Produce test bed was built to verify the concept in cooperation with industrial representatives. / Denna licentiatavhandling påvisar ett koncept för att öka flexibiliteten och samtidigt rikta om komplexiteten i automatiserade produktionssystem hos tillverkande företag på ett sätt så att deras interna personal på egen hand kan ställa om tillverkningen mot nya produkter. Anpassningar till marknadens fluktuationer och efterfrågan av nya unika produkter är en ständigt pågående process. Alltmer av produktionen flyttas tillbaka till Sverige och övriga Europa vilket ökar efterfrågan på flexibel och omställbar automation. Automation håller nere prisnivån då arbetskraften är dyr, säkerhetsställer jämn kvalité, minimerar förslitningsskador på de anställda och bidrar till säkrare och trevligare arbetsmiljö då distansen mellan operatör och process kan ökas och avskärmas. Produktion som flyttas till hemmamarknaden från låglöneländer ersätter ofta högflexibel och anpassningsbar manuell tillverkning vilket är en stor utmaning för industrin. Ett Plug &amp; Produce koncept för automatiserad tillverkning utvecklas och beskrivs i denna avhandling där automationen enkelt kan ställas om av den interna personalen och anpassas till nya produkter. Omställning med hjälp egen personal möjliggörs genom att så mycket information som möjligt utvinns från produktens datorbaserade design. Processkunskap som normalt besitts inom det tillverkande företaget adderas därtill med hjälp av grafiska användarinterface som visar flödet av tillverkningsoperationer tillsammans med processpecifika uppgifter såsom mått, bearbetningshastigheter, temperaturer och färg. Plug &amp; Produce system är uppbyggda kring processmoduler med tillverkningsresurser som kan pluggas in och ut efter behov. Till exempel kan en modul med en robot pluggas in för att avlasta befintlig robot och därmed öka produktionshastigheten. Specialdesignade resurser kan pluggas in för att öka effektiviteten och minimera energikonsumtionen. För att den inpluggade processmodulen självmant skall börja jobba och samarbeta med de andra modulerna är den försedd med egen lokal artificiell intelligens. Dessa processmoduler kan tack vare sin intelligens pluggas in i olika Plug &amp; Produce system och är därmed återvinningsbara i nya system. Intelligensen kan vara lokalt placerad i en dator på resursen eller i datormolnet kopplat till resursen. På samma sätt kan produkterna förses med intelligens och kallas då för smarta produkter. Dessa produkter har som mål att bli färdigproducerade genom delmål i form av tillverkningsoperationer. Denna intelligens förses med kunskap och erfarenheter av personalen inom det tillverkande företaget genom användarvänliga interface. När användarvänligheten Plug &amp; Produce testbädd har byggts upp tillsammans med representanter frånprefabricerade trähusindustrin. Tillverkning av prefabricerade trähus är i idag ihög grad manuell då existerande automationslösningar inte är flexibla nog eftersom husen är i hög grad är kundanpassade. Arbetet som beskrivs i denna avhandling gynnar trähusindustrin och därmed klimatet då trä binder kol för en lång tid framåt. / <p>Paper A is not included in the digital licentiate thesis due to copyright . </p>

Temperature is one of the most important parameters for the performance, safety, and aging of lithium-ion batteries and has been linked to all main barriers for widespread commercial success of electric vehicles. The aim of this thesis is to highlight the importance of temperature effects, as well as to provide engineering tools to study these. The mass transport phenomena of the electrolyte with LiPF6  in EC:DEC was fully characterized in between 10 and 40 °C and 0.5 and 1.5 M, and all mass transport properties were found to vary strongly with temperature. A superconcentrated electrolyte with LiTFSI in ACN was also fully characterized at 25 °C, and was found to have very different properties and interactions compared to LiPF6  in EC:DEC. The benefit of using the benchmarking method termed electrolyte masstransport resistivity (EMTR) compared to using only ionic conductivity was illustrated for several systems, including organic liquids, ionic liquids, solid polymers, gelled polymers, and electrolytes containing flame-retardant additives. TPP, a flame-retardant electrolyte additive, was evaluated using a HEV load cycle and was found to be unsuitable for high-power applications such as HEVs. A large-format commercial battery cell with a thermal management system was characterized using both experiments and a coupled electrochemical and thermal model during a PHEV load cycle. Different thermal management strategies were evaluated using the model, but were found to have only minor effects since the limitations lie in the heat transfer of the jellyroll. / Temperatur är en av de viktigaste parametrarna gällande ett litiumjonbatteris prestanda, säkerhet och åldring och har länkats till de främsta barriärerna för en storskalig kommersiell framgång för elbilar. Syftet med den här avhandlingen är att belysa vikten av temperatureffekter, samt att bidra med ingenjörsverktyg att studera dessa. Masstransporten för elektrolyten LiPF6  i EC:DEC karakteriserades fullständigt i temperaturintervallet 10 till 40 °C för LiPF6-koncentrationer på 0.5 till 1.5 M. Alla masstransport-egenskaper fanns variera kraftigt med temperaturen. Den superkoncentrerade elektrolyten med LiTFSI i ACN karakteriserades även den fullständigt vid 25 °C. Dess egenskaper och interaktioner fanns vara väldigt annorlunda jämfört med LiPF6  i EC:DEC. Fördelen med att använda utvärderingsmetoden elektrolytmasstransportresistivitet (EMTR) jämfört med att endast mäta konduktivitet illustrerades för flertalet system, däribland organiska vätskor, jonvätskor, fasta polymerer, gellade polymerer, och elektrolyter med flamskyddsadditiv. Flamskyddsadditivet TPP utvärderades med en hybridbils-lastcykel och fanns vara olämplig för högeffektsapplikationer, som hybridbilar. Ett kommersiellt storformatsbatteri med ett temperatur-kontrollsystem karakteriserades med b.de experiment och en kopplad termisk och elektrokemisk modell under en lastcykel utvecklad för plug-inhybridbilar. Olika strategier för kontroll av temperaturen utvärderades, men fanns bara ha liten inverkan på batteriets temperatur då begränsningarna för värmetransport ligger i elektrodrullen, och inte i batteriets metalliska ytterhölje. / <p>QC 20150522</p> / Swedish Hybrid Vehicle Center

Master of Science / Department of Computing and Information Sciences / Mitchell L. Neilsen / ImageJ and its recent upgrade, Fiji, are image processing tools that provide extensibility via Java plug-ins and recordable macros [2]. The aim of this project is to develop a plug-in compatible with ImageJ/Fiji, which extracts length information from images for scale factor determination used in 3-D Photogrammetry for plant analysis [5]. Plant images when processed using Agisoft software, gives an image consisting of the images processed merged into a single 3-D model. The coordinate system of the 3-D image generated is a relative coordinate system. The distances in the relative coordinate system are proportional to but not numerically the same as the real world distances. To know the length of any feature represented in 3-D model in real world distance, a scale factor is required. This scale factor when multiplied by some distance in the relative coordinate system, yields the actual length of that feature in the real coordinate system. For determining the scale factor we process images consisting of unsharpened yellow colored pencils which are all the same shape, color and size. The plug-in considers each pencil as a unique region by assigning unique value and unique color to all its pixels. The distance between the end midpoints of each pencil is calculated. The date and time on which the image file gets processed, name of the image file, image file creation and modification date and time, total number of valid (complete) pencils processed, the midpoints of ends of each valid pencil, length (distance) i.e., the number of pixels between the two end midpoints are all written to the output file. The length of the pencils written to the output file is used by the researchers to calculate the scale factor. Plug-in was tested on real images and the results obtained were same as the expected result.

The purpose of this study is to determine the effect of three improvement methods on nonparametric kernel regression estimators. The improvement methods are applied to the Nadaraya-Watson estimator with crossvalidation bandwidth selection, the Nadaraya-Watson estimator with plug-in bandwidth selection, the local linear estimator with plug-in bandwidth selection and a bias corrected nonparametric estimator proposed by Yao (2012). The di erent resulting regression estimates are evaluated by minimising a global discrepancy measure, i.e. the mean integrated squared error (MISE). In the machine learning context various improvement methods, in terms of the precision and accuracy of an estimator, exist. The rst two improvement methods introduced in this study are bootstrapped based. Bagging is an acronym for bootstrap aggregating and was introduced by Breiman (1996a) from a machine learning viewpoint and by Swanepoel (1988, 1990) in a functional context. Bagging is primarily a variance reduction tool, i.e. bagging is implemented to reduce the variance of an estimator and in this way improve the precision of the estimation process. Bagging is performed by drawing repetitive bootstrap samples from the original sample and generating multiple versions of an estimator. These replicates of the estimator are then used to obtain an aggregated estimator. Bragging stands for bootstrap robust aggregating. A robust estimator is obtained by using the sample median over the B bootstrap estimates instead of the sample mean as in bagging. The third improvement method aims to reduce the bias component of the estimator and is referred to as boosting. Boosting is a general method for improving the accuracy of any given learning algorithm. The method starts of with a sensible estimator and improves iteratively, based on its performance on a training dataset. Results and conclusions verifying existing literature are provided, as well as new results for the new methods. / MSc (Statistics), North-West University, Potchefstroom Campus, 2015

The purpose of this study is to determine the effect of three improvement methods on nonparametric kernel regression estimators. The improvement methods are applied to the Nadaraya-Watson estimator with crossvalidation bandwidth selection, the Nadaraya-Watson estimator with plug-in bandwidth selection, the local linear estimator with plug-in bandwidth selection and a bias corrected nonparametric estimator proposed by Yao (2012). The di erent resulting regression estimates are evaluated by minimising a global discrepancy measure, i.e. the mean integrated squared error (MISE). In the machine learning context various improvement methods, in terms of the precision and accuracy of an estimator, exist. The rst two improvement methods introduced in this study are bootstrapped based. Bagging is an acronym for bootstrap aggregating and was introduced by Breiman (1996a) from a machine learning viewpoint and by Swanepoel (1988, 1990) in a functional context. Bagging is primarily a variance reduction tool, i.e. bagging is implemented to reduce the variance of an estimator and in this way improve the precision of the estimation process. Bagging is performed by drawing repetitive bootstrap samples from the original sample and generating multiple versions of an estimator. These replicates of the estimator are then used to obtain an aggregated estimator. Bragging stands for bootstrap robust aggregating. A robust estimator is obtained by using the sample median over the B bootstrap estimates instead of the sample mean as in bagging. The third improvement method aims to reduce the bias component of the estimator and is referred to as boosting. Boosting is a general method for improving the accuracy of any given learning algorithm. The method starts of with a sensible estimator and improves iteratively, based on its performance on a training dataset. Results and conclusions verifying existing literature are provided, as well as new results for the new methods. / MSc (Statistics), North-West University, Potchefstroom Campus, 2015

ITC/USA 2007 Conference Proceedings / The Forty-Third Annual International Telemetering Conference and Technical Exhibition / October 22-25, 2007 / Riviera Hotel & Convention Center, Las Vegas, Nevada / CTEIP has launched the integrated Network Enhanced Telemetry (iNET) project to foster advances in networking and telemetry technology to meet emerging needs of major test programs. In the past these programs have been constrained by vendor proprietary equipment configuration utilities that force a significant learning curve on the part of instrumentation personnel to understand hardware idiosyncrasies and require significant human interaction and manipulation of data to be exchanged between different components of the end-to-end test system. This paper describes an ongoing effort to develop a measurement-centric data model of airborne data acquisition systems. The motivation for developing such a model is to facilitate hardware and software interoperability and to alleviate the need for vendor-specific knowledge on the part of the instrumentation engineer. This goal is driven by requirements derived from scenarios collected by the iNET program. This approach also holds the promise of decreased human interaction with and manipulation of data to be exchanged between system components.

Renewable and green energy development is vigorously supported by most countries to suppress the continuously increasing greenhouse gas (GHG) emissions. However, as the total renewable capacity expands, the growth rate of emissions is not effectively restrained. An unforeseen factor contributing to this growth is the regulation service, which aims to mitigate power frequency deviations caused by the intermittent renewable power generation and unbalanced power supply and demand. Regulation services, normally issued by supply-side balancing authorities, leads to inefficient operations of regulating generators, thus directly contributing to the emissions growth. Therefore, it is urged to find solutions that can stabilize the power frequency with an increased energy using efficiency. Demand response (DR) is an ideal candidate to solve this problem. The current smart grid infrastructure enables a high penetration of smart residential electric loads, including heating, ventilation, and air conditioning systems (HVACs), air conditioners (A/Cs), electric water heaters (EWHs), and plug-in hybrid electric vehicles (PHEVs). Beyond simply drawing power from the grid for local electric demand, those loads can also adjust their power consumption patterns by responding to the control signals sent to them. It has been proved that, if appropriately aggregated and controlled, power consumption of demand-side residential loads possesses a huge potential for providing regulation services. The research of DR is pivotal from the the application perspective due to the efficient usage of renewable energy generation and the high power quality. However, many problems remain open in this area due to the load heterogeneity, device physical constraints, and computational and communication restrictions. In order to move one step further toward industry applications, this PhD thesis is concerned with two cruxes in DR program design: Aggregation Modeling and Control; it deals with two main types of terminal loads: Thermostatically Controlled Appliances (TCAs) (Chapters 2-4) and PHEVs (Chapter 5). This thesis proceeds with Chapter 1 by reviewing the state-of-the-art of DR. Then in Chapter 2, the focus is put on modeling and control of TCAs for secondary frequency control. In order to explicitly describe local TCA dynamics and to provide the aggregator a clear global view, TCAs are aggregated by directly stacking their individual dynamics. Terminal TCAs are assumed in a general case that an arbitrary number of TCAs are equipped with varying frequency drives (VFDs). A centralized model predictive control (MPC) scheme is firstly constructed. In the design, to tackle the TCA lockout effect and to facilitate the MPC scheme, a novel approach for converting time-integrated interdependent logic constraints into inequality constraints are proposed. Since a centralized MPC scheme may introduce non-trivial computational load by using this aggregation model, especially when the number of TCAs increases, a distributed MPC (DMPC) scheme is proposed. This DMPC scheme is validated through a more practical case study that all TCAs are subject to pure ON/OFF control. Chapter 3 targets on aggregation modeling and control of TCAs for the provision of primary frequency control. To efficiently reduce the computational load to facilitate the primary frequency control, the explicit monitoring of terminal TCAs must be compromised. To this end, a 2-D population-based model is proposed, in which TCAs are clustered into state bins according to their temperature information and running status. Within the proposed aggregation framework, individual TCA dynamics' evolutions develop into TCA population migration probabilities, thus the computational load of the centralized controller is dramatically reduced. Based on this model, a centralized MPC scheme is proposed for the primary frequency control. The previously proposed population-based model provides a promising direction for the centralized control. However, in traditional population-based model, TCA lockout effect can only be considered when implementing the control signals. This will cause a mismatch between the nominal control signals and the actually implemented ones. To conquer this, in Chapter 4, an improved population-based model is studied to explicitly formulate the TCA lockout effect in the aggregation model. A DMPC scheme is firstly constructed based on this model. Furthermore, since the predictions of regulation signals may not be available or they may include severe disturbances, a control scheme that does not require future regulation signals is urged. To this end, an optimal control scheme, in which a novel penalty is included to maximize the regulation capability, is proposed to facilitate the most practical scenario. Another type of terminal loads that has a huge potential in providing grid services is PHEV. At this point, Chapter 5 presents the aggregation and charging control of heterogeneous PHEVs for the provision of DR. In contrast to using battery state-of-charge (SOC) solely as the system state, a new aggregation model is proposed by introducing a novel concept, i.e., charging requirement index. This index combines the SOC with drivers' specified charging requirements, thus inherently providing the aggregation model with richer information. A centralized MPC scheme is proposed based on this novel model. Both of the model and controller are validated through an overnight valley-filling case study. Finally, the conclusions of the thesis are summarized and future research topics are presented. / Graduate / 0537 / 0544 / 0548 / mingxiliu419@gmail.com