Дисертації з теми "Smart data management"
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UGLIOTTI, FRANCESCA MARIA. "BIM and Facility Management for smart data management and visualization." Doctoral thesis, Politecnico di Torino, 2017. http://hdl.handle.net/11583/2696432.
Повний текст джерелаBIM is for all buildings. As a disruptive technology, BIM completely changes the traditional way of working of the Construction Industry, starting from the design stage. However, the challenging issue is to establish a framework that brings together methods and tools for the buildings lifecycle, focusing on the existing buildings management. Smart city means smart data, including, therefore, intelligent use of Real Estate information. Involving Facility Management in the process is the key to ensure the availability of the proper dataset of information, supporting the idea of a BIM-based knowledge management system. According to this approach, BIM Management is achievable applying a reverse engineering process to guarantee the BIM effectiveness and to provide Facility 4.0 smart services.
Fares, Tony Yussef. "Digital rights management for smart containment objects." Access electronically, 2005. http://www.library.uow.edu.au/adt-NWU/public/adt-NWU20060511.151012/index.html.
Повний текст джерелаMoreira, Helder. "Sensor data integration and management of smart environments." Master's thesis, Universidade de Aveiro, 2016. http://hdl.handle.net/10773/17884.
Повний текст джерелаNum mundo de constante desenvolvimento tecnológico e acelerado crescimento populacional, observa-se um aumento da utilização de recursos energéticos. Sendo os edifícios responsáveis por uma grande parte deste consumo energético, desencadeiam-se vários esforços de investigações de forma a criarem-se edifícios energeticamente eficientes e espaços inteligentes. Esta dissertação visa, numa primeira fase, apresentar uma revisão das atuais soluções que combinam sistemas de automação de edifícios e a Internet das Coisas. Posteriormente, é apresentada uma solução de automação para edifícios, com base em princípios da Internet das Coisas e explorando as vantagens de sistemas de processamento complexo de eventos, de forma a fornecer uma maior integração dos múltiplos sistemas existentes num edifício. Esta solução é depois validada através de uma implementação, baseada em protocolos leves desenhados para a Internet das Coisas, plataformas de alto desempenho, e métodos complexos para análise de grandes fluxos de dados. Esta implementação é ainda aplicada num cenário real, e será usada como a solução padrão para gestão e automação num edifício existente.
In a world of constant technological development and accelerated population growth, an increased use of energy resources is being observed. With buildings responsible for a large share of this energy consumption, a lot of research activities are pursued with the goal to create energy efficient buildings and smart spaces. This dissertation aims to, in a first stage, present a review of the current solutions combining Building Automation Systems (BAS) and Internet of Things (IoT). Then, a solution for building automation is presented based on IoT principles and exploiting the advantages of Complex Event Processing (CEP) systems, to provide higher integration of the multiple building subsystems. This solution was validated through an implementation, based on standard lightweight protocols designed for IoT, high performance and real time platforms, and complex methods for analysis of large streams of data. The implementation is also applied to a real world scenario, and will be used as a standard solution for management and automation of an existing building
DEL, GIUDICE MATTEO. "Smart data management with BIM for Architectural Heritage." Doctoral thesis, Politecnico di Torino, 2016. http://hdl.handle.net/11583/2652020.
Повний текст джерелаSimonet, Anthony. "Active Data - Enabling Smart Data Life Cycle Management for Large Distributed Scientific Data Sets." Thesis, Lyon, École normale supérieure, 2015. http://www.theses.fr/2015ENSL1004/document.
Повний текст джерелаIn all domains, scientific progress relies more and more on our ability to exploit ever growing volumes of data. However, as datavolumes increase, their management becomes more difficult. A key point is to deal with the complexity of data life cycle management,i.e. all the operations that happen to data between their creation and there deletion: transfer, archiving, replication, disposal etc.These formerly straightforward operations become intractable when data volume grows dramatically, because of the heterogeneity ofdata management software on the one hand, and the complexity of the infrastructures involved on the other.In this thesis, we introduce Active Data, a meta-model, an implementation and a programming model that allow to represent formally and graphically the life cycle of data distributed in an assemblage of heterogeneous systems and infrastructures, naturally exposing replication, distribution and different data identifiers. Once connected to existing applications, Active Data exposes the progress of data through their life cycle at runtime to users and programs, while keeping their track as it passes from a system to another.The Active Data programming model allows to execute code at each step of the data life cycle. Programs developed with Active Datahave access at any time to the complete state of data in any system and infrastructure it is distributed to.We present micro-benchmarks and usage scenarios that demonstrate the expressivity of the programming model and the implementationquality. Finally, we describe the implementation of a Data Surveillance framework based on Active Data for theAdvanced Photon Source experiment that allows scientists to monitor the progress of their data, automate most manual tasks,get relevant notifications from huge amount of events, and detect and recover from errors without human intervention.This work provides interesting perspectives in data provenance and open data in particular, while facilitating collaboration betweenscientists from different communities
Christiansen, Filip, and Matilda Tranell. "Data Management and Business Opportunities inEmerging Smart Metering Market." Thesis, KTH, Energiteknik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-206975.
Повний текст джерелаUppkomsten av smarta elnät och elmätare möjliggör ett dubbelriktat flöde av information i elnät. Detta ger upphov till stora datamängder och för marknadsaktiviteter och elnätsrelaterade åtaganden krävs därför en effektiv datahantering. Dessutom uppstår en ny marknad för tredjepartsaktörer som kan använda datan och göra om den till värdefull information. Strategier för hur datahanteringen ska gå till skiljer sig åt mellan länder och mångfalden är stor. Europeiska Kommissionen har tagit fram tre olika teoretiska referensmodeller för att uppnå konsensus inom detta område. Dessa modeller kan fungera som verktyg för tredjepartsaktörer i syfte att identifiera verkliga modeller för datahantering. Dessutom kan de ge värdefull information om relationen mellan datahantering och försvårande omständigheter; något som är viktigt att förstå för att bedöma marknadsmöjligheter. Målet med denna rapport är att presentera marknadsmöjligheter för tredjepartsaktörer i två europeiska länder som har olika modeller för datahantering. Utifrån särskilda kriterier väljs Nederländerna och England. Med hjälp av existerande teori kring referensmodellerna definieras de reella modellerna i länderna. Därefter utreder rapporten hur lämpliga de reella modellerna är i relation till identifierade barriärer. Därmed fungerar de två länderna även som fallstudier för utvärdering av applicerbarheten hos referensmodellerna. I Nederländerna identifieras den verkliga modellen för datahantering som en variant av modell 1 av referensmodellerna, och en utveckling mot modell 2 kan observeras. Den avgörande barriären är integritetsrelaterad, men kundengagemang blir ett alltmer centralt fokus. I relation till dessa problem kan det konstateras att specifika regleringar har större positiv genomslagskraft än själva modellen. Den holländska marknaden befinner sig i ett tidigt utvecklingsstadie men det har visat sig att kunder är positivt inställda till innovativa tjänster. Effektiv datahantering främjas av en central åtkomstpunkt, men detta inkluderar endast data med en uppdateringsfrekvens om 15 minuter. Data med uppdateringsfrekvens om 10 sekunder är tillgänglig via en fysisk port på själva elmätaren. I England identifieras den verkliga modellen för datahantering som delar av både referensmodell 2 och 3, och den största barriären är brist på kundengagemang. Tidigare utbredda integritetsproblem har delvis utformat modellen, men trots detta återfinns positiva funktioner sett till rådande utmaning då modellen främjar högre innovationsnivåer för tjänster. Regleringar har dock tidigare begränsat utbudet av sådana tjänster till endast s.k In Home Displays. Under 2015 förändrades denna reglering vilket medför lovande marknadsmöjligheter för tredjepartsaktörer. Datatillgång sker antingen via en central åtkomstpunkt, med en uppdateringsfrekvensen om 30 minuter, eller via s.k Consumer Access Devices där uppdateringsfrekvensen är 10 sekunder. Ett gap mellan de teoretiska modellerna och den verkliga implementeringen kan observeras eftersom teoretiskt beskrivna fördelar inte alltid förekommer i praktiken. En annan viktig upptäckt är att visualiseringar av datamodeller inte alltid beskriver eller inkluderar samtliga dataflöden. Därmed bör tredjepartsaktörer inte enbart förlita sig på sådana kartläggningar; andra metoder kan vara nödvändiga för att bedöma tillgången till nödvändig data. Till sist kan det konstateras att integritetsproblem kan motverkas med metoder som ökar uppmärksamheten hos kunder. Ett viktigt samband mellan detta och mottagligheten för innovation hos kunderna kan påvisas.
Masilela, Mbonisi. "Supporting Data-Intensive Wireless Sensor Applications using Smart Data Fragmentation and Buffer Management." VCU Scholars Compass, 2007. http://scholarscompass.vcu.edu/etd/779.
Повний текст джерелаSinaeepourfard, Amir. "Hierarchical distributed fog-to-cloud data management in smart cities." Doctoral thesis, Universitat Politècnica de Catalunya, 2017. http://hdl.handle.net/10803/461740.
Повний текст джерелаAl món es generen diàriament una gran quantitat de dades, amb diferents formats, nivells de qualitat, etc. Aquestes noves dades, juntament amb les dades històriques arxivades, constitueixen la llavor per al descobriment de coneixement i la generació de valor en diversos camps de la ciència i grans entorns de dades (big data). Descobrir el valor de les dades és un procés complex de càlcul on les dades són el recurs clau, no només durant el seu processament, sinó també durant tot el seu cicle de vida. Tanmateix, encara hi ha una gran preocupació per com organitzar i gestionar aquestes dades en tots els camps per a un ús i explotació eficients durant tots els cicles de vida de les dades. Encara que recentment s'han definit diversos models específics de Data LifeCycle (DLC) per a escenaris particulars, argumentem que no hi ha un marc global i complet de DLC que s'utilitzi àmpliament en diferents camps. En particular, les ciutats intel·ligents són les solucions tecnològiques actuals per fer front als reptes i la complexitat de la creixent densitat urbana. Tradicionalment, la gestió de recursos de Smart City es basa en solucions basades en núvol (cloud computing) on es recopilen dades de sensors per proporcionar un conjunt de dades obert i centralitzat. Les avantatges dels entorns basats en núvol són la seva ubiqüitat, així com una capacitat (gairebé) il·limitada de recursos. Tanmateix, l'accés a dades del núvol implica un gran trànsit de xarxa i, en general, les latències elevades no són apropiades per a solucions crítiques o en temps real, així com també per a riscos de seguretat més elevats. Alternativament, el processament de boira (fog computing) sorgeix com una tecnologia prometedora per absorbir aquests inconvenients. Proposa l'ús de dispositius a la vora per proporcionar recuirsos informàtics més propers i, per tant, reduir el trànsit de la xarxa, reduint les latències dràsticament mentre es millora la seguretat. Hem definit un nou marc per a la gestió de dades en el context d'una ciutat intel·ligent a través d'una arquitectura de gestió de recursos des de la boira fins al núvol (Fog-to-Cloud computing, o F2C). Aquest model té els avantatges combinats de les tecnologies de boira i de núvol, ja que permet reduir les latències per a aplicacions crítiques mentre es poden utilitzar les grans capacitats informàtiques de la tecnologia en núvol. En aquesta tesi, proposem algunes idees noves en el disseny d'una arquitectura F2C de gestió de dades per a ciutats intel·ligents. En primer lloc, dibuixem i descrivim un model de Data LifeCycle global agnòstic que aborda amb èxit tots els reptes inclosos en els 6V i no adaptats a un entorn específic, però fàcil d'adaptar-se als requisits de qualsevol camp en concret. A continuació, presentem el model de Data LifeCycle complet per a una ciutat intel·ligent, una arquitectura de gestió de dades generada a partir d'un model agnòstic d'escenari global, adaptat a l'escenari particular de ciutat intel·ligent. Definim la gestió de cada fase de la vida de les dades i expliquem la seva implementació en una ciutat intel·ligent amb gestió de recursos F2C. I, a continuació, il·lustrem la nova arquitectura per a la gestió de dades en el context d'una Smart City a través d'una arquitectura de gestió de recursos F2C. Mostrem que aquest model té els avantatges d'ambdues, la tecnologia de boira i de núvol, ja que permet reduir les latències per a aplicacions crítiques mentre es pot utilitzar la gran capacitat de processament de la tecnologia en núvol. Com a primer experiment per a l'arquitectura de gestió de dades F2C, s'analitza una ciutat intel·ligent real, corresponent a la ciutat de Barcelona, amb especial èmfasi en les capes responsables de recollir les dades generades pels sensors desplegats. S'ha estimat la quantitat de dades de sensors diàries que es transmet a través de la xarxa i s'ha realitzat una projecció aproximada assumint un desplegament exhaustiu que cobreix tota la ciutat.
Finotto, Gianluca <1988>. "Smart Data: un nuovo asset intangibile a supporto del management." Master's Degree Thesis, Università Ca' Foscari Venezia, 2016. http://hdl.handle.net/10579/8802.
Повний текст джерелаStivanello, Alice <1993>. "Strategic Management over Data Privacy and Cyber Security Risk in Smart City and Smart Home." Master's Degree Thesis, Università Ca' Foscari Venezia, 2018. http://hdl.handle.net/10579/12673.
Повний текст джерелаZhang, Xin. "Secure Data Management and Transmission Infrastructure for the Future Smart Grid." Thesis, The University of Sydney, 2016. http://hdl.handle.net/2123/14657.
Повний текст джерелаShi, Heng. "Uncertainty analysis and application on smart homes and smart grids : big data approaches." Thesis, University of Bath, 2018. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.760978.
Повний текст джерелаHalvorsen, Anne (Anne Fire). "Improving transit demand management with Smart Card data : general framework and applications." Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/99543.
Повний текст джерелаThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 169-174).
Increases in ridership are outpacing capacity expansions in a number of transit systems. By shifting their focus to demand management, agencies can instead influence how customers use the system, getting more out of the capacity they already have. However, while demand management is well researched for personal vehicle use, its applications for public transportation are still emerging. This thesis explores the strategies transit agencies can use to reduce overcrowding, with a particular focus of how automatically collected fare data can support the design and evaluation of these measures. A framework for developing demand management policies is introduced to help guide agencies through this process. It includes establishing motivations for the program, aspects to consider in its design, as well as dimensions and metrics to evaluate its impacts. Additional considerations for updating a policy are also discussed, as are the possible data sources and methods for supporting analysis. This framework was applied to a fare incentive strategy implemented at Hong Kong's MTR system. In addition to establishing existing congestion patterns, a customer classification analysis was performed to understand the typical travel patterns among MTR users. These results were used to evaluate the promotion at three levels of customer aggregation: all users, user groups, and a panel of high frequency travelers. The incentive was found to have small but non-negligible impacts on morning travel, particularly at the beginning of the peak hour and among users with commuter-like behavior. Through a change point analysis, it was possible to identify the panel members that responded to the promotion and quantify factors that influenced their decision using a discrete choice model. The findings of these analyses are used to recommend potential improvements to MTR's current scheme.
by Anne Halvorsen.
S.M.
Rutqvist, David. "Data-Driven Emptying Detection for Smart Recycling Containers." Thesis, Luleå tekniska universitet, Institutionen för system- och rymdteknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-70892.
Повний текст джерелаHe, Dawei. "An advanced non-intrusive load monitoring technique and its application in smart grid building energy management systems." Diss., Georgia Institute of Technology, 2016. http://hdl.handle.net/1853/54951.
Повний текст джерелаAfzalan, Milad. "Data-driven customer energy behavior characterization for distributed energy management." Diss., Virginia Tech, 2020. http://hdl.handle.net/10919/99210.
Повний текст джерелаDoctor of Philosophy
Buildings account for more than 70% of electricity consumption in the U.S., in which more than 40% is associated with the residential sector. During recent years, with the advancement in Information and Communication Technologies (ICT) and the proliferation of data from consumers and devices, data-driven methods have received increasing attention for improving the energy-efficiency initiatives. With the increased adoption of renewable and distributed resources in buildings (e.g., solar panels and storage systems), an important aspect to improve the efficiency by matching the demand and supply is to add flexibility to the energy consumption patterns (e.g., trying to match the times of high energy demand from buildings and renewable generation). In this dissertation, we introduced data-driven solutions using the historical energy data of consumers with application to the flexibility provision. Specific problems include: (1) introducing a ranking score for buildings in a community to detect the candidates that can provide higher energy saving in the future events, (2) estimating the operation time of major energy-intensive appliances by analyzing the whole-house energy data using machine learning models, and (3) investigating the potential of achieving demand-supply balance in communities of buildings under the impact of different levels of solar panels, battery systems, and occupants energy consumption behavior. In the first study, a ranking score was introduced that analyzes the historical energy data from major loads such as washing machines and dishwashers in individual buildings and group the buildings based on their potential for energy saving at different times of the day. The proposed approach was investigated for real data of 400 buildings. The results for EV, washing machine, dishwasher, dryer, and AC show that the approach could successfully rank buildings by their demand reduction potential at critical times of the day. In the second study, machine learning (ML) frameworks were introduced to identify the times of the day that major energy-intensive appliances are operated. To do so, the input of the model was considered as the main circuit electricity information of the whole building either in lower-resolution data (smart meter data) or higher-resolution data (60Hz). Unlike previous studies that required considerable efforts for training the model (e.g, defining specific parameters for mathematical formulation of the appliance model), the aim was to develop data-driven approaches to learn the model either from the same building itself or from the neighbors that have appliance-level metering devices. For the lower-resolution data, the objective was that, if a few samples of buildings have already access to plug meters (i.e., appliance level data), one could estimate the operation time of major appliances through ML models by matching the energy behavior of the buildings, reflected in their smart meter information, with the ones in the neighborhood that have similar behaviors. For the higher-resolution data, an algorithm was introduced that extract the appliance signature (i.e., change in the pattern of electricity signal when an appliance is operated) to create a processed library and match the new events (i.e., times that an appliance is operated) by investigating the similarity with the ones in the processed library. The investigation on major appliances like AC, EV, dryer, and washing machine shows the >80% accuracy on standard performance metrics. In the third study, the impact of adding small-scale distributed resources to individual buildings (solar panels, battery, and users' practice in changing their energy consumption behavior) for matching the demand-supply for the communities was investigated. A community of ~250 buildings was considered to account for realistic uncertain energy behavior across households. It was shown that even when all buildings have a solar panel, during the afternoon times (after 4 pm) in which still ~30% of solar generation is possible, the community could not supply their demand. Furthermore, it was observed that including users' practice in changing their energy consumption behavior and battery could improve the utilization of solar energy around >10%-15%. The results can serve as a guideline for utilities and decision-makers to understand the impact of such different scenarios on improving the utilization of solar adoption. These series of studies in this dissertation contribute to the body of literature by introducing data-driven solutions/investigations for characterizing the energy behavior of households, which could increase the flexibility in energy consumption patterns.
Jennings, Brandon Douglas. "Leveraging smart system design to collect and analyze factory production data." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/117969.
Повний текст джерелаThesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, in conjunction with the Leaders for Global Operations Program at MIT, 2018.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 54-55).
Li & Fung deals with many factories that are very geographically dispersed. These facilities generally do not have the capital available to invest in new technologies and processes, and the extremely manual nature of garment fabrication is the standard as a result. As customers continue to demand quicker product turn-arounds and higher levels of customization, factories need to better understand their current process limitations in an effort to optimize their internal operations. Since most of these factories collect virtually no process data, managers have a hard time focusing on areas in which to improve. This project is approaching the question of "how can we use technology in a responsible and sustainable way to better understand our process?" from the perspective of a factory manager, who cannot necessarily invest in sophisticated software and hardware systems that other industries have adopted to monitor quality. As a result, this project focuses heavily on the user experience of both the operator (quality inspector) and the manager, as both need to be able to interact with the proposed data system easily and reliably. The primary goal of this thesis is to detail the design and implementation of a data collection platform (built during internship) for use in low-tech garment factories that will: -- Enable the procurement of process data (specifically as it relates to quality) from operators in real-time. -- Allow factory management to easily view and analyze collected data. -- Employ an intuitive front-end user interface that allows operators to quickly and reliably collect data. Since a substantial portion of this internship was spent designing, building, and testing this data collection interface, the thesis will reflect the nuances associated with building and implementing factory data systems in low-tech factories where human interaction is the primary driver of system adoption. The design and deployment of this system was ultimately successful and resulted in a robust prototype that continues to provide Li & Fung with insights into how to achieve their ultimate goal of connecting their factory network to a centralized data platform.
by Brandon Douglas Jennings.
M.B.A.
S.M.
Fonti, Alessandro. "Modelling approaches to smart buildings and smart districts for the definition of demand side management strategies and data models. The ENEA "Smart Village" case study." Doctoral thesis, Università Politecnica delle Marche, 2016. http://hdl.handle.net/11566/242982.
Повний текст джерелаEnergy consumption in buildings represents a challenge in the context of the reduction of greenhouse gas emissions and in a more efficient use of energy. An answer to this issue is the use of Demand side Management (DSM) systems which, through an increase in the use of technology, allow for the reduction of energy consumption. DSM systems need to be assessed during the design process by simulation tools. Moreover, they need simulation and predictive models if the control systems involved are advanced controls such as predictive or multilevel controls. With regards to multilevel controls, another important issue is the correct choice of the data model to properly structure the control systems. In this study, a real high-sensored Smart Village located in Rome composed of a smart building and a smart district of 8 buildings is taken into account. A Simulink simulator based on HAMbase is developed in order to model the building and district energy demands. The building simulator is calibrated and validated on real data taking into account the casual gain values as calibration parameters. The data are acquired in a period of 60 days during the winter of 2013. The optimal simulator configuration permits to obtain a MAPE on the daily transferred thermal energy less than 6%. Afterwards, a decision support system based on Pareto front multi-objective optimization combined with the smart building simulator is reported to show the model potential towards the definition of DSM policies. The simulator of the smart district is then derived directly from the building simulator by reprogramming the HAMbase s-function. This allows for multiple model instances in the same Simulink model. The district simulator is used to introduce the concept of data model in the context of smart districts. Finally, the accuracy of the low order grey-box models for short-term thermal behavior prediction is analyzed. An identification procedure is carried out on a real dataset acquired during the year 2015 from the sensors installed in a single building of the smart district. The identification shows that the second order resistance-capacitance (RC) models are the best choice in terms of accuracy and complexity.
Persson, Martin. "A Framework for Monitoring Data from a Smart Home Environment." Thesis, Luleå tekniska universitet, Institutionen för system- och rymdteknik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-79884.
Повний текст джерелаJan, Jonathan. "Collecting Data for Building Automation Analytics : A case study for collecting operational data with minimal human intervention." Thesis, KTH, Radio Systems Laboratory (RS Lab), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-233319.
Повний текст джерелаUngefär 40 % av den totala energikonsumtionen i E.U. och U.S.A. förbrukas av fastigheter. Om de delar av fastigheten som är ineffektiva enkelt kunde identifieras, skulle det underlätta fastighetsförvaltarnas arbete i att göra byggnader mer energisnåla. Detta har i sin tur potential att minska kostnader och byggnaders ekologiska fotavtryck. I dagens fastighetsautomationssystem samlas data in varje sekund, men på grund av att det saknas ett standardiserat sätt att beskriva den på, är det skillnad på att ha tillgång till data och att faktiskt kunna använda sig av den. Heterogeniteten gör att det blir både kostsamt och tidskrävande för fastighetsförvaltare att samla in data från sina fastigheter. Fastighetsförvaltare kan inte åtgärda något det inte kan se. Därför är det viktigt att underlätta möjligheten för visualisering av data från olika typer av fastighetsautomationssystem. Att lyckas med detta har potential att ge positiva effekter både när det gäller hållbarhet och ekonomi. I den här uppsatsen är författarens mål att komma fram till en hållbar, kostnads- och tidseffektiv integrationsstrategi för fastighetsförvaltare som vill få bättre insikter hur effektiv deras byggnad faktiskt är. Forskningsarbetet inleds med en litteraturstudie för att finna tidigare och pågående försök att lösa detta problem. Några initiativ för standardisering av semantiska modeller för att beskriva data inom fastighetsautomation hittades. Två av dessa, Brick och Project Haystack, valdes ut. En byggnad, och ett fastighetsautomationssystem testades i en pilotstudie. Resultaten från studien pekar på att data från fastighetautomationssystem kan integreras med en analysplattform, och en så kallad ETL-process, efter de engelska orden: extract, transform, load; presenteras för att uppnå det målet. Hur tidseffektivt data kan taggas och transformeras beror på det nuvarande kontrollsystemets datalagringsformat och om information om dess struktur är adekvat. Det noteras att det inte finns någon garanti till att få åtkomst till kontrollsystemets databas, eller information om dess struktur, därför presenteras även alternativa tekniker, däribland BACnet/IP och Open Platform Communications (OPC) Unified Architecture.
Martins, Daniel Filipe Catita. "Utilization of blockchain in the application of master data management." Master's thesis, Universidade de Aveiro, 2016. http://hdl.handle.net/10773/22724.
Повний текст джерелаAs the name implies, Master Data Management (MDM) manages Master Data: the set of core information needed and shared in the systems of an enterprise. Depending on the scope of the organization, master data could be data about clients if all the systems consider client information critical for their operations and decision making. A fundamental concept of MDM is the Golden Record, an entry with the best and more valuable information about an entity, formed through the application of rules and methods on the data that exists scattered over the systems. It is the single version of the truth. Master Data Management solutions are dependent of a centralized data hub that holds the most valuable information. The solution proposed disrupts the list of o ers, combining the Master Data Management concept with the Blockchain technology, resulting in an MDM solution with a distributed data hub that is truly decentralized. A Blockchain consists on a chain of blocks that requires computational work to attach new blocks to the end of the chain, and where blocks cannot be changed without redoing the computational e ort for all the following blocks, resulting on a trusted environment. Participants of the Blockchain network hold a full copy of the Blockchain, making it a distributed network of information. Its security protocols and requirements eliminate the need for an intermediary between transactions and make Blockchain ideal to save things of value. The solution involves Ethereum: a Blockchain platform where transactions have programmable functionality, known as Smart Contracts, pieces of code containing a set of data and executable functions that are available through the public address. As all the data inserted through the programmed Smart Contracts goes through the same speci c cleansing, matching and merging rules, all the network participants will be in possession of the same Golden Records, resulting in a single view of entity. To facilitate the utilization of the solution, a wrapper and user interface were developed, granting that the user does not need to interact directly with the Blockchain.
Como o nome indica, Master Data Management (MDM) gere dados mestre: o conjunto de informação nuclear necessária e partilhada pelos sistemas de uma empresa. Dependendo do âmbito da organização, dados mestre podem ser dados de clientes, caso todos os sistemas considerem a informação dos clientes critica para a suas operações e decisões. Um conceito fundamental de MDM e o Golden Record, um registo com a melhor e mais valiosa informação sobre uma determinada entidade, formada através da aplicação de regras e métodos nos dados existentes que estão espalhados pelos vários sistemas. E a versão única da verdade. As soluções de Master Data Management existentes são dependentes de um local centralizado onde fica guardada a informação mais valiosa. A solução proposta é distributiva para a lista de ofertas, combinando o conceito de Master Data Management com a tecnologia Blockchain, resultando numa solução MDM distribuída e descentralizada. A Blockchain consiste numa cadeia de blocos que requerem esforço computacional para que se adicionem novos blocos ao m da cadeia e onde os blocos não podem ser alterados sem repetir o esforço computacional para todos os blocos seguintes, o que resulta num ambiente conclave. Os participantes de uma rede Blockchain possuem uma copia total da Blockchain, tornando-a assim uma rede distribuída de informador. Os protocolos e requisitos de segurança eliminam a necessidade de existência de um intermediário entre as os participantes de uma transacção e tornam a Blockchain ideal para guardar objectos de valor. A soluçao envolve Ethereum: uma plataforma Blockchain onde as transaçoes tem funcionalidade programável, conhecida como Smart Contracts, pedaços de código que contêm um conjunto de dados e funções executáveis que estão disponíveis a partir de um endereço publico. Como todos os dados inseridos a partir de um Smart Contract est~ao sujeitos as mesmas regras especificas de standardizaçao, correspondência e fusão, todos os participantes da rede vão possuir os mesmos Golden Records, resultando na visão unica de entidade. Para facilitar a utilização da soluçao, foram desenvolvidos um wrapper e uma interface de utilizador, para permitir que o utilizador não necessite de interagir directamente com a Blockchain.
Söderberg, Anna, and Philip Dahlström. "Turning Smart Water Meter Data Into Useful Information : A case study on rental apartments in Södertälje." Thesis, KTH, Vattendragsteknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-217235.
Повний текст джерелаKoziel, Sylvie Evelyne. "From data collection to electric grid performance : How can data analytics support asset management decisions for an efficient transition toward smart grids?" Licentiate thesis, KTH, Elektroteknisk teori och konstruktion, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-292323.
Повний текст джерелаQC 20210330
Pisanò, Lorenzo. "IoT e Smart Irrigation: gestione dei Big Data attraverso un sistema di notifica intelligente." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/23531/.
Повний текст джерелаFitzgerald, Amy Lynn. "An exercise in database customized programming to compare the Smart Data Manager and dBaseIII." Thesis, Kansas State University, 1985. http://hdl.handle.net/2097/9838.
Повний текст джерелаLai, Tsz-wan, and 黎子雲. "The use of "Octopus" smart card in the secondary schooladministration." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2002. http://hub.hku.hk/bib/B4004029X.
Повний текст джерелаPrasannan, Sooraj. "A macro-micro system architecture analysis framework applied to Smart Grid meter data management systems by Sooraj Prasannan." Thesis, Massachusetts Institute of Technology, 2010. http://hdl.handle.net/1721.1/59009.
Повний текст джерелаThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student submitted PDF version of thesis.
Includes bibliographical references (p. 109-111).
This thesis proposes a framework for architectural analysis of a system at the Macro and Micro levels. The framework consists of two phases -- Formulation and Analysis. Formulation is made up of three steps -- Identifying the System Boundary, Identifying the Object-Process System levels using the Object-Process Methodology (OPM) and then creating the Dependency Matrix using a Design Structure Matrix (DSM). Analysis is composed of two steps -- Macro-Level and Micro-Level Analysis. Macro-Level analysis identifies the system modules and their interdependencies based on the OPM and DSM clustering analysis and Visibility-Dependency Signature Analysis. The Micro-Level analysis identifies the central components in the system based on the connectivity metrics of Indegree centrality, Outdegeree centrality, Visibility and Dependency. The conclusions are drawn based on simultaneously interpreting the results derived from the Macro-Level and Micro-Level Analysis. Macro-Analysis is vital in terms of comprehending system scalability and functionality. The modules and their interactions influence the scalability of the system while the absence of certain modules within a system might indicate missing system functionality. Micro-Analysis classifies the components in the system based on connectivity and can be used to guide redesign/design efforts. Understanding how the redesign of a particular node will affect the entire system helps in planning and implementation. On the other hand, design Modification/enhancement of nodes with low connectivity can be achieved without affecting the performance or architecture of the entire system. Identifying the highly central nodes also helps the system architect understand whether the system has enough redundancy built in to withstand the failure of the central nodes. Potential system bottlenecks can also be identified by using the micro-level analysis. The proposed framework is applied to two industry leading Smart Grid Meter Data Management Systems. Meter Data Management Systems are the central repository of meter data in the Smart Grid Information Technology Layer. Exponential growth is expected in managing electrical meter data and technology firms are very interested in finding ways to leverage the Smart Information Technology market. The thesis compares the two Meter Data Management System architectures, and proposes a generic Meter Data Management System by combining the strengths of the two architectures while identifying areas of collaboration between firms to leverage this generic architecture.
S.M.in System Design and Management
Stripling, Gwendolyn D. "An Empirical Assessment of Energy Management Information System Success Using Structural Equation Modeling." NSUWorks, 2017. http://nsuworks.nova.edu/gscis_etd/1019.
Повний текст джерелаKlasson, Anders, and Johan Rosengren. "Industrial IoT Management Systemfor Tubes with Integrated Sensors." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-237412.
Повний текст джерелаSandvik has developed a technique to place sensors inside tubes. This technology has great market potential and can optimize many industrial processes. The finished product should be able to stream sensor data to cloudservices for analysis and reading.The current system requires manual configuration on-site and the installation is labor intensive. This thesis investigates how the system’s hardware can be configured atomically, and how a supporting IT-system could function.A solution is presented where large portion of the installation process has been automated, along with an outline for a supporting system.The solution is evaluated by performing a measurement of the configuration complexity. The evaluation shows that the developed system had increased functionality compared to today’s manual configuration, configuration complexity was not increased. In many aspects, the configuration complexity was reduced.
Zhu, Junxiang. "Integration of Building Information Modelling and Geographic Information System at Data Level Using Semantics and Geometry Conversion Approach Towards Smart Infrastructure Management." Thesis, Curtin University, 2018. http://hdl.handle.net/20.500.11937/74945.
Повний текст джерелаBugeja, Joseph. "Smart connected homes : concepts, risks, and challenges." Licentiate thesis, Malmö universitet, Fakulteten för teknik och samhälle (TS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-7793.
Повний текст джерелаRAZZAK, FAISAL. "The Role of Semantic Web Technologies in Smart Environments." Doctoral thesis, Politecnico di Torino, 2013. http://hdl.handle.net/11583/2506366.
Повний текст джерелаErkki, Robert, and Philip Johnsson. "Quality Data Management in the Next Industrial Revolution : A Study of Prerequisites for Industry 4.0 at GKN Aerospace Sweden." Thesis, Luleå tekniska universitet, Institutionen för ekonomi, teknik och samhälle, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-69341.
Повний текст джерелаMohammad, Ammad Uddin. "UAV Routing Protocol (URP) for crop health management." Thesis, Brest, 2017. http://www.theses.fr/2017BRES0147/document.
Повний текст джерелаWireless sensor networks are now a credible means for crop data collection. The installation of a fixed communication structure to relay the monitored data from the cluster head to its final destination can either be impractical because of land topology or prohibitive due to high initial cost. A plausible solution is to use Unmanned Aerial Vehicles (UAV) as an alternative means for both data collection and limited supervisory control of sensors status. In this paper, we consider the case of disjoint farming parcels each including clusters of sensors, organized in a predetermined way according to farming objectives. This research focuses to drive an optimal solution for UAV search and data gathering from all sensors installed in a crop field. Furthermore, the sensor routing protocol will take into account a tradeoff between energy management and data dissemination overhead.The proposed system is evaluated by using a simulated model and it should find out a class among all under consideration
Weiss, Tobias, and Dorothea Reisbach. "Förderung der Kundeninteraktion zur Nutzung von Datenvisualisierungen auf Basis von Smart Metering im Privatkundenbereich." TUDpress, 2019. https://tud.qucosa.de/id/qucosa%3A36564.
Повний текст джерелаMassana, i. Raurich Joaquim. "Data-driven models for building energy efficiency monitoring." Doctoral thesis, Universitat de Girona, 2018. http://hdl.handle.net/10803/482148.
Повний текст джерелаA dia d’avui l’energia és un bé completament necessari arreu del món. Degut als avantatges que presenta en el transport i a les necessitats de les llars i la indústria, l’energia és transformada en energia elèctrica. Tenint en compte la total expansió i domini de l’electricitat, iniciatives com Horitzó 2020, tenen per objectiu un futur més sostenible: reduint les emissions de carboni i el consum i incrementant l’ús de renovables. Partint dels defectes de la xarxa elèctrica clàssica, com són gran distància al punt de consum, poca flexibilitat, baixa sostenibilitat, baixa qualitat de l’energia, dificultats per a emmagatzemar energia, etc. apareixen les Smart Grid (SG), una evolució natural de la xarxa clàssica. Un dels principals elements que permetrà a les SG millorar les xarxes clàssiques és l’Energy Management System (EMS). Així doncs, per a que l’EMS pugui dur a terme la gestió dels diversos elements, una de les necessitats bàsiques dels EMS serà un sistema de predicció, o sigui, saber per endavant quin consum hi haurà en un entorn determinat. A més, les empreses subministradores d’electricitat també requeriran de prediccions per a gestionar la generació, el manteniment i fins i tot les inversions a llarg termini. Així doncs ens calen sistemes de predicció del consum elèctric que, partint de les dades disponibles, ens subministrin el consum que hi haurà d’aquí a unes hores, uns dies o uns mesos, de la manera més aproximada possible. És dins d’aquest camp on s’ubica la recerca que presentem. Degut a la proliferació de xarxes de sensors i computadors més potents, s’han pogut desenvolupar sistemes de predicció més precisos. A tall de resum, en el primer treball, i tenint en compte que s’havia de conèixer en profunditat l’estat de la qüestió en relació a la predicció del consum elèctric, es va fer una anàlisi completa de l’estat de l’art. Un cop fet això, i partint del coneixement adquirit, en el segon treball es va dur a terme la instal•lació de les xarxes de sensors, la recollida de dades de consum i el modelatge amb models lineals d’auto-regressió (AR). En el tercer treball, un cop fets els models es va anar un pas més enllà recollint dades d’ocupació, de meteorologia i ambient interior, provant diferents models paradigmàtics com Multiple Linear Regression (MLR), Artificial Neural Network (ANN) i Support Vector Regression (SVR) i establint quines dades exògenes milloren la predicció dels models. Arribat a aquest punt, i havent corroborat que l’ús de dades d’ocupació millora la predicció, es van generar tècniques per tal de disposar de les dades d’ocupació per endavant, o sigui a hores vista. D’aquesta manera es van dissenyar diferents atributs d’ocupació artificials, permetent-nos fer prediccions horàries de consum a llarg termini. Aquests conceptes s’expliquen en profunditat al quart treball.
Tosto, Valentina. "Creazione di servizi personalizzati su dispositivi Android nell'ambito dell'Internet of Things collaborativo." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2016. http://amslaurea.unibo.it/12356/.
Повний текст джерелаPinarer, Ozgun. "Sustainable Declarative Monitoring Architecture : Energy optimization of interactions between application service oriented queries and wireless sensor devices : Application to Smart Buildings." Thesis, Lyon, 2017. http://www.theses.fr/2017LYSEI126/document.
Повний текст джерелаRecent researches and analysis reports declare that high energy consumption of buildings is major problem in developed countries. As a result, they show concretely that building energy management systems (BEMS) and deployed wireless sensor network environments are important for energy efficiency of building operations. In the literature, existing smart building management systems focus on energy consumption of the building, hardware deployed inside/outside of the building and network communication issues. They adopt static configurations for wireless sensor devices and proposed models are fitted to a single application. In this study, we propose a sustainable declarative monitoring architecture that focus on the energy optimisation of interactions between application service oriented queries and wireless sensor devices. We consider the monitoring system as a set of applications that exploit sensor measures in real time such as HVAC automation and control systems, real time supervision, security. These applications can be configured dynamically by the users or by the supervisor. In our approach, we take a data point of view: applications are declaratively expressed as a set of continuous queries on the sensor data stream. To achieve our objective of energy aware optimization of the monitoring architecture, we formalize sensor device configuration and fit data acquisition and data transmission to actual applications requirements. We present a complete monitoring architecture and an algorithm that handles dynamic sensor configuration. We introduce a platform that covers physical and also simulated wireless sensor devices
Hjälte, David. "Mot Industri 4.0 genom statistisk dataanalys : En studie om positionen av stansade hål vid Scania Ferruforms saidobalkstillverkning." Thesis, Luleå tekniska universitet, Institutionen för ekonomi, teknik, konst och samhälle, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-85648.
Повний текст джерелаThe fourth industrial revolution, also called Industry 4.0 is powered by several technologies which result in digitalization and automatization of industrial processes. The concept includes the application of big data and advanced analytics, which are said to provide great opportunities for quality improvements. For such a transition to take place, the ability to handle data is crucial. Despite this, many companies today show a lack of use of data to drive decision-making. The question is how companies can manage data and ultimately transition towards Industry 4.0. To research this topic this thesis has been carried out as a case study of a punching process at Scania Ferruform. Through a literature review, quantitative data collection, as well as observations and interviews, the thesis examined the current use of data in the process. Subsequently, data were examined with statistical tools to illustrate how data can be managed in a process to attain increased knowledge about causes of deviations. Lastly, the thesis explored future work towards Industry 4.0. Analysis tools have been used to analyse over 39 000 data points. The result of the study shows that there are opportunities for development in terms of collection, quality and use of data. A framework of how Ferruform should manage data in order to extract new knowledge from its processes is presented. Furthermore, an action plan is presented for a transition towards Industry 4.0. Finally, recommendations are given for further studies. The result of the thesis will be helpful for Ferruform in its transition towards more efficient processes and the technical development of which the company strives towards.
Kretek, František. "Smart Home - projekt inteligentního domu." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2014. http://www.nusl.cz/ntk/nusl-220641.
Повний текст джерелаSalama, Raghda Ahmed Abdelkerim. "Data-driven modeling of smart builiding energy management." Master's thesis, 2021. http://hdl.handle.net/10362/132389.
Повний текст джерелаGonçalves, Sandra de Jesus Pereira. "Data-driven disaster management in a smart city." Master's thesis, 2021. http://hdl.handle.net/10071/23563.
Повний текст джерелаOs desastres, tanto naturais quanto as provocadas pelo homem, são eventos complexos que se traduzem em perdas de vidas e/ou destruição de propriedades. Os avanços na área de Tecnologias de Informação e Big Data Analysis representam uma oportunidade para o desenvolvimento de ambientes resilientes dado que, a partir da aplicação das tecnologias de Big Data (BD), é possível não só extrair padrões de ocorrências dos eventos, mas também fazer a previsão dos mesmos. O trabalho realizado nesta dissertação visa aplicar a metodologia CRISP-DM de forma a conduzir análises descritivas e preditivas sobre os eventos que ocorreram na cidade de Lisboa, com ênfase nos eventos que afetaram os edifícios. A investigação permitiu verificar a existência de padrões temporais e espaciais eventos a ocorrer em certos períodos do ano, como é o caso das cheias e inundações que são registados com maior frequência nos períodos de alta precipitação. A análise espacial permitiu verificar que a área do centro da cidade é a área mais afetada pelas ocorrências sendo nestas áreas onde se concentram a maior proporção de edifícios com grandes necessidades de reparação. Por fim, modelos de aprendizagem automática foram aplicados aos dados tendo o modelo Random Forest obtido o melhor resultado com accuracy de 58%. Esta pesquisa contribui para melhorar o aumento da resiliência da cidade pois, a análise desenvolvida permitiu extrair insights sobre os eventos e os seus padrões de ocorrência que irá ajudar os processos de tomada de decisão.
Chiung-WenChang and 張瓊文. "On Data Analytics Framework of Smart-Project Management for Product Development." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/3mnk9v.
Повний текст джерела國立成功大學
工程管理碩士在職專班
106
Due to the advancement of technology, consumers' interests and needs for products are constantly changing. In order to obtain the market, enterprise must constantly develop new products to meet the changing and increasing needs of consumers. Therefore, New Product Development (NPD) is an important key activity of the enterprise and one of the strategies to create enterprise value and enhance competitive advantage. New product development is a multiplexed and complex technical application. The proportion of successful products is not high. Effective new product development requires a systematic process, appropriate methods and techniques, and effective management. As technologies such as computers, networks, socializing platform, and the IoT flourish, data-centric activities combine data science to maximize data value and create new knowledge value. With the big data, the artificial intelligent has become more and more mature, which has The rise of data science and AI has made the ideal of smart system gradually realized. New product development is a dynamic process and a system engineering producure. If we Can integrate data science methods and technologies into project management, we will make project management smart. The research uses data science concepts, methods, and techniques to design of Smart-Project Management for Product Development Model, according to this model design and planning Data Analytics Framework for Project Management and Analytics Method, using case to verify the analysis of the architecture and model is effective. This research will improve the performance of new product development, and thus enhance the company's competitiveness.
Ya-ChingChuang and 莊雅晴. "Smart Meter Management System for Microgrid Based on Data Distribution Service." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/c566s6.
Повний текст джерелаLeón, Palacio Ana. "SILE: A Method for the Efficient Management of Smart Genomic Information." Doctoral thesis, 2019. http://hdl.handle.net/10251/131698.
Повний текст джерела[CAT] Al llarg de les últimes dues dècades, les dades generades per les tecnologies de secuenciació de nova generació han revolucionat el nostre coneixement sobre la biologia humana. És mes, ens han permès desenvolupar i millorar el nostre coneixement sobre com els canvis (variacions) en l'ADN poden estar relacionats amb el risc de patir determinades malalties. Actualment, hi ha una gran quantitat de dades genòmiques disponibles de forma pública i que són consultats amb freqüència per la comunitat científica per a extraure conclusions significatives sobre les associacions entre gens de risc i els mecanismes que produeixen les malalties. No obstant això, el maneig d'aquesta quantitat de dades que creix de forma exponencial s'ha convertit en un repte i els investigadors es veuen obligats a submergir-se en un llac de dades molt complexes que estan dispersos en mes de mil repositoris heterogenis, representats en múltiples formats i amb diferents nivells de qualitat. A m\és, quan es tracta de resoldre una tasca en concret només una petita part de la gran quantitat de dades disponibles és realment significativa. Aquests són els que nosaltres anomenem dades "intel·ligents". El principal objectiu d'aquesta tesi és proposar un enfocament sistemàtic per al maneig eficient de dades genòmiques intel·ligents mitjançant l'ús de tècniques de modelatge conceptual i avaluació de la qualitat de les dades. Aquest enfocament està dirigit a poblar un sistema d'informació amb dades que siguen accessibles, informatius i útils per a l'extracció de coneixement de valor.
[EN] In the last two decades, the data generated by the Next Generation Sequencing Technologies have revolutionized our understanding about the human biology. Furthermore, they have allowed us to develop and improve our knowledge about how changes (variants) in the DNA can be related to the risk of developing certain diseases. Currently, a large amount of genomic data is publicly available and frequently used by the research community, in order to extract meaningful and reliable associations among risk genes and the mechanisms of disease. However, the management of this exponential growth of data has become a challenge and the researchers are forced to delve into a lake of complex data spread in over thousand heterogeneous repositories, represented in multiple formats and with different levels of quality. Nevertheless, when these data are used to solve a concrete problem only a small part of them is really significant. This is what we call "smart" data. The main goal of this thesis is to provide a systematic approach to efficiently manage smart genomic data, by using conceptual modeling techniques and the principles of data quality assessment. The aim of this approach is to populate an Information System with data that are accessible, informative and actionable enough to extract valuable knowledge.
This thesis was supported by the Research and Development Aid Program (PAID-01-16) under the FPI grant 2137.
León Palacio, A. (2019). SILE: A Method for the Efficient Management of Smart Genomic Information [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/131698
TESIS
Premiado
Liu, Chui-Yuan, and 劉騏源. "Design and Implementation of Cloud Data Integration Management for Smart Aquarium Device." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/36331735976512961997.
Повний текст джерела中原大學
電機工程研究所
105
In this thesis, we design and implement a smart aquarium system combining cloud data integration management with temperature control and aquarium water system to improve flexibility of aquarium water exchange system and user can understand the aquarium state through the smart control interface. In this smart aquarium integration control device with cloud data integration has six parts. First, the construction of cloud database is collection the aquarium state to upload google spreadsheet. Second, the temperature control by using PWM with TEC can make the aquarium temperature stably. Third, the real-time charts from cloud data can exchange the dashboard that makes users understand the state of the aquarium. Fourth, the streaming service real-time display can use website or smart phone to observe the aquarium state. Fifth, the smart aquarium water system can improve water quality in the aquarium. Finally, we experiment temperature and water quality to make a flow chart of cloud data managements system. In these studies, the contribution of the research is as follows: 1. We design aquarium water exchange system by flexibility smart UI control which can extend fish-life longer. 2. We integrate cloud of data and change to the dashboard which can make the user understand state in the aquarium. 3. We use Thermoelectric Cooler (TEC) to rapid heating by PWM technology in the aquarium. Keywords: Cloud data、smart control、streaming service、Thermoelectric Cooling Chip
YU, AN LIN, and 林宥安. "Using Big Data to Explore the Analysis of Smart Machine Management System." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/9ycf3k.
Повний текст джерела國立勤益科技大學
工業工程與管理系
106
The modern global industry is at the beginning of an era of innovation. Industry 4.0 combines machines, analysis, the Internet of Things (IoT), automation, and data exchange. We added communication capabilities to each device to connect the world of machines between devices and devices through the IoT, to establish a smart machine factory with resource efficiency and adaptability, provide perfect an after-sales service in business processes and value processes for integrating customers and business partners. In this study, we use WebAccess to let the production data and production status to do a charting, and the process can be monitored immediately to facilitate the transformation of the company in the future into a Small and Medium Enterprises with Industry 4.0 advanced technology. Through this mechanic analytical process technology, the mechanical industry can help factory to control the controllability of the robotic arm. The robotic arm is used to replace the traditional manual packaging mode. The robotic arm sucks and removes the plastic blister, grab the color pen, to pick up the paper card to insert in the plastic blister. During the process, the inspection data are transmitted to WebAccess. WebAccess collects test the production loop data and the data mining screens a large number of data and then discards the data to the inverted transmission neural network for analysis to examine the model of the production rate. Finally, the two-stage clustering method is used to verify the consistent rate. The planning of production processes through data models to achieving consistency, it can also reduce the production risks and reduce personnel costs, evolution to a lights-out manufacturing.
Taghipour, Dizaji Roshanak. "Acquiring Multimodal Disaggregate Travel Behavior Data Using Smart Phones." Thesis, 2013. http://hdl.handle.net/10012/7304.
Повний текст джерелаFirmino, Bruno Manuel Paias. "Smart Monetization - Telecom Revenue Management beyond the traditional invoice." Master's thesis, 2019. http://hdl.handle.net/10362/113609.
Повний текст джерелаWU, DE-CHANG, and 鄔德昌. "A Study on Transformer Load Management by Utilizing Smart Meter Data of Low Voltage Customers." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/17482010491175551068.
Повний текст джерела國立高雄海洋科技大學
輪機工程研究所
105
Utilities are beginning to turn to smart metering value-added application technologies to improve distribution system operations. The aim of this paper is to build a set of analysis model for value-added application of distribution transformer load management for low voltage (LV) smart metering. The low voltage network state estimation is used to obtain an estimate in transformer load with data in customer information system. The result is used for transformer load monitoring using data visualization technique accordingly. The proposed method can assist utilities in transformer load management for identifying assets requiring replacement as they reach the end of their useful life.