Дисертації з теми "Energy Efficient Machine Learning System"
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OSTA, MARIO. "Energy-efficient embedded machine learning algorithms for smart sensing systems." Doctoral thesis, Università degli studi di Genova, 2020. http://hdl.handle.net/11567/997732.
Повний текст джерелаAzmat, Freeha. "Machine learning and energy efficient cognitive radio." Thesis, University of Warwick, 2016. http://wrap.warwick.ac.uk/85990/.
Повний текст джерелаGarcía-Martín, Eva. "Extraction and Energy Efficient Processing of Streaming Data." Licentiate thesis, Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-15532.
Повний текст джерелаScalable resource-efficient systems for big data analytics
Harmer, Keith. "An energy efficient brushless drive system for a domestic washing machine." Thesis, University of Sheffield, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.265571.
Повний текст джерелаCui, Henggang. "Exploiting Application Characteristics for Efficient System Support of Data-Parallel Machine Learning." Research Showcase @ CMU, 2017. http://repository.cmu.edu/dissertations/908.
Повний текст джерелаLe, Borgne Yann-Aël. "Learning in wireless sensor networks for energy-efficient environmental monitoring." Doctoral thesis, Universite Libre de Bruxelles, 2009. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/210334.
Повний текст джерелаIn environmental monitoring studies, many applications are expected to run unattended for months or years. Sensor nodes are however constrained by limited resources, particularly in terms of energy. Since communication is one order of magnitude more energy-consuming than processing, the design of data collection schemes that limit the amount of transmitted data is therefore recognized as a central issue for wireless sensor networks.
An efficient way to address this challenge is to approximate, by means of mathematical models, the evolution of the measurements taken by sensors over space and/or time. Indeed, whenever a mathematical model may be used in place of the true measurements, significant gains in communications may be obtained by only transmitting the parameters of the model instead of the set of real measurements. Since in most cases there is little or no a priori information about the variations taken by sensor measurements, the models must be identified in an automated manner. This calls for the use of machine learning techniques, which allow to model the variations of future measurements on the basis of past measurements.
This thesis brings two main contributions to the use of learning techniques in a sensor network. First, we propose an approach which combines time series prediction and model selection for reducing the amount of communication. The rationale of this approach, called adaptive model selection, is to let the sensors determine in an automated manner a prediction model that does not only fits their measurements, but that also reduces the amount of transmitted data.
The second main contribution is the design of a distributed approach for modeling sensed data, based on the principal component analysis (PCA). The proposed method allows to transform along a routing tree the measurements taken in such a way that (i) most of the variability in the measurements is retained, and (ii) the network load sustained by sensor nodes is reduced and more evenly distributed, which in turn extends the overall network lifetime. The framework can be seen as a truly distributed approach for the principal component analysis, and finds applications not only for approximated data collection tasks, but also for event detection or recognition tasks.
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Les réseaux de capteurs sans fil forment une nouvelle famille de systèmes informatiques permettant d'observer le monde avec une résolution sans précédent. En particulier, ces systèmes promettent de révolutionner le domaine de l'étude environnementale. Un tel réseau est composé d'un ensemble de capteurs sans fil, ou unités sensorielles, capables de collecter, traiter, et transmettre de l'information. Grâce aux avancées dans les domaines de la microélectronique et des technologies sans fil, ces systèmes sont à la fois peu volumineux et peu coûteux. Ceci permet leurs deploiements dans différents types d'environnements, afin d'observer l'évolution dans le temps et l'espace de quantités physiques telles que la température, l'humidité, la lumière ou le son.
Dans le domaine de l'étude environnementale, les systèmes de prise de mesures doivent souvent fonctionner de manière autonome pendant plusieurs mois ou plusieurs années. Les capteurs sans fil ont cependant des ressources limitées, particulièrement en terme d'énergie. Les communications radios étant d'un ordre de grandeur plus coûteuses en énergie que l'utilisation du processeur, la conception de méthodes de collecte de données limitant la transmission de données est devenue l'un des principaux défis soulevés par cette technologie.
Ce défi peut être abordé de manière efficace par l'utilisation de modèles mathématiques modélisant l'évolution spatiotemporelle des mesures prises par les capteurs. En effet, si un tel modèle peut être utilisé à la place des mesures, d'importants gains en communications peuvent être obtenus en utilisant les paramètres du modèle comme substitut des mesures. Cependant, dans la majorité des cas, peu ou aucune information sur la nature des mesures prises par les capteurs ne sont disponibles, et donc aucun modèle ne peut être a priori défini. Dans ces cas, les techniques issues du domaine de l'apprentissage machine sont particulièrement appropriées. Ces techniques ont pour but de créer ces modèles de façon autonome, en anticipant les mesures à venir sur la base des mesures passées.
Dans cette thèse, deux contributions sont principalement apportées permettant l'applica-tion de techniques d'apprentissage machine dans le domaine des réseaux de capteurs sans fil. Premièrement, nous proposons une approche qui combine la prédiction de série temporelle avec la sélection de modèles afin de réduire la communication. La logique de cette approche, appelée sélection de modèle adaptive, est de permettre aux unités sensorielles de determiner de manière autonome un modèle de prédiction qui anticipe correctement leurs mesures, tout en réduisant l'utilisation de leur radio.
Deuxièmement, nous avons conçu une méthode permettant de modéliser de façon distribuée les mesures collectées, qui se base sur l'analyse en composantes principales (ACP). La méthode permet de transformer les mesures le long d'un arbre de routage, de façon à ce que (i) la majeure partie des variations dans les mesures des capteurs soient conservées, et (ii) la charge réseau soit réduite et mieux distribuée, ce qui permet d'augmenter également la durée de vie du réseau. L'approche proposée permet de véritablement distribuer l'ACP, et peut être utilisée pour des applications impliquant la collecte de données, mais également pour la détection ou la classification d'événements.
Doctorat en Sciences
info:eu-repo/semantics/nonPublished
Yurur, Ozgur. "Energy Efficient Context-Aware Framework in Mobile Sensing." Scholar Commons, 2013. http://scholarcommons.usf.edu/etd/4797.
Повний текст джерелаWestphal, Florian. "Efficient Document Image Binarization using Heterogeneous Computing and Interactive Machine Learning." Licentiate thesis, Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-16797.
Повний текст джерелаScalable resource-efficient systems for big data analytics
Chakraborty, Debaditya. "Detection of Faults in HVAC Systems using Tree-based Ensemble Models and Dynamic Thresholds." University of Cincinnati / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1543582336141076.
Повний текст джерелаSala, Cardoso Enric. "Advanced energy management strategies for HVAC systems in smart buildings." Doctoral thesis, Universitat Politècnica de Catalunya, 2019. http://hdl.handle.net/10803/668528.
Повний текст джерелаL’eficàcia dels sistemes de gestió d’energia per afrontar el consum d’energia en edificis és un tema que ha rebut un interès en augment durant els darrers anys a causa de la creixent demanda global d’energia i del gran percentatge d’energia que n’utilitzen actualment els edificis. L’escala d’aquest sector ha atret l'atenció de nombrosa investigació amb l’objectiu de descobrir possibles vies de millora i materialitzar-les amb l’ajuda de recents avenços tecnològics que es podrien aprofitar per disminuir les necessitats energètiques dels edificis. Concretament, en l’àrea d’instal·lacions de calefacció, ventilació i climatització, la disponibilitat de grans bases de dades històriques als sistemes de gestió d’edificis fa possible l’estudi de com d'eficients són realment aquests sistemes quan s’encarreguen d'assegurar el confort dels seus ocupants. En realitat, informes recents indiquen que hi ha una diferència entre el rendiment operatiu ideal i el rendiment generalment assolit a la pràctica. En conseqüència, aquesta tesi considera la investigació de noves estratègies de gestió de l’energia per a instal·lacions de calefacció, ventilació i climatització en edificis, destinades a reduir la diferència de rendiment mitjançant l’ús de mètodes basats en dades per tal d'augmentar el seu coneixement contextual, permetent als sistemes de gestió dirigir l’operació cap a zones de treball amb un rendiment superior. Això inclou tant l’avanç de metodologies de modelat capaces d’extreure coneixement de bases de dades de comportaments històrics d’edificis a través de la previsió de càrregues de consum i l’estimació del rendiment operatiu dels equips que recolzin la identificació del context operatiu i de les necessitats energètiques d’un edifici, tant com del desenvolupament d’una estratègia d’optimització multi-objectiu generalitzable per tal de minimitzar el consum d’energia mentre es satisfan aquestes necessitats energètiques. Els resultats experimentals obtinguts a partir de la implementació de les metodologies desenvolupades mostren un potencial important per augmentar l'eficiència energètica dels sistemes de climatització, mentre que són prou genèrics com per permetre el seu ús en diferents instal·lacions i suportant equips diversos. En conclusió, durant aquesta tesi es va desenvolupar, implementar i validar un marc d’anàlisi i actuació complet mitjançant una base de dades experimental adquirida en una planta pilot durant el període d’investigació de la tesi. Els resultats obtinguts demostren l’eficàcia de les contribucions de manera individual i, en conjunt, representen una solució idònia per ajudar a augmentar el rendiment de les instal·lacions de climatització sense afectar el confort dels seus ocupants
Tirumalareddy, Rohan Reddy. "BLE Beacon Based Indoor Positioning System in an Office Building using Machine Learning." Thesis, Blekinge Tekniska Högskola, Institutionen för datavetenskap, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-20221.
Повний текст джерелаGoutham, Mithun. "Machine learning based user activity prediction for smart homes." The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1595493258565743.
Повний текст джерелаHayward, Ross. "Analytic and inductive learning in an efficient connectionist rule-based reasoning system." Thesis, Queensland University of Technology, 2001.
Знайти повний текст джерелаLundström, Dennis. "Data-efficient Transfer Learning with Pre-trained Networks." Thesis, Linköpings universitet, Datorseende, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-138612.
Повний текст джерелаZayene, Mariem. "Cooperative data exchange for wireless networks : Delay-aware and energy-efficient approaches." Thesis, Limoges, 2019. http://www.theses.fr/2019LIMO0033/document.
Повний текст джерелаWith significantly growing number of smart low-power devices during recent years, the issue of energy efficiency has taken an increasingly essential role in the communication systems’ design. This thesis aims at designing distributed and energy efficient transmission schemes for wireless networks using game theory and instantly decodable network coding (IDNC) which is a promising network coding subclass. We study the cooperative data exchange (CDE) scenario in which all devices cooperate with each other by exchanging network coded packets until all of them receive all the required information. In fact, enabling the IDNC-based CDE setting brings several challenges such us how to extend the network lifetime and how to reduce the number of transmissions in order to satisfy urgent delay requirements. Therefore, unlike most of existing works concerning IDNC, we focus not only on the decoding delay, but also the consumed energy. First, we investigate the IDNC-based CDE problem within small fully connected networks across energy-constrained devices and model the problem using the cooperative game theory in partition form. We propose a distributed merge-and-split algorithm to allow the wireless nodes to self-organize into independent disjoint coalitions in a distributed manner. The proposed algorithm guarantees reduced energy consumption and minimizes the delay in the resulting clustered network structure. We do not only consider the transmission energy, but also the computational energy consumption. Furthermore, we focus on the mobility issue and we analyse how, in the proposed framework, nodes can adapt to the dynamic topology of the network. Thereafter, we study the IDNC-based CDE problem within large-scale partially connected networks. We considerate that each player uses no longer his maximum transmission power, rather, he controls his transmission range dynamically. In fact, we investigate multi-hop CDE using the IDNC at decentralized wireless nodes. In such model, we focus on how these wireless nodes can cooperate in limited transmission ranges without increasing the IDNC delay nor their energy consumption. For that purpose, we model the problem using a two-stage game theoretical framework. We first model the power control problem using non-cooperative game theory where users jointly choose their desired transmission power selfishly in order to reduce their energy consumption and their IDNC delay. The optimal solution of this game allows the players at the next stage to cooperate with each other through limited transmission ranges using cooperative game theory in partition form. Thereafter, a distributed multihop merge-and-split algorithm is defined to form coalitions where players maximize their utilities in terms of decoding delays and energy consumption. The solution of the proposed framework determines a stable feasible partition for the wireless nodes with reduced interference and reasonable complexity. We demonstrate that the co-operation between nodes in the multihop cooperative scheme achieves a significant minimization of the energy consumption with respect to the most stable cooperative scheme in maximum transmission range without hurting the IDNC delay
Kheffache, Mansour. "Energy-Efficient Detection of Atrial Fibrillation in the Context of Resource-Restrained Devices." Thesis, Luleå tekniska universitet, Datavetenskap, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-76394.
Повний текст джерелаMontgomery, Weston C. "Implementing a Data Acquisition System for the Training of Cloud Coverage Neural Networks." DigitalCommons@CalPoly, 2021. https://digitalcommons.calpoly.edu/theses/2310.
Повний текст джерелаKanwal, Summrina. "Towards a novel medical diagnosis system for clinical decision support system applications." Thesis, University of Stirling, 2016. http://hdl.handle.net/1893/25397.
Повний текст джерелаMämmelä, O. (Olli). "Algorithms for efficient and energy-aware network resource management in autonomous communications systems." Doctoral thesis, Oulun yliopisto, 2017. http://urn.fi/urn:isbn:9789526216089.
Повний текст джерелаTiivistelmä Langattoman tietoliikenteen nopean kasvun ennustetaan jatkuvan edelleen lähivuosinakin ja alan teollisuuden arvioiden mukaan matkapuhelinliikenteen määrä ylittäisi globaalisti 30,6 eksatavua vuoteen 2020 mennessä. Tämä tarkoittaisi liikennemäärän kahdeksankertaistumista ajanjaksolla 2015–2020. Älypuhelimet tuottavat suurimman osan matkapuhelinliikenteestä, ja älypuhelimien lukumäärän arvioidaan jatkavan kasvuaan vuoteen 2020 saakka, mikä johtaa nopeaan liikenteen kasvuun. Tämän lisäksi arvioidaan, että 5G verkot ja esineiden Internet tuottavat suuren määrän verkkoliikennettä. Matkapuhelinliikenteen ja laitteiden määrän kasvu tuo haasteita verkko-operaattoreille, palvelun tarjoajille, ja datakeskusoperaattoreille. Mikäli verkossa ei ole tarpeeksi siirtokapasiteettia dataliikenteen määrää varten, verkko ruuhkautuu ja lopulta palvelukokemus kärsii. Matkapuhelinverkot tulevat myös tulevaisuudessa tarvitsemaan datakeskusten laskentakapasiteettia. Datakeskusten energiankulutus on kuitenkin kasvanut viime vuosina, mikä on ongelma datakeskusoperaattoreille. Perinteinen strategia ongelmien ratkaisemiseksi on lisätä resurssien määrää tai tarjota tehokkaampaa laitteistoa. Resurssien liiallinen lisääminen kasvattaa kuitenkin sekä käyttö- että pääomakustannuksia ilman takuuta siitä, että keskimääräinen myyntitulo per käyttäjä kasvaisi. Tämän lisäksi tietoliikennejärjestelmät ovat monimutkaisia ja dynaamisia järjestelmiä, minkä vuoksi tehokas resurssienhallinta on haastavaa. Tämän vuoksi tarvitaan älykkäitä ja kestäviä metodeja, jotka pystyvät käyttämään olemassa olevia resursseja tehokkaasti tekemällä autonomisia päätöksiä ilman ylläpitäjän väliintuloa. Tämän tutkimuksen tavoitteena on toteuttaa, kehittää, mallintaa, ja testata algoritmeja, jotka mahdollistavat tehokkaan ja energiatietoisen verkkoresurssien hallinnan autonomisissa tietoliikennejärjestelmissä. Tutkimus esittää aluksi supertietokonedatakeskuksiin energiatietoisen algoritmin, jonka avulla voidaan vähentää energiankulutusta yhden datakeskuksen sisällä sekä usean eri datakeskuksen välillä. Verkkoyhteyden valintaan heterogeenisissä langattomissa verkoissa esitetään kaksi algoritmia. Ensimmäinen on käyttäjäkohtainen algoritmi, joka pyrkii optimoimaan yksittäisen käyttäjän palvelukokemusta. Toinen on verkon puolen algoritmi, joka keskittyy optimoimaan verkon kokonaisresurssien käyttöä. Lopuksi esitetään videopalvelulle algoritmi, joka parantaa videosisällön jakoa kontrolloidusti ja resurssitehokkaasti ilman että matkapuhelinverkon infrastruktuurille tarvitaan muutoksia
Bogdanov, Daniil. "The development and analysis of a computationally efficient data driven suit jacket fit recommendation system." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-222341.
Повний текст джерелаI detta masterexamensarbete designar och analyserar vi ett datadrivet rekommendationssystem för kavajer med mål att vägleda nät-handlare i deras process i att bedöma passform över internet. Systemet är uppdelat i två steg. I det första steget analyserar vi märkt data och tränar modeller i att lära sig att framställa prognoser av optimala kavajmått för shoppare som inte systemet har tidigare exponeras för. I steg två tar rekommendationssystemet resultatet ifrån steg ett och sorterar plaggkollektionen från bästa till sämsta passform. Den sorterade kollektionen är vad systemet är tänkt att retunera. I detta arbete föreslåar vi en specifik utformning gällande steg två med mål att reducera komplexiteten av systemet men till en kostnad i noggrannhet vad det gäller resultat. För- och nackdelar identifieras och vägs mot varandra. Resultatet i steg två visar att enkla modeller med linjära regressionsfunktioner räcker när de obereoende och beroende variabler sammanfaller på specifika punkter på kroppen. Om stil-preferenser också vill inkorpereras i dessa modeller bör icke-linjära regressionsfunktioner betraktas för att redogöra för den ökade komplexitet som medföljer. Resultaten i steg två visar att komplexiteten av rekommendationssystemet kan göras obereoende av komplexiteten för hur passform bedöms. Och då teknologin möjliggör för allt mer avancerade sätt att bedöma passform, såsom 3D-scannings tekniker, kan mer komplexa tekniker utnyttjas utan att påverka responstiden för systemet under körtid.
Leyva, Mayorga Israel. "On reliable and energy efficient massive wireless communications: the road to 5G." Doctoral thesis, Universitat Politècnica de València, 2019. http://hdl.handle.net/10251/115484.
Повний текст джерелаLa cinquena generació de xarxes mòbils (5G) es troba molt a la vora. S'espera que proveïsca de beneficis extraordinaris a la població i que resolga la majoria dels problemes de les xarxes 4G actuals. L'èxit de 5G, per a la qual ja ha sigut completada la primera fase del qual d'estandardització, depén de tres pilars: comunicacions tipus-màquina massives, banda ampla mòbil millorada, i comunicacions ultra fiables i de baixa latència (mMTC, eMBB i URLLC, respectivament, per les seues sigles en anglés). En aquesta tesi ens enfoquem en el primer pilar de 5G, mMTC, però també proveïm una solució per a aconseguir eMBB en escenaris de distribució massiva de continguts. Específicament, les principals contribucions són en les àrees de: 1) suport eficient de mMTC en xarxes cel·lulars; 2) accés aleatori per al report d'esdeveniments en xarxes sense fils de sensors (WSNs); i 3) cooperació per a la distribució massiva de continguts en xarxes cel·lulars. En l'apartat de mMTC en xarxes cel·lulars, aquesta tesi realitza una anàlisi profunda de l'acompliment del procediment d'accés aleatori, que és la forma mitjançant la qual els dispositius mòbils accedeixen a la xarxa. Aquestes anàlisis van ser inicialment dutes per mitjà de simulacions i, posteriorment, per mitjà d'un model analític. Els models van ser desenvolupats específicament per a aquest propòsit i inclouen un dels esquemes de control d'accés més prometedors: el access class barring (ACB). El nostre model és un dels més precisos que es poden trobar i l'únic que incorpora l'esquema d'ACB. Els resultats obtinguts per mitjà d'aquest model i per simulació són clars: els accessos altament sincronitzats que ocorren en aplicacions de mMTC poden causar congestió severa en el canal d'accés. D'altra banda, també són clars en què aquesta congestió es pot previndre amb una adequada configuració de l'ACB. No obstant això, els paràmetres de configuració de l'ACB han de ser contínuament adaptats a la intensitat d'accessos per a poder obtindre unes prestacions òptimes. En la tesi es proposa una solució pràctica a aquest problema en la forma d'un esquema de configuració automàtica per a l'ACB; l'anomenem ACBC. Els resultats mostren que el nostre esquema pot aconseguir un acompliment molt proper a l'òptim sense importar la intensitat dels accessos. Així mateix, pot ser directament implementat en xarxes cel·lulars per a suportar el trànsit mMTC, ja que ha sigut dissenyat tenint en compte els estàndards del 3GPP. A més de les anàlisis descrites anteriorment per a xarxes cel·lulars, es realitza una anàlisi general per a aplicacions de comptadors intel·ligents. És a dir, estudiem un escenari de mMTC des de la perspectiva de les WSNs. Específicament, desenvolupem un model híbrid per a l'anàlisi de prestacions i l'optimització de protocols de WSNs d'accés aleatori i basats en clúster. Els resultats mostren la utilitat d'escoltar el mitjà sense fil per a minimitzar el nombre de transmissions i també de modificar les probabilitats de transmissió després d'una col·lisió. Pel que fa a eMBB, ens enfoquem en un escenari de distribució massiva de continguts, en el qual un mateix contingut és enviat de forma simultània a un gran nombre d'usuaris mòbils. Aquest escenari és problemàtic, ja que les estacions base de la xarxa cel·lular no compten amb mecanismes eficients de multicast o broadcast. Per tant, la solució que s'adopta comunament és la de replicar el contingut per a cadascun dels usuaris que ho sol·liciten; és clar que això és altament ineficient. Per a resoldre aquest problema, proposem l'ús d'esquemes de network coding i d'arquitectures cooperatives anomenades núvols mòbils. En concret, desenvolupem un protocol per a realitzar la distribució massiva de continguts de forma eficient, juntament amb un model analític per a la seua optimització. Els resultats demostren que el model proposat és simple i precís
The 5th generation (5G) of mobile networks is just around the corner. It is expected to bring extraordinary benefits to the population and to solve the majority of the problems of current 4th generation (4G) systems. The success of 5G, whose first phase of standardization has concluded, relies in three pillars that correspond to its main use cases: massive machine-type communication (mMTC), enhanced mobile broadband (eMBB), and ultra-reliable low latency communication (URLLC). This thesis mainly focuses on the first pillar of 5G: mMTC, but also provides a solution for the eMBB in massive content delivery scenarios. Specifically, its main contributions are in the areas of: 1) efficient support of mMTC in cellular networks; 2) random access (RA) event-reporting in wireless sensor networks (WSNs); and 3) cooperative massive content delivery in cellular networks. Regarding mMTC in cellular networks, this thesis provides a thorough performance analysis of the RA procedure (RAP), used by the mobile devices to switch from idle to connected mode. These analyses were first conducted by simulation and then by an analytical model; both of these were developed with this specific purpose and include one of the most promising access control schemes: the access class barring (ACB). To the best of our knowledge, this is one of the most accurate analytical models reported in the literature and the only one that incorporates the ACB scheme. Our results clearly show that the highly-synchronized accesses that occur in mMTC applications can lead to severe congestion. On the other hand, it is also clear that congestion can be prevented with an adequate configuration of the ACB scheme. However, the configuration parameters of the ACB scheme must be continuously adapted to the intensity of access attempts if an optimal performance is to be obtained. We developed a practical solution to this problem in the form of a scheme to automatically configure the ACB; we call it access class barring configuration (ACBC) scheme. The results show that our ACBC scheme leads to a near-optimal performance regardless of the intensity of access attempts. Furthermore, it can be directly implemented in 3rd Generation Partnership Project (3GPP) cellular systems to efficiently handle mMTC because it has been designed to comply with the 3GPP standards. In addition to the analyses described above for cellular networks, a general analysis for smart metering applications is performed. That is, we study an mMTC scenario from the perspective of event detection and reporting WSNs. Specifically, we provide a hybrid model for the performance analysis and optimization of cluster-based RA WSN protocols. Results showcase the utility of overhearing to minimize the number of packet transmissions, but also of the adaptation of transmission parameters after a collision occurs. Building on this, we are able to provide some guidelines that can drastically increase the performance of a wide range of RA protocols and systems in event reporting applications. Regarding eMBB, we focus on a massive content delivery scenario in which the exact same content is transmitted to a large number of mobile users simultaneously. Such a scenario may arise, for example, with video streaming services that offer a particularly popular content. This is a problematic scenario because cellular base stations have no efficient multicast or broadcast mechanisms. Hence, the traditional solution is to replicate the content for each requesting user, which is highly inefficient. To solve this problem, we propose the use of network coding (NC) schemes in combination with cooperative architectures named mobile clouds (MCs). Specifically, we develop a protocol for efficient massive content delivery, along with the analytical model for its optimization. Results show the proposed model is simple and accurate, and the protocol can lead to energy savings of up to 37 percent when compared to the traditional approach.
Leyva Mayorga, I. (2018). On reliable and energy efficient massive wireless communications: the road to 5G [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/115484
TESIS
Ballivian, Sergio Marlon. "Anonymous Indoor Positioning System using Depth Sensors for Context-aware Human-Building Interaction." Thesis, Virginia Tech, 2019. http://hdl.handle.net/10919/89612.
Повний текст джерелаMaster of Science
Although Global Positioning System (GPS) has a satisfactory performance navigating outdoors, it fails in indoor environments due to the line of sight requirements. Physical obstacles such as walls, overhead floors, and roofs weaken GPS functionality in closed environments. This limitation has opened a new direction of studies, technologies, and research efforts to create indoor location sensing capabilities. In this study, we have explored the feasibility of using an indoor positioning system that seeks to detect occupants’ location and preferences accurately without raising privacy concerns. Context-aware systems were created to learn dynamics of interactions between human and buildings, examples are sensing, localizing, and distinguishing individuals. An example application is to enable a responsive air-conditioning system to adapt to personalized thermal preferences of occupants in an indoor environment as they move across spaces. To this end, we have proposed to leverage depth sensing technology, such as Microsoft Kinect sensor, that could provide information on human activities and unique skeletal attributes for identification. The proposed sensing technology could enable the inference of people location and preferences at any time and their activity levels across different indoor spaces. This system could be used for sustainable operations in buildings by detecting unoccupied rooms in buildings to save energy and reduce the cost of heating, lighting or air conditioning equipment by delivering air conditioning according to the preferences of occupants. This thesis has explored the feasibility and challenges of using depth-sensing technology for the aforementioned objectives. In doing so, we have conducted experimental studies, as well as data analyses, using different scenarios for human-environment interactions. The results have shown that we could achieve an acceptable level of accuracy in detecting individuals across different spaces for different actions.
Ahmed, Omar W. "Enhanced flare prediction by advanced feature extraction from solar images : developing automated imaging and machine learning techniques for processing solar images and extracting features from active regions to enable the efficient prediction of solar flares." Thesis, University of Bradford, 2011. http://hdl.handle.net/10454/5407.
Повний текст джерелаAhmed, Omar Wahab. "Enhanced flare prediction by advanced feature extraction from solar images : developing automated imaging and machine learning techniques for processing solar images and extracting features from active regions to enable the efficient prediction of solar flares." Thesis, University of Bradford, 2011. http://hdl.handle.net/10454/5407.
Повний текст джерелаKurén, Jonathan, Simon Leijon, Petter Sigfridsson, and Hampus Widén. "Fault Detection AI For Solar Panels." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-413319.
Повний текст джерелаMed en ökande användning av solpaneler runt om i världen ökar även betydelsen av att kunna upptäcka driftstörningar i panelerna. Genom att utnyttja den historiska uteffekten (kWh) från solpaneler samt meteorologisk data används maskininlärningsmodeller för att förutspå den förväntade uteffekten för ett givet solpanelssystem. Den förväntade uteffekten används sedan i en jämförelse med den faktiska uteffekten för att upptäcka om en driftstörning har uppstått i systemet. Resultatet av att använda den här metoden är att en förväntad uteffekt som efterliknar den faktiska uteffekten modelleras. Följaktligen, när ett fel simuleras (50% minskning av uteffekt), så är det möjligt för systemet att hitta alla introducerade fel vid analys över ett tidsspann på två veckor. Dessa resultat visar att det är möjligt att modellera en förväntad uteffekt av ett solpanelssystem med en maskininlärningsmodell och att använda den för att utvärdera om systemet producerar så mycket uteffekt som det bör göra. Systemet kan förbättras på några vis där tilläggandet av fler meteorologiska parametrar, öka precision av den meteorologiska datan och träna maskininlärningsmodellen på mer data är några möjligheter.
Mpawenimana, Innocent. "Modélisation et conception d’objets connectés au service des maisons intelligentes : Évaluation et optimisation de leur autonomie et de leur QoS." Thesis, Université Côte d'Azur, 2020. http://www.theses.fr/2020COAZ4107.
Повний текст джерелаThis PhD thesis is in the field of smart homes, and more specifically in the energy consumption optimization process for a home having an ambient energy source harvesting and storage system. The objective is to propose services to handle the household energy consumption and to promote self-consumption. To do so, relevant data must be first collected (current, active and reactive power consumption, temperature and so on). In this PhD, data have been first sensed using an intrusive load approach. Despite our efforts to build our own data base, we decided to use an online available dataset for the rest of this study. Different supervised machine learning algorithms have been evaluated from this dataset to identify home appliances with accuracy. Obtained results showed that only active and reactive power can be used for that purpose. To further optimize the accuracy, we proposed to use a moving average function for reducing the random variations in the observations. A non-intrusive load approach has been finally adopted to rather determine the global household active energy consumption. Using an online existing dataset, a machine learning algorithm based on Long Short-Term Memory (LSTM) has then been proposed to predict, over different time scale, the global household consumed energy. Long Short-Term Memory was also used to predict, for different weather profiles, the power that can be harvested from solar cells. Those predictions of consumed and harvested energy have been finally exploited by a Home Energy Management policy optimizing self-consumption. Simulation results show that the size of the solar cells as well as the battery impacts the self-consumption rate and must be therefore meticulously chosen
Lallement, Guénolé. "Extension of socs mission capabilities by offering near-zero-power performances and enabling continuous functionality for Iot systems." Electronic Thesis or Diss., Aix-Marseille, 2019. http://www.theses.fr/2019AIXM0573.
Повний текст джерелаRecent developments in the field of low voltage integrated circuits (IC) have paved the way towards energy efficient electronic devices in a booming global network called the internet-of-things (IoT) or the internet-of-everything (IoE). However, the sustainability of all these inter- connected sensors is still undermined by the constant need for either an on-board battery – that must be recharged or replaced – or an energy harvester with very limited power efficiency. The power consumption of present consumer electronic systems is fifty times higher than the energy available by cm 2-size harvester or limited to a few months on a small battery, thus hardly viable for lifetime solutions. Upcoming systems-on-chip (SoCs) must overcome the challenge of this energy gap by architecture optimizations from technology to system level. The technical approach of this work aims to demonstrate the feasibility of an efficient ultra-low-voltage (ULV) and ultra-low-power (ULP) SoC using exclusively latest industrial guidelines in 28 nm and 22 nm fully depleted silicon on insulator (FD-SOI) technologies. Several multi-power-domain SoCs based on ARM cores are implemented to demonstrate wake up strategies based on sensors inputs. By optimizing the system architecture, properly selecting and designing compo- nents with technology features chosen adequately, carefully tuning the implementation, a fully energy-optimized SoC is realized
Teng, Sin Yong. "Intelligent Energy-Savings and Process Improvement Strategies in Energy-Intensive Industries." Doctoral thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2020. http://www.nusl.cz/ntk/nusl-433427.
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
Denis, Nicolas. "Système de gestion d'énergie d'un véhicule électrique hybride rechargeable à trois roues." Thèse, Université de Sherbrooke, 2014. http://hdl.handle.net/11143/5856.
Повний текст джерелаBaskar, Ashish Guhan, and Araavind Sridhar. "Short Term Regulation in Hydropower Plants using Batteries : A case study of hydropower pants in lower Oreälven river." Thesis, KTH, Skolan för industriell teknik och management (ITM), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-289407.
Повний текст джерелаVattenkraft är en av de allra äldsta förnybara energikällorna och utgör idag en väsentlig del av Sveriges energimix. Trots att vattenkraft är förnybar, har den lett till vissa utmaningar i den lokala vattenmiljön. Som en följd av att fler miljölagar har implementerats för att reglera nyttjandet av vattendrag och sjöar, minskar flexibiliteten i vattenkraftproduktionen. Den av den svenska regeringen i juni 2020 beslutade nationella planen för miljöanpassning av vattenkraften i Sverige, förväntas börja genomföras med start 2025 och tros då resultera i fler flexibilitetsbegränsningar. Genom att analysera driften av batteriers energilagringssystem kombinerade med vattenkraftverk, bedöms flexibiliteten i sådana kombinerade system kunna ökas. Denna studie fokuserar på den kortsiktiga regleringen av nedre Oreälven med vattenkraftverken Skattungbyn, Unnån och Hansjö. En kombination av vattenkraftverken med batterisystem simuleras mot spot-marknaden och en teknisk-ekonomisk optimering av det kombinerade systemet utförs. Driften av det kombinerade systemet modelleras med linjärprogrammering och den framtida analysen av elmarknaden modelleras med maskininlärningstekniker. Tre olika scenarier för elmarknaden utvecklades baserade på målen för den svenska kärnkraften år 2040. Det första scenariot som utvecklades är i linje med det svenska energimålet om 100 % förnybar produktion till 2040. Det andra scenariot utvecklades med två kärnkraftverk fortfarande i drift 2040 och det tredje scenariot med samma kärnkraftskapacitet som 2020. Från resultaten kan särskilt noteras att med nuvarande batterikostnader (~3,6 miljoner SEK/MWh) kommer införandet av batterier för att kortsiktigt reglera vattenkraftverken inte att vara lönsamt om inte batterikostnaden reduceras till som högst 0,5 miljoner SEK/MWh. Denna studie diskuterar även möjligheterna att använda andrahandsbatterier samt en teknisk-ekonomisk analys för dess prestanda.
Murach, Thomas. "Monoscopic Analysis of H.E.S.S. Phase II Data on PSR B1259–63/LS 2883." Doctoral thesis, Humboldt-Universität zu Berlin, 2017. http://dx.doi.org/10.18452/18484.
Повний текст джерелаCherenkov telescopes can detect the faint Cherenkov light emitted by air showers that were initiated by cosmic particles with energies between approximately 100 GeV and 100 TeV in the Earth's atmosphere. Aiming for the detection of Cherenkov light emitted by gamma ray-initiated air showers, the vast majority of all detected showers are initiated by charged cosmic rays. In 2012 the H.E.S.S. observatory, until then comprising four telescopes with 100 m² mirrors each, was extended by adding a much larger fifth telescope with a very large mirror area of 600 m². Due to the large mirror area, this telescope has the lowest energy threshold of all telescopes of this kind. In this dissertation, a fast algorithm called MonoReco is presented that can reconstruct fundamental properties of the primary gamma rays like their direction or their energy. Furthermore, this algorithm can distinguish between air showers initiated either by gamma rays or by charged cosmic rays. Those tasks are accomplished with the help of artificial neural networks, which analyse moments of the intensity distributions in the camera of the new telescope exclusively. The energy threshold is 59 GeV and angular resolutions of 0.1°-0.3° are achieved. The energy reconstruction bias is at the level of a few percent, the energy resolution is at the level of 20-30%. Data taken around the 2014 periastron passage of the gamma-ray binary PSR B1259-63/LS 2883 were analysed with, among others, the MonoReco algorithm. This binary system comprises a neutron star in a 3.4 year orbit around a massive star with a circumstellar disk consisting of gas and plasma. For the first time the gamma-ray spectrum of this system could be measured by H.E.S.S. down to below 200 GeV. Furthermore, a local flux minimum could be measured during unprecedented measurements at the time of periastron. High fluxes were measured both before the first and after the second transit of the neutron star through the disk. In the second case measurements could be performed for the first time contemporaneously with the Fermi-LAT experiment, which has repeatedly detected very high fluxes at this part of the orbit. A good agreement between measured fluxes and predictions of a leptonic model is found.
Takhirov, Zafar. "Designing energy-efficient computing systems using equalization and machine learning." Thesis, 2018. https://hdl.handle.net/2144/27408.
Повний текст джерелаLUZI, MASSIMILIANO. "Design and implementation of machine learning techniques for modeling and managing battery energy storage systems." Doctoral thesis, 2019. http://hdl.handle.net/11573/1239311.
Повний текст джерела(9412388), Maryam Parsa. "Bayesian-based Multi-Objective Hyperparameter Optimization for Accurate, Fast, and Efficient Neuromorphic System Designs." Thesis, 2020.
Знайти повний текст джерела(9833915), G. Shafiullah. "Application of wireless sensor networking techniques for train health monitoring." Thesis, 2009. https://figshare.com/articles/thesis/Application_of_wireless_sensor_networking_techniques_for_train_health_monitoring/20380206.
Повний текст джерелаThe use of wireless sensor networking in conjunction with modern machine learning tech- niques is a growing area of interest in the development of vehicle health monitoring (VHM) system. This VHM system informs forward -looking decision making and the initiation of suitable actions to prevent any future disastrous events. The main objective of this thesis is to investigate the design and possible deployment of a less expensive, low-power VHM system for railway operations.
The performance of rail vehicles running on railway tracks is governed by the dynamic behaviours of railway bogies, especially in the cases of lateral instability and track irregular- ities. The proposed VHM system measures and interprets vertical accelerations of railway wagons attached to a moving locomotive using a wireless sensor network (WSN) and ma- chine learning techniques to monitor lateral instability and track irregularities. Therefore this system enables reduction of maintenance and inspection requirements of railway systems while preserving the necessary high levels of safety and reliability.
The thesis is divided into three major sections. First, an energy -efficient data commu- nication system is proposed for railway applications using WSN technology. Initially, a conceptual design of sensor nodes with appropriate hardware design is presented. Then an energy -efficient adaptive time division multiple access (TDMA) protocol is developed, further reducing the power consumption of the data communication system. This data communication system collects data from sensor nodes on the wagons and passes it to the locomotive. Secondly, a data acquisition model involving machine learning techniques is used to further reduce power consumption, computational load and hardware cost of the overall condition monitoring system. Only three sensor nodes are required on each railway wagon body to collect sufficient data to develop a VHM system instead of four sensor nodes in an existing system. Finally, a VHM system is developed to interpret the vertical acceler- ation behaviour of railway wagons using popular regression algorithms that predicts typical dynamic behaviour of railway wagons due to track irregularities and lateral instability.
To summarise, this study introduces wireless sensor networking technology that enables the development of an energy-efficient, reliable and low cost data communication system for railway operational applications. By using machine learning techniques, an energy -efficient VHM system is developed which can be used to continuously monitor railway systems, particularly railway track irregularities and derailment potential with integrity. A major benefit of the developed system is a reduction in maintenance and inspection requirements of railway systems.
(10506350), Amogh Agrawal. "Compute-in-Memory Primitives for Energy-Efficient Machine Learning." Thesis, 2021.
Знайти повний текст джерела"Energy-Efficient ASIC Accelerators for Machine/Deep Learning Algorithms." Doctoral diss., 2019. http://hdl.handle.net/2286/R.I.55506.
Повний текст джерелаDissertation/Thesis
Doctoral Dissertation Electrical Engineering 2019
(11180610), Indranil Chakraborty. "Toward Energy-Efficient Machine Learning: Algorithms and Analog Compute-In-Memory Hardware." Thesis, 2021.
Знайти повний текст джерелаNguyen, Kha Thi, and Kha Thi Nguyen. "Optimized Machine Learning Regression System for Efficient Forecast of Construction Corporate Stock Price." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/07898515145094636070.
Повний текст джерела國立臺灣科技大學
營建工程系
105
Time series forecasting has been widely used to determine the future prices of stock, and the analysis and modeling of finance time series importantly guide investor’s decisions and trades. In addition, in a dynamic environment such as the stock market, the non-linearity of the time series is pronounced, immediately affecting the efficacy of stock price forecasts. Thus, this work proposes an intelligent time series prediction system that uses sliding-window metaheuristic optimization for the purpose of predicting the stock prices of Taiwan construction companies one step ahead. It may be of great interest to home brokers who do not possess sufficient knowledge to invest in such companies. The system has a graphical user interface and functions as a stand-alone application. The proposed approach exploits a sliding-window metaheuristic-optimized machine learning regression technique. To illustrate the approach as well as to train and test it, it is applied to historical data of eight stock indices over six years from 2011 to 2017. The performance of the system was evaluated by calculating Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), Mean Square Error (MSE), Correlation Coefficient (R) and Non-linear Regression Multiple Correlation Coefficient (R2). The proposed hybrid prediction model exhibited outstanding prediction performance and it improves overall profit for investment performance. The proposed model is a promising predictive technique for highly non-linear time series, whose patterns are difficult to capture by traditional models.
Alqerm, Ismail. "Novel Machine Learning-Based Techniques for Efficient Resource Allocation in Next Generation Wireless Networks." Diss., 2018. http://hdl.handle.net/10754/627159.
Повний текст джерелаFeng, Menghong. "SunDown: Model-driven Per-Panel Solar Anomaly Detection for Residential Arrays." 2020. https://scholarworks.umass.edu/masters_theses_2/894.
Повний текст джерелаCheng-JuYu and 余承儒. "A Recommendation Information System for Selecting the Most Efficient Milling Machine by Using Case-Based Reasoning Technology of Machine Learning." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/3a87b6.
Повний текст джерелаZhang, Xing. "An intelligent energy allocation method for hybrid energy storage systems for electrified vehicles." Thesis, 2018. https://dspace.library.uvic.ca//handle/1828/9416.
Повний текст джерелаGraduate
Arienti, João Henrique Leal. "Time series forecasting applied to an energy management system ‐ A comparison between Deep Learning Models and other Machine Learning Models." Master's thesis, 2020. http://hdl.handle.net/10362/108172.
Повний текст джерелаA large amount of energy used by the world comes from buildings’ energy consumption. HVAC (Heat, Ventilation, and Air Conditioning) systems are the biggest offenders when it comes to buildings’ energy consumption. It is important to provide environmental comfort in buildings but indoor wellbeing is directly related to an increase in energy consumption. This dilemma creates a huge opportunity for a solution that balances occupant comfort and energy consumption. Within this context, the Ambiosensing project was launched to develop a complete energy management system that differentiates itself from other existing commercial solutions by being an inexpensive and intelligent system. The Ambiosensing project focused on the topic of Time Series Forecasting to achieve the goal of creating predictive models to help the energy management system to anticipate indoor environmental scenarios. A good approach for Time Series Forecasting problems is to apply Machine Learning, more specifically Deep Learning. This work project intends to investigate and develop Deep Learning and other Machine Learning models that can deal with multivariate Time Series Forecasting, to assess how well can a Deep Learning approach perform on a Time Series Forecasting problem, especially, LSTM (Long Short-Term Memory) Recurrent Neural Networks (RNN) and to establish a comparison between Deep Learning and other Machine Learning models like Linear Regression, Decision Trees, Random Forest, Gradient Boosting Machines and others within this context.
Chiang, Chang-Yi, and 蔣昌益. "An Application of Machine Learning for Performing Diagnoses of Functional Disturbances on an Intelligent Energy Management System." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/k2446v.
Повний текст джерела"Building Energy Modeling: A Data-Driven Approach." Doctoral diss., 2016. http://hdl.handle.net/2286/R.I.38640.
Повний текст джерелаDissertation/Thesis
Doctoral Dissertation Industrial Engineering 2016
TUCCI, FRANCESCO ALDO. "Artificial intelligence for digital twins in energy systems and turbomachinery: development of machine learning frameworks for design, optimization and maintenance." Doctoral thesis, 2023. https://hdl.handle.net/11573/1667401.
Повний текст джерела(8790188), Abhishek Navarkar. "MACHINE LEARNING MODEL FOR ESTIMATION OF SYSTEM PROPERTIES DURING CYCLING OF COAL-FIRED STEAM GENERATOR." Thesis, 2020.
Знайти повний текст джерелаGuerra, David José Santos. "Implementation of an effective and efficient anti-money laundering & counter terrorism financing system: the adoption of a behaviorally view." Master's thesis, 2019. http://hdl.handle.net/10362/79383.
Повний текст джерелаMoney laundering and the financing of terrorism has been and is increasingly becoming an even greater concern for governments and for the operating institutions. The present document describes the activities partaken by the trainee, during his one-year internship at the Millennium BCP’s Compliance Office. The trainee first started with an integration/training period, having meetings and lectures with representatives and specialists of the multiple Compliance Office departments, on their duties and respective department role on the overall Compliance Office’s schema. An e-learning component was also complied, whereas the trainee had access to a battery of mandatory courses on legal and internal operation regulations, with both learning and evaluation modules. The trainee was then integrated in the Information Systems and Analytics Department’s team, and in its AML/CFT System implementation project. Once integrated, the trainee did some data analysis to the bank’s customer database, to uncover discrepancies and inconsistences, further providing possible solutions to correct these, in order to prepare for the AML/CFT system implementation. Relative to the implementation of the AML/CFT system, the trainee participated in the whole implementation and configuration process: the ETL process; the parametrization, definition and analysis of the system; The development, definition and calibration of detection algorithms on transactions suspicious of Money Laundering and/or Terrorism Financing; the preparation/adjustment of the decision algorithms according with the evaluation algorithms' assessments; The statistical assessment on the transactions and decisions made in accordance with the reintegration on the previous decision models; And the transactional behavior analysis.