Дисертації з теми "Monitoring Smart Environment"
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Peng, Yang. "Smart sensing design for environment monitoring sensor networks." Online access for everyone, 2008. http://www.dissertations.wsu.edu/Thesis/Summer2008/y_peng_072208.pdf.
Повний текст джерелаPersson, 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 джерелаGiarola, Enrico. "Distributed Monitoring for User Localization and Profiling in Smart Environment." Doctoral thesis, Università degli studi di Trento, 2018. https://hdl.handle.net/11572/368899.
Повний текст джерелаGiarola, Enrico. "Distributed Monitoring for User Localization and Profiling in Smart Environment." Doctoral thesis, University of Trento, 2018. http://eprints-phd.biblio.unitn.it/2776/1/Ph.D.Thesis.GIAROLA-February.2018.pdf.
Повний текст джерелаBremstedt, Pedersen Ivan, and Alfred Andersson. "More than downloading : Visualization of data produced by sensors in a home environment." Thesis, KTH, Kommunikationssystem, CoS, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-97937.
Повний текст джерела”Home automation” syftar till ett system som låter användaren kontrollera och styra olika apparater i hemmet, ofta sker detta utifrån. Dessa apparater inkluderar, men är inte begränsade till ljusbrytare, termostater, termometrar, persienner eller klimatanläggningar. Potentialen för ett sådant system är enormt då antalet apparater som skulle kunna övervakas med endast minimal och billig extra hårdvara är stort. Många av dessa apparater kan dra nytta av att vara ansluten till ett system som gör det möjligt för ägaren att hantera och styra enheter i deras hem. Antalet apparater är därför mångdubbelt fler än antalet hem som är kopplade till systemet. Det finns flera system på marknaden som ger användaren ett sätt att övervaka och styra en hemmiljö, men dessa system är ofta låsta och stödjer bara specifika enheter. Genom att införa en öppen plattform för allmänheten som inte är bunden till något system, kan vi tillåta att fler enheter kan integreras i hemmet och bidra till ytterligare utveckling av smartare hem. Målet med detta projekt var att skapa en skalbar öppen plattform med möjlighet till asynkron uppdatering. Detta har gjorts genom att implementera flera logiska delar för att förse användaren med ett webbgränssnitt och för att tillåta oss hantera kommunikation och lagring av data. Alla dessa delar är sammanlänkade för att bilda ett system av servrar som hanterar alla bakgrundsprocesser. Denna avhandling diskuterar och presenterar implementeringar av alla dessa servrar, hur de genomförs, kommunicera med varandra, ger säkra anslutningar och hur de kan skala med ökad användning. I denna process diskuterar och presenterar vi de tekniker som använts, hur man använder dem och deras fördelar.
RAZZAK, FAISAL. "The Role of Semantic Web Technologies in Smart Environments." Doctoral thesis, Politecnico di Torino, 2013. http://hdl.handle.net/11583/2506366.
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
ILIE, Ana Maria Carmen. "Smart Sensor Technology for Environmental Monitoring Applications." Doctoral thesis, Università degli studi di Ferrara, 2018. http://hdl.handle.net/11392/2487882.
Повний текст джерелаIl progetto di ricerca si è concentrato sullo sviluppo di dispositivi innovativi utilizzando i sensori a basso costo per ottenere le concentrazioni di gas (GHG) quali anidride carbonica (CO2) e metano (CH4) e ottenere una buona qualità dell'acqua come secondo trattamento nelle acque reflue nell’impianto di trattamento. Oltre alla calibrazione del sensore, il prototipo di monitoraggio multiparametro è stato testato in diversi contesti: a) Nel laboratorio con colonne di terreno naturali, suoli, per determinare la risposta del sensore in condizioni controllate, calibrazione e validazione; b) Scala di campo in molti contesti geologici, per la qualità Aria-suolo (misure di metano e anidride carbonica, radon) nel sito di stoccaggio di gas naturale a Minerbio, Italia; Attività di perforazione e fratturazione idraulica in Greeley, Colorado, USA; per la qualità dell'acqua: impianto di trattamento delle acque reflue in Algarve, Portogallo. Il sistema di monitoraggio ha fornito un enorme set di dati per i quali è stato possibile utilizzare analisi statistiche, gestione ed elaborazione (Big DATA). L'identificazione della fonte delle emissioni di gas è stata identificata in diversi rapporti dell'IPCC secondo cui i cambiamenti climatici rappresentano l'emergenza principale per l'equilibrio socio / economico / ambientale del pianeta Terra. Non sono stati identificati valori anomali come concentrazioni di gas metano nel sito di stoccaggio di Minerbio (Italia) e nelle attività di perforazione in Greeley, Colorado, USA. Gli esperimenti con la colonna di terreno per i bacini di infiltrazione nell'impianto di trattamento delle acque reflue in Algarve, in Portogallo, ci hanno dato buoni risultati, la qualità dell'acqua è stata migliorata dopo il 2 ° trattamento. I sensori a basso costo (gas - acqua) per la qualita’ dell’aria e del suolo, hanno fornito una buona calibrazione e validazione con coefficiente di correlazione r2 di 0,70 - 0,96.
Ahmed, Faizan. "Global IoT Coverage Through Aerial And Satellite Network." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-281245.
Повний текст джерелаInternet of Things (IoT) och maskintypkommunikation (MTC) har fått mer fart under de senaste åren men måste fortfarande penetreras med sin fulla sväng i vårt dagliga liv. Detta kan vara möjligt med allmän ramverk som ger global nätverkstäckning. Icke- markbundna nät bestående av satelliter och flygplattformar förväntas tillhandahålla nästa generations kommunikationstjänster i undervärdiga och obetjänade områden genom att säkerställa kvaliteten påtjänster som inte kan täckas av befintliga marknät pågrund av ekonomiska och geografiska begränsningar. Syftet med den här avhandlingen är att formulera en uppsättning massiva och kritiska MTC-användningsfall som global miljöövervakning, spårning av fraktcontainrar och smart jordbruk, och utvärdera deras omfattande krav som datastorlek, sensornoddensitet och upplänkkapacitet och diskutera möjliga nätverk arkitekturer och distributioner med fokus påsatellit- eller flygnät. En rigorös diskussion om olika nätverksarkitekturer för att möta kraven har presenterats, som involverar (1) Low Earth Orbit (LEO) satellitbaserat nätverk, (2) High Altitude Platform (HAP) baserat nätverk, och (3) HAP och UAV baserat nätverk. De föreslagna nätverksarkitekturerna har simulerats och analyserats med MATLAB-verktyg för respek- tive användningsfall i termer av det nödvändiga antalet satelliter eller flygplattformar. Kriterierna för val av nätverksarkitekturer för användningsfallen är baserade pådet minsta antalet satelliter eller flygplattformar. Resultaten visar att LEO-konstellationen bestående av 260 satelliter är möjlig när det gäller distribution och hantering för globalt miljöövervakningsnätverk. Påliknande sätt ger 1440 LEO-satelliter global täckning för spårning av fraktcontainrar. Småjordbruksanvändningsfall kräver hög kapacitet, och följaktligen är HAP och UAV integrerad nätverksarkitektur mer realistisk för ett helt autonomt system jämfört med andra nätverksarkitekturer. Kooperativ kontroll och hantering av jordbruksmaskiner kan utföras vid UAV. Simuleringsresultat visar att en enda UAV kan vara kapabel att kommandera och kontrollera jordbrukssmarta maskiner i ett kvadratkilometer grödningsfält och kan skicka sammanfattningen av händelser till centralstationen via HAP.
Eichinski, Philip. "Smart sampling of environmental audio recordings for biodiversity monitoring." Thesis, Queensland University of Technology, 2018. https://eprints.qut.edu.au/123022/1/Philip_Eichinski_Thesis.pdf.
Повний текст джерелаTurza, Ashley K. "Dense, low-power environmental monitoring for smart energy profiling." Thesis, Massachusetts Institute of Technology, 2010. http://hdl.handle.net/1721.1/60206.
Повний текст джерелаCataloged from PDF version of thesis.
Includes bibliographical references (p. 49).
Recent architectural trends have included exploring open space and the extensive use of glass as building material. While the details of these large, light-exposed, open-air environments can be modeled as thermal fluid systems in CFD simulations, the use of dense sensor networks can provide real-time monitoring of a building's airflow and thermal management systems without the need for computationally-intensive theoretical models, and can use this data to inform and advance these models. Sensor networks can provide an accurate picture of the actual conditions of a building and how those conditions can change over time, due to deterioration or external influences. The information gathered from such networks will be critical in determining the energy efficiency of a building. To do this, a sensor network made of two types of sensors, temperature-humidity and airflow, was deployed in the large, glass-enclosed atrium of the recently-completed MIT Media Lab Extension (E14) in late March 2010. Their performance was calibrated, monitored, and the preliminary results analyzed in conjunction with the external weather conditions in the Boston metropolitan area. The results show that while the use of the sensors in monitoring temperature and humidity is successful, the airflow sensors currently require a different solution to solve both the need for low-power consumption and resolution, range, and stability in its measurements.
by Ashley K. Turza.
S.B.
Stinson, Jonathan William. "Smart energy monitoring technology to reduce domestic electricity and gas consumption through behaviour change." Thesis, Edinburgh Napier University, 2015. http://researchrepository.napier.ac.uk/Output/9828.
Повний текст джерелаDi, Chiappari Alain. "A Collaborative Mobile Crowdsensing System for Smart Cities." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2016. http://amslaurea.unibo.it/11874/.
Повний текст джерелаMontori, Federico <1990>. "Delivering IoT Services in Smart Cities and Environmental Monitoring through Collective Awareness, Mobile Crowdsensing and Open Data." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amsdottorato.unibo.it/8957/1/THESIS_REV.pdf.
Повний текст джерелаPark, Gyuhae. "Assessing Structural Integrity using Mechatronic Impedance Transducers with Applications in Extreme Environments." Diss., Virginia Tech, 2000. http://hdl.handle.net/10919/27719.
Повний текст джерелаPh. D.
Vladimir, Rajs. "Metode praćenja parametara životne sredine bazirane na pametnim mernim sistemima." Phd thesis, Univerzitet u Novom Sadu, Fakultet tehničkih nauka u Novom Sadu, 2015. https://www.cris.uns.ac.rs/record.jsf?recordId=95395&source=NDLTD&language=en.
Повний текст джерелаAs the effects of global warming are spreading globally, the world population encounters one of the most important social and scientific phenomena- changing the parameters of the environment due to pollution. Any conducted action requires precise and accurate measuring of the environmental parameters at several dozens of thousands points deployed around the world. Since financially, as well as practically, it is impossible to create such a large number of measuring stations which would network all over the planet, it is obvious that some alternative solutions must be found. A new measuring system is developed and measuring methods for remote measurement of environmental parameters are implemented. This system can be implemented as a stationary or mobile measuring station. The working hypothesis is based on the use of statistical analysis of measurement data. It leads to the possibility of reducing the number of sensors at measure station, as based on the monitoring of one value-gas concentration (the concentration of carbon monoxide) can be estimated values of other gas (the concentration of nitrogen - dioxide) in the case that they originate from the same source. Using prediction and regression models - interpolation and extrapolation have shown the possibility to reduce the number of measuring stations. Specifically, in the territory of one observedcity, by using interpolation curves, the estimation of concentrations of gases based on data from the measuring system can be shown. Also, based on a mathematical model (ARMA) estimation of concentrations of gases based on previous measurements is shown.
Husain, Muhammad Dawood. "Development of temperature sensing fabric." Thesis, University of Manchester, 2012. https://www.research.manchester.ac.uk/portal/en/theses/development-of-temperature-sensing-fabric(0e5e8367-c3b2-4cff-bcc9-f32fac97b50f).html.
Повний текст джерелаMahmoud, Attaallah Nour Aldin. "Demand Disaggregation for Non-Residential Water Users in the City of Logan, Utah, USA." DigitalCommons@USU, 2018. https://digitalcommons.usu.edu/etd/7401.
Повний текст джерелаRollings, Graham. "Using evolutionary algorithms to resolve 3-dimensional geometries encoded in indeterminate data-sets." Thesis, University of Exeter, 2011. http://hdl.handle.net/10036/4326.
Повний текст джерелаTelci, Ilker Tonguc. "Optimal water quality management in surface water systems and energy recovery in water distribution networks." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/45861.
Повний текст джерелаNOTARANGELO, NICLA MARIA. "A Deep Learning approach for monitoring severe rainfall in urban catchments using consumer cameras. Models development and deployment on a case study in Matera (Italy) Un approccio basato sul Deep Learning per monitorare le piogge intense nei bacini urbani utilizzando fotocamere generiche. Sviluppo e implementazione di modelli su un caso di studio a Matera (Italia)." Doctoral thesis, Università degli studi della Basilicata, 2021. http://hdl.handle.net/11563/147016.
Повний текст джерелаNegli ultimi 50 anni, le alluvioni si sono confermate come il disastro naturale più frequente e diffuso a livello globale. Tra gli impatti degli eventi meteorologici estremi, conseguenti ai cambiamenti climatici, rientrano le alterazioni del regime idrogeologico con conseguente incremento del rischio alluvionale. Il monitoraggio delle precipitazioni in tempo quasi reale su scala locale è essenziale per la mitigazione del rischio di alluvione in ambito urbano e periurbano, aree connotate da un'elevata vulnerabilità. Attualmente, la maggior parte dei dati sulle precipitazioni è ottenuta da misurazioni a terra o telerilevamento che forniscono informazioni limitate in termini di risoluzione temporale o spaziale. Ulteriori problemi possono derivare dagli elevati costi. Inoltre i pluviometri sono distribuiti in modo non uniforme e spesso posizionati piuttosto lontano dai centri urbani, comportando criticità e discontinuità nel monitoraggio. In questo contesto, un grande potenziale è rappresentato dall'utilizzo di tecniche innovative per sviluppare sistemi inediti di monitoraggio a basso costo. Nonostante la diversità di scopi, metodi e campi epistemologici, la letteratura sugli effetti visivi della pioggia supporta l'idea di sensori di pioggia basati su telecamera, ma tende ad essere specifica per dispositivo scelto. La presente tesi punta a indagare l'uso di dispositivi fotografici facilmente reperibili come rilevatori-misuratori di pioggia, per sviluppare una fitta rete di sensori a basso costo a supporto dei metodi tradizionali con una soluzione rapida incorporabile in dispositivi intelligenti. A differenza dei lavori esistenti, lo studio si concentra sulla massimizzazione del numero di fonti di immagini (smartphone, telecamere di sorveglianza generiche, telecamere da cruscotto, webcam, telecamere digitali, ecc.). Ciò comprende casi in cui non sia possibile regolare i parametri fotografici o ottenere scatti in timeline o video. Utilizzando un approccio di Deep Learning, la caratterizzazione delle precipitazioni può essere ottenuta attraverso l'analisi degli aspetti percettivi che determinano se e come una fotografia rappresenti una condizione di pioggia. Il primo scenario di interesse per l'apprendimento supervisionato è una classificazione binaria; l'output binario (presenza o assenza di pioggia) consente la rilevazione della presenza di precipitazione: gli apparecchi fotografici fungono da rivelatori di pioggia. Analogamente, il secondo scenario di interesse è una classificazione multi-classe; l'output multi-classe descrive un intervallo di intensità delle precipitazioni quasi istantanee: le fotocamere fungono da misuratori di pioggia. Utilizzando tecniche di Transfer Learning con reti neurali convoluzionali, i modelli sviluppati sono stati compilati, addestrati, convalidati e testati. La preparazione dei classificatori ha incluso la preparazione di un set di dati adeguato con impostazioni verosimili e non vincolate: dati aperti, diversi dati di proprietà del National Research Institute for Earth Science and Disaster Prevention - NIED (telecamere dashboard in Giappone accoppiate con dati radar multiparametrici ad alta precisione) e attività sperimentali condotte nel simulatore di pioggia su larga scala del NIED. I risultati sono stati applicati a uno scenario reale, con la sperimentazione attraverso una telecamera di sorveglianza preesistente che utilizza la connettività 5G fornita da Telecom Italia S.p.A. nella città di Matera (Italia). L'analisi si è svolta su più livelli, fornendo una panoramica sulle questioni relative al paradigma del rischio di alluvione in ambito urbano e questioni territoriali specifiche inerenti al caso di studio. Queste ultime includono diversi aspetti del contesto, l'importante ruolo delle piogge dal guidare l'evoluzione millenaria della morfologia urbana alla determinazione delle criticità attuali, oltre ad alcune componenti di un prototipo Web per la comunicazione del rischio alluvionale su scala locale. I risultati ottenuti e l'implementazione del modello corroborano la possibilità che le tecnologie a basso costo e le capacità locali possano aiutare a caratterizzare la forzante pluviometrica a supporto dei sistemi di allerta precoce basati sull'identificazione di uno stato meteorologico significativo. Il modello binario ha raggiunto un'accuratezza e un F1-score di 85,28% e 0,86 per il set di test e di 83,35% e 0,82 per l'implementazione nel caso di studio. Il modello multi-classe ha raggiunto un'accuratezza media e F1-score medio (macro-average) di 77,71% e 0,73 per il classificatore a 6 vie e 78,05% e 0,81 per quello a 5 classi. Le prestazioni migliori sono state ottenute nelle classi relative a forti precipitazioni e assenza di pioggia, mentre le previsioni errate sono legate a precipitazioni meno estreme. Il metodo proposto richiede requisiti operativi limitati, può essere implementato facilmente e rapidamente in casi d'uso reali, sfruttando dispositivi preesistenti con un uso parsimonioso di risorse economiche e computazionali. La classificazione può essere eseguita su singole fotografie scattate in condizioni disparate da dispositivi di acquisizione di uso comune, ovvero da telecamere statiche o in movimento senza regolazione dei parametri. Questo approccio potrebbe essere particolarmente utile nelle aree urbane in cui i metodi di misurazione come i pluviometri incontrano difficoltà di installazione o limitazioni operative o in contesti in cui non sono disponibili dati di telerilevamento o radar. Il sistema non si adatta a scene che sono fuorvianti anche per la percezione visiva umana. I limiti attuali risiedono nelle approssimazioni intrinseche negli output. Per colmare le lacune evidenti e migliorare l'accuratezza della previsione dell'intensità di precipitazione, sarebbe possibile un'ulteriore raccolta di dati. Sviluppi futuri potrebbero riguardare l'integrazione con ulteriori esperimenti in campo e dati da crowdsourcing, per promuovere comunicazione, partecipazione e dialogo aumentando la resilienza attraverso consapevolezza pubblica e impegno civico in una concezione di comunità smart.
Mataloto, Bruno Miguel Gonçalves. "IoT*(Ambisense): Smart environment monitoring using LoRa." Master's thesis, 2019. http://hdl.handle.net/10071/20267.
Повний текст джерелаNesta dissertação de mestrado, IoT * (AmbiSense), é apresentado um sistema IoT desenvolvido como uma solução para Gestão de Edifícios e Energia recorrendo a ferramentas de visualização para identificar heurísticas e criar poupanças automáticas. Os protótipos desenvolvidos comunicam utilizando LoRa, e são compostos por um conjunto de sensores ligados a um microcontrolador alimentado por bateria. Os sensores adquirem dados como temperatura, humidade, luminosidade, qualidade do ar e movimento. Para edifícios de pequena e média dimensão onde a gestão do sistema é possível, um dashboard fornece templates de visualização com dados em tempo real, permitindo extrair heurísticas, que introduzem poupanças através de um conjunto de ações predefinidas ou intervenção manual. O sistema LoBEMS (LoRa Building and Energy Management System), foi validado numa escola local durante um período de três anos. A avaliação do sistema resultou numa poupança de energia de 20% e uma melhoria significativa da qualidade do ambiente e conforto no interior da escola. Para edifícios de maior dimensão onde a gestão do sistema não é possível, criámos uma ferramenta de visualização 3D, que apresenta os dados e alertas do sistema, num modelo interativo do edifício. Este cenário foi validado no campus do ISCTE-IUL, onde foi necessária a interação da Comunidade para obter poupanças. Foi nos também solicitada uma validação do sistema no centro de dados da Universidade, onde os templates do sistema foram utilizados para detetar anomalias e sugerir alterações. A flexibilidade do sistema permite a sua implementação em qualquer edifício, sem exigir um grande investimento ou implementações complexas.
PANNONE, DANIELE. "Smart environment monitoring through micro unmanned aerial vehicles." Doctoral thesis, 2019. http://hdl.handle.net/11573/1241567.
Повний текст джерелаCHEN, PIN-CHANG, and 陳品彰. "A Monitoring and Controlling System for Smart Home Environment." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/prx5uk.
Повний текст джерела朝陽科技大學
資訊與通訊系
105
The techniques of IoT(Internet of Thing) and the technology changed into more advanced in recent year. More and more products combine with the IoT techniques. Smart home connects with variety of daily necessities. By using the IoT, products can communicate with each other and making our daily life become safer and more convenient. The point of smart home is sensor, control and immediate inform. Our system is using sensor model and control model to sensing environment’s data and control home appliances. We can monitored by using the platform on web browser and inform alarm. The monitoring platform also provide history which is recorded data’s variation in recent three days. The user can observe environment data from the chart. Moreover, we also design the system in mobile application. The user can monitor data on the platform in anytime. Mobile application also provide inform function, if sensor get the value is overflow, mobile application will inform on user’s cellphone and mobile application will tell user which sensor is overflow. Our system also provide multi-model monitor in the same time. When the multi-model sent the data to same server, this server’s monitoring platform can show multi-model’s sensing data. We use temperature and humidity sensor, smoking sensor, ultrasonic sensor and ambient light sensor in this system. The user only need to put the sensor on the Arduino board which need to follow by our system’s specifications. You can get the correspond data on the monitor.
Oliveira, João Pedro Cabeleira Claro de. "Development of a Business Intelligence Conceptual Model for Waste Collection and Transportation Monitoring." Master's thesis, 2022. http://hdl.handle.net/10362/134985.
Повний текст джерелаThe need to answer ever-increasing urban challenges and the integration of new technologies in every aspect of our daily lives prompt the creation of the Smart City concept. These new technologies gather an enormous quantity of data that reveals interesting patterns about cities, enabling opportunities to enhance public decision-making and problem-solving. One critical aspect of urban life is its relationship with the environment. There’s a real need to work on a unified global approach on how to tackle issues such as pollution and waste management, but there is not a single framework or guideline to handle these new data gathered. This work focuses on structuring a framework of analysis for waste collection and transportation purposes. The objective was to develop monitoring dashboards for Departamento de Higienização Urbana (DMHU), using proven metrics of efficiency and effectiveness, result of a comprehensive literature review. A BI framework was developed using Power BI to perform efficiency-based analysis of waste collection circuits – the main process of DMHU. The receptivity of DMHU towards the solution presented seems to indicate that a BI solution is indeed valuable for complex monitoring problems such as solid waste management.
Shen, Shih Yuan, and 沈士元. "Development and Assessment of a Sleep Activity Behavior Monitoring Platform Integrated with Biofeedback Smart Pillow for Home Environment." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/49583476332752052459.
Повний текст джерела長庚大學
醫療機電工程研究所
103
Obstructive Sleep Apnea Syndrome (OSAS) is caused by partial obstruction or complete blockage of the upper air way during sleep. Overnight polysomnography (PSG) is golden standard device for the diagnosis of OSAS. It monitors and records several biophysiological signals throughout the night by attaching several sensors to patients. This procedure is complex and requires a specialized sleep center. The purpose of this work is to develop a sleep activity behavior monitoring platform and provide self-monitoring of abnormal events in home environment. Besides, there are no clear and consistent criteria to follow in calculating the baseline of airflow or oxygen saturation for apnea and hypopnea events. Therefore, we proposed a method to calculate the baseline using tidal volume reduction ratio and oxygen saturation reduction. Experimental results demonstrated the proposed method can correctly determine the apnea and hypopnea events. In addition, previous literatures show occurrence of that the upper air way blockage may be dependent on movement of the head position during sleep. In this research, a smart pillow integrated with physiological measurements, head posture biofeedback and close-loop pneumatic control mechanism was developed. The pillow has four airbags with two degrees of freedom. It can provide the adjustment of positions with tilt angle range (0° to 21°) and rotational angle (0° to 24°) with less than the 1°. This work also evaluated the identification of apnea and hypopnea events using proposed baseline calculation method for clinical applicability.
Costa, Bárbara Nogueira da. "VAR-AS, sustained attention detection system in the learning environment." Master's thesis, 2020. http://hdl.handle.net/10071/20964.
Повний текст джерелаEsta dissertação apresenta um sistema de monitorização da variabilidade da frequência cardíaca (VFC) através da Eletrocardiograma (ECG) e Fotoplestimografria (PPG) para a deteção do estado de atenção num ambiente de aprendizagem. Os signais de ECG e PPG foram adquiridos e processados através do desenvolvimento de um sistema embebido multi-microcontrolador. Ao extrair-se as ondas destes sinais, calcula-se a frequência cardíaca através dos intervalos de tempo entre os picos R para o eletrocardiograma e entre os batimentos para a fotopletismografia. Por fim, para poder indicaro nível da atenção da parte do utente adicionou-se um input em que o voluntário marca os períodos que considera estar mais atento. Estes dados são associados a uma marca temporal com resolução ma base de dados na nuvém. Após o armazenamento destes dados, analisa-se a VFC com base em algoritmos desenvolvidos com a ferramenta Matlab. Estes algoritmos permitem estudar a variabilidade cardíaca segundo os domínios do tempo, da frequência e como tambem foram consideradas medidas VFC não lineares. Por fim, adicionou-se um módulo para a medição da condutividade da pele, relacionando-a com a avaliação do nível de stress durante o processo de aprendizagem. Para comprovar a fiabilidade do sistema realizaram-se testes a diversos voluntários em ambientes reais de acordo com um protocolo estipulado. Estes registos foram analisados como ponto de partida para classificar situações de maior atenção do voluntário perante um cenário educativo.
Santos, Diogo Alexandre Lopes dos. "EnerMon: IoT power monitoring system for smart environments." Master's thesis, 2019. http://hdl.handle.net/10071/20227.
Повний текст джерелаO presente estudo, realizado no âmbito da tese de mestrado, descreve o desenvolvimento e subsequente validação de EnerMon, um sistema IoT(Internet of Things / Internet das Coisas) LoRa flexível, eficiente e baseado em edge-computing capaz de monitorizar consumo de energia em tempo real. Através de processos de análise descritiva é também apresentada uma visão geral sobre o consumo energético ao longo do tempo com a identificação de desperdícios de energia. Neste trabalho o leitor irá tomar conhecimento do processo de desenvolvimento completo do sistema, desde a fase de design à fase de testes, criado com o uso de Arduinos, Sensores de transformadores de corrente e Raspberry PIs como servidores aplicacionais, bem como informação relacionada com a comunicação LoRa e o que lhe é expectável. Esta solução, com a ajuda de analíticas descritivas, permitem a identificação de pegadas energéticas locais, usando materiais de baixo custo por menos de 80€ por sensor de monitorização de três fases. Permite também a fácil instalação sem limitações de alcance e obstáculos na comunicação, simplificando a sua utilização em diferentes ambientes desde edifícios complexos a consumidores energéticos mais pequenos, como caldeiras elétricas ou simplesmente medir a pegada energética de turistas em alojamento local.
Cardellicchio, Angelo. "Smart sensor systems for environmental monitoring: implications and applications." Doctoral thesis, 2019. http://hdl.handle.net/11589/161062.
Повний текст джерелаClimate change is one of the biggest challenges that humanity will face in the upcoming decades. Hence, over the last few years, the environmental engineering research community has focused its effort on the development and deployment of (often distributed) smart sensor systems, specifically designed for environmental monitoring. These sensors produce large amounts of data, which can be used to describe climate changes and, hopefully, suggest future actions to prevent further damages to the environment. However, to enable the ’smart’ capabilities in such systems, researchers must pay attention to several aspects, including two on which this thesis work is focused. The first one, which is often underestimated, is the design of the data acquisition phase: a poor experimental setting will lead to biased data, and therefore ineffective results. The second one concerns the algorithm used to model data, which should be chosen to reflect their intrinsic nature. This work tries to give a first contribution to both these aspect, describing the results of two specific use case scenarios, and highlighting how experiments can greatly benefit from some simple, yet effective, design guidelines. The final goal is to define an initial working pipeline for environmental data processing, which can be both flexible to be adapted to different scenarios, and accurate enough to give an effective description of the observed phenomena.
Yang, Meng-Lin, and 楊孟霖. "Smart Wireless Sensing Network Applied to Aquaculture Water Quality Environmental Monitoring." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/p4hyey.
Повний текст джерела正修科技大學
電機工程研究所
106
This manuscript provides a smart monitoring system design of aquaculture, used for the control of aquaculture water quality to be the optimal state, by an IoT structure. This design integrates multiple sensors, such as temperature sensor, PH sensor, TDS sensor, EC sensor, ORP sensor as well as smart functions. The smart functions include communication, time scheduling, power monitoring, and safety protections. All these datas can upload into cloud server via Wifi communication. Not only it will alert when water is under the bad quality, also it can offer informations of water quality to users. Moreover, the system include solar energy to provide indipendent power supply.By comparing with the conventional controller, this design not only significantly reduces the required area of wiring, but also improves the setup efficiency.
Shih-FanWen and 溫士範. "Internet-of-Things Based Smart Home Appliance Electricity Management and Environmental Monitoring Design." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/30564605048333936877.
Повний текст джерела國立成功大學
電機工程學系碩博士班
100
This thesis presents an integration of load management hardware module and thermal humidity sensor, where Zigbee wireless network is included to establish an energy management and environmental sensing system bases on the Internet-of-Things (IoT) structure. In this developed system platform, it is equipped with the functions of real-time monitoring, load control and data storage, in which the electricity management strategies consisting of electricity usage scheduling, environment regulation and demand response are all concerned. It is found that through this scheme design, the developed system comes with high flexibility along with the improved power consumption efficiency, achieving the goal energy conservation. In order to confirm the feasibility and practicality of the proposed system design, it has been simulated and tested under different scenarios. From the test results, they have demonstrated the effectiveness of this system on environment supervision and home appliance management applications, which is meanwhile served as the beneficial reference for electricity management operating model and future IoT development.
Santoso, Hendro Agus, and 馮昌嵩. "Automated Deployment of Omnidirectional and Directional Mobile Sensors for Smart Environmental Monitoring: Protocols Design and Systems Implementation." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/385na2.
Повний текст джерела國立交通大學
電機資訊國際學程
107
Advances of micro-electromechanical system (MEMS), sensing technology, and wireless communication have significantly encouraged the development of wireless sensor networks (WSNs) in the past decade. A WSN is widely used for habitat and environmental monitoring, medical application (with the purpose of improving quality of health care), agricultural assistance, and as solutions to military problems. For monitoring applications, sufficient sensing coverage is essential. In this dissertation, we propose a Coverage-Aware Sensor Automation (CASA, which means "home" in Spanish) protocol suite, including: a global sensor deployment scheme and sensing coverage recovery in the presence of sensor failures. An Enhanced Virtual Forces Algorithm with Boundary Forces (EVFA-B) is proposed for deployment scheme of omnidirectional mobile sensors to ensure sufficient sensing coverage. In the face of sensing node failures, a Sensor Self-Organizing Algorithm (SSOA) is devised to efficiently recover the sensing void and restore the required sensing coverage. Different from omnidirectional sensors, the coverage region of a directional sensor is determined by not only the sensing radius (distance), but also its sensing orientation and spread angle. Heterogeneous sensing distance and spread angles are likely to exist among directional sensors. In this dissertation, we deal with heterogeneous directional sensors equipped with locomotion and rotation facilities to enable sensors self-deployment. Two Enhanced Deployment Algorithms (EDA-I and EDA-II) are proposed to achieve high sensing coverage ratio in the monitored field. EDA-I leverages the concept of virtual forces (for sensors movements) and virtual boundary torques (for sensors rotations), whereas EDA-II combines Voronoi diagram directed movement and boundary torques guided rotations. Performance results demonstrate that our enhanced deployment mechanisms are capable of providing desirable surveillance level, while consuming moderate moving and rotating energy under reasonable computation time. Once the deployment algorithm produces moving destinations (goals) for all sensors, however, the problem of how to schedule moving paths to reach the goals without collisions remains largely neglected in the sensor networking field. Only with proper path planning and scheduling, all robots will be able to reach their intended destination successfully. We propose a Collision-Free Path Planning (CFPP) mechanism, based on geometric formulations and batched movements, to address the sensors deployment problem. Our proposed CFPP method incurs little computation latency, moderate energy consumption, and ensures 100$\%$ goal reachability. A proof-of-concept prototype is implemented to corroborate our protocol feasibility in a real-life environment.
Chen, Yi-Xuan, and 陳怡璇. "A preliminary study for building up a smart IOT (Internet Of Things) system related to Environmental Monitoring , Simulation and Risk Assessment of Indoor Air Quality." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/74n8d7.
Повний текст джерела國立宜蘭大學
環境工程學系碩士班
104
Disclosure of critical effects of various indoor air pollutants (IAPs) for human health risk is attributable to issue of the Sick Building Syndrome (SBS) widely discussed all over the world nowadays. Hereafter, in 2010 the World Health Organization (WHO) tried to propose a set of worldwide guidelines, criteria, information, and standards on some IPAs associated with human health, benzene and formaldehyde for example, to lead any countries in the world to scientifically carry out their certain management solutions or means in terms of the indoor air quality (IAQ). Along with emissions of various IAPs from building materials, excessive interior decorations as well as redundant aged buildings, the climatic characteristics of subtropical island Taiwan, higher temperature and relative humidity for instance, have been proven to be sort of unfavorable conditions for deteriorating the IAQ. Whereas above mentioned causes speak for themselves- the IAQ will for sure play an influential role on vital health of anyone who inevitably stays approximately 90% of his/her time in any generalized indoor circumstance in Taiwan. Consequently, Taiwan was the second country in the world, following the South Korea, to promulgate an enforcement domestic law ‘The Indoor Air Quality Management Act’ for regulating IAQ of Taiwan in 2011; it has been officially implemented in 2012. A variety of principal measures were definitely declared in this Act, including the TEPA (Taiwan Environmental Protection Administration) should announce a list of locations required to follow the act beforehand plus this list will be further expanded in the future, several certain locations should employ qualified and dedicated IAQ control specialists, some specific locations should build up their continuous emission monitoring system based on real-time requirements, to name a few. Meanwhile, a number of auxiliary regulations were enacted by the TEPA to implement this Act by a comprehensively approach. Owing to drive by the accessibility of Internet, innovative development of Information and Communication Technology (ICT) as well as global adoption of the ‘Open’ thinking in regard to software, hardware and data over the years, our contemporary society has undoubtedly been evolving into a new and ubiquitous pattern of cyber world centered by the conception of Internet of Things (IOT). In brief, the idea of IOT attempts to envision real-time application of automation intelligence and smart decision making by further developing a cyber-physical system (CPS) and an added-value data Cloud analytic model, Big Data analysis for example, on the basis of a large amount of data widely collected from nature or anthropogenic surrounding. Therefore, the IOT has been globally regarded as one kind of most well-known topic, outlook or action plan with trendy, innovative, interdisciplinary attributes and opportunities for upgrading any industries as well. Take the conception of what is called Industry 4.0 ( the fourth industrial revolutio or smart factory) for example, it in a manner aims to realize and generalize the core philosophy of the IOT through the current trend of automation intelligence, smart decision making thinking and data exchange in industrial technologies to facilitate transformation opportunities or turning points for any industrial sectors such as conventional industries, manufacturing industries, and so on. Compared the priority to enact an enforcement act for regulating IAQ with other countries in the world, this study proposes that the facts of health effect and risk correlated to IAQ should be disclosed and highlighted on the basis of intention and necessity of ‘The Indoor Air Quality Management Act’ promulgated in Taiwan in 2011. Obviously, it now is in urgent opportunity and need of making integration of multidisciplinary studies, and developing a comprehensive, smart and automation framework for systematically disclosing the environmental risk information to make a lot of sense to the public via merging additional, innovative IOT-oriented and automation thinking into our current IAQ regulating mechanism and managing system. Hence, this study not only takes above mentioned urgent necessities into account, but some required elements are also weighed within this study before making the definitions of our research requirements, limitations and assumptions; such as low energy consumption, technique and cost- effectiveness feasibility, system deployment facilitation, and so on. Accordingly, this study tries to take advantage of novel open hardware, Arduino for example, and be in compliance with the methodology of software engineering to practically build up a systematic research system for coming to a preliminary IOT-oriented IAQ management framework conclusion including comprehensive and smart automation managing mechanisms, environmental risk information disclosures, and CFD Simulation. In pragmatic, the results of this study could be concluded as a workable framework suggestion for facilitating management operation as well as communicating the real-time condition and health risk information of IAQ. Furthermore, the results of this study also could expectably call attention to the issues in terms of IAQ and some inputs of other researches as well.
(10695907), Wo Jae Lee. "AI-DRIVEN PREDICTIVE WELLNESS OF MECHANICAL SYSTEMS: ASSESSMENT OF TECHNICAL, ENVIRONMENTAL, AND ECONOMIC PERFORMANCE." Thesis, 2021.
Знайти повний текст джерелаOne way to reduce the lifecycle cost and environmental impact of a product in a circular economy is to extend its lifespan by either creating longer-lasting products or managing the product properly during its use stage. Life extension of a product is envisioned to help better utilize raw materials efficiently and slow the rate of resource depletion. In the case of manufacturing equipment (e.g., an electric motor on a machine tool), securing reliable service life as well as the life extension are important for consistent production and operational excellence in a factory. However, manufacturing equipment is often utilized without a planned maintenance approach. Such a strategy frequently results in unplanned downtime, owing to unexpected failures. Scheduled maintenance replaces components frequently to avoid unexpected equipment stoppages, but increases the time associated with machine non-operation and maintenance cost.
Recently, the emergence of Industry 4.0 and smart systems is leading to increasing attention to predictive maintenance (PdM) strategies that can decrease the cost of downtime and increase the availability (utilization rate) of manufacturing equipment. PdM also has the potential to foster sustainable practices in manufacturing by maximizing the useful lives of components. In addition, advances in sensor technology (e.g., lower fabrication cost) enable greater use of sensors in a factory, which in turn is producing greater and more diverse sets of data. Widespread use of wireless sensor networks (WSNs) and plug-and-play interfaces for the data collection on product/equipment states are allowing predictive maintenance on a much greater scale. Through advances in computing, big data analysis is faster/improved and has allowed maintenance to transition from run-to-failure to statistical inference-based or machine learning prediction methods.
Moreover, maintenance practice in a factory is evolving from equipment “health management” to equipment “wellness” by establishing an integrated and collaborative manufacturing system that responds in real-time to changing conditions in a factory. The equipment wellness is an active process of becoming aware of the health condition and of making choices that achieve the full potential of the equipment. In order to enable this, a large amount of machine condition data obtained from sensors needs to be analyzed to diagnose the current health condition and predict future behavior (e.g., remaining useful life). If a fault is detected during this diagnosis, a root cause of a fault must be identified to extend equipment life and prevent problem reoccurrence.
However, it is challenging to build a model capturing a relationship between multi-sensor signals and mechanical failures, considering the dynamic manufacturing environment and the complex mechanical system in equipment. Another key challenge is to obtain usable machine condition data to validate a method.
A goal of the proposed work is to develop a systematic tool for maintenance in manufacturing plants using emerging technologies (e.g., AI, Smart Sensor, and IoT). The proposed method will facilitate decision-making that supports equipment maintenance by rapidly detecting a worn component and estimating remaining useful life. In order to diagnose and prognose a health condition of equipment, several data-driven models that describe the relationships between proxy measures (i.e., sensor signals) and machine health conditions are developed and validated through the experiment for several different manufacturing-oriented cases (e.g., cutting tool, gear, and bearing). To enhance the robustness and the prediction capability of the data-driven models, signal processing is conducted to preprocess the raw signals using domain knowledge. Through this process, useful features from the large dataset are extracted and selected, thus increasing computational efficiency in model training. To make a decision using the processed signals, a customized deep learning architecture for each case is designed to effectively and efficiently learn the relationship between the processed signals and the model’s outputs (e.g., health indicators). Ultimately, the method developed through this research helps to avoid catastrophic mechanical failures, products with unacceptable quality, defective products in the manufacturing process as well as to extend equipment service life.
To summarize, in this dissertation, the assessment of technical, environmental and economic performance of the AI-driven method for the wellness of mechanical systems is conducted. The proposed methods are applied to (1) quantify the level of tool wear in a machining process, (2) detect different faults from a power transmission mini-motor testbed (CNN), (3) detect a fault in a motor operated under various rotation speeds, and (4) to predict the time to failure of rotating machinery. Also, the effectiveness of maintenance in the use stage is examined from an environmental and economic perspective using a power efficiency loss as a metric for decision making between repair and replacement.