The Ergonomics Research Group at Queen’s University, supported by the Workplace and Safety Insurance Board, has been developing an on-line system to estimate peak and cumulative joint loading in the workplace. This study will aid the project by examining the muscle activation levels (MALs) in upper extremity and trunk muscles during a manual lifting task using both hands. It was hypothesized that MAL’s are correlated with the magnitude of the load in the hands and thus could be used to predict the load which in turn will be used to predict the lower back moments. Alterations in the muscle activation patterns due to lifting different loads were examined. Electromyographic signals (EMG) and kinematic data were recorded from different sites on the trunk and upper limb as subjects lifted a load from the floor to a shelf using squat, stoop and freestyle lift techniques. All raw EMG data were processed to obtain the linear envelopes (LE) which provides estimates of the MAL’s. The peak, mean and area of the linear envelopes were calculated. Using regression analysis, a relationship between the parameters and load lifted was found to exist. A non-linear parallel cascade type architecture was used to develop a model to predict the load in the hands. The model uses the EMG parameters as inputs and fits the data via linear and non-linear cascades to the output, i.e. the load in the hands. A model was successfully developed for the squat lift posture using the area, peak and mean of the zero-normalized EMG LE recorded from the erector spinae (L4 level), with a prediction error of ± 1.03kg and for the stoop posture, a prediction error of ± 2.34kg. Given the predicted loads, moments in the lower back were computed using the method of Hof (1992). / Thesis (Master, Electrical & Computer Engineering) -- Queen's University, 2007-09-28 16:15:23.077

This thesis aims to explore the use of a predictive scaling algorithm to scale a microservices-based system according to a predicted system load. A scalable system along with a predictive scaling algorithm is developed and tested by applying a periodic load to the system. The developed scaling algorithm is a combination of a reactive and a predictive algorithm, where the reactive algorithm is used to scale the system when no significant load changes are predicted. The results show that the periodical load is predicted by the algorithm, that the system can be scaled preemptively, and that the algorithm has room for improvement in terms of accuracy. / Detta examensarbete siktar på att utforska möjligheten att använda förutsägande skalningsalgoritmer för att skala ett microservices-baserat system enligt en förutspådd belastning på systemet. Ett skalbart system utvecklas tillsammans med en förutsägande skalningsalgoritm, och testas genom att applicera en periodisk belastning på systemet. Den utvecklade skalningsalgoritmen är en kombination av en reaktiv och förutsägande algoritm, där den reaktiva algoritmen används för att skala systemet då inga signifikanta belastningar förutspås. Resultaten visar att systemets belastning kan förutspås och att systemet kan skalas med hjälp av den förutspådda belastningen, samt att algoritmen har utrymme för förbättringar.

This thesis presents performance evaluation and a field validation study of a time and temperature indexed autoregressive with exogenous (4-3-5 ARX) building thermal load prediction model with an aim to integrate the model with actual predictive control systems. The 4-3-5 ARX model is very simple and computationally efficient with relatively high prediction accuracy compared to the existing sophisticated prediction models, such as artificial neural network prediction models. However, performance evaluation and field validation of the model are essential steps before implementing the model in actual practice. The performance of the model was evaluated under different climate conditions as well as under modeling uncertainty. A field validation study was carried out for three buildings at Mississippi State University. The results demonstrate that the 4-3-5 ARX model can predict building thermal loads in an accurate manner most of the times, indicating that the model can be readily implemented in predictive control systems.

This work deals with algorithms for distributing and mapping content on nodes in CDN system. Compares local and global algorithms for loading files on origin and edge servers. A high level CDN simulator is made. A matrix based approach for mapping content on CDN servers is proposed along with tranformation for solution of mapping optimalisation through genetic algorithms.

Traditional power plants only utilize about 30 percent of the primary energy that they consume, and the rest of the energy is usually wasted in the process of generating or transmitting electricity. On-site and near-site power generation has been considered by business, labor, and environmental groups to improve the efficiency and the reliability of power generation. Combined heat and power (CHP) systems are a promising alternative to traditional power plants because of the high efficiency and low CO2 emission achieved by recovering waste thermal energy produced during power generation. A CHP operational algorithm designed to optimize operational costs must be relatively simple to implement in practice such as to minimize the computational requirements from the hardware to be installed. This dissertation focuses on the following aspects pertaining the design of a practical CHP operational algorithm designed to minimize the operational costs: (a) real-time CHP operational strategy using a hierarchical optimization algorithm; (b) analytic solutions for cost-optimal power generation unit operation in CHP Systems; (c) modeling of reciprocating internal combustion engines for power generation and heat recovery; (d) an easy to implement, effective, and reliable hourly building load prediction algorithm.

The mobile traffic in cellular networks is increasing in a steady rate as we go intoa future where we are connected to the internet practically all the time in one wayor another. To map the mobile traffic and the volume pressure on the base stationduring different time periods, it is useful to have the ability to predict the trafficvolumes within cellular networks. The data in this work consists of 4G cellular trafficdata spanning over a 7 day coherent period, collected from cells in a moderately largecity. The proposed method in this work is ARIMA modeling, in both original formand with an extension where the coefficients of the ARIMA model are re-esimated byintroducing some user characteristic variables. The re-estimated coefficients produceslightly lower forecast errors in general than a isolated ARIMA model where thevolume forecasts only depends on time. This implies that the forecasts can besomewhat improved when we allow the influence of these variables to be a part ofthe model, and not only the time series itself.

Sedan industriella revolutionen har hastigheten av global uppvärmning och föroreningar i miljön ökat betydligt. Företag i fordonsindustrin arbetar aktivt för att göra sina produkter mer hållbara genom att bland annat minska utsläppen, minimera användningen av icke-förnybara resurser samt att återvinna. En batteridriven elbil (BEV) är en möjlig lösning för renare transport och marknaden har ökat signifikant. Men med den nuvarande batteriteknologin skulle stora byggmaskiner som grävmaskiner behöva tunga batterier för att möta sina energibehov, vilket ökar den totala vikten. Bränslecellshybriddrivna fordon (FCHEV) med vätgas är en potentiell lösning för medelstora och stora byggmaskiner som kombinerar bränsleceller och batterier för att tillhandahålla energin. Byggmaskiner har en växlande effekt och utför vanligtvis upprepande arbetsmönster, men en bränslecell reagerar långsammare på grund av den kemiska processen. Därför behövs ett effektivt energihanteringssystem för att möta effektbehovet, uppfylla systembegränsningar, minska vätgasförbrukningen samt att begränsa bränslecell- och batteridegraderingen. Syftet med denna avhandling är att utveckla en kontrollenhet och ett estimeringsinstrument för maskinbelastning för ett sådant FCHEV system. En ny energihanteringsstrategi föreslås genom att formulera den som ett optimeringsproblem och använda modellprediktiv reglering (MPC) för att minimera målfunktionen som involverar vätgasförbrukning och hastighetsbegränsningar. Kontrollenheten ger en optimal fördelning av bränslecell- och batterikraft över en tidsperiod som uppfyller det efterfrågade effektbehovet och följer systembegränsningarna. Maskinbelastningsestimeringen är baserad på autokorrelation och integreras med kontrollenheten. Estimeringsinstrumentet fungerar som en ingång till kontrollenheten som optimerar fördelningen av kraften mellan batteriet och bränslecellen. Jämfört med den tidigare realtidsfördelningsfunktionen för effekt som användes av Volvo Construction Equipment AB (Volvo CE) visade det sig att MPC kombinerat med autokorrelationsbaserad belastningsestimering främst använde ett mycket smalare fönster för batteriets laddningstillstånd (SoC), vilket öppnar upp möjligheten att minska batteristorleken i maskinen. Transienter i bränslecellens effekt minskar också, vilket minskar dess nedbrytning och förbättrar livslängden. / Ever since the industrial evolution, the rate of global warming and pollution in the environment have gone up significantly. Automotive companies are actively working towards making their products more sustainable in terms of reducing emissions, minimizing resource utilization of non-renewables, recycling, and several other steps. A pure battery electric vehicle (BEV) is a possible solution for cleaner transport and has seen widespread adoption among users. However, with the current battery technology, large construction machines such as excavators would need heavy batteries to meet their energy demand, pushing up the overall weight. Hydrogen driven Fuel Cell Hybrid Electric Vehicles (FCHEV) are a potential solution for medium and large sized construction machines having both fuel cells and batteries to supply energy. Construction machines have a highly transient power and generally perform repeating patterns of work but a fuel cell is slow reacting device due to the chemistry involved. Hence there is a need for an efficient energy management system to meet the power demand, satisfy system constraints, reduce hydrogen consumption and limit fuel cell and battery degradation. This thesis aims to develop a controller and a machine load predictor for such a FCHEV. A novel energy management strategy is proposed by formulating it as an optimization problem and using Model Predictive Control (MPC) to minimize the objective function that involves hydrogen consumption and rate constraints. The controller yields an optimal fuel cell and battery power split over a time-horizon that fulfills the demanded power and obeys the system constraints. An auto-correlation-based machine load predictor is integrated with the controller. The predictor serves as an input to the controller that optimizes the power split between the battery and fuel cell. Compared to the previous real-time power-split function used by Volvo Construction Equipment AB (Volvo CE), the MPC combined with the auto-correlation-based load predictor was found to primarily use a much narrower battery State of Charge (SoC) window, thus opening up the potential to reduce battery size in the machine. Transients in the fuel cell power are also reduced, thus slowing down its degradation and improving the lifetime.

This thesis gives a research on developing an automatic or guided-automatic tool to predict the hardware (HW) resource occupation, namely task load, with respect to the software (SW) application algorithm parameters in an LTE base station. For the signal processing in an LTE base station it is important to get knowledge of how many HW resources will be used when applying a SW algorithm on a specic platform. The information is valuable for one to know the system and platform better, which can facilitate a reasonable use of the available resources. The process of developing the tool is considered to be the process of building a mathematical model between HW task load and SW parameters, where the process is dened as function approximation. According to the universal approximation theorem, the problem can be solved by an intelligent method called articial neural networks (ANNs). The theorem indicates that any function can be approximated with a two-layered neural network as long as the activation function and number of hidden neurons are proper. The thesis documents a work ow on building the model with the ANN method, as well as some research on data subset selection with mathematical methods, such as Partial Correlation and Sequential Searching as a data pre-processing step for the ANN approach. In order to make the data selection method suitable for ANNs, a modication has been made on Sequential Searching method, which gives a better result. The results show that it is possible to develop such a guided-automatic tool for prediction purposes in LTE baseband signal processing under specic precision constraints. Compared to other approaches, this model tool with intelligent approach has a higher precision level and a better adaptivity, meaning that it can be used in any part of the platform even though the transmission channels are dierent. / Denna avhandling utvecklar ett automatiskt eller ett guidat automatiskt verktyg for att forutsaga behov av hardvaruresurser, ocksa kallat uppgiftsbelastning, med avseende pa programvarans algoritmparametrar i en LTE basstation. I signalbehandling i en LTE basstation, ar det viktigt att fa kunskap om hur mycket av hardvarans resurser som kommer att tas i bruk nar en programvara ska koras pa en viss plattform. Informationen ar vardefull for nagon att forsta systemet och plattformen battre, vilket kan mojliggora en rimlig anvandning av tillgangliga resurser. Processen att utveckla verktyget anses vara processen att bygga en matematisk modell mellan hardvarans belastning och programvaruparametrarna, dar processen denieras som approximation av en funktion. Enligt den universella approximationssatsen, kan problemet losas genom en intelligent metod som kallas articiella neuronnat (ANN). Satsen visar att en godtycklig funktion kan approximeras med ett tva-skiktS neuralt natverk sa lange aktiveringsfunktionen och antalet dolda neuroner ar korrekt. Avhandlingen dokumenterar ett arbets- ode for att bygga modellen med ANN-metoden, samt studerar matematiska metoder for val av delmangder av data, sasom Partiell korrelation och sekventiell sokning som dataforbehandlingssteg for ANN. For att gora valet av uppgifter som lampar sig for ANN har en andring gjorts i den sekventiella sokmetoden, som ger battre resultat. Resultaten visar att det ar mojligt att utveckla ett sadant guidat automatiskt verktyg for prediktionsandamal i LTE basbandssignalbehandling under specika precisions begransningar. Jamfort med andra metoder, har dessa modellverktyg med intelligent tillvagagangssatt en hogre precisionsniva och battre adaptivitet, vilket innebar att den kan anvandas i godtycklig del av plattformen aven om overforingskanalerna ar olika.

Les traces réelles de réseaux cellulaires représentent une mine d’information utile pour améliorer les performances des réseaux. Des traces comme les CDRs (Call detail records) contiennent des informations horodatées sur toutes les interactions des utilisateurs avec le réseau sont exploitées dans cette thèse. Nous avons proposé des nouvelles approches dans l’étude et l’analyse des problématiques des réseaux de télécommunications, qui sont basé sur les traces réelles et des algorithmes d’apprentissage automatique. En effet, un outil global d’analyse de données, pour la classification automatique des stations de base, la prédiction de la charge de réseau et la gestion de la bande passante est proposé ainsi qu’un outil pour la détection automatique des anomalies de réseau. Ces outils ont été validés par des applications directes, et en utilisant différentes topologies de réseaux comme les réseaux WMN et les réseaux basés sur les drone-cells. Nous avons montré ainsi, qu’en utilisant des outils d’analyse de données avancés, il est possible d’optimiser dynamiquement les réseaux mobiles et améliorer la gestion de la bande passante. / Real datasets of mobile network traces contain valuable information about the network resources usage. These traces may be used to enhance and optimize the network performances. A real dataset of CDR (Call Detail Records) traces, that include spatio-temporal information about mobile users’ activities, are analyzed and exploited in this thesis. Given their large size and the fact that these are real-world datasets, information extracted from these datasets have intensively been used in our work to develop new algorithms that aim to revolutionize the infrastructure management mechanisms and optimize the usage of resource. We propose, in this thesis, a framework for network profiles classification, load prediction and dynamic network planning based on machine learning tools. We also propose a framework for network anomaly detection. These frameworks are validated using different network topologies such as wireless mesh networks (WMN) and drone-cell based networks. We show that using advanced data mining techniques, our frameworks are able to help network operators to manage and optimize dynamically their networks

The automotive industry has been actively seeking ways to reduce emissions and combat global warming. While pure battery electric vehicles have shown promise in achieving zero-tailpipe emissions, they face challenges in meeting the energy demands of large construction machines like excavators and wheel loaders, due to the heavy batteries required. To overcome this issue, Fuel Cell Hybrid Electric Vehicles (FCHEV) have emerged as a potential solution. However, efficient energy management systems are crucial for FCHEV, as fuel cells are slow-reacting devices and construction machines operate with highly transient work cycles. This thesis addresses the need for an effective energy management strategy by developing a controller and machine load predictor for an FCHEV. The proposed approach utilizes Model Predictive Control (MPC) to minimize an objective function encompassing hydrogen consumption and rate constraints. The controller determines the optimal power split between the fuel cell and battery over a specific time-horizon, ensuring power demand is met while adhering to system constraints. Additionally, an auto-correlation-based machine load predictor is integrated with the controller to optimize the power split between the battery and fuel cell. By implementing the MPC combined with the auto-correlation-based load predictor, the FCHEV effectively utilizes a narrower battery State of Charge (SoC) window, potentially reducing the required battery size in the machine. Moreover, the strategy reduces transients in fuel cell power, slowing down degradation and enhancing its lifetime, in comparison to Volvo Construction Equipment AB’s (Volvo CE) previous real-time power-split function. This research contributes to the development of energy-efficient solutions for large construction machines, particularly in the context of FCHEV. The proposed energy management strategy utilizing MPC and load prediction techniques holds promise for improving overall system performance, reducing hydrogen consumption, and limiting the degradation of fuel cell and battery components. / Bilindustrin har sedan länge sökt sätt att minska utsläppen och bekämpa den globala uppvärmningen. Även om rent batteridrivna elektriska fordon är i princip avgasfria, så är det utmananade att möta energikraven för stora byggmaskiner som grävmaskiner och hjullastare med endast batterier. För att övervinna detta problem har bränslecell-hybrid-elektriska fordon (FCHEV från engelskans Fuel-cell hybrid electrical vehicle) identifierats som en potentiell lösning. Dock är effektiva energihanteringssystem avgörande för FCHEV, eftersom bränsleceller reagerar långsamt och byggmaskiner arbetar med snabbt varierande arbetscykler. Detta examensarbete försöker att möta behovet av en effektiv energihanteringsstrategi genom att utveckla en styrenhet och maskinbelastningsprognos för en FCHEV. Det föreslagna tillvägagångssättet använder modellprediktiv reglering (MPC) för att minimera en målfunktion som ta hänsyn till både vätekonsumtion och hastighetsbegränsningar. Styrenheten bestämmer den optimala effektfördelningen mellan bränslecellen och batteriet över en specifik tidshorisont, och säkerställer att effektkravet uppfylls samtidigt som systembegränsningarna följs. Dessutom integreras en auto-korrelationsbaserad maskinbelastningsprediktor med styrenheten för att optimera effektfördelningen mellan batteriet och bränslecellen. Genom att implementera MPC i kombination med den auto-korrelationsbaserade belastningsprognosen, använder FCHEV effektivt ett smalare fönster för batterets laddningstillstånd (SoC), vilket potentiellt minskar den nödvändiga batteristorleken i maskinen. Dessutom minskar strategin transienter i bränslecellseffekten och förbättrar dess livstid, jämfört med Volvo Construction Equipment AB:s (Volvo CE) tidigare lösning. Denna forskning bidrar till utvecklingen av energieffektiva lösningar för stora byggmaskiner, särskilt i sammanhanget FCHEV. Den föreslagna energihanteringsstrategin, med sin kombination av MPC och belastningsprediktionstekniker, har en potential att förbättra den totala systemprestandan, minska vätekonsumtionen och begränsa försämringen av bränslecell- och batterikomponenter.