No description available.

No description available.

No description available.

No description available.

Car following (CF) behaviour modelling has made significant progress in both traffic engi-neering and traffic psychology during recent decades. Autonomous vehicles (AVs) have been demonstrated to optimise traffic flow and increase traffic stability. Consequently, sever-al car-following models have been proposed based on various car following criteria, leading to a range of model parameter sets. In traffic engineering, Intelligent Driving Model (IDM) are commonly used as microscopic traffic flow models to simulate a single vehicle's behav-iour on a road. Observational data can be employed to parameter calibrate IDM models, which enhances their practicality for real-world applications. As a result, the calibration of model parameters is crucial in traffic simulation research and typically involves solving an optimization problem. Within the given context, the Nelder-Mead（NM）algorithm, particle swarm optimization (PSO) algorithm and genetic algorithm (GA) are utilized in this study for parameterizing the IDM model, using abundant trajectory data from five different road conditions. The study further examines the effects of various algorithms on the IDM model in different road sections, providing useful insights for traffic simulation and optimization.:Table of Contents CHAPTER 1 INTRODUCTION 1 1.1 BACKGROUND AND MOTIVATION 1 1.2 STRUCTURE OF THE WORK 3 CHAPTER 2 BACKGROUND AND RELATED WORK 4 2.1 CAR-FOLLOWING MODELS 4 2.1.1 General Motors model and Gazis-Herman-Rothery model 5 2.1.2 Optimal velocity model and extended models 6 2.1.3 Safety distance or collision avoidance models 7 2.1.4 Physiology-psychology models 8 2.1.5 Intelligent Driver model 10 2.2 CALIBRATION OF CAR-FOLLOWING MODEL 12 2.2.1 Statistical Methods 13 2.2.2 Optimization Algorithms 14 2.3 TRAJECTORY DATA 21 2.3.1 Requirements of Experimental Data 22 2.3.2 Data Collection Techniques 22 2.3.3 Collected Experimental Data 24 CHAPTER 3 EXPERIMENTS AND RESULTS 28 3.1 CALIBRATION PROCESS 28 3.1.1 Objective Function 29 3.1.2 Errors Analysis 30 3.2 SOFTWARE AND METHODOLOGY 30 3.3 NM RESULTS 30 3.4 PSO RESULTS 37 3.4.1 PSO Calibrator 37 3.4.2 PSO Results 44 3.5 GA RESULTS 51 3.6 OPTIMIZATION PERFORMANCE ANALYSIS 58 CHAPTER 4 CONCLUSION 60 REFERENCES 62

[pt] Com o objetivo de desenvolver um sistema de apoio à decisão na área de manutenção embarcada, este trabalho apresenta um modelo para problemas de ordem com restrições: CPSO(mais). Este modelo é a combinação de dois modelos da literatura, o PSO(mais), que apresenta bons resultados em problemas com restrições, e o CPSO, que introduz as modificações necessárias para aplicar o PSO em problemas de ordem. O modelo proposto foi adaptado para resolver o complexo problema de definir a melhor sequência de atividades embarcadas e funcionários alocados, de forma a maximizar o lucro da prestadora de serviço no período de três meses respeitando todas as restrições de prazo de conclusão dos serviços e restrições específicas do segmento offshore. Para avaliar o desempenho deste novo modelo na resolução do problema proposto, duas variantes do CPSO(mais) foram avaliadas frente ao modelo da literatura, CPSO, em seis casos de simulação propostos. Conclui-se pelos resultados das simulações que o modelo CPSO(mais) com inicialização reduzida destaca-se dos demais avaliados por apresentar um tempo de execução moderado e com soluções melhores que as dos demais. / [en] In order to develop an offshore maintenance support system, this work presents a model for constrained combinatorial problems: CPSO(plus). This model is a combination of two models, the PSO(plus), which presented good results in problems with constrains, and the CPSO, which is an adaptation of PSO for application in combinatorial problems. The proposed model has been adapted to solve the complex problem of defining the best sequence of offshore activities and allocated staff so as to maximize service provider profitability within three months while respecting all service completion time constraints and specific offshore work constraints. To evaluate the performance of this new model in solving the proposed problem, two CPSO(plus) variants were evaluated against the literature model, CPSO, in six proposed simulation cases. It is concluded from the results of the simulations that the CPSO(plus) model with reduced initialization outperforms other evaluated models with respect to execution time and solutions to given problem.

<p dir="ltr">Tracking and characterizing the space debris population in Earth orbit is necessary to ensure that space can continue to be used safely. However, because space objects are affected by non-conservative forces like drag and solar radiation pressure, predicting the long-term evolution of their orbits is impossible without knowledge of their attitude profiles. Such knowledge may be unavailable for inactive satellites or objects of which the observer is not the owner or operator. In many cases, attitude cannot be measured directly because resolved images of space objects are unavailable due to the distance between the object and the observer, and the effects of atmospheric seeing. However, the total brightness of objects can still be measured. A set of brightness measurements over time is referred to as a "light curve.'' An object's observed brightness is influenced by its attitude and other factors such as its orbit, shape, and reflective properties. If some of these other factors are known, attitude information may be extracted from a light curve. Existing methods of solving this attitude inversion problem either require a good initial guess for an object's rotational states or do not provide a full state estimate. The work in this thesis avoids both problems and provides a full state estimate without requiring an initial state guess.</p><p><br></p><p dir="ltr">The attitude estimation process assumes that the observation geometry and the observed object's shape, reflection properties, and inertia tensor are known. In this thesis, an initial method of searching for attitudes that could correspond to each measurement using the viewing sphere is described. These possible attitudes or "pseudo-measurements'' are then used to initialize a probability hypothesis density filter that is theoretically capable of representing the multi-modal nature of the attitude estimate using a Gaussian mixture model. However, the probability hypothesis density filter is found to often diverge from the truth because it is necessary to merge and prune components of the Gaussian mixture model to avoid computational intractability. In its place, a particle swarm optimizer method for performing an attitude inversion has been developed. This method uses analytic attitude solutions to quickly propagate a large number of attitude time histories simultaneously. The particle swarm optimizer method is validated using simulated light curves for several objects. A preliminary attempt is made to estimate the attitude of an object using real light curve measurements.</p>

This dissertation explores the applicability of RC helicopters as a tool to map wind conditions. This dissertation presents the construction of a robust instrumentation system capable of wireless in-situ measurement and mapping of ship airwake. The presented instrumentation system utilizes an RC helicopter as a carrier platform and uses the helicopter's dynamics for spatial 3D mapping of wind turbulence. The system was tested with a YP676 naval training craft to map ship airwake generated in controlled heading wind conditions. Novel system modeling techniques were developed to estimate the dynamics of an instrumented RC helicopter, in conjunction with onboard sensing, to estimate spatially varying (local) wind conditions. The primary problem addressed in this dissertation is the reliable estimation and separation of pilot induced dynamics from the system measurements, followed by the use of the dynamics residuals/discrepancies to map the wind conditions. This dissertation presents two different modelling approaches to quantify ship airwake using helicopter dynamics. The helicopter systems were characterized using both machine learning and analytical aerodynamic modelling approaches. In the machine learning based approaches, neural networks, along with other models, were trained then assessed in their capability to model dynamics from pilot inputs and other measured helicopter states. The dynamics arising from the wind conditions were fused with the positioning estimates of the helicopter to generate ship airwake maps which were compared against CFD generated airwake patterns. In the analytical modelling based approach, the dynamic response of an RC helicopter to a spatially varying parameterized wind field was modeled using a 30-state nonlinear ordinary differential equation-based dynamic system, while capturing essential elements of the helicopter dynamics. The airwake patterns obtained from both types of approach were compared against anemometrically produced wind maps of turbulent wind conditions artificially generated in a controlled indoor environment. Novel hardware architecture was developed to acquire data critical for the operation and calibration of the proposed system. The mechatronics design of three prototypes of the proposed system were presented and performance evaluated using experimental testing with a modified YP676 naval training vessel in the Chesapeake Bay area. In closing, qualitative analysis of these systems along with potential applications and improvements are discussed to conclude this dissertation. / Ph. D. / Ship airwake is a trail of wind turbulence left behind the superstructure of cruising naval vessels and are considered as a serious safety concern for aviators during onboard operations. Prior knowledge of the airwake distribution around the ship can alert pilots of possible hazards ahead of time and mitigate operational risks during the launch and recovery of the aircraft on the flight deck. This dissertation presents a novel application of Remote Control (RC) helicopters as tools to measure and map ship airwake. This dissertation presents two approaches to extract wind conditions from helicopter dynamics: (1) using machine learning based modeling, and (2) using analytic aerodynamic modeling-based estimation. Machine Learning is a modern engineering tool to model and simulate any system using experimental data alone. Under the machine learning based approach, the helicopter’s response to pilot inputs was modeled using multiple algorithms, with experimental flight data collected the absence of the ship airwake. With an assumption of capturing all the aerodynamic effects with the machine learning algorithms, the deviations in the dynamics estimates during testing environment were used to characterize and map ship airwake. In contrast to the machine learning model, the analytical approach modeled all critical aerodynamic processes of the RC helicopter as functions of pilot inputs and wind conditions using well defined physics laws, thus eliminating any need for training data. This approach predicts wind conditions on the basis of the model’s capability to match the estimates of helicopter dynamics to the actual measurements. Both presented approaches were tested on wind conditions created in indoor and outdoor environments. The performance of the proposed system was evaluated in experimental testing with a modified YP676 naval training vessel in the Chesapeake Bay area. The dissertation also presents the mechatronic design details of the novel hardware prototypes and subsystems used in the various studies and experiments. Finally, qualitative analysis of these systems along with their potential applications and improvements are discussed to conclude this dissertation.

Most of the real world problems in water resources involve nonlinear formulations in their solution construction. Obtaining optimal solutions for large scale nonlinear optimization problems is always a challenging task. The conventional methods, such as linear programming (LP), dynamic programming (DP) and nonlinear programming (NLP) may often face problems in solving them. Recently, there has been an increasing interest in biologically motivated adaptive systems for solving real world optimization problems. The multi-member, stochastic approach followed in Evolutionary Algorithms (EA) makes them less susceptible to getting trapped at local optimal solutions, and they can search easier for global optimal solutions. In this thesis, efficient optimization techniques based on swarm intelligence and evolutionary computation principles have been proposed for single and multi-objective optimization in water resource systems. To overcome the inherent limitations of conventional optimization techniques, meta-heuristic techniques like ant colony optimization (ACO), particle swarm optimization (PSO) and differential evolution (DE) approaches are developed for single and multi-objective optimization. These methods are then applied to few case studies in planning and operation of reservoir systems in India. First a methodology based on ant colony optimization (ACO) principles is investigated for reservoir operation. The utility of the ACO technique for obtaining optimal solutions is explored for large scale nonlinear optimization problems, by solving a reservoir operation problem for monthly operation over a long-time horizon of 36 years. It is found that this methodology relaxes the over-year storage constraints and provides efficient operating policy that can be implemented over a long period of time. By using ACO technique for reservoir operation problems, some of the limitations of traditional nonlinear optimization methods are surmounted and thus the performance of the reservoir system is improved. To achieve faster optimization in water resource systems, a novel technique based on swarm intelligence, namely particle swarm optimization (PSO) has been proposed. In general, PSO has distinctly faster convergence towards global optimal solutions for numerical optimization. However, it is found that the technique has the problem of getting trapped to local optima while solving real world complex problems. To overcome such drawbacks, the standard particle swarm optimization technique has been further improved by incorporating a novel elitist-mutation (EM) mechanism into the algorithm. This strategy provides proper exploration and exploitation throughout the iterations. The improvement is demonstrated by applying it to a multi-purpose single reservoir problem and also to a multi reservoir system. The results showed robust performance of the EM-PSO approach in yielding global optimal solutions. Most of the practical problems in water resources are not only nonlinear in their formulations but are also multi-objective in nature. For multi-objective optimization, generating feasible efficient Pareto-optimal solutions is always a complicated task. In the past, many attempts with various conventional approaches were made to solve water resources problems and some of them are reported as successful. However, in using the conventional linear programming (LP) and nonlinear programming (NLP) methods, they usually involve essential approximations, especially while dealing withdiscontinuous, non-differentiable, non-convex and multi-objective functions. Most of these methods consider multiple objective functions using weighted approach or constrained approach without considering all the objectives simultaneously. Also, the conventional approaches use a point-by-point search approach, in which the outcome of these methods is a single optimal solution. So they may require a large number of simulation runs to arrive at a good Pareto optimal front. One of the major goals in multi-objective optimization is to find a set of well distributed optimal solutions along the true Pareto optimal front. The classical optimization methods often fail to attain a good and true Pareto optimal front due to accretion of the above problems. To overcome such drawbacks of the classical methods, there has recently been an increasing interest in evolutionary computation methods for solving real world multi-objective problems. In this thesis, some novel approaches for multi-objective optimization are developed based on swarm intelligence and evolutionary computation principles. By incorporating Pareto optimality principles into particle swarm optimization algorithm, a novel approach for multi-objective optimization has been developed. To obtain efficient Pareto-frontiers, along with proper selection scheme and diversity preserving mechanisms, an efficient elitist mutation strategy is proposed. The developed elitist-mutated multi-objective particle swarm optimization (EM-MOPSO) technique is tested for various numerical test problems and engineering design problems. It is found that the EM-MOPSO algorithm resulting in improved performance over a state-of-the-art multi-objective evolutionary algorithm (MOEA). The utility of EM-MOPSO technique for water resources optimization is demonstrated through application to a case study, to obtain optimal trade-off solutions to a reservoir operation problem. Through multi-objective analysis for reservoir operation policies, it is found that the technique can offer wide range of efficient alternatives along with flexibility to the decision maker. In general, most of the water resources optimization problems involve interdependence relations among the various decision variables. By using differential evolution (DE) scheme, which has a proven ability of effective handling of this kind of interdependence relationships, an efficient multi-objective solver, namely multi-objective differential evolution (MODE) is proposed. The single objective differential evolution algorithm is extended to multi-objective optimization by integrating various operators like, Pareto-optimality, non-dominated sorting, an efficient selection strategy, crowding distance operator for maintaining diversity, an external elite archive for storing non- dominated solutions and an effective constraint handling scheme. First, different variations of DE approaches for multi-objective optimization are evaluated through several benchmark test problems for numerical optimization. The developed MODE algorithm showed improved performance over a standard MOEA, namely non-dominated sorting genetic algorithm–II (NSGA-II). Then MODE is applied to a case study of Hirakud reservoir operation problem to derive operational tradeoffs in the reservoir system optimization. It is found that MODE is achieving robust performance in evaluation for the water resources problem, and that the interdependence relationships among the decision variables can be effectively modeled using differential evolution operators. For optimal utilization of scarce water resources, an integrated operational model is developed for reservoir operation for irrigation of multiple crops. The model integrates the dynamics associated with the water released from a reservoir to the actual water utilized by the crops at farm level. It also takes into account the non-linear relationship of root growth, soil heterogeneity, soil moisture dynamics for multiple crops and yield response to water deficit at various growth stages of the crops. Two types of objective functions are evaluated for the model by applying to a case study of Malaprabha reservoir project. It is found that both the cropping area and economic benefits from the crops need to be accounted for in the objective function. In this connection, a multi-objective frame work is developed and solved using the MODE algorithm to derive simultaneous policies for irrigation cropping pattern and reservoir operation. It is found that the proposed frame work can provide effective and flexible policies for decision maker aiming at maximization of overall benefits from the irrigation system. For efficient management of water resources projects, there is always a great necessity to accurately forecast the hydrologic variables. To handle uncertain behavior of hydrologic variables, soft computing based artificial neural networks (ANNs) and fuzzy inference system (FIS) models are proposed for reservoir inflow forecasting. The forecast models are developed using large scale climate inputs like indices of El-Nino Southern Oscialltion (ENSO), past information on rainfall in the catchment area and inflows into the reservoir. In this purpose, back propagation neural network (BPNN), hybrid particle swarm optimization trained neural network (PSONN) and adaptive network fuzzy inference system (ANFIS) models have been developed. The developed models are applied for forecasting inflows into the Malaprabha reservoir. The performances of these models are evaluated using standard performance measures and it is found that the hybrid PSONN model is performing better than BPNN and ANFIS models. Finally by adopting PSONN model for inflow forecasting and EMPSO technique for solving the reservoir operation model, the practical utility of the different models developed in the thesis are demonstrated through application to a real time reservoir operation problem. The developed methodologies can certainly help in better planning and operation of the scarce water resources.

Massive multiple input multiple output - MIMO systems are a reality and modern communication systems rely upon this technology to cope with the increasing need for capacity and network usage. Antenna arrays are at the heart of the of the massive-MIMO system and are the enabling technology. The defining cost of such a system is the number of transmit receive ports TRx as they dictate the number of control points and the associated digital control computational capacity. Typically users are spread along the azimuth and there is limited angular user spread along elevation. This enables us to group the elements in elevation which of course limits the elevation scanning performance. The element grouping result in grating lobes when we do elevation scanning. In the newly introduced frequency range 3 - FR3 in the envisioned 6G communication systems that is from 6-20 GHz it will not be allowed to transmit power above the horizon and the resulting grating lobes from the standard grouping should be mitigated. This project is structured into two parts. In the first part a grating lobe mitigation technique based on irregular subarray grouping utilizing the wellknown Penrose irregular tessellation is developed. This tessellation is based into two geometrical shapes where when put together they can fully tile the space aperiodically. Introducing this apperiodicity the grating or quantization lobes of the array are mitigated. In addition, in the first part a beam forming algorithm is developed based on particle swarm optimization that is able to produce the optimal weights for the array steering as well as optimize some of the embedded patterns of the irregular grouping. The last optimization step of the irregular subarray patterns is utilized only when the grouping results in a narrow pattern in azimuth and as a result we have static single port beamforming networks. This of course is a trade off between the broadside gain and the azimuth steerability of the array. In the second part of this thesis two low loss band pass filters have been developed with a PCB integrated suspended stripline techology. The filters were optimised for the frequencies within FR3. The resulted filtering structures can further be integrated at the input port of the proposed feeding network with the same technology. The two parts of this thesis target to introduce on one hand a antenna array architecture with subarray groupings that produce no grating lobes and on the other hand the proposed filtering structures have small enough dimensions to fit within the subarray footprint. / Dagens moderna kommunikationssystem använder sig av Massive multiple input multiple output (m-MIMO) för att kunna möta det allt större kraven på kapacitet och nätverksanvändning. Gruppantenner är den mest fundamentala delen av massive-MIMO system och möjliggör dess funktion. För ett sådant system (m-MIMO-system), så kommer den största kostnaden från antalet sändare/mottagare (TRx) -portar som används. Antalet portar i ett massiveMIMO system bestämmer vilken kapacitet systemet har till hands när det gäller lobformning. Vanligtvis är användare utspridda i det horisontella planet, samtidigt som de är begränsade i sin spridning i höjdled. Detta möjliggör användandet av en gruppantenn som grupperar sina antennelement i höjdled, vilket såklart begränsar gruppantennens lobformning i höjdled. Grupperandet av antennelement skapar sidlober när gruppantennen lobformar i höjdled. I det nya frekvensbandet, 3 - FR3 i det föreställda 6G kommunikationssystemet som opererar mellan 6-20 GHz, så kommer det inte att vara tillåtet att sända ut effekt över horisonten, samtidigt som de sidlober som kommer från standardgruppering måste begränsas. Detta projekt är strukturerat i två delar. I första delen så presenteras ett sätt att lindra sidlober, som baseras på irreguljära gruppantenner via Penrose tessellation. Denna tessellation är indelad i två geometriska former sådan att när vi sätter ihop dem så kan de framgångsrikt täcka vår geometri icke-periodvist. Genom att introducera denna icke-periodicitet så kan sidloberna från gruppanetnnen lindras. Utöver detta så är också så är en lobformningsalgoritm skapad som baseras på particle swarm optimization (PSO), som kan skapa de optimala vikterna för lobformning och lobstyrning. Det sista optimiseringssteget av de irreguljära gruppantennmönstret används bara när gruppering av antennelement resulterar i ett snävt mönster i azimut-riktning. Därför använder vi ett statiskt enportsmatningsnätverk. Detta är såklart en vägning mellan bredsideförstärkning och förmågan att kunna lobforma i det horisontella planet. I den andra delen så har två låg förlust bandpassfilter utvecklats med en PCB-integrerad suspended sripline teknik. Filtrerna optimerades för frekvenser inom FR3. De resulterande filterstrukturerna kan integreras längs input-porten av det föreslagna matningsnätverket som använder sig av den samma teknik. De två delarna i denna uppsats presenterar dels en gruppantenn med irreguljär antennelementsindelning som lindrar sidlober, samt dels filterstrukturer som kan användas tillsammans med gruppantennen.