The main goal of a scheduling process is to decide when and how to execute each of the project's activities. Despite large variety of researched scheduling problems, the majority of them can be described as generalisations of the resource-constrained project scheduling problem (RCPSP). Because of wide applicability and challenging difficulty, RCPSP has attracted vast amount of attention in the research community and great variety of heuristics have been adapted for solving it. Even though these heuristics are structurally different and operate according to diverse principles, they are designed to obtain only one solution at a time. In the recent researches on RCPSPs, it was proven that these kind of problems have complex multimodal fitness landscapes, which are characterised by a wide solution search spaces and presence of multiple local and global optima. The main goal of this thesis is twofold. Firstly, it presents a variation of the RCPSP that considers optimisation of projects in an uncertain environment where resources are modelled to adapt to their environment and, as the result of this, improve their efficiency. Secondly, modification of a novel evolutionary computation method Cuckoo Search (CS) is proposed, which has been adapted for solving combinatorial optimisation problems and modified to obtain multiple solutions. To test the proposed methodology, two sets of experiments are carried out. Firstly, the developed algorithm is applied to a real-life software development project. Secondly, the performance of the algorithm is tested on universal benchmark instances for scheduling problems which were modified to take into account specifics of the proposed optimisation model. The results of both experiments demonstrate that the proposed methodology achieves competitive level of performance and is capable of finding multiple global solutions, as well as prove its applicability in real-life projects.

Les réseaux sans fil multi-saut (WMN : Wireless multi-hop Networks) sont passés du stade de simple curiosité pour revêtir aujourd'hui un intérêt certain aussi bien du point de vue de la communauté de recherche que des opérateurs de réseaux et services. En analysant les services et applications fournis au sein des réseaux WMNs, nous pouvons constater que certaines applications telles que la visioconférence, la VoIP, etc sont sensibles au délai et nécessitent une certaine qualité de service (QoS). D'autres applications telles que le transfert de fichier, le streaming vidéo, etc. sont gourmands en terme d'utilisation de bande passante. Par conséquent, les architectures de communication des réseaux WMNs doivent intégrer des mécanismes de routage efficaces et adaptés pour répondre aux besoins des services et applications envisagés. Dans cette thèse, Nous nous intéressons à la problématique du routage dans les réseaux WMNs. Notre objectif est de proposer une nouvelle approche de routage qui prend en compte différents métriques de coûts. Tout d'abord, nous avons montré que le routage sous contraintes multiples est un problème NP complet et que trois étapes sont nécessaires à la conception d'une nouvelle solution de routage: (i) modélisation de l'interférence, (ii) l'estimation de la de la bande passante restante, (iii) l'estimation du délai à un saut. Suivant cette vision, nous avons proposé deux variantes du protocole de routage OLSR (SP-OLSR, S2P-OLSR) se basant sur la métrique SINR. Les résultats des simulations ont montré l'intérêt de la proposition dans un contexte de communication vocale (VoIP). Ensuite, nous avons proposé un algorithme d'estimation d'interférence à 2 sauts (2-HEAR) afin d'estimer la bande passante disponible. Puis, et sur la base de cet algorithme, nous avons proposé une nouvelle métrique de routage pour les WMNs: Estimated Balanced Capacity (EBC) en vue de parvenir à l'équilibrage de charge entre des différents flux. La dernière question abordée dans cette thèse est celle de l'estimation du délai à un saut. La solution proposée donne une borne du délai en se basant sur un modèle de file d'attente de type G/G/1. Enfin, nous avons englobé toutes les précédentes contributions pour mettre en place une nouvelle approche de routage hybride sous contraintes multiples. Ce protocole comporte une partie proactive utilisant la nouvelle métrique de routage (EBC) et une partie réactive qui permet de prendre en compte le délai relative à une connexion donné. / There is a growing interest in wireless multi-hop networks (WMNs) since there are promising in opening new business opportunity for network operators and service providers. This research field aims at providing wireless communication means to carry different types of applications (FTP, Web browsing, video streaming, in addition to VoIP). Such applications have different constraints and their specific requirements in terms of Quality of Service (QoS) or performance metrics (delay jitter, end-to-end delay). We examine, in this thesis, the problem of routing in WMNs. Our main goal is to propose a new multi-metrics routing capable to fit these particular needs. In this thesis, we make several contributions toward WMN multi-constrained routing. First, we show that the multi-constrained path finding problem is NP-Complete and inherently a cross-layer issue, and that three steps are necessary to design the multi-metric routing protocol: (i) modeling of the inferring signal, (ii) estimation of the remaining bandwidth, (iii) estimation of the one-hop delay. Second, moving in such direction, we propose two enhanced versions of the OLSR routing protocol. The suggested protocols consider the SINR as a routing metric to build a reliable topology graph. Performance evaluation shows that utilizing such routing metric helps to improve significantly the VoIP application quality in the context of ad hoc network while maintaining a reasonable overhead cost. Third, we have proposed a 2-Hop interference Estimation Algorithm (2-HEAR) in order to estimate the available bandwidth. Then, and based on such algorithm, we have proposed a novel routing metric for WMNs: Estimated Balanced Capacity (EBC) in order to achieve load-balancing among the different flows. The next issue tackled in this thesis is the one-hop delay estimation, the one-hop delay is estimated by means of an analytical model based on G/G/1 queue. Finally, we have encompassed all the previous contributions to address our main goal, i.e. the design of a multi-constrained routing protocol for WMNs. A hybrid routing protocol is then proposed. This protocol is a junction of two parts : a proactive part that makes use of the previously estimated constraint, and a reactive part, which is triggered ”on demand” when news applications are expressed.

abstract: In the daily life of an individual problems of varying difficulty are encountered. Each problem may include a different number of constraints placed upon the problem solver. One type of problem commonly used in research are multiply-constrained problems, such as the compound remote associates. Since their development they have been related to creativity and insight. Moreover, research has been conducted to determine the cognitive abilities underlying problem solving abilities. We sought to fully evaluate the range of cognitive abilities (i.e., working memory, episodic and semantic memory, and fluid and crystallized intelligence) linked to multiply-constrained problem solving. Additionally, we sought to determine whether problem solving ability and strategies (analytical or insightful) were task specific or domain general through the use of novel problem solving tasks (TriBond and Location Bond). Results indicated that multiply-constrained problem solving abilities were domain general, solutions derived through insightful strategies were more often correct than analytical, and crystallized intelligence was the only cognitive ability that provided unique predictive value. / Dissertation/Thesis / Masters Thesis Psychology 2019

In this dissertation, several volt-var optimization methods have been proposed to improve the expected performance of the distribution system using distributed renewable energy sources and conventional volt-var control equipment: photovoltaic inverter reactive power control for chance-constrained distribution system performance optimisation, integrated distribution system optimization using a chance-constrained formulation, integrated control of distribution system equipment and distributed generation inverters, and coordination of PV inverters and voltage regulators considering generation correlation and voltage quality constraints for loss minimization. Distributed generation sources (DGs) have important benefits, including the use of renewable resources, increased customer participation, and decreased losses. However, as the penetration level of DGs increases, the technical challenges of integrating these resources into the power system increase as well. One such challenge is the rapid variation of voltages along distribution feeders in response to DG output fluctuations, and the traditional volt-var control equipment and inverter-based DG can be used to address this challenge. These methods aim to achieve an optimal expected performance with respect to the figure of merit of interest to the distribution system operator while maintaining appropriate system voltage magnitudes and considering the uncertainty of DG power injections. The first method is used to optimize only the reactive power output of DGs to improve system performance (e.g., operating profit) and compensate for variations in active power injection while maintaining appropriate system voltage magnitudes and considering the uncertainty of DG power injections over the interval of interest. The second method proposes an integrated volt-var control based on a control action ahead of time to find the optimal voltage regulation tap settings and inverter reactive control parameters to improve the expected system performance (e.g., operating profit) while keeping the voltages across the system within specified ranges and considering the uncertainty of DG power injections over the interval of interest. In the third method, an integrated control strategy is formulated for the coordinated control of both distribution system equipment and inverter-based DG. This control strategy combines the use of inverter reactive power capability with the operation of voltage regulators to improve the expected value of the desired figure of merit (e.g., system losses) while maintaining appropriate system voltage magnitudes. The fourth method proposes a coordinated control strategy of voltage and reactive power control equipment to improve the expected system performance (e.g., system losses and voltage profiles) while considering the spatial correlation among the DGs and keeping voltage magnitudes within permissible limits, by formulating chance constraints on the voltage magnitude and considering the uncertainty of PV power injections over the interval of interest. The proposed methods require infrequent communication with the distribution system operator and base their decisions on short-term forecasts (i.e., the first and second methods) and long-term forecasts (i.e., the third and fourth methods). The proposed methods achieve the best set of control actions for all voltage and reactive power control equipment to improve the expected value of the figure of merit proposed in this dissertation without violating any of the operating constraints. The proposed methods are validated using the IEEE 123-node radial distribution test feeder.

Zero-inflated data abound in ecological studies as well as in other scientific and quantitative fields. Nonparametric regression with zero-inflated response may be studied via the zero-inflated generalized additive model (ZIGAM). ZIGAM assumes that the conditional distribution of the response variable belongs to the zero-inflated 1-parameter exponential family which is a probabilistic mixture of the zero atom and the 1-parameter exponential family, where the zero atom accounts for an excess of zeroes in the data. We propose the constrained zero-inflated generalized additive model (COZIGAM) for analyzing zero-inflated data, with the further assumption that the probability of non-zero-inflation is some monotone function of the (non-zero-inflated) exponential family distribution mean. When the latter assumption obtains, the new approach provides a unified framework for modeling zero-inflated data, which is more parsimonious and efficient than the unconstrained ZIGAM. We develop an iterative algorithm for model estimation based on the penalized likelihood approach, and derive formulas for constructing confidence intervals of the maximum penalized likelihood estimator. Some asymptotic properties including the consistency of the regression function estimator and the limiting distribution of the parametric estimator are derived. We also propose a Bayesian model selection criterion for choosing between the unconstrained and the constrained ZIGAMs. We consider several useful extensions of the COZIGAM, including imposing additive-component-specific proportional and partial constraints, and incorporating threshold effects to account for regime shift phenomena. The new methods are illustrated with both simulated data and real applications. An R package COZIGAM has been developed for model fitting and model selection with zero-inflated data.

The stomatal network in plants is a well-characterized biological system that hypothetically solves the constrained optimization problem of maximizing CO2 uptake from the air while constraining evaporative water loss during the process of photosynthesis. There are numerous such constrained optimization problems present in the real world as well as in computer science. This thesis work attempts to solve one such constrained optimization problem in a distributed manner by taking a cue from the dynamics of stomatal networks. The problem considered here is Dynamic Thermal Management (DTM) in a multi-processing element system in computing. There have been several approaches in the past that tried to solve the problem of DTM by varying the frequency of operation of blocks in the computing system. The selection of frequencies for DTM such that overall performance is maximized while temperature is constrained is a non-deterministic polynomial-time (NP) hard problem. In this thesis, a distributed approach to solve the problem of DTM using a cellular neural network is proposed. A cellular neural network is used to mimic the stomatal network with slight variations based on the problem considered.

One often wishes to understand the probability distribution of stochastic data from experiment or computer simulations. However, where no model is given, practitioners must resort to parametric or non-parametric methods in order to gain information about the underlying distribution. Others have used initially a nonparametric estimator in order to understand the underlying shape of a set of data, and then later returned with a parametric method to locate the peaks. However they are interested in estimating spectra, which may have multiple peaks, where in this work we are interested in approximating the peak position of a single-peak probability distribution. One method of analyzing a distribution of data is by fitting a curve to, or smoothing them. Polynomial regression and least-squares fit are examples of smoothing methods. Initial understanding of the underlying distribution can be obscured depending on the degree of smoothing. Problems such as under and oversmoothing must be addressed in order to determine the shape of the underlying distribution. Furthermore, smoothing of skewed data can give a biased estimation of the peak position. We propose two new approaches for statistical mode estimation based on the assumption that the underlying distribution has only one peak. The first method imposes the global constraint of unimodality locally, by requiring negative curvature over some domain. The second method performs a search that assumes a position of the distribution's peak and requires positive slope to the left, and negative slope to the right. Each approach entails a constrained least-squares fit to the raw cumulative probability distribution. We compare the relative efficiencies [12] of finding the peak location of these two estimators for artificially generated data from known families of distributions Weibull, beta, and gamma. Within each family a parameter controls the skewness or kurtosis, quantifying the shapes of the distributions for comparison. We also compare our methods with other estimators such as the kernel-density estimator, adaptive histogram, and polynomial regression. By comparing the effectiveness of the estimators, we can determine which estimator best locates the peak position. We find that our estimators do not perform better than other known estimators. We also find that our estimators are biased. Overall, an adaptation of kernel estimation proved to be the most efficient. The results for the work done in this thesis will be submitted, in a different form, for publication by D.A. Rabson and J.K. Looper.

This study investigated the efficacy of multiple indicators, multiple causes (MIMIC) methods in detecting uniform and nonuniform differential item functioning (DIF) among multiple groups, where the underlying causes of DIF was different. Three different implementations of MIMIC DIF detection were studied: sequential free baseline, free baseline, and constrained baseline. In addition, the robustness of the MIMIC methods against the violation of its assumption, equal factor variance across comparison groups, was investigated. We found that the sequential-free baseline methods provided similar Type I error and power rates to the free baseline method with a designated anchor, and much better Type I error and power rates than the constrained baseline method across four groups, resulting from the co-occurrence background variables. But, when the equal factor variance assumption was violated, the MIMIC methods yielded the inflated Type I error. Also, the MIMIC procedure had problems correctly identifying the sources DIF, so further methodological developments are needed.

<p>The need for debugging tools for declarative programming languages has increased due to the rapid development of modeling and simulation tools/programs. Declarative equation-based programming languages have the problem of equation systems being over-, or under-constrained. This means that the system of equations has more equations than variables or more variables than equations respectively, making the system of equations unsolvable. In this study a static debugger is implemented in OpenModelica compiler for the equation-based programming language Modelica to make it easier for the programmer or modeler to locate the equation/s causing the unconstrained system of equations. The debugging techniques used by the debugger are developed by Peter Bunus. Those techniques are able to detect unconstrained systems of equations and give solutions by identifying the minimal set ofequation/s that should be removed or which variable/s should be added to an equation/s to make the system solvable. In this study the debugging techniques for detecting and giving a solution for over-constrained system of equations are shown suitable to be used for the programming language Modelica in the OpenModelica compiler.</p>

<p>Functional Magnetic Resonance Imaging (fMRI) is a neuroimaging technique used to study brain functionality to enhance our understanding of the brain. This technique is based on MRI, a painless, noninvasive image acquisition method without harmful radiation. Small local blood oxygenation changes which are reflected as small intensity changes in the MR images are utilized to locate the active brain areas. Radio frequency pulses and a strong static magnetic field are used to measure the correlation between the physical changes in the brain and the mental functioning during the performance of cognitive tasks.</p><p>This master thesis presents approaches for the analysis of fMRI data. The constrained Canonical Correlation Analysis (CCA) which is able to exploit the spatio-temporal nature of an active area is presented and tested on real human fMRI data. The actual distribution of active brain voxels is not known in the case of real human data. To evaluate the performance of the diagnostic algorithms applied to real human data, a modified Receiver Operating Characteristics (modified ROC) which deals with this lack of knowledge is presented. The tests on real human data reveal the better detection efficiency with the constrained CCA algorithm.</p><p>A second aim of this thesis was to implement the promising technique of constrained CCA into the software environment SPM. To implement the constrained CCA algorithms into the fMRI part of SPM2, a toolbox containing Matlab functions has been programmed for the further use by neurological scientists. The new SPM functionalities to exploit the spatial extent of the active regions with CCA are presented and tested.</p>