An Intelligent Advisor for City Traffic Policies

Nowadays, city streets are populated not only by private vehicles but also by public transport, fleets of workers, and deliveries. Since each vehicle class has a maximum cargo capacity, we study in this article how authorities could improve the road traffic by endorsing long term policies to change the different vehicle proportions: sedans, minivans, full size vans, trucks, and motorbikes, without losing the ability of moving cargo throughout the city. We have performed our study in a realistic scenario (map, road traffic characteristics, and number of vehicles) of the city of Malaga and captured the many details into the SUMO microsimulator. After analyzing the relationship between travel times, emissions, and fuel consumption, we have defined a multiobjective optimization problem to be solved, so as to minimize these city metrics. Our results provide a scientific evidence that we can improve the delivery of goods in the city by reducing the number of heavy duty vehicles and fostering the use of vans instead.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech. This research has been partially funded by the Spanish MINECO and FEDER projects TIN2014-57341-R, TIN2016-81766-REDT, and TIN2017-88213-R. University of Malaga, Andalucia TECH. Daniel H. Stolfi is supported by a FPU grant (FPU13/00954) from the Spanish MECD. Christian Cintrano is supported by a FPI grant (BES-2015-074805) from Spanish MINECO

Design and Performance Analysis of Genetic Algorithms for Topology Control Problems

In this dissertation, we present a bio-inspired decentralized topology control mechanism, called force-based genetic algorithm (FGA), where a genetic algorithm (GA) is run by each autonomous mobile node to achieve a uniform spread of mobile nodes and to provide a fully connected network over an unknown area. We present a formal analysis of FGA in terms of convergence speed, uniformity at area coverage, and Lyapunov stability theorem. This dissertation emphasizes the use of mobile nodes to achieve a uniform distribution over an unknown terrain without a priori information and a central control unit. In contrast, each mobile node running our FGA has to make its own movement direction and speed decisions based on local neighborhood information, such as obstacles and the number of neighbors, without a centralized control unit or global knowledge. We have implemented simulation software in Java and developed four different testbeds to study the effectiveness of different GA-based topology control frameworks for network performance metrics including node density, speed, and the number of generations that GAs run. The stochastic behavior of FGA, like all GA-based approaches, makes it difficult to analyze its convergence speed. We built metrically transitive homogeneous and inhomogeneous Markov chain models to analyze the convergence of our FGA with respect to the communication ranges of mobile nodes and the total number of nodes in the system. The Dobrushin contraction coefficient of ergodicity is used for measuring convergence speed for homogeneous and inhomogeneous Markov chain models of our FGA. Furthermore, convergence characteristic analysis helps us to choose the nearoptimal values for communication range, the number of mobile nodes, and the mean node degree before sending autonomous mobile nodes to any mission. Our analytical and experimental results show that our FGA delivers promising results for uniform mobile node distribution over unknown terrains. Since our FGA adapts to local environment rapidly and does not require global network knowledge, it can be used as a real-time topology controller for commercial and military applications

Toward Fault Adaptive Power Systems in Electric Ships

Shipboard Power Systems (SPS) play a significant role in next-generation Navy fleets. With the increasing power demand from propulsion loads, ship service loads, weaponry systems and mission systems, a stable and reliable SPS is critical to support different aspects of ship operation. It also becomes the technology-enabler to improve ship economy, efficiency, reliability, and survivability. Moreover, it is important to improve the reliability and robustness of the SPS while working under different operating conditions to ensure safe and satisfactory operation of the system. This dissertation aims to introduce novel and effective approaches to respond to different types of possible faults in the SPS. According to the type and duration, the possible faults in the Medium Voltage DC (MVDC) SPS have been divided into two main categories: transient and permanent faults. First, in order to manage permanent faults in MVDC SPS, a novel real-time reconfiguration strategy has been proposed. Onboard postault reconfiguration aims to ensure the maximum power/service delivery to the system loads following a fault. This study aims to implement an intelligent real-time reconfiguration algorithm in the RTDS platform through an optimization technique implemented inside the Real-Time Digital Simulator (RTDS). The simulation results demonstrate the effectiveness of the proposed real-time approach to reconfigure the system under different fault situations. Second, a novel approach to mitigate the effect of the unsymmetrical transient AC faults in the MVDC SPS has been proposed. In this dissertation, the application of combined Static Synchronous Compensator (STATCOM)-Super Conducting Fault Current Limiter (SFCL) to improve the stability of the MVDC SPS during transient faults has been investigated. A Fluid Genetic Algorithm (FGA) optimization algorithm is introduced to design the STATCOM\u27s controller. Moreover, a multi-objective optimization problem has been formulated to find the optimal size of SFCL\u27s impedance. In the proposed scheme, STATCOM can assist the SFCL to keep the vital load terminal voltage close to the normal state in an economic sense. The proposed technique provides an acceptable post-disturbance and postault performance to recover the system to its normal situation over the other alternatives

Solving travelling salesman problem using hybrid fluid genetic algorithm (HFGA)

Gezgin Satıcı Problemi (GSP), bir satıcının bütün şehirleri sadece bir defa ziyaret ederek başlangıç noktasına dönmesini sağlayan en kısa rotanın belirlendiği problemdir. GSP, araç rotalamadan baskılı devre kartı montajına kadar birçok problemin temelini oluşturur. Bu problem, optimizasyon alanında çalışan kişilerden büyük ilgi görmüştür, ancak özellikle büyük ölçekli veri kümeleri için çözülmesi zordur. Bu çalışmada, GSP’nin çözümü için Akışkan Genetik Algoritma, En Yakın Komşu ve 2-Opt sezgiselleri üzerine kurulu melez bir yöntem sunulmaktadır. Önerilen yöntemin performansı literatürde bulunan En Yakın Komşu, Genetik Algoritma, Tabu Arama, Karınca Kolonisi Optimizasyonu ve Ağaç Fizyolojisi Optimizasyon algoritmaları kullanılarak elde edilen çözüm değerleri ile kıyaslanmıştır. Önerilen yöntemin sonuçları çözüm süresi ve kalitesi bakımından üstünlük göstermektedir

Otimização de hiperparâmetros em algoritmos de arvore de decisão utilizando computação evolutiva

Some algorithms in machine learning are parameterizable, they allow the configuration of parameters in order to increase the performance in some tasks. In most cases, these parameters are empirically found by the developer. Another approach is to use some optimization technique to find an optimized set of parameters. The aim of this project is the application of evolutionary algorithms, Genetic Algorithm (GA), Fluid Genetic Algorithm (FGA) and Genetic Algorithm using Theory of Chaos (GATC) to optimize the search for hyperparameters in decision tree algorithms. This work presents some satisfactory results within the data set tested, where the Classification and Regression Trees (CART) algorithm was used as a classifier algorithm for the tests. In these, the decision trees generated from the default values of the hyperparameters are compared with those optimized by the proposed approach. We has tried to optimize the accuracy and final size of the generated tree, which were successfully optimized by the proposed algorithms.Alguns algoritmos em aprendizado de máquina são parametrizáveis, ou seja, permitem a configuração de parâmetros de maneira a aumentar o desempenho na tarefa utilizada. Na maioria dos casos, estes parâmetros são encontrados empiricamente pelo desenvolvedor. Outra abordagem é utilizar alguma técnica de otimização para encontrar um conjunto otimizado de parâmetros. Este projeto tem por objetivo a aplicação dos algoritmos evolutivos, Algoritmo Genético (AG), Fluid Genetic Algorithm (FGA) e Genetic Algorithm using Theory of Chaos (GATC) para otimizar a busca de hiperparâmetros em algoritmos de ´arvores de decisão. Este trabalho apresenta alguns resultados satisfatórios dentro do conjunto de dados testados, onde o algoritmo Classification and. Regressivo Trees (CART) foi utilizado como algoritmo classificador para os testes. Nestes, as arvores de decisão geradas a partir dos valores padrão dos hiperparâmetros são comparados com os otimizados pela abordagem proposta. Buscou-se otimizar a acurácia e o tamanho final da ´arvore gerada, o que foram otimizadas com sucesso pelos algoritmos propostos

A NOVEL THREE DEGREE-OF-FREEDOMS OSCILLATION SYSTEM OF INSECT FLAPPING WINGS

We propose an oscillation system to replicate the dynamic behavior of flapping wings, inspired by insect flight muscles. In particular, we study the flight of the fruit fly Drosophila virilis . We model the wing as a rigid body with three degree-of-freedom, described by three Euler angles: the stroke angle, the rotation angle and the deviation angle. Insect flight muscles are separated into two types: power muscles and control muscles. One actuator and one torsional spring at the stroke angle act as the power muscles. Two torsional springs at the rotation angle and the deviation angle mimic the control muscles. A dynamic model, using a blade-element model and a quasi-steady model to calculate aerodynamic forces and moments, is set up for analysis of the system\u27s performance. Using non-dimensional analysis, we are able to identify the dynamic behavior of the system through four coefficients: stroke stiffness coefficient, rotation stiffness coefficient, deviation stiffness coefficient and input torque coefficient. We use the dynamic model to explore a large coefficients space of the oscillation system. We find that tuning deviation stiffness coefficient and rotation stiffness coefficient generates four different types of wing trajectories. Among them, the one with a high deviation stiffness coefficient and a mediate rotation stiffness coefficient produces high lift and high power loading. Its wing trajectory is quite similar to the wing trajectory in actual insects. Furthermore, a hybrid optimization algorithm (a genetic algorithm and a Nelder-Mead simplex algorithm) is implemented to find the optimal stiffness coefficients. Through these coefficients, the system minimizes power loading while still providing enough lift to maintain a time-averaged constant altitude over one stroke cycle. The results of this optimization indicate that the flapping wing with nonzero deviation achieves a better aerodynamic performance than the wing with zero deviation. The oscillatory property of this system does not only explain how insects use flight muscles to tune wing kinematics, but it also allows for design simplifications of the wing driving mechanism of flapping micro air vehicles

Congenital structural and functional fibrinogen disorders : a primer for internists

Congenital qualitative and quantitative fibrinogen disorders represent heterogeneous rare abnormalities caused by mutations in one of the 3 genes encoding individual fibrinogen polypeptide chains, located on chromosome 4q28. It is estimated that congenital fibrinogen disorder accounts for 8% of rare coagulation factor deficiencies. Most of congenital fibrinogen disorders are suspected in individuals with bleeding tendency or coincidentally discovered, for instance prior to surgery. Fibrinogen disorders could be also found in patients with thrombotic events, impaired wound healing, and recurrent spontaneous abortions. Afibrinogenemia manifests as mild to severe bleeding, while hypofibrinogenemia is often asymptomatic. Dysfibrinogenemia, a qualitative fibrinogen disorder, is associated with bleeding, thrombosis, or with no symptoms. Recent recommendations issued by the International Society on Thrombosis and Haemostasis in 2018 do not encourage routine evaluation of thrombin time or other coagulation tests in patients with suspected congenital fibrinogen disorders, highlighting the value of fibrinogen antigen measurement and genetic analysis, added to the key finding, that is, reduced fibrinogen concentration determined with a coagulometric assay. The current review summarizes practical issues in diagnostic workup and clinical management of patients with afibrinogenemia, hypofibrinogenemia, dysfibrinogenemia, and hypodysfibrinogenemia from a perspective of internists who may encounter patients with reduced fibrinogen concentration in everyday practice. Despite the fact that hematologists are in front line for the management of patients with bleeding tendency, internists should be aware of the clinical and laboratory findings in patients with inherited fibrinogen disorders including the risk of thromboembolism and management prior to invasive procedures

Statistical aspects of forensic genetics:Models for qualitative and quantitative STR data

This PhD thesis deals with statistical models intended for forensic genetics, which is the part of forensic medicine concerned with analysis of DNA evidence from criminal cases together with calculation of alleged paternity and affinity in family reunification cases. The main focus of the thesis is on crime cases as these differ from the other types of cases since the biological material often is used for person identification contrary to affinity. Common to all cases, however, is that the DNA is used as evidence in order to assess the prob-ability of observing the biological material given different hypotheses. Most countries use com-mercially manufactured DNA kits for typing a person’s DNA profile. Using these kits the DNA profile is constituted by the state of 10-15 DNA loci which has a large variation from person to person in the population. Thus, only a small fraction of the genome is typed, but due to the large variability, it is possible to identify individuals with very high probability. These probabil-ities are used when calculating the weight of evidence, which in some cases corresponds to the likelihood of observing a given suspect’s DNA profile in the population. By assessing the probability of the DNA evidence under competing hypotheses the biologica