A New Least Squares Support Vector Machines Ensemble Model for Aero Engine Performance Parameter Chaotic Prediction

Aiming at the nonlinearity, chaos, and small-sample of aero engine performance parameters data, a new ensemble model, named the least squares support vector machine (LSSVM) ensemble model with phase space reconstruction (PSR) and particle swarm optimization (PSO), is presented. First, to guarantee the diversity of individual members, different single kernel LSSVMs are selected as base predictors, and they also output the primary prediction results independently. Then, all the primary prediction results are integrated to produce the most appropriate prediction results by another particular LSSVM—a multiple kernel LSSVM, which reduces the dependence of modeling accuracy on kernel function and parameters. Phase space reconstruction theory is applied to extract the chaotic characteristic of input data source and reconstruct the data sample, and particle swarm optimization algorithm is used to obtain the best LSSVM individual members. A case study is employed to verify the effectiveness of presented model with real operation data of aero engine. The results show that prediction accuracy of the proposed model improves obviously compared with other three models

Machine Learning with Metaheuristic Algorithms for Sustainable Water Resources Management

The main aim of this book is to present various implementations of ML methods and metaheuristic algorithms to improve modelling and prediction hydrological and water resources phenomena having vital importance in water resource management

Controle de um reator de polimerização de propeno utilizando filtro de partículas e rede neural

Os materiais poliméricos estão presentes em diversos setores industriais e na vida diária da sociedade, apresentando vantagens como menores custos e maior durabilidade. O polipropileno, obtido pela formação de longas cadeias de monômero de propeno, é uma das oleofinas mais importantes da atualidade, possuindo ampla gama de aplicações. Devido ao forte interesse econômico que desperta, existe uma busca contínua por melhorias em seu processo produtivo. Vários métodos para sua fabricação podem ser encontrados, combinando tecnologias de produção e de catalizadores. Para garantir a segurança, as necessidades e atingir os objetivos das operações, torna-se necessário inserir estruturas para um controle eficaz do processo. Entretanto, sem um bom monitoramento, isto não é possível. Em plantas reais de polimerização, os dispositivos de medição estão sujeitos a incertezas e nem sempre estão disponíveis, ou o equipamento de fato não existe ou seu custo de obtenção/manutenção torna seu uso inviável. Assim, esta dissertação propõe um esquema de sensor virtual baseado em filtro de partículas (FP) e rede neural artificial (RNA), que é aplicado a um reator de polimerização de propeno simulado. Este esquema permite a redução da incerteza e a observação de variáveis latentes por meio do FP. Na sequência, a RNA permite a detecção de propriedades finais do polipropileno a partir dos dados melhorados. A preocupação foi fornecer aos controladores informações mais completas e melhoradas. Os resultados mostraram que o sensor virtual possibilitou melhorias no controle do processo, fornecendo estimativas precisas e tempo de ação consistente com intervalos de amostragens industriais, o que destaca seu potencial para aplicação prática. Palavras-chave: Modelagem e Simulação, Estimação de Estados, Filtro de Partículas, Rede Neural, Controle de Processos, Polimerização. &#8195

The use of computational intelligence for security in named data networking

Information-Centric Networking (ICN) has recently been considered as a promising paradigm for the next-generation Internet, shifting from the sender-driven end-to-end communication paradigma to a receiver-driven content retrieval paradigm. In ICN, content -rather than hosts, like in IP-based design- plays the central role in the communications. This change from host-centric to content-centric has several significant advantages such as network load reduction, low dissemination latency, scalability, etc. One of the main design requirements for the ICN architectures -since the beginning of their design- has been strong security. Named Data Networking (NDN) (also referred to as Content-Centric Networking (CCN) or Data-Centric Networking (DCN)) is one of these architectures that are the focus of an ongoing research effort that aims to become the way Internet will operate in the future. Existing research into security of NDN is at an early stage and many designs are still incomplete. To make NDN a fully working system at Internet scale, there are still many missing pieces to be filled in. In this dissertation, we study the four most important security issues in NDN in order to defense against new forms of -potentially unknown- attacks, ensure privacy, achieve high availability, and block malicious network traffics belonging to attackers or at least limit their effectiveness, i.e., anomaly detection, DoS/DDoS attacks, congestion control, and cache pollution attacks. In order to protect NDN infrastructure, we need flexible, adaptable and robust defense systems which can make intelligent -and real-time- decisions to enable network entities to behave in an adaptive and intelligent manner. In this context, the characteristics of Computational Intelligence (CI) methods such as adaption, fault tolerance, high computational speed and error resilient against noisy information, make them suitable to be applied to the problem of NDN security, which can highlight promising new research directions. Hence, we suggest new hybrid CI-based methods to make NDN a more reliable and viable architecture for the future Internet.Information-Centric Networking (ICN) ha sido recientemente considerado como un paradigma prometedor parala nueva generación de Internet, pasando del paradigma de la comunicación de extremo a extremo impulsada por el emisora un paradigma de obtención de contenidos impulsada por el receptor. En ICN, el contenido (más que los nodos, como sucede en redes IPactuales) juega el papel central en las comunicaciones. Este cambio de "host-centric" a "content-centric" tiene varias ventajas importantes como la reducción de la carga de red, la baja latencia, escalabilidad, etc. Uno de los principales requisitos de diseño para las arquitecturas ICN (ya desde el principiode su diseño) ha sido una fuerte seguridad. Named Data Networking (NDN) (también conocida como Content-Centric Networking (CCN) o Data-Centric Networking (DCN)) es una de estas arquitecturas que son objetode investigación y que tiene como objetivo convertirse en la forma en que Internet funcionará en el futuro. Laseguridad de NDN está aún en una etapa inicial. Para hacer NDN un sistema totalmente funcional a escala de Internet, todavía hay muchas piezas que faltan por diseñar. Enesta tesis, estudiamos los cuatro problemas de seguridad más importantes de NDN, para defendersecontra nuevas formas de ataques (incluyendo los potencialmente desconocidos), asegurar la privacidad, lograr una alta disponibilidad, y bloquear los tráficos de red maliciosos o al menos limitar su eficacia. Estos cuatro problemas son: detección de anomalías, ataques DoS / DDoS, control de congestión y ataques de contaminación caché. Para solventar tales problemas necesitamos sistemas de defensa flexibles, adaptables y robustos que puedantomar decisiones inteligentes en tiempo real para permitir a las entidades de red que se comporten de manera rápida e inteligente. Es por ello que utilizamos Inteligencia Computacional (IC), ya que sus características (la adaptación, la tolerancia a fallos, alta velocidad de cálculo y funcionamiento adecuado con información con altos niveles de ruido), la hace adecuada para ser aplicada al problema de la seguridad ND

Machine Learning for Informed Representation Learning

The way we view reality and reason about the processes surrounding us is intimately connected to our perception and the representations we form about our observations and experiences. The popularity of machine learning and deep learning techniques in that regard stems from their ability to form useful representations by learning from large sets of observations. Typical application examples include image recognition or language processing for which artificial neural networks are powerful tools to extract regularity patterns or relevant statistics. In this thesis, we leverage and further develop this representation learning capability to address relevant but challenging real-world problems in geoscience and chemistry, to learn representations in an informed manner relevant to the task at hand, and reason about representation learning in neural networks, in general. Firstly, we develop an approach for efficient and scalable semantic segmentation of degraded soil in alpine grasslands in remotely-sensed images based on convolutional neural networks. To this end, we consider different grassland erosion phenomena in several Swiss valleys. We find that we are able to monitor soil degradation consistent with state-of-the-art methods in geoscience and can improve detection of affected areas. Furthermore, our approach provides a scalable method for large-scale analysis which is infeasible with established methods. Secondly, we address the question of how to identify suitable latent representations to enable generation of novel objects with selected properties. For this, we introduce a new deep generative model in the context of manifold learning and disentanglement. Our model improves targeted generation of novel objects by making use of property cycle consistency in property-relevant and property-invariant latent subspaces. We demonstrate the improvements on the generation of molecules with desired physical or chemical properties. Furthermore, we show that our model facilitates interpretability and exploration of the latent representation. Thirdly, in the context of recent advances in deep learning theory and the neural tangent kernel, we empirically investigate the learning of feature representations in standard convolutional neural networks and corresponding random feature models given by the linearisation of the neural networks. We find that performance differences between standard and linearised networks generally increase with the difficulty of the task but decrease with the considered width or over-parametrisation of these networks. Our results indicate interesting implications for feature learning and random feature models as well as the generalisation performance of highly over-parametrised neural networks. In summary, we employ and study feature learning in neural networks and review how we may use informed representation learning for challenging tasks

Predicting the Future

Due to the increased capabilities of microprocessors and the advent of graphics processing units (GPUs) in recent decades, the use of machine learning methodologies has become popular in many fields of science and technology. This fact, together with the availability of large amounts of information, has meant that machine learning and Big Data have an important presence in the field of Energy. This Special Issue entitled “Predicting the Future—Big Data and Machine Learning” is focused on applications of machine learning methodologies in the field of energy. Topics include but are not limited to the following: big data architectures of power supply systems, energy-saving and efficiency models, environmental effects of energy consumption, prediction of occupational health and safety outcomes in the energy industry, price forecast prediction of raw materials, and energy management of smart buildings

Advances in Evolutionary Algorithms

With the recent trends towards massive data sets and significant computational power, combined with evolutionary algorithmic advances evolutionary computation is becoming much more relevant to practice. Aim of the book is to present recent improvements, innovative ideas and concepts in a part of a huge EA field

Recent Advances in Social Data and Artificial Intelligence 2019

The importance and usefulness of subjects and topics involving social data and artificial intelligence are becoming widely recognized. This book contains invited review, expository, and original research articles dealing with, and presenting state-of-the-art accounts pf, the recent advances in the subjects of social data and artificial intelligence, and potentially their links to Cyberspace

Women in Artificial intelligence (AI)

This Special Issue, entitled "Women in Artificial Intelligence" includes 17 papers from leading women scientists. The papers cover a broad scope of research areas within Artificial Intelligence, including machine learning, perception, reasoning or planning, among others. The papers have applications to relevant fields, such as human health, finance, or education. It is worth noting that the Issue includes three papers that deal with different aspects of gender bias in Artificial Intelligence. All the papers have a woman as the first author. We can proudly say that these women are from countries worldwide, such as France, Czech Republic, United Kingdom, Australia, Bangladesh, Yemen, Romania, India, Cuba, Bangladesh and Spain. In conclusion, apart from its intrinsic scientific value as a Special Issue, combining interesting research works, this Special Issue intends to increase the invisibility of women in AI, showing where they are, what they do, and how they contribute to developments in Artificial Intelligence from their different places, positions, research branches and application fields. We planned to issue this book on the on Ada Lovelace Day (11/10/2022), a date internationally dedicated to the first computer programmer, a woman who had to fight the gender difficulties of her times, in the XIX century. We also thank the publisher for making this possible, thus allowing for this book to become a part of the international activities dedicated to celebrating the value of women in ICT all over the world. With this book, we want to pay homage to all the women that contributed over the years to the field of AI

DATA-DRIVEN ANALYTICAL MODELS FOR IDENTIFICATION AND PREDICTION OF OPPORTUNITIES AND THREATS

During the lifecycle of mega engineering projects such as: energy facilities, infrastructure projects, or data centers, executives in charge should take into account the potential opportunities and threats that could affect the execution of such projects. These opportunities and threats can arise from different domains; including for example: geopolitical, economic or financial, and can have an impact on different entities, such as, countries, cities or companies. The goal of this research is to provide a new approach to identify and predict opportunities and threats using large and diverse data sets, and ensemble Long-Short Term Memory (LSTM) neural network models to inform domain specific foresights. In addition to predicting the opportunities and threats, this research proposes new techniques to help decision-makers for deduction and reasoning purposes. The proposed models and results provide structured output to inform the executive decision-making process concerning large engineering projects (LEPs). This research proposes new techniques that not only provide reliable timeseries predictions but uncertainty quantification to help make more informed decisions. The proposed ensemble framework consists of the following components: first, processed domain knowledge is used to extract a set of entity-domain features; second, structured learning based on Dynamic Time Warping (DTW), to learn similarity between sequences and Hierarchical Clustering Analysis (HCA), is used to determine which features are relevant for a given prediction problem; and finally, an automated decision based on the input and structured learning from the DTW-HCA is used to build a training data-set which is fed into a deep LSTM neural network for time-series predictions. A set of deeper ensemble programs are proposed such as Monte Carlo Simulations and Time Label Assignment to offer a controlled setting for assessing the impact of external shocks and a temporal alert system, respectively. The developed model can be used to inform decision makers about the set of opportunities and threats that their entities and assets face as a result of being engaged in an LEP accounting for epistemic uncertainty