Conceptual Design of Wind Farms Through Novel Multi-Objective Swarm Optimization

Wind is one of the major sources of clean and renewable energy, and global wind energy has been experiencing a steady annual growth rate of more than 20% over the past decade. In the U.S. energy market, although wind energy is one of the fastest increasing sources of electricity generation (by annual installed capacity addition), and is expected to play an important role in the future energy demographics of this country, it has also been plagued by project underperformance and concept-to-installation delays. There are various factors affecting the quality of a wind energy project, and most of these factors are strongly coupled in their influence on the socio-economic, production, and environmental objectives of a wind energy project. To develop wind farms that are profitable, reliable, and meet community acceptance, it is critical to accomplish balance between these objectives, and therefore a clean understanding of how different design and natural factors jointly impact these objectives is much needed. In this research, a Multi-objective Wind Farm Design (MOWFD) methodology is developed, which analyzes and integrates the impact of various factors on the conceptual design of wind farms. This methodology contributes three major advancements to the wind farm design paradigm: (I) provides a new understanding of the impact of key factors on the wind farm performance under the use of different wake models; (II) explores the crucial tradeoffs between energy production, cost of energy, and the quantitative role of land usage in wind farm layout optimization (WFLO); and (III) makes novel advancements on mixed-discrete particle swarm optimization algorithm through a multi-domain diversity preservation concept, to solve complex multi-objective optimization (MOO) problems. A comprehensive sensitivity analysis of the wind farm power generation is performed to understand and compare the impact of land configuration, installed capacity decisions, incoming wind speed, and ambient turbulence on the performance of conventional array layouts and optimized wind farm layouts. For array-like wind farms, the relative importance of each factor was found to vary significantly with the choice of wake models, i.e., appreciable differences in the sensitivity indices (of up to 70%) were observed across the different wake models. In contrast, for optimized wind farm layouts, the choice of wake models was observed to have no significant impact on the sensitivity indices. The MOWFD methodology is designed to explore the tradeoffs between the concerned performance objectives and simultaneously optimize the location of turbines, the type of turbines, and the land usage. More importantly, it facilitates WFLO without prescribed conditions (e.g., fixed wind farm boundaries and number of turbines), thereby allowing a more flexible exploration of the feasible layout solutions than is possible with other existing WFLO methodologies. In addition, a novel parameterization of the Pareto is performed to quantitatively explore how the best tradeoffs between energy production and land usage vary with the installed capacity decisions. The key to the various complex MO-WFLOs performed here is the unique set of capabilities offered by the new Multi-Objective Mixed-Discrete Particle Swarm Optimization (MO-MDPSO) algorithm, developed, tested and extensively used in this dissertation. The MO-MDPSO algorithm is capable of dealing with a plethora of problem complexities, namely: multiple highly nonlinear objectives, constraints, high design space dimensionality, and a mixture of continuous and discrete design variables. Prior to applying MO-MDPSO to effectively solve complex WFLO problems, this new algorithm was tested on a large and diverse suite of popular benchmark problems; the convergence and Pareto coverage offered by this algorithm was found to be competitive with some of the most popular MOO algorithms (e.g., GAs). The unique potential of the MO-MDPSO algorithm is further established through application to the following complex practical engineering problems: (I) a disc brake design problem, (II) a multi-objective wind farm layout optimization problem, simultaneously optimizing the location of turbines, the selection of turbine types, and the site orientation, and (III) simultaneously minimizing land usage and maximizing capacity factors under varying land plot availability

An Improved Bees Algorithm for Training Deep Recurrent Networks for Sentiment Classification

Recurrent neural networks (RNNs) are powerful tools for learning information from temporal sequences. Designing an optimum deep RNN is difficult due to configuration and training issues, such as vanishing and exploding gradients. In this paper, a novel metaheuristic optimisation approach is proposed for training deep RNNs for the sentiment classification task. The approach employs an enhanced Ternary Bees Algorithm (BA-3+), which operates for large dataset classification problems by considering only three individual solutions in each iteration. BA-3+ combines the collaborative search of three bees to find the optimal set of trainable parameters of the proposed deep recurrent learning architecture. Local learning with exploitative search utilises the greedy selection strategy. Stochastic gradient descent (SGD) learning with singular value decomposition (SVD) aims to handle vanishing and exploding gradients of the decision parameters with the stabilisation strategy of SVD. Global learning with explorative search achieves faster convergence without getting trapped at local optima to find the optimal set of trainable parameters of the proposed deep recurrent learning architecture. BA-3+ has been tested on the sentiment classification task to classify symmetric and asymmetric distribution of the datasets from different domains, including Twitter, product reviews, and movie reviews. Comparative results have been obtained for advanced deep language models and Differential Evolution (DE) and Particle Swarm Optimization (PSO) algorithms. BA-3+ converged to the global minimum faster than the DE and PSO algorithms, and it outperformed the SGD, DE, and PSO algorithms for the Turkish and English datasets. The accuracy value and F1 measure have improved at least with a 30–40% improvement than the standard SGD algorithm for all classification datasets. Accuracy rates in the RNN model trained with BA-3+ ranged from 80% to 90%, while the RNN trained with SGD was able to achieve between 50% and 60% for most datasets. The performance of the RNN model with BA-3+ has as good as for Tree-LSTMs and Recursive Neural Tensor Networks (RNTNs) language models, which achieved accuracy results of up to 90% for some datasets. The improved accuracy and convergence results show that BA-3+ is an efficient, stable algorithm for the complex classification task, and it can handle the vanishing and exploding gradients problem of deep RNNs

Artificial intelligence to enhance aerodynamic shape optimisation of the Aegis UAV

This article presents an optimisation framework that uses stochastic multi-objective optimisation, combined with an Artificial Neural Network (ANN), and describes its application to the aerodynamic design of aircraft shapes. The framework uses the Multi-Objective Particle Swarm Optimisation (MOPSO) algorithm and the obtained results confirm that the proposed technique provides highly optimal solutions in less computational time than other approaches to the same design problem. The main idea was to focus computational effort on worthwhile design solutions rather than exploring and evaluating all possible solutions in the design space. It is shown that the number of valid solutions obtained using ANN-MOPSO compared to MOPSO for 3000 evaluations grew from 529 to 1006 (90% improvement) with a penalty of only 8.3% (11 min) in computational time. It is demonstrated that including an ANN, the ANN-MOPSO with 3000 evaluations produced a larger number of valid solutions than the MOPSO with 5500 evaluations, and in 33% less computational time (64 min). This is taken as confirmation of the potential power of ANNs when applied to this type of design problem

Fault Diagnosis and Failure Prognostics of Lithium-ion Battery based on Least Squares Support Vector Machine and Memory Particle Filter Framework

123456A novel data driven approach is developed for fault diagnosis and remaining useful life (RUL) prognostics for lithium-ion batteries using Least Square Support Vector Machine (LS-SVM) and Memory-Particle Filter (M-PF). Unlike traditional data-driven models for capacity fault diagnosis and failure prognosis, which require multidimensional physical characteristics, the proposed algorithm uses only two variables: Energy Efficiency (EE), and Work Temperature. The aim of this novel framework is to improve the accuracy of incipient and abrupt faults diagnosis and failure prognosis. First, the LSSVM is used to generate residual signal based on capacity fade trends of the Li-ion batteries. Second, adaptive threshold model is developed based on several factors including input, output model error, disturbance, and drift parameter. The adaptive threshold is used to tackle the shortcoming of a fixed threshold. Third, the M-PF is proposed as the new method for failure prognostic to determine Remaining Useful Life (RUL). The M-PF is based on the assumption of the availability of real-time observation and historical data, where the historical failure data can be used instead of the physical failure model within the particle filter. The feasibility of the framework is validated using Li-ion battery prognostic data obtained from the National Aeronautic and Space Administration (NASA) Ames Prognostic Center of Excellence (PCoE). The experimental results show the following: (1) fewer data dimensions for the input data are required compared to traditional empirical models; (2) the proposed diagnostic approach provides an effective way of diagnosing Li-ion battery fault; (3) the proposed prognostic approach can predict the RUL of Li-ion batteries with small error, and has high prediction accuracy; and, (4) the proposed prognostic approach shows that historical failure data can be used instead of a physical failure model in the particle filter

Cybersecurity of Industrial Cyber-Physical Systems: A Review

Industrial cyber-physical systems (ICPSs) manage critical infrastructures by controlling the processes based on the "physics" data gathered by edge sensor networks. Recent innovations in ubiquitous computing and communication technologies have prompted the rapid integration of highly interconnected systems to ICPSs. Hence, the "security by obscurity" principle provided by air-gapping is no longer followed. As the interconnectivity in ICPSs increases, so does the attack surface. Industrial vulnerability assessment reports have shown that a variety of new vulnerabilities have occurred due to this transition while the most common ones are related to weak boundary protection. Although there are existing surveys in this context, very little is mentioned regarding these reports. This paper bridges this gap by defining and reviewing ICPSs from a cybersecurity perspective. In particular, multi-dimensional adaptive attack taxonomy is presented and utilized for evaluating real-life ICPS cyber incidents. We also identify the general shortcomings and highlight the points that cause a gap in existing literature while defining future research directions.Comment: 32 pages, 10 figure

Improving the accuracy of spoofed traffic inference in inter-domain traffic

Ascertaining that a network will forward spoofed traffic usually requires an active probing vantage point in that network, effectively preventing a comprehensive view of this global Internet vulnerability. We argue that broader visibility into the spoofing problem may lie in the capability to infer lack of Source Address Validation (SAV) compliance from large, heavily aggregated Internet traffic data, such as traffic observable at Internet Exchange Points (IXPs). The key idea is to use IXPs as observatories to detect spoofed packets, by leveraging Autonomous System (AS) topology knowledge extracted from Border Gateway Protocol (BGP) data to infer which source addresses should legitimately appear across parts of the IXP switch fabric. In this thesis, we demonstrate that the existing literature does not capture several fundamental challenges to this approach, including noise in BGP data sources, heuristic AS relationship inference, and idiosyncrasies in IXP interconnec- tivity fabrics. We propose Spoofer-IX, a novel methodology to navigate these challenges, leveraging Customer Cone semantics of AS relationships to guide precise classification of inter-domain traffic as In-cone, Out-of-cone ( spoofed ), Unverifiable, Bogon, and Unas- signed. We apply our methodology on extensive data analysis using real traffic data from two distinct IXPs in Brazil, a mid-size and a large-size infrastructure. In the mid-size IXP with more than 200 members, we find an upper bound volume of Out-of-cone traffic to be more than an order of magnitude less than the previous method inferred on the same data, revealing the practical importance of Customer Cone semantics in such analysis. We also found no significant improvement in deployment of SAV in networks using the mid-size IXP between 2017 and 2019. In hopes that our methods and tools generalize to use by other IXPs who want to avoid use of their infrastructure for launching spoofed-source DoS attacks, we explore the feasibility of scaling the system to larger and more diverse IXP infrastructures. To promote this goal, and broad replicability of our results, we make the source code of Spoofer-IX publicly available. This thesis illustrates the subtleties of scientific assessments of operational Internet infrastructure, and the need for a community focus on reproducing and repeating previous methods.A constatação de que uma rede encaminhará tráfego falsificado geralmente requer um ponto de vantagem ativo de medição nessa rede, impedindo efetivamente uma visão abrangente dessa vulnerabilidade global da Internet. Isto posto, argumentamos que uma visibilidade mais ampla do problema de spoofing pode estar na capacidade de inferir a falta de conformidade com as práticas de Source Address Validation (SAV) a partir de dados de tráfego da Internet altamente agregados, como o tráfego observável nos Internet Exchange Points (IXPs). A ideia chave é usar IXPs como observatórios para detectar pacotes falsificados, aproveitando o conhecimento da topologia de sistemas autônomos extraído dos dados do protocolo BGP para inferir quais endereços de origem devem aparecer legitimamente nas comunicações através da infra-estrutura de um IXP. Nesta tese, demonstramos que a literatura existente não captura diversos desafios fundamentais para essa abordagem, incluindo ruído em fontes de dados BGP, inferência heurística de relacionamento de sistemas autônomos e características específicas de interconectividade nas infraestruturas de IXPs. Propomos o Spoofer-IX, uma nova metodologia para superar esses desafios, utilizando a semântica do Customer Cone de relacionamento de sistemas autônomos para guiar com precisão a classificação de tráfego inter-domínio como In-cone, Out-of-cone ( spoofed ), Unverifiable, Bogon, e Unassigned. Aplicamos nossa metodologia em análises extensivas sobre dados reais de tráfego de dois IXPs distintos no Brasil, uma infraestrutura de médio porte e outra de grande porte. No IXP de tamanho médio, com mais de 200 membros, encontramos um limite superior do volume de tráfego Out-of-cone uma ordem de magnitude menor que o método anterior inferiu sob os mesmos dados, revelando a importância prática da semântica do Customer Cone em tal análise. Além disso, não encontramos melhorias significativas na implantação do Source Address Validation (SAV) em redes usando o IXP de tamanho médio entre 2017 e 2019. Na esperança de que nossos métodos e ferramentas sejam aplicáveis para uso por outros IXPs que desejam evitar o uso de sua infraestrutura para iniciar ataques de negação de serviço através de pacotes de origem falsificada, exploramos a viabilidade de escalar o sistema para infraestruturas IXP maiores e mais diversas. Para promover esse objetivo e a ampla replicabilidade de nossos resultados, disponibilizamos publicamente o código fonte do Spoofer-IX. Esta tese ilustra as sutilezas das avaliações científicas da infraestrutura operacional da Internet e a necessidade de um foco da comunidade na reprodução e repetição de métodos anteriores

SciTech News- 69(1)-2015

Columns and Reports From the Editor.....5 SciTech News Call for Articles....5 Conference Report, Momentum Press SLA Annual Conference Grant Recipient.. 20 Division News Science-Technology Division...6 Chemistry Division... 11 Engineering Division... 17 Aerospace Section of the Engineering Division.. 23 Architecture, Building Engineering, Construction and Design Section of the Engineering Division.. 24 Call for Nominations & Applications Sparks Award for Professional Development... 16 Reviews Sci-Tech Book News Reviews.... 25 Advertisements Annual Reviews.....3 IEEE....

Evolutionary Computation 2020

Intelligent optimization is based on the mechanism of computational intelligence to refine a suitable feature model, design an effective optimization algorithm, and then to obtain an optimal or satisfactory solution to a complex problem. Intelligent algorithms are key tools to ensure global optimization quality, fast optimization efficiency and robust optimization performance. Intelligent optimization algorithms have been studied by many researchers, leading to improvements in the performance of algorithms such as the evolutionary algorithm, whale optimization algorithm, differential evolution algorithm, and particle swarm optimization. Studies in this arena have also resulted in breakthroughs in solving complex problems including the green shop scheduling problem, the severe nonlinear problem in one-dimensional geodesic electromagnetic inversion, error and bug finding problem in software, the 0-1 backpack problem, traveler problem, and logistics distribution center siting problem. The editors are confident that this book can open a new avenue for further improvement and discoveries in the area of intelligent algorithms. The book is a valuable resource for researchers interested in understanding the principles and design of intelligent algorithms

Modular Self-Reconfigurable Robotic Systems: A Survey on Hardware Architectures

Modular self-reconfigurable robots present wide and unique solutions for growing demands in the domains of space exploration, automation, consumer products, and so forth. The higher utilization factor and self-healing capabilities are most demanded traits in robotics for real world applications and modular robotics offer better solutions in these perspectives in relation to traditional robotics. The researchers in robotics domain identified various applications and prototyped numerous robotic models while addressing constraints such as homogeneity, reconfigurability, form factor, and power consumption. The diversified nature of various modular robotic solutions proposed for real world applications and utilization of different sensor and actuator interfacing techniques along with physical model optimizations presents implicit challenges to researchers while identifying and visualizing the merits/demerits of various approaches to a solution. This paper attempts to simplify the comparison of various hardware prototypes by providing a brief study on hardware architectures of modular robots capable of self-healing and reconfiguration along with design techniques adopted in modeling robots, interfacing technologies, and so forth over the past 25 years