Optimization Methods Applied to Power Systems Ⅱ

Electrical power systems are complex networks that include a set of electrical components that allow distributing the electricity generated in the conventional and renewable power plants to distribution systems so it can be received by final consumers (businesses and homes). In practice, power system management requires solving different design, operation, and control problems. Bearing in mind that computers are used to solve these complex optimization problems, this book includes some recent contributions to this field that cover a large variety of problems. More specifically, the book includes contributions about topics such as controllers for the frequency response of microgrids, post-contingency overflow analysis, line overloads after line and generation contingences, power quality disturbances, earthing system touch voltages, security-constrained optimal power flow, voltage regulation planning, intermittent generation in power systems, location of partial discharge source in gas-insulated switchgear, electric vehicle charging stations, optimal power flow with photovoltaic generation, hydroelectric plant location selection, cold-thermal-electric integrated energy systems, high-efficiency resonant devices for microwave power generation, security-constrained unit commitment, and economic dispatch problems

Management, Technology and Learning for Individuals, Organisations and Society in Turbulent Environments

This book presents the collection of fifty papers which were presented in the Second International Conference on BUSINESS SUSTAINABILITY 2011 - Management, Technology and Learning for Individuals, Organisations and Society in Turbulent Environments , held in Póvoa de Varzim, Portugal, from 22ndto 24thof June, 2011.The main motive of the meeting was growing awareness of the importance of the sustainability issue. This importance had emerged from the growing uncertainty of the market behaviour that leads to the characterization of the market, i.e. environment, as turbulent. Actually, the characterization of the environment as uncertain and turbulent reflects the fact that the traditional technocratic and/or socio-technical approaches cannot effectively and efficiently lead with the present situation. In other words, the rise of the sustainability issue means the quest for new instruments to deal with uncertainty and/or turbulence. The sustainability issue has a complex nature and solutions are sought in a wide range of domains and instruments to achieve and manage it. The domains range from environmental sustainability (referring to natural environment) through organisational and business sustainability towards social sustainability. Concerning the instruments for sustainability, they range from traditional engineering and management methodologies towards “soft” instruments such as knowledge, learning, and creativity. The papers in this book address virtually whole sustainability problems space in a greater or lesser extent. However, although the uncertainty and/or turbulence, or in other words the dynamic properties, come from coupling of management, technology, learning, individuals, organisations and society, meaning that everything is at the same time effect and cause, we wanted to put the emphasis on business with the intention to address primarily companies and their businesses. Due to this reason, the main title of the book is “Business Sustainability 2.0” but with the approach of coupling Management, Technology and Learning for individuals, organisations and society in Turbulent Environments. Also, the notation“2.0” is to promote the publication as a step further from our previous publication – “Business Sustainability I” – as would be for a new version of software. Concerning the Second International Conference on BUSINESS SUSTAINABILITY, its particularity was that it had served primarily as a learning environment in which the papers published in this book were the ground for further individual and collective growth in understanding and perception of sustainability and capacity for building new instruments for business sustainability. In that respect, the methodology of the conference work was basically dialogical, meaning promoting dialog on the papers, but also including formal paper presentations. In this way, the conference presented a rich space for satisfying different authors’ and participants’ needs. Additionally, promoting the widest and global learning environment and participation, in accordance with the Conference's assumed mission to promote Proactive Generative Collaborative Learning, the Conference Organisation shares/puts open to the community the papers presented in this book, as well as the papers presented on the previous Conference(s). These papers can be accessed from the conference webpage (http://labve.dps.uminho.pt/bs11). In these terms, this book could also be understood as a complementary instrument to the Conference authors’ and participants’, but also to the wider readerships’ interested in the sustainability issues. The book brought together 107 authors from 11 countries, namely from Australia, Belgium, Brazil, Canada, France, Germany, Italy, Portugal, Serbia, Switzerland, and United States of America. The authors “ranged” from senior and renowned scientists to young researchers providing a rich and learning environment. At the end, the editors hope, and would like, that this book to be useful, meeting the expectation of the authors and wider readership and serving for enhancing the individual and collective learning, and to incentive further scientific development and creation of new papers. Also, the editors would use this opportunity to announce the intention to continue with new editions of the conference and subsequent editions of accompanying books on the subject of BUSINESS SUSTAINABILITY, the third of which is planned for year 2013.info:eu-repo/semantics/publishedVersio

Advanced Techniques for Design and Manufacturing in Marine Engineering

Modern engineering design processes are driven by the extensive use of numerical simulations; naval architecture and ocean engineering are no exception. Computational power has been improved over the last few decades; therefore, the integration of different tools such as CAD, FEM, CFD, and CAM has enabled complex modeling and manufacturing problems to be solved in a more feasible way. Classical naval design methodology can take advantage of this integration, giving rise to more robust designs in terms of shape, structural and hydrodynamic performances, and the manufacturing process.This Special Issue invites researchers and engineers from both academia and the industry to publish the latest progress in design and manufacturing techniques in marine engineering and to debate the current issues and future perspectives in this research area. Suitable topics for this issue include, but are not limited to, the following:CAD-based approaches for designing the hull and appendages of sailing and engine-powered boats and comparisons with traditional techniques;Finite element method applications to predict the structural performance of the whole boat or of a portion of it, with particular attention to the modeling of the material used;Embedded measurement systems for structural health monitoring;Determination of hydrodynamic efficiency using experimental, numerical, or semi-empiric methods for displacement and planning hulls;Topology optimization techniques to overcome traditional scantling criteria based on international standards;Applications of additive manufacturing to derive innovative shapes for internal reinforcements or sandwich hull structures

Optimization of A Real Time Multi Mixed Make-To-Order Assembly Line to Reduce Positive Drift

ThesisAssembly lines are critical for the realization of product manufacture. In recent times, there has been a shift from the make-to-stock (mass production) approach to a make-to-order (mass customization) approach and this has brought on a strong emphasis on product variety. Although variety can be included to a product at various phases of production, literature shows that by providing each functional module of the product with several variants, assembly lines provide the most cost-effective approach to achieve high product variety. However, there are certain challenges associated with using assembly lines to achieve product variety. One of these challenges is assembly line balancing. Assembly line balancing is the search for an optimum assignment of tasks, such that given precedence constraints according to pre-defined single or multi objective goal are met. These objectives include reducing the number of stations for a given cycle time or minimizing the cycle time for a given number of stations. Cycle time refers to the amount of time allotted to accomplish a certain process in an assembly process. This deviation from the optimal cycle time is technically referred to as drift. Drift can be negative or positive. Negative drift represents the time span during which an assembly line is idle, due to work being finished ahead of prescribed cycle time. Positive drift, meanwhile, represents time span in which an assembly line exceeds the prescribed cycle time. The problems caused by drift, especially positive drift, is so vast that there is a research niche are dedicated to this study called Assembly Line Balancing Problems. Various authors have proposed numerous solutions for solving assembly line balancing problems created by positive drift. However, there is very little information on optimizing multi model make-to order systems with real time inputs so as to reduce the effects of positive drift. This study looks at how such a system can be optimized by using the case study of a water bottling plant. This is done by initially looking at the literature in the field of assembly line balancing to isolate the research gap this study aims to fill. Secondly, the water bottling plant, described as the case study, is modelled using MATLAB/Simulink. Thirdly, the different optimization methodologies are discussed and applied to the created model. Finally, the optimized model is tested and the results are analysed. The results of this study show that positive drift, which can be a major challenge in a real time multi mixed assembly line, can be reduced by the optimization of assembly lines. The results of this study can also be seen as an addition to the knowledge base of the broader research on mixed model assembly line balancing

Mining a Small Medical Data Set by Integrating the Decision Tree and t-test

[[abstract]]Although several researchers have used statistical methods to prove that aspiration followed by the injection of 95% ethanol left in situ (retention) is an effective treatment for ovarian endometriomas, very few discuss the different conditions that could generate different recovery rates for the patients. Therefore, this study adopts the statistical method and decision tree techniques together to analyze the postoperative status of ovarian endometriosis patients under different conditions. Since our collected data set is small, containing only 212 records, we use all of these data as the training data. Therefore, instead of using a resultant tree to generate rules directly, we use the value of each node as a cut point to generate all possible rules from the tree first. Then, using t-test, we verify the rules to discover some useful description rules after all possible rules from the tree have been generated. Experimental results show that our approach can find some new interesting knowledge about recurrent ovarian endometriomas under different conditions.[[journaltype]]國外[[incitationindex]]EI[[booktype]]紙本[[countrycodes]]FI

Advanced Immunoinformatics Approaches for Precision Medicine

Genomic sequencing and other ’-omic’ technologies are slowly changing biomedical practice. As a result, patients now can be treated based on their molecular profile. Especially the immune system’s variability, in particular that of the human leukocyte antigen (HLA) gene cluster, makes such a paradigm indispensable when treating illnesses such as cancer, autoimmune diseases, or infectious diseases. It can be, however, costly and time-consuming to determine the HLA genotype with traditional means, as these methods do not utilize often pre-existing sequencing data. We therefore proposed an algorithmic approach that can use these data sources to infer the HLA genotype. HLA genotyping inference can be cast into a set covering problem under special biological constraints and can be solved efficiently via integer linear programming. Our proposed approach outperformed previously published methods and remains one of the most accurate methods to date. We then introduced two applications in which a HLA-based stratification is vital for the efficacy of the treatment and the reduction of its adverse effects. In the first example, we dealt with the optimal design of string-of-beads vaccines (SOB). We developed a mathematical model that maximizes the efficacy of such vaccines while minimizing their side effects based on a given HLA distribution. Comparisons of our optimally designed SOB with experimentally tested designs yielded promising results. In the second example, we considered the problem of anti-drug antibody (ADA) formation of biotherapeutics caused by HLA presented peptides. We combined a new statistical model for mutation effect prediction together with a quantitative measure of immunogenicity to formulate an optimization problem that finds alterations to reduce the risk of ADA formation. To efficiently solve this bi-objective problem, we developed a distributed solver that is up to 25-times faster than state-of-the art solvers. We used our approach to design the C2 domain of factor VIII, which is linked to ADA formation in hemophilia A. Our experimental evaluations of the proposed designs are encouraging and demonstrate the prospects of our approach. Bioinformatics is an integral part of modern biomedical research. The translation of advanced methods into clinical use is often complicated. To ease the translation, we developed a programming library for computational immunology and used it to implement a Galaxy-based web server for vaccine design and a KNIME extension for desktop PCs. These platforms allow researchers to develop their own immunoinformatics workflows utilizing the platform’s graphical programming capabilities

Advances on Mechanics, Design Engineering and Manufacturing III

This open access book gathers contributions presented at the International Joint Conference on Mechanics, Design Engineering and Advanced Manufacturing (JCM 2020), held as a web conference on June 2–4, 2020. It reports on cutting-edge topics in product design and manufacturing, such as industrial methods for integrated product and process design; innovative design; and computer-aided design. Further topics covered include virtual simulation and reverse engineering; additive manufacturing; product manufacturing; engineering methods in medicine and education; representation techniques; and nautical, aeronautics and aerospace design and modeling. The book is organized into four main parts, reflecting the focus and primary themes of the conference. The contributions presented here not only provide researchers, engineers and experts in a range of industrial engineering subfields with extensive information to support their daily work; they are also intended to stimulate new research directions, advanced applications of the methods discussed and future interdisciplinary collaborations

Spatial optimisation for resilient infrastructure services

Ph. D. Thesis.Infrastructure networks provide crucial services to the functioning of human settlements. Extreme weather events, especially flooding, can lead to disruption or complete loss of these crucial infrastructure services, which can have significant impacts on people’s health and wellbeing, as well as being costly to repair. Urban areas concentrate infrastructure and people, and are consequently particularly sensitive to disruptions due to natural (and human-made) disasters. Flooding alone constituted 47% of all weather-related disasters between 1995 and 2015, causing enormous loss of lives and economic damages. Climate change is projected to further exacerbate the impacts that natural disasters have on cities. Choices about where to site infrastructure have a significant impact on the impacts of extreme weather events. For example, investments in flood risk management have typically focussed on prioritising interventions to protect people, houses and businesses. Protection of infrastructure services has either been a bonus benefit of flood defence protection of property, or been implemented by individual infrastructure operators. Spatial planning is a key process to influence the distribution of people and activities over broad spatial scales. However, decision-making processes to locate infrastructure services does not typically consider resilience issues at broad spatial scales which can lead to inefficient use of resources. Moreover, spatial planning typically requires consideration of multiple, sometimes competing, objectives with solutions that are not readily tractable. Balancing multiple trade-offs in spatial planning with multiple variables at high spatial resolution is computationally demanding. This research has developed a new framework for multi-objective Pareto-optimal location-allocation problems solving. The RAO (Resource Allocation Optimisation) framework developed here is a heuristic approach that makes use of a Genetic Algorithm (GA) to produce Pareto-optimal spatial plans that balance a typical tradeoff in spatial planning: the maximisation of accessibility of a given infrastructure service vs the minimisation of the costs of providing that service. The method is applied to two case studies: (i) Storage of temporary flood defences, and (ii) Location of healthcare facilities. The RAO is first applied to a flood risk management case study in the Humber Estuary, UK, to optimise the strategic allocation of storing space for emergency resources (like temporary flood barriers, portable generators, pumps etc.) by maximising the accessibility of warehouses (i.e. minimising travel times from storing locations to deployment sites) and minimising costs. The evaluation of costs involves both capital and operational costs such as the length of temporary defences needed, storage site locations, number of lorries and personnel to enable their deployment, and maintenance costs. A baseline is tested against a number of scenarios, including a flood disrupting road network and thereby deployment operations, as well as variable infrastructure and land use costs, different transportation and deployment strategies and changing the priority of protecting different critical infrastructures. Key findings show investment in strategically located warehouses decreases deployment time across the whole region by several hours, while prioritising the protection of the infrastructure assets serving larger shares of population can cut costs by 30%. Moreover, the analysis of the ensemble of all scenarios provides crucial insights for spatial planners. For example, storage sites in Hull or Hedon, and in the areas of Withernsea and Drax are robust choices under all scenarios. Meanwhile, the Humber Bridge is shown to play a crucial role in enabling regional coverage of temporary barriers. The second case study shows how emergency response strategies can be enhanced by optimal allocation of healthcare facilities at a regional scale. The RAO framework allocates healthcare facilities in Northland (New Zealand) balancing the trade-off between maximisation of accessibility (i.e. minimisation of travel times between households and GP clinics) and minimisation of costs (i.e. number of clinics and doctors). Results show how c.80% of Northland’s population lives within a 20 minutes drive from the closest GP, but this can be increased to 90% with strategic investment and relocation of doctors and clinics. By accounting for flood and landslide risk, the RAO is used to identify strategies that improve accessibility to healthcare services by up to 5% even during extreme events (when compared to the current business as usual service accessibility). Application to these two problems demonstrates that the RAO framework can identify optimal strategies to deploy finite resources to maximise the resilience of infrastructure services. Moreover, it provides an analytical appreciation of the sensitivity between planning tradeoffs and therefore the overall robustness of a strategy to uncertainty. The method is consequently of benefit to local authorities, infrastructure operators and agencies responsible for disaster management. Following successful application to regional scale case studies, it is recommended that future work scale the analysis to consider resource allocation to protect infrastructure at a national scaleEngineering and Physical Sciences Research Counci