89 research outputs found

    Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5

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    This fifth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered. First Part of this book presents some theoretical advances on DSmT, dealing mainly with modified Proportional Conflict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classifiers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes. Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identification of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classification. Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classification, and hybrid techniques mixing deep learning with belief functions as well

    Tradition and Innovation in Construction Project Management

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    This book is a reprint of the Special Issue 'Tradition and Innovation in Construction Project Management' that was published in the journal Buildings

    Internet and Biometric Web Based Business Management Decision Support

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    Internet and Biometric Web Based Business Management Decision Support MICROBE MOOC material prepared under IO1/A5 Development of the MICROBE personalized MOOCs content and teaching materials Prepared by: A. Kaklauskas, A. Banaitis, I. Ubarte Vilnius Gediminas Technical University, Lithuania Project No: 2020-1-LT01-KA203-07810

    A Reinforcement Learning-based Framework for Proactive Supply Chain Risk Identification

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    Over the past few decades, global supply chains (GSCs) have seen a significant increase with the widespread adoption of digital technologies and improved trade policies. GSCs are a network of organisations or individuals across the world involved in producing and delivering goods and services to customers. While this globalisation and the use of global technologies have increased the efficiency of supply chain operations, it has also exposed them to various additional uncertainties and risk types that can negatively impact their operations. Thus, for GSCs to function properly, such uncertainties must be managed. Hence, supply chain risk management is critical in the smooth operation of GSCs. The first task in supply chain risk management is risk identification, where risk managers identify the risk events that may negatively impact their operations for further analysis. It is crucial that risk identification is undertaken in a timely manner so that risk managers can be proactive in managing the possible impacts of the identified risks on their operations. This task can be done manually which is tedious and time-consuming, however, with the increased sophistication and capability of artificial intelligence (AI), there is a potential for AI algorithms to be used to enhance the efficacy and efficiency of this task. A review of the existing literature detailed in this thesis highlights that while AI has been widely employed in different disciplines, it has shortcomings which are specific to the area of risk identification in supply chains. In other words, the majority of the existing risk identification techniques in supply chain risk management are either reactive or predictive in their working nature. This means that such techniques either identify the risk events after they occur or predict future occurrences of the known risk events based on their past pattern of occurrences. However, as emphasised in this thesis, for the supply chain risk identification process to be effective and comprehensive, it has to be proactive in its working nature rather than reactive or predictive. By being proactive, the risk identification techniques aim to identify beforehand known or unknown events of risks that have the potential to occur and negatively impact an activity. The analysis obtained assists the risk manager to perform the steps of risk analysis and risk evaluation on the identified risks before developing plans to manage them. Existing literature on supply chain risk identification lacks techniques to achieve this aim. To address this gap in the literature, this thesis develops a framework, namely Reinforcement Learning-based Supply Chain Risk Identification, which assists risk managers in automatedly and accurately identifying the risk events that may have the potential to impact their operations and bring them to his/her attention for further follow up. The proposed framework adopts the science and engineering research approach and four different frameworks are developed that identify the risk events of interest to the risk manager, extract related news articles on these risk events and analyse them, before recommending the most important news articles to the risk manager for follow-up actions. The functionality and viability of these prototypes are validated by experiments and systematised by a supply chain case study to highlight their effectiveness

    Evaluation of optimal solutions in multicriteria models for intelligent decision support

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    La memoria se enmarca dentro de la optimización y su uso para la toma de decisiones. La secuencia lógica ha sido la modelación, implementación, resolución y validación que conducen a una decisión. Para esto, hemos utilizado herramientas del análisis multicrerio, optimización multiobjetivo y técnicas de inteligencia artificial. El trabajo se ha estructurado en dos partes (divididas en tres capítulos cada una) que se corresponden con la parte teórica y con la parte experimental. En la primera parte se analiza el contexto del campo de estudio con un análisis del marco histórico y posteriormente se dedica un capítulo a la optimización multicriterio en el se recogen modelos conocidos, junto con aportaciones originales de este trabajo. En el tercer capítulo, dedicado a la inteligencia artificial, se presentan los fundamentos del aprendizaje estadístico , las técnicas de aprendizaje automático y de aprendizaje profundo necesarias para las aportaciones en la segunda parte. La segunda parte contiene siete casos reales a los que se han aplicado las técnicas descritas. En el primer capítulo se estudian dos casos: el rendimiento académico de los estudiantes de la Universidad Industrial de Santander (Colombia) y un sistema objetivo para la asignación del premio MVP en la NBA. En el siguiente capítulo se utilizan técnicas de inteligencia artificial a la similitud musical (detección de plagios en Youtube), la predicción del precio de cierre de una empresa en el mercado bursátil de Nueva York y la clasificación automática de señales espaciales acústicas en entornos envolventes. En el último capítulo a la potencia de la inteligencia artificial se le incorporan técnicas de análisis multicriterio para detectar el fracaso escolar universitario de manera precoz (en la Universidad Industrial de Santander) y, para establecer un ranking de modelos de inteligencia artificial de se recurre a métodos multicriterio. Para acabar la memoria, a pesar de que cada capítulo contiene una conclusión parcial, en el capítulo 8 se recogen las principales conclusiones de toda la memoria y una bibliografía bastante exhaustiva de los temas tratados. Además, el trabajo concluye con tres apéndices que contienen los programas y herramientas, que a pesar de ser útiles para la comprensión de la memoria, se ha preferido poner por separado para que los capítulos resulten más fluidos

    Increasing Sustainability in Buildings Through Energy-Efficient Concrete

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    The energy performance of buildings is influenced by a wide range of climatic and design-related variables, including but not limited to ambient temperature, heating and cooling systems, and thermal properties of building elements. This thesis explored the magnitude of the impact of the thermal properties of concrete compared to other influential factors and assessed their critical role in the energy performance of buildings. To this end, several approaches have been employed to improve the thermal performance of concrete, such as partial to full replacement of cement and natural aggregates with supplementary cementitious materials and recycled concrete aggregates, respectively, resulting in the production of lightweight concrete. However, incorporating recycled contents into concrete mixes beyond certain percentages can negatively impact the mechanical performance of concrete, which poses a challenge for engineers and designers balancing thermal, environmental, and mechanical performances. With the goal of spanning the mentioned requirements, this thesis proposed an AI-assisted framework integrating data-driven modelling techniques and multi-objective optimisation algorithms to optimise recycled aggregate concrete mixes targeting energy performance-related and economic objectives without compromising their mechanical strength. In this sense, incorporating recycled contents and air bubbles into concrete mixes was found to be an effective approach to address some hurdles associated with concrete 3D printing, which is a promising technique for large-scale construction projects due to its speed and cost-efficiency. The results showed that increasing air voids allowed for replacing recycled content beyond commonly used percentages, resulting in lightweight and ultra-lightweight 3D printable cementitious composites with significant thermal conductivity improvements

    Sustainability Analysis and Environmental Decision-Making Using Simulation, Optimization, and Computational Analytics

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    Effective environmental decision-making is often challenging and complex, where final solutions frequently possess inherently subjective political and socio-economic components. Consequently, complex sustainability applications in the “real world” frequently employ computational decision-making approaches to construct solutions to problems containing numerous quantitative dimensions and considerable sources of uncertainty. This volume includes a number of such applied computational analytics papers that either create new decision-making methods or provide innovative implementations of existing methods for addressing a wide spectrum of sustainability applications, broadly defined. The disparate contributions all emphasize novel approaches of computational analytics as applied to environmental decision-making and sustainability analysis – be this on the side of optimization, simulation, modelling, computational solution procedures, visual analytics, and/or information technologies

    Advanced Modeling, Control, and Optimization Methods in Power Hybrid Systems - 2021

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    The climate changes that are becoming visible today are a challenge for the global research community. In this context, renewable energy sources, fuel cell systems and other energy generating sources must be optimally combined and connected to the grid system using advanced energy transaction methods. As this reprint presents the latest solutions in the implementation of fuel cell and renewable energy in mobile and stationary applications such as hybrid and microgrid power systems based on the Energy Internet, blockchain technology and smart contracts, we hope that they will be of interest to readers working in the related fields mentioned above

    Z-Numbers-Based Approach to Hotel Service Quality Assessment

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    In this study, we are analyzing the possibility of using Z-numbers for measuring the service quality and decision-making for quality improvement in the hotel industry. Techniques used for these purposes are based on consumer evalu- ations - expectations and perceptions. As a rule, these evaluations are expressed in crisp numbers (Likert scale) or fuzzy estimates. However, descriptions of the respondent opinions based on crisp or fuzzy numbers formalism not in all cases are relevant. The existing methods do not take into account the degree of con- fidence of respondents in their assessments. A fuzzy approach better describes the uncertainties associated with human perceptions and expectations. Linguis- tic values are more acceptable than crisp numbers. To consider the subjective natures of both service quality estimates and confidence degree in them, the two- component Z-numbers Z = (A, B) were used. Z-numbers express more adequately the opinion of consumers. The proposed and computationally efficient approach (Z-SERVQUAL, Z-IPA) allows to determine the quality of services and iden- tify the factors that required improvement and the areas for further development. The suggested method was applied to evaluate the service quality in small and medium-sized hotels in Turkey and Azerbaijan, illustrated by the example
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