ESSE 2017. Proceedings of the International Conference on Environmental Science and Sustainable Energy

Environmental science is an interdisciplinary academic field that integrates physical-, biological-, and information sciences to study and solve environmental problems. ESSE - The International Conference on Environmental Science and Sustainable Energy provides a platform for experts, professionals, and researchers to share updated information and stimulate the communication with each other. In 2017 it was held in Suzhou, China June 23-25, 2017

Artificial Intelligence in Landscape Architecture: A Survey of Theory, Culture, and Practice

This dissertation explores the role of artificial intelligence (AI) in shaping the landscape architecture profession. It looks at how AI has evolved in the field, its current influence, and its potential to change research, teaching, and professional practice. The research includes a detailed review of existing literature to identify trends in AI applications and gaps in knowledge. It also examines landscape architects\u27 attitudes towards AI, revealing a mix of enthusiasm for its benefits and concerns about its impact on creativity and design processes, and proposes new ways of thinking about and working with AI. The work brings a unique perspective on AI in the field and gives valuable insights for future research and practice

Intelligent Real-Time Decision Support Systems for Road Traffic Management. Multi-agent based Fuzzy Neural Networks with a GA learning approach in managing control actions of road traffic centres.

The selection of the most appropriate traffic control actions to solve non-recurrent traffic congestion is a complex task which requires significant expert knowledge and experience. In this thesis we develop and investigate the application of an intelligent traffic control decision support system for road traffic management to assist the human operator to identify the most suitable control actions in order to deal with non-recurrent and non-predictable traffic congestion in a real-time situation. Our intelligent system employs a Fuzzy Neural Networks (FNN) Tool that combines the capabilities of fuzzy reasoning in measuring imprecise and dynamic factors and the capabilities of neural networks in terms of learning processes. In this work we present an effective learning approach with regard to the FNN-Tool, which consists of three stages: initializing the membership functions of both input and output variables by determining their centres and widths using self-organizing algorithms; employing an evolutionary Genetic Algorithm (GA) based learning method to identify the fuzzy rules; tune the derived structure and parameters using the back-propagation learning algorithm. We evaluate experimentally the performance and the prediction capability of this three-stage learning approach using well-known benchmark examples. Experimental results demonstrate the ability of the learning approach to identify all relevant fuzzy rules from the training data. A comparative analysis shows that the proposed learning approach has a higher degree of predictive capability than existing models. We also address the scalability issue of our intelligent traffic control decision support system by using a multi-agent based approach. The large network is divided into sub-networks, each of which has its own associated agent. Finally, our intelligent traffic control decision support system is applied to a number of road traffic case studies using the traffic network in Riyadh, in Saudi Arabia. The results obtained are promising and show that our intelligent traffic control decision support system can provide an effective support for real-time traffic control

The resilience of road transport networks redundancy, vulnerability and mobility characteristics

This thesis is concerned with the development of a composite resilience index for road transport networks. The index employs three characteristics, namely redundancy, vulnerability and mobility, measuring resilience at network junction, link and origin-destination levels, respectively. Various techniques have been adopted to quantify each characteristic and the composite resilience index as summarised below. The redundancy indicator for road transport network junctions is based on the entropy concept, due to its ability to measure the system configuration in addition to being able to model the inherent uncertainty in road transport network conditions. Various system parameters based on different combinations of link flow, relative link spare capacity and relative link speed were examined. The developed redundancy indicator covers the static aspect of redundancy, i.e. alternative paths, and the dynamic feature of redundancy reflected by the availability of spare capacity under different network loading and service level. The vulnerability indicator for road transport network links is developed by combining vulnerability attributes (e.g. link capacity, flow, length, free flow and traffic congestion density) with different weights using a new methodology based on fuzzy logic and exhaustive search optimisation techniques. Furthermore, the network vulnerability indicators are calculated using two different aggregations: an aggregated vulnerability indicator based on physical characteristics and the other based on operational characteristics. The mobility indicator for road transport networks is formulated from two mobility attributes reflecting the physical connectivity and level of service. The combination of the two mobility attributes into a single mobility indicator is achieved by a fuzzy logic approach. Finally, the interdependence of the proposed characteristics is explored and the composite resilience index is estimated from the aggregation of the three characteristics indicators using two different approaches, namely equal weighting and principal component analysis methods. Moreover, the impact of real-time travel information on the proposed resilience characteristics and the composite resilience index has been investigated. The application of the proposed methodology on a synthetic road transport network of Delft city (Netherlands) and other real life case studies shows that the developed indicators for the three characteristics and the composite resilience index responded well to traffic load change and supply variations. The developed composite resilience index will be of use in various ways; first, helping decision makers in understanding the dynamic nature of resilience under different disruptive events, highlighting weaknesses in the network and future planning to mitigate the impact of disruptive events. Furthermore, each developed indicator for the three characteristics considered can be used as a tool to assess the effectiveness of different management policies or technologies to improve the overall network performance or the daily operation of road transport networks

Natural Language Processing: Emerging Neural Approaches and Applications

This Special Issue highlights the most recent research being carried out in the NLP field to discuss relative open issues, with a particular focus on both emerging approaches for language learning, understanding, production, and grounding interactively or autonomously from data in cognitive and neural systems, as well as on their potential or real applications in different domains

Algebraic structures of neutrosophic triplets, neutrosophic duplets, or neutrosophic multisets. Volume II

The topics approached in this collection of papers are: neutrosophic sets; neutrosophic logic; generalized neutrosophic set; neutrosophic rough set; multigranulation neutrosophic rough set (MNRS); neutrosophic cubic sets; triangular fuzzy neutrosophic sets (TFNSs); probabilistic single-valued (interval) neutrosophic hesitant fuzzy set; neutro-homomorphism; neutrosophic computation; quantum computation; neutrosophic association rule; data mining; big data; oracle Turing machines; recursive enumerability; oracle computation; interval number; dependent degree; possibility degree; power aggregation operators; multi-criteria group decision-making (MCGDM); expert set; soft sets; LA-semihypergroups; single valued trapezoidal neutrosophic number; inclusion relation; Q-linguistic neutrosophic variable set; vector similarity measure; fundamental neutro-homomorphism theorem; neutro-isomorphism theorem; quasi neutrosophic triplet loop; quasi neutrosophic triplet group; BE-algebra; cloud model; fuzzy measure; clustering algorithm; and many more

Proceedings of the 2018 Canadian Society for Mechanical Engineering (CSME) International Congress

Published proceedings of the 2018 Canadian Society for Mechanical Engineering (CSME) International Congress, hosted by York University, 27-30 May 2018