A intelligent particle swarm optimization for short-term traffic flow forecasting using on-road sensor systems

On-road sensor systems installed on freeways are used to capture traffic flow data for short-term traffic flow predictors for traffic management, in order to reduce traffic congestion and improve vehicular mobility. This paper intends to tackle the impractical time-invariant assumptions which underlie the methods currently used to develop short-term traffic flow predictors: i) the characteristics of current data captured by on-road sensors are assumed to be time-invariant with respect to those of the historical data, which is used to developed short-term traffic flow predictors; and ii) the configuration of the on-road sensor systems is assumed to be time-invariant. In fact, both assumptions are impractical in the real world, as the current traffic flow characteristics can be very different from the historical ones, and also the on-road sensor systems are time-varying in nature due to damaged sensors or component wear. Therefore, misleading forecasting results are likely to be produced when short-term traffic flow predictors are designed using these two time-invariant assumptions. To tackle these time-invariant assumptions, an intelligent particle swarm optimization algorithm, namely IPSO, is proposed to develop short-term traffic flow predictors by integrating the mechanisms of particle swarm optimization, neural network and fuzzy inference system, in order to adapt to the time-varying traffic flow characteristics and the time-varying configurations of the on-road sensor systems. The proposed IPSO was applied to forecast traffic flow conditions on a section of freeway in Western Australia, whose traffic flow information can be captured on-line by the on-road sensor system. These results clearly demonstrate the effectiveness of using the proposed IPSO for real-time traffic flow forecasting based on traffic flow data captured by on-road sensor systems

Fuel Costs Minimization on a Steel Billet Reheating Furnace Using Genetic Algorithms

Metallurgy industries often use steel billets, at a proper temperature, to achieve the desired metallurgical, mechanical, and dimensional properties of manufactured products. Optimal operation of steel billet reheating furnaces requires the minimization of fuel consumption while maintaining a homogeneous material thermal soak. In this study, the operation of a reheating furnace is modeled as a nonlinear optimization problem with the goal of minimizing fuel cost while satisfying a desired discharge temperature. For this purpose, a genetic algorithms approach is developed. Computational simulation results show that it is possible to minimize costs for different charge temperatures and production rates using the implemented method. Additionally, practical results are validated with actual data, in a specific scenario, showing a reduction of 3.36% of fuel consumption

Multi-objective optimization of a magnetically levitated planar motor with multi-layer windings

In this paper, a novel magnetically levitated coreless planar motor with three-layer orthogonal overlapping windings is shown to have higher power density and higher space utilization compared to other coreless planar motors. In order to achieve maximum forces with minimum cost and minimum space, a multi-objective optimization of the novel planar motor is carried out. In order to reduce the computational resources required for finite element analyses, a fast but accurate analytical tool is developed, based on expressions of the flux density of the permanent magnet array, which are derived from the scalar magnetic potential method. The validity and accuracy is verified by 3D FE results. Based on the force formulas and the multi-objective function derived from the analytical models, a particle swarm optimization (PSO) algorithm is applied to optimize the dimensions of the planar motor. The design and optimization of the planar motor is validated with experimental results, measured on a built prototype, thus proving the validity of the analytical tools

Optimal excitation controllers, and location and sizing of energy storage for all-electric ship power system

The Navy\u27s future all-electric ship power system is based on the integrated power system (IPS) architecture consisting of power generation, propulsion systems, hydrodynamics, and DC zonal electric distribution system (DC-ZEDS). To improve the power quality, optimal excitation systems, and optimal location and sizing of energy storage modules (ESMs) are studied. In this dissertation, clonal selection algorithm (CSA) based controller design is firstly introduced. CSA based controller design shows better exploitation ability with relatively long search time when compared to a particle swarm optimization (PSO) based design. Furthermore, \u27optimal\u27 small population PSO (SPPSO) based excitation controller is introduced. Parameter sensitivity analysis shows that the parameters of SPPSO for regeneration can be fined tuned to achieve fast optimal controller design, and thus exploiting SPPSO features for problem of particles get trapped in local minima and long search time. Furthermore, artificial immune system based concepts are used to develop adaptive and coordinated excitation controllers for generators on ship IPS. The computational approaches for excitation controller designs have been implemented on digital signal processors interfaced to an actual laboratory synchronous machine, and to multimachine electric ship power systems simulated on a real-time digital simulator. Finally, an approach to evaluate ESM location and sizing is proposed using three metrics: quality of service, survivability and cost. Multiple objective particle swarm optimization (MOPSO) is used to optimize these metrics and provide Pareto fronts for optimal ESM location and sizing --Abstract, page iv

Green Technologies for Production Processes

This book focuses on original research works about Green Technologies for Production Processes, including discrete production processes and process production processes, from various aspects that tackle product, process, and system issues in production. The aim is to report the state-of-the-art on relevant research topics and highlight the barriers, challenges, and opportunities we are facing. This book includes 22 research papers and involves energy-saving and waste reduction in production processes, design and manufacturing of green products, low carbon manufacturing and remanufacturing, management and policy for sustainable production, technologies of mitigating CO2 emissions, and other green technologies

Modelling, Monitoring, Control and Optimization for Complex Industrial Processes

This reprint includes 22 research papers and an editorial, collected from the Special Issue "Modelling, Monitoring, Control and Optimization for Complex Industrial Processes", highlighting recent research advances and emerging research directions in complex industrial processes. This reprint aims to promote the research field and benefit the readers from both academic communities and industrial sectors

Recent Development of Hybrid Renewable Energy Systems

Abstract: The use of renewable energies continues to increase. However, the energy obtained from renewable resources is variable over time. The amount of energy produced from the renewable energy sources (RES) over time depends on the meteorological conditions of the region chosen, the season, the relief, etc. So, variable power and nonguaranteed energy produced by renewable sources implies intermittence of the grid. The key lies in supply sources integrated to a hybrid system (HS)

INTER-ENG 2020

These proceedings contain research papers that were accepted for presentation at the 14th International Conference Inter-Eng 2020 ,Interdisciplinarity in Engineering, which was held on 8–9 October 2020, in Târgu Mureș, Romania. It is a leading international professional and scientific forum for engineers and scientists to present research works, contributions, and recent developments, as well as current practices in engineering, which is falling into a tradition of important scientific events occurring at Faculty of Engineering and Information Technology in the George Emil Palade University of Medicine, Pharmacy Science, and Technology of Târgu Mures, Romania. The Inter-Eng conference started from the observation that in the 21st century, the era of high technology, without new approaches in research, we cannot speak of a harmonious society. The theme of the conference, proposing a new approach related to Industry 4.0, was the development of a new generation of smart factories based on the manufacturing and assembly process digitalization, related to advanced manufacturing technology, lean manufacturing, sustainable manufacturing, additive manufacturing, and manufacturing tools and equipment. The conference slogan was “Europe’s future is digital: a broad vision of the Industry 4.0 concept beyond direct manufacturing in the company”

Bibliography of Lewis Research Center technical publications announced in 1993

This compilation of abstracts describes and indexes the technical reporting that resulted from the scientific and engineering work performed and managed by the Lewis Research Center in 1993. All the publications were announced in the 1993 issues of STAR (Scientific and Technical Aerospace Reports) and/or IAA (International Aerospace Abstracts). Included are research reports, journal articles, conference presentations, patents and patent applications, and theses

Full Proceedings, 2018

Full conference proceedings for the 2018 International Building Physics Association Conference hosted at Syracuse University