A New Manufacturing Service Selection and Composition Method Using Improved Flower Pollination Algorithm

With an increasing number of manufacturing services, the means by which to select and compose these manufacturing services have become a challenging problem. It can be regarded as a multiobjective optimization problem that involves a variety of conflicting quality of service (QoS) attributes. In this study, a multiobjective optimization model of manufacturing service composition is presented that is based on QoS and an environmental index. Next, the skyline operator is applied to reduce the solution space. And then a new method called improved Flower Pollination Algorithm (FPA) is proposed for solving the problem of manufacturing service selection and composition. The improved FPA enhances the performance of basic FPA by combining the latter with crossover and mutation operators of the Differential Evolution (DE) algorithm. Finally, a case study is conducted to compare the proposed method with other evolutionary algorithms, including the Genetic Algorithm, DE, basic FPA, and extended FPA. The experimental results reveal that the proposed method performs best at solving the problem of manufacturing service selection and composition

Frontiers in Ultra-Precision Machining

Ultra-precision machining is a multi-disciplinary research area that is an important branch of manufacturing technology. It targets achieving ultra-precision form or surface roughness accuracy, forming the backbone and support of today’s innovative technology industries in aerospace, semiconductors, optics, telecommunications, energy, etc. The increasing demand for components with ultra-precision accuracy has stimulated the development of ultra-precision machining technology in recent decades. Accordingly, this Special Issue includes reviews and regular research papers on the frontiers of ultra-precision machining and will serve as a platform for the communication of the latest development and innovations of ultra-precision machining technologies

Smart Manufacturing

This book is a collection of 11 articles that are published in the corresponding Machines Special Issue “Smart Manufacturing”. It represents the quality, breadth and depth of the most updated study in smart manufacturing (SM); in particular, digital technologies are deployed to enhance system smartness by (1) empowering physical resources in production, (2) utilizing virtual and dynamic assets over the Internet to expand system capabilities, (3) supporting data-driven decision-making activities at various domains and levels of businesses, or (4) reconfiguring systems to adapt to changes and uncertainties. System smartness can be evaluated by one or a combination of performance metrics such as degree of automation, cost-effectiveness, leanness, robustness, flexibility, adaptability, sustainability, and resilience. This book features, firstly, the concepts digital triad (DT-II) and Internet of digital triad things (IoDTT), proposed to deal with the complexity, dynamics, and scalability of complex systems simultaneously. This book also features a comprehensive survey of the applications of digital technologies in space instruments; a systematic literature search method is used to investigate the impact of product design and innovation on the development of space instruments. In addition, the survey provides important information and critical considerations for using cutting edge digital technologies in designing and manufacturing space instruments

Energy-efficient resource allocation scheme based on enhanced flower pollination algorithm for cloud computing data center

Cloud Computing (CC) has rapidly emerged as a successful paradigm for providing ICT infrastructure. Efficient and environmental-friendly resource allocation mechanisms, responsible for allocatinpg Cloud data center resources to execute user applications in the form of requests are undoubtedly required. One of the promising Nature-Inspired techniques for addressing virtualization, consolidation and energyaware problems is the Flower Pollination Algorithm (FPA). However, FPA suffers from entrapment and its static control parameters cannot maintain a balance between local and global search which could also lead to high energy consumption and inadequate resource utilization. This research developed an enhanced FPA-based energy efficient resource allocation scheme for Cloud data center which provides efficient resource utilization and energy efficiency with less probable Service Level Agreement (SLA) violations. Firstly, an Enhanced Flower Pollination Algorithm for Energy-Efficient Virtual Machine Placement (EFPA-EEVMP) was developed. In this algorithm, a Dynamic Switching Probability (DSP) strategy was adopted to balance the local and global search space in FPA used to minimize the energy consumption and maximize resource utilization. Secondly, Multi-Objective Hybrid Flower Pollination Resource Consolidation (MOH-FPRC) algorithm was developed. In this algorithm, Local Neighborhood Search (LNS) and Pareto optimisation strategies were combined with Clustering algorithm to avoid local trapping and address Cloud service providers conflicting objectives such as energy consumption and SLA violation. Lastly, Energy-Aware Multi-Cloud Flower Pollination Optimization (EAM-FPO) scheme was developed for distributed Multi-Cloud data center environment. In this scheme, Power Usage Effectiveness (PUE) and migration controller were utilised to obtain the optimal solution in a larger search space of the CC environment. The scheme was tested on MultiRecCloudSim simulator. Results of the simulation were compared with OEMACS, ACS-VMC, and EA-DP. The scheme produced outstanding performance improvement rate on the data center energy consumption by 20.5%, resource utilization by 23.9%, and SLA violation by 13.5%. The combined algorithms have reduced entrapment and maintaned balance between local and global search. Therefore, based on the findings the developed scheme has proven to be efficient in minimizing energy consumption while at the same time improving the data center resource allocation with minimum SLA violation

Metaheuristic approach on feature extraction and classification algorithm for handwrittten character recognition

Handwritten Character Recognition (HCR) is a process of converting handwritten text into machine readable form and it comprises three stages; preprocessing, feature extraction and classification. This study acknowledged the issues regarding HCR performances particularly at the feature extraction and classification stages. In relation to feature extraction stage, the problem identified is related to continuous and minimum chain code feature extraction at its starting and revisit points due to branches of handwritten character. As for the classification stage, the problems identified are related to the input feature for classification that results in low accuracy of classification and classification model particularly in Artificial Neural Network (ANN) learning problem. Thus, the aim of this study is to extract the continuous chain code feature for handwritten character along with minimising its length and then proceed to develop and enhance the ANN classification model based on the extracted chain code in order to identify the handwritten character better. Four phases were involved in accomplishing the aim of this study. First, thinning algorithm was applied to remove the redundancies of pixel in handwritten character binary image. Second, graph based-metaheuristic feature extraction algorithm was proposed to extract the continuous chain code feature of the handwritten character image while minimising the route length of the chain code. Graph theory was then utilised as a solution representation. Hence, two metaheuristic approaches were adopted; Harmony Search Algorithm (HSA) and Flower Pollination Algorithm (FPA). As a result, HSA graphbased metaheuristic feature extraction algorithm was proposed to extract the continuous chain code feature for handwritten character. Based on the experiment conducted, it was demonstrated that the HSA graph-based metaheuristic feature extraction algorithm showed better performance in generating the shortest route length of chain code with minimum computational time compared to FPA. Furthermore, based on the evaluation of previous works, the proposed algorithm showed notable performance in terms of shortest route length of chain code for extracting handwritten character. Third, a feature vector was derived to address the input feature issue. The derivation of feature vector based on proposed formation rule namely Local Value Formation Rule (LVFR) and Global Value Formation Rule (GVFR) was adopted to create the image features for classification purpose. ANN was applied to classify the handwritten character based on the derived feature vector. Fourth, a hybrid of Firefly Algorithm (FA) and ANN (FA-ANN) classification model was proposed to solve the ANN network learning issue. Confusion Matrix was generated to evaluate the performance of the model in terms of precision, sensitivity, specificity, F-score, accuracy and error rate. As a result, the proposed hybrid FA-ANN classification model is superior in classifying the handwritten characters compared to the proposed feature vector-based ANN with 1.59 percent incremental in terms of accuracy model. Furthermore, the proposed hybrid FA-ANN also exhibits better performances compared to previous related works on HCR

PROGRAM and PROCEEDINGS THE NEBRASKA ACADEMY OF SCIENCES 1880-2017 Including the Nebraska Association of Teachers of Science (NATS) Division Nebraska Junior Academy of Sciences (NJAS) Affiliate and Affiliated Societies

FRIDAY, APRIL 21, 2017 7:30 a.m. REGISTRATION FOR ACADEMY, Lobby of Lecture wing, Olin Hall 8:00 Aeronautics and Space Science, Session A, Olin 249 Aeronautics and Space Science, Session B, Olin 224 Chemistry and Physics, Section A, Chemistry, Olin A Collegiate Academy, Biology, Session A, Olin B Collegiate Academy, Biology, Session B, Olin 112 Collegiate Academy, Chemistry and Physics, Session A, Olin 324 8:30 Biological and Medical Sciences, Session A, Smith Callen Conference Center 9:10 Aeronautics and Space Science, Poster Session, Olin 249 9:40 Applied Science and Technology, Olin 325 10:00 Chemistry and Physics, Physics, Section B, Planetarium 10:30 Aeronautics and Space Science, Poster Session, Olin 249 11:00 MAIBEN MEMORIAL LECTURE, OLIN B – Scholarship and Friend of Science Recipients also announced. 12:00 LUNCH, PATIO ROOM, STORY STUDENT CENTER Aeronautics Group, Sunflower Room 1:00 p.m. Anthropology, Olin 111 Biological and Medical Sciences, Session B, Smith Callen Conference Center Collegiate Academy, Biology, Session A, Olin B Collegiate Academy, Biology, Session B, Olin 112 Collegiate Academy, Chemistry and Physics, Session B, Olin 324 Earth Science, Olin 249 1:05 Applied Science and Technology, Olin 325 1:15 Teaching of Science and Math, Olin 224 Chemistry and Physics, Section A, Chemistry, Olin A 2:45 Environmental Sciences, Olin 249 4:30 BUSINESS MEETING, OLIN B Abstracts of papers 2016-2017 EXECUTIVE COMMITTEE 2016-2017 PROGRAM COMMITTEE 2016-2017 POLICY COMMITTEE FRIENDS OF THE ACADEMY FRIEND OF SCIENCE AWARD WINNERS FRIEND OF SCIENCE AWARD TO KACIE BAUM FRIEND OF SCIENCE AWARD TO TODD YOUNG Author Index 141 p

Advanced Signal Processing Techniques Applied to Power Systems Control and Analysis

The work published in this book is related to the application of advanced signal processing in smart grids, including power quality, data management, stability and economic management in presence of renewable energy sources, energy storage systems, and electric vehicles. The distinct architecture of smart grids has prompted investigations into the use of advanced algorithms combined with signal processing methods to provide optimal results. The presented applications are focused on data management with cloud computing, power quality assessment, photovoltaic power plant control, and electrical vehicle charge stations, all supported by modern AI-based optimization methods

PROGRAM, THE NEBRASKA ACADEMY OF SCIENCES: One Hundred-Thirty-First Annual Meeting, APRIL 23-24, 2021. ONLINE

AFFILIATED SOCIETIES OF THE NEBRASKA ACADEMY OF SCIENCES, INC. 1.American Association of Physics Teachers, Nebraska Section: Web site: http://www.aapt.org/sections/officers.cfm?section=Nebraska 2.Friends of Loren Eiseley: Web site: http://www.eiseley.org/ 3.Lincoln Gem & Mineral Club: Web site: http://www.lincolngemmineralclub.org/ 4.Nebraska Chapter, National Council for Geographic Education 5.Nebraska Geological Society: Web site: http://www.nebraskageologicalsociety.org Sponsors of a $50 award to the outstanding student paper presented at the Nebraska Academy of SciencesAnnual Meeting, Earth Science /Nebraska Chapter, National Council Sections 6.Nebraska Graduate Women in Science 7.Nebraska Junior Academy of Sciences: Web site: http://www.nebraskajunioracademyofsciences.org/ 8.Nebraska Ornithologists’ Union: Web site: http://www.noubirds.org/ 9.Nebraska Psychological Association: http://www.nebpsych.org/ 10.Nebraska-Southeast South Dakota Section Mathematical Association of America: Web site: http://sections.maa.org/nesesd/ 11.Nebraska Space Grant Consortium: Web site: http://www.ne.spacegrant.org/ CONTENTS AERONAUTICS & SPACE SCIENCE ANTHROPOLOGY APPLIED SCIENCE & TECHNOLOGY BIOLOGICAL & MEDICAL SCIENCES COLLEGIATE ACADEMY: BIOLOGY COLLEGIATE ACADEMY: CHEMISTRY & PHYSICS EARTH SCIENCES ENVIRONMENTAL SCIENCES GENERAL CHEMISTRY GENERAL PHYSICS TEACHING OF SCIENCE & MATHEMATICS 2020-2021 PROGRAM COMMITTEE 2020-2021 EXECUTIVE COMMITTEE FRIENDS OF THE ACADEMY NEBRASKA ACADEMY OF SCIENCS FRIEND OF SCIENCE AWARD WINNERS FRIEND OF SCIENCE AWARD TO DR PAUL KAR