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2018 International Symposium on Power Electronics, Electrical Drives, Automation and Motion, SPEEDAM 2018

作者： Coelho, Leandro Dos Santos Maidl, Gabriel Pierezan, Juliano Mariani, Viviana Cocco Da Luz, Mauricio V. Ferreira Leite, Jean Vianei Industrial and Systems Eng. Graduate Program -PPGEPS Federal University of Parana -UFPR Department of Electrical Engineering -DEE/PPGEE Pontifical Catholic University of Parana -PUCPR Curitiba PR Brazil Department of Electrical Engineering-DEE/PPGEE Federal University of Parana -UFPR Curitiba PR Brazil Florianopolis SC Brazil

ISBN: (纸本)9781538649411

Metaheuristic algorithm is a generic computational approach aiming at efficiently solving optimization problems, mainly global optimization problems. The No Free Lunch theorem states that no single algorithm can perform well on every optimization problem, encouraging the development of new optimization metaheuristics. Ant lion optimizer (ALO) is a nature inspired stochastic metaheuristic algorithm which mimics the hunting behavior of ant lions in nature using steps of hunting prey such as the random walk of ants, building traps, entrapment of ants in traps, catching preys, and re-building traps. In this paper, an ALO approach is adapted to multiobjective optimization (MOALO) using external archiving and ranking with crowding distance. Furthermore, a MOALO version with the control parameter setup based on Lozi map with chaotic dynamical behavior is also proposed to solve a Transformer Design Optimization (TDO) problem with two competing objectives. The effectiveness of the proposed algorithms is demonstrated by the simulations applied to a TDO problem. © 2018 IEEE.

关键词： Multiobjective optimization

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Regularization of Extreme Learning Machines with information of spatial relations of the projected data

Regularization of Extreme Learning Machines with information...

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International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Lourenço R. G. Araújo Luiz C. B. Torres Leonardo J. Silvestre Carla Takahashi Antônio P. Braga Graduate Program in Electrical Engineering Universidade Federal de Minas Gerais Belo Horizonte MG Brazil Department of Computing and Systems Federal University of Ouro Preto Jo??o Monlevade Brazil Department of Computing and Electronics Federal University of Esp??rito Santo S??o Mateus ES Brazil Loggi Tecnologia Alameda Santos S??o Paulo SP Brazil Department of Electronics Engineering Federal University of Minas Gerais Belo Horizonte MG Brazil

The following work presents a new approach to automatic selection of Tikhonov's regularization parameter, responsible for controlling the weight value of an ELM neural network. A strategy based on measurements obtained from data projection (Fisher-Score) is introduced. Seven datasets are tested and results are compared to those obtained when the regularization parameter is selected through cross-validation. The strategy shows satisfactory classification performance (in terms of p-value), while presenting significant training time reduction.

关键词： Neurons Training Cybernetics Minimization Testing Biological neural networks Aerospace electronics

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Impact of Scheduling Policies on the Performance of an Additive Manufacturing Production System

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Procedia Manufacturing 2019年 39卷 447-456页

作者： Maaz Saleem Kapadia Binil Starly Alec Thomas Reha Uzsoy Donald Warsing Graduate Program in Operations Research North Carolina State University 2500 Stinson Dr Raleigh NC 27607 USA Edward P.Fitts Department of Industrial and Systems Engineering North Carolina State University 111 Lampe Dr Raleigh NC 27607 USA Poole College of Management North Carolina State University 2801 Founders Dr Raleigh NC 27695 USA

We study the impact of scheduling policies on the cycle time and throughput of an Additive Manufacturing (AM) facility. Orders for irregularly shaped parts with specified due dates and surface quality requirements arrive randomly at the facility. The parts are fabricated in one of several AM build chambers, followed by post-processing operations based on surface finishing requirements. We combine a part placement heuristic with different scheduling policies (FIFO, Earliest Due Date) to allocate parts to AM build chambers and lay out the parts within the chambers to approximately optimize delivery performance. Simulation experiments using an Iterative Optimization-based Simulation (IOS) model integrating a simulation engine (implemented in SIMIO) with a computational engine (implemented in MATLAB) show that the scheduling policies have a significant impact on cycle time and order lateness. Selection and peer review under the responsibility of ICPR25 International Scientific & Advisory and Organizing Committee members.

关键词： Iterative Optimization-based Simulation Additive Manufacturing Genetic Algorithm

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Model mismatch in multi-objective optimisation and preservation of trade-off order. ⁎

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IFAC-PapersOnLine 2019年第26期52卷 249-254页

作者： Gilberto Reynoso-Meza Alejandro Vignoni Yadira Boada Jesüs Picü Enric Picü Industrial and Systems Engineering Graduate Program - PPGEPS. Polytechnic School. Pontifcia Universidade Catülica do Paraná PUCPR. Rua Imaculada Conceição 1155 Zip code 80215-901 Curitiba PR Brazil Synthetic Biology and Biosystems Control Lab I.U. de Automática e Informática Industrial (ai2) Universitat Politècnica de Valencia 46022 Camino de Vera S/N Valencia Spain

Multi-objective optimisation is a valuable tool for tuning dynamical systems when simultaneous optimisation performance objectives are in conflict. When the goal is tuning the parameters of a synthetic biology device, mismatch between the model implemented in silico -a more or less coarse simplification of the real system- and the actual in vivo implementation might lead to a disagreement between the in silico and in vivo design objectives for a given solution from the Pareto front. Here, we propose an iterative closed-loop multi-objective optimisation approach where the new information provided by the difference between the in silico Pareto front and its in vivo implementation is used to improve the parametric model. This aims to minimise the discrepancies between in silico and in vivo performance objectives while preserving the trade-off order among solutions. As a proof-of-concept we consider the problem of tuning a synthetic gene circuit used as feedforward-feedback controller for the expression of a protein of interest. We use an extended parametric model of the gene synthetic circuit to represent the in vivo set up and a simplified one for the in silico one.

关键词： Multi-objective optimisation model mismatch synthetic biology synthetic gene circuit

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Spatiotemporal dynamics of ecosystem fires and biomass burning-induced carbon emissions in China over the past two decades

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Geography and Sustainability 2020年第1期1卷 47-58页

作者： Anping Chen Rongyun Tang Jiafu Mao Chao Yue Xiran Li Mengdi Gao Xiaoying Shi Mingzhou Jin Daniel Ricciuto Sam Rabin Phillippe Ciais Shilong Piao Department of Biology Colorado State UniversityFort CollinsCO 80523USA Graduate Degree Program in Ecology Colorado State UniversityFort CollinsCO 80523USA Department of Industrial and Systems Engineering University of TennesseeKnoxvilleTN 37996USA Environmental Sciences Division and Climate Change Science Institute Oak Ridge National LaboratoryOak RidgeTN 37831USA State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau Northwest Agriculture&Forestry UniversityYanglingShanxi 712100China College of Urban and Environmental Sciences Central China Normal UniversityWuhanHubei 430079China Sino-French Institute for Earth System Science College of Urban and Environmental SciencesPeking UniversityBeijing 100871China Atmospheric Environmental Research(IMK-IFU) Institute of Meteorology and Climate ResearchKarlsruhe Institute of Technology(KIT)Kreuzeckbahnstraße 19Garmisch-Partenkirchen 82467Germany LSCE UMR CEA-CNRSBat.709CEL’Orme des MerisiersGif-sur-Yvette 91191France

Fire is a major type of disturbance that has important influences on ecosystem dynamics and carbon *** our understanding of ecosystem fires and their carbon cycle consequences is still limited,largely due to the difficulty of large-scale fire monitoring and the complex interactions between fire,vegetation,climate,and anthropogenic ***,using data from satellite-derived fire observations and ecosystem model simulations,we performed a comprehensive investigation of the spatial and temporal dynamics of China’s ecosystem fire disturbances and their carbon emissions over the past two decades(1997–2016).Satellite-derived results showed that on average about 3.47-4.53×10^(4) km^(2) of the land was burned annually during the past two decades,among which annual burned forest area was about 0.81-1.25×10^(4) km^(2),accounting for 0.33-0.51%of the forest area in *** burning emitted about 23.02 TgC per *** to satellite products,simulations from the Energy Exascale Earth System Land Model(ELM)strongly overestimated China’s burned area and fire-induced carbon *** burned area and fire-induced carbon emissions were high for boreal forest in Northeast China’s Daxing’anling region and subtropical dry forest in South Yunnan,as revealed by both the satellite product and the model *** results suggest that climate and anthropogenic factors play critical roles in controlling the spatial and seasonal distribution of China’s ecosystem fire *** findings highlight the importance of multiple complementary approaches in assessing ecosystem fire disturbance and its carbon *** studies are required to improve the methods of observing and modelling China’s ecosystem fire disturbances,which will provide valuable information for fire management and ecosystem sustainability in an era when both human activities and the natural environment are rapidly changing.

关键词： Fire emission Burned area Fire models China

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Self-adaptive Evolutionary Algorithm for DNA Codeword Design

Self-adaptive Evolutionary Algorithm for DNA Codeword Design

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Congress on Evolutionary Computation

作者： Jeisson Prieto Elizabeth León Max H. Garzon Computing Systems and Industrial Engineering Department Universidad Nacional de Colombia Bogotá Colombia Department of Computer Science and the Bioinformatics Program University of Memphis Memphis USA

DNA has emerged as a new computational resource for data encoding and processing. The fundamental problem of DNA Codeword Design (CWD) calls for finding effective ways to encode and process data in DNA. The problem has shown to be of interest in other areas as well, including computational memories, self-assembly and phylogenetic analysis, among others. In prior work, a framework to analyze this problem has been developed and simple versions of CWD have been shown to be NP-complete using any single reasonable metric that approximates the Gibbs energy, thus practically making it very difficult to find a general procedure for finding optimal efficient encodings. We present a Self-adaptive Evolutionary Algorithm for CWD (SaEA-CWD) as an extension of the Hybrid Adaptive Evolutionary algorithm (HAEA). SaEA-CWD is a parameter adaptation technique that automatically adapts the rates of its genetic operator applications to exploit structural properties of the search space to improve the speed and quality of the solutions. An implementation and preliminary results are evaluated in spaces where searches are already prohibitive to ordinary methods (such as 8- and 10-mers) due to the combinatorial explosion of the solution DNA space. Applications to other problems are suggested, such as a general technique for dimensionality reduction based on SaEA-CWD.

关键词： DNA Evolutionary computation Encoding Extraterrestrial measurements Self-assembly

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The Application of Reference Ontologies for Semantic Interoperability in an Integrated Product Development Process in Smart Factories

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Procedia Manufacturing 2017年 11卷 1375-1384页

作者： Szejka, Anderson Luis Junior, Osiris Canciglieri Industrial and Systems Engineering Graduate Program Pontifical Catholic University of Parana Imaculada Conceição 1155 Curitiba 80215-901 Paraná Brazil

Efficient information sharing demands formal information structures in order to make sure the semantic interoperability across different phases of Product Development Process. This research presents an application of reference ontologies to support semantic interoperability during the Product Development Process. The concepts inherent to the product development process are formalized in reference ontologies and these ontologies are specialized according to the specific product data, respecting semantic rules. As a result, the potential benefits and limitations of the ontology-driven interoperability were discussed, contributing to the semantic interoperability field and improving the product and manufacturing design. © 2017 The Authors

关键词： Decision-Making support Integrated Product Development Process Multiple Domains Ontology Semantic Interoperability Smart Factories

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The association for the advancement of artificial intelligence 2020 workshop program

The association for the advancement of artificial intelligen...

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作者： Bang, Grace Barash, Guy Bea, Ryan Cali, Jacques Castillo-Effen, Mauricio Chen, Xin Cynthia Chhaya, Niyati Dhoopar, Rohan Dumancic, Sebastijan Espinoza, Huáscar Farchi, Eitan Fioretto, Ferdinando Fuentetaja, Raquel Galley, Michel Geib, Christopher Hernández-Orallo, José Huang, Xiaowei Keren, Sarah Kim, Seokhwan Jaidka, Kokil Liu, Xiaomo Lu, Tyler Ma, Zhiqiang Mallah, Richard McDermid, John Michalowski, Martin Mirsky, Reuth Héigeartaigh, Seán Ó. Ramachandran, Deepak Segovia-Aguas, Javier Shaban-Nejad, Arash Shehory, Onn Shwartz, Vered Srivastava, Siddharth Talamadupula, Kartik Tang, Jian Zhang, Dell Zhang, Jian S&P Global. Western Digital University of Southampton United Kingdom Blue Prism Group PLC Lockheed Martin's Advanced Technology Laboratories ArlingtonVA United States University of Hong Kong Hong Kong Adobe Research Google Research Katholieke Universiteit Leuven Belgium Commissariat à ĺÉnergie Atomique France IBM Research Department of Electrical Engineering and Computer Science Syracuse University United States Universidad Carlos III de Madrid Spain Microsoft Research AI SIFT LLC Universitat Politècnica de València Spain Leverhulme Centre for the Future of Intelligence United Kingdom Department of Computer Science University of Liverpool United Kingdom National University of Singapore Singapore Harvard School of Engineering and Applied Sciences in the of Fields of Multiagent Environment Design and Goal Recognition Design Amazon Alexa AI Future of Life Institute United States University of York Assuring Autonomy International Program United Kingdom University of Minnesota School of Nursing MinneapolisMN United States University of Texas at Austin AustinTX United States Institut de Robòtica i Informàtica Industrial Spanish Council for Scientific Research Technical University of Catalonia Spain Oak Ridge National Laboratory Center for Biomedical Informatics Department of Pediatrics University of Tennessee Health Science Center United States Bar Ilan University Israel University of Washington SeattleWA United States Autonomous Agents and Intelligent Robots lab of the School of Computing Informatics Decision Systems Arizona State University United States DiDi AI Labs School of Industrial and System Engineering Georgia Institute of Technology United States University of London United Kingdom Microsoft Azure

The Association for the Advancement of Artificial Intelligence 2020 Workshop program included twenty-three workshops covering a wide range of topics in artificial intelligence. This report contains the required reports, which were submitted by most, but not all, of the workshop chairs. Copyright © 2020, Association for the Advancement of Artificial Intelligence. All rights reserved.

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Author Correction: The high-dimensional space of human diseases built from diagnosis records and mapped to genetic loci

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Nature computational science 2023年第7期3卷 658页

作者： Gengjie Jia Yu Li Xue Zhong Kanix Wang Milton Pividori Rabab Alomairy Aniello Esposito Hatem Ltaief Chikashi Terao Masato Akiyama Koichi Matsuda David E Keyes Hae Kyung Im Takashi Gojobori Yoichiro Kamatani Michiaki Kubo Nancy J Cox James Evans Xin Gao Andrey Rzhetsky Shenzhen Branch Guangdong Laboratory of Lingnan Modern Agriculture Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs Agricultural Genomics Institute at Shenzhen Chinese Academy of Agricultural Sciences Shenzhen China. jiagengjie@***. Computational Bioscience Research Center King Abdullah University of Science and Technology Thuwal Saudi Arabia. Computer Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia. Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong People's Republic of China. Department of Medicine and Vanderbilt Genetics Institute Vanderbilt University Medical Center Nashville TN US. Department of Medicine Institute of Genomics and Systems Biology Committee on Genomics Genetics and Systems Biology University of Chicago Chicago IL US. Department of Operations Business Analytics and Information Systems University of Cincinnati Cincinnati OH US. Department of Systems Pharmacology and Translational Therapeutics Perelman School of Medicine University of Pennsylvania Philadelphia PA US. Extreme Computing Research Center Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia. College of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia. HPE HPC/AI EMEA Research Laboratory Bristol UK. RIKEN Center for Integrative Medical Sciences Yokohama Japan. Clinical Research Center Shizuoka General Hospital Shizuoka Japan. Department of Applied Genetics The School of Pharmaceutical Sciences University of Shizuoka Shizuoka Japan. Department of Ophthalmology Graduate School of Medical Sciences Kyushu University Fukuoka Japan. Department of Computational Biology and Medical Sciences Graduate School of Frontier Sciences The University of Tokyo Tokyo Japan. Biological and Environmenta

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Studying operations strategy through cause and effect correlation 24

Studying operations strategy through cause and effect correl...

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24th International Conference on Production Research, ICPR 2017

作者： Okoshi, C.Y. de Lima, E. Pinheiro da Costa, S.E. Gouvêa Industrial and Systems Engineering Graduate Program Pontifical Catholic University of Parana Rua Imaculada Conceição 1155 Prado Velho Curitiba Parana Brazil Industrial and Systems Engineering Graduate Program Federal University of Technology - Parana Via do Conhecimento km 1 Pato Branco Parana Brazil

ISBN: (纸本)9781605955070

The companies use the performance results to identify their main problems and to analyse the cause roots in their production systems, supplier network and process technology. The purpose of this paper is to analyze cause and effect relationships among the performance dimensions and decision areas in the framework of operations strategy. The explored dataset is a research project named 4th Round of High Performance Manufacturing Project. The dataset holds indicators from 304 manufacturing companies in 13 countries. The techniques used for the analysis were descriptive statistics, normality test, factor analysis, and Multiple Linear Regression. As main results, the following cause and effect correlations for each manufacture performance dimension were found: innovation, flexibility, quality, cost, speed, and reliability. The results support the building of cause and effect diagrams regarding performance dimensions and decision areas to be used on improvements to the companies. © 2017 DEStech Publications. All rights reserved.

关键词： Manufacture

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