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Effect of chemical composition and microstructure on the thermal conductivity of Mg-Al-Ca alloys

Keikinzoku/Journal of Japan Institute of Light Metals 2024年第1期74卷 9-13页

作者： Kirimoto, Yuichi Kondo, Natsuki Ito, Mizuki Tsukeda, Tadayoshi Aida, Tetsuo Tabata, Hironobu Kurihara, Hideki Suzuki, Mayumi Department of Nano and Functional Material Science Graduate School of Science and Engineering for Education University of Toyama 3190 Gofuku Toyama-shi Toyama930-8555 Japan School of Sustainable Design Materials and Engineering University of Toyama Toyama-shi Toyama Japan Program of Materials Science and Engineering Studies in Science and Engineering Graduate School of Science and Engineering University of Toyama Toyama-shi Toyama Japan Faculty of Sustainable Design Academic Assembly University of Toyama Toyama-shi Toyama Japan CHUETSU METAL WORKS CO. LTD. Nakaniikawa-gun Toyama Japan Saitama Industrial Technology Center Kawaguchi-shi Saitama Japan Department of Mechanical Systems Engineering Faculty of Engineering Toyama Prefectural University Imizu-shi Toyama Japan

In recent years, the increasing demand for electric vehicles, digital devices such as PCs and smartphones, and renewable energy storage systems has created a need for higher-performance batteries. Accordingly, the thermal conductivity of the case that houses the battery is becoming increasingly important. In this study, compositions with high thermal conductivity and good castability were investigated, focusing on Mg-Al-Ca alloys, and the relationship between the two was examined through thermal conductivity measurements and microstructural observations. As a result, the Mg-6%Al-4%Ca alloy was optimal in terms of castability and thermal conductivity. It was confirmed that a lower Al solid solution capacity and a higher purity of the matrix phase are important to improve the thermal conductivity. It is also suggested that the second phase, platelet Al2Ca, is likely to be recognized and enriched in the matrix, contributing to the improvement of thermal conductivity. The amount of Ca solid solution in the matrix was suggested to be reduced by the enrichment of Al in the matrix. The Ca solid solution in the matrix was found to be less effective in reducing thermal conductivity than the Al solid solution. © 2024 The Japan Institute of Light Metals.

关键词： Magnesium alloys

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Classification of Abandoned Areas for Solar Energy Projects Using Artificial Intelligence and Quantum Mechanics

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Journal of Geographic Information System 2021年第3期13卷 318-339页

作者： David Gabriel de Barros Franco Maria Teresinha Arns Steiner Undergraduate Program in Logistics Universidade Federal do Tocantins (UFT) Araguaína Brasil Graduate Program in Industrial Engineering and Systems Pontifícia Universidade Católica do Paraná (PUC-PR) Curitiba Brasil

The increasing demand for energy has intensified recently, requiring alternative sources to fossil fuels, which have become economically and environmentally unfeasible. On the other hand, the increasing land occupation in recent centuries is a growing problem, demanding greater efficiency, particularly in the reuse of abandoned areas, which has become an alternative. An interesting alternative would be installing energy facilities like solar, wind, biomass, and geothermal, in these areas. The objective of this paper is to develop a classification methodology, based on Artificial Intelligence (AI) and Quantum Theory (QT), to automatically carry out the classification of abandoned areas suitable for the settlement of these power plants. Artificial Neural Networks (ANNs) improved by the hybrid algorithm Quantum-behaved Particle Swarm Optimization (QPSO) together with the Levenberg-Marquardt Algorithm (LMA) were used for the classification task. In terms of Mean Squared Error (MSE), the QPSO-LMA approach achieved a decrease of 19.6% in relation to the classical LMA training with random initial weights. Moreover, the model’s accuracy showed an increase of 7.3% for the QPSO-LMA over the LMA. To validate this new approach, it was also tested on six different datasets available in the UCI Machine Learning Repository and seven classical techniques established in the literature. For the problem of installing photovoltaic plants in abandoned areas, the knowledge acquired with the solar dataset can be extrapolated to other regions.

关键词： Solar Energy Soil Reuse Artificial Neural Networks Quantum-Behaved Particle Swarm Optimization

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Factors for design of performance measurement systems for not-for-profit organizations

Factors for design of performance measurement systems for no...

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2016 industrial and systems engineering Research Conference, ISERC 2016

作者： Moura, Louisi Francis de Lima, Edson Pinheiro Deschamps, Fernando van Aken, Eileen Gouvea da Costa, Sergio E. Industrial and Systems Engineering Graduate Program Pontifical Catholic University of Parana Curitiba Brazil Federal University of Technology - Parana Curitiba Brazil Department of Mechanical Engineering Federal University of Parana Curitiba Brazil Grado Department of Industrial and Systems Engineering Virginia Tech Blacksburg United States

The scope of this research is delimited by the study of not-for-profit organizations performance through operations management lens. According to a systematic literature review, the performance measurement frameworks applied to not-for-profit organizations are adaptations of models developed for for-profit companies. However, the specific characteristics related to participatory governance, responsibility to declare their tangible and intangible results, and accountability of different revenue sources that combine donations, governmental aids, volunteering or public financing are usually not properly considered. In this context, this paper intends to identify factors that intervene in not-for-profit organizations' performance measurement systems, and to organize them in a conceptual framework that could assist not-for-profit organizations to better design their performance measurement systems. The paper also analyzes how enterprise engineering design principles can be applied in this context, relating the intervening factors to enterprise engineering guidelines. © 2016 Proceedings of the 2016 industrial and systems engineering Research Conference, ISERC 2016. All rights reserved.

关键词： industrial research

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Deep Reinforcement Learning Applied to a Sparse-Reward Trading Environment with Intraday Data

SSRN

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SSRN 2023年

作者： Takara, Lucas de Azevedo Alves Portela Santos, André Mariani, Viviana Cocco dos Santos Coelho, Leandro Department of Electrical Engineering PPGEE Federal University of Parana UFPR Avenida Coronel Francisco Herácito dos Santos Parana Curitiba81530–000 Brazil CUNEF Universidad Calle Almansa 101 Madrid28040 Spain Mechanical Engineering Graduate Program PPGEM Pontifical Catholic University of Parana PUCPR Imaculada Conceição Parana Curitiba80215–901 Brazil Industrial and Systems Engineering Graduate Program PPGEPS Pontifical Catholic University of Parana PUCPR Imaculada Conceição Parana Curitiba80215–901 Brazil

Deep reinforcement learning (DRL) achieved significant progress in several areas enabling computers to perform complex decision-making tasks. Applied to quantitative trading, DRL trading agents can optimize their decisions during distinct market scenarios to reach a profitable strategy by learning from previous experiences. This work proposes a trading system containing a variant of the Deep Q-Network (DQN) algorithm as its main component called Extended Trading DQN (ETDQN). It can adapt its learning to trade across numerous market-behavior moments, receiving feedback only when a trade is liquidaded, differently from recent approaches that provide constant feedback. Based on distributional learning and other independent extensions suggested by the DRL community, the algorithm optimizes its decision-making process by replaying prioritized experiences containing different sub-goals, assisting the agent in collecting the maximum value of profits and removing the need for fine-tuning rewards. ETDQN learned to trade on three distinct financial time series signals, identifying trading opportunities during volatile price moments. The algorithm showed more aggressive behavior regarding the volatility of its annual returns than the Trading DQN benchmark, achieving a 1.46 and 7.13 better performance with respect to average daily cumulative returns in Western Digital Corporation and Cosmos cryptocurrency historical market data. © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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An Analysis of Flexible Manufacturing on the Support of the Development of Smart Product-Service systems 17th

An Analysis of Flexible Manufacturing on the Support of the ...

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17th IFIP WG 5.1 International Conference on Product Lifecycle Management, PLM 2020

作者： de M. Leite, Athon F. C. S. Canciglieri, Matheus B. Szejka, Anderson L. Goh, Yee Mey Monfared, Radmehr P. de F. R. Loures, Eduardo Junior, Osiris Canciglieri Industrial and Systems Engineering Graduate Program Pontifical Catholic University of Paraná CuritibaParaná Brazil School of Mechanical Electrical and Manufacturing Engineering Loughborough University Loughborough Leicestershire United Kingdom

ISBN: (纸本)9783030628062

Currently, changes in the customer’s mindset and increased flexibility in manufacturing shifted competition between companies to focus on the product’s value rather than its cost. In this context, Product-Service systems (PSSs) are an alternative to traditional value propositions (i.e. pure products, pure services) by combining services with products and providing an experience to customers. In recent decades, PSSs were expanded from solely business aspects to engineering aspects and technical considerations in their scope, such as the paradigms of Technical PSS, industrial PSS, Digitalized PSS and Cyber-Physical PSS. More recently, due to the application of Industry 4.0 concepts, there is a significant increase of "smart technologies", i.e. sensors;connectivity;mobile application, in PSSs, naming it a new paradigm on the field, known as Smart PSSs. In this context, this research aims to explore and discuss current issues related to the integration of flexible manufacturing processes and Smart PSSs through a literature review and analysis, regarding information sharing, traceability and system reconfiguration based on customer’s requirements. Found literature points to gaps in the theme related to semantic issues and design issues regarding the dynamic reconfiguration of the manufacturing process and the Smart PSS. The modern approaches to the theme, as found in literature, are still not able to cope with the semantic barriers and high volume of heterogeneous information in a holistic manner. Conclusions point that further investigation of the cited issues is required in a systematic manner, as well as the development of models that can cope with the presented issues. © 2020, IFIP International Federation for Information Processing.

关键词： Semantics

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GNN-Based Point Cloud Maps Feature Extraction and Residual Feature Fusion for 3D Object Detection

GNN-Based Point Cloud Maps Feature Extraction and Residual F...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Wei-Hsiang Liao Chieh-Chih Wang Wen-Chieh Lin Graduate Degree Program of Artificial Intelligence National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan Mechanical and Mechatronics Systems Research Laboratories Industrial Technology Research Institute Hsinchu Taiwan Institute of Multimedia Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan

LiDAR detection of long-range vehicles is challenging because very few and sparse points are measured in long distances and vehicles with similar shapes of targets could lead to false positives easily. To tackle these challenges, taking the environment information (HD maps) into account could be beneficial to predetermine where targets are more or less likely to appear. Compared with semantic maps, HD maps formed by point clouds provide much richer information from surrounding static objects and scenes. In this work, we construct a GNN-based feature extraction of point cloud maps to increase the receptive fields of learning map features. Our work is based on PVRCNN, the state-of-the-art LiDAR object detection method. With point-wise and voxel-wise features obtained from PVRCNN, residual feature fusion is proposed to fuse the features from PVRCNN and the map features from GNN. Our approach is evaluated on NuScenes dataset. It achieves a 24.78% average precision improvement for long-range objects at 40–50 meters, the farthest areas with ground truth annotation. Our approach also has a 4.22% reduction of false positives in the entire sensing areas.

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Big data in healthcare:Conceptual network structure,key challenges and opportunities

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Digital Communications and Networks 2023年第4期9卷 856-868页

作者： Leonardo B.Furstenaua Pedro Leivas Michele Kremer Sott Michael S.Dohan Jose Ricardo Lopez-Robles Manuel J.Cobo Nicola Luigi Bragazzi Kim-Kwang Raymond Choo Post-Graduate Program in Industrial Engineering Federal University of Rio Grande Do SulPorto Alegre90035-190Brazil University of Santa Cruz Do Sul Av.Independencia 2293Santa Cruz Do SulRSBrazil Business School Unisinos UniversityAv.Dr.Nilo Peçanha 160091330-002Porto AlegreBrazil Faculty of Business Administration Lakehead UniversityThunder BayOntarioCanada Autonomous University of Zacatecas Av.Ramon Lopez Velarde Col.CentroZacatecas98000Mexico Department of Computer Science and Artificial Intelligence Andalusian Research Institute in Data Science and Computational Intelligence(DaSCI)University of GranadaGranada 18071Spain York University ON M3J 1P3Canada Department of Information Systems and Cyber Security University of Texas at San AntonioSan AntonioTX78249USA

Big data is a concept that deals with large or complex data sets by using data analysis tools(e.g.,data mining,machine learning)to analyze information extracted from several sources *** data has attracted wide attention from academia,for example,in supporting patients and health professionals by improving the accuracy of decision-making,diagnosis and disease *** research aimed to perform a Bibliometric Performance and Network Analysis(BPNA)supported by a Scoping Review(SR)to depict the strategic themes,thematic evolution structure,main challenges and opportunities related to the concept of big data applied in the healthcare *** this goal in mind,4857 documents from the Web of Science covering the period between 2009 to June 2020 were analyzed with the support of SciMAT *** bibliometric performance showed the number of publications and citations over time,scientific productivity and the geographic distribution of publications and research *** strategic diagram yielded 20 clusters and their relative importance in terms of centrality and *** thematic evolution structure presented the most important themes and how it changes over ***,we presented the main challenges and future opportunities of big data in healthcare.

关键词： Big data Healthcare digitalization Bibliometric Strategic intelligence Co-word analysis SciMAT

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industrial energy efficiency assessment and prioritization model: An approach based on multi-criteria method PROMETHEE

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International Journal of Sustainable Energy Planning and Management 2023年 37卷 41-60页

作者： Richter, Bernardo Keller Marcondes, Gabriela Hansen Monteiro, Nathalia Juca da Costa, Sergio Eduardo Gouvea Loures, Eduardo R. Deschamps, Fernando de Lima, Edson Pinheiro Industrial and Systems Engineering Graduate Program Pontifical Catholic University of Parana Rua Imaculada Conceição 1155 Curitiba80215-901 Brazil Department of Engineering State University of Pará Travessa Dr. Enéas Pinheiro 2626 Belém66095-015 Brazil Federal University of Technology-Parana Via do Conhecimento Pato Branco85503-390 Brazil Mechanical Engineering Department Federal University of Parana Rua XV de Novembro 1299 Curitiba80060–000 Brazil

In today’s scenario of increasing energy prices, new legislations, and rising consumer concerns regarding environmental issues, industries face an unprecedented challenge of reducing their energy consumption without negatively impacting their profit and productivity. Based on this, companies are focusing on analyzing their energy efficiency, which has various criteria to be considered, and at least three organizational levels. To close this gap, this study developed an industrial energy efficiency assessment and prioritization model based on energy assessment literature. It utilized multi-criteria analysis for the prioritization of industrial energy efficiency measures. To achieve the goal, a literature review was conducted to map relevant energy efficiency practices from which an industrial energy efficiency assessment tool was developed through the lens of three organizational levels (plant, process, and machine). Subsequently, an energy-efficiency project prioritization tool was proposed using the multi-criteria PROMETHEE II method. The assessment and prioritization model was applied to an energy industry for refinement. It generated an overview of the company’s energy efficiency maturity and a ranking of the most recommended measures for the optimal use of energy resources according to established criteria and their weights. Four subcategories (lighting, HVAC systems, compressed air, and motors) were analyzed for the organizational levels, and lighting presented the higher result of a maturity of 2.77 on a scale from 0 to 3, also the maturity of the company was 2.01, which means that is still space for improvement. The improvements were highlighted according to each subcategory studied, pointing to actions that needed to be developed to improve energy efficiency. © 2023, Aalborg University press. All rights reserved.

关键词： Energy efficiency

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Applying machine learning to AHP multicriteria decision making method to assets prioritization in the context of industrial maintenance 4.0

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IFAC-PapersOnLine 2019年第13期52卷 2152-2157页

作者： Lima, Erick Gorski, Ewerton Loures, Eduardo F.R. Portela Santos, Eduardo A. Deschamps, Fernando Industrial and Systems Engineering Graduate Program Pontifícia Universidade Católica do Paraná Curitiba Brazil

The increasing competition among industries has led to the emergence of numerous tools and methods to support decision making focused on assets maintenance in a company, since ensuring good maintenance is directly linked with greater reliability and uptime for equipment, reducing losses in production processes and consequently increasing profitability. This work aims to use Machine Learning (ML) algorithms - Bayesian Networks (BN) and attribute relevance analysis (ARA), implemented in the Weka® platform, to process a dataset of event logs failure of industrial machine components. The approach aims to use the conditional probability relations generated by the BN and the ranking of criteria relevance for the design of an AHP decision-making model within the scope of industrial maintenance to prioritize which components of a specific machine are more susceptible to failures. The proposed integration mechanism aims to bring greater reliability to the weights assigned to the criteria of the AHP model, and consequently, a more accurate decision support. The results showed that the AHP model generated from a Bayesian Network is consistent with the conditional probabilities estimated by the BN, giving robustness to the decision sphere in the context of industrial maintenance. This AHP model can serve as a basis to be complemented by qualitative analysis criteria according to the need of the individual specialist, allowing the construction of strategic maintenance action plans. © 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Machine learning Analytic hierarchy process Bayesian networks Competition Decision making Decision support systems Hierarchical systems Maintenance Conditional probabilities Decision making models industrial maintenance Integration mechanisms Multi criteria decision making methods Qualitative analysis Relevance analysis Strategic maintenance

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Towards a Predictive Approach for Omni-channel Retailing Supply Chains

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IFAC-PapersOnLine 2019年第13期52卷 844-850页

作者： Pereira, Marina Meireles Frazzon, Enzo Morosini Graduate Program in Production Engineering Industrial and Systems Engineering Department Technology Center Federal University of Santa Catarina Florianópolis Santa Catarina Brazil

The adoption of omni-channel strategy has changed the relation between retailers and customers and brought more complexity to the retailing supply chains. To address the increasing complexity, it is necessary to adopt innovative approaches based on information technologies and intelligent decision methods. The challenges to retailers are improving the accuracy of offline and online channels demand forecasting, better managing offline and online customer's needs, thus reducing the uncertainties of the omni-channel retailing supply chain. In this context, this research paper aims to propose a predictive approach for omni-channel retailing supply chain combining clustering with artificial neural network to handle demand uncertainty. © 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Neural networks Complex networks Learning systems Sales Supply chain management Channel strategies Clustering Demand forecasting Demand uncertainty Innovative approaches Intelligent decisions Omni channel Retail supply chain

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