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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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Applications of multi-objective optimisation for PID-like controller tuning: a 2015-2019 review and analysis

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IFAC-PapersOnLine 2020年第2期53卷 7933-7940页

作者： Tainara Marques Gilberto Reynoso-Meza Industrial and Systems Engineering Graduate Program (PPGEPS) Pontifical Catholic University of Parana (PUCPR) CuritibaPR Brazil

The first open invited track in multi-objective optimisation for control systems was organised in 2017 with the idea of exchanging ideas and research about how those techniques are valuable for control engineers. Given that control engineering problems are generally multi-objective problems, multi-objective optimisation offers an interesting approach via the simultaneous optimisation of all design objectives. Controller tuning is not except from this. In this paper we perform a review and analysis of the literature, limited to the IFAC environment, to appreciate and detect new tendencies in controller tuning applications via multi-objective optimisation. Time window under consideration is from 2015 to date, coinciding with a previous review on the topic, as well as the emigration of IFAC proceedings to Elsevier.

关键词： Multi-objective optimisation Parametric optimisation Evolutionary Algorithms Intelligent Control Process Control

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Improvement of corrosion resistance and heat dissipation of Mg-6%Al-4%Ca alloy by the addition of Mn, Mm, and the surface treatment

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Keikinzoku/Journal of Japan Institute of Light Metals 2023年第12期73卷 646-650页

作者： Ito, Mizuki Kondo, Natsuki Kirimoto, Yuichi Tsukeda, Tadayoshi Aida, Tetsuo Tabata, Hironobu Kurihara, Hideki Suzuki, Mayumi Program of Materials Science and Engineering Studies in Science and Engineering Graduate School of Science and Engineering University of Toyama 3190 Gofuku Toyama-shi Toyama930-8555 Japan School of Sustainable Design Materials and Engineering University of Toyama Toyama Toyama-shi Japan Department of Nano and Functional Material Science Graduate School of Science and Engineering for Education University of Toyama Toyama Toyama-shi Japan Faculty of Sustainable Design Academic Assembly University of Toyama Toyama Toyama-shi 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 Toyama Imizu-shi Japan

With the recent digitization, electronic equipment housings are required to be thin-walled and have rigidity, heat dissipation, corrosion resistance, and electromagnetic shielding properties. Magnesium alloys are promising materials that satisfy these requirements. Mg-Al alloys have good castability and high strength, but their thermal conductivity decreases. In general, adding alloying elements to magnesium decreases thermal conductivity the effect of aluminum addition is significant. It was found that adding calcium to Mg-Al alloys to form Mg-Al-Ca (AX) alloys results in higher thermal conductivity than that of Mg-Al alloys. However, AX alloys have poor corrosion resistance and require post-treatment such as surface treatment. In this study, AX64, which has high thermal conductivity among the AX series alloys, was used as the basic composition, and corrosion resistance was improved by adding manganese and misch metal, and an attempt was made to improve corrosion resistance and emissivity through surface treatment. The change in thermal conductivity by heat treatment was also investigated. Experimental results showed that the addition of manganese and misch metal improved corrosion resistance and thermal conductivity. Plasma anodization also improved corrosion resistance and emissivity. Heat treatment of these alloys at 400°C improved the thermal conductivity with longer holding time. © 2023 The Japan Institute of Light Metals.

关键词： Corrosion resistance

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Competitive Advantage Model Through Knowledge Management systems Implementation to Optimize Business Sustainability 2021

Competitive Advantage Model Through Knowledge Management Sys...

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2021 The 9th International Conference on Information Technology: IoT and Smart City

作者： Wahyu Sardjono Erma Lusia Hargo Utomo Samudra Sukardi Aninda Rahmasari Yuri Delano Regent Montororing Information Systems Management Department BINUS Graduate Program BINA NUSANTARA University Indonesia Research Capacity Building Research & Development BINA NUSANTARA University Indonesia Department of Management Faculty of Economic and Business Gajahmada University Indonesia Graduate Program Faculty of Economy and Business University of Indonesia Indonesia International Relations Department Faculty of Humanities BINA NUSANTARA University Indonesia Industrial Engineering Study Program Bhayangkara University Indonesia

ISBN: (纸本)9781450384971

Complaint resolution that arise due to internal and external factors in a company can be monitored through the Service Recovery Index (SRI), and SRI is developed through a number of factors that influence it. Meanwhile, the SRI model needs to be developed to see the business sustainability through implementation knowledge management system in the organization. The data collection method was carried out, starting with building a research instrument based on knowledge management theory and continued with observation and distributing questionnaires to respondents who were involved in the business process of managing SRI. The data analysis method was used to process the data from the questionnaire, factor analysis method was used and the method to build the model used was the regression analysis method. The results obtained from this study are the discovery of 4 (four) factors with a number of indicators that are grouped on each formed factor, which can then be made and built a readiness model that reflects current conditions, so that it can be used to build predictions in the future related to readiness. implementation of knowledge management system development in order to support increased understanding of the service recovery index in the organization.

关键词： sustainability knowledge management service recovery index business factor analysis

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An Analysis Method for Metric-Level Switching in Beat Tracking

arXiv

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arXiv 2022年

作者： Chiu, Ching-Yu Müller, Meinard Davies, Matthew E.P. Su, Alvin Wen-Yu Yang, Yi-Hsuan Graduate Program of Multimedia Systems and Intelligent Computing National Cheng Kung University Academia Sinica Taiwan International Audio Laboratories Erlangen Germany Department of Informatics Engineering Centre for Informatics and Systems The University of Coimbra University of Coimbra Portugal Department of CSIE National Cheng Kung University Taiwan Taiwan AI Labs Taiwan

For expressive music, the tempo may change over time, posing challenges to tracking the beats by an automatic model. The model may first tap to the correct tempo, but then may fail to adapt to a tempo change, or switch between several incorrect but perceptually plausible ones (e.g., half- or double-tempo). Existing evaluation metrics for beat tracking do not reflect such behaviors, as they typically assume a fixed relationship between the reference beats and estimated beats. In this paper, we propose a new performance analysis method, called annotation coverage ratio (ACR), that accounts for a variety of possible metric-level switching behaviors of beat trackers. The idea is to derive sequences of modified reference beats of all metrical levels for every two consecutive reference beats, and compare every sequence of modified reference beats to the subsequences of estimated beats. We show via experiments on three datasets of different genres the usefulness of ACR when being utilized alongside existing metrics, and discuss the new insights that can be gained. © 2022, CC BY.

关键词： Taps

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Deep Learning with Greedy Layer-Wise Compound Scaling for Temperature and Humidity Prediction in Solar Dryer Dome

SSRN

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SSRN 2022年

作者： Cenggoro, Tjeng Wawan Elwirehardja, Gregorius Natanael Dominic, Nicholas Setiawan, Karli Eka Rahutomo, Reza Djuana, Endang Gunawan, Fergyanto E. Budiman, Arief S. Romeli, Sugiarto Pardamean, Bens Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta11480 Indonesia Electrical Engineering Department Faculty of Industrial Technology Universitas Trisakti Jakarta11440 Indonesia Industrial Engineering Department BINUS Graduate Program Master of Industrial Engineering Bina Nusantara University Jakarta11480 Indonesia Department of Manufacturing and Mechanical Engineering and Technology Oregon Institute of Technology Klamath FallsOR97601 United States PT. Impack Pratama Industri Jakarta Indonesia

Drying has been an eco-friendly and cost-efficient method to reduce post-harvest losses of agricultural crops. In various countries, the technique has been widely utilized in the form of Solar Dryer Dome (SDD) buildings. Accurate indoor microclimate forecasting is very important to implement smart control systems in SDDs that can maintain the ideal conditions, as the availability of solar energy is limited only at daytimes and the temperature may drop significantly during nighttimes in remote areas. The microclimate variables that play an important role in maintaining ideal environmental conditions include temperature and relative humidity. © 2022, The Authors. All rights reserved.

关键词： Forecasting

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Speaker-Specific Articulatory Feature Extraction Based on Knowledge Distillation for Speaker Recognition

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APSIPA Transactions on Signal and Information Processing 2023年第2期12卷

作者： Qian-Bei Hong Hsin-Min Wang Chung-Hsien Wu Graduate Program of Multimedia Systems and Intelligent Computing National Cheng Kung University and Academia Sinica Taiwan Graduate Program of Multimedia Systems and Intelligent Computing National Cheng Kung University and Academia Sinica and Department of Computer Science and Information Engineering National Cheng Kung University Taiwan

This paper proposes a novel speaker-specific articulatory feature (AF) extraction model based on knowledge distillation (KD) for speaker recognition. First, an AF extractor is trained as a teacher model for extracting the AF profiles of the input speaker dataset. Next, a KD-based speaker embedding extraction method is proposed to distill the speaker-specific information from the AF profiles in the teacher model to a student model based on multi-task learning, in which the lower layers not only capture the speaker characteristics from acoustic features, but also learn the speaker-specific features from the AF profiles for robust speaker representation. Finally, speaker embeddings are extracted from the high-level layer, and the obtained speaker embeddings are further used to train a probabilistic linear discriminant analysis (PLDA) model for speaker recognition. In the experiments, speaker embedding models were trained using the VoxCeleb2 dataset and the AF extractor was trained based on the LibriSpeech dataset, and the performance was evaluated using the VoxCeleb1 dataset. The experiments showed that the proposed KD-based models outperformed the baseline models without KD. Furthermore, feature concatenation of multimodal results can further improve the performance.

关键词： Speaker recognition articulatory feature knowledge distillation

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Mine-to-client planning with Markov Decision Process 18

Mine-to-client planning with Markov Decision Process

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18th European Control Conference, ECC 2020

作者： Leal Gomes Leite, Joao Marcelo Arruda, Edilson F. Bahiense, Laura Marujo, Lino G. Federal University of Rio de Janeiro Industrial Engineering Program Alberto Luiz Coimbra Institute Graduate School and Research in Engineering Rio de Janeiro Brazil Federal University of Rio de Janeiro Systems Engineer and Computer Science Program Alberto Luiz Coimbra Institute Graduate School and Research in Engineering Rio de Janeiro Brazil

ISBN: (纸本)9783907144015

This works presents an innovative application of Markov Decision Process (MDP) to a medium-term mining logistics planning problem considering the mine-to-client supply chain. We implemented three distinct algorithms based on state-of-the-art approaches to solve large-scale problems, and compared their results. Furthermore, we combined all three variants in a single novel algorithm that attained fast convergence and may be an alternative to circumvent the curse of dimensionality underlying large scale problems. © 2020 EUCA.

关键词： Supply chains

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Using Online Grocery Applications during the COVID-19 Pandemic: Their Relationship with Open Innovation

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Journal of Open Innovation: Technology, Market, and Complexity 2022年第2期8卷

作者： Gumasing, Ma. Janice J. Prasetyo, Yogi Tri Persada, Satria Fadil Ong, Ardvin Kester S. Young, Michael Nayat Nadlifatin, Reny Redi, Anak Agung Ngurah Perwira School of Industrial Engineering and Engineering Management Mapúa University 658 Muralla St. Intramuros Manila 1002 Philippines School of Graduate Studies Mapúa University 658 Muralla St. Intramuros Manila 1002 Philippines Entrepreneurship Department BINUS Business School Undergraduate Program Bina Nusantara University Malang 65154 Indonesia Department of Information Systems Institut Teknologi Sepuluh Nopember Kampus ITS Sukolilo Surabaya 60111 Indonesia Industrial Engineering Department BINUS Graduate Program—Master of Industrial Engineering Bina Nusantara University Jakarta 11480 Indonesia

This present research examines the behavioral intentions of Filipinos to use online grocery applications during the novel COVID-19 pandemic. The study proposes an integration of the health belief model (HBM) and the Unified Theory of Acceptance and Use of Technology (UTAUT2) to identify the factors affecting the acceptance and usage of Filipinos of online grocery applications in terms of the impact of health risk for COVID-19. To accurately measure the factors and their relationship to behavioral intentions and usage behavior, a questionnaire was developed and distributed to 373 residents in the Philippines. Partial least squares structural equation modeling (PLS-SEM) was applied as an analytical method for this study. The results revealed that performance expectancy, perceived benefits, perceived severity, and cues to action significantly influenced the behavioral intentions and usage of online grocery apps during the COVID-19 pandemic. The study’s findings can be utilized as a theoretical framework for future researchers of consumer behavior;e-commerce developers;and grocery industry retailers, to enhance the innovation and services of online grocery applications. The results of this study may also be used and capitalized on by investors and managers to apply in strategizing when developing and marketing online grocery applications among con-sumers. Moreover, the framework of this study may be adopted and utilized by other online markets, even in different counties. Further theoretical and practical aspects are discussed in this paper. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.

关键词： health belief model online grocery applications structural equation modeling UTAUT2

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