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Finite Element Investigation of Torque Ripple in Roller-Cycloidal Contact: Examining Gear Ratio, Mesh Density, and Transmitted Power Dependencies 1

引用

3rd International Symposium on Industrial Engineering and Automation

作者： Fraccaroli, Lorenzo Maccioni, Lorenzo Concli, Franco Blagojevic, Mirko Rotini, Federico Free Univ Bozen Bolzano Fac Engn I-39100 Bolzano Italy Univ Kragujevac Fac Engn Kragujevac 34000 Serbia Univ Firenze Dept Ind Engn I-50139 Florence Italy

ISBN: (数字)9783031704628

ISBN: (纸本)9783031704611;9783031704628

The low backlash of cycloidal gearboxes is undoubtedly a distinctive feature. However, due to their architecture, cycloidal drives exhibit torque oscillations, i.e. Torque Ripple (TR), caused by variations in system stiffness as contact points change over time. Such oscillations can be critical in position control applications. One of the most common approaches to analyze torque ripple, namely finite element analysis, is exploited and discussed in this paper. Specifically, the aim is to investigate how modeling parameters such as mesh density, in different geometries and operating torques, impact torque ripple estimation. Preliminary results from 12 simulations, conducted at two gear ratios, two levels of transmissible power, and three mesh densities (coarse, medium, fine), demonstrate that with an equal number of elements per cycloidal disk lobe, models with smaller GR exhibit larger TR. It is observed that changes in Mesh density significantly affect TR, without a corresponding significant change in maximum stress levels. Additionally, lower applied torques result in higher TR, though this effect diminishes with denser meshes. These findings highlight the critical need for thorough mesh sensitivity analysis in TR studies and the necessity to establish a contact zone discretization approach that is independent of GR, to accurately assess and mitigate TR in cycloidal drives.

关键词： FEM FEA Cycloidal Drive Dynamic high-power density

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Comparing Effectiveness of Statistical Versus Deep Learning for Time Series Forecasting 8th

Comparing Effectiveness of Statistical Versus Deep Learning ...

引用

8th Smart Trends in Computing and Communications (SmartCom)

作者： Yadav, Hemant N. Thakkar, Amit Rochwani, Nesh Patel, Rudra Charotar Univ Sci & Technol Changa Chandubhai S Patel Inst Technol Fac Technol Smt Kundanben Dinsha Patel Dept Informat Technol Anand Gujarat India Charotar Univ Sci & Technol CHARUSAT Changa Chandubhai S Patel Inst Technol CSPIT Dept Comp Sci & Engn Anand Gujarat India

ISBN: (纸本)9789819713127;9789819713134

One of the most popular academic subjects is the forecasting of time series. Machine learning, or ML, approaches have been proposed as substitutes to statistical ones in academic literature for forecasting time series. However, information on their respective effectiveness when it comes to both precision and computational demands is scarce. Machine learning approaches are increasingly being used to handle these predicted challenges, instead of traditional statistical methods. However, there is a limitation of comparative data for advanced models developed in the past few years such as DeepAR and Prophet. We have also utilized models such as LSTM, GRU, RNN, ARIMA. In this research, we addressed this problem and compared six different models for the Jena Climate dataset and the Appliances Energy forecast dataset.

关键词： Time series Forecasting Deep learning Statistical forecasting LSTM

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Electric Vehicle Issues, Challenges and Technological Solutions: A Systematic Review 7th

Electric Vehicle Issues, Challenges and Technological Soluti...

引用

7th International Conference on Logistics Operations Management (GOL)

作者： Jraif, Amine El Idrissi, Adiba El Bouzekri Messaoud, Elhassania Chouaib Doukkali Univ Natl Sch Appl Sci Sci Engineer Lab Energy El Jadida Morocco Hassania Sch Publ Works Casablanca Morocco

ISBN: (纸本)9783031686337;9783031686344

The electrification of transport is necessary in order to reduce greenhouse gases, as well as to guarantee sustainability, as conventional vehicles, use of limited natural resources such as oil. Despite the considerable progress in terms of autonomy and energy efficiency of these vehicles, it is still necessary to take advantage of technological progress and that of battery efficiency, to ensure a wider deployment of electric vehicles (EV). In this paper we present a systematic review of the impact of the different technological aspects on electric vehicle efficiency, we investigate the energy efficiency of EVs, as well as the issues and constraints considered in electric vehicle routing problems (EVRP). The strengths of the electrification of transport and the use of EVs in distribution logistics are clearly highlighted, and gaps in EVRP models are identified.

关键词： Energy consumption Electric vehicle EVRP EV Battery

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From Data to Decision: Exploring Machine Learning's Impact on Shaping Smart Cities 4th

From Data to Decision: Exploring Machine Learning's Impact o...

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4th International Conference on Digital Technologies and Applications (ICDTA)

作者： Adraoui, Meriem Diop, El Bachir Azmi, Rida Chenal, Jerome Abdem, Seyid Abdellahi Ebnou Mohammed VI Polytech Univ UM6P Ctr Urban Syst CUS Benguerir Morocco Ecole Polytech Fed Lausanne EPFL Urban & Reg Planning Community CEAT Lausanne Switzerland

ISBN: (纸本)9783031686528;9783031686535

In today's era of rapid urbanization, smart cities are emerging as a beacon of efficiency and sustainability. At the heart of this evolution is the integration of Machine Learning (ML), which can transform vast streams of data into informed decisions. This article examines through a literature review the transformative potential of machine learning in urban environments, highlighting advances and challenges in the key smart city domains: Infrastructure, Environment, Mobility and Transport. The review highlights both the promising breakthroughs achieved and the challenges ahead in this interdisciplinary field. Our analysis aims to provide readers with a comprehensive understanding of how machine learning not only improves data processing capabilities, but also leads to actionable and impactful decisions in the urban context. By tracing the path from raw data to informed urban solutions, this paper highlights the central role of machine learning in shaping the smart cities of the future.

关键词： Smart Cities Machine Learning Infrastructure Environment Mobility and Transport Sustainability Urban Data

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AI-Driven EoL Aircraft Treatment: A Research Perspective

AI-Driven EoL Aircraft Treatment: A Research Perspective

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Intelligent systems Conference

作者： Amirnia, Ashkan Keivanpour, Samira 2500 Chem Polytech Dept Math & Ind Engn Montreal PQ H3T 1J4 Canada

ISBN: (纸本)9783031664274;9783031664281

End-of-life (EoL) aircraft treatment is a crucial step in the circular manufacturing of the aircraft industry, delivering considerable economic and environmental benefits. Despite these advantages, complex and sensitive operations are a serious challenge in this field due to the high complexity of an aircraft structure and its constituent materials and composites. Poor operations can significantly reduce output quality and increase operational costs. Consequently, the value of the recovered materials may not exceed the operational costs. Artificial intelligence (AI) is a valuable tool for enhancing the effectiveness and sustainability of EoL aircraft management by automating and optimizing the identification, separation, processing, and transformation of materials. AI can also help to reduce waste and emissions, increase material recovery and reuse, and create new markets and jobs. This paper comprehensively reviews the AI applications in EoL aircraft treatment. It discusses the current state-of-the-art AI models across three domains: recycling, maintenance, and dismantling/disassembly. This article then carefully highlights the existing gaps based on the analyses. It also describes the possible directions of future research.

关键词： Artificial intelligence EoL aircraft Sustainable aircraft management Circular manufacturing

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Solver Based Heuristics for Rolling Stocks Corrective Maintenance Scheduling 7th

Solver Based Heuristics for Rolling Stocks Corrective Mainte...

引用

7th International Conference on Logistics Operations Management (GOL)

作者： Ray, Tom Bocquillon, Ronan T'kindt, Vincent SNCF Voyageurs Ingn Mat Cluster Ouest St Pierre Des Corps France Univ Tours Lab Informat Fondamentale & Appl Tours LIFAT Tours France

ISBN: (纸本)9783031686337;9783031686344

Trains have a constrained schedule and are not available on demand. If a malfunction is detected, the moment and the place to fix the problem while keeping the network at its optimal use may not be easy to find, given that there will be multiple problems throughout the network. Deciding of the maintenance requests the solution of a scheduling problem. The goal of this research is to provide an efficient solution to this problem. To achieve this aim, we develop a mixed integer linear programming model, a constraint programming model and multiple heuristic algorithms such as a Local Branching (LB) over the mixed integer linear programming model and a Variable Partitioning Local Search (VPLS) over the constraint programming model.

关键词： Rolling Stock maintenance Mathematical Programming Constraint Programming Heuristics

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Control Techniques for Vision-Based Autonomous Vehicles for Agricultural Applications: A Meta-analytic Review 1

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International Conference on Artificial Intelligence: Theory and Applications (AITA)

作者： Thakur, Abhishek Kumar, Ankit Mishra, Sudhansu Kumar BIT Mesra Ranchi 835215 Jharkhand India

ISBN: (数字)9789819984763

ISBN: (纸本)9789819984756;9789819984763

The agriculture industry continues to expand, showing significant potential in meeting the escalating food demand. Renowned for its plentiful resources and substantial agricultural yield, India is progressively leveraging advancements in technology for mass production of superior quality goods. Notably, the integration of robotics has revolutionized farming practices, encompassing activities such as fruit picking, sheep herding, pruning, weeding, spraying, and other farming tasks. The move toward automation not only mitigates the reliance on human labor but also enhances the overall productivity of the agriculture sector. This paper conducts a detailed examination of the progress in the realm of vision-based autonomous vehicles (AV) tailored for agricultural applications. Previous research has employed a range of machine learning algorithms, from linear and logistic regression, decision trees, support vector machines (SVM), naive Bayes, K-nearest neighbor (KNN) algorithms, to K-means. Nowadays, hybrid algorithms are increasingly employed in AV designs, addressing the constraints of single algorithms while elevating their efficiency. Furthermore, ensemble techniques are being used for efficient handling and accurate prediction over vast data quantities. These techniques amalgamate the predictive prowess of numerous base estimators, boosting robustness, and are predominantly employed today utilizing Python and R codes.

关键词： Autonomous vehicles Artificial intelligence Vision-based control Machine learning algorithms Control techniques

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Applying DevOps Practices for Machine Learning: Case Study Predicting Academic Performance 12th

Applying DevOps Practices for Machine Learning: Case Study P...

引用

12th World Conference on Information systems and Technologies (WorldCIST)

作者： Valdiviezo-Diaz, Priscila Guaman, Daniel Univ Tecn Particular Loja Dept Comp Sci San Cayetano Alto Loja 1101608 Ecuador

ISBN: (纸本)9783031602269;9783031602276

This paper presents the application of DevOps practices in the development and implementation of a machine learning model to predict the academic performance of students enrolled in a Higher Education Institution in Ecuador. The study encompasses three key phases: 1) Dataset preparation to build and validate the model, 2) Experimental evaluation of machine learning algorithms for academic performance prediction, and 3) Implementation of DevOps practices to design and build a software application using Microservices architecture, that supports the prediction model and visualize the results to take decisions. The dataset employed for the model and software application incorporates socio-demographic data and academic records of undergraduate students within the Information Technology career. Logistic Regression, Random Forest, and Neural Network algorithms are applied directly to the student dataset. Compared with other algorithms, experimental results present favorable outcomes with Random Forest, particularly in precision, sensitivity, and f-score metrics.

关键词： academic performance software application microservices machine learning prediction

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New Measurement Techniques Describing the Sinter Process 1

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11st World Conference on Information systems and Technologies (WorldCIST)

作者： Thienpont, Emanuel Kashi Hauck, Thorsten Erolglu, Tolga Kleinert, Tobias Walter, Kerstin VDEH Betriebsforschungsinst gmbH Sohnstr 69 D-40237 Dusseldorf Germany Rhein Westfal TH Aachen Turmstr 46 D-52062 Aachen Germany DK Recycling & Roheisen GmbH Werthauser Str 182 D-47053 Duisburg Germany

ISBN: (数字)9783031456510

ISBN: (纸本)9783031456503;9783031456510

Sinter with high and consistent quality, produced with low costs and emissions is very important for iron production. Transport and storage degrade sinter quality, generating fines and segregation effects. Conventional sinter quality monitoring is insufficient as it is slow and expensive. Consequently, sinter quality and the impact of different sinter quality on daily BF operation is extremely nontransparent. In this work, a new approach will be implemented to strengthen the data base that describes the sinter quality at a sinter plant. New on-line measurements will be established, combined, and analyzed with Big Data technologies. This break-through in continuous quality monitoring should help to get continuous quality indices for sinter and will enable combined optimization of sinter plant and BF.

关键词： Sinter Plant Hot Crusher Acoustic Measurement Technique Sinter Quality Indices Data Mining

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Attending to Entity Class Attributes for Named Entity Recognition with Few-Shot Learning

Attending to Entity Class Attributes for Named Entity Recogn...

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Intelligent systems Conference (IntelliSys)

作者： Patel, Raj Nath Dutta, Sourav Assem, Haytham Huawei Res Dublin Ireland Amazon Alexa AI Cambridge England

ISBN: (纸本)9783031477140;9783031477157

Named Entity Recognition (NER) serves as the foundation for several natural language applications like question answering, chat-bots and intent classification. Identification of entity boundaries and its categorization into entity types poses a significant challenge in domain-dependent and low-resource settings, with limited training data availability. To this end, we propose AtEnA, a novel NER framework utilizing entity class attributes from external knowledge source for few-shot learning. We use a two-stage fine-tuning process, wherein a language model is initially trained to "attend" to the different entity class attributes along with the textual context, and is then fine-tuned for the downstream application data with few annotated training examples. Experiments on benchmark NER datasets depict AtEnA to perform around 10 F1 score points better than the existing NER methodologies, specifically for fewshot limited training scenarios.

关键词： Named entity recognition Knowledge graph Few-shot learning Transformers Attention mask

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