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SSRN 2024年

作者： Verbel, Angel Augusto Schutesky, Maria Emilia Gregory, Daniel D. Verbel, Arturo University of Brasilia Institute of Geosciences Graduate Program in Geology DF Brasília70910-900 Brazil University of Toronto Department of Earth Sciences TorontoONM5S 3B1 Canada Universidad de los Andes Systems and Computing Engineering Department School of Engineering Bogotá111711 Colombia

Systematic sampling of gold grains and their characterization in abundance and morphology can be used as a vectoring tool for mineral deposits. However, the processes that lead to the formation of an ore deposit are multiscale and nonlinear. As a result, the elemental compositions recorded in gold grains during the mineralization process seem mostly irregular and unpredictable. Here, we took advantage of the capabilities of neural networks to approximate, a natural nonlinear function that better explains the chemical variability observed in gold allowing us to make predictions. this was achieved by using published data of trace elements in gold of four types of gold deposits (i.e., Orogenic, VMS, porphyry and epithermal) from 47 different localities, obtained by laser ablation-inductively coupled plasma-mass spectrometry (LA- ICP-MS). The model was optimized by training five different architectures on the most influential elements, determined by principal component analysis (PCA). As a result, it was found that two hidden layers with 10 neurons each, is the best architecture for using trace elements as predictors of the type of deposit that formed a natural gold grain. In order to encourage the use of the findings made in this paper, we introduce OreGenes, an app developed in Matlab2023b based on the best-obtained model. It allows any user who possesses compatible data, the ability to import and process it to obtain a prediction with an average accuracy level of 88.9% confidence to determine the mineral deposit type that an unknown gold grain came from which grants the user the ability to have a powerful tool for exploration or research. © 2024, The Authors. All rights reserved.

关键词： Deposits

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Predicting Factors Influencing the Actual Use of E-learning Platform Among Medical Students in the Philippines

Predicting Factors Influencing the Actual Use of E-learning ...

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IEEE International Conference on Industrial engineering and engineering Management

作者： Omar Paolo Benito Yogi Tri Prasetyo Ralph Andre C. Roque Fernan Patrick Flores Maela Madel L. Cahigas Reny Nadlifatin Irene Dyah Ayuwati International Bachelor Program in Engineering Yuan Ze University Taoyuan Taiwan Department of Industrial Engineering and Management Yuan Ze University Taoyuan Taiwan School of Industrial Engineering and Engineering Management Mapúa University Manila Philippines School of Graduate Studies Mapúa University Manila Philippines Department of Information Systems Institut Teknologi Sepuluh Nopember Surabaya Indonesia Rectorate University of Surabaya Surabaya Indonesia

ISBN: (数字)9798350386097

ISBN: (纸本)9798350386103

This study aimed to examine the factors influencing the acceptance of eLearning platforms in medical education during the COVID-19 pandemic in the Philippines, using the latent variables from the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) and two additional variables derived from relevant literature. A total of $\mathbf{3 6 0}$ medical students participated by completing an online survey comprising 40 questions. The analysis, conducted using stepwise multiple linear regression, revealed that Performance Expectancy (PE), Habit (HB), and Instructor Characteristics (IC) were significant predictors of Actual Use (AU) with an accuracy of 54.22%. Higher levels of PE, HB, and IC were found to positively influence the acceptance and utilization of medical eLearning platforms. The findings from this study can provide valuable insights for the Commission on Higher Education in the Philippines, aiding in the enhancement of eLearning platforms for medical education. Although this research primarily focused on UTAUT2 and two additional variables-Learning Value and Instructor Characteristicsthe results offer substantial implications for the broader development f eLearning platforms in the medical field.

关键词： COVID-19 Surveys Gold Technological innovation Electronic learning Accuracy Pandemics Education Linear regression Industrial engineering

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Diabetes Classification Using Support Vector Machine: Binary Classification Model 4

Diabetes Classification Using Support Vector Machine: Binary...

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4th International Conference on Information and Communications Technology, ICOIACT 2021

作者： Rabiha, Suciana Ghadati Wibowo, Antoni Lukas Heryadi, Yaya Computer Science Department BINUS Graduate Program-Doctor of Computer Science. Information Systems Department BINUS Online Learning Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program-Doctor of Computer Science Bina Nusantara University 11480 Indonesia Faculty of Engineering Universitas Katolik Indonesia Atma Jaya Indonesia Computer Science Department BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University 11480 Indonesia

ISBN: (纸本)9781665433945

One of the health problems that require special attention is diabetes, besides the growth of this disease infection is increasing in various circles ranging from children, adults, men, women and the elderly. So to detect diabetes at an early stage, machine learning algorithms are the right choice to help detect indications of diabetes. This paper presents experimental results related to the exploration of the use of a Super Vector Machine (SVM) for data classification of Diabetes. The model used in this experiment is the Binary Classification Model. Based on the test results, the highest accuracy is obtained by using a total of 700 lines of data, which is 75,7143%. The Best estimated feasible point in Box Constraint is 272.29 with a KernelScale area of 72.627. © 2021 IEEE

关键词： Face recognition

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Heterogeneity of the Dominant Causes of Performance Loss in End-of-Life Cathodes and Their Consequences for Direct Recycling

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Advanced Energy Materials 2025年第0期

作者： Popeil, Melissa Usseglio-Viretta, Francois L.E. Pu, Xiaofei Gasper, Paul Dutta, Nikita Wang, Evelyna Allen, Eva Mangum, John S. Sunderlin, Nathaniel Fink, Kae Allen, Jeffery M. Weddle, Peter J. DeCaluwe, Steven C. Finegan, Donal P. Mines/NREL Advanced Energy Systems Graduate Program Colorado School of Mines GoldenCO80401 United States National Renewable Energy Laboratory GoldenCO80401 United States Argonne National Laboratory LemontIL60439 United States Department of Mechanical Engineering Colorado School of Mines GoldenCO80401 United States

Recycling Li-ion batteries from electric vehicles is critical for reducing costs and supporting the development of a domestic battery supply chain. Direct recycling of cathodes, like LiNixMnyCozO2 (NMC), is attractive due to its low cost, energy use, and emissions compared to traditional recycling techniques. However, a comprehensive understanding of the active material properties at end-of-life is needed to guide direct recycling processes and the performance-dependent reuse applications. Here, NMC material from an end-of-life commercial pouch cell is characterized and bench-marked against pristine non-cycled counterparts with respect to capacity, impedance, crystallography, morphology, and microstructure to identify major degradation modes and understand variability in the end-of-life material. The spatial heterogeneity of each property throughout the cell is also quantified. While the degraded material demonstrated similar capacity as the pristine, its impedance and rate capability are severely diminished. Furthermore, samples from the periphery of the electrode layers showed more severe performance loss compared to samples extracted from central regions. The dominant culprit of performance loss is the material microstructure, where the magnitude of particle cracking showed the strongest correlation to the impedance components that are most unfavorably impacted. This work suggests severe cracks in cathode active materials are the primary challenge that direct recycling methods must overcome. © 2025 The Author(s). Advanced Energy Materials published by Wiley-VCH GmbH.

关键词： battery NMC recycling

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Single Inductor Dual Buck Inverter Classical and Predictive Linear Voltage Controller Comparison

Single Inductor Dual Buck Inverter Classical and Predictive ...

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IEEE Annual Southern Power Electronics Conference (SPEC)

作者： Luis David Patarroyo-Gutierrez Nelson Nayoshi Nakamoto Yamaguti Oscar Mauricio Hernández-Gómez Daniel Abreu Macedo Da Silva School of electronic Engineering Universidad Pedagógica y Tecnológica de Colombia Tunja-Boyacá Colombia Dep. of Automation and Systems Federal University of Santa Catarina Florianopolis SC Brazil Electrical Engineering Graduate Program Federal University of Pará Belem Brazil

This article presents the mathematical model of a 2 KW DBI (Dual Buck Inverter) converter that can be used in renewable energy applications. The inverter operating modes are described with the equations that model their behavior with the activation signals of the MOSFETs. The switching model is built. Simulation results are presented by applying linear control through a PI controller and GPC (Generalized Predictive Controller) hybridized in PID structure; simulation results are recorded in PSIM© verifying the control response due to changes in the load and the input DC voltage level.

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Optimized Filling of a Given Cuboid with Spherical Powders for Additive Manufacturing

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Journal of the Operations Research Society of China 2021年第4期9卷 853-868页

作者： Zoya Duriagina Igor Lemishka Igor Litvinchev Jose Antonio Marmolejo Alexander Pankratov Tatiana Romanova Georgy Yaskov Lviv Polytechnic National University Lviv 79013Ukraine Katolicki Uniwersytet Lubelski Jana Pawła II Al.Racławickie 1420950 LublinPoland Graduate Program in Systems Engineering Nuevo Leon State University(UANL)MonterreyCP 66455 San Nicolas de los GarzaNuevo LeonMexico Engineering Department Panamerican UniversityCP 03920 Mexico CityMexico Department of Mathematical Modeling and Optimal Design Institute for Mechanical Engineering Problemsthe National Academy of Sciences of UkraineKharkiv 61046Ukraine Department of Systems Engineering of Kharkiv National University of Radio Electronics Kharkiv 61166Ukraine

In additive manufacturing(also known as 3D printing),a layer-by-layer buildup process is used for manufacturing *** laser 3D printers can work with various materials including metal *** particular,mixing various-sized spherical powders of titanium alloys is considered most promising for the aerospace *** achieve desired mechanical properties of the final product,it is necessary to maintain a certain proportional ratio between different powder *** this paper,a modeling approach for filling up a rectangular 3D volume by unequal spheres in a layer-by-layer manner is proposed.A relative number of spheres of a given radius(relative frequency)are known and have to be fulfilled in the final packing.A fast heuristic has been developed to solve this special packing *** results are compared with experimental findings for titanium alloy spherical *** relative frequencies obtained by using the imposed algorithm are very close to those obtained by the *** provides an opportunity for using a cheap numerical modeling instead of expensive experimental study.

关键词： Sphere packing Heuristic algorithm Titanium alloy spherical powders 3D printing

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Case Study: Gain Scheduling Adaptive Control Applied on a Didactic Level System

Case Study: Gain Scheduling Adaptive Control Applied on a Di...

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International Carpathian Control Conference (ICCC)

作者： Vinícius Berno Da Silva Paulo César Barros Rodrigues Murillo Ferreira Dos Santos Paolo Mercorelli Milena Faria Pinto Wolmar Araújo Neto Department of Electroelectronics CEFET-MG Leopoldina Brasil Institute for Production Technology and Systems (IPTS) Leuphana University of Lüneburg Germany Graduate Program in Electrical Engineering CEFET-RJ Rio de Janeiro Brasil Federal University of Espírito Santo UFES Vitória Brazil

ISBN: (数字)9798350350708

ISBN: (纸本)9798350350715

Gain scheduling has been successfully applied in many different areas due to the positive results achieved when applied to nonlinear control. Its utilization enables the controller to respond rapidly to changes in operating conditions. This study demonstrates the application of gain scheduling to a SMAR didactic level system to implement adaptive control. The results exhibited a strong response with minor errors using a Proportional and Integral (PI) controller, which facilitated the development of two generic equations for determining the values of K p (proportional gain) and K i (integral gain) based on the desired set point.

关键词： Adaptive scheduling PI control Liquids System dynamics Integral equations Noise Gain measurement

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Techno-economic and life-cycle assessment of electrochemical captured CO2 conversion process in monoethanolamine

Techno-economic and life-cycle assessment of electrochemical...

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2022 AIChE Annual Meeting

作者： Lee, Suhyun Choi, Woong Kim, Jae Hyung Hwang, Yun Jeong Na, Jonggeol Ewha Womans University School of Chemical Engineering and Materials Science Korea Republic of Ewha Womans University Graduate program in System Health Science and Engineering Korea Republic of Clean Energy Research Center Korea Institute of Science and Technology Korea Republic of Seoul National University School of Chemistry Korea Republic of Korea Republic of Ewha Womans University Dept. of Chemical Engineering & Material science Advanced Manufacturing Systems Laboratory Korea Republic of

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TEAM: Task-Clustering and Enhanced Adaptability for Meta-Reinforcement Learning on Robotics Through Multi-Task Diffusion and Optimization

TEAM: Task-Clustering and Enhanced Adaptability for Meta-Rei...

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IEEE International Conference on Robotic Computing (IRC)

作者： Joshua Ho Chien-Min Wang Chung-Ta King Chi-Wei Feng Shih-Yun Sunkuan Social Networks and Human-Centered Computing Program Taiwan International Graduate Program Institute of Information Science Academia Sinica Taipei Taiwan Institute of Information Systems and Applications National Tsing Hua University Hsinchu Taiwan Department of Computer Science National Tsing Hua University Hsinchu Taiwan Department of Electrical Engineering National Taiwan University Taipei Taiwan

ISBN: (数字)9798331521554

ISBN: (纸本)9798331521561

The ability of robots to imitate human learning strategies-rapidly adapting to new tasks without large datasets-has garnered significant attention in meta-learning. Meta-reinforcement learning seeks to enhance robotic agent flexibility across diverse tasks and contexts, offering promise where single-task learning often fails. Despite advancements like multi-task diffusion models and task-weighted optimization mechanisms, effectively training tasks with varying complexities simultaneously remains a major challenge. This paper introduces a novel meta-reinforcement learning method that addresses this issue by clustering the training tasks of robotic arms based on semantic and trajectory similarities, while leveraging adaptive learning rates and task-specific weights proposed by the multitask optimization techniques. Our approach, TEAM, emphasizes performance-driven semantic clustering, optimizing based on robotic task similarity, complexity, and convergence objectives. We also integrate fast adaptive and multi-task optimization of the diffusion model to enhance computational efficiency and adaptability. More specifically, we introduce a cluster-specific optimization technique, using specialized parameters for each group to allow more refined task handling. The experimental validation demonstrates the effectiveness of this scalable method in improving performance, adaptability, and efficiency in real-world, heterogeneous robotic tasks, further advancing robotic computing in meta-reinforcement learning.

关键词： Training Metalearning program processors Semantics Multitasking Diffusion models Complexity theory Trajectory Robots Optimization

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Towards Real-Time Machining Tool Failure Forecast Approach for Smart Manufacturing systems

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IFAC-PapersOnLine 2022年第2期55卷 548-553页

作者： Nicolas Nebelung Mario D.S. de Oliveira Santos Sofia T. Helena Athon F.C.S. de Moura Leite Matheus B. Canciglieri Anderson L. Szejka Control and Automation Engineering Department Pontifical Catholic University of Paraná (PUCPR) Curitiba Brazil Junior Scientific Initiation Program at the Nossa Senhora de Medianeira School and Pontifical Catholic University of Paraná Curitiba – Paraná Brazil Industrial and Systems Engineering Graduate Program (PPGEPS) Pontifical Catholic University of Paraná (PUCPR) Curitiba Brazil

Industry 4.0 is characterized by a dynamic market that constantly looking for new methods to optimize and integrate manufacturing processes. In this context, Artificial Intelligence has gained prominence in problem-solving, such as failure prediction and decision making, thus improving product quality, and consequently bringing competitiveness to the company. Aiming to contribute to this scenario, this research develops a data treatment system that, from an intelligent tool and an interoperable ontological model, automates the prediction and detection of failures in machining machines lines. The system was developed for the prediction of faults in machining lines includes an artificial intelligence formed from prediction algorithms and inferences, it is possible to guarantee the correct treatment and communication of data at different stages of the process. For the experimental research, was used data collected from a machining line of a public dataset. The information is collected and classified by Artificial Intelligence that supports a decision system. The prediction of tool wear would enable the system to infer the type of problem that is causing this wear, a possible root cause, and the needed maintenance based on the ontological inference tool. By this classification of data, it is possible to achieve, through inferences, a reduction in the decision scope, bringing the possible problems caused by the incoming value. The semantic interoperability ensures correct data exchange and processing, which generates a more assertive view of production failures. The system may help companies to increase their productive process by helping them identify future failures in production if applied in a real scenario.

关键词： Industry 4.0 Smart System Artificial Intelligence Machine Learning Ontology Machine Failure Forecast

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