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检索条件"丛书名=Lecture notes in artificial intelligence,"

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Optimization of Electrostatic Adhesion Using Concentric Ring Electrodes 17th

Optimization of Electrostatic Adhesion Using Concentric Ring...

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17th International Conference on Intelligent Robotics and Applications

作者： Yuan, KangLong Chen, Xinxian Peng, Jun Xi An Jiao Tong Univ Natl & Local Joint Engn Res Ctr Equipment Operat Xian 710049 Peoples R China Xi An Jiao Tong Univ Sch Mech Engn Xian 710049 Peoples R China Xi An Jiao Tong Univ Sch Future Technol Xian 710049 Peoples R China

ISBN: (纸本)9789819607792;9789819607808

Controllable adhesion different from permanent sticky connection, can achieve repeatable attachment and detachment to surfaces of nonconductive substrates, and obtain extensive attractions and applications in climbing robots, silicon-based electronics, clinic treatments, etc. Whereas the electrostatic adhesion technique offers controllable adhesions by switching power, the adhesion strength is much weaker than permanent glue and tapes. We derive electrostatic adhesion strength expression according to the Maxwell stress tensor method and develop the effective width model to optimize adhesion strength. By combining dimensional parameters of electrode rings into a single variable, we pinpoint the optimum condition with one defined parameter of effective width, leading to predictions of the maximum limit of adhesion. We reveal the importance of optimizing the dimensional parameters of electrode rings, not only applied voltages and air gaps. Although the effect of thickness of substrates was typically ignored, it rarely affects adhesion analysis only when substrates are thicker enough. Electrostatic adhesion pads with various electrode ring numbers were fabricated and assembled. Experiment results and reference data supported this strength optimization strategy. We also designed a soft robot based on our optimized electrostatic adhesion pad and origami pneumatic muscle. It moves in an inchworm pattern, and it is highly impact-resistant due to the soft materials.

关键词： Electrostatic Adhesion Concentric Ring Electrode Optimization Analyze Soft Robot

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Going Bananas! - Unfolding Program Synthesis with Origami 34th

Going Bananas! - Unfolding Program Synthesis with Origami

引用

34th Brazilian Conference on Intelligent Systems

作者： Fernandes, Matheus Campos de Franca, Fabricio Olivetti Francesquini, Emilio Fed Univ ABC UFABC Santo Andre SP Brazil

ISBN: (纸本)9783031790317;9783031790324

Automatically creating a computer program using input-output examples can be a challenging task, especially when trying to synthesize computer programs that require loops or recursion. Even though the use of recursion can make the algorithmic description more succinct and declarative, this concept creates additional barriers to program synthesis algorithms such as the creation and the attempt to evaluate non-terminating programs. One reason is that the recursive function must define how to traverse (or generate) the data structure and, at the same time, how to process it. In functional programming, the concept of Recursion Schemes decouples these two tasks by putting a major focus on the data processing. This can also help to avoid some of the pitfalls of recursive functions during program synthesis, as argued in the paper that introduced the Origami technique. The authors showed how this technique can be effective in finding solutions for programs that require folding a list. In this work, we incorporate other Recursion Schemes into Origami, such as accumulated folding, unfolding, and the combination of unfolding and folding. We evaluated it on the 29 problems of the standard General Program Synthesis Benchmark Suite 1, obtaining favorable results against other well-known algorithms. Overall, Origami achieves the best results in 25% more problems than its predecessor (HOTGP) and an even higher increase when compared to other approaches. Not only that, but it can also consistently find a solution to problems for which other concurrent algorithms report a low success rate.

关键词： Program Synthesis Genetic Programming Recursion

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Exploring Climatic Shifts in Brazilian Climates: Insights from ARMAX, Decision Trees, and artificial Neural Networks 34th

Exploring Climatic Shifts in Brazilian Climates: Insights fr...

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34th Brazilian Conference on Intelligent Systems

作者： de Andrade, Joao V. R. da Silva, Igor L. B. de Souza Junior, Teobaldo G. Silva, Leandro H. de S. Freire, Agostinho Lucena, Daisy Fernandes, Bruno J. T. Univ Pernambuco UPE Escola Politecn Pernambuco Recife PE Brazil Inst Fed Educ Ciencia & Tecnol Paraiba IFPB Joao Pessoa Paraiba Brazil Univ Fed Paraiba UFPB Joao Pessoa Paraiba Brazil

ISBN: (纸本)9783031790379;9783031790386

Over the years, humanity has deepened its understanding of its actions' profound impacts on global temperatures. Beyond the rise in global temperatures driven by global warming, local biomes also significantly influence regional temperature variations, often due to changes in land use. Historically, climatologists have attributed local temperature increases to deforestation, urban development, and agricultural activities. Although these factors are well-known, there remains a need for methodologies to objectively quantify the impact of land use changes on temperature increases. This paper proposes an explanation for temperature increase utilizing advanced statistical models such as ARMAX and neural networks with explainable AI components aiming to quantify the effects of these land use changes on local temperature trends over several years. For this purpose, two cities were chosen to apply the methodology. Sao Felix do Xingu, in the Brazilian Amazon rainforest (a city that has experienced significant deforestation due to agriculture over the last decade), and Cajazeiras, in the Brazilian semi-arid region (a city that has had substantial urban development over the years). The Data for this analysis were sourced from the MapBiomas Brazilian database and temperature records collected by the Brazilian National Institute of Meteorology. Our approach offers insights into human activities' climatic impact, giving weight to soil changes and providing a methodology for further interdisciplinary studies, potentially serving as a starting point for specialist systems across various domains.

关键词： Explainable AI ARMAX models Neural networks Global warming Land use changes

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A Topology-Inspired Approach to AGM Belief Change 34th

A Topology-Inspired Approach to AGM Belief Change

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34th Brazilian Conference on Intelligent Systems

作者： Souza, Marlo Univ Fed Bahia Salvador BA Brazil

ISBN: (纸本)9783031790379;9783031790386

AGM's belief revision is one of the main paradigms in the study of belief change operations. Despite its popularity, it is well recognised that AGM's work relies on a strong idealisation of the agent's capabilities and on the supporting logic. Recent results in the literature have shed light on the definability of AGM-like belief change operations in general logics, showing how the properties of a logic can be leveraged to construct these operations. Exploring well-known connections between Logic, Algebra and Topology, our work explores a general notion of belief change contraction, which can be connected to well-studied operations in the literature, such as AGM rational contractions, partial meet contractions, and multiple contractions, employing the framework of Abstract Model Theory and Topological Semantics. We show that AGM rational contractions can be characterised in our framework, and we can establish connections to different results in the literature.

关键词： Belief Change Model Theory Non-classical Logics

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Spatial Intelligent Estimation of Energy Consumption 23rd

Spatial Intelligent Estimation of Energy Consumption

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23rd Mexican International Conference on artificial intelligence

作者： Gallegos, Jessica Borunda, Monica Garduno, Raul Garcia-Beltran, Carlos Daniel Univ Nacl Autonoma Mexico Fac Ciencias Mexico City Mexico Consejo Nacl Ciencia & Technol Mexico City Mexico Tecnol Nacl Mexico Ctr Nacl Invest & Desarrollo Tecnol Mexico City Mexico Inst Nacl Elect & Energias Limpias Mexico City Mexico

ISBN: (纸本)9783031755422;9783031755439

Energy consumption estimation plays a crucial role in sustainable development and resource allocation. In this study, energy consumption in the municipalities of Mexico is estimated from night-time light from satellite images. The application of various Statistical and Machine Learning models to estimate energy consumption, such as Linear Regression, Decision Tree, Random Forest, and Neural Network, is explored. This study is the first step toward the creation of a model that allows the prediction of energy consumption in isolated areas where energy consumption data is scarce or there is no information, but night-time light data. The results demonstrate a strong relationship between energy consumption and these satellite images. Consequently, all tested models provided accurate estimations, with the Random Forest and Neural Network methods yielding the best performance.

关键词： Energy consumption Machine Learning Satellite images of Night-time Lights

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Acoustic Features and Autoencoders for Fault Detection in Rotating Machines: A Case Study 34th

Acoustic Features and Autoencoders for Fault Detection in Ro...

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34th Brazilian Conference on Intelligent Systems

作者： Bortoni, Leonardo Afonso Ferreira Jaskowiak, Pablo Andretta Joinville Technol Ctr CTJ Fed Univ Santa Catarina UFSC Joinville Brazil

ISBN: (纸本)9783031790348;9783031790355

Traditional Machine Fault Detection (MFD) techniques usually rely on multiple sensor data sources, such as vibration, temperature, force, and audio/acoustic signals. Acoustic signals, in particular, are quite appealing in the context of MFD, as they are often among the first manifestations of machine failure. Furthermore, they are associated with high sensitivity, environmental resilience, and do not require machine interference. Given these compelling characteristics, MFD based exclusively on acoustic signals can be highly beneficial. In this work, we evaluate an unsupervised MFD approach based on Autoencoders (AE) trained exclusively on features extracted from acoustic signals of a rotating machine. The data employed in this work comes from the Machine Fault Database (MaFaulDa), which includes information from vibration and velocity sensors, besides the acoustic measurements. This allows us to compare the performance of the AE models to that of supervised models (such as MLPs) trained on the same acoustic-based feature set, as well as feature sets that incorporate all sensors from MaFaulDa. Our results support that unsupervised MFD based on Autoencoders and acoustic signals is particularly appealing, as it requires only normal machine operation for training. Indeed, we obtained AUC values of 0.86 for the task.

关键词： Machine Fault Detection Acoustic Audio Autoencoders

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Efficient Visual Navigation with Bio-inspired Route Learning Algorithms 17th

Efficient Visual Navigation with Bio-inspired Route Learning...

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17th International Conference on Simulation of Adaptive Behavior (SAB)

作者： Kagioulis, Efstathios Knight, James C. Philippides, Andrew Ghosh, Anindya Amin, Amany Graham, Paul Nowotny, Thomas Univ Sussex Ctr Computat Neurosci & Robot Brighton BN1 9QJ E Sussex England

ISBN: (纸本)9783031715327;9783031715334

Visual navigation through complex environments is a challenging task, yet ants navigate in them easily and accurately with low-resolution vision and limited neural resources. Inspired by ants, we have developed a series of visual familiarity-based navigation algorithms for teach-and-repeat style navigation. These algorithms learn the egocentric visual appearance of the world on a training route and, during subsequent navigation, move in the direction that leads to the best match of the current view with one of the scenes encountered during training. Because they do not depend on accurate feature extraction or map building these algorithms use relatively unprocessed low-resolution panoramic views, making them computationally efficient. However, the computational cost of comparing the current view with all training images is still quite large and the algorithm can get confused if the path crosses itself. Here we develop and test novel algorithms where the agent uses sequence information to adaptively select a window of route memories to navigate with. This algorithm is shown to successfully navigate real-world routes through ant-like habitats, including a figure of 8-route, as well as a long route along a corridor, with all computation performed onboard the robot.

关键词： Bio-inspired Robotics Visual Navigation Teach and Repeat Autonomous Robotics

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Diversity Population Metrics in Diploid and Haploid Genetic Algorithm Variants 19th

Diversity Population Metrics in Diploid and Haploid Genetic ...

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19th International Conference on Hybrid artificial intelligence Systems

作者： Petrovan, Adrian Matei, Oliviu Pop, Petrica C. Sabo, Cosmin Tech Univ Cluj Napoca Dept Elect Engn Elect & Comp Sci Cluj Napoca Romania Tech Univ Cluj Napoca Dept Math & Comp Sci Cluj Napoca Romania

ISBN: (纸本)9783031741821;9783031741838

Genetic Algorithms (GAs) are powerful optimization techniques inspired by the principles of natural selection and genetics. One critical aspect of their success lies in the diversity of solutions within their populations. Diversity ensures exploration of a broader solution space, preventing premature convergence to sub-optimal solutions. Consequently, the evaluation and maintenance of diversity metrics in GA populations have garnered significant attention in the field of evolutionary computation. This article presents the efficiency of diploid genetic algorithms against the haploid variant through measures of population diversity.

关键词： diploid genetic algorithm diversity metrics entropy

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artificial intelligence in Portfolio Selection Problem: A Review and Future Perspectives 19th

Artificial Intelligence in Portfolio Selection Problem: A Re...

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19th International Conference on Hybrid artificial intelligence Systems

作者： Sanchez-Fernandez, Alvaro Diez-Gonzalez, Javier Perez, Hilde Univ Leon Dept Mech Comp & Aerosp Engn Leon 24071 Spain

ISBN: (纸本)9783031741852;9783031741869

Portfolio selection, the process of selecting the optimal combination of securities to achieve investors objectives, is a leading problem in finance. Ever since its inception, the problem has been widely addressed theoretically but its practical applications in portfolio management have seen few changes, with new technologies like artificial intelligence (AI) having limited impact in practice. The present study addresses this gap by identifying six main areas of the portfolio selection process and analyzing literature trends and gaps in AI methods within these areas. Additionally, it proposes four primary research avenues where AI can enhance portfolio selection in both theory and practice: i) utilizing alternative datasets and big-data analysis, ii) implementing robust predictive portfolio optimization techniques, iii) applying Reinforcement Learning (RL) methods for dynamic portfolio optimization, and iv) leveraging AI to make portfolio optimization more applicable in real-world scenarios.

关键词： artificial intelligence Machine Learning Metaheuristics Portfolio Optimization Portfolio Selection

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Variable Stiffness of Continuum Robotics: A Review 17th

Variable Stiffness of Continuum Robotics: A Review

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17th International Conference on Intelligent Robotics and Applications

作者： Huang, Dongbao Yang, Laihao Sun, Yu Xi An Jiao Tong Univ Natl Key Lab Aerosp Power Syst & Plasma Technol Xian Peoples R China

ISBN: (纸本)9789819607792;9789819607808

Continuum robots have advantages such as strong adaptability, high safety, and excellent flexibility, allowing them to move flexibly in unstructured environments, thereby attracting extensive attention. In practical applications, continuum robots need high compliance to adapt to complex and narrow environments while maintaining a certain degree of stiffness to ensure stable and precise operations. Therefore the ability to achieve the transition between rigidity and flexibility in continuum manipulators is crucial for ensuring the safety of human-robot interaction and functional performance. This paper reviews the mechanisms of variable stiffness in continuum robots, including material-based methods, structural methods, and hybrid methods. The comparison of different variable stiffness methods is based on the application needs, design requirements, and performance metrics of continuum robots, including stiffening capability, response time, space occupation, and implementation safety. A comprehensive analysis of existing methods is provided, and future development directions are proposed.

关键词： Continuum Robots Variable Stiffness Function-Structure Integration

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