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A Fitness-Based Migration Policy for Biased Random-Key Genetic Algorithms 26th

A Fitness-Based Migration Policy for Biased Random-Key Gene...

26th International Conference on Applications of Evolutionary Computation, EvoApplications 2023, held as part of EvoStar 2023

作者： Boiani, Mateus Parpinelli, Rafael Stubs Dorn, Márcio Institute of Informatics Federal University of Rio Grande do Sul Rio Grande do Sul Porto Alegre Brazil Graduate Program in Applied Computing Santa Catarina State University Santa Catarina Joinville Brazil Center for Biotechnology Federal University of Rio Grande do Sul Rio Grande do Sul Porto Alegre Brazil National Institute of Science and Technology - Forensic Science Rio Grande do Sul Porto Alegre Brazil

ISBN: (纸本)9783031302282

Population diversity management is crucial for the quality of solutions in Evolutionary Algorithms. Many techniques require assistance to handle diverse problem characteristics and may prematurely converge in local optima. Maintaining diversity enables the algorithm to search the space and produce better results effectively. Parallel models are a common approach to preserving diversity;however, design decisions affect optimization process characteristics. For example, the Island model’s migration policy affects convergence speed. This study proposes and evaluates a fitness-based migration policy for the Biased Random-Key Genetic Algorithm (BRKGA) and compares it to two traditional strategies. The results in continuous search spaces demonstrate that the proposed policy can enhance BRKGA optimization with appropriate parameters. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Genetic algorithms

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2D Basement Relief Inversion using Sparse Regularization.

arXiv

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arXiv 2024年

作者： Barboza, Francisco Márcio da Cunha Romão Silva, E. Arthur Anthony de Carvalho, Bruno Motta Department of Computing and Technology Federal University of Rio Grande do Norte Rio Grande do Norte Caicó Brazil Graduate Program on Systems and Computing Federal University of Rio Grande do Norte Rio Grande do Norte Natal Brazil Department of Informatics and Applied Mathematics Federal University of Rio Grande do Norte Rio Grande do Norte Natal Brazil

Basement relief gravimetry is a key application in geophysics, particularly important for oil exploration and mineral prospecting. It involves solving an inverse geophysical problem, where the parameters of a geological model are inferred from observed data. In this context, the geological model consists of the depths of constant-density prisms representing the basement relief, and the data correspond to the gravitational anomalies caused by these prisms. Inverse geophysical problems are typically ill-posed, as defined by Hadamard, meaning that small perturbations in the data can result in large variations in the solutions. To address this instability, regularization techniques, such as those proposed by Tikhonov, are employed to stabilize the solutions. This study presents a comparative analysis of various regularization techniques applied to the gravimetric inversion problem, including Smoothness Constraints, Total Variation, the Discrete Cosine Transform (DCT), and the Discrete Wavelet Transform (DWT) using Daubechies D4 wavelets. Optimization methods are commonly used in inverse geophysical problems because of their ability to find optimal parameters that minimize the objective function—in this case, the depths of the prisms that best explain the observed gravitational anomalies. The Genetic Algorithm (GA) was selected as the optimization technique. GA is based on Darwinian evolutionary theory, specifically the principle of natural selection, where the fittest individuals in a population are selected to pass on their traits. In optimization, this translates to selecting solutions that most effectively minimize the objective function. The results, evaluated using fit metrics and cumulative error analysis, demonstrate the effectiveness of all the regularization techniques and the Genetic Algorithm. Among the methods tested, the Smoothness constraint was briefly the most effective for the first and second synthetic models. For the third model, which was based on re

关键词： Discrete wavelet transforms

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Saasport model: Exploring protocol portability, resource elasticity and microservice architecture in the efficient execution of iot applications 20

Saasport model: Exploring protocol portability, resource ela...

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20th International Conferences on WWW/Internet 2021 and applied computing 2021

作者： da Silveira, Maria Gisele Flores da Silva Silveira, Wagner da Rosa Righi, Rodrigo da Costa, Cristiano André Griebler, Dalvan Três de Maio - RS Brazil Applied Computing Graduate Program Unisinos University Av. Unisinos 950 RS São Leopoldo Brazil Av. Ipiranga 6681 RS Porto Alegre Brazil

ISBN: (纸本)9789898704344

Despite the high growth of the Internet of Things and the multitude of applications that use the information generated, it is estimated that 90 % of these data are not yet fully used. This is because IoT systems are based on heterogeneous networks and devices with resource restrictions, which cannot execute significant processing. So one of the alternatives that have been addressed is the use of cloud applications, where the data can be appropriately processed, stored, and analyzed. In the cloud, applications can then employ resource elasticity and be developed in microservice architectures. In state-of-the-art, the main efforts to provide portability and efficient treatment of IoT applications are described. However, a platform that provides protocol portability, resource elasticity using microservices architecture for the efficient execution of IoT applications has not yet been implemented. In this context, this work presents SaaSport, which among its contributions, will allow the efficient treatment of data generated by IoT devices that use the MQTT and CoAP protocols. The final result of this work demonstrates that the model provides an efficient treatment of the data generated in IoT environments. © 20th International Conferences on WWW/Internet 2021 and applied computing 2021.

关键词： Elasticity

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Evaluation of Resampling Techniques to Provide Better Synthesized Input Data to Super-Resolution Deep Learning Model Training

Evaluation of Resampling Techniques to Provide Better Synthe...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Vinicius Sales Ademir Marques Graciela Racolte Anderson Nunes Tainá Guimaraes Daniel Zanotta André Spigolon Luiz Gonzaga Mauricio Roberto Veronez Vizlab - X-Reality and GeoInformatics Lab UNISINOS-São Leopoldo RS Brazil Graduate Program in Applied Computing at Vale do Rio dos Sinos University São Leopoldo RS Brazil CENPES - PETROBRAS Rio de Janeiro Brazil

Hyperspectral images often have low spatial resolution due to the sensor sizes required to capture the required spectral response. Super-resolution (SR) techniques try to mitigate this by injecting more detail in the upscaled image, either with numerical methods or deep learning and Convolution Neural Networks. In the deep learning methods, the models learn image details by inferring a high-resolution (HR) image from a synthetic low-resolution (LR) image that simulates the natural degradation of sensors by applying resampling (to reduce the image detail) and noising (to add small errors and interference). Often disregarded in the literature, the resampling method applied to generate the synthetic image can impact greatly the deep learning model training. This work, evaluate several resampling techniques to measure this impact using the Harvard hyperspectral dataset. Results showed that the Lanczos filter was the best among eight other resampling methods. The Nemenyi and Friedman ranking statistical tests also indicated that the Cubic-Spline, Bicubic, and RMS achieved good results.

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Investigating Cases and Clusters-Based Reuse Policies for Card-Playing Agents

Investigating Cases and Clusters-Based Reuse Policies for Ca...

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IEEE Symposium on Computational Intelligence and Games, CIG

作者： Gustavo B. Paulus Daniel P. Vargas Joaquim V. C. Assunção Luis A. L. Silva Federal Institute of Education Science and Technology of Rio Grande do Sul Ibirubá Brazil Graduate Program in Computer Science Federal University of Santa Maria Santa Maria Brazil Applied Computing Department Federal University of Santa Maria Santa Maria Brazil

Computer games continue to present challenging experimental fields for developing Artificial Intelligence (AI) models. With Case-Based Reasoning and Clustering, this work proposes novel cases and clusters-based reuse criteria for implementing card-playing agents with diversified playing skills. Using the game of Truco, a common game in South America, we detail how game actions are reused from past cases selected as query answers for given game problems. In doing so, the majority rule, the probability-based lottery, the probability of victory, and the number of points won reuse policies are used to select a cluster of cases. Then these policies are also used to select game actions from the cases within the selected cluster. Investigating the combined exploration of reuse policies, experiments of different natures evaluate the performance of implemented Truco bots disputing matches against each other. Players in these tournaments are equipped with varied policies and use case bases constructed differently.

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MILPdM: A Predictive Maintenance Architecture for the Military Domain

MILPdM: A Predictive Maintenance Architecture for the Milita...

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International Conference on Machine Learning and Applications (ICMLA)

作者： Jovani Dalzochio Rafael Kunst Jorge Barbosa Edison Pignaton De Freitas Alécio Binotto Graduate Program in Applied Computing University of Vale do Rio dos Sinos São Leopoldo Brazil Institute of Informatics Federal University of Rio Grande do SuI Porto Alegre Brazil Asset and Edge Optimization IBM Consulting Munich Germany

Equipment monitoring for failure prediction is receiving attention from different sectors of society, such as industry, healthcare, and defense. In the defense domain, assets like military vehicles generate data that one can use to identify behavior changes and anticipate possible real-time failures, avoiding unnecessary maintenance interventions. Failure anticipation is crucial, as assets operated in the military domain perform critical tasks in which unexpected equipment failures result in high material and human costs. Approaches found in the literature typically deal with failure generation, aiming at analyzing the equipment's behavior. This paper proposes a broader approach called MILPdM. This proposal is a failure prediction architecture covering the whole failure prediction and predictive maintenance procedures. We evaluate MILPdM architecture by analyzing an engine-failure scenario where we train models to predict time series by collecting vibration data that describes the degradation of the vehicle's health. Considering the implementation of an LSTM-based neural network and Random Forest, the acquired results lead to a root mean square error of 0.15015 in the best case, which allows to predict the failure status two minutes in advance with only 3 hours of data history. This result shows that MILPdM is capable of anticipating failures with high assertiveness.

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Clustering Fracture Data in 3D Outcrop Models with Hierarchical Agglomerative Clustering and Fisher Statistics

Clustering Fracture Data in 3D Outcrop Models with Hierarchi...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Graciela Racolte Ademir Marques Vinicius Ferreira Sales Daniel Capella Zanotta Delano Menecucci Ibanez Mauricio Roberto Veronez Luiz Gonzaga Vizlab — X-Reality and GeoInformatics Lab UNISINOS São Leopoldo RS Brazil Graduate Program in Applied Computing at Vale do Rio dos Sinos University São Leopoldo RS Brazil CENPES PETROBRAS Rio de Janeiro Brazil

Most fracture properties, such as orientation and density, are acquired by interpreting data obtained at the wellbores, while fracture properties between wells are typically derived from seismic data. However, this information is sparse or has low resolution leading to the study and analysis of outcrops. The data acquisition in outcrops is facilitated by its 3D representation in Digital Outcrop Models (DOM) obtained from LiDAR and Photogrammetry. It also allows virtual interpretation and fracture detection. In either case, the 3D fracture data need to be clustered in fracture sets to allow the statistical analysis necessary to upscale and resample the DFN. This clusterization is carried out by methods like k-means and fuzzy, however with caveats, like the prior definition of the number of clusters. In this work, we propose the use of agglomerative hierarchical clustering to cluster 3D fracture data obtained by a KD-Tree segmentation algorithm. Results showed that when compared to k-means, the proposed method presented more compact clusters and balance when considering Fisher’s statistics of dispersion.

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Modeling Of Mean Tropospheric Temperature From Convolutional Neural Networks

Modeling Of Mean Tropospheric Temperature From Convolutional...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Diego Brum Vinicius Francisco Rofatto Leonardo Scalco Rafaela de Oliveira Pena Luiz Gonzaga Daniel C. Zanotta Luiz Fernando Sapucci Mauricio Roberto Veronez VIZLAB—X-Reality and Geoinformatics Laboratory UNISINOS Sao Leopoldo RS Brazil Graduate Program on Applied Computing UNISINOS Sao Leopoldo RS Brazil Institute of Geography Federal University of Uberlandia Monte Carmelo MG Brazil Graduate Program in Agriculture and Geospatial Information Federal University of Uberlandia Monte Carmelo MG Brazil Brazilian Institute of Space Research (INPE) Cachoeira Paulista SP Brazil

The Mean Tropospheric Temperature (Tm) is important for the sake of the conversion between Zenith Path Delay (ZPD) to Precipitable Water Vapor (PWV) because of the proportionality constant that takes Tm to be calculated. In Brazil, Tm modeling’s been traditionally performed using Multiple Linear Regression Models (MLR). However, recent works suggest the use of Deep Learning methods to model Tm values. In this work, we propose models based on Convolutional Neural Networks (CNN) using radiosonde data from 1961 to 2010. The results show that CNN models can outperform the traditional methods considering different statistic metrics like R, standard deviation, and RMSE.

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Designing Classical Header Bits for Quantum Wrapper Networking with O-band Quantum Signal

Designing Classical Header Bits for Quantum Wrapper Networki...

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Frontiers in Optics + Laser Science 2023, FiO, LS 203: Part of Frontiers in Optics + Laser Science 2023

作者： Gül, G. Berkay On, M. Singh, S.K. Proietti, R. Kanter, G.S. Yoo, S.J.B. Kumar, P. Graduate Program in Applied Physics Northwestern University 2145 Sheridan Road EvanstonIL60208 United States Department of Electrical and Computer Engineering University of California Davis DavisCA95616 United States Department of Electronics and Communication Eng. Indian Institute of Technology Roorkee247667 India Department of Electronics and Telecommunications Engineering Politecnico di Torino Italy Center for Photonic Communication and Computing Dept. of Electrical and Computer Eng. Northwestern University EvanstonIL60208 United States

We investigate the impact of wavelength choice for the classical headers in quantum wrapper networking with O-band quantum payload. We show that a C-band header performs substantially better than an O-band header. ... 详细信息

ISBN: (纸本)9781957171296

关键词： Bandpass filters Beam splitters Bit error rate Entanglement distribution Quantum teleportation Raman scattering

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Designing Classical Header Bits for Quantum Wrapper Networking with O-band Quantum Signal

Designing Classical Header Bits for Quantum Wrapper Networki...

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2023 Laser Science, LS 2023

作者： Gül, G. On, M. Berkay Singh, S.K. Proietti, R. Kanter, G.S. Yoo, S.J.B. Kumar, P. Graduate Program in Applied Physics Northwestern University 2145 Sheridan Road EvanstonIL60208 United States Department of Electrical and Computer Engineering University of California Davis DavisCA95616 United States Department of Electronics and Communication Eng. Indian Institute of Technology Roorkee247667 India Department of Electronics and Telecommunications Engineering Politecnico di Torino Italy Center for Photonic Communication and Computing Dept. of Electrical and Computer Eng. Northwestern University EvanstonIL60208 United States

关键词： Quantum electronics

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