Desarrollo de una aplicación híbrida para cálculos de siembra

En este trabajo se presentan los requerimientos, detalles técnicos y herramientas utilizadas para el diseño e implementación de una aplicación híbrida orientada a dispositivos móviles que permite realizar cálculos de parámetros y verificación del sistema de siembra y fertilización, como así también el cálculo de diversos insumos referidos a esta labor.Sociedad Argentina de Informática e Investigación Operativ

Li-Ion Batteries Parameter Estimation with Tiny Neural Networks Embedded on Intelligent IoT Microcontrollers

Lithium-ion (Li-Ion) batteries are rechargeable batteries which can maximize battery lifespan thanks to their chemical abilities, at the same time increasing power energy density. For these reasons, Li-Ion batteries have earned considerable popularity, and they are widely used both in mobile computing devices (e.g. smartphones and smartwatches) and automotive systems (e.g. hybrid and electric vehicles). A fundamental parameter for battery health monitoring is the State of Health (SoH), which is computed from the maximum releasable capacity, and which represents battery functionality in energy storage and delivery. Among the most used data-driven approaches are Machine Learning (ML) algorithms, such as Support Vector Machines (SVMs), Random Forest (RF) regressions, and Artificial Neural Networks (ANNs). This article presents a comparison of different ML algorithms for estimating maximum releasable capacity of Li-Ion batteries, with a special focus on the implementation of both Forward and Recurrent ANNs (FNNs and RNNs, respectively), using prognostic Li-Ion battery data sets provided by the National Aeronautics and Space Administration (NASA). After an evaluation of models performances in terms of RMSE and MAE, STM32Cube.AI tool was used to convert pre-trained ANNs to optimized ANSI C code for STM32 microcontrollers (MCUs), and to profile their complexity automatically. Finally, in order to decrease models size with minimal accuracy loss, the implemented ANNs were quantized via STM32Cube.AI, converting weights and activations from 32-bit floating-point to 8-bit integer precision. TensorFlow Lite for Microcontrollers (TFLM) was used as benchmark in the analysis and validation of both non-quantized and quantized models, and the performances obtained via STM32Cube.AI and TFLM were compared

Deeprank2

What's Changed Fix fix: check only 1 pssm for variant queries by @DaniBodor in https://github.com/DeepRank/deeprank2/pull/430 fix: pdb files with underscore in the filename gives unexpected query ids by @joyceljy in https://github.com/DeepRank/deeprank2/pull/447 fix: dataset_train inheritance warnings by @gcroci2 in https://github.com/DeepRank/deeprank2/pull/461 fix: cast hse feature to float64 by @DanLep97 in https://github.com/DeepRank/deeprank2/pull/465 fix: readthedocs after deeprank2 renaming by @gcroci2 in https://github.com/DeepRank/deeprank2/pull/472 fix: force scipy version for fixing deeprank2 installation by @gcroci2 in https://github.com/DeepRank/deeprank2/pull/478 fix: warning messages for invalid data in test_dataset.py by @joyceljy in https://github.com/DeepRank/deeprank2/pull/442 fix: make scipy 1.11.2 work by @cbaakman in https://github.com/DeepRank/deeprank2/pull/482 Refactor refactor: inherit information from training set for valid/test sets by @joyceljy in https://github.com/DeepRank/deeprank2/pull/446 refactor: rename deeprankcore to deeprank2 by @gcroci2 in https://github.com/DeepRank/deeprank2/pull/469 Build build: improve installation making use of pyproject.toml file only and setuptools by @gcroci2 in https://github.com/DeepRank/deeprank2/pull/491 CI CI: decrease sensitivity of test_graph_augmented_write_as_grid_to_hdf5 by @DaniBodor in https://github.com/DeepRank/deeprank2/pull/445 CI: fewer triggers by @DaniBodor in https://github.com/DeepRank/deeprank2/pull/457 Docs docs: update README.md by @gcroci2 in https://github.com/DeepRank/deeprank2/pull/443 docs: create tutorial README by @DaniBodor in https://github.com/DeepRank/deeprank2/pull/455 docs: improve installation instructions by @DaniBodor in https://github.com/DeepRank/deeprank2/pull/452 docs: add tutorials for PPIs by @gcroci2 in https://github.com/DeepRank/deeprank2/pull/434 docs: add tutorials for variants by @gcroci2 in https://github.com/DeepRank/deeprank2/pull/459 docs: minor improvements to install instructions by @DaniBodor in https://github.com/DeepRank/deeprank2/pull/484 docs: type hinting and docstrings in molstruct by @DaniBodor in https://github.com/DeepRank/deeprank2/pull/497 docs: joss paper by @gcroci2 in https://github.com/DeepRank/deeprank2/pull/423 docs: clarify ppi scoring metrics and add doc strings and tests by @gcroci2 in https://github.com/DeepRank/deeprank2/pull/498 docs: add performances table for deeprank2 by @gcroci2 in https://github.com/DeepRank/deeprank2/pull/493 Style style: auto-scrape trailing whitespace upon save in VS code by @DaniBodor in https://github.com/DeepRank/deeprank2/pull/483 Full Changelog: https://github.com/DeepRank/deeprank2/compare/v2.0.0...v2.1.