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6 results for “Smart algorithms”
Smart Battery Management System for Electric Vehicles: Selflearning Algorithms for Simultaneous State and Parameter Estimation, and Stress Detection
<p>The project proposes to develop parameter-varying SOH-coupled models for lithium-ion battery and self-learning algorithms to learn the model for simultaneous state and parameter estimation and fault detection. The traditional battery models use constant parameters, limiting their accuracy for predicting the state of the charge and health over the complete life-cycle. In practice, the battery parameters vary with the change in the state of charge and state of health. SOH-coupled models can be used to estimate the state of charge and health accurately. Further, obtaining the model parameters is also a challenging task for designing filters or observers for state estimation. A self-learning algorithm can eliminate the requirement of the model parameters. In this project, three SOH-coupled models are proposed and validated experimentally. The models are also used to design extended Kalman filters (EKF) for the state of charge, state of health, core and surface temperature, and internal resistance estimation. The results showed that the SOHcoupled models are more effective when compared to the uncoupled models in the literature. Further, it was found that EKFs based state estimation errors were within 1%. The self-learning algorithm using a two-layer neural network showed the ability to learn the models in real-time. However, the state estimation errors are higher for the self-learning scheme compared to the EKF based approaches. This is due to the limited measurement and online training schemes utilized to train neural networks. This requires further investigation in hyper-parameter tuning for implementation. Finally, a model-based fault detection scheme was proposed to detect internal thermal fault at its onset. The SOHcoupled model is reformulated to incorporate the internal resistance as a state. The EKF is used as a fault detection observer. The proposed fault detection scheme is validated using numerical simulation. It was observed that the fault detection scheme with SOH coupled electro-thermal-aging model could effectively detect a thermal fault at its incipient state.</p>
Simple Multimodal Algorithmic Reasoning Task Dataset (SMART-101)
<p><strong>Introduction</strong></p> <p>Recent times have witnessed an increasing number of applications of deep neural networks towards solving tasks that require superior cognitive abilities, e.g., playing Go, generating art, ChatGPT, etc. Such a dramatic progress raises the question: how generalizable are neural networks in solving problems that demand broad skills? To answer this question, we propose SMART: a Simple Multimodal Algorithmic Reasoning Task (and the associated SMART-101 dataset) for evaluating the abstraction, deduction, and generalization abilities of neural networks in solving visuo-linguistic puzzles designed specifically for children of younger age (6--8). Our dataset consists of 101 unique puzzles; each puzzle comprises a picture and a question, and their solution needs a mix of several elementary skills, including pattern recognition, algebra, and spatial reasoning, among others. To train deep neural networks, we programmatically augment each puzzle to 2,000 new instances; each instance varied in appearance, associated natural language question, and its solution. To foster research and make progress in the quest for artificial general intelligence, we are publicly releasing our SMART-101 dataset, consisting of the full set of programmatically-generated instances of 101 puzzles and their solutions.</p> <p>The dataset was introduced in our paper <a href="https://arxiv.org/pdf/2212.09993.pdf">Are Deep Neural Networks SMARTer than Second Graders?</a> by Anoop Cherian, Kuan-Chuan Peng, Suhas Lohit, Kevin A. Smith, and Joshua B. Tenenbaum, CVPR 2023</p> <p>Files in the unzipped folder:</p> <ol> <li>./README.md: This Markdown file</li> <li>./SMART101-Data: Folder containing all the puzzle data. See below for details.</li> <li>./puzzle_type_info.csv: Puzzle categorization (into 8 skill classes).</li> </ol> <p><strong>Dataset Organization</strong></p> <p>The dataset consists of `101` folders (numbered from 1-101); each folder corresponds to one distinct puzzle (root puzzle). There are 2000 puzzle instances programmatically created for each root puzzle, numbered from 1-2000. Every root puzzle index (in [1,101]) folder contains: (i) `img/` and (ii) `puzzle_<index>.csv`. The folder `img/` is the location where the puzzle instance images are stored, and `puzzle_<index>.csv` the non-image part of a puzzle. Specifically, a row of `puzzle_<index>.csv` is the following tuple: `<id, Question, image, A, B, C, D, E, Answer>`, where `id` is the puzzle instance id (in [1,2000]), `Question` is the puzzle question associated with the instance, `image` is the name of the image (in `img/` folder) corresponding to this instance `id`, `A, B, C, D, E` are the five answer candidates, and `Answer` is the answer to the question. </p> <p><strong>At a Glance</strong></p> <ul> <li>The size of the unzipped dataset is ~12GB. </li> <li>The dataset consists of `101` folders (numbered from 1-101); each folder corresponds to one distinct puzzle (root puzzle). </li> <li>There are 2000 puzzle instances programmatically created for each root puzzle, numbered from 1-2000. </li> <li>Every root puzzle index (in [1,101]) folder contains: (i) `img/` and (ii) `puzzle_<index>.csv`. </li> <li>The folder `img/` is the location where the puzzle instance images are stored, and `puzzle_<index>.csv` contains the non-image part of a puzzle. Specifically, a row of `puzzle_<index>.csv` is the following tuple: `<id, Question, image, A, B, C, D, E, Answer>`, where `id` is the puzzle instance id (in [1,2000]), `Question` is the puzzle question associated with the instance, `image` is the name of the image (in `img/` folder) corresponding to this instance `id`, `A, B, C, D, E` are the five answer candidates, and `Answer` is the correct answer to the question. </li> </ul> <p><strong>Other Details</strong><br> In our paper <a href="https://arxiv.org/pdf/2212.09993.pdf">Are Deep Neural Networks SMARTer than Second Graders?</a>, we provide four different dataset splits for evaluation: (i) Instance Split (IS), (ii) Answer Split (AS), (iii) Puzzle Split (PS), and (iv) Few-shot Split (FS). Below, we provide the details of each split to make fair comparisons to the results reported in our paper. </p> <p><em>Puzzle Split (PS)</em><br> We use the following root puzzle ids as the `Train` and `Test` sets. </p> <table> <thead> <tr> <th scope="col">Split</th> <th scope="col">Root Puzzle Id Sets</th> </tr> </thead> <tbody> <tr> <td>`Test`</td> <td>{ 94,95, 96, 97, 98, 99, 101, 61,62, 65, 66,67, 69, 70, 71,72,73,74,75,76,77}</td> </tr> <tr> <td>`Train`</td> <td>{1,2,...,101} \ Test</td> </tr> </tbody> </table> <p>Evaluation is done on all the `Test` puzzles and their accuracies averaged. For the 'Test' puzzles, we use the instance indices 1701-2000 in the evaluation.</p> <p><em>Few-shot Split (FS)</em></p> <p>We randomly select `k` number of instances from the `Test` sets (that are used in the PS split above) for training in FS split (e.g., `k=100`). These `k` few-shot samples are taken from instance indices 1-1600 of the respective puzzles and evaluation is conducted on all instance ids from 1701-2000.</p> <p><em>Instance Split (IS)</em></p> <p>We split the instances under every root puzzle as: Train = 1-1600, Val = 1601-1700, Test = 1701-2000. We train the neural network models using the `Train` split puzzle instances from all the root puzzles together and evaluate on the `Test` split of all puzzles.</p> <p><em>Answer Split (AS)</em></p> <p>We find the median answer value among all the 2000 instances for every root puzzle and only use this set of the respective instances (with the median answer value) as the `Test` set for evaluation (this set is excluded from the training of the neural networks).</p> <p><em>Puzzle Categorization</em></p> <p>Please see puzzle_type_info.csv for details on the categorization of the puzzles into eight classes, namely (i) counting, (ii) logic, (iii) measure, (iv) spatial, (v) arithmetic, (vi) algebra, (vii) pattern finding, and (viii) path tracing.</p> <p><strong>Other Resources</strong></p> <p>PyTorch code for using the dataset to train deep neural networks is available <a href="https://www.merl.com/publications/TR2023-014">here</a>.</p> <p><strong>Contact</strong><br> Anoop Cherian (cherian@merl.com), Kuan-Chuan Peng (kpeng@merl.com), or Suhas Lohit (slohit@merl.com)</p> <p><br> <strong>Citation</strong><br> If you use the SMART-101 dataset in your research, please cite our paper:</p> <pre><code>@article{cherian2022deep, title={Are Deep Neural Networks SMARTer than Second Graders?}, author={Cherian, Anoop and Peng, Kuan-Chuan and Lohit, Suhas and Smith, Kevin and Tenenbaum, Joshua B}, journal={arXiv preprint arXiv:2212.09993}, year={2022} }</code></pre> <p><strong>Copyright and Licenses</strong></p> <p>The SMART-101 dataset is released under `CC-BY-SA-4.0`.</p> <pre><code>Created by Mitsubishi Electric Research Laboratories (MERL), 2022-2023 SPDX-License-Identifier: CC-BY-SA-4.0 </code></pre> <p> </p>
Data for: "Random forest algorithms for recognizing daily life activities using plantar pressure information: A smart-shoe study"
<p>Data and software related to the manuscript "Random forest algorithms for recognizing daily life activities using plantar pressure information: A smart-shoe study" (2020). </p> <p>========</p> <p>Typo in README_DataRep.txt:</p> <p>' 2) "one_config" [...] Selected results requiring these data are shown in Fig. <strong>13</strong>'<strong> </strong>(not 11).</p>
Smart hydroponic agriculture using genetic algorithm based k-nearest neighbors
Open the record for dataset details and reuse information.
Simple, Mobile-based Artificial Intelligence AlgoRithms in the Detection of Diabetic ReTinopathy (SMART) Study
ClinicalTrials.gov study NCT03572699. IPD Sharing: NO. Countries: 1. Publications: 0.
Validation of Sleep Monitoring Algorithm Based on Smart Watches
ClinicalTrials.gov study NCT06696196. IPD Sharing: NO. Countries: 1. Publications: 0.
ScienceDex guides
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