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Machine Learning Generalization of Lumped Parameter Models for the Optimal Cooling of Embedded Systems
2020
Studies in Informatics and Control
Preserved Fulltext
The novelty of the study lies in the integration of Machine Learning for supporting Lumped Parameter simulations. ...
The total heat transfer rate is estimated based on the software instruction cycle, allowing an accurate selection of the cooling components. ...
The other six features are extracted from the datasheet of the product, providing technical specifications that encompass relevant heat generation characteristics: manufacturer, program memory size, CPU ...
doi:10.24846/v29i2y202003
fatcat:jksyprdomnedxdwotaqbxivrca
Efficient Information Sharing in ICT Supply Chain Social Network via Table Structure Recognition
[article]
2022
arXiv
pre-print
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Information sharing plays a crucial role in improving the efficiency of the supply chain, and datasheets are the most common data format to describe e-component commodities in the ICT supply chain because ...
However, with the surging number of electronic documents, it has been far beyond the capacity of human readers, and it is also challenging to process tabular data automatically because of the complex table ...
Study [14] propose a deep model cost-sensitive model that can learn robust features from both minority and majority samples by training the classdependent cost and model parameters jointly. ...
arXiv:2211.02128v1
fatcat:da65bk7vrvewdfnpwgpwhpvkdq
Machine Learning and Rules Induction in Support of Analog Amplifier Design
2022
Computation
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The aim of the paper is to present a two-step method for facilitating the design of analog amplifiers taking into account the bottom–top approach and utilizing machine learning techniques. ...
The X-chart and a framework describing the specificity of analog circuit design using machine learning are introduced. ...
Machine learning-based approaches to design rely on the collected data, a strong understanding of the theory in electronics, and the practically proven methods. ...
doi:10.3390/computation10090145
fatcat:brhce3fmgbdelgfp3stimsxaka
Intelligent - Web search for EMI filter optimization
2021
International Workshop on Fuzzy Logic and Applications
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This paper proposes an intelligent system that aids the electronic designer to achieve compliance with design constraints exploiting the overwhelming set of components available on the Web. ...
The proposed system aids the designer in finding components based on the main constraints and performs an optimization. ...
The second one is an information extractor module which has been designed to extract information from specific pages of the RS site 2 , a well-known electronic components site. ...
dblp:conf/wilf/PilatoRVV21
fatcat:pf7py2bogvckjgzw75escya4mm
A Survey on EEG Signal Processing Techniques and Machine Learning: Applications to the Neurofeedback of Autobiographical Memory Deficits in Schizophrenia
2021
Electronics
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A deep review of the most used machine learning algorithms and the advantages/drawbacks of most used methods is presented. ...
The role of machine learning in this regard has become of key importance during the last years due to its high capacity to analyze complex amounts of data. ...
Deep learning is mainly based on multi-layered (input, hidden and output layers) neural networks that learn from large amounts of data, emulating the functions and workings of the human brain, computing ...
doi:10.3390/electronics10233037
fatcat:rcxbh2oxuvh6nl3sdpnpousuze
Fonduer
2018
Proceedings of the 2018 International Conference on Management of Data - SIGMOD '18
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Fonduer uses a new deep-learning model to automatically capture the representation (i.e., features) needed to learn how to extract relations from richly formatted data. ...
In contrast to KBC from text or tabular data, KBC from richly formatted data aims to extract relations conveyed jointly via textual, structural, tabular, and visual expressions. ...
, of DARPA, DOE, NIH, ONR, or the U.S. ...
doi:10.1145/3183713.3183729
pmid:29937618
pmcid:PMC6013301
dblp:conf/sigmod/0002HCHRLR18
fatcat:smuhpzdqbjh33bpysjlesbozbq
Deep learning in a sensor array system based on the distribution of volatile compounds from meat cuts using GC–MS analysis
2020
Sensing and Bio-Sensing Research
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To achieve the objective in differentiating two meat cuts from three types of meat, this study uses statistical parameters for extraction feature, PCA for reducing the dimension, and deep learning. ...
Furthermore, to get more improvements from the previous researches, this study aims to optimize the parameters of deep learning. ...
This paper proposes the architecture for the deep-learning system in Fig. 3 based on the optimal combination of hyperparameters. ...
doi:10.1016/j.sbsr.2020.100371
fatcat:wbvutrhxybdanbndgam6h7hqcu
Leveraging Deep Learning for Designing Healthcare Analytics Heuristic for Diagnostics
2021
Neural Processing Letters
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For this task researchers are doing explorations in big data analytics, deep learning (advanced form of machine learning known as deep neural nets), predictive analytics and various other algorithms to ...
Louvain Mani-Hierarchical Fold Learning healthcare analytics showed maximum 0.952 correlations between two clusters with Spearman when applied on 240 instances extracted from comorbidities diagnostic data ...
to come up with these observations mentioned in Section 5 from experimental study. ...
doi:10.1007/s11063-021-10425-w
pmid:33551665
pmcid:PMC7852051
fatcat:nfs3pi5ed5b6vjzjfcz6odm4vm
Adaptive Data Mining Approach for PCB Defect Detection and Classification
2016
Indian Journal of Science and Technology
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Objective: To develop a model for PCB defect detection and classification with the help of soft computing technique. ...
Methodology: To improve the performance of the prediction and classification we propose a hybrid approach for feature reduction and classification. ...
Originated from insights and machine learning, information mining is the procedure of extracting and refining information from vast databases 2 . ...
doi:10.17485/ijst/2016/v9i44/98964
fatcat:pibrt574dfatnmc5igalsg32se
Survey of High-Performance Processors and FPGAs for On-Board Processing and Machine Learning Applications
2021
Zenodo
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Finally, an overview of the availability of machine learning tools for the listed devices is also presented. ...
The survey presented includes device parameters such as the relative performance, qualification status and the availability of radiation test results. ...
• …and high cost of ultra deep sub-micron technologies•However, process node usage in RHBD is (slowly) catching up to COTS equivalent• But number of components are low, mainly driven by high manufacturing ...
doi:10.5281/zenodo.5639642
fatcat:2esr2dqhwnayfor6ileyoxof3a
Deep learning based buck-boost converter for PV modules
2024
Heliyon
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The deep learning-based model is trained using data collected from the conventional PID controller. ...
Here, a deep learning-based model is proposed to reduce the steady-state time and achieve the desired buck- or boost mode for PV modules. ...
In machine learning, feature extraction is manual, while feature extraction is automatic in deep learning. ...
doi:10.1016/j.heliyon.2024.e27405
pmid:38562510
pmcid:PMC10982980
fatcat:ocbscs2j75evriketk2trdni4y
Differentiating Birds and Animals using Deep Learning Neural Network with Image Processing Approach
2020
International Journal of Innovative Science and Research Technology
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The movement of birds and animals cannot be controlled by any method. We can only drive away them. To drive away them, humans are used. ...
Classification can we carried out from unconstrained images based on colour feature extraction. Which means birds appeared in different size, pose and angle of view can be identified. ...
To differentiate it as birds and animals the deep learning neural network with image processing approach is used. ...
doi:10.38124/ijisrt20sep399
fatcat:76okqzxr6verzjwb3fkdy4adai
Datasheets for Machine Learning Sensors: Towards Transparency, Auditability, and Responsibility for Intelligent Sensing
[article]
2024
arXiv
pre-print
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2023 pre-print arXiv:2306.08848v1 fatcat:ahiqngwknrfxlpx5ba5kk56jsqOther Versions
To provide a case study of the application of our datasheet template, we also designed and developed two examples for ML sensors performing computer vision-based person detection: one an open-source ML ...
To this end, we introduce a standard datasheet template for these ML sensors and discuss and evaluate the design and motivation for each section of the datasheet in detail including: standard dasheet components ...
study environment, to Matt Taylor, for his assistance with the data nutrition labels, to facilities management in the Harvard SEC for helping us to set up the experimental study environment, and to all of ...
arXiv:2306.08848v3
fatcat:wbvowxisrfhkjmlicrk5kx5q3q
Table Detection in the Wild: A Novel Diverse Table Detection Dataset and Method
[article]
2023
arXiv
pre-print
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2022 pre-print arXiv:2209.09207v1 fatcat:6tnrbki6wbh6terjldi24ck5ky
Experimental results show the superiority of applying convolutional deep learning methods over classical computer vision-based methods. ...
The introduction of this diverse table detection dataset will enable the community to develop high throughput deep learning methods for understanding document layout and tabular data processing. ...
We gratefully acknowledge the support of team members in verifying the dataset annotation quality. ...
arXiv:2209.09207v2
fatcat:d2vlo7pgzbf33kd6dcqqgtntfq
Development of an Evaluation Platform for Statistical Characterization of MOSFET Model Parameters
2018
U Porto Journal of Engineering
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In discrete electronics, the statistical variability of device parameters is seldom given in datasheets, at least in such a way that this information can still be useful at design phase. ...
Secondly, it also finds pedagogical purposes, having practical applicability in undergraduate courses of fundamental electronics, as it targets the use of a straightforward approach to derive parameters ...
This work finds an adequate use in courses of fundamental electronics, with the potential of improving both the interest and learning of undergraduate students in analog electronics. ...
doi:10.24840/2183-6493_003.001_0004
fatcat:giqbqukcufeejlbyci4tz7pbsm
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