scholarly journals Towards domain-specific surrogate models for smart grid co-simulation

2019 ◽  
Vol 2 (S1) ◽  
Author(s):  
Stephan Balduin ◽  
Martin Tröschel ◽  
Sebastian Lehnhoff
Keyword(s):  
Deep Learning ◽  
Low Voltage ◽  
Large Data ◽  
Surrogate Models ◽  
Computational Effort ◽  
Point Of View ◽  
Training Process ◽  
Data Set ◽  
Domain Specific ◽  
Dimensional Approximation

Abstract Surrogate models are used to reduce the computational effort required to simulate complex systems. The power grid can be considered as such a complex system with a large number of interdependent inputs. With artificial neural networks and deep learning, it is possible to build high-dimensional approximation models. However, a large data set is also required for the training process. This paper presents an approach to sample input data and create a deep learning surrogate model for a low voltage grid. Challenges are discussed and the model is evaluated under different conditions. The results show that the model performs well from a machine learning point of view, but has domain-specific weaknesses.

scholarly journals Conversation Modeling with Neural Network

2018 ◽  
pp. 1-10
Author(s):  
Jivan Y. Patil ◽  
Girish P. Potdar
Keyword(s):  
Neural Networks ◽  
Intelligent System ◽  
Large Data ◽  
Learning Problems ◽  
Beam Search ◽  
Training Set ◽  
Data Set ◽  
Domain Specific ◽  
Proposed Model ◽  
End To End

The ability to process, understand and interact in natural language carries high importance for building a Intelligent system, as it will greatly affect the way of communicating with the system. Deep Neural Networks (DNNs) have achieved excellent performance for many of machine learning problems and are widely accepted for applications in the field of computer vision and supervised  learning. Although DNNs work well with availability of large labeled training set, it cannot be used to map complex structures like sentences end-to-end. Existing approaches for conversational modeling are domain specific and require handcrafted rules. This paper proposes a simple approach based on use of neural networks’ recently proposed sequence to sequence framework. The proposed model generates reply by predicting sentence using chained probability for given sentence(s) in conversation. This model is trained end-to-end on large data set. Proposed approach uses Attention to focus text generation on intent of conversation as well as beam search to generate optimum output with some diversity.Primary findings show that model shows common sense reasoning on movie transcript data set.

scholarly journals A Comparative Analysis of Machine/Deep Learning Models for Parking Space Availability Prediction

Sensors ◽  
2020 ◽  
Vol 20 (1) ◽  
pp. 322 ◽  
Author(s):  
Faraz Malik Awan ◽  
Yasir Saleem ◽  
Roberto Minerva ◽  
Noel Crespi
Keyword(s):  
Deep Learning ◽  
Comparative Analysis ◽  
Random Forest ◽  
Decision Tree ◽  
Multilayer Perceptron ◽  
Large Data ◽  
Data Sets ◽  
Application Domain ◽  
Parking Space ◽  
Data Set

Machine/Deep Learning (ML/DL) techniques have been applied to large data sets in order to extract relevant information and for making predictions. The performance and the outcomes of different ML/DL algorithms may vary depending upon the data sets being used, as well as on the suitability of algorithms to the data and the application domain under consideration. Hence, determining which ML/DL algorithm is most suitable for a specific application domain and its related data sets would be a key advantage. To respond to this need, a comparative analysis of well-known ML/DL techniques, including Multilayer Perceptron, K-Nearest Neighbors, Decision Tree, Random Forest, and Voting Classifier (or the Ensemble Learning Approach) for the prediction of parking space availability has been conducted. This comparison utilized Santander’s parking data set, initiated while working on the H2020 WISE-IoT project. The data set was used in order to evaluate the considered algorithms and to determine the one offering the best prediction. The results of this analysis show that, regardless of the data set size, the less complex algorithms like Decision Tree, Random Forest, and KNN outperform complex algorithms such as Multilayer Perceptron, in terms of higher prediction accuracy, while providing comparable information for the prediction of parking space availability. In addition, in this paper, we are providing Top-K parking space recommendations on the basis of distance between current position of vehicles and free parking spots.

scholarly journals Fish Detection Using Deep Learning

2020 ◽  
Vol 2020 ◽  
pp. 1-13 ◽  
Author(s):  
Suxia Cui ◽  
Yu Zhou ◽  
Yonghui Wang ◽  
Lujun Zhai
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Data Augmentation ◽  
Autonomous Underwater Vehicle ◽  
Training Data ◽  
Training Process ◽  
Data Set ◽  
Speed Up ◽  
Fish Detection ◽  
Process Speed

Recently, human being’s curiosity has been expanded from the land to the sky and the sea. Besides sending people to explore the ocean and outer space, robots are designed for some tasks dangerous for living creatures. Take the ocean exploration for an example. There are many projects or competitions on the design of Autonomous Underwater Vehicle (AUV) which attracted many interests. Authors of this article have learned the necessity of platform upgrade from a previous AUV design project, and would like to share the experience of one task extension in the area of fish detection. Because most of the embedded systems have been improved by fast growing computing and sensing technologies, which makes them possible to incorporate more and more complicated algorithms. In an AUV, after acquiring surrounding information from sensors, how to perceive and analyse corresponding information for better judgement is one of the challenges. The processing procedure can mimic human being’s learning routines. An advanced system with more computing power can facilitate deep learning feature, which exploit many neural network algorithms to simulate human brains. In this paper, a convolutional neural network (CNN) based fish detection method was proposed. The training data set was collected from the Gulf of Mexico by a digital camera. To fit into this unique need, three optimization approaches were applied to the CNN: data augmentation, network simplification, and training process speed up. Data augmentation transformation provided more learning samples; the network was simplified to accommodate the artificial neural network; the training process speed up is introduced to make the training process more time efficient. Experimental results showed that the proposed model is promising, and has the potential to be extended to other underwear objects.

scholarly journals Micro Clustering Methodology for Document Objects using Deep Learning Techniques

2019 ◽  
Vol 9 (2) ◽  
pp. 1546-1552
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Recurrent Neural Network ◽  
Large Data ◽  
Heterogeneous Data ◽  
Machine Learning Algorithms ◽  
Volume Data ◽  
Data Set ◽  
Clustering And Classification

Large data clustering and classification is a very challenging task in data mining. Various machine learning and deep learning systems have been proposed by many researchers on a different dataset. Data volume, data size and structure of data may affect the time complexity of the system. This paper described a new document object classification approach using deep learning (DL) and proposed a recurrent neural network (RNN) for classification with a micro-clustering approach.TF-IDF and a density-based approach are used to store the best features. The plane work used supervised learning method and it extracts features set called as BK of the desired classes. once the training part completed then proceeds to figure out the particular test instances with the help of the planned classification algorithm. Recurrent Neural Network categorized the particular test object according to their weights. The system can able to work on heterogeneous data set and generate the micro-clusters according to classified results. The system also carried out experimental analysis with classical machine learning algorithms. The proposed algorithm shows higher accuracy than the existing density-based approach on different data sets.

scholarly journals A Reduced Electrically-Equivalent Model of the IEEE European Low Voltage Test Feeder

2021 ◽  
Author(s):  
Maman Ahmad Khan ◽  
Barry Hayes
Keyword(s):  
Distribution System ◽  
Low Voltage ◽  
Computation Time ◽  
Computational Effort ◽  
Equivalent Model ◽  
Full Data ◽  
Data Set ◽  
Network Loading ◽  
Wide Range ◽  
Bus Network

<div>This letter presents a reduced, electrically equivalent model of the IEEE European Low Voltage Test Feeder for use in distribution network studies. The original test feeder is made up of 906 buses, of which only 55 have loads connected. This work proposes an equivalent 116 bus network which accurately represents all of the characteristics of the original test feeder, but significantly reduces the computational effort required when applied in a range of distribution system applications. The model reduction technique applied is explained in detail, and the performance of the modified network is tested under a wide range of network loading conditions. The analysis in this letter demonstrates that the modified 116 bus network produces identical results with 80% less computation time when compared to the original 906 bus network. The full data set for the modified network is provided on IEEE Dataport. Available: https://dx.doi.org/10.21227/0d2n-j565.</div>

scholarly journals Deep learning the holographic black hole with charge

2019 ◽  
Vol 28 (12) ◽  
pp. 1950153 ◽  
Author(s):  
Jing Tan ◽  
Chong-Bin Chen
Keyword(s):  
Neural Network ◽  
Black Hole ◽  
Deep Learning ◽  
Deep Neural Network ◽  
Learning Algorithm ◽  
Equation Of Motion ◽  
Batch Size ◽  
Training Process ◽  
Data Set ◽  
Deep Learning Algorithm

We use the deep learning algorithm to learn the Reissner–Nordström (RN) black hole metric by building a deep neural network. Plenty of data are determined in boundary of AdS and we propagate them to the black hole horizon through AdS metric and equation of motion (e.o.m). We label these data according to the values near the horizon, and together with initial data they constitute a data set. Then we construct corresponding deep neural network and train it with the data set to obtain the Reissner–Nordström (RN) black hole metric. Finally, we discuss the effects of learning rate, batch-size and initialization on the training process.

scholarly journals Molecular Transformer-aided Biocatalysed Synthesis Planning

2021 ◽  
Author(s):  
Daniel Probst ◽  
Matteo Manica ◽  
Yves Gaëtan Nana Teukam ◽  
Alessandro Castrogiovanni ◽  
Federico Paratore ◽  
...  
Keyword(s):  
Green Chemistry ◽  
Enzymatic Catalysis ◽  
Large Data ◽  
Single Step ◽  
Specific Knowledge ◽  
Data Set ◽  
Domain Specific ◽  
Forward Prediction ◽  
Pathway Prediction ◽  
Domain Specific Knowledge

Enzyme catalysts are an integral part of green chemistry strategies towards a more sustainable and resource-efficient chemical synthesis. However, the use of enzymes on unreported substrates and their specific stereo- and regioselectivity are domain-specific knowledge factors that require decades of field experience to master. This makes the retrosynthesis of given targets with biocatalysed reactions a significant challenge. Here, we use the molecular transformer architecture to capture the latent knowledge about enzymatic activity from a large data set of publicly available biochemical reactions, extending forward reaction and retrosynthetic pathway prediction to the domain of biocatalysis. We introduce the use of a class token based on the EC classification scheme that allows to capture catalysis patterns among different enzymes belonging to the same hierarchical families. The forward prediction model achieves an accuracy of 49.6% and 62.7%, top-1 and top-5 respectively, while the single-step retrosynthetic model shows a round-trip accuracy of 39.6% and 42.6%, top-1 and top-10 respectively. Trained models and curated data are made publicly available with the hope of promoting enzymatic catalysis and making green chemistry more accessible through the use of digital technologies.

scholarly journals Deep Learning Applications in Geosciences: Insights into Ichnological Analysis

2021 ◽  
Vol 11 (16) ◽  
pp. 7736
Author(s):  
Korhan Ayranci ◽  
Isa E. Yildirim ◽  
Umair bin Waheed ◽  
James A. MacEachern
Keyword(s):  
Deep Learning ◽  
Oil And Gas ◽  
Full Range ◽  
Large Data ◽  
Training Data ◽  
Critical Parameters ◽  
Deep Convolutional Neural Networks ◽  
Data Set ◽  
Robust Solution ◽  
Automated Technique

Ichnological analysis, particularly assessing bioturbation index, provides critical parameters for characterizing many oil and gas reservoirs. It provides information on reservoir quality, paleodepositional conditions, redox conditions, and more. However, accurately characterizing ichnological characteristics requires long hours of training and practice, and many marine or marginal marine reservoirs require these specialized expertise. This adds more load to geoscientists and may cause distraction, errors, and bias, particularly when continuously logging long sedimentary successions. In order to alleviate this issue, we propose an automated technique to determine the bioturbation index in cores and outcrops by harnessing the capabilities of deep convolutional neural networks (DCNNs) as image classifiers. In order to find a fast and robust solution, we utilize ideas from deep learning. We compiled and labeled a large data set (1303 images) composed of images spanning the full range (BI 0–6) of bioturbation indices. We divided these images into groups based on their bioturbation indices in order to prepare training data for the DCNN. Finally, we analyzed the trained DCNN model on images and obtained high classification accuracies. This is a pioneering work in the field of ichnological analysis, as the current practice is to perform classification tasks manually by experts in the field.

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