scholarly journals Using Recurrent Neural Networks to Model Spatial Grammars for Design Creation

2020 ◽  
Vol 142 (10) ◽  
Author(s):  
Michael A Yukish ◽  
Gary M Stump ◽  
Simon W Miller
Keyword(s):  
Neural Network ◽  
Success Rate ◽  
Recurrent Neural Network ◽  
High Reliability ◽  
Complex Problem ◽  
Geometric Constraints ◽  
Test Case ◽  
Data Set ◽  
Spatial Grammar ◽  
Feasible Solutions

Abstract The authors present preliminary results on successfully training a recurrent neural network to learn a spatial grammar embodied in a data set, and then generate new designs that comply with the grammar but are not from the data set, demonstrating generalized learning. For the test case, the data were created by first exercising generative context-free spatial grammar representing physical layouts that included infeasible designs due to geometric interferences and then removing the designs that violated geometric constraints, resulting in a data set from a design grammar that is of a higher complexity context-sensitive grammar. A character recurrent neural network (char-RNN) was trained on the positive remaining results. Analysis shows that the char-RNN was able to effectively learn the spatial grammar with high reliability, and for the given problem with tuned hyperparameters, having up to 98% success rate compared to a 62% success rate when randomly sampling the generative grammar. For a more complex problem where random sampling results in only 18% success, a trained char-RNN generated feasible solutions with an 89% success rate. Further, the char-RNN also generated designs differing from the training set at a rate of over 99%, showing generalized learning.

scholarly journals COMPUTER VISION BASED TRAFFIC SIGN SENSING FOR SMART TRANSPORT

2019 ◽  
Vol 1 (01) ◽  
pp. 11-19 ◽  
Author(s):  
James Deva Koresh H
Keyword(s):  
Neural Network ◽  
High Reliability ◽  
Traffic Data ◽  
Traffic Sign ◽  
Spatial Variance ◽  
Convolutional Network ◽  
Data Set ◽  
Real Time Traffic ◽  
Frame Work ◽  
Detection And Recognition

The paper puts forward a real time traffic sign sensing (detection and recognition) frame work for enhancing the vehicles capability in order to have a save driving, path planning. The proposed method utilizes the capsules neural network that outperforms the convolutional neural network by eluding the necessities for the manual effort. The capsules network provides a better resistance for the spatial variance and the high reliability in the sensing of the traffic sign compared to the convolutional network. The evaluation of the capsule network with the Indian traffic data set shows a 15% higher accuracy when compared with the CNN and the RNN.

scholarly journals Real-time event detection using recurrent neural network in social sensors

2019 ◽  
Vol 15 (6) ◽  
pp. 155014771985649 ◽  
Author(s):  
Van Quan Nguyen ◽  
Tien Nguyen Anh ◽  
Hyung-Jeong Yang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Language Processing ◽  
Recurrent Neural Network ◽  
Event Detection ◽  
Word Embedding ◽  
Series Data ◽  
Accuracy Score ◽  
Data Set ◽  
Size Limitation

We proposed an approach for temporal event detection using deep learning and multi-embedding on a set of text data from social media. First, a convolutional neural network augmented with multiple word-embedding architectures is used as a text classifier for the pre-processing of the input textual data. Second, an event detection model using a recurrent neural network is employed to learn time series data features by extracting temporal information. Recently, convolutional neural networks have been used in natural language processing problems and have obtained excellent results as performing on available embedding vector. In this article, word-embedding features at the embedding layer are combined and fed to convolutional neural network. The proposed method shows no size limitation, supplementation of more embeddings than standard multichannel based approaches, and obtained similar performance (accuracy score) on some benchmark data sets, especially in an imbalanced data set. For event detection, a long short-term memory network is used as a predictor that learns higher level temporal features so as to predict future values. An error distribution estimation model is built to calculate the anomaly score of observation. Events are detected using a window-based method on the anomaly scores.

scholarly journals Learning Dynamic Factors to Improve the Accuracy of Bus Arrival Time Prediction via a Recurrent Neural Network

2019 ◽  
Vol 11 (12) ◽  
pp. 247
Author(s):  
Xin Zhou ◽  
Peixin Dong ◽  
Jianping Xing ◽  
Peijia Sun
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Public Transportation ◽  
Prediction Accuracy ◽  
Arrival Time ◽  
Attention Mechanism ◽  
Experimental Results ◽  
Support Vector ◽  
Data Set ◽  
Dynamic Factors

Accurate prediction of bus arrival times is a challenging problem in the public transportation field. Previous studies have shown that to improve prediction accuracy, more heterogeneous measurements provide better results. So what other factors should be added into the prediction model? Traditional prediction methods mainly use the arrival time and the distance between stations, but do not make full use of dynamic factors such as passenger number, dwell time, bus driving efficiency, etc. We propose a novel approach that takes full advantage of dynamic factors. Our approach is based on a Recurrent Neural Network (RNN). The experimental results indicate that a variety of prediction algorithms (such as Support Vector Machine, Kalman filter, Multilayer Perceptron, and RNN) have significantly improved performance after using dynamic factors. Further, we introduce RNN with an attention mechanism to adaptively select the most relevant input factors. Experiments demonstrate that the prediction accuracy of RNN with an attention mechanism is better than RNN with no attention mechanism when there are heterogeneous input factors. The experimental results show the superior performances of our approach on the data set provided by Jinan Public Transportation Corporation.

scholarly journals Identification of a Typical CSTR Using Optimal Focused Time Lagged Recurrent Neural Network Model with Gamma Memory Filter

2009 ◽  
Vol 2009 ◽  
pp. 1-7
Author(s):  
S. N. Naikwad ◽  
S. V. Dudul
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Recurrent Neural Network ◽  
Neural Network Model ◽  
Learning Rule ◽  
Continuous Stirred Tank Reactor ◽  
Back Propagation Algorithm ◽  
Data Set ◽  
Highly Nonlinear ◽  
Time Lagged

A focused time lagged recurrent neural network (FTLR NN) with gamma memory filter is designed to learn the subtle complex dynamics of a typical CSTR process. Continuous stirred tank reactor exhibits complex nonlinear operations where reaction is exothermic. It is noticed from literature review that process control of CSTR using neuro-fuzzy systems was attempted by many, but optimal neural network model for identification of CSTR process is not yet available. As CSTR process includes temporal relationship in the input-output mappings, time lagged recurrent neural network is particularly used for identification purpose. The standard back propagation algorithm with momentum term has been proposed in this model. The various parameters like number of processing elements, number of hidden layers, training and testing percentage, learning rule and transfer function in hidden and output layer are investigated on the basis of performance measures like MSE, NMSE, and correlation coefficient on testing data set. Finally effects of different norms are tested along with variation in gamma memory filter. It is demonstrated that dynamic NN model has a remarkable system identification capability for the problems considered in this paper. Thus FTLR NN with gamma memory filter can be used to learn underlying highly nonlinear dynamics of the system, which is a major contribution of this paper.

Machine Learning for Formation Tightness Prediction and Mobility Prediction

2021 ◽  
Author(s):  
Zhaoya Fan ◽  
Jichao Chen ◽  
Tao Zhang ◽  
Ning Shi ◽  
Wei Zhang
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Linear Regression ◽  
Success Rate ◽  
Support Vector ◽  
Svm Classifier ◽  
Mobility Prediction ◽  
Data Set ◽  
Pressure Buildup ◽  
Pressure Test

Abstract From the perspective of wireline formation test (WFT), formation tightness reflects the "speed" of pressure buildup while the pressure test is being conducted. We usually define a pressure test point that has a very low pressure-buildup speed as a tight point. The mobility derived from this kind of pressure point is usually less than 0.01md/cP; otherwise, the pressure points will be defined as valid points with valid formation pressure and mobility. Formation tightness reflects the formation permeability information and can be an indicator to estimate the difficulty of the WFT pumping and sampling operation. Mobility, as compared to permeability, reflects the dynamic supply capacity of the formation. A rapid and good mobility prediction based on petrophysical logging can not only directly provide valid formation productivity but can also evaluate the feasibility of the WFT and doing optimization work in advance. Compared to a time-consuming and costly drillstem test (DST) operation, the WFT is the most efficient and cost-saving method to confirm hydrocarbon presence. However, the success rate of WFT sampling operations in the deep Kuqa formation is less than 50% overall, mostly due to the formation tightness exceeding the capability of the tools. Therefore, a rapid mobility evaluation is necessary to meet WFT feasibility analysis. As companion work to a previous WFT optimization study(SPE-195932-MS), we further studied and discuss the machine learning for mobility prediction. In the previous study, we formed a mobility prediction workflow by doing a statistical analysis of more than 1000 pressure test points with several statistical mathematic methods, such as univariate linear regression (ULR), multivariate linear regression (MLR), neural network regression analysis (NNA), and decision tree classification analysis (DTA) methods. In this paper, the methods and principles of machine learning are expounded. A series of machine learning methods were tested. The algorithms that are appropriate for these specific data set were selected. Includes DTA, discriminant analysis (DA), logistic regression, support vector machine (SVM), K-nearest neighbor (KNN) for formation tightness prediction and linear regression, DTA, SVM, Gaussian process regression SVM, random tree, neural network analysis for mobility prediction. Contrastive analysis reveals that: The SVM classifier has the best result over other methods for formation tightness probability prediction. Based on R squared and RMSE analysis, linear regression, GPR, and NNA delivered relatively good results compared with other mobility prediction methods. An optimized data processing workflow was proposed, and it delivered a better result than the workflow proposed in SPE-195932-MS under the same training and testing dataset condition. The comparison between measured mobility and predicted mobility results reveals that, in most situations, the predicted mobility and measured mobility matched very well with each other. WFT were conducted in newly drilled wells. Sampling success rate also achieved 100% in these wells by optimizing the WFT tool string and sampling stations selection in advance, and NPT is significantly reduced.

Refined Clustering of Software Components by Using K-Mean and Neural Network

2015 ◽  
Vol 4 (2) ◽  
pp. 62
Author(s):  
Indu Verma ◽  
Amarjeet Kaur ◽  
Iqbaldeep Kaur
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Search Time ◽  
Search Space ◽  
Relevant Information ◽  
Test Case ◽  
Test Cases ◽  
Similar Data ◽  
Data Set ◽  
Single Cluster

Data Mining is extraction of relevant information about data set. A data-warehouse is a location where information is stored. There are various services of data mining, clustering is one of them. Clustering is an effort to group similar data onto single cluster. In this paper we propose and implement k-mean and neural network for clustering same components in single cluster. Clustering reduces the search space by grouping similar test cases together according to the requirements and, hence minimizing the search time, for the retrieval of the test cases, resulting in reduced time complexity. In this research paper we proposed approach for re-usability of test cases by unsupervised approach and supervised approach. In unsupervised learning we proposed k-mean and in supervised learning neural network. We have designed the algorithm for requirement and test case document clustering according to its tf-idf vector space and the output is set of highly cohesive pattern groups.

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 SISTEM PERINGKAS OTOMATIS ABSTRAKTIF DENGAN MENGGUNAKAN RECURRENT NEURAL NETWORK

2018 ◽  
Vol 2 (1) ◽  
pp. 65 ◽  
Author(s):  
Kuncoro Yoko ◽  
Viny Christanti Mawardi ◽  
Janson Hendryli
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Model System ◽  
Text Summarization ◽  
Word Embedding ◽  
System Evaluation ◽  
Data Set ◽  
Hidden States ◽  
Bahasa Indonesia

Abstractive Text Summarization try to creates a shorter version of a text while preserve its meaning. We try to use Recurrent Neural Network (RNN) to create summaries of Bahasa Indonesia text. We get corpus from Detik dan Kompas site news. We used word2vec to create word embedding from our corpus then train our data set with RNN to create a model. This model used to generate news. We search the best model by changing word2vec size and RNN hidden states. We use system evaluation and Q&A Evaluation to evaluate our model. System evaluation showed that model with 6457 data set, 200 word2vec size, and 256 RNN hidden states gives best accuracy for 99.8810%. This model evaluated by Q&A Evaluation. Q&A Evaluation showed that the model gives 46.65% accurary.

scholarly journals Music Feature Classification Based on Recurrent Neural Networks with Channel Attention Mechanism

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Jie Gan
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Traditional Music ◽  
Attention Mechanism ◽  
Feature Classification ◽  
Data Set ◽  
Hard Drives ◽  
Music Classification ◽  
Online Music ◽  
Convolution Structure

With the advancement of multimedia and digital technologies, music resources are rapidly increasing over the Internet, which changed listeners’ habits from hard drives to online music platforms. It has allowed the researchers to use classification technologies for efficient storage, organization, retrieval, and recommendation of music resources. The traditional music classification methods use many artificially designed acoustic features, which require knowledge in the music field. The features of different classification tasks are often not universal. This paper provides a solution to this problem by proposing a novel recurrent neural network method with a channel attention mechanism for music feature classification. The music classification method based on a convolutional neural network ignores the timing characteristics of the audio itself. Therefore, this paper combines convolution structure with the bidirectional recurrent neural network and uses the attention mechanism to assign different attention weights to the output of the recurrent neural network at different times; the weights are assigned for getting a better representation of the overall characteristics of the music. The classification accuracy of the model on the GTZAN data set has increased to 93.1%. The AUC on the multilabel labeling data set MagnaTagATune has reached 92.3%, surpassing other comparison methods. The labeling of different music labels has been analyzed. This method has good labeling ability for most of the labels of music genres. Also, it has good performance on some labels of musical instruments, singing, and emotion categories.

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