scholarly journals Kohonen Network-Based Adaptation of Non Sequential Data for Use in Convolutional Neural Networks

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7221
Author(s):  
Michał Bereta
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Particle Physics ◽  
Time Series Data ◽  
Hybrid Approach ◽  
Measurement Data ◽  
Series Data ◽  
Sequential Data ◽  
Convolutional Network ◽  
Kohonen Network

Convolutional neural networks have become one of the most powerful computing tools of artificial intelligence in recent years. They are especially suitable for the analysis of images and other data that have an inherent sequence structure, such as time series data. In the case of data in the form of vectors of features, the order of which does not matter, the use of convolutional neural networks is not justified. This paper presents a new method of representing non-sequential data as images that can be analyzed by a convolutional network. The well-known Kohonen network was used for this purpose. After training on non-sequential data, each example is represented by so-called U-image that can be used as input to a convolutional layer. A hybrid approach was also presented, where the neural network uses two types of input signals, both U-image representation and the original features. The results of the proposed method on traditional machine learning databases as well as on a difficult classification problem originating from the analysis of measurement data from experiments in particle physics are presented.

scholarly journals A New Method of Mixed Gas Identification Based on a Convolutional Neural Network for Time Series Classification

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 1960 ◽  
Author(s):  
Lu Han ◽  
Chongchong Yu ◽  
Kaitai Xiao ◽  
Xia Zhao
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Convolutional Neural Networks ◽  
Time Series Data ◽  
Series Data ◽  
Time Series Classification ◽  
Mixed Gas ◽  
Gas Identification ◽  
Mixed Gases

This paper proposes a new method of mixed gas identification based on a convolutional neural network for time series classification. In view of the superiority of convolutional neural networks in the field of computer vision, we applied the concept to the classification of five mixed gas time series data collected by an array of eight MOX gas sensors. Existing convolutional neural networks are mostly used for processing visual data, and are rarely used in gas data classification and have great limitations. Therefore, the idea of mapping time series data into an analogous-image matrix data is proposed. Then, five kinds of convolutional neural networks—VGG-16, VGG-19, ResNet18, ResNet34 and ResNet50—were used to classify and compare five kinds of mixed gases. By adjusting the parameters of the convolutional neural networks, the final gas recognition rate is 96.67%. The experimental results show that the method can classify the gas data quickly and effectively, and effectively combine the gas time series data with classical convolutional neural networks, which provides a new idea for the identification of mixed gases.

scholarly journals Convolutional neural networks for modeling and forecasting nonlinear nonstationary processes

ScienceRise ◽  
2021 ◽  
pp. 12-20
Author(s):  
Andrii Belas ◽  
Petro Bidyuk
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Convolutional Neural Networks ◽  
Recurrent Neural Networks ◽  
Time Series Data ◽  
Autoregressive Models ◽  
Series Data ◽  
Nonstationary Processes ◽  
Modeling And Forecasting ◽  
Technological Product

The object of research. The object of research is modeling and forecasting nonlinear nonstationary processes presented in the form of time-series data. Investigated problem. There are several popular approaches to solving the problems of adequate model constructing and forecasting nonlinear nonstationary processes, such as autoregressive models and recurrent neural networks. However, each of them has its advantages and drawbacks. Autoregressive models cannot deal with the nonlinear or combined influence of previous states or external factors. Recurrent neural networks are computationally expensive and cannot work with sequences of high length or frequency. The main scientific result. The model for forecasting nonlinear nonstationary processes presented in the form of the time series data was built using convolutional neural networks. The current study shows results in which convolutional networks are superior to recurrent ones in terms of both accuracy and complexity. It was possible to build a more accurate model with a much fewer number of parameters. It indicates that one-dimensional convolutional neural networks can be a quite reasonable choice for solving time series forecasting problems. The area of practical use of the research results. Forecasting dynamics of processes in economy, finances, ecology, healthcare, technical systems and other areas exhibiting the types of nonlinear nonstationary processes. Innovative technological product. Methodology of using convolutional neural networks for modeling and forecasting nonlinear nonstationary processes presented in the form of time-series data. Scope of the innovative technological product. Nonlinear nonstationary processes presented in the form of time-series data.

scholarly journals Supervised Health Stage Prediction Using Convolutional Neural Networks for Bearing Wear

Sensors ◽  
2020 ◽  
Vol 20 (20) ◽  
pp. 5846
Author(s):  
Sungho Suh ◽  
Joel Jang ◽  
Seungjae Won ◽  
Mayank Shekhar Jha ◽  
Yong Oh Lee
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Domain Knowledge ◽  
Time Series Data ◽  
Graphical Representation ◽  
Ground Truth ◽  
Threshold Value ◽  
Series Data ◽  
Health State ◽  
Feature Extraction Method

Early detection of faults in rotating machinery systems is crucial in preventing system failure, increasing safety, and reducing maintenance costs. Current methods of fault detection suffer from the lack of efficient feature extraction method, the need for designating a threshold producing minimal false alarm rates, and the need for expert domain knowledge, which is costly. In this paper, we propose a novel data-driven health division method based on convolutional neural networks using a graphical representation of time series data, called a nested scatter plot. The proposed method trains the model with a small amount of labeled data and does not require a threshold value to predict the health state of rotary machines. Notwithstanding the lack of datasets that show the ground truth of health stages, our experiments with two open datasets of run-to-failure bearing demonstrated that our method is able to detect the early symptoms of bearing wear earlier and more efficiently than other threshold-based health indicator methods.

Using deep convolutional neural networks to forecast spatial patterns of Amazonian deforestation

2021 ◽  
Author(s):  
James George Clifford Ball ◽  
Katerina Petrova ◽  
David Coomes ◽  
Seth Flaxman
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Tropical Forest ◽  
Convolutional Neural Networks ◽  
Time Series Data ◽  
Short Term Memory ◽  
Series Data ◽  
Surface Model ◽  
Observation Data ◽  
Deep Convolutional Neural Networks

1. Tropical forests are subject to diverse deforestation pressures but their conservation is essential to achieve global climate goals. Predicting the location of deforestation is challenging due to the complexity of the natural and human systems involved but accurate and timely forecasts could enable effective planning and on-the-ground enforcement practices to curb deforestation rates. New computer vision technologies based on deep learning can be applied to the increasing volume of Earth observation data to generate novel insights and make predictions with unprecedented accuracy. 2. Here, we demonstrate the ability of deep convolutional neural networks to learn spatiotemporal patterns of deforestation from a limited set of freely available global data layers, including multispectral satellite imagery, the Hansen maps of historic deforestation (2001-2020) and the ALOS JAXA digital surface model, to forecast future deforestation (2021). We designed four original deep learning model architectures, based on 2D Convolutional Neural Networks (2DCNN), 3D Convolutional Neural Networks (3DCNN), and Long Short-Term Memory (LSTM) Recurrent Neural Networks (RNN) to produce spatial maps that indicate the risk to each forested pixel (~30 m) in the landscape of becoming deforested within the next year.. They were trained and tested on data from two ~80,000 km2 tropical forest regions in the Southern Peruvian Amazon. 3. We found that the networks could predict the likely location of future deforestation to a high degree of accuracy. Our best performing model - a 3DCNN - had the highest pixel-wise accuracy (80-90%) when validated on 2020 deforestation based 2014-2019 training. Visual examination of the forecasts indicated that the 3DCNN network could automatically discern the drivers of forest loss from the input data. For example, pixels around new access routes (e.g. roads) were assigned high risk whereas this was not the case for recent, concentrated natural loss events (e.g. remote landslides). 4. CNNs can harness limited time-series data to predict near-future deforestation patterns, an important step in using the growing volume of satellite remote sensing data to curb global deforestation. The modelling framework can be readily applied to any tropical forest location and used by governments and conservation organisations to prevent deforestation and plan protected areas.

scholarly journals Causal Discovery with Attention-Based Convolutional Neural Networks

2019 ◽  
Vol 1 (1) ◽  
pp. 312-340 ◽  
Author(s):  
Meike Nauta ◽  
Doina Bucur ◽  
Christin Seifert
Keyword(s):  
Complex System ◽  
Neural Networks ◽  
Decision Making ◽  
Time Series ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Time Series Data ◽  
Causal Discovery ◽  
Series Data ◽  
Causal Relationships

Having insight into the causal associations in a complex system facilitates decision making, e.g., for medical treatments, urban infrastructure improvements or financial investments. The amount of observational data grows, which enables the discovery of causal relationships between variables from observation of their behaviour in time. Existing methods for causal discovery from time series data do not yet exploit the representational power of deep learning. We therefore present the Temporal Causal Discovery Framework (TCDF), a deep learning framework that learns a causal graph structure by discovering causal relationships in observational time series data. TCDF uses attention-based convolutional neural networks combined with a causal validation step. By interpreting the internal parameters of the convolutional networks, TCDF can also discover the time delay between a cause and the occurrence of its effect. Our framework learns temporal causal graphs, which can include confounders and instantaneous effects. Experiments on financial and neuroscientific benchmarks show state-of-the-art performance of TCDF on discovering causal relationships in continuous time series data. Furthermore, we show that TCDF can circumstantially discover the presence of hidden confounders. Our broadly applicable framework can be used to gain novel insights into the causal dependencies in a complex system, which is important for reliable predictions, knowledge discovery and data-driven decision making.

Neural Network Techniques for Time Series Prediction: A Review

2019 ◽  
Vol 3 (3) ◽  
Author(s):  
Muhammad Faheem Mushtaq ◽  
Urooj Akram ◽  
Muhammad Aamir ◽  
Haseeb Ali ◽  
Muhammad Zulqarnain
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Time Series Data ◽  
Weather Prediction ◽  
Time Series Prediction ◽  
Series Data ◽  
Prediction Problem ◽  
Neural Network Models ◽  
Physical Time

It is important to predict a time series because many problems that are related to prediction such as health prediction problem, climate change prediction problem and weather prediction problem include a time component. To solve the time series prediction problem various techniques have been developed over many years to enhance the accuracy of forecasting. This paper presents a review of the prediction of physical time series applications using the neural network models. Neural Networks (NN) have appeared as an effective tool for forecasting of time series.  Moreover, to resolve the problems related to time series data, there is a need of network with single layer trainable weights that is Higher Order Neural Network (HONN) which can perform nonlinearity mapping of input-output. So, the developers are focusing on HONN that has been recently considered to develop the input representation spaces broadly. The HONN model has the ability of functional mapping which determined through some time series problems and it shows the more benefits as compared to conventional Artificial Neural Networks (ANN). The goal of this research is to present the reader awareness about HONN for physical time series prediction, to highlight some benefits and challenges using HONN.

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