Vibration Anomaly Detection using Deep Neural Network and Convolutional Neural Network

Identifying the “health state” of the equipment is the domain of condition monitoring. The paper proposes a study of two models: DNN (Deep Neural Network) and CNN (Convolutional Neural Network) over an existent dataset provided by Case Western Reserve University for analyzing vibrations in fault diagnosis. After the model is trained on the windowed dataset using an optimal learning rate, minimizing the cost function, and is tested by computing the loss, accuracy and precision across the results, the weights are saved, and the models can be tested on other real data. The trained model recognizes raw time series data collected by micro electro-mechanical accelerometer sensors and detects anomalies based on former times series entries.

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Method of determination of the text direction on the image with the use of convolutional neural network

Informatization and communication ◽

10.34219/2078-8320-2020-11-2-96-99 ◽

2020 ◽

pp. 96-99

Author(s):

P.L. Nikolaev

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Deep Neural Network ◽

Binary Classification ◽

Synthetic Data ◽

Real Data ◽

Method Of Determination ◽

Classification Of Images

This article deals with method of binary classification of images with small text on them Classification is based on the fact that the text can have 2 directions – it can be positioned horizontally and read from left to right or it can be turned 180 degrees so the image must be rotated to read the sign. This type of text can be found on the covers of a variety of books, so in case of recognizing the covers, it is necessary first to determine the direction of the text before we will directly recognize it. The article suggests the development of a deep neural network for determination of the text position in the context of book covers recognizing. The results of training and testing of a convolutional neural network on synthetic data as well as the examples of the network functioning on the real data are presented.

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Outlier Detection Using Convolutional Neural Network for Wireless Sensor Network

International Journal of Business Data Communications and Networking ◽

10.4018/ijbdcn.286705 ◽

2021 ◽

Vol 17 (2) ◽

pp. 0-0

Keyword(s):

Neural Network ◽

Time Series ◽

Deep Learning ◽

Wireless Sensor Network ◽

Convolutional Neural Network ◽

Sensor Network ◽

Time Series Data ◽

Wireless Sensor ◽

Series Data ◽

Detection Accuracy

Over the recent years, the term deep learning has been considered as one of the primary choice for handling huge amount of data. Having deeper hidden layers, it surpasses classical methods for detection of outlier in wireless sensor network. The Convolutional Neural Network (CNN) is a biologically inspired computational model which is one of the most popular deep learning approaches. It comprises neurons that self-optimize through learning. EEG generally known as Electroencephalography is a tool used for investigation of brain function and EEG signal gives time-series data as output. In this paper, we propose a state-of-the-art technique designed by processing the time-series data generated by the sensor nodes stored in a large dataset into discrete one-second frames and these frames are projected onto a 2D map images. A convolutional neural network (CNN) is then trained to classify these frames. The result improves detection accuracy and encouraging.

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An Ultrashort-Term Net Load Forecasting Model Based on Phase Space Reconstruction and Deep Neural Network

Applied Sciences ◽

10.3390/app9071487 ◽

2019 ◽

Vol 9 (7) ◽

pp. 1487 ◽

Cited By ~ 7

Author(s):

Fei Mei ◽

Qingliang Wu ◽

Tian Shi ◽

Jixiang Lu ◽

Yi Pan ◽

...

Keyword(s):

Neural Network ◽

Phase Space ◽

Deep Neural Network ◽

Time Series Data ◽

Load Forecasting ◽

Phase Space Reconstruction ◽

Series Data ◽

Forecasting Model ◽

Model Based ◽

Net Load

Recently, a large number of distributed photovoltaic (PV) power generations have been connected to the power grid, which resulted in an increased fluctuation of the net load. Therefore, load forecasting has become more difficult. Considering the characteristics of the net load, an ultrashort-term forecasting model based on phase space reconstruction and deep neural network (DNN) is proposed, which can be divided into two steps. First, the phase space reconstruction of the net load time series data is performed using the C-C method. Second, the reconstructed data is fitted by the DNN to obtain the predicted value of the net load. The performance of this model is verified using real data. The accuracy is high in forecasting the net load under high PV penetration rate and different weather conditions.

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Deep Neural Network Algorithm Feedback Model with Behavioral Intelligence and Forecast Accuracy

Symmetry ◽

10.3390/sym12091465 ◽

2020 ◽

Vol 12 (9) ◽

pp. 1465

Author(s):

Taikyeong Jeong

Keyword(s):

Neural Network ◽

Classification Accuracy ◽

Deep Neural Network ◽

Time Series Data ◽

Training Data ◽

Series Data ◽

Context Aware ◽

Data Set ◽

Feedback Model ◽

Data Connection

When attempting to apply a large-scale database that holds the behavioral intelligence training data of deep neural networks, the classification accuracy of the artificial intelligence algorithm needs to reflect the behavioral characteristics of the individual. When a change in behavior is recognized, that is, a feedback model based on a data connection model is applied, an analysis of time series data is performed by extracting feature vectors and interpolating data in a deep neural network to overcome the limitations of the existing statistical analysis. Using the results of the first feedback model as inputs to the deep neural network and, furthermore, as the input values of the second feedback model, and interpolating the behavioral intelligence data, that is, context awareness and lifelog data, including physical activities, involves applying the most appropriate conditions. The results of this study show that this method effectively improves the accuracy of the artificial intelligence results. In this paper, through an experiment, after extracting the feature vector of a deep neural network and restoring the missing value, the classification accuracy was verified to improve by about 20% on average. At the same time, by adding behavioral intelligence data to the time series data, a new data connection model, the Deep Neural Network Feedback Model, was proposed, and it was verified that the classification accuracy can be improved by about 8 to 9% on average. Based on the hypothesis, the F (X′) = X model was applied to thoroughly classify the training data set and test data set to present a symmetrical balance between the data connection model and the context-aware data. In addition, behavioral activity data were extrapolated in terms of context-aware and forecasting perspectives to prove the results of the experiment.

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Epileptic Detection from the Eeg Signal using the Anti colony Optimization Technique with Deep Neural Network

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.a2971.059120 ◽

2020 ◽

Vol 9 (1) ◽

pp. 2726-2733

Keyword(s):

Neural Network ◽

Deep Neural Network ◽

Time Series Data ◽

Optimization Technique ◽

Signal Decomposition ◽

Ant Colony ◽

Series Data ◽

Eeg Signal ◽

Eeg Signals ◽

Mode Decomposition

Extensively used technique to diagnose the epilepsy is EEG. The research objective is to check the variations of frequency found in the epileptic EEG signals.. The EEG dataset were acquired from online database of the Bonn University (BU). Then, butterworth type two filter was implemented to remove the unwanted artifacts from the acquired EEG signals. Further, Multivariate Variational Mode Decomposition (MVMD) methodology was applied to decompose the denoised EEG signals. The signal decomposition helps in finding the necessary information, which required to model the complex time series data. Then, the features were extracted from decomposed signals by using fifteen entropy, linear and statistical features. In addition, ant colony optimization technique was proposed for optimizing the extracted features. The optimized feature vectors were classified by Deep Neural Network (DNN) that includes two circumstances (seizure and healthy), and (Interictal, ictal, and normal). The accuracy attained using the ant colony with deep neural network is 98.12% using the BU EEG dataset, respectively related to the existing models.

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A Double-Channel Hybrid Deep Neural Network Based on CNN and BiLSTM for Remaining Useful Life Prediction

Sensors ◽

10.3390/s20247109 ◽

2020 ◽

Vol 20 (24) ◽

pp. 7109

Author(s):

Chengying Zhao ◽

Xianzhen Huang ◽

Yuxiong Li ◽

Muhammad Yousaf Iqbal

Keyword(s):

Neural Network ◽

Prediction Model ◽

Spatial Data ◽

Deep Neural Network ◽

Time Series Data ◽

Remaining Useful Life ◽

Series Data ◽

Useful Life ◽

Lstm Network ◽

Double Channel

In recent years, prognostic and health management (PHM) has played an important role in industrial engineering. Efficient remaining useful life (RUL) prediction can ensure the development of maintenance strategies and reduce industrial losses. Recently, data-driven based deep learning RUL prediction methods have attracted more attention. The convolution neural network (CNN) is a kind of deep neural network widely used in RUL prediction. It shows great potential for application in RUL prediction. A CNN is used to extract the features of time-series data according to the spatial feature method. This way of processing features without considering the time dimension will affect the prediction accuracy of the model. On the contrary, the commonly used long short-term memory (LSTM) network considers the timing of the data. However, compared with CNN, it lacks spatial data extraction capabilities. This paper proposes a double-channel hybrid prediction model based on the CNN and a bidirectional LSTM network to avoid those drawbacks. The sliding time window is used for data preprocessing, and an improved piece-wise linear function is used for model validating. The prediction model is evaluated using the C-MAPSS dataset provided by NASA. The predicted results show the proposed prediction model to have a better prediction performance compared with other state-of-the-art models.

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Classification of Chaotic Signals of the Recurrence Matrix Using a Convolutional Neural Network and Verification through the Lyapunov Exponent

Applied Sciences ◽

10.3390/app11010077 ◽

2020 ◽

Vol 11 (1) ◽

pp. 77

Author(s):

Jaehyeon Nam ◽

Jaeyoung Kang

Keyword(s):

Neural Network ◽

Lyapunov Exponent ◽

Dynamic System ◽

Convolutional Neural Network ◽

Dynamic Characteristics ◽

Time Series Data ◽

Fundamental Property ◽

Recurrence Plot ◽

Series Data ◽

Nonsmooth Problems

This study classified chaotic time series data, including smooth and nonsmooth problems in a dynamic system, using a convolutional neural network (CNN) and verified it through the Lyapunov exponent. For this, the classical nonlinear differential equation by the Lorenz model was used to analyze a smooth dynamic system. The vibro-impact model was used for the nonsmooth dynamic system. Recurrence is a fundamental property of a dynamic system, and a recurrence plot is a representative method to visualize the recurrence characteristics of reconstructed phase space. Therefore, this study calculated the Lyapunov exponent by parametric analysis and visualized the corresponding recurrence matrix to show the dynamic characteristics as an image. In addition, the dynamic characteristics were classified using the proposed CNN model. The proposed CNN model determined chaos with an accuracy of more than 92%.

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Indoor 3D Localization Scheme Based on BLE Signal Fingerprinting and 1D Convolutional Neural Network

Electronics ◽

10.3390/electronics10151758 ◽

2021 ◽

Vol 10 (15) ◽

pp. 1758

Author(s):

Shangyi Yang ◽

Chao Sun ◽

Youngok Kim

Keyword(s):

Neural Network ◽

Time Series ◽

Convolutional Neural Network ◽

Time Series Data ◽

Indoor Navigation ◽

Series Data ◽

Positioning Accuracy ◽

3D Localization ◽

Localization Scheme ◽

3D Positioning

Indoor localization schemes have significant potential for use in location-based services in areas such as smart factories, mixed reality, and indoor navigation. In particular, received signal strength (RSS)-based fingerprinting is used widely, given its simplicity and low hardware requirements. However, most studies tend to focus on estimating the 2D position of the target. Moreover, it is known that the fingerprinting scheme is computationally costly, and its positioning accuracy is readily affected by random fluctuations in the RSS values caused by fading and the multipath effect. We propose an indoor 3D localization scheme based on both fingerprinting and a 1D convolutional neural network (CNN). Instead of using the conventional fingerprint matching method, we transform the 3D positioning problem into a classification problem and use the 1D CNN model with the RSS time-series data from Bluetooth low-energy beacons for classification. By using the 1D CNN with the time-series data from multiple beacons, the inherent drawback of RSS-based fingerprinting, namely, its susceptibility to noise and randomness, is overcome, resulting in enhanced positioning accuracy. To evaluate the proposed scheme, we developed a 3D positioning system and performed comprehensive tests, whose results confirmed that the scheme significantly outperforms the conventional common spatial pattern classification algorithm.

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Simulating Time-Series Data for Improved Deep Neural Network Performance

IEEE Access ◽

10.1109/access.2019.2940701 ◽

2019 ◽

Vol 7 ◽

pp. 131248-131255 ◽

Cited By ~ 1

Author(s):

Jordan Yeomans ◽

Simon Thwaites ◽

William S. P. Robertson ◽

David Booth ◽

Brian Ng ◽

...

Keyword(s):

Neural Network ◽

Time Series ◽

Network Performance ◽

Deep Neural Network ◽

Time Series Data ◽

Series Data

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Vector Auto-Regressive Deep Neural Network: A Data-Driven Deep Learning-Based Directed Functional Connectivity Estimation Toolbox

Frontiers in Neuroscience ◽

10.3389/fnins.2021.764796 ◽

2021 ◽

Vol 15 ◽

Author(s):

Takuto Okuno ◽

Alexander Woodward

Keyword(s):

Neural Network ◽

Deep Learning ◽

Functional Connectivity ◽

Deep Neural Network ◽

Time Series Data ◽

Evaluation Framework ◽

Series Data ◽

Ground Truth Data ◽

Functional Connectome ◽

Auto Regressive

An important goal in neuroscience is to elucidate the causal relationships between the brain’s different regions. This can help reveal the brain’s functional circuitry and diagnose lesions. Currently there are a lack of approaches to functional connectome estimation that leverage the state-of-the-art in deep learning architectures and training methodologies. Therefore, we propose a new framework based on a vector auto-regressive deep neural network (VARDNN) architecture. Our approach consists of a set of nodes, each with a deep neural network structure. These nodes can be mapped to any spatial sub-division based on the data to be analyzed, such as anatomical brain regions from which representative neural signals can be obtained. VARDNN learns to reproduce experimental time series data using modern deep learning training techniques. Based on this, we developed two novel directed functional connectivity (dFC) measures, namely VARDNN-DI and VARDNN-GC. We evaluated our measures against a number of existing functional connectome estimation measures, such as partial correlation and multivariate Granger causality combined with large dimensionality counter-measure techniques. Our measures outperformed them across various types of ground truth data, especially as the number of nodes increased. We applied VARDNN to fMRI data to compare the dFC between 41 healthy control vs. 32 Alzheimer’s disease subjects. Our VARDNN-DI measure detected lesioned regions consistent with previous studies and separated the two groups well in a subject-wise evaluation framework. Summarily, the VARDNN framework has powerful capabilities for whole brain dFC estimation. We have implemented VARDNN as an open-source toolbox that can be freely downloaded for researchers who wish to carry out functional connectome analysis on their own data.

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