scholarly journals Nonintrusive Residential Electricity Load Decomposition Based on Transfer Learning

2021 ◽  
Vol 13 (12) ◽  
pp. 6546
Author(s):  
Mingzhi Yang ◽  
Yue Liu ◽  
Quanlong Liu
Keyword(s):  
Neural Network ◽  
Transfer Learning ◽  
Residential Buildings ◽  
Electricity Consumption ◽  
Electrical Load ◽  
Prediction Ability ◽  
Training Time ◽  
Load Monitoring ◽  
Open Datasets ◽  
Improved Model

Monitoring electricity consumption in residential buildings is an important way to help reduce energy usage. Nonintrusive load monitoring is a technique to separate the total electrical load of a single household into specific appliance loads. This problem is difficult because we aim to extract the energy consumption of each appliance by only using the total electrical load. Deep transfer learning is expected to solve this problem. This paper proposes a deep neural network model based on an attention mechanism. This model improves the traditional sequence-to-sequence model with a time-embedding layer and an attention layer so that it can be better applied in nonintrusive load monitoring. In particular, the improved model abandons the recurrent neural network structure and shortens the training time, which means it is more appropriate for use in model pretraining with large datasets. To verify the validity of the model, we selected three open datasets and compared them with the current leading model. The results show that transfer learning can effectively improve the prediction ability of the model, and the model proposed in this study has a better performance than the most advanced available model.

scholarly journals Surface EMG-Based Instantaneous Hand Gesture Recognition Using Convolutional Neural Network with the Transfer Learning Method

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2540
Author(s):  
Zhipeng Yu ◽  
Jianghai Zhao ◽  
Yucheng Wang ◽  
Linglong He ◽  
Shaonan Wang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Gesture Recognition ◽  
Recognition System ◽  
Surface Emg ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Training Time ◽  
Generalization Ability

In recent years, surface electromyography (sEMG)-based human–computer interaction has been developed to improve the quality of life for people. Gesture recognition based on the instantaneous values of sEMG has the advantages of accurate prediction and low latency. However, the low generalization ability of the hand gesture recognition method limits its application to new subjects and new hand gestures, and brings a heavy training burden. For this reason, based on a convolutional neural network, a transfer learning (TL) strategy for instantaneous gesture recognition is proposed to improve the generalization performance of the target network. CapgMyo and NinaPro DB1 are used to evaluate the validity of our proposed strategy. Compared with the non-transfer learning (non-TL) strategy, our proposed strategy improves the average accuracy of new subject and new gesture recognition by 18.7% and 8.74%, respectively, when up to three repeated gestures are employed. The TL strategy reduces the training time by a factor of three. Experiments verify the transferability of spatial features and the validity of the proposed strategy in improving the recognition accuracy of new subjects and new gestures, and reducing the training burden. The proposed TL strategy provides an effective way of improving the generalization ability of the gesture recognition system.

scholarly journals Voiceprint Identification for Limited Dataset Using the Deep Migration Hybrid Model Based on Transfer Learning

Sensors ◽  
2018 ◽  
Vol 18 (7) ◽  
pp. 2399 ◽  
Author(s):  
Cunwei Sun ◽  
Yuxin Yang ◽  
Chang Wen ◽  
Kai Xie ◽  
Fangqing Wen
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Hybrid Model ◽  
Data Augmentation ◽  
Small Sample ◽  
Restricted Boltzmann Machine ◽  
Small Samples ◽  
Boltzmann Machine ◽  
Training Time

The convolutional neural network (CNN) has made great strides in the area of voiceprint recognition; but it needs a huge number of data samples to train a deep neural network. In practice, it is too difficult to get a large number of training samples, and it cannot achieve a better convergence state due to the limited dataset. In order to solve this question, a new method using a deep migration hybrid model is put forward, which makes it easier to realize voiceprint recognition for small samples. Firstly, it uses Transfer Learning to transfer the trained network from the big sample voiceprint dataset to our limited voiceprint dataset for the further training. Fully-connected layers of a pre-training model are replaced by restricted Boltzmann machine layers. Secondly, the approach of Data Augmentation is adopted to increase the number of voiceprint datasets. Finally, we introduce fast batch normalization algorithms to improve the speed of the network convergence and shorten the training time. Our new voiceprint recognition approach uses the TLCNN-RBM (convolutional neural network mixed restricted Boltzmann machine based on transfer learning) model, which is the deep migration hybrid model that is used to achieve an average accuracy of over 97%, which is higher than that when using either CNN or the TL-CNN network (convolutional neural network based on transfer learning). Thus, an effective method for a small sample of voiceprint recognition has been provided.

Self-organizing probability neural network-based intelligent non-intrusive load monitoring with applications to low-cost residential measuring devices

2020 ◽  
pp. 014233122095086
Author(s):  
Zejian Zhou ◽  
Yingmeng Xiang ◽  
Hao Xu ◽  
Yishen Wang ◽  
Di Shi ◽  
...  
Keyword(s):  
Neural Network ◽  
Probabilistic Neural Network ◽  
Low Cost ◽  
Back Propagation ◽  
Learning Technologies ◽  
Training Time ◽  
The Public ◽  
Measuring Devices ◽  
Load Monitoring ◽  
Self Organizing

Non-intrusive load monitoring (NILM) is a critical technique for advanced smart grid management due to the convenience of monitoring and analysing individual appliances’ power consumption in a non-intrusive fashion. Inspired by emerging machine learning technologies, many recent non-intrusive load monitoring studies have adopted artificial neural networks (ANN) to disaggregate appliances’ power from the non-intrusive sensors’ measurements. However, back-propagation ANNs have a very limit ability to disaggregate appliances caused by the great training time and uncertainty of convergence, which are critical flaws for low-cost devices. In this paper, a novel self-organizing probabilistic neural network (SPNN)-based non-intrusive load monitoring algorithm has been developed specifically for low-cost residential measuring devices. The proposed SPNN has been designed to estimate the probability density function classifying the different types of appliances. Compared to back-propagation ANNs, the SPNN requires less iterative synaptic weights update and provides guaranteed convergence. Meanwhile, the novel SPNN has less space complexity when compared with conventional PNNs by the self-organizing mechanism which automatically edits the neuron numbers. These advantages make the algorithm especially favourable to low-cost residential NILM devices. The effectiveness of the proposed algorithm is demonstrated through numerical simulation by using the public REDD dataset. Performance comparisons with well-known benchmark algorithms have also been provided in the experiment section.

scholarly journals Improving Deep Learning-Based UWB LOS/NLOS Identification with Transfer Learning: An Empirical Approach

Electronics ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 1714
Author(s):  
JiWoong Park ◽  
SungChan Nam ◽  
HongBeom Choi ◽  
YoungEun Ko ◽  
Young-Bae Ko
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Transfer Learning ◽  
Classification Accuracy ◽  
Ultra Wideband ◽  
Line Of Sight ◽  
Training Time ◽  
Identification Scheme ◽  
Office Environments ◽  
Non Line Of Sight

This paper presents an improved ultra-wideband (UWB) line of sight (LOS)/non-line of sight (NLOS) identification scheme based on a hybrid method of deep learning and transfer learning. Previous studies have limitations, in that the classification accuracy significantly decreases in an unknown place. To solve this problem, we propose a transfer learning-based NLOS identification method for classifying the NLOS conditions of the UWB signal in an unmeasured environment. Both the multilayer perceptron and convolutional neural network (CNN) are introduced as classifiers for NLOS conditions. We evaluate the proposed scheme by conducting experiments in both measured and unmeasured environments. Channel data were measured using a Decawave EVK1000 in two similar indoor office environments. In the unmeasured environment, the existing CNN method showed an accuracy of approximately 44%, but when the proposed scheme was applied to the CNN, it showed an accuracy of up to 98%. The training time of the proposed scheme was measured to be approximately 48 times faster than that of the existing CNN. When comparing the proposed scheme with learning a new CNN in an unmeasured environment, the proposed scheme demonstrated an approximately 10% higher accuracy and approximately five times faster training time.

scholarly journals Research on GRU Neural Network Satellite Traffic Prediction Based on Transfer Learning

2021 ◽  
Author(s):  
Ning Li ◽  
Lang Hu ◽  
Zhong-Liang Deng ◽  
Tong Su ◽  
Jiang-Wang Liu
Keyword(s):  
Neural Network ◽  
Particle Filter ◽  
Transfer Learning ◽  
Network Traffic ◽  
Prediction Algorithm ◽  
Traffic Prediction ◽  
Satellite Network ◽  
Training Time ◽  
Gradient Descent Algorithm ◽  
Particle Filter Algorithm

AbstractIn this paper, we propose a Gated Recurrent Unit(GRU) neural network traffic prediction algorithm based on transfer learning. By introducing two gate structures, such as reset gate and update gate, the GRU neural network avoids the problems of gradient disappearance and gradient explosion. It can effectively represent the characteristics of long correlation traffic, and can realize the expression of nonlinear, self-similar, long correlation and other characteristics of satellite network traffic. The paper combines the transfer learning method to solve the problem of insufficient online traffic data and uses the particle filter online training algorithm to reduce the training time complexity and achieve accurate prediction of satellite network traffic. The simulation results show that the average relative error of the proposed traffic prediction algorithm is 35.80% and 8.13% lower than FARIMA and SVR, and the particle filter algorithm is 40% faster than the gradient descent algorithm.

scholarly journals Update of specific electric loads of public premises located in residential buildings

2021 ◽  
Vol 23 (3) ◽  
pp. 62-72
Author(s):  
Yu. I. Soluyanov ◽  
A. R. Akhmetshin ◽  
V. I. Soluyanov
Keyword(s):  
Energy Consumption ◽  
Residential Buildings ◽  
Electricity Consumption ◽  
Normative Values ◽  
Electrical Load ◽  
Smart Meters ◽  
Apartment Buildings ◽  
Constant Change ◽  
Electrical Loads ◽  
Electric Loads

THE PURPOSE. To determine the composition of electricity consumers in apartment buildings. To analyze the power consumption of organizations located on the first two floors of apartment buildings. To justify the need to update the standards for electrical loads for public premises built into residential buildings. METHODS. Information on electricity consumption was received by automated electricity metering system from smart meters installed directly at consumers. To achieve this goal, statistical methods for analyzing energy consumption were used. RESULTS. The article describes the relevance of the topic, provides a rationale for adjusting the normative values of specific electrical loads for public premises built into residential buildings. The percentage of consumer groups is shown on the example of several apartment buildings. The annual specific average monthly graphs of electricity consumption are presented: shops, offices, pharmacies, restaurants. CONCLUSION. In an effort to increase the level of comfort, developers are interested in developing the infrastructure of the facilities, mainly for this, they use ground and first floors, in which retail and office areas are most often located. Research by the Roselectromontazh Association has shown that to determine the electrical load of non-residential commercial premises, one has to use one averaged value due to the constant change in the purpose of premises and the complexity of determining the occupied area.

Small aircraft detection using deep learning

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Emre Kiyak ◽  
Gulay Unal
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Convolutional Neural Networks ◽  
Fine Tuning ◽  
Deep Convolutional Neural Networks ◽  
Content Type ◽  
Training Time

Purpose The paper aims to address the tracking algorithm based on deep learning and four deep learning tracking models developed. They compared with each other to prevent collision and to obtain target tracking in autonomous aircraft. Design/methodology/approach First, to follow the visual target, the detection methods were used and then the tracking methods were examined. Here, four models (deep convolutional neural networks (DCNN), deep convolutional neural networks with fine-tuning (DCNNFN), transfer learning with deep convolutional neural network (TLDCNN) and fine-tuning deep convolutional neural network with transfer learning (FNDCNNTL)) were developed. Findings The training time of DCNN took 9 min 33 s, while the accuracy percentage was calculated as 84%. In DCNNFN, the training time of the network was calculated as 4 min 26 s and the accuracy percentage was 91%. The training of TLDCNN) took 34 min and 49 s and the accuracy percentage was calculated as 95%. With FNDCNNTL, the training time of the network was calculated as 34 min 33 s and the accuracy percentage was nearly 100%. Originality/value Compared to the results in the literature ranging from 89.4% to 95.6%, using FNDCNNTL, better results were found in the paper.

scholarly journals Deep Transfer Learning for Vulnerable Road Users Detection using Smartphone Sensors Data

2020 ◽  
Vol 12 (21) ◽  
pp. 3508
Author(s):  
Mohammed Elhenawy ◽  
Huthaifa I. Ashqar ◽  
Mahmoud Masoud ◽  
Mohammed H. Almannaa ◽  
Andry Rakotonirainy ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Classification Accuracy ◽  
High Accuracy ◽  
Training Time ◽  
Road Users ◽  
Smartphone Sensors ◽  
Vulnerable Road Users ◽  
And Behavior

As the Autonomous Vehicle (AV) industry is rapidly advancing, the classification of non-motorized (vulnerable) road users (VRUs) becomes essential to ensure their safety and to smooth operation of road applications. The typical practice of non-motorized road users’ classification usually takes significant training time and ignores the temporal evolution and behavior of the signal. In this research effort, we attempt to detect VRUs with high accuracy be proposing a novel framework that includes using Deep Transfer Learning, which saves training time and cost, to classify images constructed from Recurrence Quantification Analysis (RQA) that reflect the temporal dynamics and behavior of the signal. Recurrence Plots (RPs) were constructed from low-power smartphone sensors without using GPS data. The resulted RPs were used as inputs for different pre-trained Convolutional Neural Network (CNN) classifiers including constructing 227 × 227 images to be used for AlexNet and SqueezeNet; and constructing 224 × 224 images to be used for VGG16 and VGG19. Results show that the classification accuracy of Convolutional Neural Network Transfer Learning (CNN-TL) reaches 98.70%, 98.62%, 98.71%, and 98.71% for AlexNet, SqueezeNet, VGG16, and VGG19, respectively. Moreover, we trained resnet101 and shufflenet for a very short time using one epoch of data and then used them as weak learners, which yielded 98.49% classification accuracy. The results of the proposed framework outperform other results in the literature (to the best of our knowledge) and show that using CNN-TL is promising for VRUs classification. Because of its relative straightforwardness, ability to be generalized and transferred, and potential high accuracy, we anticipate that this framework might be able to solve various problems related to signal classification.

scholarly journals MORED: A Moroccan Buildings’ Electricity Consumption Dataset

Energies ◽  
2020 ◽  
Vol 13 (24) ◽  
pp. 6737
Author(s):  
Mohamed Aymane Ahajjam ◽  
Daniel Bonilla Licea ◽  
Chaimaa Essayeh ◽  
Mounir Ghogho ◽  
Abdellatif Kobbane
Keyword(s):  
Electricity Consumption ◽  
Ground Truth ◽  
Ground Truth Data ◽  
Disadvantaged Neighborhoods ◽  
Consumption Data ◽  
Load Monitoring ◽  
Open Datasets

This paper consists of two parts: an overview of existing open datasets of electricity consumption and a description of the Moroccan Buildings’ Electricity Consumption Dataset, a first of its kind, coined as MORED. The new dataset comprises electricity consumption data of various Moroccan premises. Unlike existing datasets, MORED provides three main data components: whole premises (WP) electricity consumption, individual load (IL) ground-truth consumption, and fully labeled IL signatures, from affluent and disadvantaged neighborhoods. The WP consumption data were acquired at low rates (1/5 or 1/10 samples/s) from 12 households; the IL ground-truth data were acquired at similar rates from five households for extended durations; and IL signature data were acquired at high and low rates (50 k and 4 samples/s) from 37 different residential and industrial loads. In addition, the dataset encompasses non-intrusive load monitoring (NILM) metadata.

scholarly journals Convolutional Neural Network Coupled with a Transfer-Learning Approach for Time-Series Flood Predictions

Water ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 96 ◽  
Author(s):  
Nobuaki Kimura ◽  
Ikuo Yoshinaga ◽  
Kenji Sekijima ◽  
Issaku Azechi ◽  
Daichi Baba
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Learning Approach ◽  
Target Domain ◽  
Training Time ◽  
Source Domain ◽  
Flood Model

East Asian regions in the North Pacific have recently experienced severe riverine flood disasters. State-of-the-art neural networks are currently utilized as a quick-response flood model. Neural networks typically require ample time in the training process because of the use of numerous datasets. To reduce the computational costs, we introduced a transfer-learning approach to a neural-network-based flood model. For a concept of transfer leaning, once the model is pretrained in a source domain with large datasets, it can be reused in other target domains. After retraining parts of the model with the target domain datasets, the training time can be reduced due to reuse. A convolutional neural network (CNN) was employed because the CNN with transfer learning has numerous successful applications in two-dimensional image classification. However, our flood model predicts time-series variables (e.g., water level). The CNN with transfer learning requires a conversion tool from time-series datasets to image datasets in preprocessing. First, the CNN time-series classification was verified in the source domain with less than 10% errors for the variation in water level. Second, the CNN with transfer learning in the target domain efficiently reduced the training time by 1/5 of and a mean error difference by 15% of those obtained by the CNN without transfer learning, respectively. Our method can provide another novel flood model in addition to physical-based models.

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