scholarly journals Multi-State Household Appliance Identification Based on Convolutional Neural Networks and Clustering

Energies ◽  
2020 ◽  
Vol 13 (4) ◽  
pp. 792 ◽  
Author(s):  
Ying Zhang ◽  
Bo Yin ◽  
Yanping Cong ◽  
Zehua Du
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Energy Consumption ◽  
Convolutional Neural Network ◽  
Neural Network Model ◽  
Clustering Algorithm ◽  
Identification Accuracy ◽  
Household Appliance ◽  
Load Monitoring

Non-intrusive load monitoring, a convenient way to discern the energy consumption of a house, has been studied extensively. However, most research works have been carried out based on a hypothetical condition that each electric appliance has only one running state. This leads to low identification accuracy for multi-state electric appliances. To deal with this problem, a method for identifying the type and state of electric appliances based on a power time series is proposed in this paper. First, to identify the type of appliance, a convolutional neural network model was constructed that incorporated residual modules. Then, a k-means clustering algorithm was applied to calculate the number of states of the appliance. Finally, in order to identify the states of the appliances, different k-means clustering models were established for different multi-state electric appliances. Experimental results show effectiveness of the proposed method in identifying both the type and the running state of electric appliances.

scholarly journals Convolutional Neural Network Classification of Telematics Car Driving Data

Risks ◽  
2019 ◽  
Vol 7 (1) ◽  
pp. 6 ◽  
Author(s):  
Guangyuan Gao ◽  
Mario Wüthrich
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Convolutional Neural Network ◽  
High Frequency ◽  
Location Data ◽  
Neural Network Classification ◽  
Feature Information ◽  
Car Driving

The aim of this project is to analyze high-frequency GPS location data (second per second) of individual car drivers (and trips). We extract feature information about speeds, acceleration, deceleration, and changes of direction from this high-frequency GPS location data. Time series of this feature information allow us to appropriately allocate individual car driving trips to selected drivers using convolutional neural networks.

scholarly journals NEURAL NETWORK FORECASTING OF ENERGY CONSUMPTION OF A METALLURGICAL ENTERPRISE

2021 ◽  
pp. 14-22
Author(s):  
Anna Bakurova ◽  
Olesia Yuskiv ◽  
Dima Shyrokorad ◽  
Anton Riabenko ◽  
Elina Tereschenko
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Energy Consumption ◽  
Short Term ◽  
Metallurgical Enterprise ◽  
Input Information ◽  
The Neural Network ◽  
Artificial Neural

The subject of the research is the methods of constructing and training neural networks as a nonlinear modeling apparatus for solving the problem of predicting the energy consumption of metallurgical enterprises. The purpose of this work is to develop a model for forecasting the consumption of the power system of a metallurgical enterprise and its experimental testing on the data available for research of PJSC "Dneprospetsstal". The following tasks have been solved: analysis of the time series of power consumption; building a model with the help of which data on electricity consumption for a historical period is processed; building the most accurate forecast of the actual amount of electricity for the day ahead; assessment of the forecast quality. Methods used: time series analysis, neural network modeling, short-term forecasting of energy consumption in the metallurgical industry. The results obtained: to develop a model for predicting the energy consumption of a metallurgical enterprise based on artificial neural networks, the MATLAB complex with the Neural Network Toolbox was chosen. When conducting experiments, based on the available statistical data of a metallurgical enterprise, a selection of architectures and algorithms for learning neural networks was carried out. The best results were shown by the feedforward and backpropagation network, architecture with nonlinear autoregressive and learning algorithms: Levenberg-Marquard nonlinear optimization, Bayesian Regularization method and conjugate gradient method. Another approach, deep learning, is also considered, namely the neural network with long short-term memory LSTM and the adam learning algorithm. Such a deep neural network allows you to process large amounts of input information in a short time and build dependencies with uninformative input information. The LSTM network turned out to be the most effective among the considered neural networks, for which the indicator of the maximum prediction error had the minimum value. Conclusions: analysis of forecasting results using the developed models showed that the chosen approach with experimentally selected architectures and learning algorithms meets the necessary requirements for forecast accuracy when developing a forecasting model based on artificial neural networks. The use of models will allow automating high-precision operational hourly forecasting of energy consumption in market conditions. Keywords: energy consumption; forecasting; artificial neural network; time series.

Predicting drug-target affinity based on recurrent neural networks and graph convolutional neural networks

2021 ◽  
Vol 24 ◽  
Author(s):  
Qingyu Tian ◽  
Mao Ding ◽  
Hui Yang ◽  
Caibin Yue ◽  
Yue Zhong ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Convolutional Neural Network ◽  
Drug Target ◽  
Channel Model ◽  
Search Space ◽  
Topological Features ◽  
Latent Features ◽  
Chemical Background

Background: Drug development requires a lot of money and time, and the outcome of the challenge is unknown. So, there is an urgent need for researchers to find a new approach that can reduce costs. Therefore, the identification of drug-target interactions (DTIs) has been a critical step in the early stages of drug discovery. These computational methods aim to narrow the search space for novel DTIs and to elucidate the functional background of drugs. Most of the methods developed so far use binary classification to predict the presence or absence of interactions between the drug and the target. However, it is more informative, but also more challenging, to predict the strength of the binding between a drug and its target. If the strength is not strong enough, such a DTI may not be useful. Hence, the development of methods to predict drug-target affinity (DTA) is of significant importance. Method: We have improved the Graph DTA model from a dual-channel model to a triple-channel model. We interpreted the target/protein sequences as time series and extracted their features using the LSTM network. For the drug, we considered both the molecular structure and the local chemical background, retaining the four variant networks used in Graph DTA to extract the topological features of the drug and capturing the local chemical background of the atoms in the drug by using BiGRU. Thus, we obtained the latent features of the target and two latent features of the drug. The connection of these three feature vectors is then input into a 2-layer FC network, and a valuable binding affinity is output. Result: We use the Davis and Kiba datasets, using 80% of the data for training and 20% of the data for validation. Our model shows better performance by comparing it with the experimental results of Graph DTA. Conclusion: In this paper, we altered the Graph DTA model to predict drug-target affinity. It represents the drug as a graph, and extracts the two-dimensional drug information using a graph convolutional neural network. Simultaneously, the drug and protein targets are represented as a word vector, and the convolutional neural network is used to extract the time series information of the drug and the target. We demonstrate that our improved method has better performance than the original method. In particular, our model has better performance in the evaluation of benchmark databases.

Week-ahead Forecasting of Household Energy Consumption Using CNN and Multivariate Data

2021 ◽  
Vol 20 ◽  
pp. 182-188
Author(s):  
Vanita Agrawal ◽  
Pradyut K. Goswami ◽  
Kandarpa K. Sarma
Keyword(s):  
Neural Network ◽  
Energy Consumption ◽  
Power System ◽  
Convolutional Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Smart Grids ◽  
Smart Meters ◽  
Household Energy ◽  
Household Energy Consumption

Short-Term Load Forecasting for buildings has gained a lot of importance in recent times due to the ongoing penetration of renewable energy and the upgradation of power system networks to Smart Grids embedded with smart meters. Power System expansion is not able to keep pace with the energy consumption demands. In this scenario, accurate household energy forecasting is one of the key solutions to managing the demand side energy. Even a small percentage of improvement in forecasting error, translates to a lot of saving for both producers and consumers. In this paper, it was found out that Aggregated 1-Dimensional Convolutional Neural Networks can be effectively modeled to predict the household consumption with greater accuracy than a basic 1-Dimensional Convolutional Neural Network model or a classical Auto Regressive Integrated Moving Average model. The proposed Aggregated Convolutional Neural Network model was tested on a 4 year household energy consumption dataset and gave very promising Root Mean Square Error reduction

RECOGNITION OF THE TEXT BY MEANS OF DEEP LEARNING

2020 ◽  
Vol 69 (1) ◽  
pp. 378-383
Author(s):  
T.A. Nurmukhanov ◽  
◽  
B.S. Daribayev ◽  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Neural Network Model ◽  
Input Data ◽  
Optimization Methods ◽  
Text Recognition ◽  
Activation Functions

Using neural networks, various variations of the classification of objects can be performed. Neural networks are used in many areas of recognition. A big area in this area is text recognition. The paper considers the optimal way to build a network for text recognition, the use of optimal methods for activation functions, and optimizers. Also, the article checked the correctness of text recognition with different optimization methods. This article is devoted to the analysis of convolutional neural networks. In the article, a convolutional neural network model will be trained with a teacher. Teaching with a teacher is a type of training for neural networks in which you provide the input data and the desired result, that is, the student looking at the input data will understand that you need to strive for the result that was provided to him.

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.

scholarly journals Convolutional Neural Network Model for Predicting Skin based Diseases and Evaluation of Risk Assessment

2019 ◽  
Vol 8 (2) ◽  
pp. 989-993
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Risk Assessment ◽  
Convolutional Neural Network ◽  
Neural Network Model ◽  
Skin Diseases ◽  
Disease Prediction ◽  
Prediction System ◽  
User Input ◽  
Near Future

In today’s dynamic lifestyle, with people not prioritizing hygiene as an essential entity, they tend to get more easily prone to skin diseases. As a result, it has become highly significant to devise an automated mechanism which helps users to predict a disease using simple methodologies such as input in the form of images. In our case we have used Convolutional Neural Networks to diagnose the same. The disease prediction system uses simple methodologies including taking user input in the form of images, which aids in providing more accurate results. As the concept used is Convolutional Neural Network (CNN), the system not just analyses the input and predicts the disease based on it, but also guesses the nearest possible result based on its feature and adds that record to that class of disease for future predictions. Over a longer period of time, this majorly helps in preventing hazardous skin diseases from causing widespread damage, as spreading of skin diseases is highly rapid and difficult to control, the ultimate aim through this project is to overcome the above limitations and help create awareness amongst people on skin diseases, which happens to be a growing concern in the near future.

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