scholarly journals A Cross Entropy Based Deep Neural Network Model for Road Extraction from Satellite Images

Entropy ◽  
2020 ◽  
Vol 22 (5) ◽  
pp. 535 ◽  
Author(s):  
Bowei Shan ◽  
Yong Fang
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Satellite Images ◽  
Cross Entropy ◽  
Road Extraction ◽  
Training Time ◽  
Deep Model ◽  
Spatial Pyramid Pooling ◽  
Asynchronous Training

This paper proposes a deep convolutional neural network model with encoder-decoder architecture to extract road network from satellite images. We employ ResNet-18 and Atrous Spatial Pyramid Pooling technique to trade off between the extraction precision and running time. A modified cross entropy loss function is proposed to train our deep model. A PointRend algorithm is used to recover a smooth, clear and sharp road boundary. The augmentated DeepGlobe dataset is used to train our deep model and the asynchronous training method is applied to accelerate the training process. Five salellite images covering Xiaomu village are taken as input to evaluate our model. The proposed E-Road model has fewer number of parameters and shorter training time. The experiments show E-Road outperforms other state-of-the-art deep models with 5.84% to 59.09% improvement, and can give the accurate predictions for the images with complex environment.

scholarly journals Neural Network Model for Land Cover Classification from Satellite Images

2007 ◽  
Vol 67 (4) ◽  
Author(s):  
Mónica Bocco ◽  
Gustavo Ovando ◽  
Silvina Sayago ◽  
Enrique Willington
Keyword(s):  
Neural Network ◽  
Land Cover ◽  
Network Model ◽  
Neural Network Model ◽  
Satellite Images ◽  
Land Cover Classification

Learning Chinese Word Segmentation Based on Bidirectional GRU-CRF and CNN Network Model

2019 ◽  
Vol 15 (3) ◽  
pp. 47-62 ◽  
Author(s):  
Chenghai Yu ◽  
Shupei Wang ◽  
Jiajun Guo
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Network Models ◽  
Word Segmentation ◽  
Chinese Word ◽  
Chinese Word Segmentation ◽  
Neural Network Models ◽  
Long Distance ◽  
Training Time

Chinese word segmentation is the basis of the Chinese natural language processing (NLP). With the development of the deep learning, various neural network models are applied to the Chinese word segmentation. However, current neural network models have the characteristics of artificial feature extraction, nonstandard word-weight, inability to effectively use long-distance information and long training time of models in Chinese word segmentation. To solve a series of problems, this article presents a CNN-Bidirectional GRU-CRF neural network model (CNN Bidirectional GRU CRF Network, CBiGCN), which breaks through the limit of conventional method window, truly realizes end-to-end processing and applies to the neural network model by the five-Tag set method, bias-variable-weight greedy strategy and supplements by Goldstein-Armijo guidelines. Besides, this model, with simple structure, is easy to be operated. And it can automatically learn features, reduces large amounts of tasks on specific knowledge in the form of handcrafted features and data pre-processing, makes use of context information effectively. The authors set an experiment with two data corpuses for Chinese word segmentation to evaluate their system. The experiment verified their new model can obtain better Chinese word segmentation results and greatly reduce training time.

scholarly journals Simplified Neural Network Model Design with Sensitivity Analysis and Electricity Consumption Prediction in a Commercial Building

Energies ◽  
2019 ◽  
Vol 12 (7) ◽  
pp. 1201 ◽  
Author(s):  
Moon Kim ◽  
Jaehoon Cha ◽  
Eunmi Lee ◽  
Van Pham ◽  
Sanghyuk Lee ◽  
...  
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Electricity Consumption ◽  
Shopping Mall ◽  
Commercial Building ◽  
Rapid Urbanization ◽  
Data Set ◽  
Training Time ◽  
Building Model

With growing urbanization, it has become necessary to manage this growth smartly. Specifically, increased electrical energy consumption has become a rapid urbanization trend in China. A building model based on a neural network was proposed to overcome the difficulties of analytical modelling. However, increased amounts of data, repetitive computation, and training time become a limitation of this approach. A simplified model can be used instead of the full order model if the performance is acceptable. In order to select effective data, Mean Impact Value (MIV) has been applied to select meaningful data. To verify this neural network method, we used real electricity consumption data of a shopping mall in China as a case study. In this paper, a Bayesian Regularization Neural Network (BRNN) is utilized to avoid overfitting due to the small amount of data. With the simplified data set, the building model showed reasonable performance. The mean of Root Mean Square Error achieved is around 10% with respect to the actual consumption and the standard deviation is low, which reflects the model’s reliability. We also compare the results with our previous approach using the Levenberg–Marquardt back propagation (LM-BP) method. The main difference is the output reliability of the two methods. LM-BP shows higher error than BRNN due to overfitting. BRNN shows reliable prediction results when the simplified neural network model is applied.

scholarly journals A Neural Network Model for Driver’s Lane-Changing Trajectory Prediction in Urban Traffic Flow

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Chenxi Ding ◽  
Wuhong Wang ◽  
Xiao Wang ◽  
Martin Baumann
Keyword(s):  
Neural Network ◽  
Traffic Flow ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Driving Simulator ◽  
Back Propagation ◽  
Urban Traffic ◽  
Lane Changing ◽  
Training Time

The neural network may learn and incorporate the uncertainties to predict the driver’s lane-changing behavior more accurately. In this paper, we will discuss in detail the effectiveness of Back-Propagation (BP) neural network for prediction of lane-changing trajectory based on the past vehicle data and compare the results between BP neural network model and Elman Network model in terms of the training time and accuracy. Driving simulator data and NGSIM data were processed by a smooth method and then used to validate the availability of the model. The test results indicate that BP neural network might be an accurate prediction of driver’s lane-changing behavior in urban traffic flow. The objective of this paper is to show the usefulness of BP neural network in prediction of lane-changing process and confirm that the vehicle trajectory is influenced previously by the collected data.

scholarly journals Performance analysis of the TensorFlow library with different optimisation algorithms

2021 ◽  
Vol 21 ◽  
pp. 330-335
Author(s):  
Maciej Wadas ◽  
Jakub Smołka
Keyword(s):  
Neural Network ◽  
Performance Analysis ◽  
Network Model ◽  
Neural Network Model ◽  
Deep Neural Networks ◽  
Network Models ◽  
Neural Network Models ◽  
Training Time ◽  
The Neural Network ◽  
Training Efficiency

This paper presents the results of performance analysis of the Tensorflow library used in machine learning and deep neural networks. The analysis focuses on comparing the parameters obtained when training the neural network model for optimization algorithms: Adam, Nadam, AdaMax, AdaDelta, AdaGrad. Special attention has been paid to the differences between the training efficiency on tasks using microprocessor and graphics card. For the study, neural network models were created in order to recognise Polish handwritten characters. The results obtained showed that the most efficient algorithm is AdaMax, while the computer component used during the research only affects the training time of the neural network model used.

Application of Elm Neural Network in Dam Displacement Early Warning Model

2013 ◽  
Vol 864-867 ◽  
pp. 2363-2366
Author(s):  
Tu Tu ◽  
Feng Zhu ◽  
Ai Wu Cao ◽  
Lu He ◽  
Geng Ying
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Early Warning ◽  
Bp Neural Network ◽  
Training Time ◽  
Multiple Factors ◽  
Bp Neural Network Model ◽  
Warning Model ◽  
Temperature Water

The dam displacement is related to multiple factors such as time, temperature, water level and etc. And it presents a strong nonlinear and certain randomness.Neural network model because of its inherent characteristics can better simulate the dam displacement.Nowadays,It has methods to estimate the displacement of the dam by constructing physical model and BP neural network model.But BP neural network's training time is too long and the forecast effect is not very good.So this paper introduces Elm neural network model,establishs Elm neural network model of dam displacement early warning considering multiple factors to estimate the displacement.By a simple example and compared with BP neural network model to reflect the rationality and scientificity of this method.

A Fault Diagnosis Intelligent Algorithm Based on Improved BP Neural Network

2019 ◽  
Vol 33 (09) ◽  
pp. 1959028 ◽  
Author(s):  
Pingfeng Liu ◽  
Wang Zhang
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Reference Value ◽  
Intelligent Algorithm ◽  
Neural Network Models ◽  
Training Time ◽  
Bp Neural Network Model

The fault diagnosis intelligent algorithm makes full use of the associative memory and pattern recognition function of the neural network to compare the abnormal value of various parameters of the engine fault with the reference value of the known fault mode, which can shorten the fault diagnosis time and improve the diagnosis efficiency. BP neural network model as one of the most widely used neural network models in the world is of significance to solve nonlinear complex problems. Of course, there are also some deficiencies in it, such as long training time and ease to trap into local minimum. This paper utilized the global search advantage of genetic algorithm to optimize the optimal weight and threshold value of BP neural network. Furthermore, an improved BP neural network was put forward, which is greatly improved in stability, generalization and convergence rate. Taking fault diagnosis of automobile engine as an example, a simulation experiment was carried out on the established model. The research results indicate that improved neural network model owns a higher accuracy than pure GA model or BP neural network model (with an average accuracy improved by 19.04% than traditional model), and its effect is satisfactory.

scholarly journals Estimation of State of Charge of Lithium-Ion Batteries Based on Wide and Deep Neural Network Model

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Di Mu ◽  
Shuning Wang
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Deep Neural Network ◽  
Estimation Error ◽  
Lithium Ion ◽  
Soc Estimation ◽  
Deep Model ◽  
Proposed Model ◽  
Bpnn Model

It is important to accurately estimate the SOC to ensure that the lithium-ion battery is within a safe working range, prevent over-charging and over-discharging, and ultimately improve battery life. However, SOC is an internal state of the battery and cannot be directly measured. This paper proposes a SOC estimation method based on the wide and deep neural network model, which combines the linear regression (LR) model and the backpropagation neural network (BPNN) model. This article uses the dataset provided by the Advanced Energy Storage and Applications (AESA) group to verify the performance of the model. The performance of the proposed model is compared with the common BPNN model in terms of root mean square error (RMSE), average absolute proportional error (MAPE), and SOC estimation error. The validation results prove that the effect of the proposed model in estimating SOC is better than that of the ordinary BPNN model. Compared with the BPNN model, the RMSE values of the SOC predicted value of the wide and deep model in the charging and discharging stages were reduced by 10.2% and 15.4%, respectively. Experimental results show that the maximum SOC estimation error of the model in predicting the SOC during charging and discharging is 0.42% and 0.86%, respectively.

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