scholarly journals Polarization-Based Haze Removal Using Self-Supervised Network

2022 ◽  
Vol 9 ◽  
Author(s):  
Yingjie Shi ◽  
Enlai Guo ◽  
Lianfa Bai ◽  
Jing Han
Keyword(s):  
Neural Network ◽  
Gradient Descent ◽  
Transmission Model ◽  
Neural Network Training ◽  
Original Image ◽  
Atmospheric Transmission ◽  
Atmospheric Scattering ◽  
Haze Removal ◽  
Network Training ◽  
The Neural Network

Atmospheric scattering caused by suspended particles in the air severely degrades the scene radiance. This paper proposes a method to remove haze by using a neural network that combines scene polarization information. The neural network is self-supervised and online globally optimization can be achieved by using the atmospheric transmission model and gradient descent. Therefore, the proposed method does not require any haze-free image as the constraint for neural network training. The proposed approach is far superior to supervised algorithms in the performance of dehazing and is highly robust to the scene. It is proved that this method can significantly improve the contrast of the original image, and the detailed information of the scene can be effectively enhanced.

scholarly journals Gradient Descent is a Technique for Learning to Learn

2018 ◽  
Vol 5 (2) ◽  
pp. 145-156 ◽  
Author(s):  
Taposh Kumar Neogy ◽  
Naresh Babu Bynagari
Keyword(s):  
Neural Network ◽  
Gradient Descent ◽  
Optimization Methods ◽  
Neural Network Training ◽  
Learning To Learn ◽  
Learning Problem ◽  
Convex Problems ◽  
Network Training ◽  
Simple Convex ◽  
Learned Features

In machine learning, the transition from hand-designed features to learned features has been a huge success. Regardless, optimization methods are still created by hand. In this study, we illustrate how an optimization method's design can be recast as a learning problem, allowing the algorithm to automatically learn to exploit structure in the problems of interest. On the tasks for which they are taught, our learning algorithms, implemented by LSTMs, beat generic, hand-designed competitors, and they also adapt well to other challenges with comparable structure. We show this on a variety of tasks, including simple convex problems, neural network training, and visual styling with neural art.  

scholarly journals Neural network training for vehicle fuel level control

2021 ◽  
Vol 4 (135) ◽  
pp. 12-22
Author(s):  
Vladimir Gerasimov ◽  
Nadija Karpenko ◽  
Denys Druzhynin
Keyword(s):  
Neural Network ◽  
Fuel Consumption ◽  
Confusion Matrix ◽  
Training Model ◽  
Neural Network Training ◽  
Raw Data ◽  
Network Training ◽  
The Neural Network ◽  
Fuel Level ◽  
Distorted Data

The goal of the paper is to create a training model based on real raw noisy data and train a neural network to determine the behavior of the fuel level, namely, to determine the time and volume of vehicle refueling, fuel consumption / excessive consumption / drainage.Various algorithms and data processing methods are used in fuel control and metering systems to get rid of noise. In some systems, primary filtering is used by excluding readings that are out of range, sharp jumps and deviations, and averaging over a sliding window. Research is being carried out on the use of more complex filters than simple averaging – by example, the Kalman filter for data processing.When measuring the fuel level using various fuel level sensor the data is influenced by many external factors that can interfere with the measurement and distort the real fuel level. Since these interferences are random and have a different structure, it is very difficult to completely remove them using classical noise suppression algorithms. Therefore, we use artificial intelligence, namely a neural network, to find patterns, detect noise and correct distorted data. To correct distorted data, you first need to determine which data is distorted, classify the data.In the course of the work, the raw data on the fuel level were transformed for use in the neural network training model. To describe the behavior of the fuel level, we use 4 possible classes: fuel consumption is observed, the vehicle is refueled, the fuel level does not change (the vehicle is idle), the data is distorted by noise. Also, in the process of work, additional tools of the DeepLearning4 library were used to load data training and training a neural network. A multilayer neural network model is used, namely a three-layer neural network, as well as used various training parameters provided by the DeepLearning4j library, which were obtained because of experiments.After training the neural network was used on test data, because of which the Confusion Matrix and Evaluation Metrics were obtained.In conclusion, finding a good model takes a lot of ideas and a lot of experimentation, also need to correctly process and transform the raw data to get the correct data for training. So far, a neural network has been trained to determine the state of the fuel level at a point in time and classify the behavior into four main labels (classes). Although we have not reduced the error in determining the behavior of the fuel level to zero, we have saved the states of the neural network, and in the future we will be able to retrain and evolve our neural network to obtain better results.

scholarly journals TRAINING NEURAL NETWORK FOR TAXI PASSENGER DEMAND FORECASTING USING GRAPHICS PROCESSING UNITS

2020 ◽  
Vol 2 (1) ◽  
pp. 29-36
Author(s):  
M. I. Zghoba ◽  
◽  
Yu. I. Hrytsiuk ◽  
Keyword(s):  
Neural Network ◽  
Graphics Processing Units ◽  
Neural Network Training ◽  
Training Time ◽  
Passenger Demand ◽  
Network Training ◽  
The Neural Network ◽  
Input Dataset ◽  
Speed Up ◽  
Graphics Processing

The peculiarities of neural network training for forecasting taxi passenger demand using graphics processing units are considered, which allowed to speed up the training procedure for different sets of input data, hardware configurations, and its power. It has been found that taxi services are becoming more accessible to a wide range of people. The most important task for any transportation company and taxi driver is to minimize the waiting time for new orders and to minimize the distance from drivers to passengers on order receiving. Understanding and assessing the geographical passenger demand that depends on many factors is crucial to achieve this goal. This paper describes an example of neural network training for predicting taxi passenger demand. It shows the importance of a large input dataset for the accuracy of the neural network. Since the training of a neural network is a lengthy process, parallel training was used to speed up the training. The neural network for forecasting taxi passenger demand was trained using different hardware configurations, such as one CPU, one GPU, and two GPUs. The training times of one epoch were compared along with these configurations. The impact of different hardware configurations on training time was analyzed in this work. The network was trained using a dataset containing 4.5 million trips within one city. The results of this study show that the training with GPU accelerators doesn't necessarily improve the training time. The training time depends on many factors, such as input dataset size, splitting of the entire dataset into smaller subsets, as well as hardware and power characteristics.

scholarly journals Power Quality Disturbance Identification and Optimization Based on Machine Learning

2021 ◽  
Author(s):  
Fei Long ◽  
Fen Liu ◽  
Xiangli Peng ◽  
Zheng Yu ◽  
Huan Xu ◽  
...  
Keyword(s):  
Neural Network ◽  
Power Quality ◽  
Classification Method ◽  
Neural Network Training ◽  
Training Set ◽  
Network Training ◽  
The Neural Network ◽  
Recognition Ability ◽  
Power Quality Disturbance ◽  
Depth Learning

In order to improve the electrical quality disturbance recognition ability of the neural network, this paper studies a depth learning-based power quality disturbance recognition and classification method: constructing a power quality perturbation model, generating training set; construct depth neural network; profit training set to depth neural network training; verify the performance of the depth neural network; the results show that the training set is randomly added 20DB-50DB noise, even in the most serious 20dB noise conditions, it can reach more than 99% identification, this is a tradition. The method is impossible to implement. Conclusion: the deepest learning-based power quality disturbance identification and classification method overcomes the disadvantage of the selection steps of artificial characteristics, poor robustness, which is beneficial to more accurately and quickly discover the category of power quality issues.

scholarly journals Automatic building footprint extraction from UAV images using neural networks

2020 ◽  
Vol 64 (04) ◽  
pp. 545-561
Author(s):  
Zoran Kokeza ◽  
Miroslav Vujasinović ◽  
Miro Govedarica ◽  
Brankica Milojević ◽  
Gordana Jakovljević
Keyword(s):  
Neural Network ◽  
Surface Model ◽  
Neural Network Training ◽  
Implementation Evaluation ◽  
Network Training ◽  
Area Of Interest ◽  
Cadastral Maps ◽  
The Neural Network ◽  
Building Footprint ◽  
Uav Images

Up-to-date cadastral maps are crucial for urban planning. Creating those maps with the classical geodetic methods is expensive and time-consuming. Emerge of Unmanned Aerial Vehicles (UAV) made a possibility for quick acquisition of data with much more details than it was possible before. The topic of the research refers to the challenges of automatic extraction of building footprints on high-resolution orthophotos. The objectives of this study were as follows: (1) to test the possibility of using different publicly available datasets (Tanzania, AIRS and Inria) for neural network training and then test the generalisation capability of the model on the Area Of Interest (AOI); (2) to evaluate the effect of the normalised digital surface model (nDSM) on the results of neural network training and implementation. Evaluation of the results shown that the models trained on the Tanzania (IoU 36.4%), AIRS (IoU 64.4%) and Inria (IoU 7.4%) datasets doesn't satisfy the requested accuracy to update cadastral maps in study area. Much better results are achieved in the second part of the study, where the training of the neural network was done on tiles (256x256) of the orthophoto of AOI created from data acquired using UAV. A combination of RGB orthophoto with nDSM resulted in a 2% increase of IoU, achieving the final IoU of over 90%.

Neural Network-WPCA Based Method for Multi-Objective Optimal Redundancy Allocation

2017 ◽  
Vol 24 (05) ◽  
pp. 1750024
Author(s):  
Yuanchen Fang ◽  
Huyang Xu ◽  
Nasser Fard
Keyword(s):  
Neural Network ◽  
Design Method ◽  
Principal Component ◽  
Estimation Accuracy ◽  
Neural Network Training ◽  
Performance Measurements ◽  
Redundancy Allocation ◽  
Training Performance ◽  
Network Training ◽  
The Neural Network

For systems with multiple redundancies, reliability evaluation in the redundancy allocation problem (RAP) constitutes a computational complexity. It has been demonstrated that neural network training provides an efficient approach to estimate the complex system reliability function. When executing the neural network algorithm, there are many parameters that need to be determined for improving the training performance. Therefore, robust experimental design method can be used to determine the neural network parameters. The traditional robust design methods are intended for a single response variable. However, the application of neural network method includes more than one measurement, such as estimation accuracy and time efficiency. In this paper, utility function is first estimated by neural network training, in which the algorithm parameters are determined by weighted principal component (WPCA)-based multi-response optimization which simultaneously optimizes more than one training performance measurements. Moreover, it is always desirable to simultaneously optimize several objectives in designing a system, such as reliability, cost, etc. Therefore, continuous WPCA-based multi-response design is then applied to obtain the best design of redundancies in RAP, which simultaneously optimize multiple objectives by taking into account the correlations between them.

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