scholarly journals Real-Time Maritime Traffic Anomaly Detection Based on Sensors and History Data Embedding

Sensors ◽  
2019 ◽  
Vol 19 (17) ◽  
pp. 3782 ◽  
Author(s):  
Julius Venskus ◽  
Povilas Treigys ◽  
Jolita Bernatavičienė ◽  
Gintautas Tamulevičius ◽  
Viktor Medvedev
Keyword(s):  
Neural Network ◽  
Data Processing ◽  
Processing Time ◽  
Heavy Traffic ◽  
Sensor Data ◽  
Identification System ◽  
Automated Identification ◽  
Maritime Traffic ◽  
Abnormal Movements ◽  
The Neural Network

The automated identification system of vessel movements receives a huge amount of multivariate, heterogeneous sensor data, which should be analyzed to make a proper and timely decision on vessel movements. The large number of vessels makes it difficult and time-consuming to detect abnormalities, thus rapid response algorithms should be developed for a decision support system to identify abnormal movements of vessels in areas of heavy traffic. This paper extends the previous study on a self-organizing map application for processing of sensor stream data received by the maritime automated identification system. The more data about the vessel’s movement is registered and submitted to the algorithm, the higher the accuracy of the algorithm should be. However, the task cannot be guaranteed without using an effective retraining strategy with respect to precision and data processing time. In addition, retraining ensures the integration of the latest vessel movement data, which reflects the actual conditions and context. With a view to maintaining the quality of the results of the algorithm, data batching strategies for the neural network retraining to detect anomalies in streaming maritime traffic data were investigated. The effectiveness of strategies in terms of modeling precision and the data processing time were estimated on real sensor data. The obtained results show that the neural network retraining time can be shortened by half while the sensitivity and precision only change slightly.

scholarly journals Combined neural network/Phillips-Tikhonov approach to aerosol retrievals over land from the NASA Research Scanning Polarimeter

2017 ◽  
Author(s):  
Antonio Di Noia ◽  
Otto P. Hasekamp ◽  
Lianghai Wu ◽  
Bastiaan van Diedenhoven ◽  
Brian Cairns ◽  
...  
Keyword(s):  
Neural Network ◽  
Land Surface ◽  
Processing Time ◽  
Iterative Scheme ◽  
Initial Guess ◽  
Effective Radius ◽  
Aerosol Layer ◽  
Large Dataset ◽  
The Neural Network ◽  
Fine Mode

Abstract. In this paper, an algorithm for the retrieval of aerosol and land surface properties from airborne spectropolarimetric measurements – combining neural networks and an iterative scheme based on Phillips-Tikhonov regularization – is described. The algorithm – which is an extension of a scheme previously designed for ground-based retrievals – is applied to measurements from the Research Scanning Polarimeter (RSP) onboard the NASA ER-2 aircraft. A neural network, trained on a large dataset of synthetic measurements, is applied to perform aerosol retrievals from real RSP data, and the neural network retrievals are subsequently used as first guess for the Phillips-Tikhonov retrieval. The resulting algorithm appears capable of accurately retrieving aerosol optical thickness, fine mode effective radius and aerosol layer height from RSP data. Among the advantages of using a neural network as initial guess for an iterative algorithm are a decrease in processing time and an increase in the number of converging retrievals.

scholarly journals DESIGN AND ANALYSIS OF AN INTELLIGENT FIRE DETECTION SYSTEM FOR AIRCRAFT

2020 ◽  
Vol 5 (2) ◽  
pp. 260-273
Author(s):  
Amit Yadav ◽  
Abhijeet Agawal ◽  
Pramod Kumar ◽  
Tejaswi Sachwani
Keyword(s):  
Neural Network ◽  
Detection System ◽  
Fire Detection ◽  
Sensor Data ◽  
Correct Prediction ◽  
The Neural Network ◽  
Fire Condition ◽  
Fire Alarm System ◽  
Actual Fire ◽  
Fire Detection System

Fire detection system and fire warning are design features of an aircraft. Fire detection system protects the aircraft and passengers both in case of actual fire during flight. But spurious fire warning during flight creates a panic situation in flight crews and passengers. The conventional fire alarm system of an aircraft is triggered by false signal. ANN based fire detection system provides real observation of deployed zones. An intelligent fire detection system is developed based on artificial neural network using three detection information such as heat (temperature), smoke density and CO gas. This Information helps in determining the probability of three representative of Fire condition which is Fire, smoke and no fire. The simulated MATLAB results Show that the errors in identification are very less. The neural network based fire detection system integrates different types of sensor data and improves the ability of system to correct prediction of fires. It gives early alarm when any kind of fire broke out and helps to decrease in spurious warning.

scholarly journals Improving the energy efficiency of oil production using identification and prediction of operating modes of production wells based on data analysis methods, machine learning and neural networks

2019 ◽  
Vol 124 ◽  
pp. 05031 ◽  
Author(s):  
A.M. Sagdatullin
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Control System ◽  
Oil And Gas ◽  
Gas Production ◽  
Operating Conditions ◽  
Identification System ◽  
Oil And Gas Production ◽  
The Neural Network ◽  
And Control

Currently, there is a need to improve the systems and control of pumping equipment in the oil and gas production and oil and gas transport industries. Therefore, an adaptive neural network control system for an electric drive of a production well was developed. The task of expanding the functional capabilities of asynchronous electric motors control of the oil and gas production system using the methods of neural networks is solved. We have developed software modules of the well drive control system based on the neural network, an identification system, and a scheme to adapt the control processes to changing load parameters, that is, to dynamic load, to implement the entire system for real-time control of the highspeed process. In this paper, based on a model of an identification block that includes a multilayered neural network of direct propagation, the control of the well system was implemented. The neural network of the proposed system was trained on the basis of the error back-propagation algorithm, and the identification unit works as a forecaster of system operation modes based on the error prediction. In the initial stage of the model adaptation, some fluctuations of the torque are observed at the output of the neural network, which is associated with new operating conditions and underestimated level of learning. However, the identification object and control system is able to maintain an error at minimum values and adapt the control system to a new conditions, which confirms the reliability of the proposed scheme.

Human Identification System Research with the Electrocardiograms Based on the Neural Network

2014 ◽  
Vol 926-930 ◽  
pp. 1104-1107
Author(s):  
Jia Lun Lin
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Identification System ◽  
Frequency Noise ◽  
Ecg Signal ◽  
Feature Subset ◽  
Neural Network Classifier ◽  
Main Research ◽  
The Neural Network ◽  
Dna Algorithm

Based on existing researches at home and abroad, an intensive study of ECG signal preprocessing, feature extraction, feature analysis and feature weight analysis was made in the Paper neural network classifier was designed to realize the ECG identification and it was optimized by GA algorithm and DNA algorithm. The main research was concluded as follows. Firstly, extracting the preprocessing and feature of ECG signal. We have analyzed the frequency of ECG signal and the noise signal included by using wavelet and wavelet threshold methods filter the low and high frequency noise in ECG signal. Secondly, analyzing weight of ECG feature and selecting the optimal feature subset. Evaluated by the accuracy rate of BP neural network classification, the optimal characteristics for identification subset is determined then. Thirdly, designing and optimizing the neural network classifier. As the BP neural network has the Problems of easily falling into local minimum and being not convergence, GA and DNA algorithm are used to optimize it.

scholarly journals Heterogeneous convolutional neural networks to build movement routes of the objects in spatial environment with accumulated energy potential

2021 ◽  
Vol 16 (93) ◽  
pp. 21-37
Author(s):  
Yuriy N. Lavrenkov ◽  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Data Processing ◽  
Optimal Path ◽  
Rhythmic Activity ◽  
Main Role ◽  
Energy Potential ◽  
Convolutional Network ◽  
The Neural Network ◽  
Neural Populations

We consider the synthesis of a hybrid neural convolutional network with the modular topology-based architecture, which allows to arrange a parallel convolutional computing system to combine both the energy transfer and data processing, in order to simulate complex functions of natural biological neural populations. The system of interlayer neural commutation, based on the distributed resonance circuits with the layers of electromagnetic metamaterial between the inductive elements, is a base for simulation of the interaction between the astrocyte networks and the neural clusters responsible for information processing. Consequently, the data processing is considered both at the level of signal transmission through neural elements, and as interaction of artificial neurons and astrocytic networks ensuring their functioning. The resulting two-level neural system of data processing implements a set of measures to solve the issue based on the neural network committee. The specific arrangement of the neural network enables us to implement and configure the educational procedure using the properties absent in the neural networks consisting of neural populations only. The training of the convolutional network is based on a preliminary analysis of rhythmic activity, where artificial astrocytes play the main role of interneural switches. The analysis of the signals moving through the neural network enables us to adjust variable components to present information from training bunches in the available memory circuits in the most efficient way. Moreover, in the training process we observe the activity of neurons in various areas to evenly distribute the computational load on neural network modules to achieve maximum performance. The trained and formed convolutional network is used to solve the problem of determining the optimal path for the object moving due to the energy from the environment

scholarly journals Expert Sensor for Site Specification Application of Agricultural Chemicals

1995 ◽  
Author(s):  
Gaines E. Miles ◽  
Yael Edan ◽  
F. Tom Turpin ◽  
Avshalom Grinstein ◽  
Thomas N. Jordan ◽  
...  
Keyword(s):  
Neural Network ◽  
Field Conditions ◽  
Hill Climbing ◽  
Real Field ◽  
Design Parameters ◽  
Identification System ◽  
Site Specific ◽  
The Neural Network ◽  
Block Based ◽  
A Site

In this work multispectral reflectance images are used in conjunction with a neural network classifier for the purpose of detecting and classifying weeds under real field conditions. Multispectral reflectance images which contained different combinations of weeds and crops were taken under actual field conditions. This multispectral reflectance information was used to develop algorithms that could segment the plants from the background as well as classify them into weeds or crops. In order to segment the plants from the background the multispectrial reflectance of plants and background were studied and a relationship was derived. It was found that using a ratio of two wavelenght reflectance images (750nm and 670nm) it was possible to segment the plants from the background. Once ths was accomplished it was then possible to classify the segmented images into weed or crop by use of the neural network. The neural network developed for this work is a modification of the standard learning vector quantization algorithm. This neural network was modified by replacing the time-varying adaptation gain with a constant adaptation gain and a binary reinforcement function. This improved accuracy and training time as well as introducing several new properties such as hill climbing and momentum addition. The network was trained and tested with different wavelength combinations in order to find the best results. Finally, the results of the classifier were evaluated using a pixel based method and a block based method. In the pixel based method every single pixel is evaluated to test whether it was classified correctly or not and the best weed classification results were 81% and its associated crop classification accuracy is 57%. In the block based classification method, the image was divided into blocks and each block was evaluated to determine whether they contained weeds or not. Different block sizes and thesholds were tested. The best results for this method were 97% for a block size of 8 inches and a pixel threshold of 60. A simulation model was developed to 1) quantify the effectiveness of a site-specific sprayer, 2) evaluate influence of diffeent design parameters on efficiency of the site-specific sprayer. In each iteration of this model, infected areas (weed patches) in the field were randomly generated and the amount of required herbicides for spraying these areas were calculated. The effectiveness of the sprayer was estimated for different stain sizes, nozzle types (conic and flat), nozzle sizes and stain detection levels of the identification system. Simulation results indicated that the flat nozzle is much more effective as compared to the conic nozzle and its relative efficiency is greater for small nozzle sizes. By using a site-specific sprayer, the average ratio between the spraying areas and the stain areas is about 1.1 to 1.8 which can save up to 92% of herbicides, especially when the proportion of the stain areas is small.

Concepts of the neural network model for tomography data processing

1995 ◽  
Author(s):  
Yuri N. Kulchin ◽  
Oleg B. Vitrik ◽  
Oleg T. Kamenev ◽  
Oleg V. Kirichenko ◽  
Yuri S. Petrov ◽  
...  
Keyword(s):  
Neural Network ◽  
Data Processing ◽  
Network Model ◽  
Neural Network Model ◽  
The Neural Network ◽  
Tomography Data

Unsupervised physics-based neural networks for seismic migration

2019 ◽  
Vol 7 (3) ◽  
pp. SE189-SE200 ◽  
Author(s):  
Janaki Vamaraju ◽  
Mrinal K. Sen
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Data Processing ◽  
Seismic Data ◽  
Minimum Energy ◽  
Machine Learning Algorithms ◽  
Hopfield Neural Networks ◽  
Seismic Data Processing ◽  
Forward Models ◽  
The Neural Network

We have developed a novel framework for combining physics-based forward models and neural networks to advance seismic processing and inversion algorithms. Migration is an effective tool in seismic data processing and imaging. Over the years, the scope of these algorithms has broadened; today, migration is a central step in the seismic data processing workflow. However, no single migration technique is suitable for all kinds of data and all styles of acquisition. There is always a compromise on the accuracy, cost, and flexibility of these algorithms. On the other hand, machine-learning algorithms and artificial intelligence methods have been found immensely successful in applications in which big data are available. The applicability of these algorithms is being extensively investigated in scientific disciplines such as exploration geophysics with the goal of reducing exploration and development costs. In this context, we have used a special kind of unsupervised recurrent neural network and its variants, Hopfield neural networks and the Boltzmann machine, to solve the problems of Kirchhoff and reverse time migrations. We use the network to migrate seismic data in a least-squares sense using simulated annealing to globally optimize the cost function of the neural network. The weights and biases of the neural network are derived from the physics-based forward models that are used to generate seismic data. The optimal configuration of the neural network after training corresponds to the minimum energy of the network and thus gives the reflectivity solution of the migration problem. Using synthetic examples, we determine that (1) Hopfield neural networks are fast and efficient and (2) they provide reflectivity images with mitigated migration artifacts and improved spatial resolution. Specifically, the presented approach minimizes the artifacts that arise from limited aperture, low subsurface illumination, coarse sampling, and gaps in the data.

scholarly journals Vessel Trajectory Prediction Model Based on AIS Sensor Data and Adaptive Chaos Differential Evolution Support Vector Regression (ACDE-SVR)

2019 ◽  
Vol 9 (15) ◽  
pp. 2983 ◽  
Author(s):  
Jiao Liu ◽  
Guoyou Shi ◽  
Kaige Zhu
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Differential Evolution ◽  
Support Vector Regression ◽  
Prediction Accuracy ◽  
Sensor Data ◽  
Support Vector ◽  
Identification System ◽  
Trajectory Prediction ◽  
Model Based

There are difficulties in obtaining accurate modeling of ship trajectories with traditional prediction methods. For example, neural networks are prone to falling into local optima and there are a small number of Automatic Identification System (AIS) information samples regarding target ships acquired in real time at sea. In order to improve the accuracy of ship trajectory predictions and solve these problems, a trajectory prediction model based on support vector regression (SVR) is proposed. Ship speed, course, time stamp, longitude and latitude from AIS data were selected as sample features and the wavelet threshold de-noising method was used to process the ship position data. The adaptive chaos differential evolution (ACDE) algorithm was used to optimize the internal model parameters to improve convergence speed and prediction accuracy. AIS sensor data corresponding to a certain section of the Tianjin Port ships were selected, on which SVR, Recurrent Neural Network (RNN) and Back Propagation (BP) neural network model trajectory prediction simulations were carried out. A comparison of the results shows that the trajectory prediction model based on ACDE-SVR has higher and more stable prediction accuracy, requires less time and is simple, feasible and efficient.

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