Developed Incident Detection Algorithm Compared with Neural Network Algorithms

2003 ◽  
Vol 1836 (1) ◽  
pp. 83-92 ◽  
Author(s):  
Hualiang(Harry) Teng ◽  
Yi(Grace) Qi ◽  
David R. Martinelli
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Detection Algorithm ◽  
Incident Detection ◽  
Decision Threshold ◽  
Network Algorithms ◽  
Detection Delay ◽  
The Neural Network ◽  
Before And After ◽  
Neural Network Algorithm

The CUSUM (cumulative sum of log-likelihood ratio) algorithm is an optimization-based algorithm that is attractive for many applications because it can minimize detection delay and can explicitly incorporate the characteristics of processes before and after changes. One such application is freeway incident detection, where field-measurable traffic-flow parameters are used to flag incidents in real time in an expedient and reliable manner. In the presented study, the special characteristics of traffic processes associated with incidents are incorporated into the CUSUM algorithm for freeway incident detection. In the algorithm evaluation, the most recently developed neural networks are compared with an enhanced CUSUM algorithm. The neural network algorithms are systematically evaluated first among themselves, and then the best of them is compared with the CUSUM algorithm. The results demonstrate that the CUSUM incident detection algorithm can perform better than the neural network algorithms. The neural network algorithm may show inferior performance because it cannot adjust its decision threshold in real time.

scholarly journals Application of Neural Network Algorithm in Optimal Control of Ethylene Distillation Tower

2021 ◽  
pp. 19-29
Author(s):  
Ruyang Mo ◽  
Huihui Wang
Keyword(s):  
Neural Network ◽  
Large Scale ◽  
Simulation Software ◽  
Network Algorithms ◽  
System Description ◽  
Software Interface ◽  
Operation Speed ◽  
The Neural Network ◽  
Neural Network Algorithm ◽  
Rectification Process

For some nonlinear dynamic systems with uncertainties or disturbances, neural networks can perform intelligent cognition and simulation on them, achieve a good system description, and further realize intelligent control. Aiming at the ethylene rectification process, in order to avoid the time delay of complex rectification process modeling and large-scale process simulation software interface program, and to improve the simulation operation speed, the optimization model combined with the learning function of the neural network is used for the simulation calculation of the rectification process. It can meet the time and accuracy requirements of online optimization. This article outlines several commonly used neural network algorithms and their related applications in ethylene distillation, aiming to provide reference for the development and innovation of industry technology.

scholarly journals Analysis of DES Plaintext Recovery Based on BP Neural Network

2019 ◽  
Vol 2019 ◽  
pp. 1-5
Author(s):  
Sijie Fan ◽  
Yaqun Zhao
Keyword(s):  
Neural Network ◽  
Error Rate ◽  
Bp Neural Network ◽  
Layer By Layer ◽  
Network Simulator ◽  
Analysis Model ◽  
Backpropagation Neural Network ◽  
Network Algorithms ◽  
The Neural Network ◽  
Neural Network Algorithm

Backpropagation neural network algorithms are one of the most widely used algorithms in the current neural network algorithm. It uses the output error rate to estimate the error rate of the direct front layer of the output layer, so that we can get the error rate of each layer through the layer-by-layer backpropagation. The purpose of this paper is to simulate the decryption process of DES with backpropagation algorithm. By inputting a large number of plaintext and ciphertext pairs, a neural network simulator for the decryption of the target cipher is constructed, and the ciphertext given is decrypted. In this paper, how to modify the backpropagation neural network classifier and apply it to the process of building the regression analysis model is introduced in detail. The experimental results show that the final result of restoring plaintext of the neural network model built in this paper is ideal, and the fitting rate is higher than 90% compared with the true plaintext.

scholarly journals An Enhanced Novel GA-based Malware Detection in End Systems Using Structured and Unstructured Data by Comparing Support Vector Machine and Neural Network

2021 ◽  
Vol 11 (2) ◽  
pp. 1514-1525
Author(s):  
Sai Tejeshwar Reddy T
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Malware Detection ◽  
Unstructured Data ◽  
Support Vector ◽  
Network Algorithms ◽  
Android Malware ◽  
Android Malware Detection ◽  
The Neural Network ◽  
Neural Network Algorithm

Aim: The aim of the work is to perform android malware detection using Structured and Unstructured data by comparing Neural Network algorithms and SVM. Materials and Methods: consider two groups such as Support Vector Machine and Neural Network. For each algorithm take N=10 samples from the dataset collected and perform two iterations on each algorithm to identify the Malware Detection. Result: The accuracy results of the Neural Network model has potential up to (82.91%) and the Support Vector Machine algorithm has an accuracy of (79.67%) for Android malware detection with the significance value of (p=0.007). Conclusion: classification of android malware detection using Neural Network algorithm shows better accuracy than SVM.

scholarly journals The implementation of an object detection algorithm on the FT processor

2018 ◽  
Vol 173 ◽  
pp. 03081
Author(s):  
Bin Li ◽  
Shuming Chen ◽  
Chao Yang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Object Detection ◽  
Real Time ◽  
Detection Algorithm ◽  
Utilization Rate ◽  
Time Requirement ◽  
Processor Performance ◽  
Network Algorithm ◽  
Neural Network Algorithm

With the continuous development of automatic drive and neural networks, it is possible to use neural network algorithm to carry out object detection in unmanned driving. Usually, the computation of neural network algorithm is huge. How to efficiently compute the algorithm and meet the real-time requirement is a challenge. In this paper, a sparse neural network algorithm is proposed, which can improve the utilization rate of processors. The object detection algorithm YOLO is implemented on the processor. Its performance is equivalent to the current best processor performance.

scholarly journals Analysis and Prediction of Childhood Pneumonia Deaths using Machine Learning Algorithms

2021 ◽  
Author(s):  
Felipe A. L. Soares ◽  
Efrem E. O. Lousada ◽  
Tiago B. Silveira ◽  
Raquel A. F. Mini ◽  
Luis E. Zárate ◽  
...  
Keyword(s):  
Neural Network ◽  
Community Acquired Pneumonia ◽  
Machine Learning Algorithms ◽  
Cardiac Insufficiency ◽  
Network Algorithms ◽  
Childhood Pneumonia ◽  
The Neural Network ◽  
Scatter Plots ◽  
Neural Network Algorithm ◽  
Tract Infections

Acute Respiratory Tract Infections are among the leading causes of child mortality worldwide. Specifically, community-acquired pneumonia has different causes, such as: passive smoking, air pollution, poor hygiene, cardiac insufficiency, oropharyngeal colonization, nutritional deficiency, immunosuppression, and environmental, economic and social factors. Due to the variation of these causes, knowledge discovery in this area of health has been a great challenge for researchers. Thus, this paper presents the steps for the construction of a database and evaluation results applied to the analysis and prediction of potential deaths caused by childhood pneumonia using the Pictorea method. For this, the Random Forest and Artificial Neural Network algorithms were used, and after comparison, the Neural Network algorithm showed higher accuracy by up to 87.57%. This algorithm was used to analyze and predict the number of deaths from pneumonia in children up to 5 years old, and the results were presented using Root Mean Square Error and scatter plots. A domain specialist validated the results and defined that the pattern found is relevant for future studies in the medical field, helping to analyze the behavior of countries and predict future scenarios.

Performance Analysis of Sequential and Parallel Neural Network Algorithm for Stock Price Forecasting

2013 ◽  
pp. 97-121
Author(s):  
Rashedur M. Rahman ◽  
Ruppa K. Thulasiram ◽  
Parimala Thulasiraman
Keyword(s):  
Neural Network ◽  
Stock Price ◽  
Price Forecasting ◽  
Memory Architecture ◽  
Backpropagation Algorithm ◽  
Network Algorithms ◽  
Training Time ◽  
Stock Price Forecasting ◽  
The Neural Network ◽  
Neural Network Algorithm

The neural network is popular and used in many areas within the financial field, such as credit authorization screenings, regularities in security price movements, simulations of market behaviour, and so forth. In this research, the authors use a neural network technique for stock price forecasting of Great West Life, an insurance company based in Winnipeg, Canada. The Backpropagation algorithm is a popular algorithm to train a neural network. However, one drawback of traditional Backpropagation algorithm is that it takes a substantial amount of training time. To expedite the training process, the authors design and develop different parallel and multithreaded neural network algorithms. The authors implement parallel neural network algorithms on both shared memory architecture using OpenMP and distributed memory architecture using MPI and analyze the performance of those algorithms. They also compare the results with traditional auto-regression model to establish accuracy.

scholarly journals Implementation of a Load Sensitizing Bridge Spherical Bearing Based on Low-Coherent Fiber-Optic Sensors Combined with Neural Network Algorithms

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 37
Author(s):  
Jingjing Guo ◽  
Tiesuo Geng ◽  
Huaizhi Yan ◽  
Lize Du ◽  
Zhe Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Temperature Compensation ◽  
Fiber Optic ◽  
Fiber Optic Sensors ◽  
Network Algorithms ◽  
Load Sensing ◽  
The Neural Network ◽  
Spherical Bearing ◽  
Neural Network Algorithm ◽  
Set Up

Low-coherent fiber-optic sensors combined with neural network algorithms were designed to carry out a load-sensitizing spherical bearing. Four sensing fibers were wound around the outside of the pot support of the spherical bearing uniformly deployed from upper to bottom. The upper three were configured in a distributed way to respond to the applied load as a function of the three strain sensors. The bottom one was employed as a temperature compensation sensor. A loading experiment was implemented to test the performance of the designed system. The results showed that there was a hysteresis in all the three sensors between loading and unloading process. The neural network algorithm is proposed to set up a function of the three sensors, treated as a set of input vectors to establish the input-output relationship between the applied loads and the constructed input vectors, in order to overcome the hysteresis existing in each sensor. An accuracy of 6% for load sensing was approached after temperature compensation.

scholarly journals Augmented Reality Maintenance Assistant Using YOLOv5

2021 ◽  
Vol 11 (11) ◽  
pp. 4758
Author(s):  
Ana Malta ◽  
Mateus Mendes ◽  
Torres Farinha
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Object Recognition ◽  
Augmented Reality ◽  
Real Time ◽  
Recognition System ◽  
High Accuracy ◽  
Video Streams ◽  
The Neural Network ◽  
Deep Learning Neural Network

Maintenance professionals and other technical staff regularly need to learn to identify new parts in car engines and other equipment. The present work proposes a model of a task assistant based on a deep learning neural network. A YOLOv5 network is used for recognizing some of the constituent parts of an automobile. A dataset of car engine images was created and eight car parts were marked in the images. Then, the neural network was trained to detect each part. The results show that YOLOv5s is able to successfully detect the parts in real time video streams, with high accuracy, thus being useful as an aid to train professionals learning to deal with new equipment using augmented reality. The architecture of an object recognition system using augmented reality glasses is also designed.

Unmanned aerial vehicle reconnaissance image recognition based on convolutional neural network

2021 ◽  
pp. 1-10
Author(s):  
Lipeng Si ◽  
Baolong Liu ◽  
Yanfang Fu
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Image Recognition ◽  
Error Detection ◽  
Convolution Neural Network ◽  
Data Sets ◽  
Small Target ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Real Time Tracking

The important strategic position of military UAVs and the wide application of civil UAVs in many fields, they all mark the arrival of the era of unmanned aerial vehicles. At present, in the field of image research, recognition and real-time tracking of specific objects in images has been a technology that many scholars continue to study in depth and need to be further tackled. Image recognition and real-time tracking technology has been widely used in UAV aerial photography. Through the analysis of convolution neural network algorithm and the comparison of image recognition technology, the convolution neural network algorithm is improved to improve the image recognition effect. In this paper, a target detection technique based on improved Faster R-CNN is proposed. The algorithm model is implemented and the classification accuracy is improved through Faster R-CNN network optimization. Aiming at the problem of small target error detection and scale difference in aerial data sets, this paper designs the network structure of RPN and the optimization scheme of related algorithms. The structure of Faster R-CNN is adjusted by improving the embedding of CNN and OHEM algorithm, the accuracy of small target and multitarget detection is improved as a whole. The experimental results show that: compared with LENET-5, the recognition accuracy of the proposed algorithm is significantly improved. And with the increase of the number of samples, the accuracy of this algorithm is 98.9%.

Construction of first-class university course based on artificial intelligence and neural network algorithm

2020 ◽  
pp. 1-12
Author(s):  
Yingli Duan
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Vocational Training ◽  
Network Algorithms ◽  
Teaching Efficiency ◽  
Neural Network Algorithm ◽  
Solid Foundation ◽  
University Level ◽  
University Course

Curriculum is the basis of vocational training, its development level and teaching efficiency determine the realization of vocational training objectives, as well as the quality and level of major vocational academic training. Therefore, the development of curriculum is an important issue. And affect the school’s teaching capacity building. The analysis of the latest developments in the main courses shows that there are some deviations or irrationalities in the curriculum in some colleges and universities, and the general problems of understanding the latest courses, such as lack of solid foundation in curriculum setting, unclear direction of objectives, unclear reform ideas, inadequate and systematic construction measures, lack of attention to the quality of education. This paper explains the rules for the establishment of first-level courses, clarifies the ideas and priorities of architecture, and explores strategies for building university-level courses using knowledge of artificial intelligence and neural network algorithms in order to gain experience from them.

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