Arithmetic Evaluation System Based on MixNet-YOLOv3 and CRNN Neural Networks

2021 ◽  
pp. 344-355
Author(s):  
Tianliang Liu ◽  
Congcong Liang ◽  
Xiubin Dai ◽  
Jiebo Luo
Keyword(s):  
Neural Networks ◽  
Evaluation System

Dynamic statistical evaluation of safety emergency management in coal enterprises based on neural network algorithms

2020 ◽  
Vol 39 (4) ◽  
pp. 5521-5534
Author(s):  
Ying Liu ◽  
Zhongqi Fan ◽  
Hongliang Qi
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Emergency Management ◽  
Coal Mine ◽  
Evaluation System ◽  
Statistical Evaluation ◽  
Specific Situation ◽  
Network Algorithms ◽  
Management Capability ◽  
Coal Enterprises

By establishing the evaluation system of emergency management capability for coal mine enterprises, we can identify the problems and shortcomings in coal mine emergency management, improve and improve its emergency management capability for coal mine emergencies. In this paper, the authors analyze the dynamic statistical evaluation of safety emergency management in coal enterprises based on neural network algorithms. Neural networks can form any form of topological structure through neurons, so they can directly simulate fuzzy reasoning in structure, that is to say, the equivalent structure of neural networks and fuzzy systems can be formed. This paper constructs the index system based on accident causes, and verifies the scientific rationality of the system. On this basis, according to the specific situation of coal mine emergency management, we design the evaluation criteria of coal mine emergency management capability evaluation index. Because coal mine accidents have the characteristics of complexity, variability and sudden dynamic, it is necessary to adjust and improve the accidents dynamically at any time. The model combines qualitative and quantitative indicators, and can make an overall evaluation of coal mine emergency management capability. It has the characteristics of clear results and strong fitting of simulation results.

A NEURAL NETWORK MODEL FOR CREDIT RISK EVALUATION

2009 ◽  
Vol 19 (04) ◽  
pp. 285-294 ◽  
Author(s):  
ADNAN KHASHMAN
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Credit Risk ◽  
Network Model ◽  
Neural Network Model ◽  
Risk Evaluation ◽  
Evaluation System ◽  
Learning Algorithm ◽  
Back Propagation ◽  
Learning Schemes

Credit scoring is one of the key analytical techniques in credit risk evaluation which has been an active research area in financial risk management. This paper presents a credit risk evaluation system that uses a neural network model based on the back propagation learning algorithm. We train and implement the neural network to decide whether to approve or reject a credit application, using seven learning schemes and real world credit applications from the Australian credit approval datasets. A comparison of the system performance under the different learning schemes is provided, furthermore, we compare the performance of two neural networks; with one and two hidden layers following the ideal learning scheme. Experimental results suggest that neural networks can be effectively used in automatic processing of credit applications.

Nondestructive Evaluation System for White Ginseng Quality Using Image Processing Technique

2006 ◽  
Vol 321-323 ◽  
pp. 1225-1228
Author(s):  
Seong Min Kim ◽  
Chul Soo Kim ◽  
Chong Ho Lee ◽  
Myung Ho Kim ◽  
Seung Jae Park
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Artificial Neural Networks ◽  
Evaluation System ◽  
Image Acquisition ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Gray Level ◽  
White Ginseng ◽  
Artificial Neural

A real-time white ginseng quality evaluation system based on a machine vision technique and artificial neural networks was developed to replace the current manual grading and its efficiency was tested. The system consisted of conveyor, image acquisition system synchronized with a sample-detecting sensor, and image processing and decision-making system. Software running under Windows system was developed. The algorithm included three consecutive stages of (a) image acquisition and preprocessing, (b) mathematical feature extraction, and (c) grade decision using artificial neural networks. Mathematical features such as area ratio, mean and standard deviation of gray level, skewness of gray level histogram, and the number of run segment, were extracted from five equally divided parts of a specimen. An artificial neural network model was used to classify samples into three grading categories. The grading error of the system was about 26%, which is comparable to the 30% in case of manual grading. The grading rate was one sample per a second.

A comparison of neural network and multiple regression predictions for 305-day lactation yield using partial lactation records

2003 ◽  
Vol 83 (2) ◽  
pp. 307-310 ◽  
Author(s):  
W. Grzesiak ◽  
R. Lacroix ◽  
J. Wójcik ◽  
P. Blaszczyk
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Multiple Regression ◽  
Milk Yield ◽  
Evaluation System ◽  
Multiple Regression Model ◽  
Yield Prediction ◽  
The Neural Network ◽  
Prediction Test ◽  
Artificial Neural

Milk yield predictions based on artificial neural etworks and multiple regression were studied. The 305-d lactation yield predictions were based on milk yield of the first 4 test days. Average 305-d milk production of the herd, number of days in milk and month of calving. The predictions made with either the neural network or the multiple regression model did not differ (P > 0.05) from the values estimated with the current Polish dairy cattle evaluation system. The neural network model may be alternative method of predicting these traits. Key words: Artificial neural networks, multiple linear regression, milk yield prediction, test day data

Study on Construction Bidding Management Based on GA-BP Neural Networks

2014 ◽  
Vol 584-586 ◽  
pp. 2423-2426
Author(s):  
Tian Bao Wu ◽  
Xun Liu ◽  
Tai Quan Zhou
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Bp Neural Network ◽  
Evaluation System ◽  
Construction Project ◽  
Basic Theory ◽  
Slow Convergence ◽  
Artificial Neural

In the bidding evaluation, the deviations are likely to be brought about by experts' subjectivity, arbitrary and tendentiousness. A method for construction project bidding based on the BP neural network improved by GA (Genetic Algorithm) is proposed. On the basis of the basic theory of the BP neural network, discussions are provided on how to rectify the drawbacks of slow convergence and prone to convergence to minimum with the use of GA. The model is successfully applied GA - BP artificial neural networks to project, which are in concert with the result of experts. The study makes contribution to research about the evaluation system of construction bidding management.

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