scholarly journals Application of Traditional Cultural Symbols in Art Design under the Background of Artificial Intelligence

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Cuifang Lin
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Nonlinear Function ◽  
Fractal Model ◽  
Nonlinear Transformations ◽  
Cultural Symbols ◽  
Fractal Models ◽  
Neural Network Algorithm ◽  
Art Design

In order to solve the declining influence of traditional cultural symbols, the research on traditional cultural symbols has become more meaningful. This article aims to study the application of traditional cultural symbols in art design under the background of artificial intelligence. In this paper, a fractal model with self-combined nonlinear function changes is constructed. By combining nonlinear transformations and multiparameter adjustments, various types of fractal models can be automatically rendered. The convolutional neural network algorithm is used to extract the characteristics of the style picture, and it is compared with the trained picture many times to avoid the problem of excessive tendency of the image with improper weight. The improved L-BFGS algorithm is also used to optimize the loss of the traditional L-BFGS, which improves the quality of the generated pictures and reduces the noise of the chessboard. The experimental results in this paper show that the improved L-BFGS algorithm has the least loss and the shortest time in the time used for more than 500 s. Compared with the traditional AdaGrad method, its loss is reduced by about 62%; compared with the traditional AdaDelta method, its loss is reduced by 46%. Its loss is reduced by about 8% compared with the newly optimized Adam method, which is a great improvement.

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.

scholarly journals Weeds Detection in Agricultural Fields using Convolutional Neural Network

2019 ◽  
Vol 8 (11) ◽  
pp. 292-296
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Weed Control ◽  
Classification System ◽  
Agricultural Fields ◽  
Use Of Technology ◽  
Neural Network Algorithm

Weeds are very annoying for farmers and also not very good for the crops. Its existence might damage the growth of the crops. Therefore, weed control is very important for farmers. Farmers need to ensure their agricultural fields are free from weeds for at least once a week, whether they need to spray weeds herbicides to their plantation or remove it using tools or manually. The aim of this research is to build an automated weed control robot using the Lego Mindstorm EV3 which connected to a computer. The robot consists of motors, servo motors and a camera which we use to capture the image of the crops and weeds. An automated image classification system has been designed to differentiate between weeds and crops. The robot will spray the weed herbicides directly to the area that have been detected weeds near or at it. For the image classification method, we employ the convolutional neural network algorithm to process the image of the object. Therefore, by the use of technology especially in artificial intelligence, farmers can reduce the amount of workload and workforce they need to monitor their plantation. In addition, this technology also can improve the quality of the crops.

scholarly journals A Method of Ore Blending Based on the Quality of Beneficiation and Its Application in a Concentrator

2021 ◽  
Vol 11 (11) ◽  
pp. 5092
Author(s):  
Bingyu Liu ◽  
Dingsen Zhang ◽  
Xianwen Gao
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Industrial Test ◽  
Modeling Method ◽  
Daily Work ◽  
Production Site ◽  
Neural Network Algorithm ◽  
The Relationship ◽  
Ore Dressing

Ore blending is an essential part of daily work in the concentrator. Qualified ore dressing products can make the ore dressing more smoothly. The existing ore blending modeling usually only considers the quality of ore blending products and ignores the effect of ore blending on ore dressing. This research proposes an ore blending modeling method based on the quality of the beneficiation concentrate. The relationship between the properties of ore blending products and the total concentrate recovery is fitted by the ABC-BP neural network algorithm, taken as the optimization goal to guarantee the quality of ore dressing products at the source. The ore blending system was developed and operated stably on the production site. The industrial test and actual production results have proved the effectiveness and reliability of this method.

scholarly journals Demand Modelling in Telecommunications

10.14311/1121 ◽  
2009 ◽  
Vol 49 (2) ◽  
Author(s):  
M. Chvalina
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Network Models ◽  
Short Term ◽  
Neural Network Models ◽  
Term Forecast ◽  
Artificial Intelligence Methods ◽  
Demand Modelling

This article analyses the existing possibilities for using Standard Statistical Methods and Artificial Intelligence Methods for a short-term forecast and simulation of demand in the field of telecommunications. The most widespread methods are based on Time Series Analysis. Nowadays, approaches based on Artificial Intelligence Methods, including Neural Networks, are booming. Separate approaches will be used in the study of Demand Modelling in Telecommunications, and the results of these models will be compared with actual guaranteed values. Then we will examine the quality of Neural Network models. 

Artificial Intelligence in Aviation Industries

2020 ◽  
pp. 1-35
Author(s):  
Tetiana Shmelova ◽  
Arnold Sterenharz ◽  
Serge Dolgikh
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Decision Making ◽  
Unmanned Aircraft ◽  
Innovative Technology ◽  
Sociotechnical System ◽  
Air Traffic Controllers ◽  
High Degree ◽  
Difficult Situations

This chapter presents opportunities to use Artificial Intelligence (AI) in aviation and aerospace industries. The AI used an innovative technology for improving the effectiveness of building aviation systems in each stage of the lifecycle for enhancing the security of aviation systems and the characteristic ability to learn, improve, and predict difficult situations. The AI is presented in Air Navigation Sociotechnical system (ANSTS) because the activity of ANSTS, is accompanied by a high degree of risk of causing catastrophic outcomes. The operator's models of decision making in AI systems are presented such as Expert Systems, Decision Support Systems for pilots of manned and unmanned aircraft, air traffic controllers, engineers, etc. The quality of operator's decisions depends on the development and use of innovative technology of AI and related fields (Big Data, Data Mining, Multicriteria Decision Analysis, Collaboration Decision Making, Blockchain, Artificial Neural Network, etc.).

An artificial intelligence algorithm that identifies middle turbinate pneumatisation (concha bullosa) on sinus computed tomography scans

2020 ◽  
Vol 134 (4) ◽  
pp. 328-331 ◽  
Author(s):  
P Parmar ◽  
A-R Habib ◽  
D Mendis ◽  
A Daniel ◽  
M Duvnjak ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Computed Tomography ◽  
Diagnostic Accuracy ◽  
Convolutional Neural Network ◽  
Middle Turbinate ◽  
Concha Bullosa ◽  
Computed Tomography Images ◽  
Computed Tomography Scans ◽  
Neural Network Algorithm

AbstractObjectiveConvolutional neural networks are a subclass of deep learning or artificial intelligence that are predominantly used for image analysis and classification. This proof-of-concept study attempts to train a convolutional neural network algorithm that can reliably determine if the middle turbinate is pneumatised (concha bullosa) on coronal sinus computed tomography images.MethodConsecutive high-resolution computed tomography scans of the paranasal sinuses were retrospectively collected between January 2016 and December 2018 at a tertiary rhinology hospital in Australia. The classification layer of Inception-V3 was retrained in Python using a transfer learning method to interpret the computed tomography images. Segmentation analysis was also performed in an attempt to increase diagnostic accuracy.ResultsThe trained convolutional neural network was found to have diagnostic accuracy of 81 per cent (95 per cent confidence interval: 73.0–89.0 per cent) with an area under the curve of 0.93.ConclusionA trained convolutional neural network algorithm appears to successfully identify pneumatisation of the middle turbinate with high accuracy. Further studies can be pursued to test its ability in other clinically important anatomical variants in otolaryngology and rhinology.

scholarly journals Pattern Recognition using a Neural Network on a Microcontroller with I2C Ultrasonic Sensors

2019 ◽  
Vol 3 (1) ◽  
pp. 9-19 ◽  
Author(s):  
Fazal Noor
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Pattern Recognition ◽  
Integrated Circuit ◽  
High Accuracy ◽  
Ultrasonic Sensors ◽  
Motion Patterns ◽  
The Neural Network ◽  
Proposed Model ◽  
Neural Network Algorithm

Ultrasonic sensors have been used in a variety of applications to measure ranges to objects. Hand gestures via ultrasonic sensors form unique motion patterns for controls. In this research, patterns formed by placing a set of objects in a grid of cells are used for control purposes. A neural network algorithm is implemented on a microcontroller which takes in range signals as inputs read from ultrasonic sensors and classifies them in one of four classes. The neural network is then trained to classify patterns based on objects’ locations in real-time. The testing of the neural network for pattern recognition is performed on a testbed consisting of Inter-Integrated Circuit (I2C) ultrasonic sensors and a microcontroller. The performance of the proposed model is presented and it is observed the model is highly scalable, accurate, robust and reliable for applications requiring high accuracy such as in robotics and artificial intelligence.

scholarly journals Improving the Neural Network Algorithm for Assessing the Quality of Facial Images

2020 ◽  
pp. paper28-1-paper28-12
Author(s):  
Nikita Lisin ◽  
Alexander Gromov ◽  
Vadim Konushin ◽  
Anton Konushin
Keyword(s):  
Neural Network ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Experimental Studies ◽  
Recognition System ◽  
Surveillance Systems ◽  
Neural Network Algorithm ◽  
Baseline Algorithm ◽  
Facial Images

The paper considers the task of obtaining a quality assessment of facial images for usage in various video surveillance systems, video analytics, and biometric identification. The accuracy of person recognition and classification depends on the quality of the input images. We consider an approach to obtaining single face image quality assessment using a neural network model, which is trained on pairs of images that are split into two possible classes: the quality of the first image is better or worse than the quality of the second one. Two modifications of the selected baseline algorithm are proposed. A face recognition system is applied to change the loss function and image and face quality attributes are used when training the model. Experimental studies of the proposed modifications show their effectiveness. The accuracy of selecting the best and worst frame is increased by 1.3% and 1.9%, respectively.

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