scholarly journals FNet: A Two-Stream Model for Detecting Adversarial Attacks against 5G-Based Deep Learning Services

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Guangquan Xu ◽  
Guofeng Feng ◽  
Litao Jiao ◽  
Meiqi Feng ◽  
Xi Zheng ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Communication Networks ◽  
Learning Technology ◽  
Stream Network ◽  
Fifth Generation ◽  
Decision Network ◽  
Artificial Intelligence Technology ◽  
Adversarial Examples ◽  
The Difference

With the extensive application of artificial intelligence technology in 5G and Beyond Fifth Generation (B5G) networks, it has become a common trend for artificial intelligence to integrate into modern communication networks. Deep learning is a subset of machine learning and has recently led to significant improvements in many fields. In particular, many 5G-based services use deep learning technology to provide better services. Although deep learning is powerful, it is still vulnerable when faced with 5G-based deep learning services. Because of the nonlinearity of deep learning algorithms, slight perturbation input by the attacker will result in big changes in the output. Although many researchers have proposed methods against adversarial attacks, these methods are not always effective against powerful attacks such as CW. In this paper, we propose a new two-stream network which includes RGB stream and spatial rich model (SRM) noise stream to discover the difference between adversarial examples and clean examples. The RGB stream uses raw data to capture subtle differences in adversarial samples. The SRM noise stream uses the SRM filters to get noise features. We regard the noise features as additional evidence for adversarial detection. Then, we adopt bilinear pooling to fuse the RGB features and the SRM features. Finally, the final features are input into the decision network to decide whether the image is adversarial or not. Experimental results show that our proposed method can accurately detect adversarial examples. Even with powerful attacks, we can still achieve a detection rate of 91.3%. Moreover, our method has good transferability to generalize to other adversaries.

scholarly journals Design of College Evaluation System Based on Artificial Intelligence and Deep Learning Technology

CONVERTER ◽  
2021 ◽  
pp. 651-658
Author(s):  
Jiang Yan, Wang Peipei
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Performance Appraisal ◽  
Evaluation System ◽  
Learning Technology ◽  
Functional Requirements ◽  
Appraisal System ◽  
Performance Appraisal System ◽  
Artificial Intelligence Technology ◽  
Business Requirements

Artificial intelligence and deep learning technology are important technologies widely used in manufacturing industry.With the help of performance appraisal system to comprehensively evaluate the performance of teachers is a good measure. Therefore, it is very necessary to develop a performance appraisal system for university teachers by using artificial intelligence technology. This paper first demonstrates the feasibility of the development of performance appraisal system, and scientifically divides the user roles. According to the business requirements, the core business process of the system is established, and the system architecture and functional modules are designed. At the same time, this paper establishes the conceptual model and logical model of database. Finally, SSH framework and extjs framework are used to realize the functions of the system. In this paper, the reliability, stability and security of the system are tested to ensure that the system meets the functional and non functional requirements. The operation results show that the system has stable functions, simple operation and convenient maintenance, and basically meets the needs of users at different levels.

Rapid and accurate intraoperative pathological diagnosis by artificial intelligence with deep learning technology

2017 ◽  
Vol 107 ◽  
pp. 98-99 ◽  
Author(s):  
Jing Zhang ◽  
Yanlin Song ◽  
Fan Xia ◽  
Chenjing Zhu ◽  
Yingying Zhang ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Pathological Diagnosis ◽  
Learning Technology

Reform and innovation of artificial intelligence technology for information service in university physical education

2020 ◽  
pp. 1-11
Author(s):  
Jianye Zhang
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Physical Education ◽  
Information Service ◽  
Management Decision ◽  
Negative Effects ◽  
Education Management ◽  
Depth Analysis ◽  
Artificial Intelligence Technology ◽  
The Difference

This article analyzes the reform of information services in university physical education based on artificial intelligence technology and conducts in-depth and innovative research on it. In-depth analysis of the relationship between big data and the development and application of information technology such as the Internet, Internet of Things, cloud computing, to clarify the difference and connection between big data, informatization and intelligence. Artificial intelligence will bring opportunities for changes in data collection, management decision-making, governance models, education and teaching, scientific research services, evaluation and evaluation of physical education in our university. At the same time, big data education management in colleges and universities faces many challenges such as the balance of privacy and freedom, data hegemony, data junk, data standards, and data security, and they have many negative effects. In accordance with the requirements of educational modernization, centering on the goal of intelligent and humanized education management, it aims existing issues in college physical education management.

scholarly journals Application of Artificial Intelligence Technology Optimized by Deep Learning to Rural Financial Development and Rural Governance

2022 ◽  
Vol 30 (7) ◽  
pp. 1-23
Author(s):  
Hongwei Hou ◽  
Kunzhi Tang ◽  
Xiaoqian Liu ◽  
Yue Zhou
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Short Term Memory ◽  
Research Result ◽  
Customer Information ◽  
Rural Governance ◽  
Artificial Intelligence Technology ◽  
Reasonable Prediction

The aim of this article is to promote the development of rural finance and the further informatization of rural banks. Based on DL (deep learning) and artificial intelligence technology, data pre-processing and feature selection are conducted on the customer information of rural banks in a certain region, including the historical deposit and loan, transaction record, and credit information. Besides, four DL models are proposed with a precision of more than 87% by test to improve the simulation effect and explore the application of DL. The BLSTM-CNN (Bi-directional Long Short-Term Memory-Convolutional Neural Network) model with a precision of 95.8%, which integrates RNN (Recurrent Neural Network) and CNN (Convolutional Neural Network) in parallel, solves the shortcomings of RNN and CNN separately. The research result can provide a more reasonable prediction model for rural banks, and ideas for the development of rural informatization and promoting rural governance.

scholarly journals Using CycleGAN to Achieve the Sketch Recognition Process of Sketch-Based Modeling

2021 ◽  
pp. 26-34
Author(s):  
Yuqian Li ◽  
Weiguo Xu
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Deep Learning ◽  
Learning Technology ◽  
Modeling Technology ◽  
Image Translation ◽  
Mapping Process ◽  
And Training ◽  
Difficult Part ◽  
2D Sketches

AbstractArchitects usually design ideation and conception by hand-sketching. Sketching is a direct expression of the architect’s creativity. But 2D sketches are often vague, intentional and even ambiguous. In the research of sketch-based modeling, it is the most difficult part to make the computer to recognize the sketches. Because of the development of artificial intelligence, especially deep learning technology, Convolutional Neural Networks (CNNs) have shown obvious advantages in the field of extracting features and matching, and Generative Adversarial Neural Networks (GANs) have made great breakthroughs in the field of architectural generation which make the image-to-image translation become more and more popular. As the building images are gradually developed from the original sketches, in this research, we try to develop a system from the sketches to the images of buildings using CycleGAN algorithm. The experiment demonstrates that this method could achieve the mapping process from the sketches to images, and the results show that the sketches’ features could be recognised in the process. By the learning and training process of the sketches’ reconstruction, the features of the images are also mapped to the sketches, which strengthen the architectural relationship in the sketch, so that the original sketch can gradually approach the building images, and then it is possible to achieve the sketch-based modeling technology.

Deep learning in bioengineering and biofabrication: a powerful technology boosting translation from research to clinics

2021 ◽  
Author(s):  
João B Costa ◽  
Joana Silva-Correia ◽  
Rui L Reis ◽  
Joaquim M Oliveira
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Cost Effectiveness ◽  
Clinical Setting ◽  
Emerging Technologies ◽  
Cutting Edge ◽  
Learning Technology ◽  
3D Bioprinting ◽  
Massive Datasets

Bioengineering has been revolutionizing the production of biofunctional tissues for tackling unmet clinical needs. Bioengineers have been focusing their research in biofabrication, especially 3D bioprinting, providing cutting-edge approaches and biomimetic solutions with more reliability and cost–effectiveness. However, these emerging technologies are still far from the clinical setting and deep learning, as a subset of artificial intelligence, can be widely explored to close this gap. Thus, deep-learning technology is capable to autonomously deal with massive datasets and produce valuable outputs. The application of deep learning in bioengineering and how the synergy of this technology with biofabrication can help (more efficiently) bring 3D bioprinting to clinics, are overviewed herein.

scholarly journals An On-Device Deep Learning Approach to Battery Saving on Industrial Mobile Terminals

Sensors ◽  
2020 ◽  
Vol 20 (14) ◽  
pp. 4044
Author(s):  
Inyeop Choi ◽  
Hyogon Kim
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Energy Consumption ◽  
Energy Use ◽  
Freezing Point ◽  
Learning Technology ◽  
Mobile Terminals ◽  
Logistics Industry ◽  
Varying Environments ◽  
Current Dissipation

The mobile terminals used in the logistics industry can be exposed to wildly varying environments, which may hinder effective operation. In particular, those used in cold storages can be subject to frosting in the scanner window when they are carried out of the warehouses to a room-temperature space outside. To prevent this, they usually employ a film heater on the scanner window. However, the temperature and humidity conditions of the surrounding environment and the temperature of the terminal itself that cause frosting vary widely. Due to the complicated frost-forming conditions, existing industrial mobile terminals choose to implement rather simple rules that operate the film heater well above the freezing point, which inevitably leads to inefficient energy use. This paper demonstrates that to avoid such waste, on-device artificial intelligence (AI) a.k.a. edge AI can be readily employed to industrial mobile terminals and can improve their energy efficiency. We propose an artificial-intelligence-based approach that utilizes deep learning technology to avoid the energy-wasting defrosting operations. By combining the traditional temperature-sensing logic with a convolutional neural network (CNN) classifier that visually checks for frost, we can more precisely control the defrosting operation. We embed the CNN classifier in the device and demonstrate that the approach significantly reduces the energy consumption. On our test terminal, the net ratio of the energy consumption by the existing system to that of the edge AI for the heating film is almost 14:1. Even with the common current-dissipation accounted for, our edge AI system would increase the operating hours by 86%, or by more than 6 h compared with the system without the edge AI.

Self-organizing cognitive model synthesis with deep learning support

2018 ◽  
Vol 7 (2.28) ◽  
pp. 168 ◽  
Author(s):  
A Raikov ◽  
A Ermakov ◽  
A Merkulov
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Cognitive Model ◽  
Complete Information ◽  
Big Data Analysis ◽  
Cognitive Models ◽  
Learning Technology ◽  
Problem Situation ◽  
Learning Support ◽  
Model Synthesis

Cognitive models are created by experts and the process takes a lot of time. Furthermore, the result of expert work needs to be verified especially in cases when experts do not have complete information and cannot understand the problem situation quickly. As was previously shown cognitive models’ factors and their mutual relationships could be verified with applying Big Data analysis technology. This paper addresses the issue of automated cognitive models synthesis on the base of author’s convergent methodology, artificial intelligence and deep learning technology. 

scholarly journals Design of Fault Diagnosis Algorithm for Electromechanical System Based on Artificial Intelligence

2021 ◽  
Vol 2050 (1) ◽  
pp. 012011
Author(s):  
Fuyou Zhao ◽  
Mingying Huo ◽  
Naiming Qi ◽  
Lianfeng Li ◽  
Weiwei Cui
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Deep Learning ◽  
Fault Diagnosis ◽  
Data Sets ◽  
Learning Technology ◽  
Electrical System ◽  
Fault Recognition ◽  
Fault Features ◽  
Diagnosis Method

Abstract A relatively perfect system for the fault diagnosis of mechanical and electrical products has been formed through decades of development. Nevertheless, the traditional fault diagnosis methods fail to cope with the gradual huge mechanical and electrical system. As a result, the advantages of fault diagnosis mode driven by data are increasingly prominent. Meanwhile, the effect of fault diagnosis has exceeded the traditional fault diagnosis methods in many fields. Through the use of the deep learning technology based on artificial intelligence, it carries out mapping and fitting. By fully taking advantages of neural network, it can effectively obtain the accurate classification of fault data. A fault diagnosis method based on the fault data of mechanical and electrical system is designed in this thesis. When it comes to the basic process, it is to take data sets for different mechanical and electrical products. Through the use of feature engineering method, it extracts the fault features of data. Through the use of deep learning technology, it carries out the intelligent diagnosis. According to the experimental results, it indicates that the fault diagnosis method based on deep learning technology can distinguish a variety of fault modes in mechanical and electrical system in an effective way. What’s more, good classification results in fault recognition have been achieved by a variety of deep convolutional neural network structures, so the feasibility of the method is further verified.

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