scholarly journals Quality Inspection of Digital Archives of Application and Installation in Power Business Expanding based on Artificial Intelligence Technology

2021 ◽  
Vol 2079 (1) ◽  
pp. 012030
Author(s):  
Haihong Liang ◽  
Ling Zeng ◽  
Xiaozhou Shen ◽  
Weiwei Shi ◽  
Jiujiao Cang
Keyword(s):  
Artificial Intelligence ◽  
Classification Accuracy ◽  
Detection Method ◽  
Detection Methods ◽  
Digital Archive ◽  
Quality Inspection ◽  
Digital Archives ◽  
Evaluation Standard ◽  
Quality Detection ◽  
Artificial Intelligence Technology

Abstract The existing quality detection methods of business expansion digital archives have the problem of fuzzy evaluation standard, which leads to low classification accuracy. This paper designs a quality detection method of business expansion Digital Archives based on artificial intelligence technology. The business characteristics of business development are extracted, the minimum business data unit is described, the digital archive catalogue database is established, the digital archive evaluation standard is defined, the text similarity is calculated, the user model is established, and the quality inspection mode is established by using artificial intelligence technology. Experimental results: the average classification accuracy of the designed method based on artificial intelligence technology and the other two quality detection methods is 55.763, 43.560 and 42.605, which proves that the quality detection method based on artificial intelligence technology has higher use value.

scholarly journals A comparative study of motion detection with FMG and sEMG methods for assistive applications

2020 ◽  
Vol 7 ◽  
pp. 205566832093858
Author(s):  
Muhammad Raza Ul Islam ◽  
Asim Waris ◽  
Ernest Nlandu Kamavuako ◽  
Shaoping Bai
Keyword(s):  
Motion Detection ◽  
Classification Accuracy ◽  
Detection Method ◽  
Daily Basis ◽  
Detection Methods ◽  
Feed Forward Neural Network ◽  
Training Scheme ◽  
Training Schemes ◽  
And Training

Introduction While surface-electromyography (sEMG) has been widely used in limb motion detection for the control of exoskeleton, there is an increasing interest to use forcemyography (FMG) method to detect motion. In this paper, we review the applications of two types of motion detection methods. Their performances were experimentally compared in day-to-day classification of forearm motions. The objective is to select a detection method suitable for motion assistance on a daily basis. Methods Comparisons of motion detection with FMG and sEMG were carried out considering classification accuracy (CA), repeatability and training scheme. For both methods, classification of motions was achieved through feed-forward neural network. Repeatability was evaluated on the basis of change in CA between days and also training schemes. Results The experiments shows that day-to-day CA with FMG can reach 84.9%, compared with a CA of 77.8% with sEMG, when the classifiers were trained only on the first day. Moreover, the CA with FMG can reach to 86.5%, comparable to CA of 84.1% with sEMG, if classifiers were trained daily. Conclusions Results suggest that data recorded from FMG is more repeatable in day-to-day testing and therefore FMG-based methods can be more useful than sEMG-based methods for motion detection in applications where exoskeletons are used as needed on a daily basis.

scholarly journals Joint Banknote Recognition and Counterfeit Detection Using Explainable Artificial Intelligence

Sensors ◽  
2019 ◽  
Vol 19 (16) ◽  
pp. 3607 ◽  
Author(s):  
Miseon Han ◽  
Jeongtae Kim
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
European Union ◽  
Detection Method ◽  
Computation Time ◽  
Learning System ◽  
Detection Methods ◽  
The European Union ◽  
Explainable Artificial Intelligence ◽  
Banknote Recognition

We investigated machine learning-based joint banknote recognition and counterfeit detection method. Unlike existing methods, since the proposed method simultaneously recognize banknote type and detect counterfeit detection, it is significantly faster than existing serial banknote recognition and counterfeit detection methods. Furthermore, we propose an explainable artificial intelligence method for visualizing regions that contributed to the recognition and detection. Using the visualization, it is possible to understand the behavior of the trained machine learning system. In experiments using the United State Dollar and the European Union Euro banknotes, the proposed method shows significant improvement in computation time from conventional serial method.

scholarly journals Quality Detection System of Transparent Nonel Tubes Based on Image Processing

2015 ◽  
Vol 9 (1) ◽  
pp. 697-702
Author(s):  
Guodong Sun ◽  
Wei Xu ◽  
Lei Peng
Keyword(s):  
Image Processing ◽  
Detection Method ◽  
Detection System ◽  
Recognition Rate ◽  
Human Vision ◽  
Detection Methods ◽  
Detection Accuracy ◽  
External Diameter ◽  
Quality Detection

The traditional quality detection method for transparent Nonel tubes relies on human vision, which is inefficient and susceptible to subjective factors. Especially for Nonel tubes filled with the explosive, missed defects would lead to potential danger in blasting engineering. The factors affecting the quality of Nonel tubes mainly include the uniformity of explosive filling and the external diameter of Nonel tubes. The existing detection methods, such as Scalar method, Analysis method and infrared detection technology, suffer from the following drawbacks: low detection accuracy, low efficiency and limited detection items. A new quality detection system of Nonel tubes has been developed based on machine vision in order to overcome these drawbacks. Firstly the system architecture for quality detection is presented. Then the detection method of explosive dosage and the relevant criteria are proposed based on mapping relationship between the explosive dosage and the gray value in order to detect the excessive explosive faults, insufficient explosive faults and black spots. Finally an algorithm based on image processing is designed to measure the external diameter of Nonel tubes. The experiments and practical operations in several Nonel tube manufacturers have proved the defect recognition rate of proposed system can surpass 95% at the detection speed of 100m/min, and system performance can meet the quality detection requirements of Nonel tubes. Therefore this quality detection method can save human resources and ensure the quality of Nonel tubes.

scholarly journals Research on Multi-level Cooperative Detection of Power Grid Dispatching Fault Based on Artificial Intelligence Technology

2021 ◽  
Author(s):  
Jianzhong Dou ◽  
Zhicheng Liu ◽  
Wei Xiong ◽  
Hongzhong Chen ◽  
Yifei Wu ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Fault Detection ◽  
Simulation Experiment ◽  
Detection Method ◽  
Detection Efficiency ◽  
Power Grid ◽  
False Detection ◽  
Cooperative Detection ◽  
Artificial Intelligence Technology ◽  
Multi Level

 The traditional power grid dispatching fault detection method has low detection efficiency and accuracy due to the lack of uncertainty in modeling. Aiming at the above problems, a multi-level cooperative fault detection method based on artificial intelligence technology is studied. After the preliminary processing of the dispatching data, the multilevel fault detection architecture is established. BP neural network is used to realize the multi-level cooperative detection of scheduling faults in the multi-level detection architecture. Through simulation experiment, it is proved that the failure rate and false detection rate of the proposed method are far lower than those of traditional methods, and the method has high stability and advantages.

scholarly journals Deepfake Detection Using the Rate of Change between Frames Based on Computer Vision

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7367
Author(s):  
Gihun Lee ◽  
Mihui Kim
Keyword(s):  
Artificial Intelligence ◽  
Computer Vision ◽  
Detection Rate ◽  
Detection Method ◽  
Big Data Analysis ◽  
Rate Of Change ◽  
Digital Content ◽  
Machine Learning Method ◽  
The Matrix ◽  
Artificial Intelligence Technology

Recently, artificial intelligence has been successfully used in fields, such as computer vision, voice, and big data analysis. However, various problems, such as security, privacy, and ethics, also occur owing to the development of artificial intelligence. One such problem are deepfakes. Deepfake is a compound word for deep learning and fake. It refers to a fake video created using artificial intelligence technology or the production process itself. Deepfakes can be exploited for political abuse, pornography, and fake information. This paper proposes a method to determine integrity by analyzing the computer vision features of digital content. The proposed method extracts the rate of change in the computer vision features of adjacent frames and then checks whether the video is manipulated. The test demonstrated the highest detection rate of 97% compared to the existing method or machine learning method. It also maintained the highest detection rate of 96%, even for the test that manipulates the matrix of the image to avoid the convolutional neural network detection method.

scholarly journals A Shadow Capture Deep Neural Network for Underwater Forward-Looking Sonar Image Detection

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Taowen Xiao ◽  
Zijian Cai ◽  
Cong Lin ◽  
Qiong Chen
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Wireless Communication ◽  
Lightweight Design ◽  
Object Identification ◽  
Feature Representation ◽  
Data Set ◽  
Evaluation Standard ◽  
Artificial Intelligence Technology ◽  
Underwater Wireless Communication

Image sonar is a widely used wireless communication technology for detecting underwater objects, but the detection process often leads to increased difficulty in object identification due to the lack of equipment resolution. In view of the remarkable results achieved by artificial intelligence techniques in the field of underwater wireless communication research, we propose an object detection method based on convolutional neural network (CNN) and shadow information capture to improve the object recognition and localization effect of underwater sonar images by making full use of the shadow information of the object. We design a Shadow Capture Module (SCM) that can capture the shadow information in the feature map and utilize them. SCM is compatible with CNN models that have a small increase in parameters and a certain degree of portability, and it can effectively alleviate the recognition difficulties caused by the lack of device resolution through referencing shadow features. Through extensive experiments on the underwater sonar data set provided by Pengcheng Lab, the proposed method can effectively improve the feature representation of the CNN model and enhance the difference between class and class features. Under the main evaluation standard of PASCAL VOC 2012, the proposed method improved from an average accuracy (mAP) of 69.61% to 75.73% at an IOU threshold of 0.7, which exceeds many existing conventional deep learning models, while the lightweight design of our proposed module is more helpful for the implementation of artificial intelligence technology in the field of underwater wireless communication.

scholarly journals Quality inspection method of layered compacted subgrade and engineering example analysis

2021 ◽  
Vol 248 ◽  
pp. 03068
Author(s):  
Binbin Xu
Keyword(s):  
Detection Method ◽  
Sampling Point ◽  
Quality Inspection ◽  
Road Engineering ◽  
Inspection Method ◽  
Quality Detection ◽  
Detection Technology ◽  
Filling Method ◽  
Point Detection ◽  
Compaction Method

The effective detection of layered roller compacted subgrade quality is the key of road engineering quality control. The traditional sand filling compaction method belongs to random sampling point detection method, and it is not easy to detect the subgrade compaction condition below the sand filling pit. Based on the summary of the current domestic and foreign subgrade detection technology, this paper innovatively combines the geological radar method with sand filling method, and through the fixed point detection method. The results show that the traditional sand filling method can directly and quantitatively reflect the compactness of sampling points, while the geological radar can realize the continuous detection, and can judge the compaction layer from the loose state to the interlaminar line after compaction through the geological radar image At the same time, the GPR can identify the under compacted area in the subgrade compaction layer and reflect the overall compaction effect of the subgrade. The detection method of combining the GPR method and sand filling method has obvious technical advantages in the subgrade quality detection.

scholarly journals Malicious Powershell Detection Using Graph Convolution Network

2021 ◽  
Vol 11 (14) ◽  
pp. 6429
Author(s):  
Sunoh Choi
Keyword(s):  
Artificial Intelligence ◽  
Adjacency Matrix ◽  
Rapid Growth ◽  
Detection Rate ◽  
Detection Method ◽  
Malware Detection ◽  
Detection Methods ◽  
Jaccard Similarity ◽  
Malicious Behaviors

The internet’s rapid growth has resulted in an increase in the number of malicious files. Recently, powershell scripts and Windows portable executable (PE) files have been used in malicious behaviors. To solve these problems, artificial intelligence (AI) based malware detection methods have been widely studied. Among AI techniques, the graph convolution network (GCN) was recently introduced. Here, we propose a malicious powershell detection method using a GCN. To use the GCN, we needed an adjacency matrix. Therefore, we proposed an adjacency matrix generation method using the Jaccard similarity. In addition, we show that the malicious powershell detection rate is increased by approximately 8.2% using GCN.

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