scholarly journals Straightness Detection Method and Accuracy Analysis of Guide Rail Parts

2021 ◽  
Vol 2133 (1) ◽  
pp. 012020
Author(s):  
Xiaoying Wang
Keyword(s):  
Machine Tools ◽  
Detection Method ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Accuracy Analysis ◽  
Geometric Accuracy ◽  
Guide Rail ◽  
The Common ◽  
The Stability

Abstract Guide rail is widely used in various machine tools. It mainly plays the role of guidance and support. The geometric accuracy of the guide rail, especially the straightness accuracy, directly affects the stability of the machine tool and the accuracy of the workpiece. By analyzing the common detection methods, application scope and detection accuracy of straightness of guide rail parts, the application occasions and detection accuracy of each detection method are clarified. It provides a theoretical basis and guidance for testers to detect straightness and deal with errors.

The Study on High Precision 3D Detection Method of Steel Plate Surface Based on Structured Light

2012 ◽  
Vol 572 ◽  
pp. 338-342 ◽  
Author(s):  
Zhi Guo Liang ◽  
Quan Yang ◽  
Ke Xu ◽  
Fei He ◽  
Xiao Chen Wang ◽  
...  
Keyword(s):  
Surface Quality ◽  
Steel Plate ◽  
Detection Method ◽  
Structured Light ◽  
Detection Methods ◽  
Measurement Technology ◽  
Detection Accuracy ◽  
Plate Surface ◽  
Factors Affecting

Structured light 3D measurement technology with its simple structure, non-contact measurement, fast measurement speed and other advantages, has been widely used. Steel plate surface quality detection is not confined to the two-dimensional feature of gray detection, and local topography measurement for surface quality of steel plate detection becomes increasingly important. In this paper, steel plate surface 3D detection method based on structured light and the factors affecting the measurement accuracy are analyzed. Several effective methods of improving 3D detection accuracy are put forward. Compared with the traditional structured light 3D detection methods, the detection accuracy of new methods is remarkably improved, thus possessing better application values.

scholarly journals Bacterial colonies detecting and counting based on enhanced CNN detection method

2021 ◽  
Vol 233 ◽  
pp. 02012
Author(s):  
Shousheng Liu ◽  
Zhigang Gai ◽  
Xu Chai ◽  
Fengxiang Guo ◽  
Mei Zhang ◽  
...  
Keyword(s):  
Target Detection ◽  
Error Detection ◽  
Detection Rate ◽  
Detection Method ◽  
Difficult Problem ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Detection Model ◽  
Confidence Threshold ◽  
Small Targets

Bacterial colonies detecting and counting is tedious and time-consuming work. Fortunately CNN (convolutional neural network) detection methods are effective for target detection. The bacterial colonies are a kind of small targets, which have been a difficult problem in the field of target detection technology. This paper proposes a small target enhancement detection method based on double CNNs, which can not only improve the detection accuracy, but also maintain the detection speed similar to the general detection model. The detection method uses double CNNs. The first CNN uses SSD_MOBILENET_V1 network with both target positioning and target recognition functions. The candidate targets are screened out with a low confidence threshold, which can ensure no missing detection of small targets. The second CNN obtains candidate target regions according to the first round of detection, intercepts image sub-blocks one by one, uses the MOBILENET_V1 network to filter out targets with a higher confidence threshold, which can ensure good detection of small targets. Through the two-round enhancement detection method has been transplanted to the embedded platform NVIDIA Jetson AGX Xavier, the detection accuracy of small targets is significantly improved, and the target error detection rate and missed detection rate are reduced to less than 1%.

scholarly journals Permission Sensitivity-Based Malicious Application Detection for Android

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yubo Song ◽  
Yijin Geng ◽  
Junbo Wang ◽  
Shang Gao ◽  
Wei Shi
Keyword(s):  
Detection Method ◽  
Malware Detection ◽  
Feature Selection Method ◽  
Machine Learning Algorithms ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Android Malware ◽  
Android Malware Detection ◽  
Private Data ◽  
Weight Allocation

Since a growing number of malicious applications attempt to steal users’ private data by illegally invoking permissions, application stores have carried out many malware detection methods based on application permissions. However, most of them ignore specific permission combinations and application categories that affect the detection accuracy. The features they extracted are neither representative enough to distinguish benign and malicious applications. For these problems, an Android malware detection method based on permission sensitivity is proposed. First, for each kind of application categories, the permission features and permission combination features are extracted. The sensitive permission feature set corresponding to each category label is then obtained by the feature selection method based on permission sensitivity. In the following step, the permission call situation of the application to be detected is compared with the sensitive permission feature set, and the weight allocation method is used to quantify this information into numerical features. In the proposed method of malicious application detection, three machine-learning algorithms are selected to construct the classifier model and optimize the parameters. Compared with traditional methods, the proposed method consumed 60.94% less time while still achieving high accuracy of up to 92.17%.

Detection method for stability of microcontroller accessories based on biosensor

2021 ◽  
pp. 1-14
Author(s):  
Yan Ren ◽  
Jiayong Liu
Keyword(s):  
Process Design ◽  
Detection Method ◽  
Detection Accuracy ◽  
Stability Testing ◽  
Term Operation ◽  
Existing Problems ◽  
Testing Method ◽  
And Performance ◽  
The Stability

In order to solve the problem of poor accuracy of traditional microcontroller attachment stability testing method, a microcontroller attachment stability testing method based on biosensor was designed to solve the existing problems. The reliability test index of the microcontroller is established, then the interference of the microcontroller accessory is detected and responded, and the interference detection signal of the microcontroller accessory is selected. The process design of stability detection of microcontroller accessories based on biosensor is completed. The experimental results show that the stability detection method based on biosensor designed in this paper can ensure the stability detection accuracy of microcontroller accessories above 80%, which is more accurate than traditional methods. It can be used to evaluate the stability, reliability and performance of microcontroller accessories in long-term operation.

scholarly journals Detecting Shilling Attacks with Automatic Features from Multiple Views

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Yaojun Hao ◽  
Fuzhi Zhang ◽  
Jian Wang ◽  
Qingshan Zhao ◽  
Jianfang Cao
Keyword(s):  
Principal Component Analysis ◽  
Recommender Systems ◽  
Detection Method ◽  
Principal Component ◽  
Component Analysis ◽  
Experimental Results ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Multiple Views ◽  
Fine Grained

Due to the openness of the recommender systems, the attackers are likely to inject a large number of fake profiles to bias the prediction of such systems. The traditional detection methods mainly rely on the artificial features, which are often extracted from one kind of user-generated information. In these methods, fine-grained interactions between users and items cannot be captured comprehensively, leading to the degradation of detection accuracy under various types of attacks. In this paper, we propose an ensemble detection method based on the automatic features extracted from multiple views. Firstly, to collaboratively discover the shilling profiles, the users’ behaviors are analyzed from multiple views including ratings, item popularity, and user-user graph. Secondly, based on the data preprocessed from multiple views, the stacked denoising autoencoders are used to automatically extract user features with different corruption rates. Moreover, the features extracted from multiple views are effectively combined based on principal component analysis. Finally, according to the features extracted with different corruption rates, the weak classifiers are generated and then integrated to detect attacks. The experimental results on the MovieLens, Netflix, and Amazon datasets indicate that the proposed method can effectively detect various attacks.

A Survey on Detecting Methods for Harmonic of Power System

2012 ◽  
Vol 614-615 ◽  
pp. 907-910
Author(s):  
Xue Song Zhou ◽  
Guang Zhu Chen ◽  
You Jie Ma
Keyword(s):  
Power System ◽  
Reactive Power ◽  
Detection Method ◽  
Response Speed ◽  
Development Trend ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Harmonic Detection ◽  
Advantages And Disadvantages ◽  
Power System Harmonics

This paper describes Detecting Methods for Harmonics of power system has been considered one of the serious harms for power system. The research of harmonics has obtained high attention among people. The researches of harmonics detection have many methods, such as Based on Fryze theory of harmonic power detection, Instantaneous reactive power theory detection method, Fourier Transformation harmonic detection method, wavelet transform detection method, neural networks harmonic detection method etc. Aiming at harmonic detection, the different detection methods of power system harmonics are summarized and compared. This paper reviews the existing harmonic detection methods, and discuss their advantages and disadvantages in terms of detection accuracy and response speed, and finally summarizes the development trend of the harmonic detection method.

scholarly journals Optimized EEG–fNIRS Based Mental Workload Detection Method for Practical Applications

2021 ◽  
Author(s):  
Hongzuo Chu ◽  
Yong Cao ◽  
Jin Jiang ◽  
Jiehong Yang ◽  
Mengyin Huang ◽  
...  
Keyword(s):  
Task Difficulty ◽  
Detection Method ◽  
Mental Workload ◽  
Systems Design ◽  
Detection Methods ◽  
Complex Signal ◽  
Detection Accuracy ◽  
Signal Acquisition ◽  
Detection Techniques ◽  
Channel Configuration

Abstract Background: Mental workload is a critical consideration in complex man–machine systems design. Among various mental workload detection techniques, multimodal detection techniques integrating EEG and fNIRS signals have attracted considerable attention. However, existing EEG–fNIRS-based mental workload detection methods have certain defects, such as complex signal acquisition channels and low detection accuracy, which restrict their practical application.Method: The signal acquisition configuration was optimized and a more accurate and convenient EEG–fNIRS-based mental workload detection method was constructed. A classical MATB task was conducted with 20 participating volunteers. Subjective scale data, 64-channel EEG data, and two-channel fNIRS data were collected.Results: A higher number of EEG channels correspond to higher detection accuracy. However, there is no obvious improvement in accuracy once the number of EEG channels reaches 26, with a four-level mental workload detection accuracy of 78.25±4.71%. Partial results of physiological analysis verify the results of previous studies, such as that the θ power of EEG and concentration of O2Hb in the prefrontal region increase while the concentration of HHb decreases with task difficulty. It was further observed, for the first time, that the energy of each band of EEG signals was significantly different in the occipital lobe region, and the power of 𝛽1 and 𝛽2 bands in the occipital region increased significantly with task difficulty. The changing range and the mean amplitude of O2Hb in high-difficulty tasks were significantly higher compared with those in low-difficulty tasks.Conclusions: The channel configuration of EEG–fNIRS-based mental workload detection was optimized to 26 EEG channels and two frontal fNIRS channels. A four-level mental workload detection accuracy of 78.25±4.71% was obtained, which is higher than previously reported results. The proposed configuration can promote the application of mental workload detection technology in military, driving, and other complex human–computer interaction systems.

scholarly journals Approaches to Reforming Pension Systems in the World and Recommendations for Russia

2021 ◽  
Vol 13 (1) ◽  
pp. 125-135
Author(s):  
Kristina V. Shvandar ◽  
◽  
Anastasia A. Anisimova ◽  
Keyword(s):  
Pension System ◽  
Effective Interaction ◽  
Pension Plans ◽  
Population Coverage ◽  
Global Trends ◽  
Pension Systems ◽  
The Common ◽  
The Stability ◽  
Main Components

Global trends in the pension sector show that a funded pension, in addition to a pay-as-you-go component, increases the reliability of pension protection for retired people and improves the stability of the pension system. The article analyzes the main directions of reforms of both distribution and funded components. The common features of the considered pension systems are the presence of several levels and their effective interaction as well as the expansion of the role of accumulative pension systems. Reforms related to increasing the population coverage with accumulative pension plans are among the most common ones in the framework of the analysis. The main directions for improving the Russian funded pension system are highlighted, among which the voluntary payment of contributions and the ability to set the desired amount of contributions are the main components of the proposed changes, based on the analysis of international experience in reforming pension systems.

Structural damage detection via combining weighted strategy with trace Lasso

2018 ◽  
Vol 22 (3) ◽  
pp. 597-612 ◽  
Author(s):  
Chengbin Chen ◽  
Chudong Pan ◽  
Zepeng Chen ◽  
Ling Yu
Keyword(s):  
Damage Detection ◽  
Swarm Intelligence ◽  
Structural Damage ◽  
Optimization Problem ◽  
Detection Method ◽  
Rapid Development ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Structural Damage Detection ◽  
Selection Operator

With the rapid development of computation technologies, swarm intelligence–based algorithms become an innovative technique used for addressing structural damage detection issues, but traditional swarm intelligence–based structural damage detection methods often face with insufficient detection accuracy and lower robustness to noise. As an exploring attempt, a novel structural damage detection method is proposed to tackle the above deficiency via combining weighted strategy with trace least absolute shrinkage and selection operator (Lasso). First, an objective function is defined for the structural damage detection optimization problem by using structural modal parameters; a weighted strategy and the trace Lasso are also involved into the objection function. A novel antlion optimizer algorithm is then employed as a solution solver to the structural damage detection optimization problem. To assess the capability of the proposed structural damage detection method, two numerical simulations and a series of laboratory experiments are performed, and a comparative study on effects of different parameters, such as weighted coefficients, regularization parameters and damage patterns, on the proposed structural damage detection methods are also carried out. Illustrated results show that the proposed structural damage detection method via combining weighted strategy with trace Lasso is able to accurately locate structural damages and quantify damage severities of structures.

scholarly journals ATMOSPHERIC ENVIRONMENT DETECTION METHOD BASED ON MULTI-ROTOR UAV PLATFORM

2018 ◽  
Vol XLII-3/W5 ◽  
pp. 67-71
Author(s):  
A. Shen ◽  
S. Zhou ◽  
S. Peng
Keyword(s):  
Detection Method ◽  
Center Of Gravity ◽  
Atmospheric Environment ◽  
Detection Accuracy ◽  
Flight Safety ◽  
Rotor Speed ◽  
Gradual Process ◽  
The Stability ◽  
Environment Detection ◽  
Improve Stability

<p><strong>Abstract.</strong> For the method of atmospheric detection based on multi-rotor UAV, the effects of flight safety and airflow on detection accuracy are analyzed. The detector mounted on the multi-rotor drone should be highly symmetrical and the center of gravity should be on the same vertical line as the center of gravity of the drone. Mounting the detector above the drone's fuselage will reduce the stability of the drone, and lowering it will improve stability, but for models with high stability, placing it below will affect the maneuverability of the drone. The airflow interference in the area where the detector can be placed near the fuselage is related to the drone: the faster the rotor speed, and the closer the rotor is to the fuselage, the stronger the airflow interference. At the same time, the influence of airflow disturbance on temperature and humidity has a gradual process. Waiting for a period of time after starting the drone can change the error into a fixed error for post-processing. This method was used to detect a pollution process and achieved good results.</p>

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