scholarly journals A Novel Zero Velocity Detection Method Based on the Plantar Pressure

2020 ◽  
Author(s):  
Chengbin Wang ◽  
Meifeng Guo ◽  
Juan Wu ◽  
Bin Zhou ◽  
Qi Wei
Keyword(s):  
Plantar Pressure ◽  
Detection Method ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Pedestrian Navigation ◽  
Zero Velocity ◽  
High Detection ◽  
Walking Environment ◽  
Motion Characteristics ◽  
Indoor And Outdoor

The zero velocity update(ZUPT) algorithm is the core of a foot-mounted pedestrian navigation system. The zero velocity detection method is the premise and guarantee of the effective application of the ZUPT algorithm. To make ZUPT work properly, it is necessary to detect zero velocity intervals correctly. The detection accuracy of the existing zero velocity detection methods is easy to be affected by users and environment. A novel zero velocity detection method based on the plantar pressure is proposed in this paper, which has higher detection accuracy and better environmental adaptability. First, the paper analyzes the motion characteristics of foot during walking. Second, the inherent relationship between the plantar pressure and the gait change during walking is studied based on the pressure sensor. Then, the model of the zero velocity detection method using the plantar pressure is established. Finally, the indoor and outdoor multi-scene experiments show that this method not only has a high detection accuracy, but also has good adaptability to users and walking environment.

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%.

scholarly journals An Area-Context-Based Credibility Detection for Big Data in IoT

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Bo Zhao ◽  
Xiang Li ◽  
Jiayue Li ◽  
Jianwen Zou ◽  
Yifan Liu
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Detection Method ◽  
Big Data Analysis ◽  
Detection Methods ◽  
High Detection ◽  
Data Credibility ◽  
High Level

In order to improve the credibility of big data analysis platform’s results in IoT, it is necessary to improve the quality of IoT data. Many detection methods have been proposed to filter out incredible data, but there are certain deficiencies that performance is not high, detection is not comprehensive, and process is not credible. So this paper proposes an area-context-based credibility detection method for IoT data, which can effectively detect point anomalies, behavioral anomalies, and contextual anomalies. The performance of the context determination and the data credibility detection of the device satisfying the area characteristics is superior to the similar algorithms. As the experiments show, the proposed method can reach a high level of performance with more than 97% in metrics, which can effectively improve the quality of IoT data.

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.

scholarly journals An Adaptive Dual-Window Step Detection Method for a Waist-Worn Inertial Navigation System

2015 ◽  
Vol 69 (3) ◽  
pp. 659-672 ◽  
Author(s):  
Yanshun Zhang ◽  
Yunqiang Xiong ◽  
Yixin Wang ◽  
Chunyu Li ◽  
Zhanqing Wang
Keyword(s):  
Detection Method ◽  
Detection Accuracy ◽  
Vertical Acceleration ◽  
Navigation Systems ◽  
Step Frequency ◽  
Step Detection ◽  
Pedestrian Navigation ◽  
Centre Of Gravity ◽  
Window Approach ◽  
Dual Window

In waist-worn pedestrian navigation systems, the periodic vertical acceleration peak signal at body centre of gravity is widely used for detecting steps. Due to vibration and waist shaking interference, accelerometer output signals contain false peaks and thus reduce step detection accuracy. This paper analyses the relationship between periodic acceleration at pedestrian centre of gravity and walking stance during walking. An adaptive dual-window step detection method is proposed based on this analysis. The peak signal is detected by a dual-window and the window length is adjusted according to the change in step frequency. The adaptive dual window approach is shown to successfully suppress the effects of vibration and waist shaking, thereby improving the step detection accuracy. The effectiveness of this method is demonstrated through step detection experiments and pedestrian navigation positioning experiments respectively. The step detection error rate was found to be less than 0·15% in repeated experiments consisting of 345 steps, while the longer (about 1·3 km) pedestrian navigation experiments demonstrated typical positioning error was around 0·67% of the distance travelled.

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.

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 A Deep Learning-Aided Detection Method for FTN-Based NOMA

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Jianxiong Pan ◽  
Neng Ye ◽  
Aihua Wang ◽  
Xiangming Li
Keyword(s):  
Deep Learning ◽  
Computational Complexity ◽  
Smart City ◽  
Mean Squared Error ◽  
Detection Method ◽  
High Reliability ◽  
Detection Methods ◽  
Low Latency ◽  
Detection Accuracy ◽  
Minimum Mean Squared Error

The rapid booming of future smart city applications and Internet of things (IoT) has raised higher demands on the next-generation radio access technologies with respect to connection density, spectral efficiency (SE), transmission accuracy, and detection latency. Recently, faster-than-Nyquist (FTN) and nonorthogonal multiple access (NOMA) have been regarded as promising technologies to achieve higher SE and massive connections, respectively. In this paper, we aim to exploit the joint benefits of FTN and NOMA by superimposing multiple FTN-based transmission signals on the same physical recourses. Considering the complicated intra- and interuser interferences introduced by the proposed transmission scheme, the conventional detection methods suffer from high computational complexity. To this end, we develop a novel sliding-window detection method by incorporating the state-of-the-art deep learning (DL) technology. The data-driven offline training is first applied to derive a near-optimal receiver for FTN-based NOMA, which is deployed online to achieve high detection accuracy as well as low latency. Monte Carlo simulation results validate that the proposed detector achieves higher detection accuracy than minimum mean squared error-frequency domain equalization (MMSE-FDE) and can even approach the performance of the maximum likelihood-based receiver with greatly reduced computational complexity, which is suitable for IoT applications in smart city with low latency and high reliability requirements.

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