Design of Bamboo Strip Detection System Based on Labview and Matlab Mixed Programming

2013 ◽  
Vol 302 ◽  
pp. 772-775 ◽  
Author(s):  
Li Ping He ◽  
Shu Xiang Song ◽  
Lei Liu ◽  
Xian Ming Jiang
Keyword(s):  
Real Time ◽  
Detection Rate ◽  
Surface Defects ◽  
Detection System ◽  
Detection Accuracy ◽  
The Novel ◽  
Large Area ◽  
Accuracy Requirement ◽  
Detection Technology ◽  
Mixed Programming

According to the domestic situation that bamboo strip detection technology is artificial intervention and low automation, the novel automatic bamboo strip detection system is designed based on Labview and Matlab mixed programming. The real-time detection on bamboo surface defects is realized in the system such as serious damage, stripe, scratch, large area dim. Moreover, the system provides more than 92% detection accuracy and eight strips per second detection rate, both of which can meet the accuracy requirement of practical production. In general, this paper provides a new kind of method for detection system.

scholarly journals An Improved MB-LBP Defect Recognition Approach for the Surface of Steel Plates

2019 ◽  
Vol 9 (20) ◽  
pp. 4222 ◽  
Author(s):  
Yang Liu ◽  
Ke Xu ◽  
Jinwu Xu
Keyword(s):  
Real Time ◽  
Surface Defects ◽  
Recognition Accuracy ◽  
Detection System ◽  
Detection Algorithm ◽  
Steel Plates ◽  
Detection Accuracy ◽  
Scale Invariant ◽  
Speeded Up Robust Features ◽  
Real Time Detection

The detection of surface defects is very important for the quality improvement of steel plates. In actual production, as the steel plate production line runs faster, the steel surface defect detection algorithm is required to meet the requirements of real-time detection (less than 100 ms/image), and the detection accuracy is improved (at least 90%). In this paper, an improved multi-block local binary pattern (LBP) algorithm is proposed. This algorithm not only has the simplicity and efficiency of the LBP algorithm, but also finds a suitable scale to describe the defect features by changing the block sizes, thus ensuring high recognition accuracy. The experiment proves that the method satisfies the requirements of online real-time detection in terms of speed (63 ms/image), and surpasses the widely-used scale invariant feature transform (SIFT), speeded up robust features (SURF), gray-level co-occurrence matrix (GLCM), and LBP algorithms in recognition accuracy (94.30%), which prove that the MB-LBP has practical application value in an online real-time detection system.

scholarly journals Power-Based Non-Intrusive Condition Monitoring for Terminal Device in Smart Grid

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3635 ◽  
Author(s):  
Guoming Zhang ◽  
Xiaoyu Ji ◽  
Yanjie Li ◽  
Wenyuan Xu
Keyword(s):  
Smart Grid ◽  
Power Consumption ◽  
Real Time ◽  
Condition Monitoring ◽  
Resource Constraints ◽  
Detection System ◽  
Stable Operation ◽  
Detection Accuracy ◽  
Monitoring Method ◽  
Dsp Processors

As a critical component in the smart grid, the Distribution Terminal Unit (DTU) dynamically adjusts the running status of the entire smart grid based on the collected electrical parameters to ensure the safe and stable operation of the smart grid. However, as a real-time embedded device, DTU has not only resource constraints but also specific requirements on real-time performance, thus, the traditional anomaly detection method cannot be deployed. To detect the tamper of the program running on DTU, we proposed a power-based non-intrusive condition monitoring method that collects and analyzes the power consumption of DTU using power sensors and machine learning (ML) techniques, the feasibility of this approach is that the power consumption is closely related to the executing code in CPUs, that is when the execution code is tampered with, the power consumption changes accordingly. To validate this idea, we set up a testbed based on DTU and simulated four types of imperceptible attacks that change the code running in ARM and DSP processors, respectively. We generate representative features and select lightweight ML algorithms to detect these attacks. We finally implemented the detection system on the windows and ubuntu platform and validated its effectiveness. The results show that the detection accuracy is up to 99.98% in a non-intrusive and lightweight way.

Detection Accuracy on X-Ray Real Time Imaging for Cast Aluminum Parts

2012 ◽  
Vol 452-453 ◽  
pp. 1513-1517
Author(s):  
Ai Guo Wang ◽  
Dong Lin Yang ◽  
Peng Zhao
Keyword(s):  
Real Time ◽  
Influence Factors ◽  
Experimental Result ◽  
Detection Accuracy ◽  
Cast Aluminum ◽  
X Ray ◽  
Imaging Quality ◽  
Real Time Imaging ◽  
Detection Technology ◽  
Imaging Detection

x-ray real time imaging detection technology is a kind of important way for industrial nondestructive test. On the basis of basic theory on X-ray detection, The influence factors on x-ray real time imaging detection precision is analyzed in this article. Through analysis for the focus of X-ray source and the unintelligibility of geometric image, the relation between the optimal amplification multiple and the imaging quality is presented and the electric collimator to solve the influence on imaging quality from the scattered ray. The experimental result shows that the detection resolution ratio is up to 50PL/cm and the sensitivity is up to 1.4 % to solve the on-line real time detection for pore, inclusion and looseness and verify the application feasibility in the detection of cast aluminum parts for x-ray real time imaging detection technology.

scholarly journals Visual Detection of Surface Defects Based on Self-Feature Comparison in Robot 3-D Printing

2019 ◽  
Vol 10 (1) ◽  
pp. 235 ◽  
Author(s):  
Hongyao Shen ◽  
Wangzhe Du ◽  
Weijun Sun ◽  
Yuetong Xu ◽  
Jianzhong Fu
Keyword(s):  
Feature Extraction ◽  
3D Printing ◽  
Defect Detection ◽  
Point Cloud ◽  
Fused Deposition Modeling ◽  
Surface Defects ◽  
Detection Accuracy ◽  
Feature Extraction Method ◽  
Fused Deposition ◽  
Detection Technology

Fused Deposition Modeling (FDM) additive manufacturing technology is widely applied in recent years. However, there are many defects that may affect the surface quality, accuracy, or even cause the collapse of the parts in the printing process. In the existing defect detection technology, the characteristics of parts themselves may be misjudged as defects. This paper presents a solution to the problem of distinguishing the defects and their own characteristics in robot 3-D printing. A self-feature extraction method of shape defect detection of 3D printing products is introduced. Discrete point cloud after model slicing is used both for path planning in 3D printing and self-feature extraction at the same time. In 3-D printing, it can generate G-code and control the shooting direction of the camera. Once the current coordinates have been received, the self-feature extraction begins, whose key steps are keeping a visual point cloud of the printed part and projecting the feature points to the picture under the equal mapping condition. After image processing technology, the contours of pictured projected and picture captured will be detected. At last, the final defects can be identified after evaluation of contour similarity based on empirical formula. This work will help to detect the defects online, improve the detection accuracy, and reduce the false detection rate without being affected by its own characteristics.

A Research on Real-Time Hand-Detection Technology Oriented to the Somatic Games

2013 ◽  
Vol 748 ◽  
pp. 999-1002 ◽  
Author(s):  
Ren Chen ◽  
Hui Li
Keyword(s):  
Real Time ◽  
Failure Rate ◽  
Skin Color ◽  
Detection Rate ◽  
Detection Method ◽  
Hand Detection ◽  
Key Technology ◽  
Video Frames ◽  
Detection Technology ◽  
Adaboost Classifier

Hand-detection is a key technology to the somatic games. In this paper, we present a real-time hand-detection method based on Adaboost and skin-color characteristic. By processing the video frames with Adaboost classifier, we abstract the target regions which may contain the hand gestures. Then a filter based on skin color is proposed to select the correct regions. The best detection rate reaches above 89% with an acceptable failure rate and misjudgment rate. Experimental results show that this method is a lightweight and rapid approach to implement real-time hand detection in somatic games.

scholarly journals A Fusion of Multiagent Functionalities for Effective Intrusion Detection System

2017 ◽  
Vol 2017 ◽  
pp. 1-15 ◽  
Author(s):  
Dhanalakshmi Krishnan Sadhasivan ◽  
Kannapiran Balasubramanian
Keyword(s):  
Intrusion Detection ◽  
Real Time ◽  
Intrusion Detection System ◽  
Detection System ◽  
Detection Accuracy ◽  
The Real ◽  
Common Path ◽  
Research Areas ◽  
Water Pipeline ◽  
Active Research

Provision of high security is one of the active research areas in the network applications. The failure in the centralized system based on the attacks provides less protection. Besides, the lack of update of new attacks arrival leads to the minimum accuracy of detection. The major focus of this paper is to improve the detection performance through the adaptive update of attacking information to the database. We propose an Adaptive Rule-Based Multiagent Intrusion Detection System (ARMA-IDS) to detect the anomalies in the real-time datasets such as KDD and SCADA. Besides, the feedback loop provides the necessary update of attacks in the database that leads to the improvement in the detection accuracy. The combination of the rules and responsibilities for multiagents effectively detects the anomaly behavior, misuse of response, or relay reports of gas/water pipeline data in KDD and SCADA, respectively. The comparative analysis of the proposed ARMA-IDS with the various existing path mining methods, namely, random forest, JRip, a combination of AdaBoost/JRip, and common path mining on the SCADA dataset conveys that the effectiveness of the proposed ARMA-IDS in the real-time fault monitoring. Moreover, the proposed ARMA-IDS offers the higher detection rate in the SCADA and KDD cup 1999 datasets.

scholarly journals Ambulatory Phonation Monitoring With Wireless Microphones Based on the Speech Energy Envelope: Algorithm Development and Validation (Preprint)

2019 ◽  
Author(s):  
Chi-Te Wang ◽  
Ji-Yan Han ◽  
Shih-Hau Fang ◽  
Ying-Hui Lai
Keyword(s):  
Noise Reduction ◽  
Real Time ◽  
Background Noise ◽  
Detection System ◽  
Minimum Mean Square Error ◽  
Threshold Function ◽  
Detection Accuracy ◽  
Real Time Processing ◽  
Speech Detection ◽  
Reduction Function

BACKGROUND Voice disorders mainly result from chronic overuse or abuse, particularly in occupational voice users such as teachers. Previous studies proposed a contact microphone attached to the anterior neck for ambulatory voice monitoring; however, the inconvenience associated with taping and wiring, along with the lack of real-time processing, has limited its clinical application. OBJECTIVE This study aims to (1) propose an automatic speech detection system using wireless microphones for real-time ambulatory voice monitoring, (2) examine the detection accuracy under controlled environment and noisy conditions, and (3) report the results of the phonation ratio in practical scenarios. METHODS We designed an adaptive threshold function to detect the presence of speech based on the energy envelope. We invited 10 teachers to participate in this study and tested the performance of the proposed automatic speech detection system regarding detection accuracy and phonation ratio. Moreover, we investigated whether the unsupervised noise reduction algorithm (ie, log minimum mean square error) can overcome the influence of environmental noise in the proposed system. RESULTS The proposed system exhibited an average accuracy of speech detection of 89.9%, ranging from 81.0% (67,357/83,157 frames) to 95.0% (199,201/209,685 frames). Subsequent analyses revealed a phonation ratio between 44.0% (33,019/75,044 frames) and 78.0% (68,785/88,186 frames) during teaching sessions of 40-60 minutes; the durations of most of the phonation segments were less than 10 seconds. The presence of background noise reduced the accuracy of the automatic speech detection system, and an adjuvant noise reduction function could effectively improve the accuracy, especially under stable noise conditions. CONCLUSIONS This study demonstrated an average detection accuracy of 89.9% in the proposed automatic speech detection system with wireless microphones. The preliminary results for the phonation ratio were comparable to those of previous studies. Although the wireless microphones are susceptible to background noise, an additional noise reduction function can alleviate this limitation. These results indicate that the proposed system can be applied for ambulatory voice monitoring in occupational voice users.

scholarly journals A Method for Detecting Surface Defects in Railhead by Magnetic Flux Leakage

2021 ◽  
Vol 11 (20) ◽  
pp. 9489
Author(s):  
Yinliang Jia ◽  
Shicheng Zhang ◽  
Ping Wang ◽  
Kailun Ji
Keyword(s):  
Magnetic Flux ◽  
Surface Defects ◽  
Rapid Development ◽  
Magnetic Flux Leakage ◽  
Detection Accuracy ◽  
Physical Experiments ◽  
Detection Technology ◽  
Element Simulation ◽  
Lift Off ◽  
Flux Leakage

With the rapid development of the world’s railways, rail is vital to ensure the safety of rail transit. This article focuses on the magnetic flux leakage (MFL) non-destructive detection technology of the surface defects in railhead. A Multi-sensors method is proposed. The main sensor and four auxiliary sensors are arranged in the detection direction. Firstly, the root mean square (RMS) of the x-component of the main sensor signal is calculated. In the data more significant than the threshold, the defects are determined by the relative values of the sensors signal. The optimal distances among these sensors are calculated to the size of a defect and the lift-off. From the finite element simulation and physical experiments, it is shown that this method can effectively suppress vibration interference and improve the detection accuracy of defects.

scholarly journals The value of single-molecule real-time technology in the diagnosis of rare thalassemia variants and analysis of phenotype–genotype correlation

2021 ◽  
Author(s):  
Shiqiang Luo ◽  
Xingyuan Chen ◽  
Dingyuan Zeng ◽  
Ning Tang ◽  
Dejian Yuan ◽  
...  
Keyword(s):  
Real Time ◽  
Single Molecule ◽  
Detection Rate ◽  
Molecular Characteristics ◽  
Dot Blot ◽  
Gene Screening ◽  
Positive Detection Rate ◽  
Detection Technology ◽  
Conventional Technology ◽  
Diagnostic Technology

AbstractTo compare single-molecule real-time technology (SMRT) and conventional genetic diagnostic technology of rare types of thalassemia mutations, and to analyze the molecular characteristics and phenotypes of rare thalassemia gene variants, we used 434 cases with positive hematology screening as the cohort, then used SMRT technology and conventional gene diagnosis technology [(Gap-PCR, multiple ligation probe amplification technology (MLPA), PCR-reverse dot blot (RDB)] for thalassemia gene screening. Among the 434 enrolled cases, conventional technology identified 318 patients with variants (73.27%) and 116 patients without variants (26.73%), SMRT identified 361 patients with variants (83.18%), and 73 patients without variants (16.82%). The positive detection rate of SMRT was 9.91% higher than conventional technology. Combination of the two methods identified 485 positive alleles among 49 types of variant. The genotypes of 354 cases were concordant between the two methods, while 80 cases were discordant. Among the 80 cases, 76 cases had variants only identified in SMRT method, 3 cases had variants only identified in conventional method, and 1 false positive result by the traditional PCR detection technology. Except the three variants in HS40 and HBG1-HBG2 loci, which was beyond the design of SMRT method in this study, all the other discordant variants identified by SMRT were validated by further Sanger sequencing or MLPA. The hematological phenotypic parameters of 80 discordant cases were also analyzed. SMRT technology increased the positive detection rate of thalassemia genes, and detected rare thalassemia cases with variable phenotypes, which had great significance for clinical thalassemia gene screening.

Reach on Wireless Sensor Networks Security Issues and IDS Model

2014 ◽  
Vol 530-531 ◽  
pp. 45-49
Author(s):  
Jian Gang Tang
Keyword(s):  
Intrusion Detection ◽  
Real Time ◽  
Detection System ◽  
Detection Accuracy ◽  
Security Measures ◽  
Security Technology ◽  
Single Node ◽  
Security Issues ◽  
Network Intrusion ◽  
Wsn Security

Security measures could not absolutely prevent network intrusion. The security technology of intrusion detection system had made up for the lack of preventive measures; it could provide real-time intrusion detection and take appropriate protection for network. The research directions of WSN security were how to improve security strength and prolong the life of nodes, how to enhance the preventive ability of intelligent security system and real-time detection with high detection accuracy. This paper analyzed the typical network intrusion and defensive strategies, and researched WSN intrusion detection model by analyzing the typical algorithm. IDS model was divided into three types the first was based on single-node detection, the other was based on Multi-node peer cooperative, and the third was based on task decomposition level. Finally the paper gave the main research topic and direction for WSN security issues.

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