A multiple pattern complex event detection scheme based on decomposition and merge sharing for massive event streams

Quickly detecting related primitive events for multiple complex events from massive event stream usually faces with a great challenge due to their single pattern characteristic of the existing complex event detection methods. Aiming to solve the problem, a multiple pattern complex event detection scheme based on decomposition and merge sharing is proposed in this article. The achievement of this article lies that we successfully use decomposition and merge sharing technology to realize the high-efficient detection for multiple complex events from massive event streams. Specially, in our scheme, we first use decomposition sharing technology to decompose pattern expressions into multiple subexpressions, which can provide many sharing opportunities for subexpressions. We then use merge sharing technology to construct a multiple pattern complex events by merging sharing all the same prefix, suffix, or subpattern into one based on the above decomposition results. As a result, our proposed detection method in this article can effectively solve the above problem. The experimental results show that the proposed detection method in this article outperforms some general detection methods in detection model and detection algorithm in multiple pattern complex event detection as a whole.

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Multi-Level Clustering-Based Outlier’s Detection (MCOD) Using Self-Organizing Maps

Big Data and Cognitive Computing ◽

10.3390/bdcc4040024 ◽

2020 ◽

Vol 4 (4) ◽

pp. 24

Author(s):

Menglu Li ◽

Rasha Kashef ◽

Ahmed Ibrahim

Keyword(s):

Outlier Detection ◽

Detection Method ◽

Detection Algorithm ◽

Detection Methods ◽

Self Organizing Maps ◽

Business Outcomes ◽

Online Transactions ◽

Business Applications ◽

Efficient Detection ◽

Multi Level

Outlier detection is critical in many business applications, as it recognizes unusual behaviours to prevent losses and optimize revenue. For example, illegitimate online transactions can be detected based on its pattern with outlier detection. The performance of existing outlier detection methods is limited by the pattern/behaviour of the dataset; these methods may not perform well without prior knowledge of the dataset. This paper proposes a multi-level outlier detection algorithm (MCOD) that uses multi-level unsupervised learning to cluster the data and discover outliers. The proposed detection method is tested on datasets in different fields with different sizes and dimensions. Experimental analysis has shown that the proposed MCOD algorithm has the ability to improving the outlier detection rate, as compared to the traditional anomaly detection methods. Enterprises and organizations can adopt the proposed MCOD algorithm to ensure a sustainable and efficient detection of frauds/outliers to increase profitability (and/or) to enhance business outcomes.

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Bacterial colonies detecting and counting based on enhanced CNN detection method

E3S Web of Conferences ◽

10.1051/e3sconf/202123302012 ◽

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%.

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Research on a Surface Defect Detection Algorithm Based on MobileNet-SSD

Applied Sciences ◽

10.3390/app8091678 ◽

2018 ◽

Vol 8 (9) ◽

pp. 1678 ◽

Cited By ~ 45

Author(s):

Yiting Li ◽

Haisong Huang ◽

Qingsheng Xie ◽

Liguo Yao ◽

Qipeng Chen

Keyword(s):

Defect Detection ◽

Surface Defect ◽

Surface Defects ◽

Detection Method ◽

Detection Algorithm ◽

Single Shot ◽

Detection Model ◽

Machine Learning Methods ◽

Network Methods ◽

Surface Defect Detection

This paper aims to achieve real-time and accurate detection of surface defects by using a deep learning method. For this purpose, the Single Shot MultiBox Detector (SSD) network was adopted as the meta structure and combined with the base convolution neural network (CNN) MobileNet into the MobileNet-SSD. Then, a detection method for surface defects was proposed based on the MobileNet-SSD. Specifically, the structure of the SSD was optimized without sacrificing its accuracy, and the network structure and parameters were adjusted to streamline the detection model. The proposed method was applied to the detection of typical defects like breaches, dents, burrs and abrasions on the sealing surface of a container in the filling line. The results show that our method can automatically detect surface defects more accurately and rapidly than lightweight network methods and traditional machine learning methods. The research results shed new light on defect detection in actual industrial scenarios.

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An Efficient Pedestrian Detection Method Based on YOLOv2

Mathematical Problems in Engineering ◽

10.1155/2018/3518959 ◽

2018 ◽

Vol 2018 ◽

pp. 1-10 ◽

Cited By ~ 5

Author(s):

Zhongmin Liu ◽

Zhicai Chen ◽

Zhanming Li ◽

Wenjin Hu

Keyword(s):

Detection Method ◽

Size Range ◽

State Of The Art ◽

Pedestrian Detection ◽

Semantic Segmentation ◽

Detection Methods ◽

Detection Model ◽

Great Progress ◽

Good Trade ◽

Speed And Accuracy

In recent years, techniques based on the deep detection model have achieved overwhelming improvements in the accuracy of detection, which makes them being the most adapted for the applications, such as pedestrian detection. However, speed and accuracy are a pair of contradictions that always exist and have long puzzled researchers. How to achieve the good trade-off between them is a problem we must consider while designing the detectors. To this end, we employ the general detector YOLOv2, a state-of-the-art method in the general detection tasks, in the pedestrian detection. Then we modify the network parameters and structures, according to the characteristics of the pedestrians, making this method more suitable for detecting pedestrians. Experimental results in INRIA pedestrian detection dataset show that it has a fairly high detection speed with a small precision gap compared with the state-of-the-art pedestrian detection methods. Furthermore, we add weak semantic segmentation networks after shared convolution layers to illuminate pedestrians and employ a scale-aware structure in our model according to the characteristics of the wide size range in Caltech pedestrian detection dataset, which make great progress under the original improvement.

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Importance And Ill-Effects Of Nanoparticles: Sensors For Its Identification

Current Analytical Chemistry ◽

10.2174/1573411016666200102113529 ◽

2020 ◽

Vol 16 ◽

Author(s):

Vallidevi Krishnamurthy ◽

Kannappan Panchamoorthy Gopinath ◽

Ganeshraj Vanathi ◽

Suresh Ganapathy Shivanirudh

Keyword(s):

Analytical Techniques ◽

Cost Effective ◽

Nano Particles ◽

Detection Methods ◽

Spectroscopic Techniques ◽

Detection Techniques ◽

Efficient Detection ◽

High Efficient ◽

Living Organisms ◽

Application Fields

Background: Nanoparticle has become an important part of many modern scientific technologies. Also, there are many naturally occurring nanoparticle that play important role in various natural and synthetic processes. Detection of these nanoparticles is expensive and challenging. This review paper explains in detail about various sensor based methods used in detection of nanoparticles. Methodology: Sensor based analytical techniques are more accurate than other techniques. Nanoparticles may occur in air, water, solid surface and human or other living organisms’ physical environments. The detection methods include spectroscopic techniques, electrochemical methods, microcativities and microlasers based detection, optical techniques and many other high sensitive methods. All these methods and their principles are explained in this study. The importance and the ill effects of the nano particles are explained in this article. Further, the detection of a particular single nano particle is also discussed. Conclusions: The detailed comparative analysis of these methods showed that sensor based methods is highly efficient in detection of nanoparticles. The further research in this field may introduce many cost effective and high efficient detection techniques that would revolutionize the medical and other application fields.

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Machine Committee Framework for Power Grid Disturbances Analysis Using Synchrophasors Data

Smart Cities ◽

10.3390/smartcities4010001 ◽

2020 ◽

Vol 4 (1) ◽

pp. 1-16

Author(s):

Haoran Niu ◽

Olufemi A. Omitaomu ◽

Qing C. Cao

Keyword(s):

Event Detection ◽

Change Point ◽

Power Grid ◽

Detection Algorithm ◽

Change Point Detection ◽

Detection Methods ◽

Detection Accuracy ◽

Disturbance Analysis ◽

Events Detection ◽

Point Detection

Events detection is a key challenge in power grid frequency disturbances analysis. Accurate detection of events is crucial for situational awareness of the power system. In this paper, we study the problem of events detection in power grid frequency disturbance analysis using synchrophasors data streams. Current events detection approaches for power grid rely on individual detection algorithm. This study integrates some of the existing detection algorithms using the concept of machine committee to develop improved detection approaches for grid disturbance analysis. Specifically, we propose two algorithms—an Event Detection Machine Committee (EDMC) algorithm and a Change-Point Detection Machine Committee (CPDMC) algorithm. Both algorithms use parallel architecture to fuse detection knowledge of its individual methods to arrive at an overall output. The EDMC algorithm combines five individual event detection methods, while the CPDMC algorithm combines two change-point detection methods. Each method performs the detection task separately. The overall output of each algorithm is then computed using a voting strategy. The proposed algorithms are evaluated using three case studies of actual power grid disturbances. Compared with the individual results of the various detection methods, we found that the EDMC algorithm is a better fit for analyzing synchrophasors data; it improves the detection accuracy; and it is suitable for practical scenarios.

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Arching Detection Method of Slab Track in High-Speed Railway Based on Track Geometry Data

Applied Sciences ◽

10.3390/app10196799 ◽

2020 ◽

Vol 10 (19) ◽

pp. 6799

Author(s):

Zhuoran Ma ◽

Liang Gao ◽

Yanglong Zhong ◽

Shuai Ma ◽

Bolun An

Keyword(s):

High Speed ◽

Large Scale ◽

Detection Method ◽

Low Cost ◽

Detection Methods ◽

Data Conversion ◽

Track Geometry ◽

Slab Track ◽

Detection Model ◽

Characteristics Analysis

During the long-term service of slab track, various external factors (such as complicated temperature) can result in a series of slab damages. Among them, slab arching changes the structural mechanical properties, deteriorates the track geometry conditions, and even threatens the operation of trains. Therefore, it is necessary to detect slab arching accurately to achieve effective maintenance. However, the current damage detection methods cannot satisfy high accuracy and low cost simultaneously, making it difficult to achieve large-scale and efficient arching detection. To this end, this paper proposed a vision-based arching detection method using track geometry data. The main works include: (1) data nonlinear deviation correction and arching characteristics analysis; (2) data conversion and augmentation; (3) design and experiments of convolutional neural network- based detection model. The results show that the proposed method can detect arching damages effectively, and the F1-score reaches 98.4%. By balancing the sample size of each pattern, the performance can be further improved. Moreover, the method outperforms the plain deep learning network. In practice, the proposed method can be employed to detect slab arching and help to make maintenance plans. The method can also be applied to the data-based detection of other structural damages and has broad prospects.

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Establishment and Application of Reverse Transcription Recombinase-Aided Amplification as A Rapid Detection Method for SARS-COV-2

10.21203/rs.3.rs-92818/v1 ◽

2020 ◽

Author(s):

Lele Ai ◽

Wei Liu ◽

Fuqiang Ye ◽

Chenxi Ding ◽

Han Dai ◽

...

Keyword(s):

Nucleic Acid ◽

Reverse Transcription ◽

Rapid Detection ◽

Detection Method ◽

Detection Methods ◽

Influenza B Virus ◽

Influenza B ◽

The Novel ◽

Efficient Detection ◽

Novel Coronavirus

Abstract Background: By the end of August 2020, >23 million cases and 800,000 deaths were attributed to SARS-CoV-2 in >200 countries. The improvement of simple, rapid, and efficient detection methods is of great significance for the early detection, timely isolation, and protection of susceptible populations. This study aimed to provide an alternative method for the rapid detection of viral nucleic acid.Methods: This study provided a rapid nucleic acid detection method mediated by recombinant enzyme based on the novel coronavirus (SARS-CoV-2). Primers and probes were designed based on the N gene sequence of coronavirus. The method was performed at 39 °C, the detection time was short (<20 min), and the detection limit was up to 101 copies/mL.Results: The primer-probe did not show any cross-reaction with adenovirus, Zika virus, influenza B virus, and chikungunya virus, with good specificity. A total of 106 clinical throat swab samples were compared by reverse transcription recombinase-aided amplification (RT-RAA) and commercial reverse transcription-quantitative real-time polymerase chain reaction (RT-qPCR); the results were identical.Conclusions: The novel coronavirus RT-RAA method established in this study had high sensitivity, strong specificity, simple operation, and fast detection speed, and hence, is suitable for the rapid detection of novel coronavirus under the current epidemic situation.

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A Framework to Test Resistency of Detection Algorithms for Stepping-Stone Intrusion on Time-Jittering Manipulation

Wireless Communications and Mobile Computing ◽

10.1155/2021/1807509 ◽

2021 ◽

Vol 2021 ◽

pp. 1-8

Author(s):

Lixin Wang ◽

Jianhua Yang ◽

Michael Workman ◽

Peng-Jun Wan

Keyword(s):

Data Stream ◽

Detection Method ◽

Detection Algorithm ◽

Efficient Algorithms ◽

Detection Methods ◽

The Internet ◽

Stepping Stones ◽

Stepping Stone ◽

Detection Algorithms ◽

A Chain

Hackers on the Internet usually send attacking packets using compromised hosts, called stepping-stones, in order to avoid being detected and caught. With stepping-stone attacks, an intruder remotely logins these stepping-stones using programs like SSH or telnet, uses a chain of Internet hosts as relay machines, and then sends the attacking packets. A great number of detection approaches have been developed for stepping-stone intrusion (SSI) in the literature. Many of these existing detection methods worked effectively only when session manipulation by intruders is not present. When the session is manipulated by attackers, there are few known effective detection methods for SSI. It is important to know whether a detection algorithm for SSI is resistant on session manipulation by attackers. For session manipulation with chaff perturbation, software tools such as Scapy can be used to inject meaningless packets into a data stream. However, to the best of our knowledge, there are no existing effective tools or efficient algorithms to produce time-jittered network traffic that can be used to test whether an SSI detection method is resistant on intruders’ time-jittering manipulation. In this paper, we propose a framework to test resistency of detection algorithms for SSI on time-jittering manipulation. Our proposed framework can be used to test whether an existing or new SSI detection method is resistant on session manipulation by intruders with time-jittering.

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ACCUMULATIVE INTERSECTION SPACE BASED CORNER DETECTION ALGORITHM

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001408006909 ◽

2008 ◽

Vol 22 (08) ◽

pp. 1559-1586 ◽

Cited By ~ 3

Author(s):

NA LU ◽

ZUREN FENG

Keyword(s):

Monte Carlo ◽

Detection Method ◽

Detection Algorithm ◽

Search Method ◽

The Other ◽

Corner Detection ◽

Detection Methods ◽

Local Maxima ◽

Edge Detectors ◽

Theory And Experiments

There is no parametric formulation of corner, so the conventional Hough transform cannot be employed to detect corners directly. A random corner detection method is developed in this paper based on a new concept "accumulative intersection space" under Monte Carlo scheme. This method transforms the corner detection in the image space into local maxima localization in the accumulative intersection space where the intersections are accumulated by random computations. The proposed algorithm has been demonstrated by both theory and experiments. The proposed algorithm is isotropic, robust to image rotation, insensitive to noise and false corners on diagonal edges. Unlike the other existing contour based corner detection methods, our algorithm can effectively avoid the influence of the edge detectors, such as rounding corners or line interceptions. Extensive comparisons among our approach and the other detectors including Harris operator, Fei Shen and Han Wang detector, Han Wang and Brady detector, Foveated Visual Search method and SIFT feature, have shown the effectiveness of our method.

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