A real time expert system for anomaly detection of aerators based on computer vision and surveillance cameras

Liu, Yeqi; Yu, Huihui; Gong, Chuanyang; Chen, Yingyi

doi:10.1016/j.jvcir.2020.102767

Cited by 19 publications

(13 citation statements)

References 35 publications

(43 reference statements)

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“…The number of classification and regression instances considered are 20 and 40, respectively. The RF-KLT [25], MOD-3D LIDAR [24], and UFO [15] methods are used in the comparison.…”

Section: Resultsmentioning

confidence: 99%

“…The introduced system maximizes the object detection region and minimizes the misclassification error rate. Liu et al [25] implemented a reference frame Kanade-Lucas-Tomasi (RF-KLT) algorithm for extracting the features in fixed regions. The dimensions of the features are reduced to detect the class boundaries.…”

Section: Related Workmentioning

confidence: 99%

“…In this work, two-level visual information processing is considered for performing semantic object detection. The efficiency of the system is evaluated against the methods used in [15,24], and [25] because these methods work perfectly while analyzing objects. In other words, they are effective in detecting objects and regions in a robust and cost-effective manner.…”

Section: Related Workmentioning

confidence: 99%

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A two-level computer vision-based information processing method for improving the performance of human–machine interaction-aided applications

Alfarraj

Tolba

2020

Complex Intell. Syst.

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The computer vision (CV) paradigm is introduced to improve the computational and processing system efficiencies through visual inputs. These visual inputs are processed using sophisticated techniques for improving the reliability of human–machine interactions (HMIs). The processing of visual inputs requires multi-level data computations for achieving application-specific reliability. Therefore, in this paper, a two-level visual information processing (2LVIP) method is introduced to meet the reliability requirements of HMI applications. The 2LVIP method is used for handling both structured and unstructured data through classification learning to extract the maximum gain from the inputs. The introduced method identifies the gain-related features on its first level and optimizes the features to improve information gain. In the second level, the error is reduced through a regression process to stabilize the precision to meet the HMI application demands. The two levels are interoperable and fully connected to achieve better gain and precision through the reduction in information processing errors. The analysis results show that the proposed method achieves 9.42% higher information gain and a 6.51% smaller error under different classification instances compared with conventional methods.

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“…The number of classification and regression instances considered are 20 and 40, respectively. The RF-KLT [25], MOD-3D LIDAR [24], and UFO [15] methods are used in the comparison.…”

Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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A two-level computer vision-based information processing method for improving the performance of human–machine interaction-aided applications

Alfarraj

Tolba

2020

Complex Intell. Syst.

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“…Based on the facts and rules, a decision is made on whether the monitored behavior is "normal" or whether there is an anomaly [10]. The main disadvantage of such systems is their high computational complexity (in the general case), in particular at the detection of anomalies [27].…”

Section: Background On Network Anomaly Detection Methodsmentioning

confidence: 99%

A Software Deep Packet Inspection System for Network Traffic Analysis and Anomaly Detection

Song

Beshley

Przystupa

et al. 2020

Sensors

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In this paper, to solve the problem of detecting network anomalies, a method of forming a set of informative features formalizing the normal and anomalous behavior of the system on the basis of evaluating the Hurst (H) parameter of the network traffic has been proposed. Criteria to detect and prevent various types of network anomalies using the Three Sigma Rule and Hurst parameter have been defined. A rescaled range (RS) method to evaluate the Hurst parameter has been chosen. The practical value of the proposed method is conditioned by a set of the following factors: low time spent on calculations, short time required for monitoring, the possibility of self-training, as well as the possibility of observing a wide range of traffic types. For new DPI (Deep Packet Inspection) system implementation, algorithms for analyzing and captured traffic with protocol detection and determining statistical load parameters have been developed. In addition, algorithms that are responsible for flow regulation to ensure the QoS (Quality of Services) based on the conducted static analysis of flows and the proposed method of detection of anomalies using the parameter Hurst have been developed. We compared the proposed software DPI system with the existing SolarWinds Deep Packet Inspection for the possibility of network traffic anomaly detection and prevention. The created software components of the proposed DPI system increase the efficiency of using standard intrusion detection and prevention systems by identifying and taking into account new non-standard factors and dependencies. The use of the developed system in the IoT communication infrastructure will increase the level of information security and significantly reduce the risks of its loss.

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“…But this device has a low sorting accuracy, which is not applicable for precious minerals. An interesting example is the contactless sorting of products using video cameras [11]- [14] and the processing of the resulting images for sorting by the average diameter of the products.…”

Section: Introductionmentioning

confidence: 99%

Intelligent machine for sorting semi-precious minerals

Polishchuk

Tkach

Parkhomey

et al. 2021

IJEECS

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This paper proposes is devoted to solving the problem of sorting workpieces from minerals and semi-precious stones that are used in jewelry production. Intensive development of production leads to a reduction of natural resources, including semi-precious minerals. Therefore, the task of saving the use of minerals is relevant. The solution is based on the idea a new method for sorting semi-precious minerals is proposed, as well as the design of an intelligent installation for implementing this method. An increase in the accuracy of sorting semiprecious minerals has been achieved through the use of charge-coupled device (CCD) matrices, which automatically determine the volume of each mineral in 3D space. From the simulation work, it was found in comparison with manual sorting, the proposed method has reduced the amount of waste in the manufacture of jewelry by 15% ... 20%. Experimental studies were carried out using the example of such a semiprecious mineral as amber. The cost of raw amber in the amount of one kilogram on the modern market is in the range of 1200…2200 US dollars. The result confirms that the method is superior to the alternative method, with the achieved average waste saving of 20%, the proposed sorting technology makes it possible to obtain a profit of 240...440 US dollars per kilogram of amber. Ultimately, these solutions can improve the efficiency of jewelry production.

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A real time expert system for anomaly detection of aerators based on computer vision and surveillance cameras

Cited by 19 publications

References 35 publications

A two-level computer vision-based information processing method for improving the performance of human–machine interaction-aided applications

A two-level computer vision-based information processing method for improving the performance of human–machine interaction-aided applications

A Software Deep Packet Inspection System for Network Traffic Analysis and Anomaly Detection

Intelligent machine for sorting semi-precious minerals

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