Soft Fault Detection Algorithms for Multi-Parallel Data Streams Under the Cloud Computing

2018 ◽  
Vol 22 (7) ◽  
pp. 1114-1119
Author(s):  
Hongbing Meng ◽  
Keyword(s):  
Fault Detection ◽  
Data Streams ◽  
Data Stream ◽  
Error Probability ◽  
Detection Efficiency ◽  
Detection Algorithm ◽  
Traditional Methods ◽  
Detection Algorithms ◽  
Parallel Data ◽  
Soft Fault

In the fault detection of multi-parallel data streams, the error probability of traditional methods is large, which cannot effectively meet the soft fault detection for multi-parallel data stream, causing the problem of low detection efficiency. A soft fault detection algorithm based on adaptive multi-parallel data stream is proposed. The soft fault feature in the data stream is extracted, and the adaptive soft fault detection algorithm is used to detect the fault of the multi-parallel data stream, which can overcome the disadvantages of traditional methods, effectively improve the efficiency, safety and the accuracy. Experimental results showed that the proposed method can effectively improve the efficiency of fault detection.

scholarly journals An Efficient Robust Multiple Watermarking Algorithm for Vector Geographic Data

Information ◽  
2018 ◽  
Vol 9 (12) ◽  
pp. 296 ◽  
Author(s):  
Yingying Wang ◽  
Chengsong Yang ◽  
Changqing Zhu ◽  
Kaimeng Ding
Keyword(s):  
Detection Efficiency ◽  
Detection Threshold ◽  
Detection Algorithm ◽  
Multiple Watermarking ◽  
Detection Model ◽  
Detection Algorithms ◽  
Geographic Data ◽  
Multiple Watermarks ◽  
Watermark Algorithm ◽  
Made In

Vector geographic data play an important role in location information services. Digital watermarking has been widely used in protecting vector geographic data from being easily duplicated by digital forensics. Because the production and application of vector geographic data refer to many units and departments, the demand for multiple watermarking technology is increasing. However, multiple watermarking algorithm for vector geographic data draw less attention, and there are many urgent problems to be solved. Therefore, an efficient robust multiple watermark algorithm for vector geographic data is proposed in this paper. The coordinates in vector geographic data are first randomly divided into non-repetitive sets. The multiple watermarks are then embedded into the different sets. In watermark detection correlation, the Lindeberg theory is used to build a detection model and to confirm the detection threshold. Finally, experiments are made in order to demonstrate the detection algorithm, and to test its robustness against common attacks, especially against cropping attacks. The experimental results show that the proposed algorithm is robust against the deletion of vertices, addition of vertices, compression, and cropping attacks. Moreover, the proposed detection algorithm is compatible with single watermarking detection algorithms, and it has good performance in terms of detection efficiency.

scholarly journals Detecting Metachanges in Data Streams from the Viewpoint of the MDL Principle

Entropy ◽  
2019 ◽  
Vol 21 (12) ◽  
pp. 1134 ◽  
Author(s):  
Shintaro Fukushima ◽  
Kenji Yamanishi
Keyword(s):  
Data Streams ◽  
Data Stream ◽  
Minimum Description Length ◽  
Detection Algorithm ◽  
Change Points ◽  
Detection Methods ◽  
Code Length ◽  
Warning Signals ◽  
Mdl Principle ◽  
Synthetic Datasets

This paper addresses the issue of how we can detect changes of changes, which we call metachanges, in data streams. A metachange refers to a change in patterns of when and how changes occur, referred to as “metachanges along time” and “metachanges along state”, respectively. Metachanges along time mean that the intervals between change points significantly vary, whereas metachanges along state mean that the magnitude of changes varies. It is practically important to detect metachanges because they may be early warning signals of important events. This paper introduces a novel notion of metachange statistics as a measure of the degree of a metachange. The key idea is to integrate metachanges along both time and state in terms of “code length” according to the minimum description length (MDL) principle. We develop an online metachange detection algorithm (MCD) based on the statistics to apply it to a data stream. With synthetic datasets, we demonstrated that MCD detects metachanges earlier and more accurately than existing methods. With real datasets, we demonstrated that MCD can lead to the discovery of important events that might be overlooked by conventional change detection methods.

The Design of MIMO Precoders Based on Decomposition Algorithms

2021 ◽  
Vol I (I) ◽  
Author(s):  
S Markkandan ◽  
S Lakshmi Narayanan
Keyword(s):  
Data Streams ◽  
Data Stream ◽  
Transmission Rate ◽  
Geometric Mean ◽  
Mimo Channel ◽  
Decomposition Algorithms ◽  
Parallel Data ◽  
Multiple Input ◽  
Value Decomposition ◽  
Channel Decomposition

The Wireless Communication over Multiple Input and Multiple Output (MIMO) channel increases transmission rate by splitting the input data stream into a plethora of parallel data streams that are transmitted in simultaneously. The goal of precoding at the transmitter is to divide the channel into numerous unconnected subchannels so that many data streams may be sent out at the same time. This article examines a MIMO precoder's performance utilising different channel decomposition method, as well as its computational complexity in terms of the number of floating-point operations (FLOPs). Singular Value Decomposition (SVD), Geometric Mean Decomposition (GMD), LDLH, LU, Schur, QR, and Jordan decomposition are among the techniques discussed. According to simulation findings, precoding for MIMO based on QR decomposition beats all other precoding techniques based on channel decomposition in terms of BER performance and requires less FLOPs.

DESIGN AND APPLICATION OF BOUNDING VOLUME HIERARCHY COLLISION DETECTION ALGORITHM BASED ON VIRTUAL SPHERE

2019 ◽  
Vol 19 (07) ◽  
pp. 1940044
Author(s):  
MONAN WANG ◽  
SHAOYONG CHEN ◽  
QIYOU YANG
Keyword(s):  
Collision Detection ◽  
Detection Efficiency ◽  
Vastus Lateralis ◽  
Detection Algorithm ◽  
Virtual Surgery ◽  
Vastus Lateralis Muscle ◽  
Detection Algorithms ◽  
Bounding Volume ◽  
High Detection Efficiency ◽  
Bounding Volume Hierarchy

The result of collision detection is closely related to the further deformation or cutting action of soft tissue. In order to further improve the efficiency and stability of collision detection, in this paper, a collision detection algorithm of bounding volume hierarchy based on virtual sphere was proposed. The proposed algorithm was validated and the results show that the detection efficiency of the bounding volume hierarchy algorithm based on virtual sphere is higher than that of the serial hybrid bounding volume hierarchy algorithm and the parallel hybrid bounding volume hierarchy algorithm. Different collision detection algorithms were tested and the results show that the collision detection algorithm based on virtual sphere has high detection efficiency and good stability. As the number of triangular patches increased, the advantage was more and more obvious. Finally, the proposed algorithm was applied to two large and medium-sized virtual scenes to implement the collision detection between the vastus lateralis muscle, thigh and surgical instrument. Based on the virtual sphere, the collision detection algorithm of bounding volume hierarchy can implement efficient and stable collision detection in a virtual surgery system. Meanwhile, the algorithm can be combined with other acceleration algorithms (such as the multithread acceleration algorithm) to further improve detection efficiency.

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

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.

scholarly journals Improved Streaming Quotient Filter: A Duplicate Detection Approach for Data Streams

2020 ◽  
Vol 17 (5) ◽  
pp. 769-777
Author(s):  
Shiwei Che ◽  
Wu Yang ◽  
Wei Wang
Keyword(s):  
Data Streams ◽  
Data Stream ◽  
Parallel Implementation ◽  
Academic Research ◽  
Bloom Filter ◽  
Error Rates ◽  
Data Detection ◽  
Detection Algorithms ◽  
Redundant Data ◽  
Detection Approach

The unprecedented development and popularization of the Internet, combined with the emergence of a variety of modern applications, such as search engines, online transactions, climate warning systems and so on, enables the worldwide storage of data to grow unprecedented. Efficient storage, management and processing of such huge amounts of data has become an important academic research topic. The detection and removal of duplicate and redundant data from such multi-trillion data, while ensuring resource and computational efficiency, has constituted a challenging area of research.Because of the fact that all the data of potentially unbounded data streams can not be stored, and the need to delete duplicated data as accurately as possible, intelligent approximate duplicate data detection algorithms are urgently required. Many well-known methods based on the bitmap structure, Bloom Filter and its variants are listed in the literature. In this paper, we propose a new data structure, Improved Streaming Quotient Filter (ISQF), to efficiently detect and remove duplicate data in a data stream. ISQF intelligently stores the signatures of elements in a data stream, while using an eviction strategy to provide near zero error rates. We show that ISQF achieves near optimal performance with fairly low memory requirements, making it an ideal and efficient method for repeated data detection. It has a very low error rate. Empirically, we compared ISQF with some existing methods (especially Steaming Quotient Filter (SQF)). The results show that our proposed method outperforms theexisting methods in terms of memory usage and accuracy.We also discuss the parallel implementation of ISQF

Large Tanning Machine Fault Detection Algorithm Research

2014 ◽  
Vol 602-605 ◽  
pp. 2035-2037
Author(s):  
Yi Li
Keyword(s):  
Fault Detection ◽  
Least Squares ◽  
External Environment ◽  
Detection Algorithm ◽  
Experimental Results ◽  
Support Vector ◽  
Traditional Methods ◽  
Detection Process ◽  
Machine Fault ◽  
Random Fluctuations

In fault detection process of large tanning machine, fault signal fluctuations are susceptibly caused by interference of external environment. The traditional methods are difficult to accurately classify fault detection of such random fluctuations, resulting in latter detection with low accuracy. To avoid these shortcomings, support vector algorithm based on least squares is proposed for fault detection of large tanning machine. Experimental results show that the algorithm can improve the accuracy of fault detection.

scholarly journals Ablation studies on YOLOFruit detection algorithm for fruit harvesting robot using deep learning

2021 ◽  
Vol 922 (1) ◽  
pp. 012001
Author(s):  
O M Lawal ◽  
Z Huamin ◽  
Z Fan
Keyword(s):  
Detection Efficiency ◽  
Activation Function ◽  
Detection Algorithm ◽  
Detection Accuracy ◽  
Detection Time ◽  
Detection Algorithms ◽  
Spatial Pyramid Pooling ◽  
Feature Pyramid ◽  
Harvesting Robot ◽  
Spatial Pyramid

Abstract Fruit detection algorithm as an integral part of harvesting robot is expected to be robust, accurate, and fast against environmental factors such as occlusion by stem and leaves, uneven illumination, overlapping fruit and many more. For this reason, this paper explored and compared ablation studies on proposed YOLOFruit, YOLOv4, and YOLOv5 detection algorithms. The final selected YOLOFruit algorithm used ResNet43 backbone with Combined activation function for feature extraction, Spatial Pyramid Pooling Network (SPPNet) for detection accuracies, Feature Pyramid Network (FPN) for feature pyramids, Distance Intersection Over Union-Non Maximum Suppression (DIoU-NMS) for detection efficiency and accuracy, and Complete Intersection Over Union (CIoU) loss for faster and better performance. The obtained results showed that the average detection accuracy of YOLOFruit at 86.2% is 1% greater than YOLOv4 at 85.2% and 4.3% higher than YOLOv5 at 81.9%, while the detection time of YOLOFruit at 11.9ms is faster than YOLOv4 at 16.6ms, but not with YOLOv5 at 2.7ms. Hence, the YOLOFruit detection algorithm is highly prospective for better generalization and real-time fruit detection.

Estimation of Prepressure in Hydraulic Piston Accumulators for Industrial Wind Turbines Using Multi-Model Adaptive Estimation

2019 ◽  
Author(s):  
Fredrik F. Sørensen ◽  
Malte S. von Benzon ◽  
Sigurd S. Klemmensen ◽  
Kenneth Schmidt ◽  
Jesper Liniger
Keyword(s):  
Fault Detection ◽  
Time Constant ◽  
Wind Turbines ◽  
Adaptive Estimation ◽  
Detection Algorithm ◽  
Thermal Time ◽  
Extended Kalman Filters ◽  
Gas Leakage ◽  
Detection Algorithms ◽  
Thermal Time Constant

Abstract Failures in pitch systems may cause fatal damage to industrial wind turbines. One of the main reasons for failures in pitch systems is gas leakages of hydraulic accumulators. Due to the limited accessibility of offshore turbines, automated fault detection algorithms potentially increase turbine availability. The gas leakage is detected without downtime by using a model-based approach together with a bank and extended Kalman filters (EKF’s). The residual is analyzed using multi-model adaptive estimation (MMAE). The applied accumulator model relies on a thermal time constant describing the heat flux from the gas to the surroundings. The thermal time constant has been empirically derived from a prepressure of 50 to 172 bar. The fault detection algorithm is tested experimentally in a laboratory on a 25 liters piston accumulator using a load scenario obtained from real turbine data and a prepressure range of 50–140 bar. The Bank of EKF’s can classify the prepressure within a range and thereby detect if a gas leakage has occurred before it results in failure.

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