scholarly journals An Intuitionistic Calculus to Complex Abnormal Event Recognition on Data Streams

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Zhao Lijun ◽  
Hu Guiqiu ◽  
Li Qingsheng ◽  
Ding Guanhua
Keyword(s):  
Data Streams ◽  
Data Stream ◽  
Parametric Representation ◽  
Event Recognition ◽  
Time Interval ◽  
Time Data ◽  
Multiple Systems ◽  
Analysis System ◽  
Data Analysis System ◽  
High Level

Data mining in real-time data streams is associated with multiple types of uncertainty, which often leads the respective categorizers to make erroneous predictions related to the presence or absence of complex events. But recognizing complex abnormal events, even those that occur in extremely rare cases, offers significant support to decision-making systems. Therefore, there is a need for robust recognition mechanisms that will be able to predict or recognize when an abnormal event occurs or will occur on a data stream. Considering this need, this paper presents an Intuitionistic Tumbling Windows event calculus (ITWec) methodology. It is an innovative data analysis system that combines for the first time in the literature a set of multiple systems for Complex Abnormal Event Recognition (CAER). In the proposed system, the probabilities of the existence of a high-level complex abnormal event for each period are initially calculated nonparametrically, based on the probabilities of the low-level events associated with it. Because cumulative results are sought in consecutive, nonoverlapping sections of the data stream, the method uses the clearly defined rules of initialization and termination of the tumbling windows method, where there is an explicit determination of the time interval within which several blocks of a particular stream are investigated window. Finally, the number of maximum probable intervals in which an event is likely to occur based on a certain probability threshold is calculated, based on a parametric representation of intuitively fuzzy sets.

Knowledge Discovery From Evolving Data Streams

2019 ◽  
pp. 19-39
Author(s):  
Prasanna Lakshmi Kompalli
Keyword(s):  
Real Time ◽  
Data Streams ◽  
Data Stream ◽  
Concept Drift ◽  
Data Stream Mining ◽  
Time Data ◽  
Stream Mining ◽  
New Challenges ◽  
Mining Data Streams ◽  
Different Sources

Data coming from different sources is referred to as data streams. Data stream mining is an online learning technique where each data point must be processed as the data arrives and discarded as the processing is completed. Progress of technologies has resulted in the monitoring these data streams in real time. Data streams has created many new challenges to the researchers in real time. The main features of this type of data are they are fast flowing, large amounts of data which are continuous and growing in nature, and characteristics of data might change in course of time which is termed as concept drift. This chapter addresses the problems in mining data streams with concept drift. Due to which, isolating the correct literature would be a grueling task for researchers and practitioners. This chapter tries to provide a solution as it would be an amalgamation of all techniques used for data stream mining with concept drift.

scholarly journals Real-time detection of neural oscillation bursts allows behaviourally relevant neurofeedback

2019 ◽  
Author(s):  
Golan Karvat ◽  
Artur Schneider ◽  
Mansour Alyahyaey ◽  
Florian Steenbergen ◽  
Ilka Diester
Keyword(s):  
Real Time ◽  
Neurofeedback Training ◽  
Time Data ◽  
Beta Band ◽  
Neural Oscillation ◽  
Oscillatory Power ◽  
Analysis System ◽  
Data Analysis System ◽  
Induced Changes

AbstractNeural oscillations are increasingly interpreted as transient bursts, yet a method to measure these short-lived events in real-time is missing. Here we present a real-time data analysis system, capable to detect short and narrowband bursts, and demonstrate its usefulness for volitional increase of beta-band burst-rate in rats. This neurofeedback-training induced changes in overall oscillatory power, and bursts could be decoded from the movement of the rats, thus enabling future investigation of the role of oscillatory bursts.

scholarly journals Targeted Adaptable Sample for Accurate and Efficient Quantile Estimation in Non-Stationary Data Streams

2019 ◽  
Vol 1 (3) ◽  
pp. 848-870
Author(s):  
Ognjen Arandjelović
Keyword(s):  
Data Streams ◽  
Data Stream ◽  
Buffer Capacity ◽  
Comprehensive Evaluation ◽  
Estimation Algorithm ◽  
Quantile Estimation ◽  
Motion Features ◽  
Synthetic Datasets ◽  
High Level ◽  
Stochastic Properties

The need to detect outliers or otherwise unusual data, which can be formalized as the estimation a particular quantile of a distribution, is an important problem that frequently arises in a variety of applications of pattern recognition, computer vision and signal processing. For example, our work was most proximally motivated by the practical limitations and requirements of many semi-automatic surveillance analytics systems that detect abnormalities in closed-circuit television (CCTV) footage using statistical models of low-level motion features. In this paper, we specifically address the problem of estimating the running quantile of a data stream with non-stationary stochasticity when the absolute (rather than asymptotic) memory for storing observations is severely limited. We make several major contributions: (i) we derive an important theoretical result that shows that the change in the quantile of a stream is constrained regardless of the stochastic properties of data; (ii) we describe a set of high-level design goals for an effective estimation algorithm that emerge as a consequence of our theoretical findings; (iii) we introduce a novel algorithm that implements the aforementioned design goals by retaining a sample of data values in a manner adaptive to changes in the distribution of data and progressively narrowing down its focus in the periods of quasi-stationary stochasticity; and (iv) we present a comprehensive evaluation of the proposed algorithm and compare it with the existing methods in the literature on both synthetic datasets and three large “real-world” streams acquired in the course of operation of an existing commercial surveillance system. Our results and their detailed analysis convincingly and comprehensively demonstrate that the proposed method is highly successful and vastly outperforms the existing alternatives, especially when the target quantile is high-valued and the available buffer capacity severely limited.

Real-time EELS spectra classification using Principal Components Analysis (PCA) and Artificial Neural Networks (ANN)

2001 ◽  
Vol 7 (S2) ◽  
pp. 1164-1165
Author(s):  
P-G Åstrand ◽  
S. Csillag
Keyword(s):  
Data Analysis ◽  
Real Time ◽  
Short Exposure ◽  
Data Recording ◽  
Time Data ◽  
Real Time Data ◽  
Recent Developments ◽  
Analysis System ◽  
Data Analysis System ◽  
Exciting Possibility

Recent developments in detector technology [1] for EELS and Energy Filtered TEM has made possible to obtain large number of spectra and energy filtered images during very short exposure times. This in turn opens the exciting possibility of studying time dependent processes in the electron microscope, during exposure to the electron beam as well as the study of different radiation sensitive samples which are being degraded during lengthily data recording. This kind of data recording generates a large amount of data and manual data analysis should be avoided in order to be able to fully benefit from the improved sensitivity and increased speed of these new detectors. Thus a fast, real-time data analysis system is highly desirable.A system for real-time data analysis (spectra classification) of data generated from such a detector has been simulated in a program based on the object oriented C++ framework ROOT [2][3].

scholarly journals MCDAStream: a real-time data stream clustering based on micro-cluster density and attraction

2018 ◽  
Vol 7 (2) ◽  
pp. 270 ◽  
Author(s):  
Shyam Sunder Reddy K ◽  
Shoba Bindu C
Keyword(s):  
Real Time ◽  
Data Streams ◽  
Data Stream ◽  
Clustering Algorithm ◽  
Positional Information ◽  
Time Data ◽  
Stream Clustering ◽  
Real Time Data ◽  
Cluster Density ◽  
Data Stream Clustering

Real-time data stream clustering has been widely used in many fields, and it can extract useful information from massive sets of data. Most of the existing density-based algorithms cluster the data streams based on the density within the micro-clusters. These algorithms completely omit the data density in the area between the micro-clusters and recluster the micro-clusters based on erroneous assumptions about the distribution of the data within and between the micro-clusters that lead to poor clustering results. This paper describes a novel density-based clustering algorithm for evolving data streams called MCDAStream, which clusters the data stream based on micro-cluster density and attraction between the micro-clusters. The attraction of micro-clusters characterizes the positional information of the data points in each micro-cluster. We generate better clustering results by considering both micro-cluster density and attraction of micro-clusters. The quality of the proposed algorithm is evaluated on various synthetic and real-time datasets with distinct characteristics and quality metrics.

The Design and Implementation of Leveling Model and Data Analysis System of Eleven Roller Leveler

2010 ◽  
Vol 145 ◽  
pp. 443-448 ◽  
Author(s):  
Ying Hu ◽  
Xiao Hong Hao ◽  
Qing Xue Huang ◽  
Qiu Shu Wang ◽  
Shao Zhen Jin ◽  
...  
Keyword(s):  
Process Control ◽  
Data Management ◽  
Management System ◽  
Process Model ◽  
Data Management System ◽  
Automatic Process ◽  
Time Data ◽  
Automatic Process Control ◽  
Analysis System ◽  
Data Analysis System

For establishing an automatic process control and management system of leveler, we need to combine the software of the automatic process control and the software of real-time data management system. This article introduces something about the Leveling automatic process model, which include software’s structure, configuration, working principle and achievement. What's more, this system achieved the data management of leveling process and introduced the system configuration, functional structure. The whole system has been applied in multiple leveler and obtained good results, improving the system automation and straightening accuracy.

scholarly journals Ambient backscatter communication-based smart 5G IoT network

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Qiang Liu ◽  
Songlin Sun ◽  
Xueguang Yuan ◽  
Yang’an Zhang
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Data Transmission ◽  
Big Data Analysis ◽  
Time Data ◽  
Backscatter Communication ◽  
Data Transmission System ◽  
Analysis System ◽  
Data Analysis System ◽  
Ambient Backscatter

AbstractIn this paper, we propose an ambient backscatter communication-based smart 5G IoT network. The network consists of two parts, namely a real-time data transmission system based on ambient backscatter communication and a real-time big data analysis system based on the combination of shallow neural networks and deep neural networks. The real-time data transmission system based on ambient backscatter communication can extend the standby time of data collection equipment, reduce the size of the equipment, and increase the comfort of wearing. The real-time big data analysis system combining the shallow neural network and the deep neural network can greatly reduce the pressure caused by the frequent deep neural network calculations of the MEC and greatly reduce the energy consumed by the MEC for remote real-time monitoring.

Solving the Authentication Problem for Real-Time Data Streams

2014 ◽  
Vol 513-517 ◽  
pp. 1356-1360
Author(s):  
Fang Nian Wang ◽  
Shen Shen Wang ◽  
Wan Fang Che ◽  
Yun Bai ◽  
Cong Niu
Keyword(s):  
Real Time ◽  
Data Streams ◽  
Digital Signature ◽  
Hash Function ◽  
Data Stream ◽  
Transmission Characteristics ◽  
Time Data ◽  
Real Time Data ◽  
Authentication Schemes ◽  
Simulation Results

For the authentication problem of real-time data stream, the authentication schemes for real-time data stream based on digital signature are studied in this paper. According to the transmission characteristics of real-time data stream, the concept of authentication window is proposed, and three authentication schemes are designed based on the hash function and digital signature technology. In the three schemes, the authentication information of a packet is taken by itself, the next one, and the previous one, respectively. The structure and process of each scheme are presented. The authentication efficiency is studied from the two aspects of transmission density and authentication window, and the dynamic value of authentication window is also researched. Simulation results show that the second scheme has better performance, suitable for the authentication of real-time data stream travelling over insecure network.

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