scholarly journals Subdiffusive Source Sensing by a Regional Detection Method

Sensors ◽  
2019 ◽  
Vol 19 (16) ◽  
pp. 3504
Author(s):  
Weijing Song ◽  
Fudong Ge ◽  
YangQuan Chen
Keyword(s):  
Detection Method ◽  
Fractional Diffusion ◽  
Energy Resources ◽  
Source Detection ◽  
Diffusion Systems ◽  
Pollution Problem ◽  
Detection Approach ◽  
Hilbert Uniqueness Method ◽  
Uniqueness Method ◽  
Source Of Pollution

Motivated by the fact that the danger may increase if the source of pollution problem remains unknown, in this paper, we study the source sensing problem for subdiffusion processes governed by time fractional diffusion systems based on a limited number of sensor measurements. For this, we first give some preliminary notions such as source, detection and regional spy sensors, etc. Secondly, we investigate the characterizations of regional strategic sensors and regional spy sensors. A regional detection approach on how to solve the source sensing problem of the considered system is then presented by using the Hilbert uniqueness method (HUM). This is to identify the unknown source only in a subregion of the whole domain, which is easier to be implemented and could save a lot of energy resources. Numerical examples are finally included to test our results.

scholarly journals Enlarged Controllability of Riemann–Liouville Fractional Differential Equations

2018 ◽  
Vol 13 (9) ◽  
Author(s):  
Touria Karite ◽  
Ali Boutoulout ◽  
Delfim F. M. Torres
Keyword(s):  
Minimum Energy ◽  
Fractional Diffusion ◽  
Liouville Type ◽  
Energy Control ◽  
Penalization Method ◽  
Diffusion Systems ◽  
Minimum Energy Control ◽  
Hilbert Uniqueness Method ◽  
Uniqueness Method ◽  
Fractional Differential

We investigate exact enlarged controllability (EEC) for time fractional diffusion systems of Riemann–Liouville type. The Hilbert uniqueness method (HUM) is used to prove EEC for both cases of zone and pointwise actuators. A penalization method is given and the minimum energy control is characterized.

A Fault Detection Approach Based on Sound Signal Analysis for Equipment Monitoring

2020 ◽  
pp. 129-139
Author(s):  
Weihai Sun ◽  
Lemei Han
Keyword(s):  
Fault Detection ◽  
Signal Analysis ◽  
Energy Analysis ◽  
Detection Method ◽  
Sound Signal ◽  
Practical Significance ◽  
Time Frequency ◽  
Detection Approach ◽  
Machine Fault ◽  
Learning Data

Machine fault detection has great practical significance. Compared with the detection method that requires external sensors, the detection of machine fault by sound signal does not need to destroy its structure. The current popular audio-based fault detection often needs a lot of learning data and complex learning process, and needs the support of known fault database. The fault detection method based on audio proposed in this paper only needs to ensure that the machine works normally in the first second. Through the correlation coefficient calculation, energy analysis, EMD and other methods to carry out time-frequency analysis of the subsequent collected sound signals, we can detect whether the machine has fault.

scholarly journals A note on the exact boundary controllability for an imperfect transmission problem

2021 ◽  
Author(s):  
S. Monsurrò ◽  
A. K. Nandakumaran ◽  
C. Perugia
Keyword(s):  
Exact Controllability ◽  
Dirichlet Condition ◽  
Adjoint Problem ◽  
Imperfect Interface ◽  
External Boundary ◽  
Exact Boundary Controllability ◽  
Multipliers Method ◽  
Exact Boundary ◽  
Hilbert Uniqueness Method ◽  
Uniqueness Method

AbstractIn this note, we consider a hyperbolic system of equations in a domain made up of two components. We prescribe a homogeneous Dirichlet condition on the exterior boundary and a jump of the displacement proportional to the conormal derivatives on the interface. This last condition is the mathematical interpretation of an imperfect interface. We apply a control on the external boundary and, by means of the Hilbert Uniqueness Method, introduced by J. L. Lions, we study the related boundary exact controllability problem. The key point is to derive an observability inequality by using the so called Lagrange multipliers method, and then to construct the exact control through the solution of an adjoint problem. Eventually, we prove a lower bound for the control time which depends on the geometry of the domain, on the coefficients matrix and on the proportionality between the jump of the solution and the conormal derivatives on the interface.

A Novel Fast Pedestrian Detection Method

2015 ◽  
Vol 738-739 ◽  
pp. 538-541
Author(s):  
Fu Qiang Zhou ◽  
Yan Li
Keyword(s):  
Video Streaming ◽  
Local Binary Pattern ◽  
Detection Method ◽  
Pedestrian Detection ◽  
Original Method ◽  
Histograms Of Oriented Gradients ◽  
Detection Approach

This paper presents novel pedestrian detection approach in video streaming, which could process frames rapidly. The method is based on cascades of HOG-LBP (Histograms of Oriented Gradients-Local Binary Pattern), but combines non-negative factorization to reduce the length of the feature, aiming at realizing a more efficient way of detection, remedying the slowness of the original method. Experiments show our method can process faster than HOG and HOG-LBP, and more accurate than HOG, which has better performance in pedestrian detection in video streaming.

scholarly journals Numerical solution of time fractional diffusion systems

2017 ◽  
Vol 116 ◽  
pp. 82-94 ◽  
Author(s):  
Kevin Burrage ◽  
Angelamaria Cardone ◽  
Raffaele D'Ambrosio ◽  
Beatrice Paternoster
Keyword(s):  
Numerical Solution ◽  
Fractional Diffusion ◽  
Diffusion Systems

A Dynamic Malware Detection Approach by Mining the Frequency of API Calls

2014 ◽  
Vol 519-520 ◽  
pp. 309-312 ◽  
Author(s):  
Jin Rong Bai ◽  
Zhen Zhou An ◽  
Guo Zhong Zou ◽  
Shi Guang Mu
Keyword(s):  
Detection Rate ◽  
Detection Method ◽  
Malware Detection ◽  
Experimental Results ◽  
Detection Approach ◽  
Dynamic Detection

Dynamic detection method based on software behavior is an efficient and effective way for anti-virus technology. Malware and benign executable differ mainly in the implementation of some special behavior to propagation and destruction. A program's execution flow is essentially equivalent to the stream of API calls. Analyzing the API calls frequency from six kinds of behaviors in the same time has the very well differentiate between malicious and benign executables. This paper proposed a dynamic malware detection approach by mining the frequency of sensitive native API calls and described experiments conducted against recent Win32 malware. Experimental results indicate that the detection rate of proposed method is 98% and the value of the AUC is 0.981. Furthermore, proposed method can identify known and unknown malware.

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