A NONLINEAR DEGENERATE PARABOLIC EQUATION WITH NON-LOCAL SOURCE

The global existence and finite time blow up of the positive solution for a nonlinear degenerate parabolic equation with non- local source are studied

Source localization in signed networks with effective distance

While progress has been made in information source localization,it has overlooked the prevalent friend and adversarial relationships in social networks.This paper addresses this gap by focusing on source localization in signed network models.Leveraging the topological characteristics of signed networks and transforming the propagation probability into effective distance,we propose an optimization method for observer selection.Additionally,by using the reverse propagation algorithm we present a method for information source localization in signed networks.Extensive experimental results demonstrate that a higher proportion of positive edges within signed networks contributes to more favorable source localization,and the higher the ratio of propagation rates between positive and negative edges,the more accurate the source localization becomes.Interestingly,this aligns with our observation that,in reality,the number of friends tends to be greater than the number of adversaries,and the likelihood of information propagation among friends is often higher than among adversaries.In addition,the source located at the periphery of the network is not easy to identify.Furthermore,our proposed observer selection method based on effective distance achieves higher operational efficiency and exhibits higher accuracy in information source localization,compared with three strategies for observer selection based on the classical full-order neighbor coverage.

A robust triaxial localization method of AE source using refraction path

Acoustic emission(AE)localization algorithms based on homogeneous media or single-velocity are less accurate when applied to the triaxial localization experiments.To the end,a robust triaxial localization method of AE source using refraction path is proposed.Firstly,the control equation of the refraction path is established according to the sensor coordinates and arrival times.Secondly,considering the influence of time-difference-of-arrival(TDOA)errors,the residual of the governing equation is calculated to estimate the equation weight.Thirdly,the refraction points in different directions are solved using Snell’s law and orthogonal constraints.Finally,the source coordinates are iteratively solved by weighted correction terms.The feasibility and accuracy of the proposed method are verified by pencil-lead breaking experiments.The simulation results show that the new method is almost unaffected by the refraction ratio,and always holds more stable and accurate positioning performance than the traditional method under different ratios and scales of TDOA outliers.

The effect of right versus left long-term single-sided deafness on sound source localization

Purpose:To analyze the effect of right versus left long-term single-sided deafness(SSD)on sound source localization(SSL),discuss the necessity of intervention and treatment for SSD patients,and analyze the therapeutic effect of long-term unilateral cochlear implantation(UCI)from the perspective of SSL.Methods:This study included 25 patients with SSD,11 patients with UCI,and 30 participants with normal hearing(NH).Their SSL ability was tested by obtaining their average root mean square(RMS)error values of SSL test.Results:The results showed that the RMS error value of SSD,UCI and NH groups were 52.26±20.25◦,69.84±12.14◦and 4.27±2.66◦,respectively.The ability of SSL was better in the SSD-L group than that in the SSD-R group,and no significant difference existed in the SSD-R and the UCI group.Conclusion:When bilateral deafness patients select unilateral treatment,right-side cochlear implantation may be more beneficial in terms of SSL,which means that the central auditory cortex in long-term SSD patients is affected differently based on which side their deafness occurs.

Localization method of subsynchronous oscillation source based on high-resolution time-frequency distribution image and CNN

The penetration of new energy sources such as wind power is increasing,which consequently increases the occurrence rate of subsynchronous oscillation events.However,existing subsynchronous oscillation source-identification methods primarily analyze fixed-mode oscillations and rarely consider time-varying features,such as frequency drift,caused by the random volatility of wind farms when oscillations occur.This paper proposes a subsynchronous oscillation sourcelocalization method that involves an enhanced short-time Fourier transform and a convolutional neural network(CNN).First,an enhanced STFT is performed to secure high-resolution time-frequency distribution(TFD)images from the measured data of the generation unit ports.Next,these TFD images are amalgamated to form a subsynchronous oscillation feature map that serves as input to the CNN to train the localization model.Ultimately,the trained CNN model realizes the online localization of subsynchronous oscillation sources.The effectiveness and accuracy of the proposed method are validated via multimachine system models simulating forced and natural oscillation events using the Power Systems Computer Aided Design platform.Test results show that the proposed method can localize subsynchronous oscillation sources online while considering unpredictable fluctuations in wind farms,thus providing a foundation for oscillation suppression in practical engineering scenarios.

Localization for mixed near-field and far-field sources under impulsive noise

In order to solve the problem that the performance of traditional localization methods for mixed near-field sources(NFSs)and far-field sources(FFSs)degrades under impulsive noise,a robust and novel localization method is proposed.After eliminating the impacts of impulsive noise by the weighted out-lier filter,the direction of arrivals(DOAs)of FFSs can be estimated by multiple signal classification(MUSIC)spectral peaks search.Based on the DOAs information of FFSs,the separation of mixed sources can be performed.Finally,the estimation of localizing parameters of NFSs can avoid two-dimension spectral peaks search by decomposing steering vectors.The Cramer-Rao bounds(CRB)for the unbiased estimations of DOA and range under impulsive noise have been drawn.Simulation experiments verify that the proposed method has advantages in probability of successful estimation(PSE)and root mean square error(RMSE)compared with existing localization methods.It can be concluded that the proposed method is effective and reliable in the environment with low generalized signal to noise ratio(GSNR),few snapshots,and strong impulse.

ROBUST ACOUSTIC SOURCE LOCALIZATION FOR DIGITAL HEARING AIDS IN NOISE AND REVERBERANT ENVIRONMENT

A new method in digital hearing aids to adaptively localize the speech source in noise and reverberant environment is proposed. Based on the room reverberant model and the multichannel adaptive eigenvalue decomposition （MCAED） algorithm, the proposed method can iteratively estimate impulse response coefficients between the speech source and microphones by the adaptive subgradient projection method. Then, it acquires the time delays of microphone pairs, and calculates the source position by the geometric method. Compared with the traditional normal least mean square （NLMS） algorithm, the adaptive subgradient projection method achieves faster and more accurate convergence in a low signal-to-noise ratio （SNR） environment. Simulations for glasses digital hearing aids with four-component square array demonstrate the robust performance of the proposed method.

A novel AE source localization method using clustering detection to eliminate abnormal arrivals

Due to the significant effect of abnormal arrivals on localization accuracy,a novel acoustic emission(AE)source localization method using clustering detection to eliminate abnormal arrivals is proposed in the paper.Firstly,iterative weight estimation is utilized to obtain accurate equation residuals.Secondly,according to the distribution of equation residuals,clustering detection is used to identify and exclude abnormal arrivals.Thirdly,the AE source coordinate is recalculated with remaining normal arrivals.Experimental results of pencil-lead breaks indicate that the proposed method can achieve a better localization result with and without abnormal arrivals.The results of simulation tests further demonstrate that the proposed method possesses higher localization accuracy and robustness under different anomaly ratios and scales;even with abnormal arrivals as high as 30%,the proposed localization method still holds a correct detection rate of 91.85%.

A Quasilinear Parabolic System with Nonlocal Boundary Conditions and Localized Sources

This paper investigates the properties of solutions to a quasilinear parabolic system with nonlocal boundary conditions and localized sources. Conditions for the existence of global or blow-up solutions are given. Global blow-up property and blow- up rate estimates are also derived.

Passive Localization of Multiple Sources Using Joint RSS and AOA Measurements in Spectrum Sharing System

In spectrum sharing systems,locating mul-tiple radiation sources can efficiently find out the in-truders,which protects the shared spectrum from ma-licious jamming or other unauthorized usage.Com-pared to single-source localization,simultaneously lo-cating multiple sources is more challenging in prac-tice since the association between measurement pa-rameters and source nodes are not known.More-over,the number of possible measurements-source as-sociations increases exponentially with the number of sensor nodes.It is crucial to discriminate which measurements correspond to the same source before localization.In this work,we propose a central-ized localization scheme to estimate the positions of multiple sources.Firstly,we develop two computa-tionally light methods to handle the unknown RSS-AOA measurements-source association problem.One method utilizes linear coordinate conversion to com-pute the minimum spatial Euclidean distance sum-mation of measurements.Another method exploits the long-short-term memory(LSTM)network to clas-sify the measurement sequences.Then,we propose a weighted least squares(WLS)approach to obtain the closed-form estimation of the positions by linearizing the non-convex localization problem.Numerical re-sults demonstrate that the proposed scheme could gain sufficient localization accuracy under adversarial sce-narios where the sources are in close proximity and the measurement noise is strong.

A search-free near-field source localization method with exact signal model

Most of the near-field source localization methods are developed with the approximated signal model,because the phases of the received near-field signal are highly non-linear.Nevertheless,the approximated signal model based methods suffer from model mismatch and performance degradation while the exact signal model based estimation methods usually involve parameter searching or multiple decomposition procedures.In this paper,a search-free near-field source localization method is proposed with the exact signal model.Firstly,the approximative estimates of the direction of arrival(DOA)and range are obtained by using the approximated signal model based method through parameter separation and polynomial rooting operations.Then,the approximative estimates are corrected with the exact signal model according to the exact expressions of phase difference in near-field observations.The proposed method avoids spectral searching and parameter pairing and has enhanced estimation performance.Numerical simulations are provided to demonstrate the effectiveness of the proposed method.

A Learning Particle Swarm Optimization Algorithm for Odor Source Localization

This paper is concerned with the problem of odor source localization using multi-robot system. A learning particle swarm optimization algorithm, which can coordinate a multi-robot system to locate the odor source, is proposed. First, in order to develop the proposed algorithm, a source probability map for a robot is built and updated by using concentration magnitude information, wind information, and swarm information. Based on the source probability map, the new position of the robot can be generated. Second, a distributed coordination architecture, by which the proposed algorithm can run on the multi-robot system, is designed. Specifically, the proposed algorithm is used on the group level to generate a new position for the robot. A consensus algorithm is then adopted on the robot level in order to control the robot to move from the current position to the new position. Finally, the effectiveness of the proposed algorithm is illustrated for the odor source localization problem.

Sound Source Localization Based on SRP-PHAT Spatial Spectrum and Deep Neural Network

Microphone array-based sound source localization(SSL)is a challenging task in adverse acoustic scenarios.To address this,a novel SSL algorithm based on deep neural network(DNN)using steered response power-phase transform(SRP-PHAT)spatial spectrum as input feature is presented in this paper.Since the SRP-PHAT spatial power spectrum contains spatial location information,it is adopted as the input feature for sound source localization.DNN is exploited to extract the efficient location information from SRP-PHAT spatial power spectrum due to its advantage on extracting high-level features.SRP-PHAT at each steering position within a frame is arranged into a vector,which is treated as DNN input.A DNN model which can map the SRP-PHAT spatial spectrum to the azimuth of sound source is learned from the training signals.The azimuth of sound source is estimated through trained DNN model from the testing signals.Experiment results demonstrate that the proposed algorithm significantly improves localization performance whether the training and testing condition setup are the same or not,and is more robust to noise and reverberation.

Fast parallel factor decomposition technique for coherently distributed source localization

This paper links parallel factor（PARAFAC） analysis to the problem of nominal direction-of-arrival（DOA） estimation for coherently distributed（CD） sources and proposes a fast PARAFACbased algorithm by establishing the trilinear PARAFAC model.Relying on the uniqueness of the low-rank three-way array decomposition and the trilinear alternating least squares regression, the proposed algorithm achieves nominal DOA estimation and outperforms the conventional estimation of signal parameter via rotational technique CD（ESPRIT-CD） and propagator method CD（PM-CD）methods in terms of estimation accuracy. Furthermore, by means of the initialization via the propagator method, this paper accelerates the convergence procedure of the proposed algorithm with no estimation performance degradation. In addition, the proposed algorithm can be directly applied to the multiple-source scenario,where sources have different angular distribution shapes. Numerical simulation results corroborate the effectiveness and superiority of the proposed fast PARAFAC-based algorithm.

Comparison of two Bayesian-point-estimation methods in multiple-source localization

Environmental uncertainty represents the limiting factor in matched-field localization. Within a Bayesian framework, both the environmental parameters, and the source parameters are considered to be unknown variables. However, including environmental parameters in multiple-source localization greatly increases the complexity and computational demands of the inverse problem. In the paper, the closed-form maximumlikelihood expressions for source strengths and noise variance at each frequency allow these parameters to be sampled implicitly, substantially reducing the dimensionality and difficulty of the inversion. This paper compares two Bayesian-point-estimation methods： the maximum a posteriori（MAP） approach and the marginal posterior probability density（PPD） approach to source localization. The MAP approach determines the sources locations by maximizing the PPD over all source and environmental parameters. The marginal PPD approach integrates the PPD over the unknowns to obtain a sequence of marginal probability distribution over source range or depth.Monte Carlo analysis of the two approaches for a test case involving both geoacoustic and water-column uncertainties indicates that：（1） For sensitive parameters such as source range, water depth and water sound speed, the MAP solution is better than the marginal PPD solution.（2） For the less sensitive parameters, such as,bottom sound speed, bottom density, bottom attenuation and water sound speed, when the SNR is low, the marginal PPD solution can better smooth the noise, which leads to better performance than the MAP solution.Since the source range and depth are sensitive parameters, the research shows that the MAP approach provides a slightly more reliable method to locate multiple sources in an unknown environment.

Dimension decomposition algorithm for multiple source localization using uniform circular array

A dimension decomposition(DIDE)method for multiple incoherent source localization using uniform circular array(UCA)is proposed.Due to the fact that the far-field signal can be considered as the state where the range parameter of the nearfield signal is infinite,the algorithm for the near-field source localization is also suitable for estimating the direction of arrival(DOA)of far-field signals.By decomposing the first and second exponent term of the steering vector,the three-dimensional(3-D)parameter is transformed into two-dimensional(2-D)and onedimensional(1-D)parameter estimation.First,by partitioning the received data,we exploit propagator to acquire the noise subspace.Next,the objective function is established and partial derivative is applied to acquire the spatial spectrum of 2-D DOA.At last,the estimated 2-D DOA is utilized to calculate the phase of the decomposed vector,and the least squares(LS)is performed to acquire the range parameters.In comparison to the existing algorithms,the proposed DIDE algorithm requires neither the eigendecomposition of covariance matrix nor the search process of range spatial spectrum,which can achieve satisfactory localization and reduce computational complexity.Simulations are implemented to illustrate the advantages of the proposed DIDE method.Moreover,simulations demonstrate that the proposed DIDE method can also classify the mixed far-field and near-field signals.

GLOBAL BLOW-UP FOR A HEAT SYSTEM WITH LOCALIZED SOURCES AND ABSORPTIONS

In this paper there are established the global existence and finite time blow-up results of nonnegative solution for the following parabolic system ut = △u ＋ v^P（x0, t） - au^τ, x ∈ Ω, t 〉 0, △u ＋ v^P（x0, t） - bu^τ, x ∈ Ω, t 〉 0 subject to homogeneous Dirichlet conditions and nonnegative initial data, where x0 ∈ Ω is a fixed point, p, q, r, s ≥ 1 and a, b 〉 0 are constants. In the situation when nonnegative solution （u, v） of the above problem blows up in finite time, it is showed that the blow-up is global and this differs from the local sources case. Moreover, for the special case r = s = 1, lim t→T＊（T＊-t）^p＋1/pq-1u（x,t）=（p＋1）^1/pq-1（q＋1）^p/pq-1（pq-1）^-p＋1/pq-1, lim t→T＊（T＊-t）^q＋1/pq-1u（x,t）=（p＋1）^1/pq-1（q＋1）^p/pq-1（pq-1）^-p＋1/pq-1 are obtained uniformly on compact subsets of/2, where T＊ is the blow-up time.

Source localization with minimum variance distortionless response for spherical microphone arrays

To improve localization accuracy, the spherical microphone arrays are used to capture high-order wavefield in- formation. For the far field sound sources, the array signal model is constructed based on plane wave decomposition. The spatial spectrum function is calculated by minimum variance distortionless response （MVDR） to scan the three-dimensional space. The peak values of the spectrum function correspond to the directions of multiple sound sources. A diagonal loading method is adopted to solve the ill-conditioned cross spectrum matrix of the received signals. The loading level depends on the alleviation of the ill-condition of the matrix and the accuracy of the inverse calculation. Compared with plane wave decomposition method, our proposed localization algorithm can acquire high spatial resolution and better estimation for multiple sound source directions, especially in low signal to noise ratio （SNR）.

Distributed Plume Source Localization Using Hierarchical Sensor Networks

A hierarchical wireless sensor networks(WSN) was proposed to estimate the plume source location.Such WSN can be of tremendous help to emergency personnel trying to protect people from terrorist attacks or responding to an accident.The entire surveillant field is divided into several small sub-regions.In each sub-region,the localization algorithm based on the improved particle filter(IPF) was performed to estimate the location.Some improved methods such as weighted centroid,residual resampling were introduced to the IPF algorithm to increase the localization performance.This distributed estimation method eliminates many drawbacks inherent with the traditional centralized optimization method.Simulation results show that localization algorithm is efficient for estimating the plume source location.

A FAST SEARCH METHOD OF STEERED RESPONSE POWER WITH SMALL-APERTURE MICROPHONE ARRAY FOR SOUND SOURCE LOCALIZATION

The Steered Response Power(SRP)method works well for sound source localization in noisy and reverberant environment.However,the large computation complexity limits its practical application.In this paper,a fast SRP search method is proposed to reduce the computational complexity using small-aperture microphone array.The proposed method inspired by the SRP spatial spectrum includes two steps:first,the proposed method estimates the azimuth of the sound source roughly and determines whether the sound source is in far field or near field;then,different fine searching operations are performed according to the sound source being in far field or near field.Experiments both in simulation environments and real environments have been performed to compare the localization accuracy and computation complexity of the proposed method with those of the conventional SRP-PHAT algorithm.The results show that,the proposed method has a comparative accuracy with the conventional SRP algorithm,and achieves a reduction of 93.62%in computation complexity compared to the conventional SRP algorithm.