Rejection of Direct Blast Interference Based on Signal Phase-Matching Array Processing

In bistaic acoustic testing, there will be strong direct blast interference. An algorithm based on signal phase-matching array processing that rejects direct blast interference in bistatic acoustic testing has been studied, through which the object scattering signal is accurately extracted. Characteristics of bistatic acoustic testing and signal phase matching processing principle are fully integrated in this algorithm. Firstly, the direct blast interference is calculated from the receiving signal based on three subarrays signal phase matching processing. Secondly, the direct blast is rejected by subtraction from the receiving signal. In this way the limitations of the high signal to noise ratio that signal phase matching processing required for direct calculating the object scattering signal can be avoided. Simulation and sea trial results show that, when the ratio of signal to interference is greater than -20 dB, this algorithm of direct blast interference rejection based phase matching signal processing can accurately extract the object scattering signal.

Quantitative evaluation of multi-process collaborative operation in steelmaking–continuous casting sections

The quantitative evaluation of multi-process collaborative operation is of great significance for the improvement of production planning and scheduling in steelmaking–continuous casting sections(SCCSs). However, this evaluation is difficult since it relies on an in-depth understanding of the operating mechanism of SCCSs, and few existing methods can be used to conduct the evaluation, due to the lack of full-scale consideration of the multiple factors related to the production operation. In this study, three quantitative models were developed, and the multiprocess collaborative operation level was evaluated through the laminar-flow operation degree, the process matching degree, and the scheduling strategy availability degree. Based on the evaluation models for the laminar-flow operation and process matching levels, this study investigated the production status of two steelmaking plants, plants A and B, based on actual production data. The average laminar-flow operation(process matching) degrees of SCCSs were obtained as 0.638(0.610) and 1.000(0.759) for plants A and B, respectively, for the period of April to July 2019. Then, a scheduling strategy based on the optimization of the furnace-caster coordinating mode was suggested for plant A. Simulation experiments showed higher availability than the greedy-based and manual strategies. After the proposed scheduling strategy was applied,the average process matching degree of the SCCS of plant A increased by 4.6% for the period of September to November 2019. The multi-process collaborative operation level was improved with fewer adjustments and interruptions in casting.

An Intelligent Graph Edit Distance-Based Approach for Finding Business Process Similarities

There are numerous application areas of computing similarity between process models.It includes finding similar models from a repository,controlling redundancy of process models,and finding corresponding activities between a pair of process models.The similarity between two process models is computed based on their similarity between labels,structures,and execution behaviors.Several attempts have been made to develop similarity techniques between activity labels,as well as their execution behavior.However,a notable problem with the process model similarity is that two process models can also be similar if there is a structural variation between them.However,neither a benchmark dataset exists for the structural similarity between process models nor there exist an effective technique to compute structural similarity.To that end,we have developed a large collection of process models in which structural changes are handcrafted while preserving the semantics of the models.Furthermore,we have used a machine learning-based approach to compute the similarity between a pair of process models having structural and label differences.Finally,we have evaluated the proposed approach using our generated collection of process models.

Source-Space Compressive Matched Field Processing for Source Localization

Source localization by matched-field processing （MFP） can be accelerated by building a database of Green＇s functions which however requires a bulk-storage memory. According to the sparsity of the source locations in the search grids of MFP, compressed sensing inspires an approach to reduce the database by introducing a sensing matrix to compress the database. Compressed sensing is further used to estimate the source locations with higher resolution by solving the β -norm optimization problem of the compressed Green＇s function and the data received by a vertieal/horizontal line array. The method is validated by simulation and is verified with the experimental data.

Geoacoustic inversion for sediments in the South China Sea based on a hybrid inversion scheme

We performed a long range acoustic propagation experiment in the South China Sea(SCS) in November 2004.The environment of the experiment was with an isothermal sound speed profile,where influence of water volume fluctuation was small,meaning that bottom parameters can be well estimated from acoustic signals.We inverted the acoustic parameters of sediment by using a hybrid inversion scheme that combines the matched field processing inversion with Hamilton sediment empirical relationship and transmission loss data.The numerical results show excellent agreement with the experiment data,indicating validity of the inverted parameters.

Matched Field Localization Based on CS-MUSIC Algorithm

The problem caused by shortness or excessiveness of snapshots and by coherent sources in underwater acoustic positioning is considered.A matched field localization algorithm based on CS-MUSIC(Compressive Sensing Multiple Signal Classification) is proposed based on the sparse mathematical model of the underwater positioning.The signal matrix is calculated through the SVD(Singular Value Decomposition) of the observation matrix.The observation matrix in the sparse mathematical model is replaced by the signal matrix,and a new concise sparse mathematical model is obtained,which means not only the scale of the localization problem but also the noise level is reduced;then the new sparse mathematical model is solved by the CS-MUSIC algorithm which is a combination of CS(Compressive Sensing) method and MUSIC(Multiple Signal Classification) method.The algorithm proposed in this paper can overcome effectively the difficulties caused by correlated sources and shortness of snapshots,and it can also reduce the time complexity and noise level of the localization problem by using the SVD of the observation matrix when the number of snapshots is large,which will be proved in this paper.

An Optimized Approach to Vehicle-Type Classification Using a Convolutional Neural Network

There are numerous application areas of computing similarity between process models.It includes finding similar models from a repository,controlling redundancy of process models,and finding corresponding activities between a pair of process models.The similarity between two process models is computed based on their similarity between labels,structures,and execution behaviors.Several attempts have been made to develop similarity techniques between activity labels,as well as their execution behavior.However,a notable problem with the process model similarity is that two process models can also be similar if there is a structural variation between them.However,neither a benchmark dataset exists for the structural similarity between process models nor there exist an effective technique to compute structural similarity.To that end,we have developed a large collection of process models in which structural changes are handcrafted while preserving the semantics of the models.Furthermore,we have used a machine learning-based approach to compute the similarity between a pair of process models having structural and label differences.Finally,we have evaluated the proposed approach using our generated collection of process models.

An experimental study on matched field processing in shallow water

Matched field processing (MFP) is a generalized beamforming method which uses the spatial complexities of acoustic field in an ocean waveguide to localize sources in range, depth and azimuth or to infer parameters of the waveguide itself. In the paper, we present simulated and experimental results on narrow-band point source localization in shallow water by the matched field processing of a vertical array. Range-depth ambiguity surfaces are obtained by the spatial correlation of the incident field (modeled or realistic) with a modeled replica of that field. The simulated results indicate that a high-quality ambiguity surface can be obtained in case of perfect match between the 'true' environmental parameters and those used to compute the replica field. The effects of mismatches result in a degraded ambiguity surface and incorrect localization. Examples of localizations obtained with real sea test data are presented. It is shown that the conventional methods have better robustness than the minimum variance distortionless response (MVDR) based method. By employing the reduced minimum variance beamforming (RMVB), we can also get better results.

Bayesian localization in an uncertain ocean environment

In order to improve the ability to localize a source in an uncertain acoustic environment,a Bayesian approach,referred to here as Bayesian localization is used by including the environment in the parameter search space.Genetic algorithms are used for the parameter optimization.This method integrates the a posterior probability density（PPD） over environmental parameters to obtain a sequence of marginal probability distributions over source range and depth,from which the most-probable source location and localization uncertainties can be extracted.Considering that the seabed density and attenuation are less sensitive to the objective function of matched field processing,we utilize the empirical relationship to invert those parameters indirectly.The broadband signals recorded by a vertical line array in a Yellow Sea experiment in 2000 are processed and analyzed.It was found that,the Bayesian localization method that incorporates the environmental variability into the processor,made it robust to the uncertainty in the ocean environment.In addition,using the empirical relationship could enhance the localization accuracy.