Microseismic event waveform classification using CNN-based transfer learning models

The efficient processing of large amounts of data collected by the microseismic monitoring system(MMS),especially the rapid identification of microseismic events in explosions and noise,is essential for mine disaster prevention.Currently,this work is primarily performed by skilled technicians,which results in severe workloads and inefficiency.In this paper,CNN-based transfer learning combined with computer vision technology was used to achieve automatic recognition and classification of multichannel microseismic signal waveforms.First,data collected by MMS was generated into 6-channel original waveforms based on events.After that,sample data sets of microseismic events,blasts,drillings,and noises were established through manual identification.These datasets were split into training sets and test sets according to a certain proportion,and transfer learning was performed on AlexNet,GoogLeNet,and ResNet50 pre-training network models,respectively.After training and tuning,optimal models were retained and compared with support vector machine classification.Results show that transfer learning models perform well on different test sets.Overall,GoogLeNet performed best,with a recognition accuracy of 99.8%.Finally,the possible effects of the number of training sets and the imbalance of different types of sample data on the accuracy and effectiveness of classification models were discussed.

Identification of Convective and Stratiform Clouds Based on the Improved DBSCAN Clustering Algorithm

A convective and stratiform cloud classification method for weather radar is proposed based on the density-based spatial clustering of applications with noise(DBSCAN)algorithm.To identify convective and stratiform clouds in different developmental phases,two-dimensional(2D)and three-dimensional(3D)models are proposed by applying reflectivity factors at 0.5°and at 0.5°,1.5°,and 2.4°elevation angles,respectively.According to the thresholds of the algorithm,which include echo intensity,the echo top height of 35 dBZ(ET),density threshold,andεneighborhood,cloud clusters can be marked into four types:deep-convective cloud(DCC),shallow-convective cloud(SCC),hybrid convective-stratiform cloud(HCS),and stratiform cloud(SFC)types.Each cloud cluster type is further identified as a core area and boundary area,which can provide more abundant cloud structure information.The algorithm is verified using the volume scan data observed with new-generation S-band weather radars in Nanjing,Xuzhou,and Qingdao.The results show that cloud clusters can be intuitively identified as core and boundary points,which change in area continuously during the process of convective evolution,by the improved DBSCAN algorithm.Therefore,the occurrence and disappearance of convective weather can be estimated in advance by observing the changes of the classification.Because density thresholds are different and multiple elevations are utilized in the 3D model,the identified echo types and areas are dissimilar between the 2D and 3D models.The 3D model identifies larger convective and stratiform clouds than the 2D model.However,the developing convective clouds of small areas at lower heights cannot be identified with the 3D model because they are covered by thick stratiform clouds.In addition,the 3D model can avoid the influence of the melting layer and better suggest convective clouds in the developmental stage.

Application of GC-qqqMS/MS in Gasoline Grades and Regions Identification

Based on GC-qqqMS/MS,a qualitative and quantitative analysis method for identifying characteristic markers in gasoline samples was established.According to the established method,different grades(#92,#95,#98)of gasoline samples collected from different regions(southern,central,northeastern,and northwestern China)were studied and analyzed.The results show that the gasolines can be classified by the relative contents of aromatics,naphthalene series,indene and other characteristic substances.On the basis of the high sensitivity and selectivity of GC-qqqMS/MS,the experiment has identified the characteristic substances,and used the characteristic-ratios methods as well as stoichiometric tools to study the grades and regional differences of gasoline products.It is conducive to the identification and classification of ILR in public security in fire cases,and can also meet the actual handling demand.

Fast identification of mural pigments at Mogao Grottoes using a LIBS-based spectral matching algorithm

To quickly identify the mineral pigments in the Dunhuang murals,a spectral matching algorithm(SMA)based on four methods was combined with laser-induced breakdown spectroscopy(LIBS)for the first time.The optimal range of LIBS spectrum for mineral pigments was determined using the similarity value between two different types of samples of the same pigment.A mineral pigment LIBS database was established by comparing the spectral similarities of tablets and simulated samples,and this database was successfully used to identify unknown pigments on tablet,simulated,and real mural debris samples.The results show that the SMA method coupled with the LIBS technique has great potential for identifying mineral pigments.

Application of 16S rDNA Sequence Analysis Technique in Microbial Detection

With conserved regions and regions of high variations, 16s rDNA is an important molecular basis for the biological species identification and system evolu- tion. The modem molecular biology with 16s rDNA as the primer can accurately re- veal the diversity of microorganisms species and inheritance, thereby 16s rDNA se- quence analysis has become the main basis for classification and identification of microorganisms. Having overcome the limitations of traditional microculture methods, this method is easy to operate, quick and accurate to detect with high sensitivity, making it widely apply to species identification, community comparative analysis, phytecoenogenesis and the assessment of population diversity. It is a objective classification method with high credibility.

Low Impedance Fault Identification and Classification Based on Boltzmann Machine Learning for HVDC Transmission Systems

Identification and classification of DC faults are considered as fundamentals of DC grid protection.A sudden rise of DC fault current must be identified and classified to immediately operate the corresponding interrupting mechanism.In this paper,the Boltzmann machine learning(BML)approach is proposed for identification and classification of DC faults using travelling waves generated at fault point in voltage source converter based high-voltage direct current(VSC-HVDC)transmission system.An unsupervised way of feature extraction is performed on the frequency spectrum of the travelling waves.Binomial class logistic regression(BCLR)classifies the HVDC transmission system into faulty and healthy states.The proposed technique reduces the time for fault identification and classification because of reduced tagged data with few characteristics.Therefore,the faults near or at converter stations are readily identified and classified.The performance of the proposed technique is assessed via simulations developed in MATLAB/Simulink and tested for pre-fault and post-fault data both at VSC1 and VSC2,respectively.Moreover,the proposed technique is supported by analyzing the root mean square error to show practicality and realization with reduced computations.

Quantifying the characteristics of particulate matters captured by urban plants using an automatic approach

It is widely accepted that urban plant leaves can capture airborne particles. Previous studies on the particle capture capacity of plant leaves have mostly focused on particle mass and/or size distribution. Fewer studies, however, have examined the particle density, and the size and shape characteristics of particles, which may have important implications for evaluating the particle capture efficiency of plants, and identifying the particle sources. In addition, the role of different vegetation types is as yet unclear. Here, we chose three species of different vegetation types, and firstly applied an object-based classification approach to automatically identify the particles from scanning electron microscope（SEM）micrographs. We then quantified the particle capture efficiency, and the major sources of particles were identified. We found（1） Rosa xanthina Lindl（shrub species） had greater retention efficiency than Broussonetia papyrifera（broadleaf species） and Pinus bungeana Zucc.（coniferous species）, in terms of particle number and particle area cover.（2） 97.9% of the identified particles had diameter ≤10 μm, and 67.1% of them had diameter ≤2.5 μm. 89.8% of the particles had smooth boundaries, with 23.4% of them being nearly spherical.（3） 32.4%–74.1% of the particles were generated from bare soil and construction activities, and 15.5%–23.0% were mainly from vehicle exhaust and cooking fumes.