Relative energy zero ratio-based approach for identifying pulse-like ground motions

Pulse-like ground motions are capable of inflicting significant damage to structures. Efficient classification of pulse-like ground motion is of great importance when performing the seismic assessment in near-fault regions. In this study, a new method for identifying the velocity pulses is proposed, based on different trends of two parameters: the short-time energy and the short-time zero crossing rate of a ground motion record. A new pulse indicator, the relative energy zero ratio(REZR), is defined to qualitatively identify pulse-like features. The threshold for pulse-like ground motions is derived and compared with two other identification methods through statistical analysis. The proposed procedure not only shows good accuracy and efficiency when identifying pulse-like ground motions but also exhibits good performance for classifying records with high-frequency noise and discontinuous pulses. The REZR method does not require a waveform formula to express and fit the potential velocity pulses;it is a purely signal-based classification method. Finally, the proposed procedure is used to evaluate the contribution of pulse-like motions to the total input energy of a seismic record, which dramatically increases the seismic damage potential.

Design and Simulation of an Audio Signal Alerting and Automatic Control System

A large part of our daily lives is spent with audio information. Massive obstacles are frequently presented by the colossal amounts of acoustic information and the incredibly quick processing times. This results in the need for applications and methodologies that are capable of automatically analyzing these contents. These technologies can be applied in automatic contentanalysis and emergency response systems. Breaks in manual communication usually occur in emergencies leading to accidents and equipment damage. The audio signal does a good job by sending a signal underground, which warrants action from an emergency management team at the surface. This paper, therefore, seeks to design and simulate an audio signal alerting and automatic control system using Unity Pro XL to substitute manual communication of emergencies and manual control of equipment. Sound data were trained using the neural network technique of machine learning. The metrics used are Fast Fourier transform magnitude, zero crossing rate, root mean square, and percentage error. Sounds were detected with an error of approximately 17%;thus, the system can detect sounds with an accuracy of 83%. With more data training, the system can detect sounds with minimal or no error. The paper, therefore, has critical policy implications about communication, safety, and health for underground mine.

Jaya Honey Badger optimization- based deep neuro-fuzzy network structure for detection of (SARS- CoV) Covid-19 disease by using respiratory sound signals

Purpose-The Covid 19 prediction process is more indispensable to handle the spread and deathocurred rate because of Covid-19.However early and precise prediction of Covid-19 is more difcult because of different sizes and resolutions of input image Thus these challenges and problems experienced by traditional Covid-19 detection methods are considered as major motivation to develop JHBO-based DNFN.Design/methodology/approach-The major contribution of this research is to desigm an ffectualCovid-19 detection model using devised JHBObased DNFN,Here,the audio signal is considered as input for detecting Covid-19.The Gaussian filter is applied to input signal for removing the noises and then feature extraction is performed.The substantial features,like spectral rlloff.spectral bandwidth,Mel-frequency,cepstral coefficients (MFCC),spectral flatness,zero crossing rate,spectral centroid,mean square energy and spectral contract are extracted for further processing.Finally,DNFN is applied for detecting Covid 19 and the deep leaning model is trained by designed JHBO algorithm.Accordingly.the developed JHBO method is newly desigmed by inoorporating Honey Badger optimization Algorithm(HBA)and.Jaya algorithm.Findings-The performance of proposed hybrid optimization-based deep learming algorithm is estimated by meansof twoperformance metrics,namely testing accuracy,sensitivity and speificity of 09176,09218 and 09219.Research limitations/implications-The JHBO-based DNFN approach is developed for Covid-19 detection.The developed approach can be extended by including other hybrid optimization algorithms as well as other features can be extracted for further improving the detection performance.Practical implications-The proposed Covid-19 detection method is useful in various applications,like medical and so on,Originality/value-Developed JHBO-enabled DNFN for Covid-19 detection:An effective Covid-19 detection technique is introduced based on hybrid optimization-driven deep learning model The DNFN is used for detecting Covid-19,which classifies the feature vector as Covid-19 or non-Covid 19.Moreover,the DNFN is trained by devised JHB0 approach,which is introduced by combining HBA and Jaya algorithm.