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.

Anti-noise performance of the pulse coupled neural network applied in discrimination of neutron and gamma-ray

In this study,the anti-noise performance of a pulse-coupled neural network(PCNN)was investigated in the neutron and gamma-ray(n-γ)discrimination field.The experiments were conducted in two groups.In the first group,radiation pulse signals were pre-processed using a Fourier filter to reduce the original noise in the signals,whereas in the second group,the original noise was left untouched to simulate an extremely high-noise scenario.For each part,artificial Gaussian noise with different intensity levels was added to the signals prior to the discrimination process.In the aforementioned conditions,the performance of the PCNN was evaluated and compared with five other commonly used methods of n-γdiscrimination:(1)zero crossing,(2)charge comparison,(3)vector projection,(4)falling edge percentage slope,and(5)frequency gradient analysis.The experimental results showed that the PCNN method significantly outperforms other methods with outstanding FoM-value at all noise levels.Furthermore,the fluctuations in FoM-value of PCNN were significantly better than those obtained via other methods at most noise levels and only slightly worse than those obtained via the charge comparison and zerocrossing methods under extreme noise conditions.Additionally,the changing patterns and fluctuations of the FoMvalue were evaluated under different noise conditions.Hence,based on the results,the parameter selection strategy of the PCNN was presented.In conclusion,the PCNN method is suitable for use in high-noise application scenarios for n-γdiscrimination because of its stability and remarkable discrimination performance.It does not rely on strict parameter settings and can realize satisfactory performance over a wide parameter range.

SiZer for exploration of inhomogeneous structure in temporal distribution of earth-quakes

In this paper, the inhomogeneous structure of generalized seismic strain release time series (GSSRTS) of earth- quakes in East, West China and their subtectonic regions as Xinjiang, Qinghai-Xizang (Tibetan) Plateau, Northeast China, North China, South China and Taiwan have been analyzed by using the method of significant analysis on zero crossings of derivatives (SiZer). Results show that when index η for estimating GSSRTS is close to zero and bandwidth is large enough, GSSRTSs feature significant increasing in Xinjiang, Northeast China, South China and Taiwan tectonic regions and decreasing in Qinghai-Xizang (Tibetan Platean) and North China tectonic regions from January 1, 1970 to January 1, 2000. While with the dwindling of bandwidth GSSRTSs in all the above tec- tonic regions characterize clustering, that is to say, significant increasing and decreasing emerge alternatively. When η is large enough, GSSRTSs would have no significant statistical variation in most of above tectonic regions except Qinghai-Xizang (Tibetan Platean) and Taiwan where significant increasing or decreasing hold in several time intervals within limited bandwidths.

Design and Construction of A Single-Phase Power Factor Meter

It is known that the power consumption and efficiency of an equipment owes directly to its power factor.The lower the power factor of the equipment the more the energy consumption of such equipment and vice-versa.Hence,the need to develop an equipment to measure accurately the operating power factor of domestic and industrial equipment and appliances[1].The operating principle of this power factor meter design is based on Zero Crossing detection principle,the principle is utilized using Arduino Nano,instrument transformers,LM324 operational amplifier,generic resistor,generic XOR Gate 7488 and 2X16LCD.The input current and voltage signal is taken by the transformers and sent to the op-amp which carries out the zero crossing detection in order to get the time difference after which the microcontroller does the calculation to determine the power factor and the deficit reactive power which is then displayed on an interface[2].

Fixed Point Iteration Chaos Controlled ZCDPLL

The stable operation of first and second order Zero Crossing Digital Phase Locked Loop (ZCDPLL) is extended by using a Fixed Point Iteration (FPI) method with relaxation. The non-linear components of ZCDPLL such as sampler phase detector and Digital Controlled Oscillator (DCO) lead to unstable and chaotic operation when the filter gains are high. FPI will be used to stabilize the chaotic operation and consequently extend the lock range of the loop. The proposed stabilized loop can work in higher filter gains which are needed for faster signal acquisition.

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.