Relationship between polyhedral structures formed by tangent planes of ellipsoidal particles and system sound velocity

Internal polyhedral structures of a granular system can be investigated using the Voronoi tessellations.This technique has gained increasing recognition in research of kinetic properties of granular flows.For systems with mono-sized spherical particles,Voronoi tessellations can be utilized,while radial Voronoi tessellations are necessary for analyzing systems with multi-sized spherical particles.However,research about polyhedral structures of non-spherical particle systems is limited.We utilize the discrete element method to simulate a system of ellipsoidal particles,defined by the equation(x a)2+(y1)2+(z 1/a)2=1,where a ranges from 1.1 to 2.0.The system is then dissected by using tangent planes at the contact points,and the geometric quantities of the resulting polyhedra in different shaped systems,such as surface area,volume,number of vertices,number of edges,and number of faces,are calculated.Meanwhile,the longitudinal and transverse wave velocities within the system are calculated with the time-of-flight method.The results demonstrate a strong correlation between the sound velocity of the system and the geometry of the dissected polyhedra.The sound velocity of the system increases with the increase in a,peaking at a=1.3,and then decreases as a continues to increase.The average volume,surface area,number of vertices,number of edges,and number of faces of the polyhedra decrease with the increase in sound velocity.That is,these quantities initially decrease with the increase in a,reaching minima at a=1.3,and then increase with further increase of a.The relationship between sound velocity and the geometric quantities of the dissected polyhedra can serve as a reference for acoustic material design.

A New Method for Deriving High-Vertical-Resolution Wind Vector Data from the L-Band Radar Sounding System in China

High-vertical-resolution radiosonde wind data are highly valuable for describing the dynamics of the meso-and microscale atmosphere. However, the current algorithm used in China's L-band radar sounding system for calculating highvertical-resolution wind vectors excessively smooths the data, resulting in significant underestimation of the calculated kinetic energy of gravity waves compared to similar products from other countries, which greatly limits the effective utilization of the data. To address this issue, this study proposes a novel method to calculate high-vertical-resolution wind vectors that utilizes the elevation angle, azimuth angle, and slant range from L-band radar. In order to obtain wind data with a stable quality, a two-step automatic quality control procedure, including the RMSE-F(root-mean-square error F) test and elemental consistency test are first applied to the slant range data, to eliminate continuous erroneous data caused by unstable signals or radar malfunctions. Then, a wind calculation scheme based on a sliding second-order polynomial fitting is utilized to derive the high-vertical-resolution radiosonde wind vectors. The evaluation results demonstrate that the wind data obtained through the proposed method show a high level of consistency with the high-resolution wind data observed using the Vaisala Global Positioning System and the data observed by the new Beidou Navigation Sounding System. The calculation of the kinetic energy of gravity waves in the recalculated wind data also reaches a level comparable to the Vaisala observations.

Diagnostic Accuracy of Computerized Bowel Sound Analysis with Non-Invasive Devices for Irritable Bowel Syndrome:A Systematic Review and Meta-Analysis

Objective To assess the diagnostic accuracy of bowel sound analysis for irritable bowel syndrome(IBS)with a systematic review and meta-analysis.Methods We searched MEDLINE,Embase,the Cochrane Library,Web of Science,and IEEE Xplore databases until September 2023.Cross-sectional and case-control studies on diagnostic accuracy of bowel sound analysis for IBS were identified.We estimated the pooled sensitivity,specificity,positive likelihood ratio,negative likeli-hood ratio,and diagnostic odds ratio with a 95% confidence interval(CI),and plotted a summary receiver operat-ing characteristic curve and evaluated the area under the curve.Results Four studies were included.The pooled diagnostic sensitivity,specificity,positive likelihood ratio,nega-tive likelihood ratio,and diagnostic odds ratio were 0.94(95%CI,0.87‒0.97),0.89(95%CI,0.81‒0.94),8.43(95%CI,4.81‒14.78),0.07(95%CI,0.03‒0.15),and 118.86(95%CI,44.18‒319.75),respectively,with an area under the curve of 0.97(95%CI,0.95‒0.98).Conclusions Computerized bowel sound analysis is a promising tool for IBS.However,limited high-quality data make the results'validity and applicability questionable.There is a need for more diagnostic test accuracy studies and better wearable devices for monitoring and analysis of IBS.

Sound Transmission Loss Analysis of a Double Plate-Acoustic Cavity Coupling System with In-Plane Functionally Graded Materials

In this paper, the isogeometric analysis (IGA) is employed to develop an acoustic radiation model for a double plate-acoustic cavity coupling system, with a focus on analyzing the sound transmission loss (STL). The functionally graded (FG) plate exhibits a different material properties in-plane, and the power-law rule is adopted as the governing principle for material mixing. To validate the harmonic response and demonstrate the accuracy and convergence of the isogeometric modeling, ANASYS is utilized to compare with numerical examples. A plane wave serves as the acoustic excitation, and the Rayleigh integral is applied to discretize the radiated plate. The STL results are compared with the literature, confirming the reliability of the coupling system. Finally, the investigation is conducted to study impact of cavity depth and power-law parameter on the STL.

Deep seismic sounding investigation into the deep structure of the magma system in Changbaishan-Tianchi volcanic region

The magma system of Changbaishan-Tianchi Volcanic region is studied with three-dimensional deep seismic sounding (DSS) technique. The results show that the magma system of Changbaishan-Tianchi volcanic region, mainly characterized by low velocity of P wave, can be divided into three parts in terms of depth. At the depth range of 9-15 km, the distribution of the magma system is characterized by extensiveness, large scale and near-SN orientation. This layer is the major place for magma storage. From the depth of 15 km down to the lower crust, it is characterized by small lateral scale, which indicates the 'trace' of magma intrusion from the upper mantle into the crust and also implies that the magma system most probably extends to the upper mantle, or even deeper.(less than 8-9 km deep), the range of magma distribution is even smaller, centering on an SN-oriented area just north of the Tianchi crater. If low velocity of P wave is related to the magma system, it then reflects that the magma here is still in a state of relatively high temperature. In this sense, the magma system of Changbaishan-Tianchi volcanic region is at least not 'remains', in other words, it is in an 'active' state.

A Miniature Robotic Plane Meteorological Sounding System

This article presents a miniature robotic plane meteorological sounding system (RPMSS), which consists of three major subsystems: a miniature robotic plane, an air-borne meteorological sounding and ?ight control system, and a ground-based system. Take-o? and landing of the miniature aircraft are guided by radio control, and the ?ight of the robotic plane along a pre-designed trajectory is automatically piloted by an onboard navigation system. The observed meteorological data as well as all ?ight information are sent back in real time to the ground, then displayed and recorded by the ground-based computer. The ground-based subsystem can also transmit instructions to the air-borne control subsystem. Good system performance has been demonstrated by more than 300 hours of ?ight for atmospheric sounding.

Adaptive Method for State Estimation of Sound Environment System with Uncertainty and its Application to Psychological Evaluation

The actual sound environment system exhibits various types of linear and non-linear characteristics, and it often contains uncertainty. Furthermore, the observations in the sound environment are often in the level-quantized form. In this paper, two types of methods for estimating the specific signal for sound envi-ronment systems with uncertainty and the quantized observation are proposed by introducing newly a system model of the conditional probability type and moment statistics of fuzzy events. The effectiveness of the proposed theoretical methods is confirmed by applying them to the actual problem of psychological evalua-tion for the sound environment.

Experimental research of traction motor system for electric vehicle base on sound tensity

The traction motor of electric vehicle is differing from the general industry traction motor completely. Not only frequently start, parking, accelerate, decelerate and low speed, but also high torque in climbing slope, low torque in high speed and wide range speed are requested. Base on the theory of sound intensi- ty, the experiment of noise are study through the measurement at discrete points. The sizing grid is 10mm × 10mm, The sound intensity map of traction motor are protracted at 1000r/min and the result show that the main noise sources are fan, gear-box and the traction motor in turn.

State Estimation for Sound Environment System with Nonlinear Observation Characteristics by Introducing Wide-Sense Particle Filter

In this study, a modified particle filter considering non-Gaussian properties of noises is proposed in a form applicable to real situation in sound environment system where the observation data are contaminated by the external noise (i.e., background noise) of arbitrary probability distribution and measured in decibel scale. More specifically, a nonlinear observation model in decibel scale with a quantized level is first paid considered by introducing the additive property of energy variables (i.e., sound intensity) in sound environment system. Next, a wide-sense particle filter of an expansion expression type is derived in a form suitable for the nonlinear observation characteristics and the signal processing considering higher-order correlation information between the specific signal and observation. Furthermore, the effectiveness of the proposed theory is confirmed by applying it to the observed data measured in real sound environment.

Geoelectrical structures of northern segment of Tan-Lu fault system in China and Russia areas based on magnetotelluric soundings

From China to Russia,Tan-Lu fault system stretches for thousands of kilometers,towards NE 30°.The authors have collected more than ten magnetotelluric profile that China and Russia have carried out,in the studied area of the Tan-Lu fault's northern section,and have analyzed electrical characteristics of the relevant profiles.Deriving the following conclusions:(1)Jiamusi-Bureya plots,etc.,demonstrate the high resistance of Kernel;(2)Fold belt attributed to the land showed high and low resistance stitching,associated with not only terrane accretion,but also upper mantle upwelling;below the sedimentary basin,the resistivity of the upper mantle is lower and closer to the surface comparing with the adjacent area(100-150);(3)In the system of Tan-Lu fracture,Yishu and Dunmi fracture,etc.are expressed as vertical low-resistivity zone;(4)There may exist subducted old-fashioned piece under Jiamusi and Bureya plots.

ST-LSTM-SA:A New Ocean Sound Velocity Field Prediction Model Based on Deep Learning

The scarcity of in-situ ocean observations poses a challenge for real-time information acquisition in the ocean.Among the crucial hydroacoustic environmental parameters,ocean sound velocity exhibits significant spatial and temporal variability and it is highly relevant to oceanic research.In this study,we propose a new data-driven approach,leveraging deep learning techniques,for the prediction of sound velocity fields(SVFs).Our novel spatiotemporal prediction model,STLSTM-SA,combines Spatiotemporal Long Short-Term Memory(ST-LSTM) with a self-attention mechanism to enable accurate and real-time prediction of SVFs.To circumvent the limited amount of observational data,we employ transfer learning by first training the model using reanalysis datasets,followed by fine-tuning it using in-situ analysis data to obtain the final prediction model.By utilizing the historical 12-month SVFs as input,our model predicts the SVFs for the subsequent three months.We compare the performance of five models:Artificial Neural Networks(ANN),Long ShortTerm Memory(LSTM),Convolutional LSTM(ConvLSTM),ST-LSTM,and our proposed ST-LSTM-SA model in a test experiment spanning 2019 to 2022.Our results demonstrate that the ST-LSTM-SA model significantly improves the prediction accuracy and stability of sound velocity in both temporal and spatial dimensions.The ST-LSTM-SA model not only accurately predicts the ocean sound velocity field(SVF),but also provides valuable insights for spatiotemporal prediction of other oceanic environmental variables.

Sound transducer calibration of ambulatory audiometric system utilizing delta learning rule

An efficient calibration algorithm for an ambulatory audiometric test system is proposed. This system utilizes a personal digital assistant (PDA) device to generate the correct sound pressure level (SPL) from an audiometric transducer such as an earphone. The calibrated sound intensities for an audio-logical examination can be obtained in terms of the sound pressure levels of pure-tonal sinusoidal signals in eight-banded frequency ranges (250, 500, 1 000, 2 000, 3 000, 4 000, 6 000 and 8 000 Hz), and with mapping of the input sound pressure levels by the weight coefficients that are tuned by the delta learning rule. With this scheme, the sound intensities, which evoke eight-banded sound pressure levels by 5 dB steps from a minimum of 25 dB to a maximum of 80 dB, can be generated without volume displacement. Consequently, these sound intensities can be utilized to accurately determine the hearing threshold of a subject in the ambulatory audiometric testing environment.

A Method for the Removal of Ray Refraction Effects in Multibeam Echo Sounder Systems

To a multibeam echo sounder system (MBES), under water sound refraction plays an important role in depth measure- ment accuracy, and errors in sound velocity profile lead to inaccuracies in the measured depth (especially for outer beams). A method is developed to estimate the sound velocity profile based on the depth measured by vertical beam. Using this depth and other pa- rameters, such as t (sound pulse propagation time), θ (beam inclination angle), etc. We can estimate a simple sound velocity profile with which the depth error has been reduced. This method has been tested with a real dataset acquired in the East China Sea.

Intelligent Sound-Based Early Fault Detection System for Vehicles

An intelligent sound-based early fault detection system has been proposed for vehicles using machine learning.The system is designed to detect faults in vehicles at an early stage by analyzing the sound emitted by the car.Early detection and correction of defects can improve the efficiency and life of the engine and other mechanical parts.The system uses a microphone to capture the sound emitted by the vehicle and a machine-learning algorithm to analyze the sound and detect faults.A possible fault is determined in the vehicle based on this processed sound.Binary classification is done at the first stage to differentiate between faulty and healthy cars.We collected noisy and normal sound samples of the car engine under normal and different abnormal conditions from multiple workshops and verified the data from experts.We used the time domain,frequency domain,and time-frequency domain features to detect the normal and abnormal conditions of the vehicle correctly.We used abnormal car data to classify it into fifteen other classical vehicle problems.We experimented with various signal processing techniques and presented the comparison results.In the detection and further problem classification,random forest showed the highest results of 97%and 92%with time-frequency features.

Recognition System for Diagnosing Pneumonia and Bronchitis Using Children’s Breathing Sounds Based on Transfer Learning

Respiratory infections in children increase the risk of fatal lung disease,making effective identification and analysis of breath sounds essential.However,most studies have focused on adults ignoring pediatric patients whose lungs are more vulnerable due to an imperfect immune system,and the scarcity of medical data has limited the development of deep learning methods toward reliability and high classification accuracy.In this work,we collected three types of breath sounds from children with normal(120 recordings),bronchitis(120 recordings),and pneumonia(120 recordings)at the posterior chest position using an off-the-shelf 3M electronic stethoscope.Three features were extracted from the wavelet denoised signal:spectrogram,mel-frequency cepstral coefficients(MFCCs),and Delta MFCCs.The recog-nition model is based on transfer learning techniques and combines fine-tuned MobileNetV2 and modified ResNet50 to classify breath sounds,along with software for displaying analysis results.Extensive experiments on a real dataset demonstrate the effectiveness and superior performance of the proposed model,with average accuracy,precision,recall,specificity and F1 scores of 97.96%,97.83%,97.89%,98.89%and 0.98,respectively,achieving superior performance with a small dataset.The proposed detection system,with a high-performance model and software,can help parents perform lung screening at home and also has the potential for a vast screening of children for lung disease.

State Estimation for Fuzzy Sound Environment System with Finite Amplitude Fluctuation

Internal physical mechanism of actual sound environment system is often difficult to recognize analytically, and it contains unknown structural characteristics. Furthermore, the observation data often contain fuzziness due to several causes and exhibit level saturation owing to the existence of a finite dynamic range. Therefore, it is necessary to propose a new state estimation method by considering fuzziness and finite amplitude fluctuation of observation data. In this paper, a method for estimating the specific signal for sound environment system with unknown structure is proposed in an appropriate form for the finite level range of the measured fuzzy observation data by introducing an expansion expression of probability distribution with Bata distribution in the first term and new type of membership function. The effectiveness of the proposed theoretical method is confirmed by applying it to the actual problem in the sound environment.

A State Estimation Method for Sound Environment System with Unknown Observation Mechanism by Introducing Fuzzy Inference

The observed phenomena in real sound environment system often contain uncertainty such as the additional external noise with unknown statistics. Furthermore, there is complex nonlinear relationship between the specific signal and the observations, and it cannot be exactly expressed in any definite functional form. In these situations, it is one of reasonable analysis methods to treat the objective sound environment system as a fuzzy system. In this study, a state estimation method for a specific signal under the existence of an unknown observation mechanism and external noise of unknown statistics is proposed by introducing fuzzy inference. The effectiveness of the proposed theoretical method is experimentally confirmed by applying it to the actually observed data in the sound environment.

Design of Security System by Means of Sound Print

Sound Recognition becomes an important tool for intrusion detection or for the monitoring of public premises exposed to personal hostility. It could further identify different sounds. The main idea of the sound recognition process in a security system is to store samples of different sound signals in the memory of the computer as references,?and?to be analyzed with respect to their frequencies components. In this paper, the sound signal of an unknown source would be analyzed and compared with all the available reference samples,?and?then recognition is made according to the closest sample. The developed security system consists of two main parts: the spectrum analyzer that converts the sound signal to spectrograms. It is designed based on the real-time analyzes, and the recognizer which compares the spectrograms and gives the decision of the recognition by using a special criterion. Experimental results prove that the accuracy of the proposed system can be 98.33% for the selected sample of signals.

Sound Card Based ASK Communication System for Teaching Communication Principle Course

This paper deals with the issue of using the MATLAB tool in teaching the course of communication principles via constructing an Amplitude Shift Keying (ASK) communication system. Different from conventional MATLAB based simulations, the constructed system transmits modulated signals through a wire audio channel by exploiting sound card. Synchronization is required before the received signal being demodulated. Many practical problems should be considered as in real system. The designed system can be extended easily, and not only stimulates students’ interest in communication course, but also helps them understanding the principles from system viewpoints.

A Bayesian Filter for Sound Environment System with Quantized Observation*

In the real sound environment, the observation data are usually contaminated by additional background noise of arbitrary distribution type. In order to estimate several evaluation quantities for specific signal based on the observed noisy data, it is fundamental to estimate the fluctuating wave form of the specific signal. On the other hand, the observation data are very often measured in a digital level form at discrete times. This is because some signal processing methods by utilizing a digital computer are indispensable for extracting exactly various kinds of statistical evaluation for the specific signal based on the quantized level data. In this study, a Bayesian filter matched to the complicated sound environment system is derived. First, in the real situation where the sound environment system is affected by background noise of arbitrary probability distribution, a stochastic system model with quantized observation is established. Next, two types of the recursive algorithm of Bayesian filter to estimate the unknown specific signal are theoretically proposed in the quantized level form. Finally, the effectiveness of the proposed theory is experimentally confirmed by applying it to the estimation problem of real sound environment.