Main Frequency Bands Research Articles

Research on the mechanism of equal-thickness screening process formation of banana-type vibrating screens based on the signal analysis of the wavelet packet energy spectrum

ABSTRACT Banana-type vibrating screen has been widely used in classification operations in coal cleaning and mining fields due to good screening performance and large processing capacity. In this work, a new analytical method was introduced to clarify the mechanism of equal-thickness formation of banana-type vibrating screens. The vibration characteristics of the collision between the screen surface and the particles were analyzed using the wavelet packet frequency and the energy spectra. The main frequency band of the collision at the infeed and outfeed ends was the 2nd frequency band (low-frequency band). The corresponding amplitude and energy percentage value of the screen surface at the infeed end were 0.12 mm and 4.3%, respectively, which were higher than those at the outfeed end. The spectral amplitude and the energy percentage value had “high-in and low-out” characteristics which verified the mechanisms of thin-layer penetration and equal-thickness formation of banana-type vibrating screens. This work contributes to the theory of equal-thickness screenings and provides new research ideas and technical support for designing vibrating screens.

Brain Functional Connectivity in de novo Parkinson's Disease Patients Based on Clinical EEG.

In Parkinson's disease (PD), cortical–subcortical interplay plays a relevant role in affecting clinical performance. Functional MRI sequences described changes in functional connectivity at different stages of disease. Scarce are, instead, the investigations examining brain connectivity in patients with PD at early stages of disease. For this aim, here we analyzed the differences in functional connectivity between de novo, never treated, PD patients and healthy controls. The analyses were based upon custom-written scripts on the Matlab platform, combined with high-level functions of Fieldtrip, Brainstorm, and Brain Connectivity toolboxes. First, we proceeded to the spectral analysis of the EEG data in the five frequency bands (δ-θ-α-β-γ). Second, we calculated functional connectivity matrices based on both coherency (COH) and imaginary part of coherency (iCOH), in the δ-θ-α-β-γ frequency bands. Then, four network measures (density, transitivity, global efficiency, and assortativity) were computed in identified connectivity matrices. Finally, we compared the spectral density, functional connectivity matrices, and network measured between healthy controls and de novo PD patients through two-samples T-test. A total of 21 de novo PD patients and 20 healthy subjects were studied. No differences were observed in spectral analysis between the two groups, with the exception of the γ band where a significant increase in power density was found in PD patients. A reduced connectivity in the main EEG frequency bands (α-β frequency bands) was observed in PD patients compared to controls, while a hyperconnectivity was found in PD patients in γ band. Among the network measures, a reduced assortativity coefficient was found in de novo PD patients in α frequency band. Our results show the occurrence of early EEG functional connectivity alterations from the initial stages of PD. From this point of view, connectivity analysis may ease a better understanding of the complexity of PD physiopathology.

Cross-Channel Dynamic Weighting RPCA: A De-Noising Algorithm for Multi-Channel Arterial Pulse Signal

Pulse wave analysis (PWA) has been widely used in the medical field. A novel multi-channel sensor is employed in arterial pulse acquisition and brings richer physiological information to PWA. However, the noise of this sensor is distributed in the main frequency band of the pulse signal, which seriously interferes with subsequent analyses and is difficult to eliminate by existing methods. This study proposes a cross-channel dynamic weighting robust principal component analysis algorithm. A channel-scaled factor technique is used to manipulate the weighting factors in the nuclear norm. This factor can adaptively adjust the weights among the channels according to the signal pattern of each channel, optimizing the feature extraction in multi-channel signals. A series of performance evaluations were conducted, and four well-known de-noising algorithms were used for comparison. The results reveal that the proposed algorithm achieved one of the best de-noising performances in the time and frequency domains. The mean of h1 in the amplitude relative error (ARE) was 23.4% smaller than for the WRPCA algorithm. Moreover, our algorithm could accelerate convergence and reduce the computational time complexity by approximately 34.6%. These results demonstrate the performance and efficiency of the algorithm. Meanwhile, the idea can be extended to other multi-channel physiological signal de-noising and feature extraction fields.

EEG power spectral measures of cognitive workload: A meta-analysis.

Cognitive workload (CWL) is a fundamental concept in the assessment and monitoring of human performance during cognitive tasks. Numerous studies have attempted to objectively and continuously measure the CWL using neuroimaging techniques. Although the electroencephalogram (EEG) is a widely used technique, the impact of CWL on the spectral power of brain frequencies has shown inconsistent results. The present review aimed to synthesize the results of the literature and quantitatively assess which brain frequency is the most sensitive to CWL. A systematic literature search following PRISMA recommendations highlighted three main frequency bands used to measure CWL: theta (4-8 Hz), alpha (8-12 Hz), and beta (12-30 Hz). Three meta-analyses were conducted to quantitatively examine the effect of CWL on these frequencies. A total of 45 effect sizes from 24 studies involving 723 participants were computed. CWL was associated with significant effects on theta (g = 0.68, CI [0.41, 0.95]), alpha (g = -0.25, CI [-0.45, 0.04]), and beta (g = 0.50, CI [0.21, 0.79]) power. Our results suggests that theta, especially the frontal theta, is the best index of CWL. Alpha and beta power were also significantly impacted by CWL; however, their association seemed less straightforward. These results are critically analyzed considering the literature on cerebral oscillations. We conclude by emphasizing the need to investigate the interaction between CWL and other factors that may influence spectral power (e.g., emotional load), and to combine this measure with other methods of analysis of the central and peripheral nervous system (e.g., functional connectivity, heart rate).

Shaking table test for seismic optimization of soil slope reinforced by new EPS pile under earthquake

To explore the optimal seismic performance of new EPS pile, we carried out a series of shaking table test by comparing different pile types. We measured and obtained the seismic data with various attributes, such as the characteristic image of different probability horizontal seismic action, acceleration and dynamic soil pressure. By analyzing the time-domain characteristics of multi-attribute data such as deformation characteristics and acceleration, we revealed the instability mode of the slope model and the regional response characteristics of particle spatial position. Based on the Fast Fourier Transform (FFT) and Statistical Probability Sperm Matrix (SPSM), we proposed the damage level correlation between the spectral characteristics of acceleration and dynamic soil pressure. We obtained the relationship between the variation of the frequency and specific period between acceleration and dynamic soil pressure at different seismic stages through in-depth study, and then calculated the regional differences of spectrum displacement (Sd) by using Seism Signal. In the frequency domain of acceleration and dynamic soil pressure signals, the predominant frequencies were mostly concentrated in 0.5–8 Hz. Under high seismic acceleration, the amplitude frequency of acceleration spectrum of conventional pile and EPS pile was both 1.51 Hz, and the dominant frequency of dynamic soil pressure was significantly different. There was a positive correlation between seismic acceleration and dynamic soil pressure. The shift of frequency profile and main frequency band of EPS pile was mainly caused by the propagation stage after the acceleration main shock. Under high seismic acceleration, the deformation of EPS pile and conventional pile occurred to some extent. EPS plate was used as the damping layer to buffer and dissipated the deformation of the slope under earthquake, and its effect was linked to the seismic data attributes. The spectral displacement of EPS pile was reduced as a whole was higher than that of conventional pile. The slip surface of pile body was sensitive to earthquake, and it was easy to become the weak seismic link of the EPS pile. These results are helpful for us to provide a theoretical basis for improving the reliability optimization design of dynamic parameters of the pile structure.

Control of external devices based on sEMG signals

This topic studies how to use the sEMG signal portable external equipment to collect the surface EMG signal using the EMG instrument. In the signal data processing part, the causes of signal interference are analyzed from the hardware perspective, and the main frequency bands of its occurrence are recorded. In the code part, the data is processed through an analog filter. The communication mode uses TCP/IP protocol to achieve cross system interprocess communication. In the actual test process, there is unavoidable slight delay. At the same time, the voltage changes brought by the real-time adjustment mode of CPU and other computer hardware will also affect the accuracy of surface EMG signals. At the same time, the signal acquisition rate of the EMG is much higher than the transmission rate, so real-time transmission will lead to memory overflow. Therefore, this paper uses the fixed threshold algorithm as the control decision in the outgoing process, and tests the integral mean value of six different data sets and the grasping speed and reaction force of the gripper. It is finally found that when the data set has 20 elements, the delay is not large and the data is not easy to be blocked due to good real-time performance.

Features of Intraseasonal Variability Observed in the Upper-Layer Current in the Northern South China Sea

This study reveals the features of the strong intraseasonal variability (ISV) of the upper-layer current in the northern South China Sea (NSCS) based on four long-time mooring observations and altimeter data. The ISV of the upper-layer current in the NSCS consists of two dominant periods of 10–65 days and 65–110 days. The ISV with period of 10–65 days is much strong in the Luzon Strait and decays rapidly westward along the slope. The ISV with the period of 65–110 days is relatively strong along the slope with two high cores at 115 and 119°E, whereas it is weak in the Luzon Strait. The 10–65-day ISV can propagate directly from the western Pacific into the NSCS for most of the time. However, due to its long wavelength, the 65–110-day ISV propagates into the NSCS indirectly, possibly similar to the wave diffraction phenomenon. The spatial differences between the two main frequency bands are primarily due to the baroclinic and barotropic instabilities. The spatial distribution of the upper-layer ISV is closely associated with the mesoscale eddy radius of the NSCS. The eddy radius is directly proportional to the strength of 65–110-day ISV, but it is inversely proportional to the strength of 10–65-day ISV.

Improving the multi‐class classification of Alzheimer’s disease with machine learning‐based techniques: An EEG‐fNIRS hybridization study

AbstractBackgroundAs an incurable neurodegenerative disorder, Alzheimer’s Disease (AD) is being projected to become one of the most expensive diseases for understanding the pathophysiological mechanism. Exploring alterations in the AD brain network is therefore of central importance for early and accurate diagnosis of cognitive deficits levels, yet diagnostic uncertainties exist merely confirmed by postmortem brain examination. The hybrid approach of two modalities, Electroencephalography (EEG) and functional Near‐Infrared Spectroscopy (fNIRS) reveals an ideal technique‐derived diagnosis due to the inexpensive and noninvasive experiments coupled with the reliability and versatility that make it highly desirable for AD multi‐class classification tasks.MethodsIn this study, the hybrid EEG‐fNIRS was employed in constructing the classification machine learning (ML)‐based models to categorize four subject groups, including healthy controls (HC) and three AD patient classes. First, a concurrent EEG‐fNIRS setup was utilized to record data from 41 subjects during the Oddball – a cognitive ability test and 1‐back – a memory ability test. Second, while fNIRS features were computed and extracted, the event‐related spectral perturbation (ERSP) features measuring the average dynamic changes in amplitude of three main EEG frequency bands were extracted. Third, a conventional neural network and a hybrid deep Convolutional Neural Network (CNN) ‐ Long Short‐Term Memory Network (LSTM) was built for the multi‐class classification using the fNIRS and EEG features, respectively. To boost the classification accuracy, the output of different models was combined by majority voting methods.ResultsThe hybrid EEG‐fNIRS feature set was able to achieve a higher accuracy (71.01% ± 0.004) by combing their complementary properties, compared to using EEG (66.35% ± 0.016) or fNIRS (67.15% ± 0.017) alone. In addition, fNIRS and ERSP feature patterns displayed a significant difference between subject groups during the Oddball and 1‐back experimental tasks. Thus, the feature‐level ensemble shows a great ability to understand neurophysiological AD status and its related symptoms and develop a syndrome‐specific diagnostic tool for our future works.ConclusionsThese findings demonstrate the potential of the hybrid EEG‐fNIRS systems to enhance the AD diagnosis and assessment process and the capability of ML techniques to facilitate further AD classification studies.

DeepADNet: A CNN‐LSTM model for the multi‐class classification of Alzheimer’s disease using multichannel EEG

AbstractBackgroundEarly and accurate diagnosis of Alzheimer’s disease (AD) – an incurable and progressive brain disorder, along with a sharp upward trend in the incidence rate, is significantly important for patients to take prevention and appropriate treatments. However, the pathophysiological mechanism at the levels of AD severity is still poorly understood in spite of the worldwide financial and research efforts. Using an inexpensive and noninvasive modality, such as Electroencephalography (EEG) coupled with Deep Learning (DL)‐derived diagnostic tool to retain the accuracy and versatility, therefore, has gained much attention in AD multi‐class classification.MethodsThe present study proposes DL model, named DeepADNet, for the AD multi‐class classification using multichannel EEG. First, we extracted the event‐related spectral perturbation (ERSP) features measuring the average dynamic changes in amplitude of three main EEG frequency bands relative to a specific experimental event. Second, the hybrid deep Convolutional Neural Network (CNN) ‐ Long Short‐Term Memory Network (LSTM) was built to exploit the wide range of ERSP patterns on a time‐frequency domain and to generate discriminant features to classify healthy controls (HC) with two AD subject groups. To tackle the imbalance problem and improve the classification accuracy, we also applied different over‐sampling techniquesResultsThe EEG data were collected from the Oddball – a cognitive ability test with 63 subjects (23 HC, 17 Pre‐symptomatic AD (aAD) and 23 Prodromal AD (pAD) subjects) and the N‐back – a memory ability test with 36 subjects (13 HC, 11 aAD and 12 pAD subjects). We demonstrated that the ERSP patterns displayed significant differences among three subject groups during the two experimental stages. Classification results also reveal that a CNN‐LSTM model could overcome the existing methods utilizing the hand‐engineered features which demand on prior knowledge of AD analysis and achieved the highest accuracy during the Oddball (71.95% ± 0.019 and 75.95% ± 0.017) and during the N‐back (69.40% ± 0.003 and 73.70% ± 0.010) corresponding to original and SVMSMOTE‐based ERSP features.ConclusionsThese findings demonstrate the capability of the EEG systems to better underlie the AD progression through spatiotemporal‐dynamic regions in the brain and the potential of DL‐based models for further AD classification studies.

Development of multi-band tuned rail damper for rail vibration control

The intense interaction force between the wheel-rail causes the vibration of wheel and rail, and generates rolling noise. Increasing the rail damper is an effective way to suppress rail vibration. In this paper, a shear-type multi-band tuned rail damper (MTRD) consist of internal mass bars and external resilient layer to reduce rail vibration is proposed. Mass bars are wrapped inside resilient layer and placed along the length of rail. Resilient layer between two adjacent mass bars can enforce them perform shearing motion corresponding to the layer along the main vibration direction of rail. Different oscillation frequencies corresponding to different mass bars can be tuned by adjusting the structure of MTRD. It achieves an increase in the operating frequency band and a widening of the operating frequency range of MTRD. The influence of parameters of MTRD on its performance is investigated by the finite element method (FEM). One of three designed MTRD models is selected for prototype production after comparative analysis. Laboratory measurements are conducted on a rail specimen and 12 m track platform. Results show the main operating frequency band of designed MTRD is in the range of 200 Hz–800 Hz, achieves the goal of the desired frequency band. Frequency response function tests of rail on 12 m platform show the MTRD could suppress rail vibration in the range of 100–2000 Hz. To estimate the practical reduction effects of MTRD on vibration, a small radius curve of metro line is selected for field measurements. The measured track decay rate (DR) shows MTRD could increase DR at frequencies above 80 Hz with a max increment of 5 dB/m and 9.3 dB/m in vertical and lateral direction respectively. Reduction of rail vibration and rolling noise when trains pass is also measured. The overall vibration level of rail achieves a max reduction of 10.9 dB and 6.3 dB in vertical and lateral directions respectively. The overall sound pressure level is reduced by 5.9 dB(A) at the speed of 60 km/h.

Noise Reduction Characteristics of Macroporous Asphalt Pavement Based on A Weighted Sound Pressure Level Sensor.

Based on the manual of macroporous noise-reducing asphalt pavement design, the indoor main drive pavement function accelerated loading test system was applied to investigate the impact of speed, loading conditions (dry and wet) and structural depth on the noise reduction of macroporous Open Graded Friction Course (OGFC) pavement, as well as its long-term noise reduction. Combined with the noise spectrum of the weighted sound pressure level, the main components and sensitive frequency bands of pavement noise under different factors were analyzed and compared. According to experimental results, the noise reduction effect of different asphalt pavements from strong to weak is as follows: OGFC-13 > SMA-13 > AC-13 > MS-III. The noise reduction effect of OGFC concentrates on the frequency of 1–4 kHz when high porosity effectively reduces the air pump effect. As the effect of wheels increases and the depth of the road structure decreases, the noise reduction effect of OGFC decreases. It indicates the noise reduction performance attenuates at a later stage, similar to the noise level of densely graded roads.

A New Method for Prediction of the Acoustic Performance and Design Parameter Sensitivity Analysis of Multi-Chamber Perforated Resonators With an Irregular Cross Section

Abstract Aiming at reducing the high-amplitude and wide-frequency noise in charged air intake system of the powertrain, this paper proposes a new method for predicting the acoustic characteristics of an irregular cross-section multi-chamber perforated resonator under flow conditions. By this method, the presence of three-dimensional sound waves and the effects of higher-order modes are considered, and the acoustic performance of the resonator can be evaluated through the computation of transmission loss. Moreover, by discretizing the cross-section of the perforated resonator and extracting node information, this method can solve the acoustic characteristics of the perforated resonator with any cross section. Based on the transfer matrix method, the quadrupole parameters of each chamber are obtained. Then, the acoustic characteristics of the multi-chamber perforated resonator could be calculated. The theoretical prediction data and the experimental data have been compared, and the results show good agreement within the entire frequency range, which verifies the accuracy of the theoretical prediction approach. Based on this prediction approach, the influence of section ratio, structure parameters, and inlet flowrate on the acoustic characteristics of the resonator is explored. The results show that when the structural parameters change, the peak resonance frequency of the resonator will have a regular shift. With the increase of the inlet flowrate, the main frequency band of sound attenuation will decrease significantly. The theoretical method developed in this work can be used for the calculation and optimization of multi-chamber resonators in various applications.

Liquid CO2 Phase Change Fracturing and Vibration Monitoring in Roadbed Slope Excavation

In order to analyze the vibration impact of carbon dioxide fracturing tube blasting red sandstone, combined with the engineering conditions and surrounding environment of Anqing High-tech Zone, the rock-breaking blasting plan is carefully designed. The fracturing tube is arranged in two rows, the first row has 23 holes with a spacing of 1.5m, and the second batch of holes has 22 holes, which are arranged in a quincunx pattern with a distance of ‘1.3m between the front and rear rows. And design the corresponding vibration signal monitoring test to study the crack propagation caused by blasting, the attenuation law of vibration and its frequency spectrum distribution. The results show that after blasting, there is a relatively smooth crack in the plane area of the fracture zone 7m away from the critical surface. The width of the crack is between 20-40cm. The vibration velocity of the measuring point decays rapidly with the increase of the vibration source distance, and the vibration velocity decay curves in the X and Y directions are basically the same. Spectrum analysis shows that the main frequency band of each sub-velocity is generally below 100Hz, and the main vibration frequencies in the X and Y directions are concentrated in 30∼60Hz. The main vibration frequency band of the rock-breaking blasting vibration signal has little correlation with the direction and propagation distance of the vibration signal. And compared with the analysis of emulsion explosives, it is found that the particle vibration speed caused by the emulsion explosive rock-breaking blasting is much greater than that of the liquid CO2 fracturing tube rock-breaking blasting, indicating that the use of liquid carbon dioxide to break the rock can effectively reduce the vibration generated by blasting.

Evaluation of Brain Network Properties in Patients with MRI-Negative Temporal Lobe Epilepsy: An MEG Study.

Abnormal functional brain networks of temporal lobe epilepsy (TLE) patients with structural abnormalities may partially reflect structural lesions rather than either TLE per se or functional compensatory processes. In this study, we sought to investigate the brain-network properties of intractable TLE patients apart from the effects of structural abnormalities. The brain network properties of 20 left and 23 right MRI-negative TLE patients and 22 healthy controls were evaluated using magnetoencephalographic recordings in six main frequency bands. A slowing of oscillatory brain activity was observed for the left or right TLE group vs. healthy controls. The TLE groups presented significantly increased functional connectivity in the delta, theta, lower alpha and beta bands, and significantly greater values in the normalized clustering coefficient and path length, and significantly smaller values in the weighted small-world measure in the theta band when compared to healthy controls. Alterations in global and regional band powers can be attributed to spectral slowing in TLE patients. The brain networks of TLE patients displayed abnormally high synchronization in multi-frequency bands and shifted toward a more regular architecture with worse network efficiency in the theta band. Without the contamination of structural lesions, these significant findings can be helpful for better understanding of the pathophysiological mechanism of TLE. The theta band can be considered as a preferred frequency band for investigating the brain-network dysfunction of MRI-negative intractable TLE patients.

Mathematical Modeling of Brain Activity under Specific Auditory Stimulation.

Understanding the connection between different stimuli and the brain response represents a complex research area. However, the use of mathematical models for this purpose is relatively unexplored. The present study investigates the effects of three different auditory stimuli on cerebral biopotentials by means of mathematical functions. The effects of acoustic stimuli (S1, S2, and S3) on cerebral activity were evaluated by electroencephalographic (EEG) recording on 21 subjects for 20 minutes of stimulation, with a 5-minute period of silence before and after stimulation. For the construction of the mathematical models used for the study of the EEG rhythms, we used the Box-Jenkins methodology. Characteristic mathematical models were obtained for the main frequency bands and were expressed by 2 constant functions, 8 first-degree functions, a second-degree function, a fourth-degree function, 6 recursive functions, and 4 periodic functions. The values obtained for the variance estimator are low, demonstrating that the obtained models are correct. The resulting mathematical models allow us to objectively compare the EEG response to the three stimuli, both between the stimuli itself and between each stimulus and the period before stimulation.

Perceiving Excitation Characteristics from Interactions between Field Road and Vehicle via Vibration Sensing

When agricultural vehicles operate in the field, the soft road excitation makes it difficult to measure the vehicle vibration. A camera-accelerator system can solve this issue by utilizing computer vision information; however, the relationship between the field road surface and the vehicle vibration response remains an unsolved problem. This study aims to investigate the correlation of the soft road excitation of different long-wave surfaces with the vehicle vibration response. Vibration equation between the vehicle and soft road surface system was established to produce an effective roughness model of the field soft road surface. In order to simulate the vehicle vibration state under different long-wave road surfaces, the soil rectangular pits with 21 kinds of different spans and depths were applied to the road surfaces, and a tractor vibration test system was built for vibration test. The frequency spectrum analysis was performed for the vibration response and the roughness signals of the road surfaces. The results showed that coefficient (R2) of frequency correlation between the roughness excitation and the original unevenness at the excitation point at the rear end of the rectangular soil pit fell within 0.9641∼0.9969. The main frequency band of the vibration response fell within 0∼3 Hz, and the phenomenon of quadruple frequency existed. The correlation of roughness excitation with quadruple frequency fell within 0.992165∼1. The primary excitation points were located at the rear end of the rectangular soil pit. In addition, it also indicated that when the vehicle was driven without autonomous power, the vehicle vibration frequency mainly depended on the excitation frequency of the field road surface and the frequency at the maximum vehicle vibration intensity was 2 or 3 times of that at the maximum field soft road excitation. These findings may provide a reference for optimal design of vibration reduction and control for agricultural vehicles.

Phase-amplitude coupling of ripple activities during seizure evolution with theta phase

ObjectiveHigh-frequency activities (HFAs) and phase-amplitude coupling (PAC) are key neurophysiological biomarkers for studying human epilepsy. We aimed to clarify and visualize how HFAs are modulated by the phase of low-frequency bands during seizures. MethodsWe used intracranial electrodes to record seizures of focal epilepsy (12 focal-to-bilateral tonic-clonic seizures and three focal-aware seizures in seven patients). The synchronization index, representing PAC, was used to analyze the coupling between the amplitude of ripples (80–250 Hz) and the phase of lower frequencies. We created a video in which the intracranial electrode contacts were scaled linearly to the power changes of ripple. ResultsThe main low frequency band modulating ictal-ripple activities was the θ band (4–8 Hz), and after completion of ictal-ripple burst, δ (1–4 Hz)-ripple PAC occurred. The ripple power increased simultaneously with rhythmic fluctuations from the seizure onset zone, and spread to other regions. ConclusionsRipple activities during seizure evolution were modulated by the θ phase. The PAC phenomenon was visualized as rhythmic fluctuations. SignificanceRipple power associated with seizure evolution increased and spread with fluctuations. The θ oscillations related to the fluctuations might represent the common neurophysiological processing involved in seizure generation.

Frequency scheduling algorithm with the allocation of the main and additional frequency bands.

The subject of this research is the frequency planning algorithm for networks with an arbitrary topology of links over radio channels. The algorithm determines the total number of non-overlapping frequency ranges for the entire network and provides the distribution of each frequency range between communication nodes. The algorithm consists of two stages: at the first stage, there is a search and simultaneous distribution of frequency channels, the so-called main frequency range, as a result, only one frequency range is allocated to each node; at the second stage, additional frequency channels are searched for, which can be used by a separate subset of nodes, thus , some nodes can use more than one frequency range, but several at once. The novelty of this research lies in the developed frequency planning algorithm for wireless communication systems with an arbitrary topology of communications over radio channels. The result of the operation of the algorithm for a wireless communication system is the allocation of radio frequencies for communication nodes from the common frequency band allocated for the wireless communication system, in terms of reuse, eliminating the effect of interference. The result for communication nodes is the allocation of a baseband and an additional frequency band, taking into account the topology of the radio network, which can be used by a separate subset that makes wireless communication systems resistant to narrowband random interference.

Study on blasting vibration characteristics of metro tunnel based on wavelet packet technology

Wavelet packet technology has been widely applied in the study on blasting vibration transmission law of overlapping tunnels. The previous research focused on vibration transmission law of the distant area---a range more than 20m, but for the close area---a range less than 10m, research seldom has been carried out. Therefore, this paper, based on a overlapping tunnel project in Qingdao, reveals the blasting vibration transmission law of the close field as well as how the numbers of free surfaces affect the law. Firstly, at the site,the blasting vibration data both in front and rear of the working faces including the cutting area (a free face) and the spreading area (two free faces) in a newly-built tunnel are obtained; Secondly, the wavelet packet technology is applied to analyze the data of blasting vibration frequency and energy distribution characteristics. Finally, it is concluded that different free facing with different numbers can lead to different main frequency distribution range and different law of main frequency band changing with distance when blasting vibration transmit within the close field; and the vibration signal energy in front and rear of the working face in cutting area and spreading area is differently distributed in the directions of 3 channels. Combining blasting vibration signal frequence with energy factors, this paper puts forward the constructive suggestions on the safety protection of existing tunnels and provides reference for studying the law of blasting vibration transmission of overlapping tunnels.CLC classification number: 0389 TD235 Document identification code: A

Effect of wheel flat on dynamic wheel-rail impact in railway turnouts

Wheel flat is a form of wheel tread defect that reduces the service life and running safety of trains and tracks. Based on the multi-body system dynamics theory, a rigid-flexible vehicle-turnout dynamic coupling modelwas established, and the wheel diameter-varying flat simulation method used to create wheel flat models of different sizes. The most unfavourable phase of flats on the wheels of a vehicle crossing a turnout was studied by scanning and calculating the phase of the flats. The influence of flat size and vehicle speed on a vehicle with flats crossing a turnout was also analyzed. The results show that, compared with a normal wheel crossing a turnout and a wheel flat acting on the interval rail, a wheel flat acting on the wheel load transition of the frog area will excite greater vertical wheel-rail forces and vertical axle box vibration acceleration; in the turnout area, the main frequency band of the vertical vibration acceleration of the axle box is 550–800 Hz, which is rarely affected by the size of flats or the speed of the vehicle. The third-order bending mode of the wheel is excited when the wheel transits from the wing rail to the point rail.