Local Frequency Band Research Articles

A novel decomposition-based ensemble broad learning system for short-term load forecasting

Load forecasting of the power system plays a key role in the production planning and actual operation scheduling of power systems. However, as the power system becomes larger and more complex, it is very important to propose a forecasting model with high accuracy and low computational cost. In this paper, a novel short-term load forecasting model is proposed, which combines the empirical wavelet transform (EWT) and the broad learning system (BLS). The advantage of EWT is that it can decompose the signal into multiple local frequency bands, select the local wavelet function adaptively, and overcome the modal mixing problem caused by the discontinuity of the signal time-frequency scale. While, the advantage of BLS is that it allows the model to learn more by stretching the width, and uses ridge regression to make it less time consuming and faster. To verify the effectiveness, the model is compared with some other state-of-the-art methods, and several performance estimations indicate that the model has high accuracy and low computational cost.

Abnormal Low-Frequency Corticokinematic Coherence in Stroke: An Electroencephalography and Acceleration Study.

Motor control is a complex process of coordination and information interaction among neural, motor, and sensory functions. Investigating the correlation between motor-physiological information helps to understand the human motor control mechanisms and is important for the assessment of motor function status. In this manuscript, we investigated the differences in the neuromotor coupling analysis between healthy controls and stroke patients in different movements. We applied the corticokinematic coherence (CKC) function between the electroencephalogram (EEG) and acceleration (ACC) data. First, we collected the EEG and ACC data from 10 healthy controls and 10 stroke patients under the task of movement execution (ear touch and knee touch) and movement maintenance (ear touch and knee touch). After the preprocessing of raw data, we used frequency domain coherence method to analyze the full-frequency EEG and ACC data, which could be concluded that the CKC intensity in the movement execution was higher than that in the movement maintenance. However, there was no significant difference between healthy subjects and stroke patients. Secondly, the coherence results in local frequency bands showed that low-frequency bands could better reflect the difference between movement execution and maintenance. The intensity of coherence in healthy subjects was significantly higher than that in other bands, but not in stroke patients. Further comparison of coherence results in local frequency bands showed that the intensity of theta band in healthy controls was significantly higher than other rhythms, especially in the knee touch phase. Therefore, we infer that neurodynamic coupling analysis based on EEG and ACC data can show the differences between healthy subjects and stroke patients. Such researches could add to the understanding of neuro-motor control mechanisms and provide new quantitative indicators on the motor function assessment.

Lightweight sound insulation optimization of simply supported locally resonant plate

A theoretical model of the normal sound insulation of a rectangular locally resonant (LR) plate with simply supported boundary conditions is established. The accuracy of the model is verified through comparison with finite element simulation results. Considering a 1-m2 aluminum plate as the research object, a non-dominated genetic algorithm is introduced. The algorithm optimizes the lightweight sound insulation of an LR finite plate with a center frequency band of 20–800 Hz as the optimization objective of maximum average sound insulation and minimum mass. The optimization parameters are substrate thickness and local resonance parameters (including resonator target frequency (fobj), number of resonators, additional mass ratio, and resonator damping). Optimization results indicate that the average sound insulation fluctuates at approximately 2 dB due to changes in fobj. The results also provide an optimal solution to the algorithm. The optimal sound insulation value curve of LR plates corresponding to different surface densities of intermediate improved cases is fitted and compared with the sound insulation value of an equal-mass bare plate. Under lightweight optimization, the resonators are deemed capable of improving the local frequency band sound insulation. In contrast, the wideband average sound insulation can approximate but not surpass the insulation provided by the equal-mass bare plate. In practical applications, an LR plate that can improve the local frequency band sound insulation and ensure the overall lightweight sound insulation performance of the wide frequency band can be obtained. This can be achieved by analyzing the frequency spectrum of optimal points in the lightweight sound insulation optimization process. Relevant research can provide a basis for the evaluation and design of the low-frequency sound insulation of plates for transportation equipment, such as airplanes, high-speed trains, and cars.

Influence of neuromuscular electrical stimulation pulse waveform on corticomuscular coupling and the brain functional connectivity network

ObjectiveNeuromuscular electrical stimulation (NMES) has been shown to promote functional corticomuscular coupling (FCMC), but its effect on brain functional connectivity network (BFCN) remains uncertain. This study aims to propose a new method to analyze the influence of NMES with different stimulation waveforms on the motor system from two perspectives of FCMC and BFCN. ApproachThe bivariate empirical mode decomposition time-delay maximal information coefficient (BTDMIC) method was used to assess the local frequency band characteristics of the cortex and muscle. Main resultThe experimental results showed that the FCMC increment after NMES was prominent in the β1 (15–25 Hz), β2 (25–35 Hz), and γ1 (35–45 Hz) bands. Additionally, the coupling values of C3 after sine-NMES were highest in the β1 band, while the coupling values after square-NMES were highest in the β2 and γ1 bands. For BFCN, the cortical-cortical coupling of the C3 after square-NMES was high in the C3 → others direction. Moreover, in the α (8–14 Hz) and β1 bands, the clustering coefficient, density, and efficiency after square-NMES were significantly higher than those of the three other frequency bands. ConclusionNMES effectively promoted information exchange between the motor cortices of the brain, and the facilitating effect is particularly pronounced in the case of square-NMES. SignificanceThis study offers a novel perspective for evaluating rehabilitation therapies based on NMES.

Analysis of corticomuscular-cortical functional network based on time-delayed maximal information spectral coefficient

Objective. The study of brain networks has become an influential tool for investigating post-stroke brain function. However, studies on the dynamics of cortical networks associated with muscle activity are limited. This is crucial for elucidating the altered coordination patterns in the post-stroke motor control system. Approach. In this study, we introduced the time-delayed maximal information spectral coefficient (TDMISC) method to assess the local frequency band characteristics (alpha, beta, and gamma bands) of functional corticomuscular coupling (FCMC) and cortico-cortical network parameters. We validated the effectiveness of TDMISC using a unidirectionally coupled Hénon maps model and a neural mass model. Main result. A grip task with 25% of maximum voluntary contraction was designed, and simulation results demonstrated that TDMISC accurately characterizes signals’ local frequency band and directional properties. In the gamma band, the affected side showed significantly strong FCMC in the ascending direction. However, in the beta band, the affected side exhibited significantly weak FCMC in all directions. For the cortico-cortical network parameters, the affected side showed a lower clustering coefficient than the unaffected side in all frequency bands. Additionally, the affected side exhibited a longer shortest path length than the unaffected side in all frequency bands. In all frequency bands, the unaffected motor cortex in the stroke group exerted inhibitory effects on the affected motor cortex, the parietal associative areas, and the somatosensory cortices. Significance. These results provide meaningful insights into neural mechanisms underlying motor dysfunction.

A local spectrum enhancement‐based method and its application in incipient fault diagnosis of rotating machinery

AbstractIncipient faults of gears and rolling bearings in rotating machineries are very difficult to identify using traditional envelope analysis methods. To address this challenge, this paper proposes an effective local spectrum enhancement‐based diagnostic method that can identify weak fault frequencies in the original complicated raw signals. For this purpose, a traversal frequency band segmentation technique is first proposed for dividing the raw signal into a series of subfrequency bands. Then, the proposed synthetic quantitative index is constructed for selecting the most informative local frequency band (ILFB) containing fault features from the divided subfrequency bands. Furthermore, an improved grasshopper optimization algorithm‐based stochastic resonance (SR) system is developed for enhancing weak fault features contained in the selected most ILFB with less computation cost. Finally, the enhanced weak fault frequencies are extracted from the output of the SR system using a common spectrum analysis. Two experiments on a laboratory planetary gearbox and an open bearing data set are used to verify the effectuality of the proposed method. The diagnostic results demonstrate that the proposed method can identify incipient faults of gears and bearings in an effective and accurate manner. Furthermore, the advantages of the proposed method are highlighted by comparison with other methods.

AceFL: Federated Learning Accelerating in 6G-Enabled Mobile Edge Computing Networks

6G is envisioned to achieve ubiquitous Artificial Intelligence (AI) in heterogeneous and massive-scale networks, where FEderated Edge Learning (FEEL) is an effective way to assist AI technology to be implemented at the network edge. FEEL enables training a machine learning model through communication among multiple edge devices based on data from a large number of users. However, the heterogeneity among distributed edge devices and the limitation of resources may degrade the training efficiency of FEEL over 6G-enabled mobile edge computing (MEC) networks. Taking this challenge into account, a novel federated learning scheme is proposed in this paper to accelerate the training process. In particular, we formulate a training efficiency maximization problem, where a novel analytical relation among the training loss, resource consumption and heterogeneity is inferred by convergence analysis. Then, we propose a searching algorithm named IFBA to obtain the optimal inexactness of local models and frequency band allocation for edge devices in order to mitigate the straggler effect caused by the heterogeneity and resource limitation of devices. As a result, the training efficiency can be improved by adapting the inexactness of local models and frequency band allocation for edge devices on-demand. Simulation results validate that AceFL has advantages in training efficiency improvement and heterogeneity adaption, e.g., accelerating the training by <inline-formula><tex-math notation="LaTeX">$5\%$</tex-math></inline-formula> - <inline-formula><tex-math notation="LaTeX">$15\%$</tex-math></inline-formula> under different heterogeneity.

Assessment of the Road Surface Condition with Longitudinal Acceleration Signal of the Car Body

On the basis of road tests, the authors assessed the feasibility of the vehicle body acceleration values for the purposes of assessing road surface characteristics in terms of its roughness. Short-term Fourier Transform (STFT) was used for the analysis of the recorded signal. The spectra obtained in successive frequency bands demonstrate the amplitudes originating from the natural vibrations of the rolling wheel and forces resulting from the interaction with the road roughness. The article focuses on the relationships between the road roughness and the ratios of individual amplitudes in a specific frequency band of the vehicle body acceleration values. Amplitude values derived on the basis of successive windows were averaged for analogous, arbitrarily assumed local frequency bands. The value characterizing the road surface condition provided the information regarding the mean amplitude value in specific frequency ranges depending on the instantaneous velocity of the car body and the condition of the road surface on which it was moving. In cases where the road was free of any visible roughness, the obtained mean amplitude value in the analyzed spectrum window, for the adopted vehicle velocity range from 50 km h to 100 km/h, did not exceed 0.02 m/s2. It was also demonstrated that the road surface roughness leads to an increase in the mean amplitude value from 0.07 m/s2 to 0.16 m/s2.

A space-frequency localized approach of spatial filtering for motor imagery classification.

Classification of Motor Imagery (MI) signals is the heart of Brain-Computer Interface (BCI) based applications. Spatial filtering is an important step in this process that produce new set of signals for better discrimination of two classes of EEG signals. In this work, a new approach of spatial filtering called Space-Frequency Localized Spatial Filtering (SFLSF) is proposed to enhance the performances of MI classification. The SFLSF method initially divides the scalp-EEG channels into local overlapping spatial windows. Then a filter bank is used to divide the signals into local frequency bands. The group of channels, localized in space and frequency, are then processed with spatial filter, and features are subsequently extracted for classification task. Experimental results corroborate that the proposed space localization helps to increase the classification accuracy when compared to the existing methods using spatial filters. The classification performance is further improved when frequency localization is incorporated. Thus, the proposed space-frequency localized approach of spatial filtering helps to deliver better classification result which is consistently 3-5% higher than traditional methods.

Time-frequency synthesis analysis for complex signal of rotating machinery via variational mode manifold reinforcement learning

The transient signal caused by localized fault in rotating machinery always contains complex modulation information with heavy background noise distributed, which brings much difficulties for fault feature identification in the application of rotating machinery fault diagnosis. Focusing on the sensitive feature extraction from these complex signals, this paper proposes a novel variational mode manifold reinforcement learning (VM2RL) to adaptively construct time-frequency synthesis analysis for enhancement of transient features. First, a method of adaptive variational mode decomposition (VMD) is employed to divide the raw spectrum of the given signal into several sub-bands with different frequency modulation information. Second, an improved time-frequency manifold (ITFM) learning is introduced to gain the topological manifold structure from those sub-distributions in time-frequency domain. Then, a sound-enhanced signature of transient features on the whole time-frequency plane can be synthesized by combining those sub-TFMs from each modulated segment back to the corresponding frequency band. Finally, the time-frequency envelope spectrum for fault diagnosis is further obtained through statistically evaluating their amplitude distribution. Among them, short-frequency Fourier transform (SFFT) is introduced to transform local frequency bands into a series of TFDs which improves the computational efficiency of TFM learning. In this manner, the desired transient distribution on full time-frequency plane can be automatically reconstructed by VM2RL with manifold reinforced in a data-driven way. A simulation study and two experimental signals are both analyzed here, and fast spectral kurtosis and conventional VMD methods are also used to verify its effectiveness. Meanwhile, a quantitative analysis has been provided to further illustrate its superiority in the application of complex signal fault of rotating machinery.

Transfer Spectral Entropy and Application to Functional Corticomuscular Coupling.

Functional corticomuscular coupling (FCMC) with different rhythmic oscillations plays different roles in neural communication and interaction between the central nervous system and the peripheral system. Larger methods, such as coherence and Granger causality (GC), have been used to describe the frequency band characteristics in the frequency domain, but they fail to account for the inherent complexity. Considering that the transfer entropy (TE) method as an information theory has advantages in complexity and direction, we extended it and proposed a novel method named transfer spectral entropy (TSE) to explore the local frequency band characteristics between two coupling signals. To verify this, we introduced a Henon model and a neural mass model to generate the simulation signals. We then applied the proposed method to explore the FCMC by analyzing the correlation between the EEG and EMG signals during steady-state force output. Simulation results showed that the TSE method, compared with the GC method, not only described the information interaction in the local frequency band but also restrained the "false coupling." In addition, the results also revealed that the TSE method was sensitive to coupling strength but not to the data length. Further analysis of the experimental data showed that beta1 (15-25 Hz) and beta2 (25-35 Hz) bands were prominent in the FCMC for both EEG-to-EMG and EMG-to-EEG directions. In addition, the statistical analysis of the significant area indicated that the coupling in the EEG-to-EMG direction was higher at the beta1 and beta2 bands than that in the EMG-to-EEG direction, and the coupling in the EMG-to-EEG direction was higher at the gamma1 band (35-45 Hz) than that in the opposition. The FCMC results complementarily refined the previous studies that mainly focused on the beta band (15-35 Hz). The simulation and experimental data expound the effectiveness of the TSE model to describe the information interaction in the local frequency band between two time series, and this study extends the relative studies on FCMC.

Compressive sensing in electrical impedance tomography for breathing monitoring

Objective: Electrical impedance tomography (EIT) is a functional imaging technique in which cross-sectional images of structures are reconstructed based on boundary trans-impedance measurements. Continuous functional thorax monitoring using EIT has been extensively researched. Increasing the number of electrodes, number of planes and frame rate may improve clinical decision making. Thus, a limiting factor in high temporal resolution, 3D and fast EIT is the handling of the volume of raw impedance data produced for transmission and its subsequent storage. Owing to the periodicity (i.e. sparsity in frequency domain) of breathing and other physiological variations that may be reflected in EIT boundary measurements, data dimensionality may be reduced efficiently at the time of sampling using compressed sensing techniques. This way, a fewer number of samples may be taken. Approach: Measurements using a 32-electrode, 48-frames-per-second EIT system from 30 neonates were post-processed to simulate random demodulation acquisition method on 2000 frames (each consisting of 544 measurements) for compression ratios (CRs) ranging from 2 to 100. Sparse reconstruction was performed by solving the basis pursuit problem using SPGL1 package. The global impedance data (i.e. sum of all 544 measurements in each frame) was used in the subsequent studies. The signal to noise ratio (SNR) for the entire frequency band (0 Hz–24 Hz) and three local frequency bands were analysed. A breath detection algorithm was applied to traces and the subsequent error-rates were calculated while considering the outcome of the algorithm applied to a down-sampled and linearly interpolated version of the traces as the baseline. Main results: SNR degradation was generally proportional with CR. The mean degradation for 0 Hz–8 Hz (of interest for the target physiological variations) was below ~15 dB for all CRs. The error-rates in the outcome of the breath detection algorithm in the case of decompressed traces were lower than those associated with the corresponding down-sampled traces for CR ⩾ 25, corresponding to sub-Nyquist rate for breathing frequency. For instance, the mean error-rate associated with CR = 50 was ~60% lower than that of the corresponding down-sampled traces. Significance: To the best of our knowledge, no other study has evaluated the applicability of compressive sensing techniques on raw boundary impedance data in EIT. While further research should be directed at optimising the acquisition and decompression techniques for this application, this contribution serves as the baseline for future efforts.

Simultaneous frequency upconversion and phase coding of a radio-frequency signal for photonic radars.

We report a photonic approach to simultaneously realize frequency upconversion and phase coding of a radio-frequency (RF) signal based on polarization manipulation of optical signals. An intermediate frequency (IF) signal is upconverted to the local frequency (LO) band using a dual-polarization dual-parallel Mach-Zehnder modulator, while a high-speed polarization modulator is used to realize high-speed phase coding of the upconverted signal. The key advantage of the proposed method is that no optical or electrical filters are required to remove the residual IF, LO, and undesired downconverted signals, which ensures a broad operation bandwidth, excellent isolation, and wide tunability. The proposed scheme is theoretically analyzed and experimentally verified.

Synchronous analysis of corticomuscular coherence based on Gabor wavelet-transfer entropy

Synchronization analysis of electroencephalogram (EEG) and electromyogram (EMG) could reveal the functional corticomuscular coupling (FCMC) during the motor task in human. A novel method combining Gabor wavelet and transfer entropy (Gabor-TE) is proposed to quantitatively analyze the nonlinearly synchronous corticomuscular function coupling and direction characteristics under different steady-state force. Firstly, the Gabor wavelet transform method was used to acquire the local frequency-band signals of the EEG and EMG signals recorded from nine healthy controls simultaneously during performing grip task with four different steady-state forces. Secondly, the TE of local frequency-band was calculated and the unit area index of the transfer ( ATE) was defined to quantitatively analyze the synchronous corticomuscular function coupling and direction characteristics under steady-state force. Lastly, the effect of EEG and EMG signal power spectrum on Gabor-TE analysis was explored. The results showed that the coupling strength in the beta band was stronger in EEG→EMG direction than in EMG→EEG direction, and the ATE values in the beta band in EEG→EMG direction decreased with the force increasing. It is also shown that the difference in TE values of gamma band present a varying regularity as the increase of force in both directions. In addition, EMG power spectrum was significantly correlated with the result of Gabor-TE inspecific frequency band. The results of our study confirmed that Gabor-TE can quantitatively describe the nonlinearly synchronous corticomuscular function coupling in both local frequency band and information transmission. The analysis of FCMC provides basic information for exploring the motor control and the evaluation of clinical rehabilitation.

Local Band Spectral Entropy Based on Wavelet Packet Applied to Surface EMG Signals Analysis

An efficient analytical method for electromyogram (EMG) signals is of great significance to research the inherent mechanism of a motor-control system. In this paper, we proposed an improved approach named wavelet-packet-based local band spectral entropy (WP-LBSE) by introducing the concept of frequency band local-energy (ELF) into the wavelet packet entropy, in order to characterize the time-varying complexity of the EMG signals in the local frequency band. The EMG data were recorded from the biceps brachii (BB) muscle and triceps brachii (TB) muscle at 40°, 100° and 180° of elbow flexion by 10 healthy participants. Significant differences existed among any pair of the three patterns (p &lt; 0.05). The WP-LBSE values of the EMG signals in BB muscle and TB muscle demonstrated a decreased tendency from 40° to 180° of elbow flexion, while the distributions of spectral energy were decreased to a stable state as time periods progressed under the same pattern. The result of this present work is helpful to describe the time-varying complexity characteristics of the EMG signals under different joint angles, and is meaningful to research the dynamic variation of the activated motor units and muscle fibers in the motor-control system.

차세대 고속철도 차량용 알루미늄 압출재의 차음 설계

알미늄 압출재는 고속열차의 경량화를 위하여 기존의 주름강판을 대체하여 널리 사용된다. 알미늄 압출재는 고속열차 적층재 가운데 가장 큰 차음 기여도를 보이나, 동일한 중량의 평판과 비교할 때, 국부공진 주파수 대역에서 투과손실이 크게 떨어진다. 이 연구에서는 차세대 400㎞/h급 고속철도 차량용 알미늄 압출재를 대상으로 차음 문제를 검토하고, 차음성능의 향상 방안을 제시한다. 코어 구조를 변경시켜 국부공진 대역을 높이고, 우레탄 폼을 코어에 충진시킬 때의 차음성능 향상효과를 실험적으로 확인한다. 최종적으로 제시된 방법이 바닥 적층재의 총 투과손실을 어느 정도 개선시키는가를 평가한다.

Identification of multiple characteristic components with high accuracy and resolution using the zoom interpolated discrete Fourier transform

Complex systems can significantly benefit from condition monitoring and diagnosis to optimize operational availability and safety. However, for most complex systems, multi-fault diagnosis is a challenging issue, as fault-related components are often too close in the frequency domain to be easily identified. In this paper, the interpolated discrete Fourier transform (IpDFT) with maximum sidelobe decay windows is investigated for machinery fault feature identification. A novel identification method called the zoom IpDFT is proposed, which combines the idea of local frequency band zooming-in with the IpDFT and demonstrates high accuracy and frequency resolution in signal parameter estimation when different characteristic frequencies are very close. Simulation and a case study on rolling element bearing vibration data indicate that the proposed zoom IpDFT based on multiple modulations has better capability to identify characteristic components than do traditional methods, including fast Fourier transform (FFT) and zoom FFT.

Modulation of subthalamic alpha activity to emotional stimuli correlates with depressive symptoms in Parkinson's disease1

Deep brain stimulation of the subthalamic nucleus is an effective treatment for patients with advanced Parkinson's disease. However, affective side effects following subthalamic deep brain stimulation have been reported. Here, we aim to elucidate the influence of affective state on emotional processing as indexed by local field potential activity and to identify neurophysiological markers in patients at risk of developing depressive symptoms during subthalamic deep brain stimulation. Subthalamic local field potentials were directly recorded via electrodes implanted for deep brain stimulation in 12 Parkinson's disease patients while viewing emotionally salient and neutral pictures. Parkinson's disease patients were assessed for depressive symptoms using the Beck depression inventory at the time of operation and 3 months after continuous subthalamic nucleus deep brain stimulation. We found a significant event-related desynchronization in the local alpha frequency band (8-12 Hz) for emotionally arousing but not neutral pictures. The the event-related desynchronization (ERD) in the alpha frequency band was reduced for pleasant stimuli in patients with mild to moderate depressive symptoms compared with patients without depression. The alpha-ERD to unpleasant stimuli showed the opposite pattern. Consistently, the index of event-related alpha desynchronization (alpha ERD for pleasant stimuli minus alpha ERD for unpleasant stimuli) correlated with the Beck depression inventory at the time of the recordings and at 3 months after continuous deep brain stimulation. The alpha ERD to unpleasant pictures correlated significantly with the Beck depression inventory score at 3 months after chronic deep brain stimulation. In conclusion, we found mood-congruent stimulus processing in the subthalamic nucleus of Parkinson's disease patients. Electrophysiological markers such as event-related desynchronization of subthalamic alpha activity reflect state-dependent emotional processing and may potentially be used to predict depressive mood disturbances in Parkinson's disease patients with chronic subthalamic deep brain stimulation at an early stage.

Rapid quantitative assessment of myocardial perfusion: Spectral analysis of myocardial contrast echocardiographic images

We described a novel rapid spectral analysis technique performed on raw digital in-phase quadrature (IQ) data that quantitatively differentiated perfused from nonperfused myocardium based on the simultaneous comparison of local fundamental and harmonic frequency band intensity levels. In open-chest pigs after ligation of the left anterior descending coronary artery (LAD) and continuous venous contrast infusion, the fundamental-to-harmonic intensity ratio (FHIR) for samples placed within the left ventricular (LV) cavity (10.8 ± 1.7 dB) and perfused myocardium (13.7 ± 1.6 dB) were significantly (P <.001) lower than for nonperfused myocardium (27.1 ± 2.9 dB). In attenuated images, the FHIR for the LV cavity and perfused myocardium were also significantly (P <.05) lower than for the nonperfused myocardium (21.4 ± 3.0 dB, 34.4 ± 3.2 dB, and 40.2 ± 4.4 dB, respectively). Spectral properties of contrast microbubbles, as characterized by the FHIR, allow for rapid quantitative assessment of myocardial perfusion from data contained in a single-image frame, without requiring background image subtraction and image averaging. (J Am Soc Echocardiog 2002;15:63-8.)