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In milling processing, the wear state of the tool has an essential influence on the processing quality. The machining process is not continuous in the cycloid milling process, and the signal of the empty tool part increases the difficulty of identifying the tool wear state. At present, most of the researchers use experimental data and cut out the signal of the empty tool part in the signal by data processing. However, it will affect the original signal to a certain extent and destroy the confidential information in the original signal. A novel method using variational auto-encoder (VAE) for tool wear status identification is proposed. Due to VAE has structural characteristics that reduce the dimensionality of high-dimensional data to lower dimensionality. This requires VAE to find and learn the significant features, which are hidden in the complex raw data. In this paper, the signals of the empty tool do not need to be cut out; the effective value of the three-phase current signals obtained in the real processing is converted into the form of three-dimensional color images. VAE is applied to extract features from the image samples and then realize the classification of different wear states of the tool. A large number of comparison experiments are conducted, and the result shows that the presented method has a better recognition performance for the actual processing data. It is more suitable for the recognition of tool wear status in the actual milling process.

Noise elimination in current signals of three-phase induction motors, considered as energy systems for electromechanical conversion, is a critical preprocessing step for reliable condition monitoring and fault diagnosis. However, conventional wavelet-based denoising approaches often treat noise suppression as a generic filtering task, which may distort diagnostically relevant spectral components and inter-phase relationships. To address this limitation, this paper presents a physically constrained denoising framework that integrates the Clarke transformation with dyadic wavelet analysis to enable diagnostic-safe noise attenuation. The proposed method explicitly preserves frequency bands associated with supply harmonics, mechanical phenomena, and fault-related sidebands, while enforcing inter-phase coherence and zero-sequence stability in the Clarke domain. Wavelet parameters are selected through a diagnostic-oriented multi-criteria framework that jointly balances disturbance attenuation, harmonic fidelity, coherence retention, zero-sequence stability, and time-domain waveform integrity. Experimental validation using real three-phase induction motor current measurements under steady-state conditions shows that the proposed framework achieves noise reduction ratios of approximately 8–10 dB, while preserving the amplitudes of the main harmonic components with deviations below 10-3 dB. These results demonstrate that the proposed method provides a robust and physically consistent preprocessing stage for current-based monitoring of three-phase AC machines.

Here, a data mining–driven scheme based on discrete wavelet transform (DWT) is proposed for high impedance fault (HIF) detection in active distribution networks. Correlation between the phase current signal and the related details of the current wavelet transform is presented as a new index for HIF detection. The proposed HIF detection method is implemented in two subsequent stages. In the first stage, the most important features for HIF detection are extracted using support vector machine (SVM) and decision tree (DT). The parameters of SVM are optimised using the genetic algorithm (GA) over the input scenarios. In second stage, SVM is utilised to classify the input data. The efficiency of the utilised SVM-based classifier is compared with a probabilistic neural network (PNN). A comprehensive list of scenarios including load switching, inrush current, solid short-circuit faults, HIF faults in the presence of harmonic loads is generated. The performance of the proposed algorithm is investigated for two active distribution networks including IEEE 13-Bus and IEEE 34-Bus systems.

Retrieval of the optical phase information from measurement of intensity is of a high interest because this would facilitate simple and cost-efficient techniques and devices. In scientific and industrial applications that exploit multi-mode fibers, a prior knowledge of spatial mode structure of the fiber, in principle, makes it possible to recover phases using measured intensity distribution. However, current mode decomposition algorithms based on the analysis of the intensity distribution at the output of a few-mode fiber, such as optimization methods or neural networks, still have high computational costs and high latency that is a serious impediment for applications, such as telecommunications. Speed of signal processing is one of the key challenges in this approach. We present a high-performance mode decomposition algorithm with a processing time of tens of microseconds. The proposed mathematical algorithm that does not use any machine learning techniques, is several orders of magnitude faster than the state-of-the-art deep-learning-based methods. We anticipate that our results can stimulate further research on algorithms beyond popular machine learning methods and they can lead to the development of low-cost phase retrieval receivers for various applications of few-mode fibers ranging from imaging to telecommunications. Characterizing the modes at the output of a multimode fiber is time consuming due to computational cost. Here the authors present an algorithm for few-mode-fiber mode decomposition with a fast processing time and using only intensity measurements.

Macroscopic oscillations of different brain regions show multiple phase relationships that are persistent across time and have been implicated in routing information. While multiple cellular mechanisms influence the network oscillatory dynamics and structure the macroscopic firing motifs, one of the key questions is to identify the biophysical neuronal and synaptic properties that permit such motifs to arise. A second important issue is how the different neural activity coherence states determine the communication between the neural circuits. Here we analyse the emergence of phase-locking within bidirectionally delayed-coupled spiking circuits in which global gamma band oscillations arise from synaptic coupling among largely excitable neurons. We consider both the interneuronal (ING) and the pyramidal-interneuronal (PING) population gamma rhythms and the inter coupling targeting the pyramidal or the inhibitory neurons. Using a mean-field approach together with an exact reduction method, we reduce each spiking network to a low dimensional nonlinear system and derive the macroscopic phase resetting-curves (mPRCs) that determine how the phase of the global oscillation responds to incoming perturbations. This is made possible by the use of the quadratic integrate-and-fire model together with a Lorentzian distribution of the bias current. Depending on the type of gamma (PING vs. ING), we show that incoming excitatory inputs can either speed up the macroscopic oscillation (phase advance; type I PRC) or induce both a phase advance and a delay (type II PRC). From there we determine the structure of macroscopic coherence states (phase-locking) of two weakly synaptically-coupled networks. To do so we derive a phase equation for the coupled system which links the synaptic mechanisms to the coherence states of the system. We show that a synaptic transmission delay is a necessary condition for symmetry breaking, i.e. a non-symmetric phase lag between the macroscopic oscillations. This potentially provides an explanation to the experimentally observed variety of gamma phase-locking modes. Our analysis further shows that symmetry-broken coherence states can lead to a preferred direction of signal transfer between the oscillatory networks where this directionality also depends on the timing of the signal. Hence we suggest a causal theory for oscillatory modulation of functional connectivity between cortical circuits.

HighlightsThe basic theory, key merits and potential development of direct current triboelectric nanogenerator (DC-TENG) from the aspect of mechanical rectifier, tribovoltaic effect, phase control, mechanical delay switch and air-discharge are discussed in detail.This review provides a guideline for future challenges of DC-TENGs, and a strategy for improving the output performance for commercial applications.As hundreds of millions of distributed devices appear in every corner of our lives for information collection and transmission in big data era, the biggest challenge is the energy supply for these devices and the signal transmission of sensors. Triboelectric nanogenerator (TENG) as a new energy technology meets the increasing demand of today's distributed energy supply due to its ability to convert the ambient mechanical energy into electric energy. Meanwhile, TENG can also be used as a sensing system. Direct current triboelectric nanogenerator (DC-TENG) can directly supply power to electronic devices without additional rectification. It has been one of the most important developments of TENG in recent years. Herein, we review recent progress in the novel structure designs, working mechanism and corresponding method to improve the output performance for DC-TENGs from the aspect of mechanical rectifier, tribovoltaic effect, phase control, mechanical delay switch and air-discharge. The basic theory of each mode, key merits and potential development are discussed in detail. At last, we provide a guideline for future challenges of DC-TENGs, and a strategy for improving the output performance for commercial applications.

Permanent magnet synchronous motors (PMSMs) are widely recognized for their precise control capabilities, making them indispensable in numerous industrial applications. Yet, their susceptibility to faults, particularly stator faults, can lead to severe operational challenges. Effective health monitoring and prompt fault detection are, therefore, pivotal for preserving the performance and lifespan of PMSMs. In this research, we delve into an innovative approach to fault diagnosis in PMSMs by fusing vibration and current data. For the experimental setup, stator faults were simulated as inter-turn short circuits. Comprehensive datasets encompassing three-phase current signals and vibration signals were acquired from the PMSM test rig. These datasets were subsequently processed into statistical features. Leveraging the information gain for feature selection, we discerned crucial attributes for the fault assessment. A gradient boosting-based machine learning model was then employed to distinguish between various fault states, utilizing the selected features. Our findings unveiled that the combined vibration-current data fusion approach stands out, achieving an impressive diagnostic accuracy of 90.7% and an area under the curve of 95.1%. This underscores the efficacy of data fusion in conjunction with gradient boosting for fault diagnosis. The methodology presented herein promises to pave the way for timely fault detection, enabling proactive maintenance regimes, and bolstering the reliability of PMSMs in critical industrial settings.

Inhibitory control relies on coordinated beta‐band activity within a fronto‐basal ganglia network, which implements inhibition via downstream effects on (pre)motor areas. However, the causal role of beta synchrony in motor inhibition remains unclear. In this study, we employed dual‐site transcranial alternating current stimulation (tACS) targeting the right inferior frontal gyrus (rIFG) and left primary motor cortex (lM1) to directly manipulate phase relationships in the beta band and assess their effects on both functional connectivity and motor inhibition. Fifty‐two healthy participants received in‐phase, anti‐phase, and sham stimulation while performing a stop‐signal task. Connectivity between rIFG and lM1 increased following in‐phase stimulation and decreased after anti‐phase stimulation. No significant group‐level effects on stop‐signal task performance were observed. Exploratory Δ‐Δ correlations indicated that individuals with larger connectivity increases during in‐phase stimulation tended to show greater improvements in inhibitory performance, whereas greater connectivity decreases during anti‐phase stimulation were associated with faster go responses. Crucially, ANCOVA analyses revealed significant stimulation‐dependent changes in the slope of the connectivity‐behavior relationship, demonstrating that tACS altered how beta synchrony predicted inhibitory and motor performance despite unchanged mean behavior. These findings suggest that dual‐site beta‐tACS can bidirectionally modulate rIFG‐M1 connectivity in a phase‐dependent manner and selectively alter how beta synchrony predicts stopping and motor execution. This mechanistic insight may inform future research exploring dual‐site beta‐tACS as a tool to probe or potentially normalize inhibitory network dynamics in disorders characterized by impaired inhibition. Beta ds‐tACS modulates functional connectivity depending on phase alignment. In‐phase ds‐tACS enhances connectivity and predicts better inhibitory control. Anti‐phase ds‐tACS reduces connectivity, linked to faster go responses.

With the needs of environmental protection and the adjustment of energy structure, new energy vehicles are playing an increasingly important role in the field of transportation today. The permanent-magnet brushless direct-current motor has the characteristics of high efficiency, and can be used in the drive system of new energy vehicles or other auxiliary equipment. In the control process of the permanent-magnet brushless direct-current motor, based on a three-Hall position sensor, due to various factors, there are some errors in the Hall position signal, which must be corrected by appropriate measures. In this paper, the relationship between the position deviation in the commutation interval and the non-commutation-phase current is analyzed, and the current expressions in three different states are given. A new closed-loop compensation strategy for correcting the inaccurate commutation caused by the Hall signal error is proposed. Taking the position of a 30° electrical angle before and after the phase-change point as the H point, realizing the current symmetry within the 30° interval around the H point as the target and the sum of the slopes of the tangent lines at the two points symmetrical within the β (0 < β < 30) electrical angle around the H point as the deviation, a proportional-integral regulator is designed to correct the phase error of the phase-change signal. Finally, it is verified by experiments that the closed-loop compensation strategy proposed in this paper can effectively compensate the phase deviation of the commutation signal at a speed of about 2000 r/min, which improves the working efficiency of the motor to a certain extent.

Rogowski coils are increasingly being used in electricity metering systems. However, owing to their operating principle, they require an additional active integrating circuit to produce an output voltage or current that is directly proportional to the input current. A signal conditioner has the most significant impact on the overall conversion accuracy of the combined transducer. In this paper, a new measurement system and testing procedure utilizing a digital power meter and arbitrary waveform generator are proposed. This approach enables the characterization of the conversion accuracy of both types of active integrators: voltage-to-voltage and voltage-to-current converters. The conversion error for distorted input voltage harmonics and additional phase shift across a range of frequencies are determined. Instead of using the actual signal from the Rogowski coil during testing —which would be challenging owing to the required high RMS value of the distorted current for its input and difficulties in accurately measuring the RMS values of harmonics and their phase angles in relation to the output voltage or current of the tested converter—an arbitrary waveform generator is used. The input voltage to the active integrating circuit replicates the output voltage of the Rogowski coil: as the harmonic order increases, its RMS voltage rises proportionally.