Constrained structure of ancient Chinese poetry facilitates speech content grouping

Ancient Chinese poetry is constituted by structured language that deviates from ordinary language usage [1, 2]; its poetic genres impose unique combinatory constraints on linguistic elements [3]. How does the constrained poetic structure facilitate speech segmentation when common linguistic [4, 5, 6, 7, 8] and statistical cues [5, 9] are unreliable to listeners in poems? We generated artificial Jueju, which arguably has the most constrained structure in ancient Chinese poetry, and presented each poem twice as an isochronous sequence of syllables to native Mandarin speakers while conducting magnetoencephalography (MEG) recording. We found that listeners deployed their prior knowledge of Jueju to build the line structure and to establish the conceptual flow of Jueju. Unprecedentedly, we found a phase precession phenomenon indicating predictive processes of speech segmentation—the neural phase advanced faster after listeners acquired knowledge of incoming speech. The statistical co-occurrence of monosyllabic words in Jueju negatively correlated with speech segmentation, which provides an alternative perspective on how statistical cues facilitate speech segmentation. Our findings suggest that constrained poetic structures serve as a temporal map for listeners to group speech contents and to predict incoming speech signals. Listeners can parse speech streams by using not only grammatical and statistical cues but also their prior knowledge of the form of language

Modulation spectra capture EEG responses to speech signals and drive distinct temporal response functions

Speech signals have a unique shape of long-term modulation spectrum that is distinct from environmental noise, music, and non-speech vocalizations. Does the human auditory system adapt to the speech long-term modulation spectrum and efficiently extract critical information from speech signals? To answer this question, we tested whether neural responses to speech signals can be captured by specific modulation spectra of non-speech acoustic stimuli. We generated amplitude modulated (AM) noise with the speech modulation spectrum and 1/f modulation spectra of different exponents to imitate temporal dynamics of different natural sounds. We presented these AM stimuli and a 10-min piece of natural speech to 19 human participants undergoing electroencephalography (EEG) recording. We derived temporal response functions (TRFs) to the AM stimuli of different spectrum shapes and found distinct neural dynamics for each type of TRFs. We then used the TRFs of AM stimuli to predict neural responses to the speech signals, and found that (1) the TRFs of AM modulation spectra of exponents 1, 1.5, and 2 preferably captured EEG responses to speech signals in the δ band and (2) the θ neural band of speech neural responses can be captured by the AM stimuli of an exponent of 0.75. Our results suggest that the human auditory system shows specificity to the long-term modulation spectrum and is equipped with characteristic neural algorithms tailored to extract critical acoustic information from speech signals

Current sensorless model predictive torque control based on adaptive backstepping observer for PMSM drives

A novel adaptive backstepping observer is proposed and model predictive torque control (MPTC) strategy is considered for three-phase permanent magnet synchronous motor (PMSM) drives without any current sensor. Generally, instantaneous stator currents are required for successful operation of MPTC. If the stator current sensors fail, the most common technique for reconstructing stator currents mainly focuses on using information from a single current sensor in the DC-link of an inverter. Nevertheless, the existence of immeasurable regions in the output voltage hexagon results in that the three-phase currents will not be reliably detected since one or more of the active state vectors are not applied long enough to insure accurate measurements. In addition, the technique may suffer from the very noisy of DC-link current feedback. To avoid these drawbacks, making use of the technique of adaptive backstepping, a novel observer is proposed. The designed observer can be capable of concurrent estimation of stator currents and resistance under the assumption that rotor speed and inverter output voltage as well as DC-link voltage are available for measurement. Stability and convergence of the observer are analytically verified based on Lyapunov stability theory. In order to reduce the torque & flux ripples and improve drives control performance, MPTC strategy is employed. The proposed algorithm is less complicated and its implement is relatively easy. It can ensure that the whole drives system achieves satisfactory torque & speed control and strong robustness. Extensive simulation validates the feasibility and effectiveness of the proposed scheme

Fault tolerant model predictive control of three-phase permanent magnet synchronous motors

A new fault tolerant model predictive control (FTMPC) strategy is proposed for three-phase magnetically isotropic permanent magnet synchronous motor (PMSM) with complete loss of one phase (LOP) or loss of one leg (LOL) of the inverter. The dynamic model of PMSM with LOP or LOL is derived in abc- System. The principle of FTMPC is investigated, its predictive model for remaining two stator phase currents is established after LOP or LOL occurs, and the flux estimator based on current model is employed in order to calculate the stator flux & its corresponding torque. Extra-leg extra-switch inverter is used as power unit. The PI controller is put to use for regulating rotor speed and generating reference torque. Dynamic responses of healthy MPC and unhealthy FTMPC for PMSM systems are given to compare their performance via simulation and some analysis is presented. The simulation results show that the proposed FTMPC strategy not only allows for continuous and disturbance-free operation of the unhealthy PMSM with LOP or LOL but also preserves satisfactory torque and speed control. And then the effectiveness of the proposed schemes in this paper is demonstrated

Parent-adolescent attachment and peer attachment associated with Internet Gaming Disorder: a longitudinal study of first-year undergraduate students

Background and aims: Given that Internet Gaming Disorder (IGD) has tentatively been included in DSM-5 as a psychiatric disorder, it is important that the effect of parental and peer attachment in the development of IGD is further explored. Methods: Utilizing a longitudinal design, this study investigated the bidirectional association between perceived Q1 parent-adolescent attachment, peer attachment, and IGD among 1,054 first-year undergraduate students (58.8% female). The students provided demographic information (e.g., age, gender) and were assessed using the nine-item Internet Gaming Disorder Scale and the Inventory of Parent and Peer Attachment. Assessments occurred three times, six months apart (October 2017; April 2018; October 2018). Results: Cross-lagged panel models suggested that IGD weakly predicted subsequent mother attachment but significantly negatively predicted father attachment. However, father and mother attachment could not predict subsequent IGD. Moreover, peer attachment has bidirectional association with IGD. Further, the model also demonstrated stable crosssectional negative correlations between attachment and IGD across all three assessments. Discussion and conclusions: The findings of the present study did not show a bidirectional association between parental attachment and IGD, but they did show a negative bidirectional association between peer attachment and IGD. The results suggested previous cross-sectional associations between IGD and attachment, with larger links among males than females at the first measurement point. We found that peer attachment could negatively predict subsequent IGD, which indicates that peer attachment plays an important role in preventing addictive gaming behaviors for university students

GFTSM-based Model Predictive Torque Control for PMSM Drive System With Single Phase Current Sensor

Copyright © 2017 Acta Automatica Sinica. All rights reserved. A global fast terminal sliding mode (GFTSM)-based model predictive torque control (MPTC) strategy is developed for permanent magnet synchronous motor (PMSM) drive system with only one phase current sensor. Generally two phase-current sensors are indispensable for MPTC. In response to only one phase current sensor available and the change of stator resistance, a novel adaptive observer for estimating the remaining two phase currents and time-varying stator resistance is proposed to perform MPTC. Moreover, in view of the variation of system parameters and external disturbance, a new GFTSM-based speed regulator is synthesized to enhance the drive system robustness. In this paper, the GFTSM, based on sliding mode theory, employs the fast terminal sliding mode in both the reaching stage and the sliding stage. The resultant GFTSM-based MPTC PMSM drive system with single phase current sensor has excellent dynamical performance which is very close to the GFTSM-based MPTC PMSM drive system with two-phase current sensors. On the other hand, compared with proportional-integral (PI)-based and sliding mode (SM)-based MPTC PMSM drive systems, it possesses better dynamical response and stronger robustness as well as smaller total harmonic distortion (THD) index of three-phase stator currents in the presence of variation of load torque. The simulation results validate the feasibility and efiectiveness of the proposed scheme

Spiniform phase-encoded metagratings entangling arbitrary rational-order orbital angular momentum

Quantum entanglements between integer-order and fractional-order orbital angular momentums (OAMs) have been previously discussed. However, the entangled nature of arbitrary rational-order OAM has long been considered a myth due to the absence of an effective strategy for generating arbitrary rational-order OAM beams. Therefore, we report a single metadevice comprising a bilaterally symmetric grating with an aperture, creating optical beams with dynamically controllable OAM values that are continuously varying over a rational range. Due to its encoded spiniform phase, this novel metagrating enables the production of an average OAM that can be increased without a theoretical limit by embracing distributed singularities, which differs significantly from the classic method of stacking phase singularities using fork gratings. This new method makes it possible to probe the unexplored niche of quantum entanglement between arbitrarily defined OAMs in light, which could lead to the complex manipulation of microparticles, high-dimensional quantum entanglement and optical communication. We show that quantum coincidence based on rational-order OAM-superposition states could give rise to low cross-talks between two different states that have no significant overlap in their spiral spectra. Additionally, future applications in quantum communication and optical micromanipulation may be found