Research and application of stochastic resonance in quad-stable potential system

To solve the problem of low weak signal enhancement performance in the quad-stable system,a new quad-stable potential stochastic resonance(QSR)is proposed.Firstly,under the condition of adiabatic approximation theory,the stationary probability distribution(SPD),the mean first passage time(MFPT),the work(W),and the power spectrum amplification factor(SAF)are derived,and the impacts of system parameters on them are also extensively analyzed.Secondly,numerical simulations are performed to compare QSR with the classical Tri-stable stochastic resonance(CTSR)by using the genetic algorithm(GA)and the fourth-order Runge–Kutta algorithm.It shows that the signal-to-noise ratio(SNR)and mean signal-to-noise increase(MSNRI)of QSR are higher than CTSR,which indicates that QSR has superior noise immunity than CTSR.Finally,the two systems are applied in the detection of real bearing faults.The experimental results show that QSR is superior to CTSR,which provides a better theoretical significance and reference value for practical engineering application.

Characteristics of piecewise linear symmetric tri-stable stochastic resonance system and its application under different noises

Weak signal detection has become an important means of mechanical fault detections. In order to solve the problem of poor signal detection performance in classical tristable stochastic resonance system(CTSR), a novel unsaturated piecewise linear symmetric tristable stochastic resonance system(PLSTSR) is proposed. Firstly, by making the analysis and comparison of the output and input relationship between CTSR and PLSTSR, it is verified that the PLSTSR has good unsaturation characteristics. Then, on the basis of adiabatic approximation theory, the Kramers escape rate, the mean first-passage time(MFPT), and output signal-to-noise ratio(SNR) of PLSTSR are deduced, and the influences of different system parameters on them are studied. Combined with the adaptive genetic algorithm to synergistically optimize the system parameters, the PLSTSR and CTSR are used for numerically simulating the verification and detection of low-frequency, high-frequency,and multi-frequency signals. And the results show that the SNR and output amplitude of the PLSTSR are greatly improved compared with those of the CTSR, and the detection effect is better. Finally, the PLSTSR and CTSR are applied to the bearing fault detection under Gaussian white noise and Levy noise. The experimental results also show that the PLSTSR can obtain larger output amplitude and SNR, and can detect fault signals more easily, which proves that the system has better performance than other systems in bearing fault detection, and has good theoretical significance and practical value.

A robust beam tracking scheme for millimeter wave HetNets

Millimeter Wave(mmWave)communication has been widely acknowledged as an attractive solution to address high-speed transmission of massive data in 5G and beyond 5G systems due to the promising spectrum availability.However,mmWave signals are highly susceptible to blockage and may suffer from rapidly changing channels.Thus,directional/beam tracking becomes imperative yet essential for robust mmWave communications.To address this challenge,we propose a robust beam tracking scheme for mmWave Heterogeneous Networks(HetNets)with multi-connectivity.Different from most existing schemes,the proposed beam tracking scheme is effective for outage events.We first discuss theμWave-assisted beam tracking procedure with and without candidate beams,and then analyze the inherent correlation between mmWave link quality and the operating beamwidth and occlusion range to derive the optimal beamwidth.Theoretical and numerical results show that the proposed beam tracking scheme can improve the robustness of mmWave communications while guaranteeing the rate performance.

Erratum to “A robust beam tracking scheme for millimeter wave HetNets” [Digit. Commun. Network. 8 (6) (2022) 1115–1121]

The Editorial office regrets that a note about the affiliation of the first author Qing Xue was omitted in the initially published version of this paper.The note is that Qing Xue was co-first affiliated with the UESTC and CQUPT for the work of this paper.

A dynamic influence model of social network hotspot based on grey system

The outbreak of hotspot in social network may contain complex dynamic genesis. Using user behavior data from hotspots in social network, we study how different user groups play different roles for a hotspot topic. Firstly, by analyzing users' behavior records, we mine group situation that promotes the hotspot.Several major attributions in a hotspot outbreak, such as individual, peer and group triggers, are defined formally according to the view-point of social identity, social interaction, retweet depth and opinion leader. Secondly,for the problem of the uneven and sparse data in each stage of hotspot topic's life cycle, we propose a dynamic influence model based on grey system to formalize the effect of different groups. Then the process of hotspot evolution driven by distinct crowd is showed dynamically. The experimental result confirms that the model is able not only to qualify users' influence on a hotspot topic but also to predict effectively an upcoming change in a hotspot topic.

Monaural speech enhancement combining accurate ratio mask and deep neural network

Aiming at the problem that the supervised speech enhancement ignores the impact of the similarity of amplitude spectrum between clean speech,noise,and noisy speech on the enhancement effect,a method combining accurate ratio mask(ARM)and deep neural network(DNN)is proposed for monaural speech enhancement.Firstly,an accurate ratio mask based on ideal ratio mask in the time-frequency domain is designed,which utilizes the normalized cross-correlation coefficients of amplitude spectrum between clean speech and noisy speech,and between noise and noisy speech.Then,the target mask is estimated by the output of the baseline DNN which takes the amplitude spectrum of clean speech and noise as training target,and further uses the target mask to optimize the baseline DNN and gets the enhanced speech from noisy speech.Moreover,considering the discriminative information between clean speech and noise,a discriminative training function is used to replace the mean square error(MSE)as the objective function of the DNN,thus making the output of network more accurate.The experimental results show that the discriminative training function improves the enhancement effect of baseline DNN and the overall joint optimization network.Compared with other common DNN methods,the proposed method has achieved higher average PESQ,STOI and better noise suppression effect under matched and unmatched noise,and the enhanced speech also retains more speech components.