用于流式语音识别的轻量化端到端声学架构

在流式识别方法中,分块识别破坏并行性且消耗资源较大,而限制自注意力机制的上下文识别很难获得所有信息.由此,文中提出轻量化端到端声学架构(CFLASH-Transducer).为了获取细腻的局部特征,采用轻量化的FLASH(Fast Linear Attention with a Single Head)与卷积神经网络块结合.卷积块中采用Inception V2网络,提取语音信号多尺度的局部特征.再通过Coordinate Attention机制捕获特征的位置信息和多通道之间的相互关联.此外,采用深度可分离卷积,用于特征增强和层间平滑过渡.为了使其可流式化处理音频,采用RNN-T(Recurrent Neural Network Transducer)架构进行训练与解码.将当前块已经计算的全局注意力作为隐变量,传入后续块中,串联各块信息,保留训练的并行性和相关性,并且不会随着序列的增长而消耗计算资源.在开源数据集THCHS30上进行训练与测试,CFLASH-Transducer取得较高的识别率.并且相比离线识别,流式识别精度损失不超过1%.

Backstepping sliding mode control with self recurrent wavelet neural network observer for a novel coaxial twelve-rotor UAV

The robust attitude control for a novel coaxial twelve-rotor UAV which has much greater payload capacity,higher drive capability and damage tolerance than a quad-rotor UAV is studied. Firstly,a dynamical and kinematical model for the coaxial twelve-rotor UAV is designed. Considering model uncertainties and external disturbances,a robust backstepping sliding mode control( BSMC) with self recurrent wavelet neural network( SRWNN) method is proposed as the attitude controller for the coaxial twelve-rotor. A combinative algorithm of backstepping control and sliding mode control has simplified design procedures with much stronger robustness benefiting from advantages of both controllers. SRWNN as the uncertainty observer is able to estimate the lumped uncertainties effectively.Then the uniformly ultimate stability of the twelve-rotor system is proved by Lyapunov stability theorem. Finally,the validity of the proposed robust control method adopted in the twelve-rotor UAV under model uncertainties and external disturbances are demonstrated via numerical simulations and twelve-rotor prototype experiments.

Cytoreductive surgery after recurrent epithelial ovarian cancer and at other timepoints

In this descriptive review we look at the role of surgery for advanced ovarian cancer at other timepoints apart from the initial cytoreduction for front-line therapy or interval cytoreductive surgery after neoadjuvant chemotherapy. The chief surgical problem to face after primary treatment is recurrent ovarian cancer. Of far more marginal concern are the second-look surgical procedures or the palliative efforts intended to resolve the patient's symptoms with no curative intent. The role of surgery in recurrent ovarian cancer remains poorly defi ned. Current data, albeit from non-randomized studies, nevertheless clearly support surgical cytoreduction in selected patients, a rarely curative expedient that invariably yields a marked survival advantage over chemotherapy alone. Despite these fi ndings, some consider it too early to adopt secondary cytoreduction as the standard care for patients with recurrent ovarian cancer and a randomized study is needed. Two ongoing randomized trials(Arbeitsgemeinschaft Gynkologische Onkologie-Desktop Ⅲ and Gynecologic Oncology Group 213) intend to verify the role of secondary cytoreduction for platinum-sensitive ovarian cancer compared with chemotherapy considered as standard care for these patients. We await the results of these two trials for a defi nitive answer to the matter.

Adaptive Kalman filter and dynamic recurrent neural networks-based control design of macro-micro manipulator

In this paper, a composite control scheme for macro-micro dual-drive positioning stage with high accel- eration and high precision is proposed. The objective of control is to improve the precision by reducing the influence of system vibration and external noise. The positioning stage is composed of voice coil motor （VCM） as macro driver and piezoelectric actuator （PEA） as micro driver. The precision of the macro drive positioning stage is improved by the com- bined PID control with adaptive Kalman filter （AKF）. AKF is used to compensate VCM vibration （as the virtual noise） and the external noise. The control scheme of the micro drive positioning stage is presented as the integrated one with PID and intelligent adaptive inverse control approach to compensate the positioning error caused by macro drive positioning stage. A dynamic recurrent neural networks （DRNN） based inverse control approach is proposed to offset the hysteresis nonlinearity of PEA. Simulations show the positioning precision of macro-micro dual-drive stage is clearly improved via the proposed control scheme.