Lithium-ion batteries are widely used in a variety of fields due to their high energy density, high power density, long service life, and environmental friendliness. However, safety accidents with lithium-ion batteries occur frequently. The real-time safety monitoring of lithium-ion batteries is particularly important during their use. The fiber Bragg grating (FBG) sensors have some additional advantages over conventional electrochemical sensors, such as low invasiveness, electromagnetic anti-interference, and insulating properties. This paper reviews lithium-ion battery safety monitoring based on FBG sensors. The principles and sensing performance of FBG sensors are described. The single-parameter monitoring and dual-parameter monitoring of lithium-ion batteries based on FBG sensors are reviewed. The current application state of the monitored data in lithium-ion batteries is summarized. We also present a brief overview of the recent developments in FBG sensors used in lithium-ion batteries. Finally, we discuss future trends in lithium-ion battery safety monitoring based on FBG sensors.

An active disturbance rejection control based on fractional calculus is proposed to improve the motion performance and robustness of autonomous underwater vehicle (AUV). The active disturbance rejection control (ADRC) method can estimate and compensate the total disturbance of AUV automatically. The fractional-order PID (proportional integral derivative) has fast dynamic response, which can eliminate the estimation error of extended state observer. The fractional calculus active disturbance rejection strategy combines the advantages of the above two algorithms, and it is designed for AUV heading and pitch subsystems. In addition, the stability of fractional calculus ADRC heading subsystem is proven by Lyapunov stability theorem. The numerical simulations and experimental results document that the superior performance has been achieved. The fractional calculus ADRC strategy has more excellent abilities for disturbance rejection, performs better than ADRC and PID, and has important theoretical and practical value.

Three-phase PWM voltage-source rectifier (VSR) systems have been widely used in various energy conversion systems, where current sensors are the key component for state monitoring and system control. The current sensor faults may bring hidden danger or damage to the whole system; therefore, this paper proposed a random forest (RF) and current fault texture feature–based method for current sensor fault diagnosis in three-phase PWM VSR systems. First, the three-phase alternating currents (ACs) of the three-phase PWM VSR are collected to extract the current fault texture features, and no additional hardware sensors are needed to avoid causing additional unstable factors. Then, the current fault texture features are adopted to train the random forest current sensor fault detection and diagnosis (CSFDD) classifier, which is a data-driven CSFDD classifier. Finally, the effectiveness of the proposed method is verified by simulation experiments. The result shows that the current sensor faults can be detected and located successfully and that it can effectively provide fault locations for maintenance personnel to keep the stable operation of the whole system.

High-accuracy level underwater acoustical surveying plays an important role in ocean engineering applications, such as subaqueous tunnel construction, oil and gas exploration, and resources prospecting. This novel imaging method is eager to break through the existing theory to achieve a higher accuracy level of surveying. Multibeam Synthetic Aperture Sonar (MBSAS) is a kind of underwater acoustical imaging theory that can achieve 3D high-resolution detecting and overcome the disadvantages of traditional imaging methods, such as Multibeam Echo Sounder (MBES) and Synthetic Aperture Sonar (SAS). However, the resolution in the across-track direction inevitably decreases with increasing range, limited by the beamwidth of the transducer array of MBES. Furthermore, the sidelobe problem is also a significant interference of imaging sonar that introduces image noise and false peaks, which reduces the accuracy of the underwater images. Therefore, we proposed an accelerated deconvolved MBSAS beamforming method that introduces exponential acceleration and vector extrapolation to improve the convergence velocity of the classical Richardson-Lucy (R-L) iteration. The method proposed achieves a narrow beamwidth with a high sidelobe ratio in a few iterations. It can be applied to actual engineering applications, which breaks through the limitation of the actual transducer array scale. Simulations, tank, and field experiments also demonstrate the feasibility and advantages of the method proposed. 3D high-accuracy level underwater acoustical surveying can be achieved through this 2D MBES transducer array system, which can be widely promoted in the field of underwater acoustical remote sensing.

In the disturbance of unsteady flow field under the sea, the monitoring accuracy and precision of the bottom-mounted acoustic monitoring platform will decrease. In order to reduce the hydrodynamic interference, the platform wrapped with fairing structure and separated from the retrieval unit is described. The suppression effect evaluation based on the correlation theory of sound pressure and particle velocity for spherical wave in infinite homogeneous medium is proposed and the difference value between them is used to evaluate the hydrodynamic restraining performance of the bottom-mounted platform under far field condition. Through the sea test, it is indicated that the platform with sparse layers fairing structure (there are two layers for the fairing, in which the inside layer is 6-layers sparse metal net, and the outside layer is 1-layer polyester cloth, and then it takes sparse layers for short) has no attenuation in the sound pressure response to the sound source signal, but obvious suppression in the velocity response to the hydrodynamic noise. The effective frequency of the fairing structure is decreased below 10 Hz, and the noise magnitude is reduced by 10 dB. With the comparison of different fairing structures, it is concluded that the tighter fairing structure can enhance the performance of sound transmission and flow restraining.

Reflection traveltime inversion (RTI) plays an important role in the realm of exploration geophysics due to its ability to restore long-wavelength subsurface structures. However, due to the necessary migration/demigration process, every iteration of RTI requires six times as many forward calculations, resulting in high computational and storage costs. High-performance computing can accelerate RTI calculations, but it has little effect on the consumption of large storage volumes. Thus, the background and perturbed wavefields with excitation approximation are used in RTI to overcome the storage issue. In the authors’ RTI, the strategies of source-wavelet convolution and wavefield direction decomposition are introduced to solve the problems of missing source signature and multipathing in the excitation approximation wavefields. Numerical examples have demonstrated that excitation approximation RTI can provide an accurate background velocity model and reduce the storage burden of RTI.