Artificial intelligence in seismology:Advent,performance and future trends

Realistically predicting earthquake is critical for seismic risk assessment,prevention and safe design of major structures.Due to the complex nature of seismic events,it is challengeable to efficiently identify the earthquake response and extract indicative features from the continuously detected seismic data.These challenges severely impact the performance of traditional seismic prediction models and obstacle the development of seismology in general.Taking their advantages in data analysis,artificial intelligence(AI) techniques have been utilized as powerful statistical tools to tackle these issues.This typically involves processing massive detected data with severe noise to enhance the seismic performance of structures.From extracting meaningful sensing data to unveiling seismic events that are below the detection level,AI assists in identifying unknown features to more accurately predicting the earthquake activities.In this focus paper,we provide an overview of the recent AI studies in seismology and evaluate the performance of the major AI techniques including machine learning and deep learning in seismic data analysis.Furthermore,we envision the future direction of the AI methods in earthquake engineering which will involve deep learning-enhanced seismology in an internet-of-things(IoT) platform.

Sub-nanosecond-speed frequency-reconfigurable photonic radio frequency switch using a silicon modulator

Radio frequency(RF)switches are essential for implementing routing of RF signals.However,the increasing demand for RF signal frequency and bandwidth is posing a challenge of switching speed to the conventional solutions,i.e,the capability of operating at a sub-.nanosecond speed or faster.In addition,signal frequency reconfigurability is also a desirable feature to facilitate new innovations of flexible system functions.Utilizing microwave photonics as an alter-native path,we present here a photonic implementation of an RF switch providing not only the capability of switching at a sub-nanosecond speed but also options of frequency doubling of the input RF signals,allowing for flexible output waveforms.The core device is a traveling wave silicon modulator with a device size of0.2 mm × 1.8 mm and a modu-lation bandwidth of 10 GHz.Using microwave frequencies,i.e.,15 GHz and 20 GHz,as two simultaneous RF input signals,we experimentally demonstrated their amplitude and frequency switching as well as that of the doubled frequencies,ie,30 GHz and 40 GHz,at a switching frequency of 5 GHz.The results of this work point to a solution for creating high speed RF switches with high compactness and flexibility.

Hydrodynamics of high-speed robots driven by the combustion-enabled transient driving method

Underwater vehicles play important roles in underwater observation, ocean resource exploration, and sample collection.Soft robots are a unique type of underwater vehicles due to their good environmental adaptability and motion flexibility, although they are weak in terms of actuation and response ability. The transient driving method(TDM) was developed to resolve these shortcomings. However, the interaction between the robots’ swift motions and flow fields has not yet been fully studied. In this study, a computational fluid dynamic model is developed to simulate the fluid fields disturbed by transient high-speed motions generated by the robots. Focusing on the dependence of robot dynamics on thrust force and eccentricity, typical structures of both flow and turbulence fields around the robots are obtained to quantitatively analyze robot kinematic performance, velocity distribution, vortex systems, surface pressure, and turbulence. The results demonstrate the high-speed regions at the robots’ heads and tails and the vortex systems due to sudden expansion, indicating a negative relationship between the maximum fluid velocity and eccentricity. The reported results provide useful information for studying the environmental interaction abilities of robots during operating acceleration and steering tasks.

Advanced ocean wave energy harvesting:current progress and future trends

With a transition towards clean and low-carbon renewable energy,against the backdrop of the fossil-energy crisis and rising pollution,ocean energy has been proposed as a significant possibility for mitigating climate change and energy shortages for its characteristics of clean,renewable,and abundant.The rapid development of energy harvesting technology has led to extensive applications of ocean wave energy,which,however,has faced certain challenges due to the low-frequency and unstable nature of ocean waves.This paper overviews the debut and development of ocean wave energy harvesting technology,and discusses the potential and application paradigm for energy harvesting in the“intelligent ocean.”We first describe for readers the mechanisms and applications of traditional wave energy converters,and then discuss current challenges in energy harvesting performance connected to the characteristics of ocean waves.Next,we summarize the progress in wave energy harvesting with a focus on advanced technologies(e.g.,data-driven design and optimization)and multifunctional energy materials(e.g.,triboelectric metamaterials),and finally propose recommendations for future development.

Underwater minirobots actuated by hybrid driving method

Underwater minirobots have attracted significant interest due to their value in complex application scenarios.Typical underwater minirobots are driven mainly by a soft or rigid actuator.However,soft actuation is currently facing challenges,including inadequate motional control accuracy and the lack of a continuous and steady driving force,while conventional rigid actuation has limited actuation efficiency,environmental adaptability,and motional flexibility,which severely limits the accomplishment of complicated underwater tasks.In this study,we developed underwater minirobots actuated by a hybrid driving method(HDM)that combines combustion-based actuators and propeller thrusters to achieve accurate,fast,and flexible underwater locomotion performance.Underwater experiments were conducted to investigate the kinematic performance of the minirobots with respect to the motion modes of rising,drifting,and hovering.Numerical models were used to investigate the kinematic characteristics of the minirobots,and theoretical models developed to unveil the mechanical principle that governs the driving process.Satisfactory agreement was obtained from comarisons of the experimental,numerical,and theoretical results.Finally,the HDM was compared with selected hybrid driving technologies in terms of acceleration and response time.The comparison showed that the minirobots based on HDM were generally superior in transient actuation ability and reliability.

Silicon nonlinear switch as a conditional circulator for monostatic LiDAR systems

All-optical silicon-photonics-based LiDAR systems allow for desirable features in scanning resolution and speed,as well as leverage other advantages such as size, weight, and cost. Implementing optical circulators in silicon photonics enables bidirectional use of the light path for both transmitters and receivers, which simplifies the system configuration and thereby promises low system cost. In this work, to the best of our knowledge, we present the first experimental verification of all-passive silicon photonics conditional circulators for monostatic LiDAR systems using a nonlinear switch. The proposed silicon nonlinear interferometer is realized by controlling signal power distribution with power-splitting circuits, allowing the LiDAR transmitter and receiver to share the same optical path. Unlike the traditional concept requiring a permanent magnet, the present device is implemented by using common silicon photonic waveguides and a standard foundry-compatible fabrication process. With several additional phase shifters, the demonstrated device exhibits considerable flexibility using a single chip, which can be more attractive for integration with photodetector arrays in LiDAR systems.