Current Status,Challenges,and Prospects for New Types of Aerial Robots

New types of aerial robots(NTARs)have found extensive applications in the military,civilian contexts,scientific research,disaster management,and various other domains.Compared with traditional aerial robots,NTARs exhibit a broader range of morphological diversity,locomotion capabilities,and enhanced operational capacities.Therefore,this study defines aerial robots with the four characteristics of morphability,biomimicry,multi-modal locomotion,and manipulator attachment as NTARs.Subsequently,this paper discusses the latest research progress in the materials and manufacturing technology,actuation technology,and perception and control technology of NTARs.Thereafter,the research status of NTAR systems is summarized,focusing on the frontier development and application cases of flapping-wing microair vehicles,perching aerial robots,amphibious robots,and operational aerial robots.Finally,the main challenges presented by NTARs in terms of energy,materials,and perception are analyzed,and the future development trends of NTARs are summarized in terms of size and endurance,mechatronics,and complex scenarios,providing a reference direction for the follow-up exploration of NTARs.

Fuzzy-Inverse-Model-Based Networked Tracking Control Frameworks of Time-Varying Signals

Dear Editor,Tracking control in networked environment is a very challenging problem due to the contradiction of rapid response to the time-varying signal and the inevitable delay introduced by networks. This letter has proposed several fuzzy-inverse-model-based network tracking control frameworks which are helpful in handling the system with nonlinear dynamics and uncertainties.

Stability and infectivity of coronaviruses in inanimate environments

Severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)is a highly contagious virus that can transmit through respiratory droplets,aerosols,or contacts.Frequent touching of contaminated surfaces in public areas is therefore a potential route of SARS-CoV-2 transmission.The inanimate surfaces have often been described as a source of nosocomial infections.However,summaries on the transmissibility of coronaviruses from contaminated surfaces to induce the coronavirus disease 2019 are rare at present.This review aims to summarize data on the persistence of different coronaviruses on inanimate surfaces.The literature was systematically searched on Medline without language restrictions.All reports with experimental evidence on the duration persistence of coronaviruses on any type of surface were included.Most viruses from the respiratory tract,such as coronaviruses,influenza,SARS-CoV,or rhinovirus,can persist on surfaces for a few days.Persistence time on inanimate surfaces varied from minutes to up to one month,depending on the environmental conditions.SARSCoV-2 can be sustained in air in closed unventilated buses for at least 30 min without losing infectivity.The most common coronaviruses may well survive or persist on surfaces for up to one month.Viruses in respiratory or fecal specimens can maintain infectivity for quite a long time at room temperature.Absorbent materials like cotton are safer than unabsorbent materials for protection from virus infection.The risk of transmission via touching contaminated paper is low.Preventive strategies such as washing hands and wearing masks are critical to the control of coronavirus disease 2019.

ADS-B Anomaly Data Detection Model Based on Deep Learning and Difference of Gaussian Approach

Due to the influence of terrain structure,meteorological conditions and various factors,there are anomalous data in automatic dependent surveillance-broadcast(ADS-B)message.The ADS-B equipment can be used for positioning of general aviation aircraft.Aim to acquire the accurate position information of aircraft and detect anomaly data,the ADS-B anomaly data detection model based on deep learning and difference of Gaussian(DoG)approach is proposed.First,according to the characteristic of ADS-B data,the ADS-B position data are transformed into the coordinate system.And the origin of the coordinate system is set up as the take-off point.Then,based on the kinematic principle,the ADS-B anomaly data can be removed.Moreover,the details of the ADS-B position data can be got by the DoG approach.Finally,the long short-term memory(LSTM)neural network is used to optimize the recurrent neural network(RNN)with severe gradient reduction for processing ADS-B data.The position data of ADS-B are reconstructed by the sequence to sequence(seq2seq)model which is composed of LSTM neural network,and the reconstruction error is used to detect the anomalous data.Based on the real flight data of general aviation aircraft,the simulation results show that the anomaly data can be detected effectively by the proposed method of reconstructing ADS-B data with the seq2seq model,and its running time is reduced.Compared with the RNN,the accuracy of anomaly detection is increased by 2.7%.The performance of the proposed model is better than that of the traditional anomaly detection models.

Design of a Networked Tracking Control System With a Data-based Approach

Tracking control is a very challenging problem in the networked control system(NCS), especially for the process with blurred mechanism and where only input-output data are available. This paper has proposed a data-based design approach for the networked tracking control system(NTCS). The method utilizes the input-output data of the controlled process to establish a predictive model with the help of fuzzy cluster modelling(FCM)technology. Then, the deduced error and error change in the future are treated as inputs of a fuzzy sliding mode controller(FSMC) to obtain a string of future control actions. These candidate control actions in the controller side are delivered to the plant side. Thus, the network induced time delays are compensated by selecting appropriate control action. Simulation outputs prove the validity of the proposed method.

Fear can be more harmful than the severe acute respiratory syndrome coronavirus 2 in controlling the corona virus disease 2019 epidemic

The current corona virus disease 2019 outbreak caused by severe acute respiratory syndrome coronavirus 2 started in Wuhan,China in December 2019 and has put the world on alert.To safeguard Chinese citizens and to strengthen global health security,China has made great efforts to control the epidemic.Many in the global community have joined China to limit the epidemic.However,discrimination and prejudice driven by fear or misinformation have been flowing globally,superseding evidence and jeopardizing the anti-severe acute respiratory syndrome coronavirus 2 efforts.We analyze this phenomenon and its underlying causes and suggest practical solutions.

Enhancement Dataset for Low Altitude Unmanned Aerial Vehicle Detection

In recent years,the number of incidents involved with unmanned aerial vehicles(UAVs)has increased conspicuously,resulting in an increasingly urgent demand for developing anti-UAV systems. The vast requirements of high detection accuracy with respect to low altitude UAVs are put forward. In addition,the methods of UAV detection based on deep learning are of great potential in low altitude UAV detection. However,such methods need high-quality datasets to cope with the problem of high false alarm rate(FAR)and high missing alarm rate(MAR)in low altitude UAV detection,special high-quality low altitude UAV detection dataset is still lacking. A handful of known datasets for UAV detection have been rejected by their proposers for authorization and are of poor quality. In this paper,a comprehensive enhanced dataset containing UAVs and jamming objects is proposed. A large number of high-definition UAV images are obtained through real world shooting, web crawler, and data enhancement.Moreover,to cope with the challenge of low altitude UAV detection in complex backgrounds and long distance,as well as the puzzle caused by jamming objects,the noise with jamming characteristics is added to the dataset. Finally,the dataset is trained,validated,and tested by four mainstream deep learning models. The results indicate that by using data enhancement,adding noise contained jamming objects and images of UAV with complex backgrounds and long distance,the accuracy of UAV detection can be significantly improved. This work will promote the development of anti-UAV systems deeply,and more convincing evaluation criteria are provided for models optimization for UAV detection.

A multi-target stance detection based on Bi-LSTM network with position-weight

In the task of multi-target stance detection,there are problems the mutual influence of content describing different targets,resulting in reduction in accuracy.To solve this problem,a multi-target stance detection algorithm based on a bidirectional long short-term memory(Bi-LSTM)network with position-weight is proposed.First,the corresponding position of the target in the input text is calculated with the ultimate position-weight vector.Next,the position information and output from the Bi-LSTM layer are fused by the position-weight fusion layer.Finally,the stances of different targets are predicted using the LSTM network and softmax classification.The multi-target stance detection corpus of the American election in 2016 is used to validate the proposed method.The results demonstrate that the Bi-LSTM network with position-weight achieves an advantage of 1.4%in macro average F1 value in the comparison of recent algorithms.

Establishment of Constraint Relation of Robot Dynamics Equation Based on Kinematic Influence Coefficients Method

Due to the diversity of work requirements and environment,the number of degrees of freedom(DOFs)and the complexity of structure of industrial robots are constantly increasing.It is difficult to establish the accurate dynamical model of industrial robots,which greatly hinders the realization of a stable,fast and accurate trajectory tracking control.Therefore,the general expression of the constraint relation in the explicit dynamic equation of the multi-DOF industrial robot is derived on the basis of solving the Jacobian matrix and Hessian matrix by using the kinematic influence coefficients method.Moreover,an explicit dynamic equation with general constraint relation expression is established based on the Udwadia-Kalaba theory.The problem of increasing the time of establishing constraint relationship when the multi-DOF industrial robots complete complex task constraints is solved.With the SCARA robot as the research object,the simulation results show that the proposed method can provide a new idea for industrial robot system modeling with complex constraints.

Survey of Pedestrian Action Recognition Techniques for Autonomous Driving

The development of autonomous driving has brought with it requirements for intelligence,safety,and stability.One example of this is the need to construct effective forms of interactive cognition between pedestrians and vehicles in dynamic,complex,and uncertain environments.Pedestrian action detection is a form of interactive cognition that is fundamental to the success of autonomous driving technologies.Specifically,vehicles need to detect pedestrians,recognize their limb movements,and understand the meaning of their actions before making appropriate decisions in response.In this survey,we present a detailed description of the architecture for pedestrian action recognition in autonomous driving,and compare the existing mainstream pedestrian action recognition techniques.We also introduce several commonly used datasets used in pedestrian motion recognition.Finally,we present several suggestions for future research directions.

Effect of magnesium ions/Type I collagen promote the biological behavior of osteoblasts and its mechanism

Type I collagen(Col I)is a main component of extracellular matrix(ECM).Its safety,biocompatibility,hydrophilicity and pyrogen immunogenicity make it suitable for tissues engineering applications.Mg2t also control a myriad of cellular processes,including the bone development by enhancing the attachment and differentiation of osteoblasts and accelerating mineralization to enhance bone healing.In our studies,Mg2t bind collagen to promote the proliferation and differentiation of osteoblasts through the expression of integrins and downstream signaling pathways.In order to clarify the biological behavior effect of 10mM Mg2t/Col I coating,we performed 3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide(MTT),alkaline phosphatase(ALP),406-diamidino-2-phenylindole(DAPI),Alizarin red staining and Rhodamine B-isothiocyanate(RITC)-labeled phalloidin experiments and found that 10mM Mg2t group,Col I-coating group,10mM Mg2t/Col I-coating group,respectively,promoted the proliferation and differentiation of osteoblasts,especially 10mM Mg2t/Col I-coating group.We detected the mRNA expression of osteogenic-related genes(Runx2,ALP and OCN,OPN and BMP-2)and the protein expression of signaling pathway(integrin a2,integrin b1,FAK and ERK1/2),these results indicated that 10mM Mg2t/Col I coating play an critical role in up-regulating the MC3T3-E1 cells activity.The potential mechanisms of this specific performance may be through activating via integrin a2b1-FAK-ERK1/2 protein-coupled receptor pathway.

A Survey of Human Action Recognition and Posture Prediction

Human action recognition and posture prediction aim to recognize and predict respectively the action and postures of persons in videos.They are both active research topics in computer vision community,which have attracted considerable attention from academia and industry.They are also the precondition for intelligent interaction and human-computer cooperation,and they help the machine perceive the external environment.In the past decade,tremendous progress has been made in the field,especially after the emergence of deep learning technologies.Hence,it is necessary to make a comprehensive review of recent developments.In this paper,firstly,we attempt to present the background,and then discuss research progresses.Secondly,we introduce datasets,various typical feature representation methods,and explore advanced human action recognition and posture prediction algorithms.Finally,facing the challenges in the field,this paper puts forward the research focus,and introduces the importance of action recognition and posture prediction by taking interactive cognition in self-driving vehicle as an example.

MADRL-based UAV swarm non-cooperative game under incomplete information

Unmanned Aerial Vehicles(UAVs)play increasing important role in modern battlefield.In this paper,considering the incomplete observation information of individual UAV in complex combat environment,we put forward an UAV swarm non-cooperative game model based on Multi-Agent Deep Reinforcement Learning(MADRL),where the state space and action space are constructed to adapt the real features of UAV swarm air-to-air combat.The multi-agent particle environment is employed to generate an UAV combat scene with continuous observation space.Some recently popular MADRL methods are compared extensively in the UAV swarm noncooperative game model,the results indicate that the performance of Multi-Agent Soft Actor-Critic(MASAC)is better than that of other MADRL methods by a large margin.UAV swarm employing MASAC can learn more effective policies,and obtain much higher hit rate and win rate.Simulations under different swarm sizes and UAV physical parameters are also performed,which implies that MASAC owns a well generalization effect.Furthermore,the practicability and convergence of MASAC are addressed by investigating the loss value of Q-value networks with respect to individual UAV,the results demonstrate that MASAC is of good practicability and the Nash equilibrium of the UAV swarm non-cooperative game under incomplete information can be reached.

The Four Most Basic Elements In Machine Cognition

The widespread use of ChatGPT has normalized the dialogue Turing test.To meet this challenge,China's major national development strategy suggests that for a new generation of artificial intelligence,it is first necessary to answer the big questions raised by Turing in 1950 from the perspective of cognitive physics:Can machines think?How do machines think?How do machines cognize?Whether it is carbon-based human cognition or silicon-based machine cognition,it is an interaction between complex constructs composed of the four most basic elements:matter,energy,structure,and time.Both humans and machines depend on negative entropy for living,and time is the cornerstone of cognition.Structure and time are parasitic on matter and energy in physical space,forming hard-structured ware.The soft-structured ware in cognitive space is mind,which is parasitic on the hard-structured ware or other existing soft-structured ware,and constitutes a rich hierarchy of multi-scale feelings,concepts,information,and knowledge.Extending"abstraction"from the symbolic school of artificial intelligence,"association"from the connectionist school,and"interaction"from the behaviorist school,the core of cognition is established on the shoulders of such scientific giants such as Schrodinger,Turing and Wiener.Soft and hard-structured ware interact.Cognitive machine can comprise heterogeneous hard-structured ware,such as field programmable gate arrays(FPGAs),data processing units(DPUs),central processing units(CPUs),graphics processing units(GPUs),tensor processing units(TPUs),and memory.It can also be implanted with the"Baby Cognitive Nucleus"which is hard-structured ware genetically inherited and naturally evolved to form the embodied machine.Then the hard-structured ware is parasitized by rich,multi-scale soft-structured ware.By regulating matter and energy through soft-structured ware,machines produce orderly events,form coordinated and orderly thinking activities.The heterogeneous sensors configured by the machine and the speed of thinking will no longer be trapped by the extreme values of biochemical parameters of carbon-based organisms but will be able to perceive through multi-channel cross-modal means,carry out intense thinking,and maintain cognitive continuity with memory.To generate computational and memory intelligence in cognitive space which can bootstrap,self-reuse and self-replicate,imagination and creativity are improved through memory-constrained computing.The new generation of artificial intelligence will leap beyond mechanized mathematics to automation in thinking and self-driven growth of cognition,and the thinking in the cognitive space and the behavior in the physical space verify each other,from the dialogue Turing test to the embodied Turing test.Humans have entered the intelligent era of human-machine co-creation with cognitive machines iteratively inventing,discovering,and creating alongside scientists,engineers,and skilled craftsmen,each wise in its way,improving thinking ability,and amplifying human energy.