Semantic Consistency and Correctness Verification of Digital Traffic Rules

The consensus of the automotive industry and traffic management authorities is that autonomous vehicles must follow the same traffic laws as human drivers.Using formal or digital methods,natural language traffic rules can be translated into machine language and used by autonomous vehicles.In this paper,a translation flow is designed.Beyond the translation,a deeper examination is required,because the semantics of natural languages are rich and complex,and frequently contain hidden assumptions.The issue of how to ensure that digital rules are accurate and consistent with the original intent of the traffic rules they represent is both significant and unresolved.In response,we propose a method of formal verification that combines equivalence verification with model checking.Reasonable and reassuring digital traffic rules can be obtained by utilizing the proposed traffic rule digitization flow and verification method.In addition,we offer a number of simulation applications that employ digital traffic rules to assess vehicle violations.The experimental findings indicate that our digital rules utilizing metric temporal logic(MTL)can be easily incorporated into simulation platforms and autonomous driving systems(ADS).

The First Verification Test of Space-Ground Collaborative Intelligence via Cloud-Native Satellites

Recent advancements in satellite technologies and the declining cost of access to space have led to the emergence of large satellite constellations in Low Earth Orbit(LEO).However,these constellations often rely on bent-pipe architecture,resulting in high communication costs.Existing onboard inference architectures suffer from limitations in terms of low accuracy and inflexibility in the deployment and management of in-orbit applications.To address these challenges,we propose a cloud-native-based satellite design specifically tailored for Earth Observation tasks,enabling diverse computing paradigms.In this work,we present a case study of a satellite-ground collaborative inference system deployed in the Tiansuan constellation,demonstrating a remarkable 50%accuracy improvement and a substantial 90%data reduction.Our work sheds light on in-orbit energy,where in-orbit computing accounts for 17%of the total onboard energy consumption.Our approach represents a significant advancement of cloud-native satellite,aiming to enhance the accuracy of in-orbit computing while simultaneously reducing communication cost.

Continuous-Time Channel Prediction Based on Tensor Neural Ordinary Differential Equation

Channel prediction is critical to address the channel aging issue in mobile scenarios.Existing channel prediction techniques are mainly designed for discrete channel prediction,which can only predict the future channel in a fixed time slot per frame,while the other intra-frame channels are usually recovered by interpolation.However,these approaches suffer from a serious interpolation loss,especially for mobile millimeter-wave communications.To solve this challenging problem,we propose a tensor neural ordinary differential equation(TN-ODE)based continuous-time channel prediction scheme to realize the direct prediction of intra-frame channels.Specifically,inspired by the recently developed continuous mapping model named neural ODE in the field of machine learning,we first utilize the neural ODE model to predict future continuous-time channels.To improve the channel prediction accuracy and reduce computational complexity,we then propose the TN-ODE scheme to learn the structural characteristics of the high-dimensional channel by low-dimensional learnable transform.Simulation results show that the proposed scheme is able to achieve higher intra-frame channel prediction accuracy than existing schemes.

Meta-Auto-Decoder:a Meta-Learning-Based Reduced Order Model for Solving Parametric Partial Differential Equations

Many important problems in science and engineering require solving the so-called parametric partial differential equations(PDEs),i.e.,PDEs with different physical parameters,boundary conditions,shapes of computational domains,etc.Typical reduced order modeling techniques accelerate the solution of the parametric PDEs by projecting them onto a linear trial manifold constructed in the ofline stage.These methods often need a predefined mesh as well as a series of precomputed solution snapshots,and may struggle to balance between the efficiency and accuracy due to the limitation of the linear ansatz.Utilizing the nonlinear representation of neural networks(NNs),we propose the Meta-Auto-Decoder(MAD)to construct a nonlinear trial manifold,whose best possible performance is measured theoretically by the decoder width.Based on the meta-learning concept,the trial manifold can be learned in a mesh-free and unsupervised way during the pre-training stage.Fast adaptation to new(possibly heterogeneous)PDE parameters is enabled by searching on this trial manifold,and optionally fine-tuning the trial manifold at the same time.Extensive numerical experiments show that the MAD method exhibits a faster convergence speed without losing the accuracy than other deep learning-based methods.

Periodic transparent nanowires in ITO film fabricated via femtosecond laser direct writing

This paper reports the fabrication of regular large-area laser-induced periodic surface structures(LIPSSs)in indium tin oxide(ITO)films via femtosecond laser direct writing focused by a cylindrical lens.The regular LIPSSs exhibited good properties as nanowires,with a resistivity almost equal to that of the initial ITO film.By changing the laser fluence,the nanowire resistances could be tuned from 15 to 73 kΩ/mm with a consistency of±10%.Furthermore,the average transmittance of the ITO films with regular LIPSSs in the range of 1200-2000 nm was improved from 21%to 60%.The regular LIPSS is promising for transparent electrodes of nano-optoelectronic devices-particularly in the near-infrared band.

Huawei: Helping Build a Better Connected Saudi Arabia

INFORMATION and Communication Technology(ICT),which is in full swing in the modern era,has been reconstructing human society.Dedicated to innovative ICT products,services,and solutions,Huawei is committed to building a better and well-connected world and so boosting human development.It has also become one of the leading ICT

The Flipping-Free Full-Parallax Tabletop Integral Imaging with Enhanced Viewing Angle Based on Space-Multiplexed Voxel Screen and Compound Lens Array

Tabletop integral imaging display with a more realistic and immersive experience has always been a hot spot in three-dimensional imaging technology,widely used in biomedical imaging and visualization to enhance medical diagnosis.However,the traditional structural characteristics of integral imaging display inevitably introduce the flipping effect outside the effective viewing angle.Here,a full-parallax tabletop integral imaging display without the flipping effect based on space-multiplexed voxel screen and compound lens array is demonstrated,and two holographic functional screens with different parameters are optically designed and fabricated.To eliminate the flipping effect in the reconstruction process,the space-multiplexed voxel screen consisting of a projector array and the holographic functional screen is presented to constrain light beams passing through the corresponding lens.To greatly promote imaging quality within the viewing area,the aspherical structure of the compound lens is optimized to balance the aberrations.It cooperates with the holographic functional screen to modulate the light field spatial distribution.Compared with the simulation results,the distortion rate of the imaging display is reduced to less than 9%from more than 30%.In the experiment,the floating high-quality reconstructed three-dimensional image without the flipping effect can be observed with the correct 3D perception at 96°×96°viewing angle,where 44,100 viewpoints are employed.

Reconstructing in vivo spatially offset Raman spectroscopy of human skin tissue using a GPU-accelerated Monte Carlo platform

As one type of spatially offset Raman spectroscopy(SORS), inverse SORS is particularly suited to in vivo biomedical measurements due to its ring-shaped illumination scheme. To explain inhomogeneous Raman scattering during in vivo inverse SORS measurements, the light–tissue interactions when excitation and regenerated Raman photons propagate in skin tissue were studied using Monte Carlo simulation. An eight-layered skin model was first built based on the latest transmission parameters. Then, an open-source platform, Monte Carlo e Xtreme(MCX), was adapted to study the distribution of 785 nm excitation photons inside the model with an inverse spatially shifted annular beam. The excitation photons were converted to emission photons by an inverse distribution method based on excitation flux with spatial offsets Δs of 1 mm, 2 mm, 3 mm and 5 mm. The intrinsic Raman spectra from separated skin layers were measured by continuous linear scanning to improve the simulation accuracy. The obtained results explain why the spectral detection depth gradually increases with increasing spatial offset, and address how the intrinsic Raman spectrum from deep skin layers is distorted by the reabsorption and scattering of the superficial tissue constituents. Meanwhile, it is demonstrated that the spectral contribution from subcutaneous fat will be improved when the offset increases to 5 mm, and the highest detection efficiency for dermal layer spectral detection could be achieved when Δs = 2 mm. Reasonably good matching between the calculated spectrum and the measured in vivo inverse SORS was achieved, thus demonstrating great utility of our modeling method and an approach to help understand the clinical measurements.

An Exploratory Practice of Integration of Industry and Education in High-level Programming Language Course in the Background of New Engineering

This paper analyzes some problems of the current teaching situation in the course High-level Programming Language,such as the lagging content of the course compared with technology development,the emphasis on theory rather than on practice,the low enthusiasm of students for learning,and the weak practical ability of students.In response to the needs of enterprises for talents under the background of New Engineering,especially the cultivation of students’adaptability and practical abilities towards future industries to improve students’knowledge and problemsolving abilities to keep up with the modern technology,this paper proposes the ways and methods to reform and explore the teaching content and teaching methods by integrating industry and education,assisting teaching according to industrial demands,and lowering technology barriers of new technology.The practical effect is evaluated through a survey in class and the follow-up questionnaire after class,and the results show that the effect of the practice is effective.

Technique for Ecological Pond Breeding of Charybdis japonica

In 2016 and 2017,the ecological pond breeding experiment of Charybdis japonica was carried out,with a total of experimental area up to 4 ha.Through two years of experimental research,it obtained a total of 48950 kg of C.japonica,with an average output of 12237.5 kg/ha.The production output value was 2.3675 million yuan,the total production cost was 831700 yuan,the net profit was 1.5358 million yuan,the average profit was 384000 yuan/ha,and the input-output ratio was 1∶2.85.The average proportion of C.japonica population with a production weight of 150 g or more was 7.9%.The proportion of the group of 120-150 g was relatively large,with an average of 80%in two years,and the proportion of the group below 120 g was relatively small,with an average of 12%in two years.The cumulative harvest of shellfish in two years was 65700 kg,with an average output of 16425 kg/ha,the production output value of 738100 yuan,the production cost of 295500 yuan,the net profit of 442600 yuan,and the average profit of 110700 yuan/ha.

Research on the Integrated Training Path of Secondary Vocational,Vocational,and Undergraduate Talents Based on Modern Vocational Education Group

Accelerating the construction of modern vocational education groups and promoting the integrated training of secondary vocational,vocational,and undergraduate talents,providing talent support for regional economic development,are inevitable choices to enhance the adaptability of vocational education.Based on the new development and requirements in the new situation,vocational colleges actively explore the path of connecting and cultivating secondary vocational,vocational,and undergraduate talents in accordance with the talent needs of local economic development and industrial system development.The linkage between government,school,and enterprise is established to build a modern vocational education group,aiming to build a professional chain in the industrial chain and promote innovative development of the new industrial system.Relying on the development needs of professional clusters,a multi-level system of connecting and cultivating secondary vocational,vocational,and undergraduate talents is constructed,which is in line with industry and enterprise standards.It is also necessary to build a“post-course competition certification”talent training model for connecting secondary vocational,vocational,and undergraduate students,rely on the advantages of multiple schools and enterprises,establish a platform for integrating industry and education,and innovate the evaluation system for connecting secondary vocational,vocational,and undergraduate students.

Research and Practice on the Construction of Municipal Industry-Education Consortium Based on the Integration of Industrial Chain,Innovation Chain,Talent Chain,and Education Chain

The construction of a municipal industry-education consortium is one of the important measures for the“one body,two wings,and five key points”reform of China’s modern vocational education system in the new era.It is of great significance for the integrated promotion of the three major national strategies of education,technology,and talent.Exploring the construction path of municipal industry-education consortia from the perspective of“four-chain integration”is essential for improving the quality of vocational education talents,comprehensively promoting local economic and social development,and serving the national strategy of revitalizing the country through science and education.The new generation of information technology industry-education consortium in Chengdu actively explores new paths of industry-education integration,science-education integration,and vocational-education integration.It has formed a“park+alliance”to establish a diversified collaborative construction and management mechanism,a“professional+industry”school-enterprise cooperation to promote industrial transformation and upgrading,a“teaching+research and development”to build an industry-university research innovation platform to empower high-quality development,and a“cultivation+employment”to explore a matrix style talent cultivation model,thus promoting regional governments,industries enterprises,and higher education institutions.Fully leveraging complementary advantages involves capitalizing on the spatial cluster effect.It requires a foundation in the education chain,guided by the industrial chain,activation of the innovation chain,establishment of a talent chain,enhancement of the value chain,and the promotion of a modern vocational education structure.This structure should align with market demand and industry structure,ultimately promoting regional economic and social development.

Digital cancellation of multi-band passive inter-modulation based on Wiener-Hammerstein model

Utilizing multi-band and multi-carrier techniques enhances throughput and capacity in Long-Term Evolution(LTE)-Advanced and 5G New Radio(NR)mobile networks.However,these techniques introduce Passive Inter-Modulation(PIM)interference in Frequency-Division Duplexing(FDD)systems.In this paper,a novel multi-band Wiener-Hammerstein model is presented to digitally reconstruct PIM interference signals,thereby achieving effective PIM Cancellation(PIMC)in multi-band scenarios.In the model,transmitted signals are independently processed to simulate Inter-Modulation Distortions(IMDs)and Cross-Modulation Distortions(CMDs).Furthermore,the Finite Impulse Response(FIR)filter,basis function generation,and B-spline function are applied for precise PIM product estimation and generation in multi-band scenarios.Simulations involving 4 carrier components from diverse NR frequency bands at varying transmitting powers validate the feasibility of the model for multi-band PIMC,achieving up to 19 dB in PIMC performance.Compared to other models,this approach offers superior PIMC performance,exceeding them by more than 5 dB in high transmitting power scenarios.Additionally,its lower sampling rate requirement reduces the hardware complexity associated with implementing multi-band PIMC.

Early-stage latent thermal failure of single-crystal Ni-rich layered cathode

High nickel content worsens the thermal stability of layered cathodes for lithium-ion batteries,raising safety concerns for their applications.Thoroughly understanding the thermal failure process can offer valuable guidance for material optimization on thermal stability and new opportunities in monitoring battery thermal runaway(TR).Herein,this work comprehensively investigates the thermal failure process of a single-crystal nickel-rich layered cathode and finds that the latent thermal failure starts at∼120℃far below the TR temperature(225℃).During this stage of heat accumulation,sequential structure transition is revealed by atomic resolution electron microscopy,which follows the layered→cation mixing layered→LiMn_(2)O_(4)-type spinel→disordered spinel→rock salt.This progression occurs as a result of the continuous migration and densification of transition metal cations.Phase transition generates gaseous oxygen,initially confined within the isolated closed pores,thereby not showing any thermal failure phenomena at the macro-level.Increasing temperature leads to pore growth and coalescence,and eventually to the formation of open pores,causing oxygen gas release and weight loss,which are the typical TR features.We highlight that latent thermal instability occurs before the macro-level TR,suggesting that suppressing phase transitions caused by early thermal instability is a crucial direction for material optimization.Our findings can also be used for early warning of battery thermal runaway.

Neural Network and GBSM Based Time-Varying and Stochastic Channel Modeling for 5G Millimeter Wave Communications

In this work,a frame work for time-varying channel modeling and simulation is proposed by using neural network(NN)to overcome the shortcomings in geometry based stochastic model(GBSM)and simulation approach.Two NN models are developed for modeling of path loss together with shadow fading(SF)and joint small scale channel parameters.The NN models can predict path loss plus SF and small scale channel parameters accurately compared with measurement at 26 GHz performed in an outdoor microcell.The time-varying path loss and small scale channel parameters generated by the NN models are proposed to replace the empirical path loss and channel parameter random numbers in GBSM-based framework to playback the measured channel and match with its environment.Moreover,the sparse feature of clusters,delay and angular spread,channel capacity are investigated by a virtual array measurement at 28 GHz in a large waiting hall.

A Dormant Multi-Controller Model for Software Defined Networking

In order to improve the scalability and reliability of Software Defined Networking(SDN),many studies use multiple controllers to constitute logically centralized control plane to provide load balancing and fail over.In this paper,we develop a flexible dormant multi-controller model based on the centralized multi-controller architecture.The dormant multi-controller model allows part of controllers to enter the dormant state under light traffic condition for saving system cost.Meanwhile,through queueing analysis,various performance measures of the system can be obtained.Moreover,we analyze the real traffic of China Education Network and use the results as the parameters of computer simulation and verify the effects of parameters on the system characteristics.Finally,a total expected cost function is established,and genetic algorithm is employed to find the optimal values of various parameters to minimize system cost for the deployment decision making.

Simulation of arcs for DC relay considering different impacts

Recently DC relay has been concerned as a key component in DC power distribution,management and control systems like aircraft,new energy vehicle,IT and communication industries.Ordinarily,magnetic force and contact moving speed have great influence on arc behaviours in the breaking process.This paper focuses on the numerical investigation of arc during the contact opening process in a real 400V/20 A DC relay product coupling with an inductive load circuit.A 3D air arc model based on the magneto-hydrodynamic theory was built and calculated.A method coupling different computational software was used to take the nonlinear permanent magnet and contact opening process into consideration simultaneously.Arc behaviours under different magnetic field and contact opening speed were presented and discussed carefully.It has been found that the increase of the magnetic field is beneficial to the quick build-up of arc length and voltage.Arc breaking duration becomes shorter with the increase in contact opening speed from 63.5 rad s^-1 to 94.5 rad s^-1,such reduction is less significant with an increase of opening speed from 94.5 rad s^-l to 118.5 rad s^-1.

Locally Linear Back-propagation Based Contribution for Nonlinear Process Fault Diagnosis

This paper proposes a novel locally linear backpropagation based contribution(LLBBC) for nonlinear process fault diagnosis. As a method based on the deep learning model of auto-encoder(AE), LLBBC can deal with the fault diagnosis problem through extracting nonlinear features. When the on-line fault diagnosis task is in progress, a locally linear model is firstly built at the current fault sample. According to the basic idea of reconstruction based contribution(RBC), the propagation of fault information is described by using back-propagation(BP) algorithm. Then, a contribution index is established to measure the correlation between the variable and the fault, and the final diagnosis result is obtained by searching variables with large contributions. The smearing effect, which is an important factor affecting the performance of fault diagnosis, can be suppressed as well,and the theoretical analysis reveals that the correct diagnosis can be guaranteed by LLBBC. Finally, the feasibility and effectiveness of the proposed method are verified through a nonlinear numerical example and the Tennessee Eastman benchmark process.

Timely Updates in MEC-Assisted Status Update Systems: Joint Task Generation and Computation Offloading Scheme

Fresh status updates are vital to the efficient operation of network monitoring and real-time control applications. In this paper, we consider a mobile edge computing(MEC)-assisted status update system, where smart devices extract valuable status updates from sensed data to achieve timely awareness of the surroundings by exploiting computational resources at the device and edge server. To quantify the freshness of status updates obtained by executing computation tasks, we employ the concept of age of information(Ao I) to characterize the timeliness of status updates. To cope with the limited energy at devices, we investigate a joint task generation and computation offloading scheme under a given energy budget for minimizing the age of obtained status updates. The age minimization problem is modeled as a constrained Markov decision process(CMDP). To obtain the optimal policy, we derive the structural properties of the optimal deterministic policy and propose a lightweight structure-based status update algorithm in the case of known channel statistics. Moreover, we consider a more realistic scenario without prior knowledge of channel statistics, and propose a Q-learning-based status update algorithm to make online decisions. Simulation results show that the performance of our proposed algorithms is competitive when compared with existing schemes.