The Journey/DAO/TAO of Embodied Intelligence: From Large Models to Foundation Intelligence and Parallel Intelligence

THE tremendous impact of large models represented by ChatGPT[1]-[3]makes it necessary to con-sider the practical applications of such models[4].However,for an artificial intelligence(AI)to truly evolve,it needs to possess a physical“body”to transition from the virtual world to the real world and evolve through interaction with the real environments.In this context,“embodied intelligence”has sparked a new wave of research and technology,leading AI beyond the digital realm into a new paradigm that can actively act and perceive in a physical environment through tangible entities such as robots and automated devices[5].

3D Road Network Modeling and Road Structure Recognition in Internet of Vehicles

Internet of Vehicles (IoV) is a new system that enables individual vehicles to connect with nearby vehicles,people, transportation infrastructure, and networks, thereby realizing amore intelligent and efficient transportationsystem. The movement of vehicles and the three-dimensional (3D) nature of the road network cause the topologicalstructure of IoV to have the high space and time complexity.Network modeling and structure recognition for 3Droads can benefit the description of topological changes for IoV. This paper proposes a 3Dgeneral roadmodel basedon discrete points of roads obtained from GIS. First, the constraints imposed by 3D roads on moving vehicles areanalyzed. Then the effects of road curvature radius (Ra), longitudinal slope (Slo), and length (Len) on speed andacceleration are studied. Finally, a general 3D road network model based on road section features is established.This paper also presents intersection and road section recognition methods based on the structural features ofthe 3D road network model and the road features. Real GIS data from a specific region of Beijing is adopted tocreate the simulation scenario, and the simulation results validate the general 3D road network model and therecognitionmethod. Therefore, thiswork makes contributions to the field of intelligent transportation by providinga comprehensive approach tomodeling the 3Droad network and its topological changes in achieving efficient trafficflowand improved road safety.

Optimal Quota-Share and Excess-of-Loss Reinsurance and Investment with Heston’s Stochastic Volatility Model

An optimal quota-share and excess-of-loss reinsurance and investment problem is studied for an insurer who is allowed to invest in a risk-free asset and a risky asset.Especially the price process of the risky asset is governed by Heston's stochastic volatility(SV)model.With the objective of maximizing the expected index utility of the terminal wealth of the insurance company,by using the classical tools of stochastic optimal control,the explicit expressions for optimal strategies and optimal value functions are derived.An interesting conclusion is found that it is better to buy one reinsurance than two under the assumption of this paper.Moreover,some numerical simulations and sensitivity analysis are provided.

Compression Mechanical Performance Simulation and Parameter Optimization of Degradable Ureteral Stent Based on ABAQUS

The compression performance of a degradable ureteral stent is analyzed and the parameters are optimized by a finite element modeling method.The degradable ureteral stent explored in this paper is developed from poly(glycolic acid)(PGA)and poly(lactic-co-glycolic acid)(PLGA)degradable materials.Based on the actual measurement of fabric structure parameters,the three-dimensional model of the stent is established with the help of the modeling software.The finite element analysis software is used to simulate the compression process of the degradable ureteral stent.The parameters of materials,interactions and boundary conditions are set according to the compression environment of the stent for modeling and simulation.On this basis,the friction coefficient of yarns,the yarn radius,and the braided angle of the stent are further compared.The comparison test is carried out by a single variable.The experimental results show that the change of yarn friction coefficient has little influence on the compressive stress,while the yarn radius and the braided angle of the stent have a great influence on the compressive stress.

Intelligent Small Sample Defect Detection of Concrete Surface Using Novel Deep Learning Integrating Improved YOLOv5

Dear Editor,This letter presents an intelligent small sample defect detection of concrete surface using novel deep learning integrating the improved YOLOv5 based on the Wasserstein GAN(WGAN)enhancement algorithm.The proposed method is capable of producing top-notch data sets to address the issues of insufficient samples and substandard quality.

Adaptive Optimal Discrete-Time Output-Feedback Using an Internal Model Principle and Adaptive Dynamic Programming

In order to address the output feedback issue for linear discrete-time systems, this work suggests a brand-new adaptive dynamic programming(ADP) technique based on the internal model principle(IMP). The proposed method, termed as IMP-ADP, does not require complete state feedback-merely the measurement of input and output data. More specifically, based on the IMP, the output control problem can first be converted into a stabilization problem. We then design an observer to reproduce the full state of the system by measuring the inputs and outputs. Moreover, this technique includes both a policy iteration algorithm and a value iteration algorithm to determine the optimal feedback gain without using a dynamic system model. It is important that with this concept one does not need to solve the regulator equation. Finally, this control method was tested on an inverter system of grid-connected LCLs to demonstrate that the proposed method provides the desired performance in terms of both tracking and disturbance rejection.

Automatic Quality Inspection of Bakery Products Based on Shape and Color Information

An automatic intelligent system for the colour and texture inspection of bakery products is proposed.In this system,advance classification technique featuring Support Vector Machine and biologically inspired HMAX based shape descriptor integrated with biologically plausible RGB Opponent-Colour-Channel Descriptor is used to classify bakery products to their respective classes based on the shape and based on their colour referring to different baking durations. The results of this paper are compared with other methods for the automatic bakery products inspection. It is discovered that biologically inspired computer vision models performs accurately and efficiently as compared to the computer vision models which are not biologically plausible,in the bakery products quality inspection. It is also discovered that the One Versus One SVM and Directed Acyclic Graph SVM acquired the maximum accurate classification rate. The proposed method acquired classification accuracy of 95% and 100% for the biscuit shape and biscuit colour recognition,respectively. The proposed method is also consistently stable and invariant. This shows that the biologically inspired computer vision models have the capability to replace existing inspection methods as more reliable and accurate alternative.

Assembly and analysis of the Populus deltoides mitochondrial genome:the first report of a multicircular mitochondrial conformation for the genus Populus

Genomics research of Populus deltoides,an important timber species that is widely planted worldwide,is an important part of poplar breeding.Currently,the nuclear and chloroplast genome of P.deltoides have been sequenced,but its mitochondrial genome(mitogenome)has not been reported.To further explore the evolution and phylogeny of P.deltoides,the mitogenome of P.deltoides I-69 was assembled using reads from Nanopore and Illumina sequencing platforms and found to consist of 802,637 bp and three circular chromosomes(336,205,280,841,and 185,591 bp)containing 58 genes(34 protein-coding genes,21 tRNA genes,and 3 rRNA genes).RNA analysis in combination with several species showed signifi cantly fewer RNA editingsites in the mitogenomes of poplar and other angiosperms than in gymnosperms.Sequence transfer analysis showed extensive mitogenome rearrangements in Populus species,and with evolution from lower to higher plants,tRNA transfer from chloroplasts to mitochondria became increasingly frequent.In a phylogenetic analysis,the evolutionary status of P.deltoides was determined,and the section Populus was supported.Our results based on the fi rst report of a multicircular conformation of the Populus mitogenome provide a basis for further study of the evolution and genetics of P.deltoides and other Populus species and for breeding programs.

Path Planning of UAV by Combing Improved Ant Colony System and Dynamic Window Algorithm

A fusion algorithm is proposed to enhance the search speed of an ant colony system(ACS)for the global path planning and overcome the challenges of the local path planning in an unmanned aerial vehicle(UAV).The ACS search efficiency is enhanced by adopting a 16-direction 24-neighborhood search way,a safety grid search way,and an elite hybrid strategy to accelerate global convergence.Quadratic planning is performed using the moving average(MA)method.The fusion algorithm incorporates a dynamic window approach(DWA)to deal with the local path planning,sets a retracement mechanism,and adjusts the evaluation function accordingly.Experimental results in two environments demonstrate that the improved ant colony system(IACS)achieves superior planning efficiency.Additionally,the optimized dynamic window approach(ODWA)demonstrates its ability to handle multiple dynamic situations.Overall,the fusion optimization algorithm can accomplish the mixed path planning effectively.

Research and Application of Log Defect Detection and Visualization System Based on Dry Coupling Ultrasonic Method

In order to optimize the wood internal quality detection and evaluation system and improve the comprehensive utilization rate of wood,this paper invented a set of log internal defect detection and visualization system by using the ultrasonic dry coupling agent method.The detection and visualization analysis of internal log defects were realized through log specimen test.The main conclusions show that the accuracy,reliability and practicability of the system for detecting the internal defects of log specimens have been effectively verified.The system can make the edge of the detected image smooth by interpolation algorithm,and the edge detection algorithm can be used to detect and reflect the location of internal defects of logs accurately.The content mentioned above has good application value for meeting the requirement of increasing demand for wood resources and improving the automation level of wood nondestructive testing instruments.

DeepSVDNet:A Deep Learning-Based Approach for Detecting and Classifying Vision-Threatening Diabetic Retinopathy in Retinal Fundus Images

Artificial Intelligence(AI)is being increasingly used for diagnosing Vision-Threatening Diabetic Retinopathy(VTDR),which is a leading cause of visual impairment and blindness worldwide.However,previous automated VTDR detection methods have mainly relied on manual feature extraction and classification,leading to errors.This paper proposes a novel VTDR detection and classification model that combines different models through majority voting.Our proposed methodology involves preprocessing,data augmentation,feature extraction,and classification stages.We use a hybrid convolutional neural network-singular value decomposition(CNN-SVD)model for feature extraction and selection and an improved SVM-RBF with a Decision Tree(DT)and K-Nearest Neighbor(KNN)for classification.We tested our model on the IDRiD dataset and achieved an accuracy of 98.06%,a sensitivity of 83.67%,and a specificity of 100%for DR detection and evaluation tests,respectively.Our proposed approach outperforms baseline techniques and provides a more robust and accurate method for VTDR detection.

Research and Design of Consistent Project Cases for the Software Engineering Course Group

In order to enhance the abilities of practical innovation and solving complex engineering problems of students in the engineering context,we design a course cluster teaching model based on a unified enterprise-level project case.The program divides the knowledge points required by the project into the corresponding courses,and divides their realization into the practical teaching cases,so as to realize the design of teaching practice cases embodied in the unified project framework.This model allows students to practice projects based on the unified project background while learning knowledge from different courses.It not only allows students to learn abstract,fragmented,and difficult-to-understand knowledge systems thoroughly,but also integrates the knowledge into the practice of the enterprise-level project development,helping students experience the value of knowledge in complex engineering projects and thus improving their ability to solve complex engineering problems while learning theoretical knowledge.

Contrast and Fusion: The Role of Regional Culture in Shaping Jiangsu Paper-Cut Art

The Chinese paper-cut art,first recorded in the Wei,Jin,and Southern and Northern Dynasties(220 AD-589 AD),has witnessed the changes of times,yet it still retains its artistic vitality.Chinese papercuts can be divided into two schools:the northern and the southern.Jiangsu,located in the region of the Yellow River and Huai River,is the geographical dividing line between those two schools.Therefore,in Jiangsu Province,not only the rough northern art form(such as in Xuzhou papercut)but also the graceful southern art form(such as in Jintan papercut)is evident.In addition,the unique combined paper-cut style(such as in Yangzhou and Nanjing papercuts)can be appreciated here.Although several scholars have analyzed the artistic characteristics of Jiangsu papercut based on cultural background,very few have discussed the differences between the northern and the southern in terms of content,connotation,and style.Through literature review and collected works made by local craftsmen and inheritors of this tradition,this article aims to show readers the contrast and integration of papercuts in these four places under the influence of different cultural and economic backgrounds in order to better understand the role of regional factors in shaping the art form of papercuts in Jiangsu Province.Nowadays,with the change in people’s lifestyles,the living space of traditional papercuts has shrunk drastically,and its practicability in the past has faded.Instead,people are searching for and creating cultural and artistic value in museums,tourist attractions,and commodity transactions.Among them,some works have deviated from the cultural background of traditional paper-cut art.Therefore,this paper provides a basis for the current development of this art form in Jiangsu.

Reconcile the contradictory wettability requirements for the reduction and oxidation half-reactions in overall CO_(2) photoreduction via alternately hydrophobic surfaces

The overall photocatalytic CO_(2) reduction reaction(OPCRR)that can directly convert CO_(2) and H_(2)O into fuels represents a promising renewable energy conversion technology.As a typical redox reaction,the OPCRR involves two half-reactions:the CO_(2) reduction half-reaction(CRHR)and the water oxidation half-reaction(WOHR).Generally,both half-reactions can be promoted by adjusting the wettability of catalysts.However,there is a contradiction in wettability requirements for the two half-reactions.Specifically,CRHR prefers a hydrophobic surface that can accumulate more CO_(2) molecules on the active sites,ensuring the appropriate ratio of gas-phase(CO_(2))to liquid-phase(H_(2)O)reactants.Conversely,the WOHR prefers a hydrophilic surface that can promote the departure of the gaseous product(O_(2))from the catalyst surface,preventing isolation between active sites and the reactant(H_(2)O).Here,we successfully reconciled the contradictory wettability requirements for the CRHR and WOHR by creating an alternately hydrophobic catalyst.This was achieved through a selectively hydrophobic modification method and a charge-transfer-control strategy.Consequently,the collaboratively promoted CRHR and WOHR led to a significantly enhanced OPCRR with a solar-to-fuel conversion efficiency of 0.186%.Notably,in ethanol production,the catalyst exhibited a 10.64-fold increase in generation rate(271.44μmol g^(-1)h~(-1))and a 4-fold increase in selectivity(55.77%)compared to the benchmark catalyst.This innovative approach holds great potential for application in universal overall reactions involving gas participation.

THE LONG TIME BEHAVIOR OF THE FRACTIONAL ORNSTEIN-UHLENBECK PROCESS WITH LINEAR SELF-REPELLING DRIFT

Let B^(H) be a fractional Brownian motion with Hurst index 1/2≤H<1.In this paper,we consider the equation(called the Ornstein-Uhlenbeck process with a linear self-repelling drift)dX_(t)^(H)=dB_(t)^(H)+σ X_(t)^(H)dt+vdt-θ(∫_(0)^(t)(X_(t)^(H)-X_(s)^(H))ds)dt,whereθ<0,σ,v∈ℝ.The process is an analogue of self-attracting diffusion(Cranston,Le Jan.Math Ann,1995,303:87–93).Our main aim is to study the large time behaviors of the process.We show that the solution X^(H)diverges to infinity as t tends to infinity,and obtain the speed at which the process X^(H)diverges to infinity.

Set-Membership Filtering Approach to Dynamic Event-Triggered Fault Estimation for a Class of Nonlinear Time-Varying Complex Networks

The present study addresses the problem of fault estimation for a specific class of nonlinear time-varying complex networks,utilizing an unknown-input-observer approach within the framework of dynamic event-triggered mechanism(DETM).In order to optimize communication resource utilization,the DETM is employed to determine whether the current measurement data should be transmitted to the estimator or not.To guarantee a satisfactory estimation performance for the fault signal,an unknown-input-observer-based estimator is constructed to decouple the estimation error dynamics from the influence of fault signals.The aim of this paper is to find the suitable estimator parameters under the effects of DETM such that both the state estimates and fault estimates are confined within two sets of closed ellipsoid domains.The techniques of recursive matrix inequality are applied to derive sufficient conditions for the existence of the desired estimator,ensuring that the specified performance requirements are met under certain conditions.Then,the estimator gains are derived by minimizing the ellipsoid domain in the sense of trace and a recursive estimator parameter design algorithm is then provided.Finally,a numerical example is conducted to demonstrate the effectiveness of the designed estimator.

Region-Aware Fashion Contrastive Learning for Unified Attribute Recognition and Composed Retrieval

Clothing attribute recognition has become an essential technology,which enables users to automatically identify the characteristics of clothes and search for clothing images with similar attributes.However,existing methods cannot recognize newly added attributes and may fail to capture region-level visual features.To address the aforementioned issues,a region-aware fashion contrastive language-image pre-training(RaF-CLIP)model was proposed.This model aligned cropped and segmented images with category and multiple fine-grained attribute texts,achieving the matching of fashion region and corresponding texts through contrastive learning.Clothing retrieval found suitable clothing based on the user-specified clothing categories and attributes,and to further improve the accuracy of retrieval,an attribute-guided composed network(AGCN)as an additional component on RaF-CLIP was introduced,specifically designed for composed image retrieval.This task aimed to modify the reference image based on textual expressions to retrieve the expected target.By adopting a transformer-based bidirectional attention and gating mechanism,it realized the fusion and selection of image features and attribute text features.Experimental results show that the proposed model achieves a mean precision of 0.6633 for attribute recognition tasks and a recall@10(recall@k is defined as the percentage of correct samples appearing in the top k retrieval results)of 39.18 for composed image retrieval task,satisfying user needs for freely searching for clothing through images and texts.

A Hybrid Dung Beetle Optimization Algorithm with Simulated Annealing for the Numerical Modeling of Asymmetric Wave Equations

In the generalized continuum mechanics(GCM)theory framework,asymmetric wave equations encompass the characteristic scale parameters of the medium,accounting for microstructure interactions.This study integrates two theoretical branches of the GCM,the modified couple stress theory(M-CST)and the one-parameter second-strain-gradient theory,to form a novel asymmetric wave equation in a unified framework.Numerical modeling of the asymmetric wave equation in a unified framework accurately describes subsurface structures with vital implications for subsequent seismic wave inversion and imaging endeavors.However,employing finite-difference(FD)methods for numerical modeling may introduce numerical dispersion,adversely affecting the accuracy of numerical modeling.The design of an optimal FD operator is crucial for enhancing the accuracy of numerical modeling and emphasizing the scale effects.Therefore,this study devises a hybrid scheme called the dung beetle optimization(DBO)algorithm with a simulated annealing(SA)algorithm,denoted as the SA-based hybrid DBO(SDBO)algorithm.An FD operator optimization method under the SDBO algorithm was developed and applied to the numerical modeling of asymmetric wave equations in a unified framework.Integrating the DBO and SA algorithms mitigates the risk of convergence to a local extreme.The numerical dispersion outcomes underscore that the proposed SDBO algorithm yields FD operators with precision errors constrained to 0.5‱while encompassing a broader spectrum coverage.This result confirms the efficacy of the SDBO algorithm.Ultimately,the numerical modeling results demonstrate that the new FD method based on the SDBO algorithm effectively suppresses numerical dispersion and enhances the accuracy of elastic wave numerical modeling,thereby accentuating scale effects.This result is significant for extracting wavefield perturbations induced by complex microstructures in the medium and the analysis of scale effects.

Morphological analysis for thermodynamics of cavitation collapse near fractal solid wall

A fractal geometric boundary with natural wall features is introduced into a hybrid lattice-Boltzmann-method(LBM)multiphase model. The physical model of cavitation bubble collapse near the irregular geometric wall is established to study the thermodynamic characteristics of the bubble collapse. Due to the lack of periodicity, symmetry, spatial uniformity and obvious correlation in the LBM simulation of the bubble collapse near the fractal wall, the morphological analysis based on Minkowski functional is introduced into the thermodynamic investigation of cavitation bubble so as to analyze and obtain the effective information. The results show that the Minkowski functional method can employed to study the temperature information in complex physical fields hierarchically and quantitatively. The high/low temperature region of the cavitation flow is explored, and thermal effect between irregular and fractal geometric wall and cavitation bubble can be revealed. It illustrates that LBM and morphological analysis complement each other, and morphological analysis can also be used as an optional and potential tool in research field of complex multiphase flows.

A Hybrid Compensation Scheme for the Input Rate-Dependent Hysteresis of the Piezoelectric Ceramic Actuators

A hybrid compensation scheme for piezoelectric ceramic actuators(PEAs)is proposed.In the hybrid compensation scheme,the input rate-dependent hysteresis characteristics of the PEAs are compensated.The feedforward controller is a novel input rate-dependent neural network hysteresis inverse model,while the feedback controller is a proportion integration differentiation(PID)controller.In the proposed inverse model,an input ratedependent auxiliary inverse operator(RAIO)and output of the hysteresis construct the expanded input space(EIS)of the inverse model which transforms the hysteresis inverse with multi-valued mapping into single-valued mapping,and the wiping-out,rate-dependent and continuous properties of the RAIO are analyzed in theories.Based on the EIS method,a hysteresis neural network inverse model,namely the dynamic back propagation neural network(DBPNN)model,is established.Moreover,a hybrid compensation scheme for the PEAs is designed to compensate for the hysteresis.Finally,the proposed method,the conventional PID controller and the hybrid controller with the modified input rate-dependent Prandtl-Ishlinskii(MRPI)model are all applied in the experimental platform.Experimental results show that the proposed method has obvious superiorities in the performance of the system.