Neural Dynamics for Cooperative Motion Control of Omnidirectional Mobile Manipulators in the Presence of Noises: A Distributed Approach

This paper presents a distributed scheme with limited communications, aiming to achieve cooperative motion control for multiple omnidirectional mobile manipulators(MOMMs).The proposed scheme extends the existing single-agent motion control to cater to scenarios involving the cooperative operation of MOMMs. Specifically, squeeze-free cooperative load transportation is achieved for the end-effectors of MOMMs by incorporating cooperative repetitive motion planning(CRMP), while guiding each individual to desired poses. Then, the distributed scheme is formulated as a time-varying quadratic programming(QP) and solved online utilizing a noise-tolerant zeroing neural network(NTZNN). Theoretical analysis shows that the NTZNN model converges globally to the optimal solution of QP in the presence of noise. Finally, the effectiveness of the control design is demonstrated by numerical simulations and physical platform experiments.

A Brief Overview of ChatGPT:The History,Status Quo and Potential Future Development

ChatG PT,an artificial intelligence generated content (AIGC) model developed by OpenAI,has attracted worldwide attention for its capability of dealing with challenging language understanding and generation tasks in the form of conversations.This paper briefly provides an overview on the history,status quo and potential future development of ChatGPT,helping to provide an entry point to think about ChatGPT.Specifically,from the limited open-accessed resources,we conclude the core techniques of ChatGPT,mainly including large-scale language models,in-context learning,reinforcement learning from human feedback and the key technical steps for developing ChatGPT.We further analyze the pros and cons of ChatGPT and we rethink the duality of ChatGPT in various fields.Although it has been widely acknowledged that ChatGPT brings plenty of opportunities for various fields,mankind should still treat and use ChatG PT properly to avoid the potential threat,e.g.,academic integrity and safety challenge.Finally,we discuss several open problems as the potential development of ChatGPT.

Estimating the State of Health for Lithium-ion Batteries:A Particle Swarm Optimization-Assisted Deep Domain Adaptation Approach

The state of health(SOH)is a critical factor in evaluating the performance of the lithium-ion batteries(LIBs).Due to various end-user behaviors,the LIBs exhibit different degradation modes,which makes it challenging to estimate the SOHs in a personalized way.In this article,we present a novel particle swarm optimization-assisted deep domain adaptation(PSO-DDA)method to estimate the SOH of LIBs in a personalized manner,where a new domain adaptation strategy is put forward to reduce cross-domain distribution discrepancy.The standard PSO algorithm is exploited to automatically adjust the chosen hyperparameters of developed DDA-based method.The proposed PSODDA method is validated by extensive experiments on two LIB datasets with different battery chemistry materials,ambient temperatures and charge-discharge configurations.Experimental results indicate that the proposed PSO-DDA method surpasses the convolutional neural network-based method and the standard DDA-based method.The Py Torch implementation of the proposed PSO-DDA method is available at https://github.com/mxt0607/PSO-DDA.

Finite-time Stabilization of the Double Integrator Subject to Input Saturation and Input Delay

The time-optimal control design of the double integrator is extended to the finite-time stabilization design that compensates both input saturation and input delay. With the aid of the Artstein's transformation, the problem is reduced to assigning a saturated finite-time stabilizer.

Experimental Study of Effect of Temperature Variations on the Impedance Signature of PZT Sensors for Fatigue Crack Detection

Structural health monitoring(SHM)is recognized as an efficient tool to interpret the reliability of a wide variety of infrastructures.To identify the structural abnormality by utilizing the electromechanical coupling property of piezoelectric transducers,the electromechanical impedance(EMI)approach is preferred.However,in real-time SHM applications,the monitored structure is exposed to several varying environmental and operating conditions(EOCs).The previous study has recognized the temperature variations as one of the serious EOCs that affect the optimal performance of the damage inspection process.In this framework,an experimental setup is developed in current research to identify the presence of fatigue crack in stainless steel(304)beam using EMI approach and estimate the effect of temperature variations on the electrical impedance of the piezoelectric sensors.A regular series of experiments are executed in a controlled temperature environment(25°C–160°C)using 202 V1 Constant Temperature Drying Oven Chamber(Q/TBXR20-2005).It has been observed that the dielectric constantð"33 TÞwhich is recognized as the temperature-dependent constant of PZT sensor has sufficiently influenced the electrical impedance signature.Moreover,the effective frequency shift(EFS)approach is optimized in term of significant temperature compensation for the current impedance signature of PZT sensor relative to the reference signature at the extended frequency bandwidth of the developed measurement system with better outcomes as compared to the previous literature work.Hence,the current study also deals efficiently with the critical issue of the width of the frequency band for temperature compensation based on the frequency shift in SHM.The results of the experimental study demonstrate that the proposed methodology is qualified for the damage inspection in real-time monitoring applications under the temperature variations.It is capable to exclude one of the major reasons of false fault diagnosis by compensating the consequence of elevated temperature at extended frequency bandwidth in SHM.

An Ensemble Learning Model for Early Dropout Prediction of MOOC Courses

Massive open online courses(MOOCs)have become a way of online learning across the world in the past few years.However,the extremely high dropout rate has brought many challenges to the development of online learning.Most of the current methods have low accuracy and poor generalization ability when dealing with high-dimensional dropout features.They focus on the analysis of the learning score and check result of online course,but neglect the phased student behaviors.Besides,the status of student participation at a given moment is necessarily impacted by the prior status of learning.To address these issues,this paper has proposed an ensemble learning model for early dropout prediction(ELM-EDP)that integrates attention-based document representation as a vector(A-Doc2vec),feature learning of course difficulty,and weighted soft voting ensemble with heterogeneous classifiers(WSV-HC).First,A-Doc2vec is proposed to learn sequence features of student behaviors of watching lecture videos and completing course assignments.It also captures the relationship between courses and videos.Then,a feature learning method is proposed to reduce the interference caused by the differences of course difficulty on the dropout prediction.Finally,WSV-HC is proposed to highlight the benefits of integration strategies of boosting and bagging.Experiments on the MOOCCube2020 dataset show that the high accuracy of our ELM-EDP has better results on Accuracy,Precision,Recall,and F1.

Study of Intelligent Approaches to Identify Impact of Environmental Temperature on Ultrasonic GWs Based SHM:A Review

Structural health monitoring(SHM)is considered an effective approach to analyze the efficient working of several mechanical components.For this purpose,ultrasonic guided waves can cover long-distance and assess large infrastructures in just a single test using a small number of transducers.However,the working of the SHM mechanism can be affected by some sources of variations(i.e.,environmental).To improve the final results of ultrasonic guided wave inspections,it is necessary to highlight and attenuate these environmental variations.The loading parameters,temperature and humidity have been recognized as the core environmental sources of variations that affect the SHM sensing mechanism.Environmental temperature has the most significant influence on SHM results.There is still a need for extensive research to develop such a damage inspection approach that should be insensitive to environmental temperature variations.In this framework,the current research study will not only illuminate the effect of environmental temperature through different intelligent approaches but also suggest the standard mechanism to attenuate it in actual ultrasonic guided wave based SHM.Hence,the work presented in this article addresses one of the open research challenges that are the identification of the effect of environmental and operating conditions in practical applications of ultrasonic guided waves and impedance-based SHM.

Collective Entity Alignment for Knowledge Fusion of Power Grid Dispatching Knowledge Graphs

Knowledge graphs(KGs)have been widely accepted as powerful tools for modeling the complex relationships between concepts and developing knowledge-based services.In recent years,researchers in the field of power systems have explored KGs to develop intelligent dispatching systems for increasingly large power grids.With multiple power grid dispatching knowledge graphs(PDKGs)constructed by different agencies,the knowledge fusion of different PDKGs is useful for providing more accurate decision supports.To achieve this,entity alignment that aims at connecting different KGs by identifying equivalent entities is a critical step.Existing entity alignment methods cannot integrate useful structural,attribute,and relational information while calculating entities’similarities and are prone to making many-to-one alignments,thus can hardly achieve the best performance.To address these issues,this paper proposes a collective entity alignment model that integrates three kinds of available information and makes collective counterpart assignments.This model proposes a novel knowledge graph attention network(KGAT)to learn the embeddings of entities and relations explicitly and calculates entities’similarities by adaptively incorporating the structural,attribute,and relational similarities.Then,we formulate the counterpart assignment task as an integer programming(IP)problem to obtain one-to-one alignments.We not only conduct experiments on a pair of PDKGs but also evaluate o ur model on three commonly used cross-lingual KGs.Experimental comparisons indicate that our model outperforms other methods and provides an effective tool for the knowledge fusion of PDKGs.

NEW ITERATIVE SUPER-TRELLIS DECODING WITH SOURCE A PRIORI INFORMATION FOR VLCS WITH TURBO CODES

A novel Joint Source and Channel Decoding (JSCD) scheme for Variable Length Codes (VLCs) concatenated with turbo codes utilizing a new super-trellis decoding algorithm is presented in this letter. The basic idea of our decoding algorithm is that source a priori information with the form of bit transition probabilities corresponding to the VLC tree can be derived directly from sub-state transitions in new composite-state represented super-trellis. A Maximum Likelihood (ML) decoding algorithm for VLC sequence estimations based on the proposed super-trellis is also described. Simu-lation results show that the new iterative decoding scheme can obtain obvious encoding gain especially for Reversible Variable Length Codes (RVLCs),when compared with the classical separated turbo decoding and the previous joint decoding not considering source statistical characteristics.

Dynamics and Operation-State Estimation of Current-Mode Controlled Flyback Converter

By utilizing total magnetic flux φ of the primary and secondary windings of the flyback transformer as a state variable, the discrete-time model of current-mode controlled flyback converter is established, upon which the bifurcation behaviors of the converter are analyzed and two boundary classification equations of the orbit state shifting are obtained. The operation state regions of the current-mode controlled flyback converter are well classified by two boundary classification equations. The theoretical analysis results are verified by power electronics simulator （PSIM）. The estimation of operation-state regions for the flyback converter is useful for the design of circuit parameters, stability control of chaos, and chaos-based applications.

SSL-WAEIE:Self-Supervised Learning With Weighted Auto-Encoding and Information Exchange for Infrared and Visible Image Fusion

Dear editor,Infrared and visible image fusion(IVIF)technologies are to extract complementary information from source images and generate a single fused result[1],which is widely applied in various high-level visual tasks such as segmentation and object detection[2].

Refocusing and locating effect of fluorescence scattering field

Optical imaging deep inside scattering medium has always been one of the challenges in the field of bioimaging,which significantly drawbacks the employment of con-focal microscopy system.Although a variety of feedback techniques,such as acoustic or nonlinear fluorescence-based schemes have realized the refocusing of the coherent light,the problems of non-invasively refocusing and locating of linearly-excited fluorescent beads inside the scattering medium have not been thoroughly explored.In this paper,we linearly excited the fluorescent beads inside a scattering medium by using our homemade optical con-focal system,collected the fluorescence scattering light as the optimized target,and established a theoretical model of target contrast enhancement,which is consistent with the experimental data.By improving both the cost function and variation rate within the genetic algorithm,we could refocus the fluorescence scattering field while improving the contrast enhancement factor to 12.8 dB.Then,the positions of the fluorescent beads are reconstructed by subpixel accuracy centroid localization algorithm,and the corresponding error is no more than 4.2μm with several fluorescent beads within the field of view.Finally,the main factors such as the number of fluorescent beads,the thickness of the scattering medium,the modulating parameter,the experimental noise and the system long-term stability are analyzed and discussed in detail.This study proves the feasibility of reconstructing fluorescent labeled cells inside biological tissues,which provides certain reference value for deep imaging of biological tissues.

Belief Combination of Classifiers for Incomplete Data

Data with missing values,or incomplete information,brings some challenges to the development of classification,as the incompleteness may significantly affect the performance of classifiers.In this paper,we handle missing values in both training and test sets with uncertainty and imprecision reasoning by proposing a new belief combination of classifier(BCC)method based on the evidence theory.The proposed BCC method aims to improve the classification performance of incomplete data by characterizing the uncertainty and imprecision brought by incompleteness.In BCC,different attributes are regarded as independent sources,and the collection of each attribute is considered as a subset.Then,multiple classifiers are trained with each subset independently and allow each observed attribute to provide a sub-classification result for the query pattern.Finally,these sub-classification results with different weights(discounting factors)are used to provide supplementary information to jointly determine the final classes of query patterns.The weights consist of two aspects:global and local.The global weight calculated by an optimization function is employed to represent the reliability of each classifier,and the local weight obtained by mining attribute distribution characteristics is used to quantify the importance of observed attributes to the pattern classification.Abundant comparative experiments including seven methods on twelve datasets are executed,demonstrating the out-performance of BCC over all baseline methods in terms of accuracy,precision,recall,F1 measure,with pertinent computational costs.

Review of Methods of Image Segmentation Based on Quantum Mechanics

The quantum theory application is a hot research area in recent years, especially the theory of quantum mechanics. In this paper, we focus on the research of image segmentation based on quantum mechanics. Firstly,the theory of quantum mechanics is introduced; afterwards, a review of image segmentation methods based on quantum mechanics is presented; and finally, the characteristics about the quantum mechanics applied to image processing are concluded. Two main research topics are discussed in this paper. One is to emphasize that quantum mechanics can be applied in different research areas, such as image segmentation, and the second is to conclude some methods in image segmentation and give some suggestions for possible novel methods by applying quantum mechanics theory. As a summary, this is a review paper which presents some methods based on the feasible theory in quantum mechanics aiming at achieving a better performance in image segmentation.

Another Important Parameter’s Research on Estimating Self-Similarity

It is convincingly demonstrated by numerous studies that the self-similarity of modern multimedia network traffic is presented by Hurst parameter (H). The specific performance is that the similar degree is higher along with the increase of H when H is between 0.5 and 1. However, it is doubtable that whether the complicated process of self-similarity can be described comprehensively by the parameter H only. Therefore, another important parameter cf has been proposed based on the discrete wavelet decomposition in this paper. The significance of the parameters is provided and the performance of the self-similarity process is described better.

Attacks Against Cross-Chain Systems and Defense Approaches:A Contemporary Survey

The blockchain cross-chain is a significant technology for inter-chain interconnection and value transfer among different blockchain networks.Cross-chain overcomes the“information island”problem of the closed blockchain network and is increasingly applied to multiple critical areas such as finance and the internet of things(IoT).Blockchain can be divided into three main categories of blockchain networks:public blockchains,private blockchains,and consortium blockchains.However,there are differences in block structures,consensus mechanisms,and complex working mechanisms among heterogeneous blockchains.The fragility of the cross-chain system itself makes the cross-chain system face some potential security and privacy threats.This paper discusses security defects on the cross-chain implementation mechanism,and discusses the impact of the structural features of blockchain networks on cross-chain security.In terms of cross-chain intercommunication,a cross-chain attack can be divided into a multi-chain combination attack,native chain attack,and inter-chain attack diffusion.Then various security threats and attack paths faced by the cross-chain system are analyzed.At last,the corresponding security defense methods of cross-chain security threats and future research directions for cross-chain applications are put forward.

Developing nonlinear additive tree crown width models based on decomposed competition index and tree variables

Crown development is closely related to the biomass and growth rate of the tree and its width(CW)is an important covariable in growth and yield models and in forest management.To date,various CW models have been proposed.However,limited studies have explicitly focused on additive and inherent correlation of crown components and total CW as well as the influence of competition on crown radius from the corresponding direction.In this study,two model systems were used,i.e.,aggregation method system(AMS)and disaggregation method system(DMS),to develop crown width additive model systems.For calculating spatially explicit competition index(CI),four neighbor tree selection methods were evaluated.CI was decomposed into four cardinal directions and added into the model systems.Results show that the power model form was more proper for our data to fit CW growth.For each crown radius and total CW,height to the diameter at breast height(HDR)and basal area of trees larger than the subject tree(BAL)significantly contributed to the increase of prediction accuracy.The 3-m fixed radius was optimal among the four neighborhoods selection ways.After adding decomposed competition Hegyi index into model systems AMS and DMS,the prediction accuracy improved.Of the model systems evaluated,AMS based on decomposed CI provided the best performance as well as the inherent correlation and additivity properties.Our study highlighted the importance of decomposed CI in tree CW modelling for additive model systems.This study focused on methodology and could be applied to other species or stands.

Machine Learning With Data Assimilation and Uncertainty Quantification for Dynamical Systems:A Review

Data assimilation(DA)and uncertainty quantification(UQ)are extensively used in analysing and reducing error propagation in high-dimensional spatial-temporal dynamics.Typical applications span from computational fluid dynamics(CFD)to geoscience and climate systems.Recently,much effort has been given in combining DA,UQ and machine learning(ML)techniques.These research efforts seek to address some critical challenges in high-dimensional dynamical systems,including but not limited to dynamical system identification,reduced order surrogate modelling,error covariance specification and model error correction.A large number of developed techniques and methodologies exhibit a broad applicability across numerous domains,resulting in the necessity for a comprehensive guide.This paper provides the first overview of state-of-the-art researches in this interdisciplinary field,covering a wide range of applications.This review is aimed at ML scientists who attempt to apply DA and UQ techniques to improve the accuracy and the interpretability of their models,but also at DA and UQ experts who intend to integrate cutting-edge ML approaches to their systems.Therefore,this article has a special focus on how ML methods can overcome the existing limits of DA and UQ,and vice versa.Some exciting perspectives of this rapidly developing research field are also discussed.Index Terms-Data assimilation(DA),deep learning,machine learning(ML),reduced-order-modelling,uncertainty quantification(UQ).

Dual-Prior Integrated Image Reconstruction for Quanta Image Sensors Using Multi-Agent Consensus Equilibrium

Quanta image sensors(QIS) are a new type of singlephoton imaging device that can oversample the light field to generate binary bit-streams. The reconstruction for QIS refers to the recovery of original scenes from these binary measurements.Conventional reconstruction algorithms for QIS generally depend solely on one instantiated prior and are certainly insufficient for capturing the statistical properties over high-dimensional space.On the other hand, deep learning-based methods have shown promising performance, due to their excellent ability to learn feature representations from relevant databases. However, most deep models only focus on exploring local features while generally overlooking long-range similarity. In view of this, a dual-prior integrated reconstruction algorithm for QIS(DPI-QIS) is proposed, which combines a deep prior with a non-local self-similarity one using the multi-agent consensus equilibrium(MACE)framework. In comparison to the approaches that utilize a single prior, DPI-QIS fits the reconstruction model sufficiently by leveraging the respective merits of both priors. An effective yet flexible MACE framework is employed to integrate the physical forward model allying with the two prior-based models to achieve an overall better result. Extensive experiments demonstrate that the proposed algorithm achieves state-of-the-art performance in terms of objective and visual perception at multiple oversampling factors, while having stronger robustness to noise.