Optimizing near-carbon-free nuclear energy systems:advances in reactor operation digital twin through hybrid machine learning algorithms for parameter identification and state estimation

Accurate and efficient online parameter identification and state estimation are crucial for leveraging digital twin simulations to optimize the operation of near-carbon-free nuclear energy systems.In previous studies,we developed a reactor operation digital twin(RODT).However,non-differentiabilities and discontinuities arise when employing machine learning-based surrogate forward models,challenging traditional gradient-based inverse methods and their variants.This study investigated deterministic and metaheuristic algorithms and developed hybrid algorithms to address these issues.An efficient modular RODT software framework that incorporates these methods into its post-evaluation module is presented for comprehensive comparison.The methods were rigorously assessed based on convergence profiles,stability with respect to noise,and computational performance.The numerical results show that the hybrid KNNLHS algorithm excels in real-time online applications,balancing accuracy and efficiency with a prediction error rate of only 1%and processing times of less than 0.1 s.Contrastingly,algorithms such as FSA,DE,and ADE,although slightly slower(approximately 1 s),demonstrated higher accuracy with a 0.3%relative L_2 error,which advances RODT methodologies to harness machine learning and system modeling for improved reactor monitoring,systematic diagnosis of off-normal events,and lifetime management strategies.The developed modular software and novel optimization methods presented offer pathways to realize the full potential of RODT for transforming energy engineering practices.

An improved YOLOv7 for the state identification of sliding chairs in railway turnout

The sliding chairs are important components that support the switch rail conversion in the railway turnout.Due to the harsh environmental erosion and the attack from the wheel vibration,the failure rate of the sliding chairs accounts for up to 10%of the total failure number in turnout.However,there is little research carried out in the existing literature to diagnose the deterioration states of the sliding chairs.To fill out this gap,by utilizing the images containing the sliding chairs,we propose an improved You Only Look Once version 7(YOLOv7)to identify the state of the sliding chairs.Specifically,to meet the challenge brought by the small inter-class differences among the sliding chair states,we first integrate the Convolutional Block Attention Module(CBAM)into the YOLOv7 backbone to screen the information conducive to state identification.Then,an extra detector for a small object is customized into the YOLOv7 network in order to detect the small-scale sliding chairs in images.Meanwhile,we revise the localization loss in the objective function as the Efficient Intersection over Union(EIoU)to optimize the design of the aspect ratio,which helps the localization of the sliding chairs.Next,to address the issue caused by the varying scales of the sliding chairs,we employ K-means++to optimize the priori selection of the initial anchor boxes.Finally,based on the images collected from real-world turnouts,the proposed method is verified and the results show that our method outperforms the basic YOLOv7 in the state identification of the sliding chairs with 4%improvements in terms of both mean Average Precision@0.5(mAP@0.5)and F1-score.

State Space-Time and Four States of Universe

All things in the universe possess a state and characteristics of state, resultantly in presence of space-time, which is perceived by human beings. An outlook of space-time is shaped in human by perceiving the existence and change of objects. The state space is all state characteristics exhibited in objects whilst the state time refers to the duration of an object＇s state. The time is a spatial property and not an independent dimension. The state space-time is a unity of internal and external space-time. The internal space-time is stemmed from the overall internal forces and internal energies and is a covert space-time. The external space-time refers to a space-time manifested by the external characteristics and movement of an object and is an overt space-time. In physics, there are four kinds of forces and four state space-times： bonding force and three-dimensional space-time; strong interaction of exchangeable n meson and two-dimensional space-time; quark confinement and one-dimensional space-time; and weak interaction and zero-dimensional space-time. The universe is constituted by dissimilar state space-times. Newton space-time is a three-dimensional state space-time; Einstein＇s theory of relativity is a two-dimensional state space-time. Newton and Einstein were different observers. Temporal and spatial perception of human is dependent upon human＇s intemal energy and quality. Through Qigong exercises, the human is able to enter the three-dimensional, two-dimensional, one-dimensional and zero-dimensional space-times. The relativity theory of human body will solve the time problems at the interplanetary voyage of astronauts.

A Modal Identification Algorithm Combining Blind Source Separation and State Space Realization

A modal identification algorithm is developed, combining techniques from Second Order Blind Source Separation (SOBSS) and State Space Realization (SSR) theory. In this hybrid algorithm, a set of correlation matrices is generated using time-shifted, analytic data and assembled into several Hankel matrices. Dissimilar left and right matrices are found, which diagonalize the set of nonhermetian Hankel matrices. The complex-valued modal matrix is obtained from this decomposition. The modal responses, modal auto-correlation functions and discrete-time plant matrix (in state space modal form) are subsequently identified. System eigenvalues are computed from the plant matrix to obtain the natural frequencies and modal fractions of critical damping. Joint Approximate Diagonalization (JAD) of the Hankel matrices enables the under determined (more modes than sensors) problem to be effectively treated without restrictions on the number of sensors required. Because the analytic signal is used, the redundant complex conjugate pairs are eliminated, reducing the system order (number of modes) to be identified half. This enables smaller Hankel matrix sizes and reduced computational effort. The modal auto-correlation functions provide an expedient means of screening out spurious computational modes or modes corresponding to noise sources, eliminating the need for a consistency diagram. In addition, the reduction in the number of modes enables the modal responses to be identified when there are at least as many sensors as independent (not including conjugate pairs) modes. A further benefit of the algorithm is that identification of dissimilar left and right diagonalizers preclude the need for windowing of the analytic data. The effectiveness of the new modal identification method is demonstrated using vibration data from a 6 DOF simulation, 4-story building simulation and the Heritage court tower building.

On neutrino Oscillations and Predicting the 125 GEV Two Photon Emission State from p-p Collisions Based on the 5D Homogeneous Space-Time Projection Model

Previously the 5D homogeneous space-time metric was introduced with explicitly given projection operators in matrix form which map the 5D space-time manifold into a Lorentzian space-time. Based on this projection model, vector field and spinor solutions are found to be expressible in terms of SU(2)xL and SU(3)xL, where L is the 4D Lorentz space-time group. The spinor solutions give the SU(2) leptonic states arising from space-time projection, whereas the SU(3) representation arises from conformal projection and gives the quarks, and due to gauge requirement leads to mesons and baryons. This process of mapping the 5D space-time manifold into the 4D space-time is at the basis of an analysis of the recent CERN experimental results, the presence of neutrino oscillations and the observed 125 GeV resonance in the p-p collisions, respectively. In fact, it is found that the spinor solution contains an oscillating phase, and the 125 GeV resonance is shown to be predictable, thereby 1) eliminating the need to introduce a Higgs vacuum, and 2) can be shown possibly to be an indicator for a missing heavy baryon octet.

Identification of Topological Surface State in PdTe2 Superconductor by Angle-Resolved Photoemission Spectroscopy

High-resolution angle-resolved photoemission measurements are carried out on transition metal dichalcogenide PdTe2 that is a superconductor with a Tc at 1.7K. Combined with theoretical calculations, we discover for the first time the existence of topologically nontrivial surface state with Dirac cone in PbTe2 superconductor. It is located at the Brillouin zone center and possesses helical spin texture. Distinct from the usual three-dimensional topological insulators where the Dirac cone of the surface state lies at the Fermi level, the Dirac point of the surface state in PdTe2 lies deeply below the Fermi level at - 1.75 eV binding energy and is well separated from the bulk states. The identification of topological surface state in PdTe2 superconductor deeply below the Fermi level provides a unique system to explore new phenomena and properties and opens a door for finding new topological materials in transition metal ehalcogenides.

A road hypnosis identification method for drivers based on fusion of biological characteristics

Risky driving behaviors,such as driving fatigue and distraction have recently received more attention.There is also much research about driving styles,driving emotions,older drivers,drugged driving,DUI(driving under the influence),and DWI(driving while intoxicated).Road hypnosis is a special behavior significantly impacting traffic safety.However,there is little research on this phenomenon.Road hypnosis,as an unconscious state,is can frequently occur while driving,particularly in highly predictable,monotonous,and familiar environments.In this paper,vehicle and virtual driving experiments are designed to collect the biological characteristics including eye movement and bioelectric parameters.Typical scenes in tunnels and highways are used as experimental scenes.LSTM(Long Short-Term Memory)and KNN(K-Nearest Neighbor)are employed as the base learners,while SVM(Support Vector Machine)serves as the meta-learner.A road hypnosis identification model is proposed based on ensemble learning,which integrates bioelectric and eye movement characteristics.The proposed model has good identification performance,as seen from the experimental results.In this study,alternative methods and technical support are provided for real-time and accurate identification of road hypnosis.

Parameter identification and state-of-charge estimation approach for enhanced lithium–ion battery equivalent circuit model considering influence of ambient temperatures

It is widely accepted that the variation of ambient temperature has great influence on the battery model parameters and state-of-charge(SOC) estimation, and the accurate SOC estimation is a significant issue for developing the battery management system in electric vehicles. To address this problem, in this paper we propose an enhanced equivalent circuit model(ECM) considering the influence of different ambient temperatures on the open-circuit voltage for a lithium-ion battery. Based on this model, the exponential-function fitting method is adopted to identify the battery parameters according to the test data collected from the experimental platform. And then, the extended Kalman filter(EKF) algorithm is employed to estimate the battery SOC of this battery ECM. The performance of the proposed ECM is verified by using the test profiles of hybrid pulse power characterization(HPPC) and the standard US06 driving cycles(US06) at various ambient temperatures, and by comparing with the common ECM with a second-order resistance capacitor. The simulation and experimental results show that the enhanced battery ECM can improve the battery SOC estimation accuracy under different operating conditions.

Recursive State-space Model Identification of Non-uniformly Sampled Systems Using Singular Value Decomposition

In this paper a recursive state-space model identification method is proposed for non-uniformly sampled systems in industrial applications. Two cases for measuring all states and only output(s) of such a system are considered for identification. In the case of state measurement, an identification algorithm based on the singular value decomposition(SVD) is developed to estimate the model parameter matrices by using the least-squares fitting. In the case of output measurement only, another identification algorithm is given by combining the SVD approach with a hierarchical identification strategy. An example is used to demonstrate the effectiveness of the proposed identification method.

A Two－Stage Approach of Integrated Parameter Identification and State Estimation

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Identification of Fishing State of Purse Seine Fishing Vessels Based on Multi-Indices

With the popularization of vessel satellite AIS(automatic identification system)equipment and the continuous improve-ment of the AIS data’s coverage,continuity and effectiveness,AIS has become an important data source to study the navigation char-acteristics of vessel groups.This study established an identification model to extract the fishing state and intensity information of fishing vessels,based on the AIS data of purse seine fishing vessels,combined with the variables of vessel position,speed and course.Expert experience,spatial statistics and data mining analysis methods were applied to establish the model,and the Western and Cen-tral Pacific Ocean areas were studied.The results showed that the overall accuracy of identification of the fishing state using Support Vector Machine method is higher,and the method has a good modeling effect.The spatial distribution characteristics of the vessels’fishing intensity based on AIS data showed a significant cluster distribution pattern.The obtained high-intensity fishing area can be used as a prediction of purse seine fishing grounds in the Western and Central Pacific areas.Through the processing and research of AIS data,this study provided important scientific support for the identification of fishing state of purse seine fishing vessels.The spatial fishing intensity of fishing vessels based on AIS data can also be used for the analysis of fishery resources and fishing grounds,and further serve the sustainable development of marine fisheries.

Parameter identification and pressure control of dynamic system in shield tunneling using least squares method

An estimation approach using least squares method was presented for identificationof model parameters of pressure control in shield tunneling.The state equation ofthe pressure control system for shield tunneling was analytically derived based on themass equilibrium principle that the entry mass of the pressure chamber from cutting headwas equal to excluding mass from the screw conveyor.The randomly observed noise wasnumerically simulated and mixed to simulated observation values of system responses.The numerical simulation shows that the state equation of the pressure control system forshield tunneling is reasonable and the proposed estimation approach is effective even ifthe random observation noise exists.The robustness of the controlling procedure is validatedby numerical simulation results.

An Identification Method Based on the Improved NLJ Algorithm and Its Application

The accurate model is the most important and basic condition for the application of advanced process control, but the conventional methods do not provide satisfactory results in the case of unstable processes. To effec-tively control these processes, a novel identification method (Model Parameters and Initial States Identification si-multaneously in closed loop —MPISI) is proposed. The model parameters and initial states of state equation can be simultaneously identified using this method. The results of simulation and application show that this method has the advantageous of disturbance-rejection and robustness. This method proposes a novel way for the optimization and the advanced control of the process systems.

Physical Parameter Identification Method Based on Modal Analysis for Two-axis On-road Vehicles:Theory and Simulation

Physical parameters are very important for vehicle dynamic modeling and analysis.However,most of physical parameter identification methods are assuming some physical parameters of vehicle are known,and the other unknown parameters can be identified.In order to identify physical parameters of vehicle in the case that all physical parameters are unknown,a methodology based on the State Variable Method（SVM） for physical parameter identification of two-axis on-road vehicle is presented.The modal parameters of the vehicle are identified by the SVM,furthermore,the physical parameters of the vehicle are estimated by least squares method.In numerical simulations,physical parameters of Ford Granada are chosen as parameters of vehicle model,and half-sine bump function is chosen to simulate tire stimulated by impulse excitation.The first numerical simulation shows that the present method can identify all of the physical parameters and the largest absolute value of percentage error of the identified physical parameter is 0.205%;and the effect of the errors of additional mass,structural parameter and measurement noise are discussed in the following simulations,the results shows that when signal contains 30 d B noise,the largest absolute value of percentage error of the identification is 3.78%.These simulations verify that the presented method is effective and accurate for physical parameter identification of two-axis on-road vehicles.The proposed methodology can identify all physical parameters of 7-DOF vehicle model by using free-decay responses of vehicle without need to assume some physical parameters are known.

Modeling and Identification of Multirate Systems

Multirate systems are abundant in industry; for example, many soft-sensor design problems are related to modeling, parameter identification, or state estimation involving multirate systems. The study of multirate systems goes back to the early 1950's, and has become an active research area in systems and control. This paper briefly surveys the history of development in the area of multirate systems, and introduces some basic concepts and latest results on multirate systems, including a polynomial transformation technique and the lifting technique as tools for handling multirate systems, lifted state space models, parameter identification of dual-rate systems, how to determine fast single-rate models from dual-rate models and directly from dual-rate data, and a hierarchical identification method for general multirate systems. Finally, some further research topics for multirate systems are given.

Identification of navigation characteristics of single otter trawl vessel using four machine learning models

Fishing logbook records the fishing behaviors and other information of fishing vessels.However,the accuracy of the recorded information is often difficult to guarantee due to the misreport and concealment.The fishing vessel monitoring system(VMS)can monitor and record the navigation information of fishing vessels in real time,and it may be used to improve the accuracy of identifying the state of fishing vessels.If the VMS data and fishing logbook are combined to establish their relationships,then the navigation characteristics and fishing behavior of fishing vessels can be more accurately identified.Therefore,first,a method for determining the state of VMS data points using fishing log data was proposed.Secondly,the relationship between VMS data and the different states of fishing vessels was further explored.Thirdly,the state of the fishing vessel was predicted using VMS data by building machine learning models.The speed,heading,longitude,latitude,and time as features from the VMS data were extracted by matching the VMS and logbook data of three single otter trawl vessels from September 2012 to January 2013,and four machine learning models were established,i.e.,Random Forest(RF),Adaptive Boosting(AdaBoost),K-Nearest Neighbor(KNN),and Gradient Boosting Decision Tree(GBDT)to predict the behavior of fishing vessels.The prediction performances of the models were evaluated by using normalized confusion matrix and receiver operator characteristic curve.Results show that the importance rankings of spatial(longitude and latitude)and time features were higher than those of speed and heading.The prediction performances of the RF and AdaBoost models were higher than those of the KNN and GBDT models.RF model showed the highest prediction performance for fishing state.Meanwhile,AdaBoost model exhibited the highest prediction performance for non-fishing state.This study offered a technical basis for judging the navigation characteristics of fishing vessels,which improved the algorithm for judging the behavior of fishing vessels based on VMS data,enhanced the prediction accuracy,and upgraded the fishery management being more scientific and efficient.

A Material Identification Approach Based on Wi-Fi Signal

Material identification is a technology that can help to identify the type of target material.Existing approaches depend on expensive instruments,complicated pre-treatments and professional users.It is difficult to find a substantial yet effective material identification method to meet the daily use demands.In this paper,we introduce a Wi-Fi-signal based material identification approach by measuring the amplitude ratio and phase difference as the key features in the material classifier,which can significantly reduce the cost and guarantee a high level accuracy.In practical measurement of WiFi based material identification,these two features are commonly interrupted by the software/hardware noise of the channel state information(CSI).To eliminate the inherent noise of CSI,we design a denoising method based on the antenna array of the commercial off-the-shelf(COTS)Wi-Fi device.After that,the amplitude ratios and phase differences can be more stably utilized to classify the materials.We implement our system and evaluate its ability to identify materials in indoor environment.The result shows that our system can identify 10 commonly seen liquids with an average accuracy of 98.8%.It can also identify similar liquids with an overall accuracy higher than 95%,such as various concentrations of salt water.

Power control scheme for multiple antenna systems with space-time coding in Rayleigh fading channels

Two optimal power control （PC） schemes under the power constraint for space-time coded multiple input multiple output systems over the flat Rayleigh fading channel with the imperfect channel state information （CSI） are presented. One is based on the minimization of a bit error rate （BER）, and the other is based on the maximization of a fuzzy signal-to-noise ratio. In these schemes, different powers are allocated to individual transmit an- tennas rather than equal power in the conventional one. For the first scheme, the optimal PC procedure is developed. It is shown that the Lagrange multiplier for the constrained optimization in the power control does exist and is unique. A practical iterative algorithm based on Newton＇s method for finding the Lagrange multiplier is proposed. In the second scheme, some existing schemes are included, and a suboptimal PC procedure is developed by means of the asymptotic performance analysis. With this suboptimal scheme, a simple PC calculation formula is provided, and thus the calculation of the PC will be straightforward. Moreover, the suboptimal scheme has the BER performance close to the optimal scheme. Simulation results show that the two PC schemes can provide BER lower than the equal PC and antenna selection scheme under the imperfect CSI.

OPTIMAL ANTENNA SUBSET SELECTION AND BLIND DETECTION APPROACH APPLIED TO ORTHOGONAL SPACE-TIME BLOCK CODING

An approach combining optimal antenna subset selection with blind detection scheme for Orthogonal Space-Time Block Coding (OSTBC) is proposed in this paper. The optimal antenna sub- set selection is taken into account at transmitter and/or receiver sides, which chooses the optimal an- tennas to increase the diversity order of OSTBC and improve further its performance. In order to en- hance the robustness of the detection used in the conventional OSTBC scheme, a blind detection scheme based on Independent Component Analysis (ICA) is exploited which can directly extract transmitted signals without channel estimation. Performance analysis shows that the proposed ap- proach can achieve the full diversity and the flexibility of system design by using the antenna selec-tion and the ICA based blind detection schemes.

An improved state-of-charge estimation method for sodium-ion battery based on combined correction of voltage and internal resistance

ABSTRACT The accurate state-of-charge(SOC)estimation of sodium-ion batteries is the basis for their efficient application.In this paper,a new SOC estimation method suitable for sodium-ion batteries and their application conditions is proposed,which considers the combination of open circuit voltage(OCV)and internal resistance correction.First,the optimal order of equivalent circuit model is analyzed and selected,and the monotonic and stable mapping relationships between OCV and SOC,as well as between ohmic internal resistance and SOC are determined.Then,a joint estimation algorithm for battery model parameters and SOC is estab-lished,and a joint SOC correction strategy based on OCV and ohmic internal resistance is established.The test results show that OCV correction is reliable when polarization is small,that the ohmic internal resistance correction is reliable when the current fluctuation is large,and that the maximum absolute error of SOC estimation of the proposed method is not more than 2.6%.