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.

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.

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.

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.

Identification of Steady State and Transient State

Identification of steady state and transient state plays an important role in modeling,control,optimiza-tion,and fault detection of industrial processes.Many existing methods for state identification are not satisfactory in practical applications due to problems of ideal hypothesis,too many parameters,and poor robustness.In this paper,a novel state identification approach is proposed.The problem of state identification is transformed into finding the noise band of differential signal.For practical application,automatic selection of noise band amplitude is proposed to make the method convenient to be used.Problems of gross errors,low signal-to-noise ratio and online identification are considered.And comparison with other two methods shows that the proposed method has better identification performance.Simulations and experiments also prove the effectiveness and practicability of the proposed method.

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.

Contact State Estimation Based on Surface-matching in Virtual Assembly

Contact problems are one of the most challenging fields in virtual assembly. Information of contact states could be utilized to realize compliant motion of work pieces, to analyze the contact stress, to assist positioning parts and so on. Some methods have already been proposed to estimate contact states between objects and in most of these methods contact states between objects are simplified in order to realize real-time visual reality animation. While in virtual assembly contact states between parts are required to analyze contact stress, deformation and quality. Besides the contact state estimation method for virtual assembly should be able to handle a number of complex parts in real time. There are rarely known methods which could meet this requirement till now. In this study a contact state estimation algorithm based on surface-matching for virtual assembly is proposed. Contacts between parts are categorized into six basic types according to contact region of surfaces. Based on continuous collision detection of polyhedral models a novel contact state identification algorithm which is based on surface matching is proposed. Then contact evolution algorithm, which utilizes the extern force and contact information, is implemented to handle evolution of contact state. Finally a prototype system is developed to verify the above technologies. Experiment results reveal that contact state between parts could be estimated correctly in real time virtual assembly. The proposed contact state estimation algorithm provides a complete solution to obtain the contact state between parts in virtual assembly. Information of contact state between parts could be utilized to realize contact dynamic, contact stress analysis, assembly quality analysis, and so on.

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.

An on-line capacitor state identification method based on improved RLS

As an essential part of DC-Link in the power converter,capacitor plays a crucial role in absorbing ripple current and suppressing ripple voltage.The health and residual service life of the DC-Link capacitor is one of the decisive factors for the safety,stability,and efficiency of the system in which it is located.Aiming at the shortcomings of existing methods,such as low dynamic sensitivity of data update and fluctuation of identification results,a capacitor state identification method based on improved RLS is proposed in this paper.The proposed method is optimized by introducing the forgetting factor algorithm and root means square algorithm to modify the iterative formula and final identification results.Compared with existing methods,this method can identify the capacitor’s current state in real time and accurately.Finally,we successfully verified the accuracy,robustness,and adaptability of the proposed method by a series of experimental tests on a dSPACE platform.

N4SID and MOESP Algorithms to Highlight the Ill-conditioning into Subspace Identification

In this paper,an analysis for ill conditioning problem in subspace identifcation method is provided.The subspace identifcation technique presents a satisfactory robustness in the parameter estimation of process model which performs control.As a frst step,the main geometric and mathematical tools used in subspace identifcation are briefly presented.In the second step,the problem of analyzing ill-conditioning matrices in the subspace identifcation method is considered.To illustrate this situation,a simulation study of an example is introduced to show the ill-conditioning in subspace identifcation.Algorithms numerical subspace state space system identifcation(N4SID)and multivariable output error state space model identifcation(MOESP)are considered to study,the parameters estimation while using the induction motor model,in simulation(Matlab environment).Finally,we show the inadequacy of the oblique projection and validate the efectiveness of the orthogonal projection approach which is needed in ill-conditioning;a real application dealing with induction motor parameters estimation has been experimented.The obtained results proved that the algorithm based on orthogonal projection MOESP,overcomes the situation of ill-conditioning in the Hankel s block,and thereby improving the estimation of parameters.

Estimation of vehicle states and tire-road friction using parallel extended Kalman filtering

A model-based estimator design and implementation is described in this paper to undertake combined estimation of vehicle states and tire-road friction coefficients.The estimator is designed based on a vehicle model with three degrees of freedom(3-DOF) and the dual extended Kalman filter(DEKF) technique is employed.Effectiveness of the estimation is examined and validated by comparing the outputs of the estimator with the responses of the vehicle model in CarSim in three typical road adhesion conditions(high-friction,low-friction,and joint-friction roads).Simulation results demonstrate that the DEKF estimator algorithm designed is able to obtain vehicle states(e.g.,yaw rate and roll angle) as well as road friction coefficients with reasonable accuracy.