Vector modeling and track simulation in axial turn-milling motion

Through vector analysis the kinetic vector model is built in a machining cylinder surface through axial turn-milling. When building a kinetic vector model in the machining field, machining through axial turn-milling and using equilateral triangles and square prism surfaces, the kinetic vector model is given any equilateral polygon prismic surface. Kinetic tracks are simulated through these kinetic models respectively, thus it can be seen that the axial turn-milling is a very effective method in manufacturing any equilateral, polygon, prismic surface.

Segmentation Based Real Time Anomaly Detection and Tracking Model for Pedestrian Walkways

Presently,video surveillance is commonly employed to ensure security in public places such as traffic signals,malls,railway stations,etc.A major chal-lenge in video surveillance is the identification of anomalies that exist in it such as crimes,thefts,and so on.Besides,the anomaly detection in pedestrian walkways has gained significant attention among the computer vision communities to enhance pedestrian safety.The recent advances of Deep Learning(DL)models have received considerable attention in different processes such as object detec-tion,image classification,etc.In this aspect,this article designs a new Panoptic Feature Pyramid Network based Anomaly Detection and Tracking(PFPN-ADT)model for pedestrian walkways.The proposed model majorly aims to the recognition and classification of different anomalies present in the pedestrian walkway like vehicles,skaters,etc.The proposed model involves panoptic seg-mentation model,called Panoptic Feature Pyramid Network(PFPN)is employed for the object recognition process.For object classification,Compact Bat Algo-rithm(CBA)with Stacked Auto Encoder(SAE)is applied for the classification of recognized objects.For ensuring the enhanced results better anomaly detection performance of the PFPN-ADT technique,a comparison study is made using Uni-versity of California San Diego(UCSD)Anomaly data and other benchmark data-sets(such as Cityscapes,ADE20K,COCO),and the outcomes are compared with the Mask Recurrent Convolutional Neural Network(RCNN)and Faster Convolu-tional Neural Network(CNN)models.The simulation outcome demonstrated the enhanced performance of the PFPN-ADT technique over the other methods.

Method of SLAS’s ground track manipulation based on tangential impulse thrust

Satellites with altitudes below 400 km are called super low altitude satellites(SLAS),often used to achieve responsive imaging tasks.Therefore,it is important for the manipulation of its ground track.Aiming at the problem of ground track manipulation of SLAS,a control method based on tangential impulse thrust is proposed.First,the equation of the longitude difference between SLAS and the target point on the target latitude is derived based on Gauss’s variational equations.On this basis,the influence of the tangential impulse thrust on the ground track’s longitude is derived.Finally,the method for ground track manipulation of SLAS under the tangential impulse thrust is proposed.The simulation results verify the effective-ness of the method,after manipulation,the satellite can visit the target point and revisit it for multiple days.

Control Strategies for Digital Twin Systems

With the continuous breakthrough in information technology and its integration into practical applications, industrial digital twins are expected to accelerate their development in the near future. This paper studies various control strategies for digital twin systems from the viewpoint of practical applications.To make full use of advantages of digital twins for control systems, an architecture of digital twin control systems, adaptive model tracking scheme, performance prediction scheme, performance retention scheme, and fault tolerant control scheme are proposed. Those schemes are detailed to deal with different issues on model tracking, performance prediction, performance retention, and fault tolerant control of digital twin systems. Also, the stability of digital twin control systems is analysed. The proposed schemes for digital twin control systems are illustrated by examples.

A Modified Monte Carlo Model of Speckle Tracking of Shear Wave Induced by Acoustic Radiation Force for Acousto-Optic Elasticity Imaging

A modified Monte Carlo model of speckle tracking of shear wave propagation in scattering media is proposed. The established Monte Carlo model mainly concerns the variations of optical electric field and speckle. The two- dimensional intensity distribution and the time evolution of speckles in different probe locations are obtained. The fluctuation of speckle intensity tracks the acoustic-radiation-force shear wave propagation, and especially the reduction of speckle intensity implies attenuation of shear wave. Then, the shear wave velocity is estimated quantitatively on the basis of the time-to-peak algorithm and linear regression processing. The results reveal that a smaller sampling interval yields higher estimation precision and the shear wave velocity is estimated more efficiently by using speckle intensity difference than by using speckle contrast difference according to the estimation error. Hence, the shear wave velocity is estimated to be 2.25 m/s with relatively high accuracy for the estimation error reaches the minimum （0.071）.

Parametric approach to track following control of FFSM

A robust task space tracking scheme is proposed for the free-flying space manipulator system. The dynamic equations of the system are derived via the law of momentum conservation, and then a linear state space representation is formulated by local linearization. A parametric approach is applied by using the eigenstructure assignment theory and the model reference method. A feedback stabilizing controller and a feedforward compensation controller are built based on the approach. Then an optimization procedure is followed after that to obtain the desired requirement and characteristics. Simulation results are presented to show the effectiveness of the proposed method.

Thermal History of Rocks in the Shiwandashan Basin, Southern China: Evidence from Apatite Fission Track Analysis

Based on interpretations of the apatite fission track analysis data for 10 outcrop samples and forward modeling of confined fission track length distributions, the thermal history of rocks in the Shiwandashan basin and its adjacent area, southern China, has been qualitatively and semi quantitatively studied. The results reflect several features of the thermal history. Firstly, all the samples have experienced temperatures higher than 60-70 ℃. Secondly, the time that the basement strata (T 1 b ) on the northwestern side of the Shiwandashan basin were uplifted and exhumed to the unannealed upper crust (with a paleogeotemperature of below 60-70 ℃) is much earlier than the basement rocks ( γ 1 5) on the southeastern side of the basin. Thirdly, the thermal history of samples from the basin can be divided into six stages, i.e., the fast burial and heating stage (220-145 Ma), the transient cooling stage (145-135 Ma), the burial and heating stage (135-70 Ma), the rapid cooling stage (70-50 Ma), the relatively stable stage (50-20 Ma) and another rapid cooling stage (20 Ma to present).

Contour tracking using weighted structure tensor based variational level set

A novel contour tracking method using weighted structure tensor based variational level set is proposed in this paper.The image is first converted to weighted structure tensor field by extracting apositive definite symmetric covariance matrix for each pixel.Then,a level set method is employed to represent object contour implicitly which separates the image domain into two areas each modeled by tensor field based Gaussian mixture model separately.By solving agradient flow equation of energy functional with respect to the level set,the object contour will converge to its real profile in the newly arrived frame.Experimental results on several video sequences demonstrate the better performance of our method than the other two contour tracking algorithms.

Vehicle Active Steering Control Research Based on Two-DOF Robust Internal Model Control

Because of vehicle＇s external disturbances and model uncertainties,robust control algorithms have obtained popularity in vehicle stability control.The robust control usually gives up performance in order to guarantee the robustness of the control algorithm,therefore an improved robust internal model control（IMC） algorithm blending model tracking and internal model control is put forward for active steering system in order to reach high performance of yaw rate tracking with certain robustness.The proposed algorithm inherits the good model tracking ability of the IMC control and guarantees robustness to model uncertainties.In order to separate the design process of model tracking from the robustness design process,the improved 2 degree of freedom（DOF） robust internal model controller structure is given from the standard Youla parameterization.Simulations of double lane change maneuver and those of crosswind disturbances are conducted for evaluating the robust control algorithm,on the basis of a nonlinear vehicle simulation model with a magic tyre model.Results show that the established 2-DOF robust IMC method has better model tracking ability and a guaranteed level of robustness and robust performance,which can enhance the vehicle stability and handling,regardless of variations of the vehicle model parameters and the external crosswind interferences.Contradiction between performance and robustness of active steering control algorithm is solved and higher control performance with certain robustness to model uncertainties is obtained.

Typhoon wind hazard model and estimation on return period of typhoon wind speed

Typhoons are one of the most serious natural disasters that occur annually on China’s southeast coast.A technique for analyzing the typhoon wind hazard was developed based on the empirical track model,and used to generate 1000-year virtual typhoons for Northwest Pacific basin.The influences of typhoon decay model,track model,and the extreme value distribution on the predicted extreme wind speed were investigated.We found that different typhoon decay models have least influence on the predicted extreme wind speed.Over most of the southeast coast of China,the predicted wind speed by the non-simplified empirical track model is larger than that from the simplified tracking model.The extreme wind speed predicted by different extreme value distribution is quite different.Four super typhoons Meranti(2016),Hato(2017),Mangkhut(2018)and Lekima(2019)were selected and the return periods of typhoon wind speeds along the China southeast coast were estimated in order to assess the typhoon wind hazard.

A GOCE only gravity model GOSG01S and the validation of GOCE related satellite gravity models

We compile the GOCE-only satellite model GOSG01S complete to spherical harmonic degree of 220 using Satellite Gravity Gradiometry （SGG） data and the Satellite-to-Satellite Tracking （SST） observations along the GOCE orbit based on applying a least-squares analysis. The diagonal components （Vxx, Vyy, Vzz） of the gravitational gradient tensor are used to form the system of observation equations with the band-pass ARMA filter. The point-wise acceleration observations （ax, ay, az） along the orbit are used to form the system of observation equations up to the maximum spherical harmonic degree/order 130. The analysis of spectral accuracy characteristics of the newly derived gravitational model GOSG01S and the existing models GOTIM04S, GODIR04S, GOSPW04S and JYY_GOCE02S based on their comparison with the ultrahigh degree model EIGEN-6C2 reveals a significant consistency at the spectral window approximately between 80 and 190 due to the same period SGG data used to compile these models. The GOCE related satellite gravity models GOSG01S, GOTIM05S, GODIR05S, GOTIM04S, GODIR04S, GOSPW04S, JYY_- GOCE02S, EIGEN-6C2 and EGM2008 are also validated by using GPS-leveling data in China and USA. According to the truncation at degree 200, the statistic results show that all GGMs have very similar differences at GPS-leveling points in USA, and all GOCE related gravity models have better performance than EGM2008 in China. This suggests that all these models provide much more information on the gravity field than EGM2008 in areas with low terrestrial gravity coverage. And STDs of height anomaly differences in China for the selected truncation degrees show that GOCE has improved the accuracy of the global models beyond degree 90 and the accuracies of the models improve from 24 cm to 16 cm. STDs of geoid height differences in USA show that GOSG01S model has best consistency comparing with GPSleveling data for the frequency band of the degree between 20 and 160.

Real-time lane departure warning system based on principal component analysis of grayscale distribution and risk evaluation model

A technology for unintended lane departure warning was proposed. As crucial information, lane boundaries were detected based on principal component analysis of grayscale distribution in search bars of given number and then each search bar was tracked using Kalman filter between frames. The lane detection performance was evaluated and demonstrated in ways of receiver operating characteristic, dice similarity coefficient and real-time performance. For lane departure detection, a lane departure risk evaluation model based on lasting time and frequency was effectively executed on the ARM-based platform. Experimental results indicate that the algorithm generates satisfactory lane detection results under different traffic and lighting conditions, and the proposed warning mechanism sends effective warning signals, avoiding most false warning.

The two-way feedback and passing-way of human body

Two-way feedback of human body was published in 1992. The sensation of two-way feedback of body is a special system of human reaction, which maintains and regulates symmetry and balance of human body. The human two-way feedback reacts to human health. For human overall health and delay decrepitude, it is necessary to pay attention to the stimulations (passive acceptance and initiative interventions) and relevant influences in human body and the stimulative effect. In this paper, the experimental research of stimulation and an example of two-way feedback in human body are given. And lay a foundation of prevention, medical treatment and hygiene of human overall health.

A Composite Model Predictive Control Strategy for Furnaces

Tube furnaces are essential and primary energy intensive facilities in petrochemical plants. Operational optimization of furnaces could not only help to improve product quality but also benefit to reduce energy consumption and exhaust emission. Inspired by this idea, this paper presents a composite model predictive control(CMPC)strategy, which, taking advantage of distributed model predictive control architectures, combines tracking nonlinear model predictive control and economic nonlinear model predictive control metrics to keep process running smoothly and optimize operational conditions. The controllers connected with two kinds of communication networks are easy to organize and maintain, and stable to process interferences. A fast solution algorithm combining interior point solvers and Newton's method is accommodated to the CMPC realization, with reasonable CPU computing time and suitable online applications. Simulation for industrial case demonstrates that the proposed approach can ensure stable operations of furnaces, improve heat efficiency, and reduce the emission effectively.

A Bogus Typhoon Scheme and Its Application to a Movable Nested Mesh Model

A bogus typhoon scheme,designed for the initialization of a typhoon track prediction model,is developed in thispaper.This scheme includes both effects of axisymmetric wind and asymmetric wind.Experimental forecasts using atwo-way interactive movable nested mesh model show that the forecast skill of typhoon tracks has clearly improvedafter introducing the bogus typhoon into the initial fields.

Experimental and numerical investigation of liquid-solid binary fluidized beds： Radioactive particle tracking technique and dense discrete phase model simulations

Liquid-solid binary fluidized beds are widely used in many industries. However, the flow behavior of such beds is not well understood due to the lack of accurate experimental and numerical data. In the current study, the behavior of monodisperse and binary liquid-solid fluidized beds of the same density but dif- ferent sizes is investigated using radioactive particle tracking （RPT） technique and a dense discrete phase model （DDPM）. Experiments and simulations are performed in monodisperse fluidized beds containing two different sizes of glass beads （0.6 and I mm） and a binary fluidized bed of the same particles for vari- ous bed compositions. The results show that both RPT and DDPM can predict the mixing and segregation pattern in liquid-solid binary fluidized beds. The mean velocity predictions of DDPM are in good agree- ment with the experimental findings for both monodisperse and binary fluidized beds. However, the axial root mean square velocity predictions are only reasonable for bigger particles. Particle-particle interac- tions are found to be critical for predicting the flow behavior of solids in liquid-solid binary fluidized beds.

Tracking of time-evolving sound speed profiles with an auto-regressive state-space model

An approach for time-evolving sound speed profiles tracking in shallow water is discussed. The inversion of time-evolving sound speed profiles is modeled as a state-space estimation problem, which includes a state equation for predicting the time-evolving sound speed profile and a measurement equation for incorporating local acoustic measurements. In the paper, auto-regression （AR） method is introduced to obtain a high-order AR evolution model of the sound speed field time variations, and the ensemble Kalman filter is utilized to track the sound speed field. To validate the approach, the accuracy in sound speed estimation is analyzed via a numerical implementation using the ASIAEX experimental environment and the sound velocity measurement data. Compared with traditional approaches based on the state evolution represented as a random walk, simulation results show the proposed AR method can effectively reduce the tracking errors of sound speed, and still keep good tracking performance at low signal-to-noise ratios.

Fuzzy adaptive tracking control within the full envelope for an unmanned aerial vehicle

Motivated by the autopilot of an unmanned aerial vehicle（UAV） with a wide flight envelope span experiencing large parametric variations in the presence of uncertainties, a fuzzy adaptive tracking controller（FATC） is proposed. The controller consists of a fuzzy baseline controller and an adaptive increment, and the main highlight is that the fuzzy baseline controller and adaptation laws are both based on the fuzzy multiple Lyapunov function approach, which helps to reduce the conservatism for the large envelope and guarantees satisfactory tracking performances with strong robustness simultaneously within the whole envelope. The constraint condition of the fuzzy baseline controller is provided in the form of linear matrix inequality（LMI）, and it specifies the satisfactory tracking performances in the absence of uncertainties. The adaptive increment ensures the uniformly ultimately bounded（UUB） predication errors to recover satisfactory responses in the presence of uncertainties. Simulation results show that the proposed controller helps to achieve high-accuracy tracking of airspeed and altitude desirable commands with strong robustness to uncertainties throughout the entire flight envelope.

New approach to the automatic segmentation of coronary artery in X-ray angiograms

For the segmentation of X-ray angiograms （XRA）, the essential feature and the prior knowledge of angiographic image were analyzed, and a multi-feature based fuzzy recognition （MFFR） algorithm was proposed to infer the local vessel structure in this paper. Guided by the prior knowledge of artery vessel, a probability tracking operator （PTO） can rapidly track along the artery tree, and walk across the weak region or gaps because of disturbance or preprocessing to angiographic image. Another, the accurate measurement of the vascular axis-lines and diameters can be synchronously implemented in the tracking process. To correctly evaluate the proposed method, a simulated image of CAT and some clinical XRA images were used in the experimentations. The algorithms performed better than the conventional one： given one start-point, on average 92.7% of the visible segments or branches was automatically delineated; the correctness ratio of vessel structure inference reached to 90.0% on the average.

Feedback structure based entropy approach for multiple-model estimation

The variable-structure multiple-model（VSMM）approach,one of the multiple-model（MM）methods,is a popular and effective approach in handling problems with mode uncertainties.The model sequence set adaptation（MSA）is the key to design a better VSMM.However,MSA methods in the literature have big room to improve both theoretically and practically.To this end,we propose a feedback structure based entropy approach that could fnd the model sequence sets with the smallest size under certain conditions.The fltered data are fed back in real time and can be used by the minimum entropy（ME）based VSMM algorithms,i.e.,MEVSMM.Firstly,the full Markov chains are used to achieve optimal solutions.Secondly,the myopic method together with particle flter（PF）and the challenge match algorithm are also used to achieve sub-optimal solutions,a trade-off between practicability and optimality.The numerical results show that the proposed algorithm provides not only refned model sets but also a good robustness margin and very high accuracy.