Input Structure Design for Structural Controllability of Complex Networks

This paper addresses the problem of the input design of large-scale complex networks.Two types of network components,redundant inaccessible strongly connected component(RISCC)and intermittent inaccessible strongly connected component(IISCC)are defined,and a subnetwork called a driver network is developed.Based on these,an efficient method is proposed to find the minimum number of controlled nodes to achieve structural complete controllability of a network,in the case that each input can act on multiple state nodes.The range of the number of input nodes to achieve minimal control,and the configuration method(the connection between the input nodes and the controlled nodes)are presented.All possible input solutions can be obtained by this method.Moreover,we give an example and some experiments on real-world networks to illustrate the effectiveness of the method.

Fixed-time Sliding Mode Formation Control of AUVs Based on a Disturbance Observer

In this paper,we investigate formation tracking control of autonomous underwater vehicles(AUVs)with model parameter uncertainties and external disturbances.The external disturbances due to the wind,waves,and ocean currents are combined with the model parameter uncertainties as a compound disturbance.Then a disturbance observer(DO)is introduced to estimate the compound disturbance,which can be achieved within a finite time independent of the initial estimation error.Based on a DO,a novel fixed-time sliding control scheme is developed,by which the follower vehicle can track the leader vehicle with all the states globally stabilized within a given settling time.The effectiveness and performance of the method are demonstrated by numerical simulations.

Command Filtered Finite/Fixed-time Heading Tracking Control of Surface Vehicles

This paper investigates the heading tracking problem of surface vehicles with unknown model parameters.Based on finite/fixed-time control theories and in the context of command filtered control,two novel adaptive control laws are developed by which the vehicle can track the desired heading within settling time with all signals of the closed-loop system are uniformly bounded.The effectiveness and performance of the schemes are demonstrated by simulations and comparison studies.

Synchronization of stochastic complex networks with time-delayed coupling

Noise and time delay are inevitable in real-world networks. In this article, the framework of master stability function is generalized to stochastic complex networks with time-delayed coupling. The focus is on the effects of noise, time delay,and their inner interactions on the network synchronization. It is found that when there exists time-delayed coupling in the network and noise diffuses through all state variables of nodes, appropriately increasing the noise intensity can effectively improve the network synchronizability;otherwise, noise can be either beneficial or harmful. For stochastic networks, large time delays will lead to desynchronization. These findings provide valuable references for designing optimal complex networks in practical applications.

Two-Way Neural Network Performance PredictionModel Based onKnowledge Evolution and Individual Similarity

Predicting students’academic achievements is an essential issue in education,which can benefit many stakeholders,for instance,students,teachers,managers,etc.Compared with online courses such asMOOCs,students’academicrelateddata in the face-to-face physical teaching environment is usually sparsity,and the sample size is relativelysmall.It makes building models to predict students’performance accurately in such an environment even morechallenging.This paper proposes a Two-WayNeuralNetwork(TWNN)model based on the bidirectional recurrentneural network and graph neural network to predict students’next semester’s course performance using only theirprevious course achievements.Extensive experiments on a real dataset show that our model performs better thanthe baselines in many indicators.

Dynamic Reliability Sensitivity of Cemented Carbide Cutting Tool

Influence of geometric and cutting parameters of cemented carbide cutting tool on reliability of cutting tool has become more and more mature,yet influence of its physical and material parameters on reliability is still blank.In view of this,cutting test and fatigue crack growth test of YT05 cemented carbide cutting tool are conducted to measure such data as the original crack size,growth size,times of impact loading,number and time of cutting tool in failure,and stress distribution of cutting tool is also obtained by simulating cutting process of tools.Mathematical models on dynamic reliability and dynamic reliability sensitivity of cutting tool are derived respectively by taking machining time and times of impact loading into account,thus change rules of dynamic reliability sensitivity to physical and material parameters can be obtained.Theoretical and experimental results show that sensitive degree on each parameter of tools increases gradually with the increase of machining time and times of impact loading,especially for parameters such as fracture toughness,shape parameter,and cutting stress.This proposed model solves such problems as how to determine the most sensitive parameter and influence degree of physical parameters and material parameters to reliability,which is sensitivity,and can provide theoretical foundation for improving reliability of cutting tool system.

Optimization of Cutting Parameters in Helical Milling of Carbon Fiber Reinforced Polymer

To investigate cutting performance in the helical milling of carbon fiber reinforced polymer(CFRP),experiments were conducted with unidirectional laminates.The results show that the influence of cutting parameters is very significant in the helical milling process. The axial force increases with the increase of cutting speed, which is below 95 m/min; otherwise, the axial force decreases with the increase of cutting speed. The resultant force always increases when cutting speed increases; with the increase of tangential and axial feed rates, cutting forces increase gradually. In addition, damage rings can appear in certain regions of the entry edges; therefore, the relationship between machining performance(cutting forces and holemaking quality) and cutting parameters is established using the nonlinear fitting methodology. Thus, three cutting parameters in the helical milling of CFRP, under the steady state, are optimized based on the multi-objective genetic algorithm, including material removal rate and machining performance. Finally, experiments were carried out to prove the validity of optimized cutting parameters.

Robust stability and H-infinity control for uncertain discrete-time Markovian jump singular systems

The robust stability and stabilization, and H-infinity control problems for discrete-time Markovian jump singular systems with parameter uncertainties are discussed. Based on the restricted system equivalent (r.s.e.) transformation and by introducing new state vectors, the singular system is transformed into a discrete-time Markovian jump standard linear system, and the linear matrix inequality (LMI) conditions for the discrete-time Markovian jump singular systems to be regular, causal, stochastically stable, and stochastically stable with γ-disturbance attenuation are obtained, respectively. With these conditions, the robust state feedback stochastic stabilization problem and H-infinity control problem are solved, and the LMI conditions are obtained. A numerical example illustrates the effectiveness of the method given in the paper.

Automatic segmentation of foveal avascular zone based on adaptive watershed algorithm in retinal optical coherence tomography angiography images

The size and shape of the foveal avascular zone(FAZ)have a strong positive correlation with several vision-threatening ret inovascular diseases.The identification,segmentation and analysis of FAZ are of great significance to clinical diagnosis and treatment.We presented an adaptive watershed algorithm to automatically extract F AZ from retinal optical coherence tomography angiography(OCTA)images.For the traditional watershed algorithm,"over-segmentation"is the most common problem.FAZ is often incorrectly divided into multiple regions by redundant"dams".This paper analyzed the relationship between the"dams"length and the maximum inscribed circle radius of FAZ,and proposed an adaptive watershed algorithm to solve the problem of"over-segmentation".Here,132 healthy retinal images and 50 diabetic retinopathy(DR)images were used to verify the accuracy and stability of the algorithm.Three ophthal-mologists were invited to make quan titative and qualitative evaluations on the segmentation results of this algorithm.The quantitative evaluation results show that the correlation coffi-cients between the automatic and manual segmentation results are 0.945(in healthy subjects)and 0.927(in DR patients),respectively.For qualitative evaluation,the percentages of"perfect segmentation"(score of 3)and"good segmentation"(score of 2)are 99.4%(in healthy subjects)and 98.7%(in DR patients),respectively.This work promotes the application of watershed algorithm in FAZ segmentation,making it a useful tool for analyzing and diagnosing eye diseases.

Monitoring of edema progression in permanent and transient MCAO model using SS-OCT

Cerebral edema is a severe complication of acute ischemic stroke with high mortality but limited treatment.Although parameters such as brain water content and intracranial pressure may represent the global assessment of edema,optical properties can appear heterogeneously throughout the cerebral tissue relative to the site of injury.In this study,we have monitored the edema formation and progression in both permanent and transient middle cerebral artery oc-clusion models in rats.Edema was reflected by the decrease of optical attenuation coefficient(OAC)value in OCT system.By utilizing swept-source optical coherence tomography(SS-OCT),we found that in photochemically induced permanent focal stroke model,both the edema size and edema index,steadily developed until the end of monitor(7 h).Comparatively,when transient ischemia was introduced with endothelin-1(ET-1),the edema was detected as early as 15 min,and began to recover after 30 min until monitor was finished(3 h).Despite the majority of the edema being recovered to some extent,the condition of a small region within the edema kept deteriorating,presumably due to the reperfusion damage which might result in serious clinical outcomes.Our study has compared the edema characteristics from two different acute ischemic stroke situations.This work not only confirms the capability of OCT to temporal and spatial monitor of edema but is also able to locate focal conditions at some areas that might highly determine the prognosis and treatment decisions.

Classification of Domestic Refuse in Medical Institutions Based on Transfer Learning and Convolutional Neural Network

The problem of domestic refuse is becoming more and more serious with the use of all kinds of equipment in medical institutions.This matter arouses people’s attention.Traditional artificial waste classification is subjective and cannot be put accurately;moreover,the working environment of sorting is poor and the efficiency is low.Therefore,automated and effective sorting is needed.In view of the current development of deep learning,it can provide a good auxiliary role for classification and realize automatic classification.In this paper,the ResNet-50 convolutional neural network based on the transfer learning method is applied to design the image classifier to obtain the domestic refuse classification with high accuracy.By comparing the method designed in this paper with back propagation neural network and convolutional neural network,it is concluded that the CNN based on transfer learning method applied in this paper with higher accuracy rate and lower false detection rate.Further,under the shortage situation of data samples,the method with transfer learning and ResNet-50 training model is effective to improve the accuracy of image classification.

Automated retinal layer segmentation on optical coherence tomography image by combination of structure interpolation and lateral mean filtering

Segmentation of layers in retinal images obtained by optical coherence tomography(OCT)has become an important clinical tool to diagnose ophthalmic diseases.However,due to the sus-ceptibility to speckle noise and shadow of blood vessels etc.,the layer segmentation technology based on a single image still fail to reach a satisfactory level.We propose a combination method of structure interpolation and lateral mean filtering(SI-LMF)to improve the signal-to-noise ratio based on one retinal image.Before performing one-dimensional lateral mean filtering to remove noise,structure interpolation was operated to eliminate thickness fluctuations.Then,we used boundary growth method to identify boundaries.Compared with existing segmentations,the method proposed in this paper requires less data and avoids the influence of microsaccade.The automatic segmentation method was verified on the spectral domain OCT volume images obtained from four normal objects,which successfully identified the boundaries of 10 physio-logical layers,consistent with the results based on the manual determination.

Fixed-Time Leader-Following Formation Control of Fully-Actuated Underwater Vehicles Without Velocity Measurements

This paper is concerned with formation control of fully-actuated underwater vehicles(FUVs),focusing on improving system convergence speed and overcoming velocity measurement limitation.By employing the fixed-time control theory and command filtering technique,a full state feedback formation algorithm is proposed,which makes the follower track the leader in a given time with all signals in the system globally practically stabilized in fixed time.To avoid degraded control performance due to inaccurate velocity measurement,a fixed-time convergent observer is designed to estimate the velocity of FUVs.Then the authors give an observer-based fixed-time control method,with which acceptable formation performance can be achieved in fixed time without velocity measurement.The effectiveness and performance of the proposed method are demonstrated by numerical simulations.

A New Computer Assisted Orthopaedic Surgery System:WATO

Background:The traditional manual orthopaedic technology heavily relies on a surgeon's experience,so it certainly increases the instability of the surgery.Therefore,computer assisted orthopaedic surgery(CAOS) is becoming a hot research topic for its high accuracy and stability.We developed a new CAOS system WATO,which is mainly designed for total knee replacement(TKR).Methods:WATO system provides the interactive software for a surgeon's preoperative planning.Based on its two infrared cameras,infrared markers and infrared probe,WATO system gives a simple surgical positioning procedure of femur and tibia without additional surgery for the placement of fiducial markers.According to the reference alignment axis from positioning procedure,a surgeon can move the robot of WATO system to do accurate bone resection.Safety checking is also considered in WATO system.Results:Extensive experiments were conducted on phantoms and cadaver bones to verify the accuracy and stability of WATO system.Experimental results showed that TKR using WATO system had better performance compared with traditional and navigated TKR.Conclusion:WATO system shows its superiority in TKR,and has a broad application prospect in the future.We will develop its new functions for other orthopaedic surgery such as total hip replacement(THR).Current disadvantages such as bigger skin incision have to be resolved in the future.

An Ultraviolet Hybrid Plasmonic Waveguide for Nanolaser Applications

In this paper, a novel hybrid plasmonic waveguide with a metal ridge and an MgF2 dielectric layer is demonstrated at ultraviolet band. We investigate the propagation distance, the scaling factor and the figure of merit by using the finite element method. The structure enables low scaling factor and long propagation distance. Compared to the previous structure with a metal plate, this waveguide has better performance. And the structure can be used as a nanolaser and has broad application prospects in optoelectronic integrated circuits, biological detection and so on.

High-speed all-optic optical coherence tomography and photoacoustic microscopy dual-modal system for microcirculation evaluation

We propose a high-speed all-optic dual-modal system that integrates spectral domain optical coherence tomography and photoacoustic microscopy(PAM).A 3*3 coupler-based interfer-ometer is used to remotely detect the surface vibration caused by photoacoustic(PA)waves.Three outputs of the interferometer are acquired simultaneously with a multi-channel data ac-quisition card.One channel data with the highest PA signal detection sensitivity is selected for sensitivity compensation.Experiment on the phantom demonstrates that the proposed method can sucessfully compensate for the loss of intensity caused by sensitivity variation.The imaging speed of the PAM is improved compared to our previous system.The total time to image a sample with 256×256 pixels is~20s.Using the proposed system,the microvasculature in the mouse auricle is visualized and the blood flow state is accessed.

Adaptive Object Tracking Discriminate Model for Multi-Camera Panorama Surveillance in Airport Apron

Autonomous intelligence plays a significant role in aviation security.Since most aviation accidents occur in the take-off and landing stage,accurate tracking of moving object in airport apron will be a vital approach to ensure the operation of the aircraft safely.In this study,an adaptive object tracking method based on a discriminant is proposed in multi-camera panorama surveillance of large-scale airport apron.Firstly,based on channels of color histogram,the pre-estimated object probability map is employed to reduce searching computation,and the optimization of the disturbance suppression options can make good resistance to similar areas around the object.Then the object score of probability map is obtained by the sliding window,and the candidate window with the highest probability map score is selected as the new object center.Thirdly,according to the new object location,the probability map is updated,the scale estimation function is adjusted to the size of real object.From qualitative and quantitative analysis,the comparison experiments are verified in representative video sequences,and our approach outperforms typical methods,such as distraction-aware online tracking,mean shift,variance ratio,and adaptive colour attributes.

Ingenious Interlacement of CoNiO_(2) on Carbon Nanotubes for Highly Stable Lithium‑Ion Batteries

Nickel–cobalt oxide is considered as a promising anode for lithium-ion battery,owing to its high specifc capacity,simple synthesis process and high safety.However,like most transition metal oxide anode materials,nickel–cobalt oxide sufers from poor conductivity,easy agglomeration and large volume expansion in the charging and discharging process,causing an inferior cycling lifespan.Here we report a structure design that CoNiO2 particles are ingeniously interlaced on carbon nanotubes by a simple solvothermal method.These nanotubes are irregularly intertwined to obtain an independent electrode structure with high electronic conductivity,which can also alleviate the notorious volume expansion.Consequently,the corresponding lithium-ion battery shows superior electrochemical performance.It provides a discharge capacity of 1213.7 mAh g^(−1) at 0.5 A g^(−1),and can be stable over 100 cycles with a capacity retention of 96.45%.Furthermore,the battery can also deliver a reversible capacity of 544.8 mAh g^(−1) at the high current density 3 A g^(−1).This work provides a unique idea for the performance improvement of nickel–cobalt oxide anode for lithium-ion batteries.

Simulation of bubble breaking process in a jet mixing reactor for desulfurization of molten iron

The gas–liquid flow behavior of the stirred flow field,the different positions of a single bubble,the initial velocity,the surface tension and the agglomeration of multiple bubbles were studied by CFD numerical simulation.The results show that the pressure distribution and velocity distribution inside the fluid during agitation indicate that the velocity difference between the liquid and the gas phase and the collision between the bubbles caused by the turbulent behavior of the liquid are the important conditions leading to the bubble breakage.Different initial bubble positions and initial bubble velocities have important effects on single bubble breakage.The surface tension is an important condition that affects the bubble breakage.When the surface tension coefficient is 0.7,the bubble will be stretched to the smallest degree;when the surface tension coefficient is 0.1,the bubble will be stretched to the largest degree and it will be easily broken into smaller bubbles.The multi-bubble results show the states and trajectories of coalescence between bubbles.The research results can provide data support for the engineering application of desulfurization process and theoretical guidance for the research of bubble breaking mechanism.