An Underwater Robot Inspection Anomaly Localization Feedback System Based on Sonar Technology

This article introduces an underwater robot inspection anomaly localization feedback system comprising a real-time water surface tracking,detection,and positioning system located on the water surface,while the underwater robot inspection anomaly feedback system is housed within the underwater robot.The system facilitates the issuance of corresponding mechanical responses based on the water surface’s real-time tracking,detection,and positioning,enabling recognition and feedback of anomaly information.Through sonar technology,the underwater robot inspection anomaly feedback system monitors the underwater robot in real-time,triggering responsive actions upon encountering anomalies.The real-time tracking,detection,and positioning system from the water surface identifies abnormal conditions of underwater robots based on changes in sonar images,subsequently notifying personnel for necessary intervention.

Measurement of Refractive Index Ranging from 1.42847 to 2.48272 at 1064 nm Using a Quasi-Common-Path Laser Feedback System

Wavelength 1064 nm is one of the most widely used laser wavelengths in industries and science. The high-precision measurement of the refractive index of optical materials at 1064 nm is significant for improving the optical design. We study the direct measurement of refractive index at 1064nm of lasers, including cMcium fluoride （CaF2）, fused silica and zinc selenide （ZnSe）, whose refractive indices cover a large range from 1.42847 to 2.48272. The measurement system is built based on the quasi-common-path Nd：YAG laser feedback interferometry. The thickness can be measured simultaneously with the refractive index. The results demonstrate that the system has absolute uncertainties of ~10-5 and ~10-4 mm in refractive index and thickness measurement, respectively.

Wavelet Packet-based Feedback System for Medical Image Fusion

In order to meet the requirements of medical research,diagnosis and treatment,a new algorithm for image fusion based on the wavelet packet transform in conjunction with both subjective and objective assessments is put forward in the paper.Compared to the wavelet transform,the wavelet packet transform is more intricate and effective for the medical image fusion.As indicated by the experimental results,parameters of the feedback system of the new algorithm are significantly superior to those of the wavelet transform,with practicability and accuracy.

Feedback Systems on a Reflexive Banach Space—Linearization

The aim of our work is to formulate and demonstrate the results of the normality, the Lipschitz continuity, of a nonlinear feedback system described by the monotone maximal operators and hemicontinuous, defined on real reflexive Banach spaces, as well as the approximation in a neighborhood of zero, of solutions of a feedback system [A,B] assumed to be non-linear, by solutions of another linear, This approximation allows us to obtain appropriate estimates of the solutions. These estimates have a significant effect on the study of the robust stability and sensitivity of such a system see [1] [2] [3]. We then consider a linear FS , and prove that, if ; , with the respective solutions of FS’s [A,B] and corresponding to the given (u,v) in . There exists,, positive real constants such that, . These results are the subject of theorems 3.1, ... , 3.3. The proofs of these theorems are based on our lemmas 3.2, ... , 3.5, devoted according to the hypotheses on A and B, to the existence of the inverse of the operator I+BA and . The results obtained and demonstrated along this document, present an extension in general Banach space of those in [4] on a Hilbert space H and those in [5] on a extended Hilbert space .

Geometric Structures of Stable Time-Variant State Feedback Systems

A new technique for considering the stabilizing time-variant state feedback gains is proposed from the viewpoint of information geometry. First, parametrization of the set of all stabilizing time-variant state feedback gains is given. Moreover, a diffeomorphic structure between the set of stabilizing time-variant state feedback gains and the Cartesian product of positive definite matrix and skew symmetric matrix satisfying certain algebraic conditions is constructed. Furthermore, an immersion and some results about the eigenvalue locations of stable state feedback systems are derived.

Establishment of New Solution for Complex Systems in Multidisciplinary Sci-ence Based on Feedback System Analysis Method and Proven by Simulator

This paper establishes a very important scientific solution to science of complexity for physicists, and presents a multidisciplinary involved physics and engineering. The innovative solution for complex systems presented here is verified on the basis of principles in engineering such as feed-back-system analysis using the classical control theory. This paper proposes that a complex system is a closed-loop system with a negative feedback element and is a solvable problem. A complex system can be analyzed using the system analysis theory in control engineering, and its behavior can be realized using a specially designed simulator.

An Intelligent IEQ Monitoring and Feedback System:Development and Applications

Indoor environmental quality(IEQ)significantly affects human health and wellbeing.Therefore,continuous IEQ monitoring and feedback is of great concern in both the industrial and academic communities.However,most existing studies only focus on developing sensors that cost-effectively promote IEQ measurement while ignoring interactions between the human side and IEQ monitoring.In this study,an intelligent IEQ monitoring and feedback system-the Intelligent Built Enviroment(IBEM)-is developed.Firstly,the IBEM hardware instrument integrates air temperature,relative humidity,CO_(2),particulate matter with an aerodynamic diameter no greater than _(2.5)μm(PM_(2.5)),and illuminance sensors within a small device.The accuracy of this integrated device was tested through a co-location experiment with reference sensors;the device exhibited a strong correlation with the reference sensors,with a slight deviation(R^(2)>0.97 and slopes between 1.01 and 1.05).Secondly,a wireless data transmission module,a cloud storage module,and graphical user interfaces(i.e.,a web platform and mobile interface)were built to establish a pathway for dataflow and interactive feedback with the occupants of the indoor environments.Thus,the IEQ parameters can be continuously monitored with a high spatiotemporal resolution,interactive feedback can be induced,and synchronous data collection on occupant satisfaction and objective environmental parameters can be realized.IBEM has been widely applied in 131 buildings in 18cities/areas in China,with 1188 sample locations.Among these applications,we report on the targeted IEQ diagnoses of two individual buildings and the exploration of relationships between subjective and objective IEQ data in detail here.This work demonstrates the great value of IBEM in both industrial and academic research.

User Fairness Scheme with Proportional Fair Scheduling in Multi-user MIMO Limited Feedback System

In Multi-user MIMO (MU-MIMO) downlink system, suitable user selection schemes can improve spatial diversity gain. In most of previous studies, it is always assumed that the base station (BS) knows full channel state information (CSI) of each user, which does not consider the reality. However, there are only limited feedback bits in real system. Besides, user fairness is often ignored in most of current user selection schemes. To discuss the user fairness and limited feedback, in this paper, the user selection scheme with limited feedback bits is proposed. The BS utilizes codebook precoding transmitting strategy with LTE codebook. Furthermore, this paper analyzes the influence of the number of feedback bits and the number of users on user fairness and system sum capacity. Simulation results show that in order to achieve better user fairness, we can use fewer bits for feedback CSI when the number of user is small, and more feedback bits when the number of users is large.

Orbit correction algorithm for SSRF fast orbit feedback system

A fast orbit feedback system is designed at SSRF to suppress beam orbit disturbance within sub-micron in the bandwidth up to 100 Hz. The SVD (Singular value decomposition) algorithm is applied to calculate the inverse response matrix in global orbit correction. The number of singular eigenvalues will influence orbit noise suppression and corrector strengths. The method to choose singular eigenvalue rejection threshold is studied in this paper, and the simulation and experiment results are also presented.

Trifold Frequency Multiplier by using Triode for the Feedback System of BEPC Ⅱ

In BEPC Ⅱ(Upgrade of Beijing Electron-Position Collider),multi-bunches and high current operation mode is usually used.Due to the influence of high frequency cavity structure or resistance impedance,the beam will be unstable.If the beam is unstable,the luminosity and brightness of the accelerator will be decreased.In order to improve the beam current and brightness of accelerator and the collider luminosity,the beam feedback system is needed to suppress the instability.The trifold frequency multiplier by using triode is simple to operate,and it has good performance.In the experiments,the 500MHz signal was passed through the Triode Frequency Multiplier to get the 1.5GHz signal,and its output amplitude stability is 1.7mV,and its synchronization stability is 3.46ps compared with the 500MHz input signal.

Deep learning for joint channel estimation and feedback in massive MIMO systems

The great potentials of massive Multiple-Input Multiple-Output(MIMO)in Frequency Division Duplex(FDD)mode can be fully exploited when the downlink Channel State Information(CSI)is available at base stations.However,the accurate CsI is difficult to obtain due to the large amount of feedback overhead caused by massive antennas.In this paper,we propose a deep learning based joint channel estimation and feedback framework,which comprehensively realizes the estimation,compression,and reconstruction of downlink channels in FDD massive MIMO systems.Two networks are constructed to perform estimation and feedback explicitly and implicitly.The explicit network adopts a multi-Signal-to-Noise-Ratios(SNRs)technique to obtain a single trained channel estimation subnet that works well with different SNRs and employs a deep residual network to reconstruct the channels,while the implicit network directly compresses pilots and sends them back to reduce network parameters.Quantization module is also designed to generate data-bearing bitstreams.Simulation results show that the two proposed networks exhibit excellent performance of reconstruction and are robust to different environments and quantization errors.

Fault Estimation for a Class of Markov Jump Piecewise-Affine Systems: Current Feedback Based Iterative Learning Approach

In this paper, the issues of stochastic stability analysis and fault estimation are investigated for a class of continuoustime Markov jump piecewise-affine(PWA) systems against actuator and sensor faults. Firstly, a novel mode-dependent PWA iterative learning observer with current feedback is designed to estimate the system states and faults, simultaneously, which contains both the previous iteration information and the current feedback mechanism. The auxiliary feedback channel optimizes the response speed of the observer, therefore the estimation error would converge to zero rapidly. Then, sufficient conditions for stochastic stability with guaranteed performance are demonstrated for the estimation error system, and the equivalence relations between the system information and the estimated information can be established via iterative accumulating representation.Finally, two illustrative examples containing a class of tunnel diode circuit systems are presented to fully demonstrate the effectiveness and superiority of the proposed iterative learning observer with current feedback.

A NOTE ON THE GENERAL STABILIZATION OF DISCRETE FEEDBACK CONTROL FOR NON-AUTONOMOUS HYBRID NEUTRAL STOCHASTIC SYSTEMS WITH A DELAY

Discrete feedback control was designed to stabilize an unstable hybrid neutral stochastic differential delay system(HNSDDS) under a highly nonlinear constraint in the H_∞ and exponential forms.Nevertheless,the existing work just adapted to autonomous cases,and the obtained results were mainly on exponential stabilization.In comparison with autonomous cases,non-autonomous systems are of great interest and represent an important challenge.Accordingly,discrete feedback control has here been adjusted with a time factor to stabilize an unstable non-autonomous HNSDDS,in which new Lyapunov-Krasovskii functionals and some novel technologies are adopted.It should be noted,in particular,that the stabilization can be achieved not only in the routine H_∞ and exponential forms,but also the polynomial form and even a general form.

Asynchronous Learning-Based Output Feedback Sliding Mode Control for Semi-Markov Jump Systems: A Descriptor Approach

This paper presents an asynchronous output-feed-back control strategy of semi-Markovian systems via sliding mode-based learning technique.Compared with most literature results that require exact prior knowledge of system state and mode information,an asynchronous output-feedback sliding sur-face is adopted in the case of incompletely available state and non-synchronization phenomenon.The holonomic dynamics of the sliding mode are characterized by a descriptor system in which the switching surface is regarded as the fast subsystem and the system dynamics are viewed as the slow subsystem.Based upon the co-occurrence of two subsystems,the sufficient stochastic admissibility criterion of the holonomic dynamics is derived by utilizing the characteristics of cumulative distribution functions.Furthermore,a recursive learning controller is formulated to guarantee the reachability of the sliding manifold and realize the chattering reduction of the asynchronous switching and sliding motion.Finally,the proposed theoretical method is substantia-ted through two numerical simulations with the practical contin-uous stirred tank reactor and F-404 aircraft engine model,respectively.

Pure State Feedback Switching Control Based on the Online Estimated State for Stochastic Open Quantum Systems

For the n-qubit stochastic open quantum systems,based on the Lyapunov stability theorem and LaSalle’s invariant set principle,a pure state switching control based on on-line estimated state feedback(short for OQST-SFC)is proposed to realize the state transition the pure state of the target state including eigenstate and superposition state.The proposed switching control consists of a constant control and a control law designed based on the Lyapunov method,in which the Lyapunov function is the state distance of the system.The constant control is used to drive the system state from an initial state to the convergence domain only containing the target state,and a Lyapunov-based control is used to make the state enter the convergence domain and then continue to converge to the target state.At the same time,the continuous weak measurement of quantum system and the quantum state tomography method based on the on-line alternating direction multiplier(QST-OADM)are used to obtain the system information and estimate the quantum state which is used as the input of the quantum system controller.Then,the pure state feedback switching control method based on the on-line estimated state feedback is realized in an n-qubit stochastic open quantum system.The complete derivation process of n-qubit QST-OADM algorithm is given;Through strict theoretical proof and analysis,the convergence conditions to ensure any initial state of the quantum system to converge the target pure state are given.The proposed control method is applied to a 2-qubit stochastic open quantum system for numerical simulation experiments.Four possible different position cases between the initial estimated state and that of the controlled system are studied and discussed,and the performances of the state transition under the corresponding cases are analyzed.

Suppression of longitudinal coupled-bunch instabilities with RF feedback systems in CEPC main ring

Background Circular electron positron collider(CEPC)is a 100-km electron positron collider proposed by IHEP.The longitudinal coupled-bunch instability(LCBI)of CEPC main ring operating to study the Z particle(Z machine)may be a limiting factor of CEPC and needs to be considered seriously.Purpose The purposes of this paper are to calculate the LCBI caused by the fundamental mode of superconducting RF cavities in CEPC main ring,which is the most critical impedance,and to complete the design of the RF feedback systems suitable for CEPC,whose specifications can suppress the LCBI to a manageable level.Methods The LCBI growth rate in the CEPC main ring is calculated in the frequency domain.Two kinds of RF feedback,i.e.,direct feedback and one-turn delay feedback,are simulated with the program to suppress the LCBI.And according to the suppression effect of LCBI growth rate after adding RF feedback,the required design parameters are given.Results Two operation conditions of Z machine have severe LCBI without suppression,and dozens of longitudinal modes are unstable.Only the direct RF feedback is needed to suppress LCBI in the case of Z-30 MW,while both the direct RF feedback with maximum gain and one-turn feedback are needed in the case of Z-50 MW.The LCBI growth rates can be reduced to the order of half frequency of the synchronous oscillation.Conclusion The LCBI of CEPC Z machine has been studied.Selecting appropriate feedback RF feedback can reduce the LCBI to an acceptable value that bunch by bunch feedback can suppress.

Adaptive recurrent neural network for uncertainties estimation in feedback control system

In this paper,a recurrent neural network(RNN)is used to estimate uncertainties and implement feedback control for nonlinear dynamic systems.The neural network approximates the uncertainties related to unmodeled dynamics,parametric variations,and external disturbances.The RNN has a single hidden layer and uses the tracking error and the output as feedback to estimate the disturbance.The RNN weights are online adapted,and the adaptation laws are developed from the stability analysis of the controlled system with the RNN estimation.The used activation function,at the hidden layer,has an expression that simplifies the adaptation laws from the stability analysis.It is found that the adaptive RNN enhances the tracking performance of the feedback controller at the transient and steady state responses.The proposed RNN based feedback control is applied to a DC–DC converter for current regulation.Simulation and experimental results are provided to show its effectiveness.Compared to the feedforward neural network and the conventional feedback control,the RNN based feedback control provides good tracking performance.

Adaptive Optimal Discrete-Time Output-Feedback Using an Internal Model Principle and Adaptive Dynamic Programming

In order to address the output feedback issue for linear discrete-time systems, this work suggests a brand-new adaptive dynamic programming(ADP) technique based on the internal model principle(IMP). The proposed method, termed as IMP-ADP, does not require complete state feedback-merely the measurement of input and output data. More specifically, based on the IMP, the output control problem can first be converted into a stabilization problem. We then design an observer to reproduce the full state of the system by measuring the inputs and outputs. Moreover, this technique includes both a policy iteration algorithm and a value iteration algorithm to determine the optimal feedback gain without using a dynamic system model. It is important that with this concept one does not need to solve the regulator equation. Finally, this control method was tested on an inverter system of grid-connected LCLs to demonstrate that the proposed method provides the desired performance in terms of both tracking and disturbance rejection.

Fixed-Time Stabilization of a Class of Strict-Feedback Nonlinear Systems via Dynamic Gain Feedback Control

This paper presents a novel fixed-time stabilization control(FSC)method for a class of strict-feedback nonlinear systems involving unmodelled system dynamics.The key feature of the proposed method is the design of two dynamic parameters.Specifically,a set of auxiliary variables is first introduced through state transformation.These variables combine the original system states and the two introduced dynamic parameters,facilitating the closed-loop system stability analyses.Then,the two dynamic parameters are delicately designed by utilizing the Lyapunov method,ensuring that all the closed-loop system states are globally fixed-time stable.Compared with existing results,the“explosion of complexity”problem of backstepping control is avoided.Moreover,the two designed dynamic parameters are dependent on system states rather than a time-varying function,thus the proposed controller is still valid beyond the given fixedtime convergence instant.The effectiveness of the proposed method is demonstrated through two practical systems.

Do cardiopulmonary resuscitation real-time audiovisual feedback devices improve patient outcomes? A systematic review and metaanalysis

BACKGROUND Cardiac arrest is a leading cause of mortality in America and has increased in the incidence of cases over the last several years.Cardiopulmonary resuscitation(CPR)increases survival outcomes in cases of cardiac arrest;however,healthcare workers often do not perform CPR within recommended guidelines.Real-time audiovisual feedback(RTAVF)devices improve the quality of CPR performed.This systematic review and meta-analysis aims to compare the effect of RTAVF-assisted CPR with conventional CPR and to evaluate whether the use of these devices improved outcomes in both in-hospital cardiac arrest(IHCA)and out-of-hospital cardiac arrest(OHCA)patients.AIM To identify the effect of RTAVF-assisted CPR on patient outcomes and CPR quality with in-and OHCA.METHODS We searched PubMed,SCOPUS,the Cochrane Library,and EMBASE from inception to July 27,2020,for studies comparing patient outcomes and/or CPR quality metrics between RTAVF-assisted CPR and conventional CPR in cases of IHCA or OHCA.The primary outcomes of interest were return of spontaneous circulation(ROSC)and survival to hospital discharge(SHD),with secondary outcomes of chest compression rate and chest compression depth.The methodo-logical quality of the included studies was assessed using the Newcastle-Ottawa scale and Cochrane Collaboration’s“risk of bias”tool.Data was analyzed using R statistical software 4.2.0.results were statistically significant if P<0.05.RESULTS Thirteen studies(n=17600)were included.Patients were on average 69±17.5 years old,with 7022(39.8%)female patients.Overall pooled ROSC in patients in this study was 37%(95%confidence interval=23%-54%).RTAVF-assisted CPR significantly improved ROSC,both overall[risk ratio(RR)1.17(1.001-1.362);P=0.048]and in cases of IHCA[RR 1.36(1.06-1.80);P=0.002].There was no significant improvement in ROSC for OHCA(RR 1.04;0.91-1.19;P=0.47).No significant effect was seen in SHD[RR 1.04(0.91-1.19);P=0.47]or chest compression rate[standardized mean difference(SMD)-2.1;(-4.6-0.5);P=0.09].A significant improvement was seen in chest compression depth[SMD 1.6;(0.02-3.1);P=0.047].CONCLUSION RTAVF-assisted CPR increases ROSC in cases of IHCA and chest compression depth but has no significant effect on ROSC in cases of OHCA,SHD,or chest compression rate.