A hierarchical architecture of centralized monitoring and controlling system and its high-performance and interoperability protocol

This paper describes a hierarchical architecture and a high-performance and interoperability protocol for centralized monitoring and controlling systems (CMCS) . The protocol we proposed can interoperate different monitoring and controlling systems constructed by different companies, each with different functions and communication protocols. The protocol reduces the amount of traffic and has real-time and high-perfor-mance advantages. The protocol was implemented in CMCS for telecommunication power supply and air-condi-tioner used by the Telecommunication Bureau of Zhejiang Province. This paper deals with the hierarchical architecture and function of CMCS and packet format, command ID, and SDL description of its protocol. We also discuss the properties of the interoperability and performance of the protocol in this paper.

Integration Between Enterprise Process Monitoring and Controlling System and Enterprise Application

The relationships and the features of integration between Enterprise ProcessMonitoring and Controlling System (EPMCS) and Enterprise Process Related Applications (EPRA) wereanalyzed. An integration architecture centered on EPMCS was presented, in which there were fourlayers to connect from EPMCS to EPRA: EPMCS, application integration layer, transport layer andEPRA, and there were four layers used to etstablish integration: presentation layer, function layer,data layer and system layer. The frameworks to connect EPMCS and EPRA were designed, thatEnterprise-Independent Model (EIM), Enterprise-Specific Model (ESM) and meta-model to describe thesetwo models were defined. The method to integrate data based on XML was designed to exchange datafrom EPMCS to EPRA according to the mapping between EIM and ESM. The approches are suitable forintegrating EPMCS and systems in Product Data Management (PDM), project management and enterprisebusiness management.

Dynamic simulation analysis of molten salt reactor-coupled air-steam combined cycle power generation system

A nonlinear dynamic simulation model based on coordinated control of speed and flow rate for the molten salt reactor and combined cycle systems is proposed here to ensure the coordination and stability between the molten salt reactor and power system.This model considers the impact of thermal properties of fluid variation on accuracy and has been validated with Simulink.This study reveals the capability of the control system to compensate for anomalous situations and maintain shaft stability in the event of perturbations occurring in high-temperature molten salt tank outlet parameters.Meanwhile,the control system’s impact on the system’s dynamic characteristics under molten salt disturbance is also analyzed.The results reveal that after the disturbance occurs,the controlled system benefits from the action of the control,and the overshoot and disturbance amplitude are positively correlated,while the system power and frequency eventually return to the initial values.This simulation model provides a basis for utilizing molten salt reactors for power generation and maintaining grid stability.

Finite-time Prescribed Performance Time-Varying Formation Control for Second-Order Multi-Agent Systems With Non-Strict Feedback Based on a Neural Network Observer

This paper studies the problem of time-varying formation control with finite-time prescribed performance for nonstrict feedback second-order multi-agent systems with unmeasured states and unknown nonlinearities.To eliminate nonlinearities,neural networks are applied to approximate the inherent dynamics of the system.In addition,due to the limitations of the actual working conditions,each follower agent can only obtain the locally measurable partial state information of the leader agent.To address this problem,a neural network state observer based on the leader state information is designed.Then,a finite-time prescribed performance adaptive output feedback control strategy is proposed by restricting the sliding mode surface to a prescribed region,which ensures that the closed-loop system has practical finite-time stability and that formation errors of the multi-agent systems converge to the prescribed performance bound in finite time.Finally,a numerical simulation is provided to demonstrate the practicality and effectiveness of the developed algorithm.

Vibration Control of A Flexible Marine Riser System Subject to Input Dead Zone and Extraneous Disturbances

An observer-based adaptive backstepping boundary control is proposed for vibration control of flexible offshore riser systems with unknown nonlinear input dead zone and uncertain environmental disturbances.The control algorithm can update the control law online through real-time data to make the controller adapt to the environment and improve the control precision.Specifically,based on the adaptive backstepping framework,virtual control laws and Lyapunov functions are designed for each subsystem.Three direction interference observers are designed to track the timevarying boundary disturbance.On this basis,the inverse of the dead zone and linear state transformation are used to compensate for the original system and eliminate the adverse effects of the dead zone.In addition,the stability of the closed-loop system is proven by Lyapunov stability theory.All the system states are bounded,and the vibration offset of the riser converges to a small area of the initial position.Finally,four examples of flexible marine risers are simulated in MATLAB to verify the effectiveness of the proposed controller.

An Integrated Control Framework for Torque Vectoring and Active Suspension System

Four-wheel independently driven electric vehicles(FWID-EV)endow a flexible and scalable control framework to improve vehicle performance.This paper integrates the torque vectoring and active suspension system(ASS)to enhance the vehicle’s longitudinal and vertical motion control performance.While the nonlinear characteristic of the tire model leads to a relatively heavier computational burden.To facilitate the controller design and ease the load,a half-vehicle dynamics system is built and simplified to the linear-time-varying(LTV)model.Then a model predictive controller is developed by formulating the objective function by comprehensively considering the safety,energy-saving and comfort requirements.The in-wheel motor efficiency and the power loss of tire slip are treated as optimization indices in this work to reduce energy consumption.Finally,the effectiveness of the proposed controller is verified through the rapid-control-prototype(RCP)test.The results demonstrate the enhancement of the energy-saving as well as comfort on the basis of vehicle stability.

Disturbances rejection optimization based on improved two-degree-of-freedom LADRC for permanent magnet synchronous motor systems

Permanent magnet synchronous motor(PMSM)speed control systems with conventional linear active disturbance rejection control(CLADRC)strategy encounter issues regarding the coupling between dynamic response and disturbance suppression and have poor performance in suppressing complex nonlinear disturbances.In order to address these issues,this paper proposes an improved two-degree-of-freedom LADRC(TDOF-LADRC)strategy,which can enhance the disturbance rejection performance of the system while decoupling entirely the system's dynamic and anti-disturbance performance to boost the system robustness and simplify controller parameter tuning.PMSM models that consider total disturbances are developed to design the TDOF-LADRC speed controller accurately.Moreover,to evaluate the control performance of the TDOF-LADRC strategy,its stability is proven,and the influence of each controller parameter on the system control performance is analyzed.Based on it,a comparison is made between the disturbance observation ability and anti-disturbance performance of TDOF-LADRC and CLADRC to prove the superiority of TDOF-LADRC in rejecting disturbances.Finally,experiments are performed on a 750 W PMSM experimental platform,and the results demonstrate that the proposed TDOF-LADRC exhibits the properties of two degrees of freedom and improves the disturbance rejection performance of the PMSM system.

Preoperative controlling nutritional status as an optimal prognostic nutritional index to predict the outcome for colorectal cancer

BACKGROUND The controlling nutritional status(CONUT)score effectively reflects a patient’s nutritional status,which is closely related to cancer prognosis.This study invest-igated the relationship between the CONUT score and prognosis after radical surgery for colorectal cancer,and compared the predictive ability of the CONUT score with other indexes.AIM To analyze the predictive performance of the CONUT score for the survival rate of colorectal cancer patients who underwent potentially curative resection.METHODS This retrospective analysis included 217 patients with newly diagnosed colorectal.The CONUT score was calculated based on the serum albumin level,total lymphocyte count,and total cholesterol level.The cutoff value of the CONUT score for predicting prognosis was 4 according to the Youden Index by the receiver operating characteristic curve.The associations between the CONUT score and the prognosis were performed using Kaplan-Meier curves and Cox regression analysis.RESULTS Using the cutoff value of the CONUT score,patients were stratified into CONUT low(n=189)and CONUT high groups(n=28).The CONUT high group had worse overall survival(OS)(P=0.013)and relapse-free survival(RFS)(P=0.015).The predictive performance of CONUT was superior to the modified Glasgow prognostic score,the prognostic nutritional index,and the neutrophil-to-lymphocyte ratio.Meanwhile,the predictive performances of CONUT+tumor node metastasis(TNM)stage for 3-year OS[area under the receiver operating characteristics curve(AUC)=0.803]and 3-year RFS(AUC=0.752)were no less than skeletal muscle mass index(SMI)+TNM stage.The CONUT score was negatively correlated with SMI(P<0.01).CONCLUSION As a nutritional indicator,the CONUT score could predict long-term outcomes after radical surgery for colorectal cancer,and its predictive ability was superior to other indexes.The correlation between the CONUT score and skeletal muscle may be one of the factors that play a predictive role.

Distribution Characteristics and Controlling Factors of Heavy Metals in Surface Sediments from the Bay-Island-Estuary System(BIES):A Case Study in Coastal Waters of Fujian Province,China

Based on the contents of six heavy metal elements in surface sediments from coastal areas of Fujian Province,the distribution characteristics and controlling factors of six heavy metals in a bay-island-estuary system(BIES)were studied.This paper focuses on the influence of the hydrodynamic environment,and systematically discusses how grain size compositions,chemical environment,tidal current,ocean circulation and human activities influence the distribution and transportation of the heavy metals.The results indicated that the distribution and migration of Cu,Pb,Zn and Cr elements were mainly controlled by natural factors such as regional geological background,grain size compositions,and tidal residual currents.In contrast,As and Hg was mainly affected by human factors such as agriculture and industrial manufacturing.In the BIES,where the chemical environment exerted limited influence,the accumulation and migration of heavy metals are mainly influenced by human activities and enhanced by estuary processes as well as the complex sedimentary dynamic environment caused by many bays and islands.

Telepresence Robots and Controlling Techniques in Healthcare System

In this era of post-COVID-19,humans are psychologically restricted to interact less with other humans.According to the world health organization(WHO),there are many scenarios where human interactions cause severe multiplication of viruses from human to human and spread worldwide.Most healthcare systems shifted to isolation during the pandemic and a very restricted work environment.Investigations were done to overcome the remedy,and the researcher developed different techniques and recommended solutions.Telepresence robot was the solution achieved by all industries to continue their operations but with almost zero physical interaction with other humans.It played a vital role in this perspective to help humans to perform daily routine tasks.Healthcare workers can use telepresence robots to interact with patients who visit the healthcare center for initial diagnosis for better healthcare system performance without direct interaction.The presented paper aims to compare different telepresence robots and their different controlling techniques to perform the needful in the respective scenario of healthcare environments.This paper comprehensively analyzes and reviews the applications of presented techniques to control different telepresence robots.However,our feature-wise analysis also points to specific technical,appropriate,and ethical challenges that remain to be solved.The proposed investigation summarizes the need for further multifaceted research on the design and impact of a telepresence robot for healthcare centers,building on new perceptions during the COVID-19 pandemic.

A review of artificial intelligence applications in high-speed railway systems

In recent years,the global surge of High-speed Railway(HSR)revolutionized ground transportation,providing secure,comfortable,and punctual services.The next-gen HSR,fueled by emerging services like video surveillance,emergency communication,and real-time scheduling,demands advanced capabilities in real-time perception,automated driving,and digitized services,which accelerate the integration and application of Artificial Intelligence(AI)in the HSR system.This paper first provides a brief overview of AI,covering its origin,evolution,and breakthrough applications.A comprehensive review is then given regarding the most advanced AI technologies and applications in three macro application domains of the HSR system:mechanical manufacturing and electrical control,communication and signal control,and transportation management.The literature is categorized and compared across nine application directions labeled as intelligent manufacturing of trains and key components,forecast of railroad maintenance,optimization of energy consumption in railroads and trains,communication security,communication dependability,channel modeling and estimation,passenger scheduling,traffic flow forecasting,high-speed railway smart platform.Finally,challenges associated with the application of AI are discussed,offering insights for future research directions.

Design and analysis of an advanced thermal management system for the solar close observations and proximity experiments spacecraft

In this paper,the mission and the thermal environment of the Solar Close Observations and Proximity Experiments(SCOPE)spacecraft are analyzed,and an advanced thermal management system(ATMS)is designed for it.The relationship and functions of the integrated database,the intelligent thermal control system and the efficient liquid cooling system in the ATMS are elaborated upon.For the complex thermal field regulation system and extreme space thermal environment,a modular simulation and thermal field planning method are proposed,and the feasibility of the planning algorithm is verified by numerical simulation.A solar array liquid cooling system is developed,and the system simulation results indicate that the temperatures of the solar arrays meet the requirements as the spacecraft flies by perihelion and aphelion.The advanced thermal management study supports the development of the SCOPE program and provides a reference for the thermal management in other deep-space exploration programs.

Impact of Sky Conditions on Net Ecosystem Productivity over a “Floating Blanket” Wetland in Southwest China

Based on eddy covariance(EC) measurements during 2016–20, the effects of sky conditions on the net ecosystem productivity(NEP) over a subtropical “floating blanket ” wetland were investigated. Sky conditions were divided into overcast, cloudy, and sunny conditions. On the half-hourly timescale, the daytime NEP responded more rapidly to the changes in the total photosynthetic active radiation(PARt) under overcast and cloudy skies than that under sunny skies. The increase in the apparent quantum yield under overcast and cloudy conditions was the greatest in spring and the least in summer. Additionally, lower atmospheric vapor pressure deficit(VPD) and moderate air temperature were more conducive to enhancing the apparent quantum yield under cloudy skies. On the daily timescale, NEP and the gross primary production(GPP) were higher under cloudy or sunny conditions than those under overcast conditions across seasons. The daily NEP and GPP during the wet season peaked under cloudy skies. The daily ecosystem light use efficiency(LUE) and water use efficiency(WUE) during the wet season also changed with sky conditions and reached their maximum under overcast and cloudy skies, respectively. The diffuse photosynthetic active radiation(PAR_d) and air temperature were primarily responsible for the variation of daily NEP from half-hourly to monthly timescales, and the direct photosynthetic active radiation(PAR_b) had a secondary effect on NEP. Under sunny conditions, PAR_b and air temperature were the dominant factors controlling daily NEP. While daily NEP was mainly controlled by PAR_d under cloudy and overcast conditions.

Development characteristics and controlling factors of fractures in lacustrine shale and their geological significance for evaluating shale oil sweet spots in the third member of the Shahejie Formation in the Qikou Sag, Bohai Bay Basin

Natural fractures are critical for shale oil and gas enrichment and development. Due to the extremely high heterogeneity of shale, the factors controlling the formation of internal fractures, especially horizontal fractures, remain controversial. In this study, we integrate thin section analysis and microcomputed tomography(CT) data from several lacustrine shale samples from the third member(Es3) of the Shahejie Formation, Qikou Sag, Bohai Bay Basin, to assess the fractures in detail. The goal is to reveal the development characteristics, controlling factors, and geological significance for evaluating sweet spots in a shale oil play. The fractures in the Es3contain high-angle structural and horizontal bed-parallel fractures that are mostly shear and extensional. Various factors influence fracture development,including lithofacies, mineral composition, organic matter content, and the number of laminae. Structural fractures occur predominantly in siltstone, whereas bed-parallel fractures are abundant in laminated shale and layered mudstone. A higher quartz content results in higher shale brittleness, causing fractures, whereas the transformation between clay minerals contributes to the development of bedparallel fractures. Excess pore pressure due to hydrocarbon generation and expulsion during thermal advance can cause the formation of bed-parallel fractures. The density of the bed-parallel and structural fractures increases with the lamina density, and the bed-parallel fractures are more sensitive to the number of laminae. The fractures are critical storage spaces and flow conduits and are indicative of sweet spots. The laminated shale in the Es3with a high organic matter content contains natural fractures and is an organic-rich, liquid-rich, self-sourced shale play. Conversely, the siltstone, massive mudstone, and argillaceous carbonate lithofacies contain lower amounts of organic matter and do not have bed-parallel fractures. However, good reservoirs can form in these areas when structural fractures are present and the source, and storage spaces are separated.

Noise-Tolerant ZNN-Based Data-Driven Iterative Learning Control for Discrete Nonaffine Nonlinear MIMO Repetitive Systems

Aiming at the tracking problem of a class of discrete nonaffine nonlinear multi-input multi-output(MIMO) repetitive systems subjected to separable and nonseparable disturbances, a novel data-driven iterative learning control(ILC) scheme based on the zeroing neural networks(ZNNs) is proposed. First, the equivalent dynamic linearization data model is obtained by means of dynamic linearization technology, which exists theoretically in the iteration domain. Then, the iterative extended state observer(IESO) is developed to estimate the disturbance and the coupling between systems, and the decoupled dynamic linearization model is obtained for the purpose of controller synthesis. To solve the zero-seeking tracking problem with inherent tolerance of noise,an ILC based on noise-tolerant modified ZNN is proposed. The strict assumptions imposed on the initialization conditions of each iteration in the existing ILC methods can be absolutely removed with our method. In addition, theoretical analysis indicates that the modified ZNN can converge to the exact solution of the zero-seeking tracking problem. Finally, a generalized example and an application-oriented example are presented to verify the effectiveness and superiority of the proposed process.

Risk-Informed Model-Free Safe Control of Linear Parameter-Varying Systems

This paper presents a risk-informed data-driven safe control design approach for a class of stochastic uncertain nonlinear discrete-time systems.The nonlinear system is modeled using linear parameter-varying(LPV)systems.A model-based probabilistic safe controller is first designed to guarantee probabilisticλ-contractivity(i.e.,stability and invariance)of the LPV system with respect to a given polyhedral safe set.To obviate the requirement of knowing the LPV system model and to bypass identifying its open-loop model,its closed-loop data-based representation is provided in terms of state and scheduling data as well as a decision variable.It is shown that the variance of the closedloop system,as well as the probability of safety satisfaction,depends on the decision variable and the noise covariance.A minimum-variance direct data-driven gain-scheduling safe control design approach is presented next by designing the decision variable such that all possible closed-loop system realizations satisfy safety with the highest confidence level.This minimum-variance approach is a control-oriented learning method since it minimizes the variance of the state of the closed-loop system with respect to the safe set,and thus minimizes the risk of safety violation.Unlike the certainty-equivalent approach that results in a risk-neutral control design,the minimum-variance method leads to a risk-averse control design.It is shown that the presented direct risk-averse learning approach requires weaker data richness conditions than existing indirect learning methods based on system identification and can lead to a lower risk of safety violation.Two simulation examples along with an experimental validation on an autonomous vehicle are provided to show the effectiveness of the presented approach.

Nonlinear robust adaptive control for bidirectional stabilization system of all-electric tank with unknown actuator backlash compensation and disturbance estimation

Since backlash nonlinearity is inevitably existing in actuators for bidirectional stabilization system of allelectric tank,it behaves more drastically in high maneuvering environments.In this work,the accurate tracking control for bidirectional stabilization system of moving all-electric tank with actuator backlash and unmodeled disturbance is solved.By utilizing the smooth adaptive backlash inverse model,a nonlinear robust adaptive feedback control scheme is presented.The unknown parameters and unmodelled disturbance are addressed separately through the derived parametric adaptive function and the continuous nonlinear robust term.Because the unknown backlash parameters are updated via adaptive function and the backlash effect can be suppressed successfully by inverse operation,which ensures the system stability.Meanwhile,the system disturbance in the high maneuverable environment can be estimated with the constructed adaptive law online improving the engineering practicality.Finally,Lyapunov-based analysis proves that the developed controller can ensure the tracking error asymptotically converges to zero even with unmodeled disturbance and unknown actuator backlash.Contrast co-simulations and experiments illustrate the advantages of the proposed approach.

Main controlling factor and mechanism of gas-in-place content of the Lower Cambrian shale from different sedimentary facies in the western Hubei area, South China

The Lower Cambrian shale gas in the western Hubei area,South China has a great resource prospect,but the gas-in-place(GIP)content in different sedimentary facies varies widely,and the relevant mechanism has been not well understood.In the present study,two sets of the Lower Cambrian shale samples from the Wells YD4 and YD5 in the western Hubei area,representing the deep-water shelf facies and shallowwater platform facies,respectively,were investigated on the differences of pore types,pore structure and methane adsorption capacity between them,and the main controlling factor and mechanism of their methane adsorption capacities and GIP contents were discussed.The results show that the organic matter(OM)pores in the YD4 shale samples are dominant,while the inorganic mineral(IM)pores in the YD5 shale samples are primary,with underdeveloped OM pores.The pore specific surface area(SSA)and pore volume(PV)of the YD4 shale samples are mainly from micropores and mesopores,respectively,while those of the YD5 shale samples are mainly from micropores and macropores,respectively.The methane adsorption capacity of the YD4 shale samples is significantly higher than that of the YD5 shale samples,with a maximum absolute adsorption capacity of 3.13 cm^(3)/g and 1.31 cm^(3)/g in average,respectively.Compared with the shallow-water platform shale,the deep-water shelf shale has a higher TOC content,a better kerogen type and more developed OM pores,which is the main mechanism for its higher adsorption capacity.The GIP content models based on two samples with a similar TOC content selected respectively from the Wells YD4 and YD5 further indicate that the GIP content of the deep-water shelf shale is mainly 34 m^(3)/t within a depth range of 1000—4000 m,with shale gas exploration and development potential,while the shallow-water platform shale has normally a GIP content of<1 m^(3)/t,with little shale gas potential.Considering the geological and geochemical conditions of shale gas formation and preservation,the deep-water shelf facies is the most favorable target for the Lower Cambrian shale gas exploration and development in the western Hubei area,South China.

Gypsum-based Silica Gel Humidity-controlling Composite Materials:Preparation,Characterization,and Performance

Gypsum was used as substrate,and silica gel was mixed into substrate at a certain mass ratio to prepare humidity-controlling composites;moreover,the moisture absorption and desorption properties of gypsum-based composites were compared with adding different silica gel particle size and proportion.The morphological characteristics,the isothermal equilibrium moisture content curve,moisture absorption and desorption rate,moisture absorption and desorption stability,and humidity-conditioning performance were tested and analyzed.The experimental results show that,compared with pure-gypsum,the surface structure of the gypsum-based composites is relatively loose,the quantity,density and aperture of the pores in the structure increase.The absorption and desorption capacity increase along with the increase of silica gel particle size and silica gel proportion.When 3 mm silica gel particle size is added with a mass ratio of 40%,the maximum equilibrium moisture content of humidity-controlling composites is 0.161 g/g at 98% relative humidity(RH),3.22 times that of pure-gypsum.The moisture absorption and desorption rates are increased,the equilibrium moisture absorption and desorption rates are 2.68 times and 1.61 times that of pure-gypsum at 58.5% RH,respectively.The gypsum-based composites have a good stability,which has better timely response to dynamic humidity changes and can effectively regulate indoor humidity under natural conditions.

Ultimately Bounded Output Feedback Control for Networked Nonlinear Systems With Unreliable Communication Channel: A Buffer-Aided Strategy

This paper concerns ultimately bounded output-feedback control problems for networked systems with unknown nonlinear dynamics. Sensor-to-observer signal transmission is facilitated over networks that has communication constraints.These transmissions are carried out over an unreliable communication channel. In order to enhance the utilization rate of measurement data, a buffer-aided strategy is novelly employed to store historical measurements when communication networks are inaccessible. Using the neural network technique, a novel observer-based controller is introduced to address effects of signal transmission behaviors and unknown nonlinear dynamics.Through the application of stochastic analysis and Lyapunov stability, a joint framework is constructed for analyzing resultant system performance under the introduced controller. Subsequently, existence conditions for the desired output-feedback controller are delineated. The required parameters for the observerbased controller are then determined by resolving some specific matrix inequalities. Finally, a simulation example is showcased to confirm method efficacy.