Analysis of Hospital Budget Management Control Based on Smart Hospital

Hospitals are crucial healthcare facilities where patients seek treatment,and effective budget management within hospitals significantly impacts their operational efficiency and financial performance.In the age of information technology and advanced healthcare solutions,the emergence of smart hospitals represents a new trend in the medical industry’s evolution.Leveraging modern information technology can enhance the development of hospital IT systems and drive budget management toward greater intelligence.This paper begins by analyzing the influence of smart hospitals on hospital budget control.It then examines the current state of budget management control within smart hospitals.Finally,it proposes several strategies for budget management control in smart hospitals,aiming to provide guidance for relevant stakeholders.

Machine Learning-based Electric Load Forecasting for Peak Demand Control in Smart Grid

Increasing energy demands due to factors such as population,globalization,and industrialization has led to increased challenges for existing energy infrastructure.Efficient ways of energy generation and energy consumption like smart grids and smart homes are implemented to face these challenges with reliable,cheap,and easily available sources of energy.Grid integration of renewable energy and other clean distributed generation is increasing continuously to reduce carbon and other air pollutants emissions.But the integration of distributed energy sources and increase in electric demand enhance instability in the grid.Short-term electrical load forecasting reduces the grid fluctuation and enhances the robustness and power quality of the grid.Electrical load forecasting in advance on the basic historical data modelling plays a crucial role in peak electrical demand control,reinforcement of the grid demand,and generation balancing with cost reduction.But accurate forecasting of electrical data is a very challenging task due to the nonstationary and nonlinearly nature of the data.Machine learning and artificial intelligence have recognized more accurate and reliable load forecastingmethods based on historical load data.The purpose of this study is to model the electrical load of Jajpur,Orissa Grid for forecasting of load using regression type machine learning algorithms Gaussian process regression(GPR).The historical electrical data and whether data of Jajpur is taken for modelling and simulation and the data is decided in such a way that the model will be considered to learn the connection among past,current,and future dependent variables,factors,and the relationship among data.Based on this modelling of data the network will be able to forecast the peak load of the electric grid one day ahead.The study is very helpful in grid stability and peak load control management.

Enhanced Water Quality Control Based on Predictive Optimization for Smart Fish Farming

The requirement for high-quality seafood is a global challenge in today’s world due to climate change and natural resource limitations.Internet of Things(IoT)based Modern fish farming systems can significantly optimize seafood production by minimizing resource utilization and improving healthy fish production.This objective requires intensive monitoring,prediction,and control by optimizing leading factors that impact fish growth,including temperature,the potential of hydrogen(pH),water level,and feeding rate.This paper proposes the IoT based predictive optimization approach for efficient control and energy utilization in smart fish farming.The proposed fish farm control mechanism has a predictive optimization to deal with water quality control and efficient energy consumption problems.Fish farm indoor and outdoor values are applied to predict the water quality parameters,whereas a novel objective function is proposed to achieve an optimal fish growth environment based on predicted parameters.Fuzzy logic control is utilized to calculate control parameters for IoT actuators based on predictive optimal water quality parameters by minimizing energy consumption.To evaluate the efficiency of the proposed system,the overall approach has been deployed to the fish tank as a case study,and a number of experiments have been carried out.The results show that the predictive optimization module allowed the water quality parameters to be maintained at the optimal level with nearly 30%of energy efficiency at the maximum actuator control rate compared with other control levels.

Current status and construction scheme of smart geothermal field technology

To address the key problems in the application of intelligent technology in geothermal development,smart application scenarios for geothermal development are constructed.The research status and existing challenges of intelligent technology in each scenario are analyzed,and the construction scheme of smart geothermal field system is proposed.The smart geothermal field is an organic integration of geothermal development engineering and advanced technologies such as the artificial intelligence.At present,the technology of smart geothermal field is still in the exploratory stage.It has been tested for application in scenarios such as intelligent characterization of geothermal reservoirs,dynamic intelligent simulation of geothermal reservoirs,intelligent optimization of development schemes and smart management of geothermal development.However,it still faces many problems,including the high computational cost,difficult real-time response,multiple solutions and strong model dependence,difficult real-time optimization of dynamic multi-constraints,and deep integration of multi-source data.The construction scheme of smart geothermal field system is proposed,which consists of modules including the full database,intelligent characterization,intelligent simulation and intelligent optimization control.The connection between modules is established through the data transmission and the model interaction.In the next stage,it is necessary to focus on the basic theories and key technologies in each module of the smart geothermal field system,to accelerate the lifecycle intelligent transformation of the geothermal development and utilization,and to promote the intelligent,stable,long-term,optimal and safe production of geothermal resources.

Traffic Control Based on Integrated Kalman Filtering and Adaptive Quantized Q-Learning Framework for Internet of Vehicles

Intelligent traffic control requires accurate estimation of the road states and incorporation of adaptive or dynamically adjusted intelligent algorithms for making the decision.In this article,these issues are handled by proposing a novel framework for traffic control using vehicular communications and Internet of Things data.The framework integrates Kalman filtering and Q-learning.Unlike smoothing Kalman filtering,our data fusion Kalman filter incorporates a process-aware model which makes it superior in terms of the prediction error.Unlike traditional Q-learning,our Q-learning algorithm enables adaptive state quantization by changing the threshold of separating low traffic from high traffic on the road according to the maximum number of vehicles in the junction roads.For evaluation,the model has been simulated on a single intersection consisting of four roads:east,west,north,and south.A comparison of the developed adaptive quantized Q-learning(AQQL)framework with state-of-the-art and greedy approaches shows the superiority of AQQL with an improvement percentage in terms of the released number of vehicles of AQQL is 5%over the greedy approach and 340%over the state-of-the-art approach.Hence,AQQL provides an effective traffic control that can be applied in today’s intelligent traffic system.

Design of Car Based on 5G Network Control

The role of smart cars is pivotal,and this project designs and implements a four-wheel vehicle control system leveraging 5G communication technology.The system aims to enhance the portability of smart cars,reduce their costs,enable remote control functionality,and improve mobility to meet the needs of modern Internet of Things(IoT)applications.The system integrates an ESP8266 Wi-Fi module with the Blinker IoT platform to enable remote,real-time control of car movement via a smartphone app.Using Access Point(AP)mode for fast network configuration,users can input Wi-Fi credentials and a Blinker key through a web interface for easy setup.Through the custom app interface,users can send commands to control the car’s forward,backward,turning,and stopping actions,as well as adjust speed and operation delay.Additionally,the system includes Electrically Erasable Programmable Read-Only Memory(EEPROM)data storage to ensure the persistent saving of configuration information,and it features a remote wireless camera for external monitoring of the car’s surroundings.The Android-based remote control design allows users to monitor and control the car’s movement anytime and anywhere.Experimental results show that the system is stable,provides smooth control,operates at low cost and low power consumption,and offers good portability.Therefore,this intelligent car control system offers valuable insights for smart car development and application.It can also be integrated with popular smart homes,IoT,and other emerging technologies,offering broad application potential and promising development prospects.

基于西门子S7200 SMART PLC的iMoLboX反应釜控制系统设计

为了改变iMoLboX反应釜原有生产过程中由人工进行操作及操作较为繁琐的生产状态,根据实际生产工艺提出了一种反应釜生产线控制方案:以西门子S7200 SMART PLC为核心,设计一种智能设备,用于温度、搅拌器、流体输送、固液分离提纯等系统的现场调节,并将反应釜组群的内部温度、流量强度、搅拌器速度等现场检测数据实时上传,由上位云计算机系统进行分析和控制。实际应用表明,该系统能实现iMoLboX反应釜生产的智能化控制。

基于SMART原则的医院感染预防与控制效果评价

目的:医院感染在增加患者痛苦和病死率的同时,也造成了巨大的经济损失。采取有效的措施改善医院感染情况,做好医务人员手部清洁、消毒刻不容缓。SMART原则即在目标设置时遵循明确性、可衡量性、可接受性、相关性、时限性原则。本研究在医院感染预防与控制工作中结合SMART原则,围绕手卫生等医院感染问题采取一系列措施,并对实施效果进行评价。方法:本研究为回顾性研究。2020年1至6月,在中南大学湘雅医院国际医疗部运用SMART原则开展了感染预防与控制干预工作,实施感染预防与控制质量改进计划。以指标数据作为目标衡量标准,建立以医院感染预防与控制知识知晓度、手卫生依从率为过程监测,手术部位感染率、导尿管相关尿路感染率为结果监测的评价指标。将干预后(2020年7月至2021年6月)各项评价指标与干预前(2019年1至12月)进行比较。结果:纳入在国际医疗部工作的51名医务人员,其中医师12名,护士39名;男性6名,女性45名;年龄21~57(30.2±7.1)岁。干预后医务人员医院感染预防与控制知识知晓度、手卫生依从率较干预前均明显提升(均P<0.05),但手术部位感染率、导尿管相关尿路感染率干预前后差异均无统计学意义(均P>0.05)。干预后内、外科病房医务人员医院感染预防与控制知识知晓度、手卫生依从率均较干预前有所提高(均P<0.05),外科病房医务人员医院感染预防与控制知识知晓度较低但改进明显,内科病房医务人员手卫生依从率较高且改进明显;干预后男性和女性医务人员医院感染预防与控制知识知晓度、手卫生依从率均较干预前有所提高(均P<0.05),均为男性较女性提升明显;干预后年龄<30岁、30~40岁的医务人员医院感染预防与控制知识知晓度、手卫生依从率均较干预前有所提高(均P<0.05),年龄30~40岁的医务人员医院感染预防与控制知识知晓度较低但2项指标改进均最明显;干预后医生、护士的医院感染预防与控制知识知晓度均较干预前有所提高(均P<0.05),且医生知识知晓度改进较为明显,护士手卫生依从率较干预前有所提高(P<0.05);干预后初级、中级职称医务人员医院感染预防与控制知识知晓度、手卫生依从率均较干预前有所提高(均P<0.05),均为中级职称医务人员改进较为明显。结论:基于SMART原则,结合多种国内外感染预防与控制相关评价标准,实施质量改进计划,可有效提升医务人员感染预防和控制知识知晓度及手卫生依从率,强化医院感染的预防与控制,保障患者的安全。

基于S7200 SMART PLC和组态王的雾炮机控制系统设计

为了实现对多台雾炮机的集中管理、合理雾化除尘,提出了基于S7200 SMART PLC与组态王的雾炮机控制系统设计方案:将S7200 SMART PLC作为控制系统的现场控制装置,通过继电器输出控制雾炮机中的被控电机;利用组态王软件对控制系统进行设计,实现工作模式的选择、工作指令的下达及对设备状态的监视;通过TCP/IP方式实现PLC与上位机之间的实时通信。现场运行结果表明,该控制系统运行良好,操作灵活简单,提升了雾炮机的工作效率。

Stimuli-responsive Membranes： Smart Tools for Controllable Mass-transfer and Separation Processes

As emerging artificial biomimetic membranes, smart or intelligent membranes that are able to respond to environmental stimuli are attracting ever-increasing interests from various fields. Their permeation properties including hydraulic permeability and diffusional permeability can be dramatically controlled or adjusted self-regulatively in response to small chemical and/or physical stimuli in their environments. Such environmental stimuli-responsive smart membranes could find myriad applications in numerous fields ranging from controlled release to separations. Here the trans-membrane mass-transfer and membrane separation is introduced as the beginning to initiate the requirement of smart membranes, and then bio-inspired design of environmental stimuli-responsive smart membranes and four essential elements for smart membranes are introduced and discussed. Next, smart membrane types and their applications as smart tools for controllable mass-transfer in controlled release and separations are reviewed. The research tooics in the near future are also suggested.

Machine Learning and Data-Driven Techniques for the Control of Smart Power Generation Systems:An Uncertainty Handling Perspective

Due to growing concerns regarding climate change and environmental protection,smart power generation has become essential for the economical and safe operation of both conventional thermal power plants and sustainable energy.Traditional first-principle model-based methods are becoming insufficient when faced with the ever-growing system scale and its various uncertainties.The burgeoning era of machine learning(ML)and data-driven control(DDC)techniques promises an improved alternative to these outdated methods.This paper reviews typical applications of ML and DDC at the level of monitoring,control,optimization,and fault detection of power generation systems,with a particular focus on uncovering how these methods can function in evaluating,counteracting,or withstanding the effects of the associated uncertainties.A holistic view is provided on the control techniques of smart power generation,from the regulation level to the planning level.The benefits of ML and DDC techniques are accordingly interpreted in terms of visibility,maneuverability,flexibility,profitability,and safety(abbreviated as the“5-TYs”),respectively.Finally,an outlook on future research and applications is presented.

Smart homing guidance strategy with control saturation against a cooperative target-defender team

A smart homing guidance strategy with control saturation against a target-defender team is derived. It is noteworthy that a cooperative strategy of the target-defender team is applied,which has been proved more challenging for the homing guidance.The defender missile is launched by the target and guided by a cooperative augmented proportional navigation(APN). At the same time, the target performs a one-switch maneuver to cooperate and minimize the defender's acceleration requirement. The problem is analyzed for arbitrary-order linear dynamics of the agents in the linearized form but validated by the mathematical simulations by using nonlinear kinematics. The perfect information of three agents' states is assumed. Then, a method to deal with the target-defender team is proposed. It contains a combined performance index penalizing the miss distance relative to the target and energy consumption in the whole duration. Besides, the specific miss distance related to the defender is regarded as an inequality constraint. An analytical solution for the smart guidance strategy against the APN guided defender is derived. Meanwhile, the control saturations are introduced to get more realistic and reasonable insights to this practical target-missile-defender problem. A simple but effective iterative searching technique is proposed to determine the saturation time points. The solution provides an optimal homing strategy to evade the defender with a specific miss distance and intercept the target with the minimum miss distance in the minimum energy manner. Nonlinear two-dimensional simulation results are used to validate the theoretical analysis. By comparison with the optimal differential game guidance(ODGG) and the combined minimum effort guidance(CMEG), the superiority of this smart guidance strategy is concluded.

Decentralized Control for Residential Energy Management of a Smart Users' Microgrid with Renewable Energy Exchange

This paper presents a decentralized control strategy for the scheduling of electrical energy activities of a microgrid composed of smart homes connected to a distributor and exchanging renewable energy produced by individually owned distributed energy resources. The scheduling problem is stated and solved with the aim of reducing the overall energy supply from the grid, by allowing users to exchange the surplus renewable energy and by optimally planning users' controllable loads. We assume that each smart home can both buy/sell energy from/to the grid taking into account time-varying non-linear pricing signals. Simultaneously, smart homes cooperate and may buy/sell locally harvested renewable energy from/to other smart homes. The resulting optimization problem is formulated as a non-convex non-linear programming problem with a coupling of decision variables in the constraints. The proposed solution is based on a novel heuristic iterative decentralized scheme algorithm that suitably extends the Alternating Direction Method of Multipliers to a non-convex and decentralized setting. We discuss the conditions that guarantee the convergence of the presented algorithm. Finally, the application of the proposed technique to a case study under several scenarios shows its effectiveness.

Smart Material Based Complex Mode Active Control of Flexible Manipulators with Kinematic Redundancy

An active control methodology is presented for suppressing the vibratoryresponse of flexible redundant manipulators with bonded piezoceramic actuators and strain gagesensors. Firstly, the dynamic equation of the manipulator is decoupled by means of the complex modetheory and the state-space expression of the controlled system is developed. Secondly, a continuouslinear quadratic regulator (LQR) state feedback controller is designed based on the minimumprinciple. Thirdly, a full-order Luenberger state observer featuring an assigned degree of stabilityis determined via the duality between control and estimation. Finally, a numerical simulation iscarried out on a planar 3R flexible redundant manipulator. The simulation results reveal that thedynamic performance of the system is improved rapidly and significantly.

Smart Transformer-based Medium Voltage Grid Support by Means of Active Power Control

In the last decades the voltage regulation has been challenged by the increase of power variability in the electric grid,due to the spread of non-dispatchable generation sources.This paper introduces a Smart Transformer(ST)-based Medium Voltage(MV)grid support by means of active power control in the ST-fed Low Voltage(LV)grid.The aim of the proposed strategy is to improve the voltage profile in MV grids before the operation of On-Load Tap Changer in the primary substation transformer,which needs tens of seconds.This is realized through reactive power injection by the AC/DC MV converter and simultaneous decrease of the active power consumption of voltage-dependent loads in ST-fed LV grid,controlling the ST output voltage.The last feature has two main effects:the first is to reduce the active power withdrawn from MV grid,and consequently the MV voltage drop caused by the active current component.At the same time,higher reactive power injection capability in the MV converter is unlocked,due to the lower active power demand.As result,the ST increases the voltage support in MV grid.The analysis and simulation results carried out in this paper show improvements compared to similar solutions,i.e.the only reactive power compensation.The impact of the proposed solution has been finally evaluated under different voltage-dependence of the loads in the LV grid.

Smart Grid, Smart Controllers and Home Energy Automation—Creating the Infrastructure for Future

Integration of unpredictable renewable power sources into the Grid is leading to the development of wide area control algorithms and smart grid. Smart meters are the first step in the building a smart consumer interface. Much more, however, would be required in building a smart grid than just smart meters. This paper explores the conceptual architecture of smart grid. It highlights the need for additional infrastructure to realize full potential of smart grid. The information presented in this paper is an attempt to uncover what the future in smart grid could be and what infrastructure would be required to tap its potential. As smart grid evolves, more functionality would be built in the constituents. The paper also proposes mathematical basis for some of the controller algorithms.

Research and Design of Coordinated Control Strategy for Smart Electromechanical Actuator System

In order to improve the frequency response and anti-interference characteristics of the smart electromechanical actuator(EMA)system,and aiming at the force fighting problem when multiple actuators work synchronously,a multi input multi output(MIMO)position difference cross coupling control coordinated strategy based on double‑closed-loop load feedforward control is proposed and designed.In this strategy,the singular value method of return difference matrix is used to design the parameter range that meets the requirements of system stability margin,and the sensitivity function and the H_(∞)norm theory are used to design and determine the optimal solution in the obtained parameter stability region,so that the multi actuator system has excellent synchronization,stability and anti-interference.At the same time,the mathematical model of the integrated smart EMA system is established.According to the requirements of point-to-point control,the controller of double-loop control and load feedforward compensation is determined and designed to improve the frequency response and anti-interference ability of single actuator.Finally,the 270 V high-voltage smart EMA system experimental platform is built,and the frequency response,load feedforward compensation and coordinated control experiments are carried out to verify the correctness of the position difference cross coupling control strategy and the rationality of the parameter design,so that the system can reach the servo control indexes of bandwidth 6 Hz,the maximum output force 20000 N and the synchronization error≤0.1 mm,which effectively solves the problem of force fighting.

Control Effects and Material Parameters of Piezoelectric Smart Moment Controllers

The control using piezoelectric smart moment (PSM) controllers for seismically excited structures was studied.The radical principle of PSM controller was introduced firstly and then the different formulae of control shear force for different structures were derived with the stiffness ratio of columns taken into consideration.With the active control algorithm based on the theory of modern optimal control,this study proposes a simulative computation on the frame structure and mill structure respectively,and the results indicate that the installation of this smart controller with proper parameters can significantly reduce seismic responses of different structures. The optimal parameters of the damper can be identified through a parameter study.

Active Vibration Control of Macroscopically Smart Mechanisms

This paper presents an investigation on the active vibration control of flexible linkage mechanisms featuring piezoceramic actuators and strain gauge sensors. The dynamic equation of the macroscopically smart mechanism is decoupled by means of the complex mode theory. The state-space expression of the controlled system is developed, which includes the system noise and the observation noise. Moreover, a discrete linear quadratic Gaussian （LQG） state feedback controller and a discrete Kalman filter are designed separately. Finally, the proposed method is applied to the on-line vibration control of a macroscopically smart mechanism. The experimental results reveal that the strain amplitude of the flexible link ig suppressed by 80% and the dynamic performance of mechanism has been ameliorated significantly.

Finite element modeling and feedback control of piezoelectric smart structures

Presents the general formula derived with a smart beam structure bonded with piezoelectric material using the piezoelectricity theory, elastic mechanism and Hamilton principle for electromechanically coupled piezoelectric finite element and dynamic equations, the second order dynamic model built, and the expression of state space, and the analysis of conventional speed and position feedback and the design of optimum feedback controller for output, the finite element models built for a piezoelectric cantilever beam, and the feedback controller designed eventually, and concludes with simulation results that the vibration suppression obtained is very satisfactory and the algorithms proposed are very useful.