Integrated strategy for real-time wind power fluctuation mitigation and energy storage system control

To address the impact of wind-power fluctuations on the stability of power systems,we propose a comprehensive approach that integrates multiple strategies and methods to enhance the efficiency and reliability of a system.First,we employ a strategy that restricts long-and short-term power output deviations to smoothen wind power fluctuations in real time.Second,we adopt the sliding window instantaneous complete ensemble empirical mode decomposition with adaptive noise(SW-ICEEMDAN)strategy to achieve real-time decomposition of the energy storage power,facilitating internal power distribution within the hybrid energy storage system.Finally,we introduce a rule-based multi-fuzzy control strategy for the secondary adjustment of the initial power allocation commands for different energy storage components.Through simulation validation,we demonstrate that the proposed comprehensive control strategy can smoothen wind power fluctuations in real time and decompose energy storage power.Compared with traditional empirical mode decomposition(EMD),ensemble empirical mode decomposition(EEMD),and complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN)decomposition strategies,the configuration of the energy storage system under the SW-ICEEMDAN control strategy is more optimal.Additionally,the state-of-charge of energy storage components fluctuates within a reasonable range,enhancing the stability of the power system and ensuring the secure operation of the energy storage system.

Feasibility Assessment of Fast Numerical Simulations for Real-Time Monitoring and Control of PEM Fuel Cells

Computational models that ensure accurate and fast responses to the variations in operating conditions,such as the cell tem-perature and relative humidity(RH),are essential monitoring tools for the real-time control of proton exchange membrane(PEM)fuel cells.To this end,fast cell-area-averaged numerical simulations are developed and verifi ed against the present experiments under various RH levels.The present simulations and measurements are found to agree well based on the cell voltage(polarization curve)and power density under variable RH conditions(RH=40%,RH=70%,and RH=100%),which verifi es the model accuracy in predicting PEM fuel cell performance.In addition,computationally feasible reduced-order models are found to deliver a fast output dataset to evaluate the charge/heat/mass transfer phenomena as well as water production and two-phase fl ow transport.Such fast and accurate evaluations of the overall fuel cell operation can be used to inform the real-time control systems that allow for the improved optimization of PEM fuel cell performance.

Research on Financial Internal Control of Higher Vocational Colleges

Financial intemal control in higher vocational olleges is an important means to ensure the stable development of school finance.However,in practice,it was found that there are problems such as insufficient internal supervision,imperfect internal control system,and imperfect risk prevention mechanism.Therefore,this article aims to discuss the relevant overview,existing problems,and specific strategies of financial intermal control in higher vocational colleges to provide a reference for the improvement of financial internal control in higher vocational colleges.

Quality control of marine big data——a case study of real-time observation station data in Qingdao

Offshore waters provide resources for human beings,while on the other hand,threaten them because of marine disasters.Ocean stations are part of offshore observation networks,and the quality of their data is of great significance for exploiting and protecting the ocean.We used hourly mean wave height,temperature,and pressure real-time observation data taken in the Xiaomaidao station(in Qingdao,China)from June 1,2017,to May 31,2018,to explore the data quality using eight quality control methods,and to discriminate the most effective method for Xiaomaidao station.After using the eight quality control methods,the percentages of the mean wave height,temperature,and pressure data that passed the tests were 89.6%,88.3%,and 98.6%,respectively.With the marine disaster(wave alarm report)data,the values failed in the test mainly due to the influence of aging observation equipment and missing data transmissions.The mean wave height is often affected by dynamic marine disasters,so the continuity test method is not effective.The correlation test with other related parameters would be more useful for the mean wave height.

Applying real-time control to enhance the performance of nitrogen removal in CAST system

A bench-scale reactor（72 L） red with domestic sewage, was operated more than 3 months with three operation modes： traditional mode, modified mode and real-time control mode, so as to evaluate effects of the operation mode on the system performance and to develop a feasible control strategy. Results obtained from fixed-time control study indicate that the variations of the pH and oxidation-reduction potential（ORP） profiles can represent dynamic characteristics of system and the cycle sequences can be controlled and optimized by the control points on the pH and ORP profiles. A control strategy was, therefore, developed and applied to real-time control mode. Compared with traditional mode, the total nitrogen（TN） removal can be increased by approximately 16% in modified mode and a mean TN removal of 92% was achieved in real-time control mode. Moreover, approximately 12.5% aeration energy was saved in real- time control mode. The result of this study shows that the performance of nitrogen removal was enhanced in modified operation mode. Moreover, the real-time control made it possible to optimize process operation and save aeration energy.

Intercross real-time control strategy in alternating activated sludge process for short-cut biological nitrogen removal treating domestic wastewater

To develop technically feasible and economically favorable dynamic process control(DPC)strategies for an alternating activated sludge(AAS)system,a bench-scale continuous-flow alternating aerobic and anoxic reactor,performing short-cut nitrogen removal from real domestic wastewater was operated under different control strategies for more than five months.A fixed-time control(FTC) study showed that bending-points on pH and oxidation-reduction potential(ORP)profiles accurately coincided with the major biologic...

Validation of housekeeping genes as internal controls for studying the gene expression in Pyropia haitanensis(Bangiales, Rhodophyta) by quantitative real-time PCR

Pyropia haitanensis is an economically important mariculture crop in China and has a high research value for several life phenomena, for example environmental tolerance. To explore the mechanisms underlying these characteristics, gene expression has been investigated at the whole transcriptome level. Gene expression studies using quantitative real-time PCR should start by selecting an appropriate internal control gene; therefore, the absolute expression abundance of six housekeeping genes （18S rRNA （18S）, ubiquitin-conju-ating enzyme （UBC）, actin （ACT）, β-tubulin （TUB）, elongation factors 2 （EF2）, and glyceraldehyde-3-phos- phate dehydrogenase （GAPDH） examined by the quantitative real-time PCR in samples corresponding to different strains, life-cycle stages and abiotic stress treatments. Their expression stabilities were assessed by the comparative cycle threshold （Ct） method and by two different software packages： geNorm and NormFinder. The most stable housekeeping gene is UBC and the least stable housekeeping is GADPH. Thus, it is proposed that the most appropriate internal control gene for expression analyses in P. haitanensis is UBC. The results pave the way for further gene expression analyses of different aspects of P. haitanensis biology including different strains, life-history stages and abiotic stress responses.

Control performance comparison between tuned liquid damper and tuned liquid column damper using real-time hybrid simulation

Tuned liquid damper (TLD) and tuned liquid column damper (TLCD) are two types of passive control devices that are widely used in structural control. In this study, a real-time hybrid simulation (RTHS) technique is employed to investigate the diff erence in control performance between TLD and TLCD. A series of RTHSs is presented with the premise of the same liquid length, mass ratio, and structural parameters. Herein, TLD and TLCD are physically experimented, and controlled structures are numerically simulated. Then, parametric studies are performed to further evaluate the diff erent performance between TLD and TLCD. Experimental results demonstrate that TLD is more eff ective than TLCD under diff erent amplitude excitations.

Reduced differential transform and Sumudu transform methods for solving fractional financial models of awareness

In that paper,we new study has been carried out on previous studies of one of the most important mathematical models that describe the global economic movement,and that is described as a non-linear fractional financial model of awareness,where the studies are represented at the steps following:One:The schematic of the model is suggested.Two:The disease-free equilibrium point(DFE)and the stability of the equilibrium point are discussed.Three:The stability of the model is fulfilled by drawing the Lyapunov exponents and Poincare map.Fourth:The existence of uniformly stable solutions have discussed.Five:The Caputo is described as the fractional derivative.Six:Fractional optimal control for NFFMA is discussed by clarifying the fractional optimal control through drawing before and after control.Seven:Reduced differential transform method(RDTM)and Sumudu Decomposition Method(SDM)are used to take the resolution of an NFFMA.Finally,we display that SDM and RDTM are highly identical.

The influence of CEO’s financial literacy on SMEs technological innovation:the mediating effects of MCS and risk‑taking

Previous literature showed mixed results about the impact of CEOs’financial literacy(CFL)on small and medium-sized enterprises’(SMEs)innovation.This relationship can be motivated by relevant variables,which are missing in the previous literature and make a difference as mediators.In this sense,based on the theoretical framework related to upper echelon theory and resource-based view,this study focuses on the mediating effect of risk-taking attitude and management control systems(MCS)varia-bles.Empirical data from 310 SMEs gathered using a qualitative research questionnaire are analyzed using structural equation modeling methodology.Specifically,estimations are carried out considering the partial least square method.Findings show that MCS and managers’risk attitudes fully mediate the relationship between financial literacy(FL)and innovation.Between these two mediating variables,the implementation of MCS stands out because it also enables the mediating effect of CEOs’risk-taking in the CFL–technological innovation relationship.As the results do not support the significant direct relationship between FL and risk attitude,they confirm an indirect effect through MCS.Furthermore,based on the study findings,SMEs’directors and owners,business associations,and public authorities can improve SMEs’technological innovation by implementing training programs and policies to foster CFL.They can also acknowledge the interdependency between organizational factors and individual characteristics to enhance SMEs’technological innovation.

Chaotic dynamic behavior analysis and control for a financial risk system

According to the risk management process of financial markets,a financial risk dynamic system is constructed in this paper.Through analyzing the basic dynamic properties,we obtain the conditions for stability and bifurcation of the system based on Hopf bifurcation theory of nonlinear dynamic systems.In order to make the system＇s chaos disappear,we select the feedback gain matrix to design a class of chaotic controller.Numerical simulations are performed to reveal the change process of financial market risk.It is shown that,when the parameter of risk transmission rate changes,the system gradually comes into chaos from the asymptotically stable state through bifurcation.The controller can then control the chaos effectively.

Real-Time Iterative Compensation Framework for Precision Mechatronic Motion Control Systems

With regard to precision/ultra-precision motion systems,it is important to achieve excellent tracking performance for various trajectory tracking tasks even under uncertain external disturbances.In this paper,to overcome the limitation of robustness to trajectory variations and external disturbances in offline feedforward compensation strategies such as iterative learning control(ILC),a novel real-time iterative compensation(RIC)control framework is proposed for precision motion systems without changing the inner closed-loop controller.Specifically,the RIC method can be divided into two parts,i.e.,accurate model prediction and real-time iterative compensation.An accurate prediction model considering lumped disturbances is firstly established to predict tracking errors at future sampling times.In light of predicted errors,a feedforward compensation term is developed to modify the following reference trajectory by real-time iterative calculation.Both the prediction and compen-sation processes are finished in a real-time motion control sampling period.The stability and convergence of the entire control system after real-time iterative compensation is analyzed for different conditions.Various simulation results consistently demonstrate that the proposed RIC framework possesses satisfactory dynamic regulation capability,which contributes to high tracking accuracy comparable to ILC or even better and strong robustness.

Real-Time Memory Data Optimization Mechanism of Edge IoT Agent

With the full development of disk-resident databases(DRDB)in recent years,it is widely used in business and transactional applications.In long-term use,some problems of disk databases are gradually exposed.For applications with high real-time requirements,the performance of using disk database is not satisfactory.In the context of the booming development of the Internet of things,domestic real-time databases have also gradually developed.Still,most of them only support the storage,processing,and analysis of data values with fewer data types,which can not fully meet the current industrial process control system data types,complex sources,fast update speed,and other needs.Facing the business needs of efficient data collection and storage of the Internet of things,this paper optimizes the transaction processing efficiency and data storage performance of the memory database,constructs a lightweight real-time memory database transaction processing and data storage model,realizes a lightweight real-time memory database transaction processing and data storage model,and improves the reliability and efficiency of the database.Through simulation,we proved that the cache hit rate of the cache replacement algorithm proposed in this paper is higher than the traditional LRU(Least Recently Used)algorithm.Using the cache replacement algorithm proposed in this paper can improve the performance of the system cache.

Lessons from the Real World: Financial Incentives to Improve Glycemic Control in Patients with Type 2 Diabetes

Objectives: While the value of glycemic control to minimize adverse health outcomes among patients with diabetes is clear, achieving hemoglobin A1c (A1c) goals remain a challenge. We evaluated the use of financial incentives to increase enrollment and improve glycemic control among patients invited to participate in a monthly diabetes group appointment (DGA) as part of their enrollment in DaVita HealthCare Partners, a large southern California managed care organization. Methods: Adult diabetes patients (≥18 years) with a currently uncontrolled hemoglobin A1c level (>8.0% if 9.0% if ≥ 65 years) were randomized to 1) no DGA, 2) DGA with no financial incentives (non-incentive DGA) or 3) DGA with financial incentives (incentive DGA). Results: Nine sites among four regions of the greater Los Angeles area participated. Each site offered one non-incentive DGA and one incentive DGA. Over 1500 patients were identified for recruitment and at the peak of enrollment, 299 patients were enrolled in 18 DGAs. On average, hemoglobin A1c values dropped more for patients participating in the incentive DGA (9.9% to 8.7%, -1.2%) versus non-incentive DGA (9.7% to 9.0%, -0.7%) versus no DGA group (9.1% to 8.7%, -0.4%). Several unexpected implementation challenges arose which complicated evaluation but provide important learning lessons. Conclusions: Management of chronic diseases like diabetes is challenging for patients and the primary care system alike. Continuing to implement and evaluate programs under “real-world” conditions can provide further insight into how best to support patients with diabetes and their primary care teams in order to achieve glycemic control and avoid preventable complications.

An Efficient Secure Real-Time Concurrency Control Protocol

Secure real-time databases must simultaneously satisfy two requirements in guaranteeing data security and minimizing the missing deadlines ratio of transactions. However, these two requirements can conflict with each other and achieve one requirement is to sacrifice the other. This paper presents a secure real-time concurrency control protocol based on optimistic method. The concurrency control protocol incorporates security constraints in a real-time optimistic concurrency control protocol and makes a suitable tradeoff between security and real-time requirements by introducing secure influence factor and real-time influence factor. The experimental results show the concurrency control protocol achieves data security without degrading real-time perform ance significantly.

A Model Predictive Scheduling Algorithm inReal-Time Control Systems

The ineffective utilization of power resources has attracted much attention in current years. This paper proposes a real-time distributed load scheduling algorithm considering constraints of power supply. Firstly, an objective function is designed based on the constraint, and a base load forecasting model is established when aggregating renewable generation and non-deferrable load into a power system, which aims to transform the problem of deferrable loads scheduling into a distributed optimal control problem. Then, to optimize the objective function, a real-time scheduling algorithm is presented to solve the proposed control problem. At every time step, the purpose is to minimize the variance of differences between power supply and aggregate load, which can thus ensure the effective utilization of power resources. Finally, simulation examples are provided to illustrate the effectiveness of the proposed algorithm.

Real-time hybrid simulation for structural control performance assessment

Real-time hybrid simulation is an attractive method to evaluate the response of structures under earthquake loads. The method is a variation of the pseudodynamic testing technique in which the experiment is executed in real time, thus allowing investigation of structural systems with rate-dependent components. Real-time hybrid simulation is challenging because it requires performance of all calculations, application of displacements, and acquisition of measured forces, within a very small increment of time. Furthermore, unless appropriate compensation for actuator dynamics is implemented, stability problems are likely to occur during the experiment. This paper presents an approach for real-time hybrid simulation in which compensation for actuator dynamics is implemented using a model-based feedforward compensator. The method is used to evaluate the response of a semi-active control of a structure employing an MR damper. Experimental results show good agreement with the predicted responses, demonstrating the effectiveness of the method for structural control performance assessment.

Real-time Design Constraints in Implementing Active Vibration Control Algorithms

Although computer architectures incorporate fast processing hardware resources, high performance real-time implementation of a complex control algorithm requires an efficient design and software coding of the algorithm so as to exploit special features of the hardware and avoid associated architecture shortcomings. This paper presents an investigation into the analysis and design mechanisms that will lead to reduction in the execution time in implementing real-time control algorithms. The proposed mechanisms are exemplified by means of one algorithm, which demonstrates their applicability to real-time applications. An active vibration control （AVC） algorithm for a flexible beam system simulated using the finite difference （FD） method is considered to demonstrate the effectiveness of the proposed methods. A comparative performance evaluation of the proposed design mechanisms is presented and discussed through a set of experiments.

Multi-objective Optimization of Multi-Agent Elevator Group Control System Based on Real-time Particle Swarm Optimization Algorithm

In order to get a globally optimized solution for the Elevator Group Control System (EGCS) scheduling problem, an algorithm with an overall optimization function is needed. In this study, Real-time Particle Swarm Optimization (RPSO) is proposed to find an optimal solution to the EGCS scheduling problem. Different traffic patterns and controller mechanisms for EGCS are analyzed. This study focuses on up-peak traffic because of its critical importance to modern office buildings. Simulation results show that EGCS based on Multi-Agent Systems (MAS) using RPSO gives good results for up-peak EGCS scheduling problem. Besides, the elevator real-time scheduling and reallocation functions are realized based on RPSO in case new information is available or the elevator becomes busy because it is unavailable or full. This study contributes a new scheduling algorithm for EGCS, and expands the application of PSO.

Real-time Control Oriented HCCI Engine Cycle-to-cycle Dynamic Modelling

For homogeneous charge compression ignition （HCCI） combustion, the auto-ignition process is very sensitive to in-cylinder conditions, including in-cylinder temperature, in-cylinder components and concentrations. Therefore, accurate control is required for reliable and efficient HCCI combustion. This paper outlines a simplified gasoline-fueled HCCI engine model implemented in Simulink environment. The model is able to run in real-time and with fixed simulation steps with the aim of cycle-to-cycle control and hardware- in-the-loop simulation. With the aim of controlling the desired amount of the trapped exhaust gas recirculation （EGR） from the previous cycle, the phase of the intake and exhaust valves and the respective profiles are designed to vary in this model. The model is able to anticipate the auto-ignition timing and the in-cylinder pressure and temperature. The validation has been conducted using a comparison of the experimental results on Ricardo Hydro engine published in a research by Tianjin University and a JAGUAR V6 HCCI test engine at the University of Birmingham. The comparison shows the typical HCCI combustion and a fair agreement between the simulation and experimental results.