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.

Impact of observational MJO forcing on ENSO predictability in the Zebiak-Cane model: PartⅠ.Effect on the maximum prediction error

With the observational wind data and the Zebiak-Cane model, the impact of Madden-Iulian Oscillation （MJO） as external forcing on El Nino-Southern Oscillation （ENSO） predictability is studied. The observational data are analyzed with Continuous Wavelet Transform （CWT） and then used to extract MJO signals, which are added into the model to get a new model. After the Conditional Nonlinear Optimal Perturbation （CNOP） method has been used, the initial errors which can evolve into maximum prediction error, model errors and their join errors are gained and then the Nifio 3 indices and spatial structures of three kinds of errors are investigated. The results mainly show that the observational MJO has little impact on the maximum prediction error of ENSO events and the initial error affects much greater than model error caused by MJO forcing. These demonstrate that the initial error might be the main error source that produces uncertainty in ENSO prediction, which could provide a theoretical foundation for the adaptive data assimilation of the ENSO forecast and contribute to the ENSO target observation.

Complex Problems Solution as a Service Based on Predictive Optimization and Tasks Orchestration in Smart Cities

Smart cities have different contradicting goals having no apparent solution.The selection of the appropriate solution,which is considered the best compromise among the candidates,is known as complex problem-solving.Smart city administrators face different problems of complex nature,such as optimal energy trading in microgrids and optimal comfort index in smart homes,to mention a few.This paper proposes a novel architecture to offer complex problem solutions as a service(CPSaaS)based on predictive model optimization and optimal task orchestration to offer solutions to different problems in a smart city.Predictive model optimization uses a machine learning module and optimization objective to compute the given problem’s solutions.The task orchestration module helps decompose the complex problem in small tasks and deploy them on real-world physical sensors and actuators.The proposed architecture is hierarchical and modular,making it robust against faults and easy to maintain.The proposed architecture’s evaluation results highlight its strengths in fault tolerance,accuracy,and processing speed.

OPTIMAL COMBINING PREDICTION FOR BLENDING EFFICIENCY

By means of analysing the mechanism of blending materials,a general blending efficiency model was proposed.Applying this general model to an example 9 a suitable formula of blending efficiency which is more accurate than those in papers[2-3]was obtained.Finally,a high-precision optimal combining prediction formula for calculating blending efficiency was proposed.

Research on Optimize Prediction Model and Algorithm about Chaotic Time Series

We put forward a chaotic estimating model, by using the parameter of the chaotic system, sensitivity of the parameter to inching and control the disturbance of the system, and estimated the parameter of the model by using the best update option. In the end, we forecast the intending series value in its mutually space. The example shows that it can increase the precision in the estimated process by selecting the best model steps. It not only conquer the abuse of using detention inlay technology alone, but also decrease blindness of using forecast error to decide the input model directly, and the result of it is better than the method of statistics and other series means. Key words chaotic time series - parameter identification - optimal prediction model - improved change ruler method CLC number TP 273 Foundation item: Supported by the National Natural Science Foundation of China (60373062)Biography: JIANG Wei-jin (1964-), male, Professor, research direction: intelligent compute and the theory methods of distributed data processing in complex system, and the theory of software.

EMF Waveform Optimization using the Permanent Magnet Volume-Integration Method

The emf expression can be derived with the PM volume-integration method,allowing easier optimization and prediction of the emf harmonic content.An analytical expression is developed for predicting the electromotive force(emf)waveforms and flux linkage resulting from the motion of permanent magnets(PM)in the case of two cylinders,where the outer cylinder carries a surface-mounted winding and the inner cylinder carries the PMs.The expressions are based on the PM Volume-Integration Method,which uses a volume integral calculated over the magnet volume,rather than the usual surface integral over the coil surface.The specific case of surface-mounted arc PM with radial magnetization is analyzed.An outer cylinder with infinitely thin winding distribution on its inner surface is considered.The chording factor,slot factor and spread factor are included in the analytical expression.The emf waveform and related harmonics are predicted analytically and validated by comparing with a finite element analysis and with experiment.

Embracing the Future:AI and ML Transforming Urban Environments in Smart Cities

This research explores the increasing importance of Artificial Intelligence(AI)and Machine Learning(ML)with relation to smart cities.It discusses the AI and ML’s ability to revolutionize various aspects of urban environments,including infrastructure,governance,public safety,and sustainability.The research presents the definition and characteristics of smart cities,highlighting the key components and technologies driving initiatives for smart cities.The methodology employed in this study involved a comprehensive review of relevant literature,research papers,and reports on the subject of AI and ML in smart cities.Various sources were consulted to gather information on the integration of AI and ML technologies in various aspects of smart cities,including infrastructure optimization,public safety enhancement,and citizen services improvement.The findings suggest that AI and ML technologies enable data-driven decision-making,predictive analytics,and optimization in smart city development.They are vital to the development of transport infrastructure,optimizing energy distribution,improving public safety,streamlining governance,and transforming healthcare services.However,ethical and privacy considerations,as well as technical challenges,need to be solved to guarantee the ethical and responsible usage of AI and ML in smart cities.The study concludes by discussing the challenges and future directions of AI and ML in shaping urban environments,highlighting the importance of collaborative efforts and responsible implementation.The findings highlight the transformative potential of AI and ML in optimizing resource utilization,enhancing citizen services,and creating more sustainable and resilient smart cities.Future studies should concentrate on addressing technical limitations,creating robust policy frameworks,and fostering fairness,accountability,and openness in the use of AI and ML technologies in smart cities.

Stabilizing model predictive control scheme for piecewise affine systems with maximal positively invariant terminal set

An efficient algorithm is proposed for computing the solution to the constrained finite time optimal control （CFTOC） problem for discrete-time piecewise affine （PWA） systems with a quadratic performance index. The maximal positively invariant terminal set, which is feasible and invariant with respect to a feedback control law, is computed as terminal target set and an associated Lyapunov function is chosen as terminal cost. The combination of these two components guarantees constraint satisfaction and closed-loop stability for all time. The proposed algorithm combines a dynamic programming strategy with a multi-parametric quadratic programming solver and basic polyhedral manipulation. A numerical example shows that a larger stabilizable set of states can be obtained by the proposed algorithm than precious work.

An Overview to the Concept of Smart Coupling and Battery Management for Grid Connected Photovoltaic Battery System

The paper gives an overview on the need for smart coupling for battery management in grid integrated renewable energy system （RES）. Grid integrated photovoltaic （PV） battery system, as being popular and extensively used has been discussed in the paper. Smart coupling refers to intelligent grid integration such that it can foresee local network conditions and issue battery power flow management strategy accordingly to shave the peak PV and peak load. Therefore, a need for predictive energy management arises for smart integration to the grid and supervision of the power flow in accordance to the grid conditions. This is also a running project at the Institute of Energy Systems （INES）, Offenburg University of Applied Science, Germany since January, 2015. The paper should provide insights to the motivation, need and gives an outlook to the features of desired predictive energy management system （PEMS）.

Genomic selection in plant breeding:Key factors shaping two decades of progress

Genomic selection,the application of genomic prediction(GP)models to select candidate individuals,has significantly advanced in the past two decades,effectively accelerating genetic gains in plant breeding.This article provides a holistic overview of key factors that have influenced GP in plant breeding during this period.We delved into the pivotal roles of training population size and genetic diversity,and their relationship with the breeding population,in determining GP accuracy.Special emphasis was placed on optimizing training population size.We explored its benefits and the associated diminishing returns beyond an optimum size.This was done while considering the balance between resource allocation and maximizing prediction accuracy through current optimization algorithms.The density and distribution of single-nucleotide polymorphisms,level of linkage disequilibrium,genetic complexity,trait heritability,statistical machine-learning methods,and non-additive effects are the other vital factors.Using wheat,maize,and potato as examples,we summarize the effect of these factors on the accuracy of GP for various traits.The search for high accuracy in GP—theoretically reaching one when using the Pearson’s correlation as a metric—is an active research area as yet far from optimal for various traits.We hypothesize that with ultra-high sizes of genotypic and phenotypic datasets,effective training population optimization methods and support from other omics approaches(transcriptomics,metabolomics and proteomics)coupled with deep-learning algorithms could overcome the boundaries of current limitations to achieve the highest possible prediction accuracy,making genomic selection an effective tool in plant breeding.

On the quality requirements of demand prediction for dynamic public transport

As Public Transport(PT)becomes more dynamic and demand-responsive,it increasingly depends on predictions of transport demand.But how accurate need such predictions be for effective PT operation?We address this question through an experimental case study of PT trips in Metropolitan Copenhagen,Denmark,which we conduct independently of any specific prediction models.First,we simulate errors in demand prediction through unbiased noise distributions that vary considerably in shape.Using the noisy predictions,we then simulate and optimize demand-responsive PT fleets via a linear programming formulation and measure their performance.Our results suggest that the optimized performance is mainly affected by the skew of the noise distribution and the presence of infrequently large prediction errors.In particular,the optimized performance can improve under non-Gaussian vs.Gaussian noise.We also find that dynamic routing could reduce trip time by at least 23%vs.static routing.This reduction is estimated at 809,000€/year in terms of Value of Travel Time Savings for the case study.

Improved MPPT Control Strategy for PV Connected to Grid Using IncCond-PSO-MPC Approach

This paper proposes a new hybrid maximum power point tracking(MPPT)control strategy for grid-connected solar systems based on Incremental conductance—Particle Swarm Optimization and Model Predictive Controller(IncCond-PSOMPC).The purpose of the suggested method is to create as much power as feasible from a PV system during environmental changes,then transfer it to the power grid.To accomplish this,a hybrid combination of incremental conductance(IncCond)and particle swarm optimization(PSO)is proposed to locate maximum power,followed by model predictive control(MPC)to track maximum power and control the boost converter to achieve high performance regardless of parameter variations.A two-level inverter,likewise,controlled by Model Predictive Control,is employed to inject the PV power generated.In this application,the MPC is based on minimizing the difference between the reference and prediction powers,which is computed to select the switching state of the inverter.The proposed system is simulated and evaluated in a variety of dynamic conditions using Matlab/Simulink.Results reveal that the proposed control mechanism is effective at tracking the maximum power point(MPP)with fewer power oscillations.

Optimal Predictive Impedance Control in the Presence of Uncertainty for a Lower Limb Rehabilitation Robot

As an innovative concept,an optimal predictive impedance controlle is introduced here to control a lower limb rehabilitation robo in the presence of uncertainty.The desired impedance law is considered to propose a conventional model-based impedance controller for the LLRR.However,external disturbances,model imperfection,and parameters uncertainties reduce the performance of the controller in practice.In order to cope with these uncertainties,an optimal predictive compensator is introduced as a solution for a proposed convex optimization problem,which is performed on a forward finite-length horizon.As a result,the LLRR has the desired behavior even in an uncertain environment.The performance and efficiency of the proposed controller are verified by the simulation results.

Optimal Active Power Dispatching of Microgrid and Distribution Network Based on Model Predictive Control

First, a three-tier coordinated scheduling system consisting of a distribution network dispatch layer, a microgrid centralized control layer, and local control layer in the energy internet is proposed. The multi-time scale optimal scheduling of the microgrid based on Model Predictive Control（MPC） is then studied, and the optimized genetic algorithm and the microgrid multi-time rolling optimization strategy are used to optimize the datahead scheduling phase and the intra-day optimization phase. Next, based on the three-tier coordinated scheduling architecture, the operation loss model of the distribution network is solved using the improved branch current forward-generation method and the genetic algorithm. The optimal scheduling of the distribution network layer is then completed. Finally, the simulation examples are used to compare and verify the validity of the method.

Predicting saturated vapor pressure of LNG from density and temperature data with a view to improving tank pressure management

Determining the saturated vapor pressure(SVP)of LNG requires detailed thermodynamic calculations based on compositional data.Yet LNG compositions and SVPs evolve constantly for LNG stored in tanks.Moreover,the SVP of the LNG in a tank influences boil-off rates and tank pressure trends.In order to make improved tank pressure control decisions it would be beneficial for LNG tank operators to be made more constantly aware of the SVP of the LNG in a tank.Machine learning models that accurately estimate LNG SVP from density and temperature inputs offer the potential to provide such information.A dataset of five distinct,internationally traded LNG cargoes is compiled with 305 data records representing a range of temperature and density conditions.This can be used graphically to interpolate LNG SVP.However,two machine learning methods are applied to this dataset to automate the SVP predictions.A simple multi-layer perceptron artificial neural network(MLP-ANN)predicts SVP of the dataset with root mean square error(RMSE)=6.34 kPaA and R^(2)=0.975.The transparent open-box learning network(TOB),a regression-free optimized data matching algorithm predicts SVP of the dataset with RMSE=0.59 kPaA and R^(2)=0.999.When applied to infill unknown LNG compositions the superior TOB method achieves prediction accuracy of RMSE~3kPaA and R^(2)=0.996.Predicting LNG SVP to this level of accuracy is beneficial for tank-pressure management decision making.

Reduction of Linear Systems of ODEs with Optimal Replacement Variables

In this exploratory study,we present a new method of approximating a large system of ODEs by one with fewer equations,while attempting to preserve the essential dynamics of a reduced set of variables of interest.The method has the following key elements:(i)put a(simple,ad-hoc)probability distribution on the phase space of the ODE;(ii)assert that a small set of replacement variables are to be unknown linear combinations of the not-of-interest variables,and let the variables of the reduced system consist of the variables-of-interest together with the replacement variables;(iii)find the linear combinations that minimize the difference between the dynamics of the original system and the reduced system.We describe this approach in detail for linear systems of ODEs.Numerical techniques and issues for carrying out the required minimization are presented.Examples of systems of linear ODEs and variable-coefficient linear PDEs are used to demonstrate the method.We show that the resulting approximate reduced system of ODEs gives good approximations to the original system.Finally,some directions for further work are outlined.

System Reduction Using an LQR-Inspired Version of Optimal Replacement Variables

Optimal ReplacementVariables(ORV)is amethod for approximating a large system of ODEs by one with fewer equations,while attempting to preserve the essential dynamics of a reduced set of variables of interest.An earlier version of ORV[1]had some issues,including limited accuracy and in some rare cases,instability.Here we present a newversion of ORV,inspired by the linear quadratic regulator problemof control theory,which provides better accuracy,a guarantee of stability and is in some ways easier to use.

Energy Management System Design and Testing for Smart Buildings Under Uncertain Generation (Wind/Photovoltaic) and Demand

This study provides details of the energy management architecture used in the Goldwind microgrid test bed. A complete mathematical model, including all constraints and objectives, for microgrid operational management is first described using a modified prediction interval scheme. Forecasting results are then achieved every 10 min using the modified fuzzy prediction interval model, which is trained by particle swarm optimization.A scenario set is also generated using an unserved power profile and coverage grades of forecasting to compare the feasibility of the proposed method with that of the deterministic approach. The worst case operating points are achieved by the scenario with the maximum transaction cost. In summary, selection of the maximum transaction operating point from all the scenarios provides a cushion against uncertainties in renewable generation and load demand.

Design of a Digital Management System for the Sintering Material Ground

There are many kinds and a large number of raw materials in the sintering material ground to be managed,while it is difficult to obtain the precise inventory values,which often leads to high cost.Furthermore,the external factors of material ground are difficult to handle,such as weather variation,order fluctuation,measurement failure and so on.To solve such raw material management problems,a digital management system has been developed.First,the practical requirements and the raw material management processes are analyzed.Then,optimization and prediction methods are used to calculate the inventory according to the practical situation.With the help of practical technologies and production conditions,the developed system has been applied to a large-scale sintering material ground.The practical running results of the application demonstrate the validity of the proposed digital management system.