Rapid Parameter-Optimizing Strategy for Plug-and-Play Devices in DC Distribution Systems under the Background of Digital Transformation

By integrating advanced digital technologies such as cloud computing and the Internet of Things in sensor measurement,information communication,and other fields,the digital DC distribution network can efficiently and reliably access DistributedGenerator(DG)and Energy Storage Systems(ESS),exhibiting significant advantages in terms of controllability and meeting requirements of Plug-and-Play(PnP)operations.However,during device plug-in and-out processes,improper systemparametersmay lead to small-signal stability issues.Therefore,before executing PnP operations,conducting stability analysis and adjusting parameters swiftly is crucial.This study introduces a four-stage strategy for parameter optimization to enhance systemstability efficiently.In the first stage,state-of-the-art technologies in measurement and communication are utilized to correct model parameters.Then,a novel indicator is adopted to identify the key parameters that influence stability in the second stage.Moreover,in the third stage,a local-parameter-tuning strategy,which leverages rapid parameter boundary calculations as a more efficient alternative to plotting root loci,is used to tune the selected parameters.Considering that the local-parameter-tuning strategy may fail due to some operating parameters being limited in adjustment,a multiparameter-tuning strategy based on the particle swarm optimization(PSO)is proposed to comprehensively adjust the dominant parameters to improve the stability margin of the system.Lastly,system stability is reassessed in the fourth stage.The proposed parameter-optimization strategy’s effectiveness has been validated through eigenvalue analysis and nonlinear time-domain simulations.

Optimizing Sustainability:Exergoenvironmental Analysis of a Multi-Effect Distillation with Thermal Vapor Compression System for Seawater Desalination

Seawater desalination stands as an increasingly indispensable solution to address global water scarcity issues.This study conducts a thorough exergoenvironmental analysis of a multi-effect distillation with thermal vapor compression(MED-TVC)system,a highly promising desalination technology.The MED-TVC system presents an energy-efficient approach to desalination by harnessing waste heat sources and incorporating thermal vapor compression.The primary objective of this research is to assess the system’s thermodynamic efficiency and environmental impact,considering both energy and exergy aspects.The investigation delves into the intricacies of energy and exergy losses within the MED-TVC process,providing a holistic understanding of its performance.By scrutinizing the distribution and sources of exergy destruction,the study identifies specific areas for enhancement in the system’s design and operation,thereby elevating its overall sustainability.Moreover,the exergoenvironmental analysis quantifies the environmental impact,offering vital insights into the sustainability of seawater desalination technologies.The results underscore the significance of every component in the MED-TVC system for its exergoenvironmental performance.Notably,the thermal vapor compressor emerges as pivotal due to its direct impact on energy efficiency,exergy losses,and the environmental footprint of the process.Consequently,optimizing this particular component becomes imperative for achieving a more sustainable and efficient desalination system.

Optimizing a Transportation System Using Metaheuristics Approaches (EGD/GA/ACO): A Forest Vehicle Routing Case Study

The large-scale optimization problem requires some optimization techniques, and the Metaheuristics approach is highly useful for solving difficult optimization problems in practice. The purpose of the research is to optimize the transportation system with the help of this approach. We selected forest vehicle routing data as the case study to minimize the total cost and the distance of the forest transportation system. Matlab software helps us find the best solution for this case by applying three algorithms of Metaheuristics: Genetic Algorithm (GA), Ant Colony Optimization (ACO), and Extended Great Deluge (EGD). The results show that GA, compared to ACO and EGD, provides the best solution for the cost and the length of our case study. EGD is the second preferred approach, and ACO offers the last solution.

Optimizing water and nitrogen inputs for winter wheat cropping system on the Loess Plateau,China

Optimal use of water and fertilizers can enhance winter wheat yield and increase the efficiencies of water and fertilizer usage in dryland agricultural systems. In order to optimize water and nitrogen （N） management for winter wheat, we conducted field experiments from 2006 to 2008 at the Changwu Agro-ecological Experimental Station of the Chinese Academy of Sciences on the Loess Plateau, China. Regression models of wheat yield and evapotranspiration （ET） were established in this study to evaluate the water and fertilizer coupling effects and to determine the optimal coupling domain. The results showed that there was a positive effect of water and N fertilizer on crop yield, and optimal irrigation and N inputs can significantly increase the yield of winter wheat. In the drought year （2006-2007）, the maximum yield （Yma~） of winter wheat was 9.211 t/hm2 for the treatment with 324 mm irriga- tion and 310 kg/hm2 N input, and the highest water use efficiency （WUE） of 16.335 kg/（hm2.mm） was achieved with 198 mm irrigation and 274 kg/hm2 N input. While in the normal year （2007-2008）, the maximum winter wheat yield of 10.715 t/hm2 was achieved by applying 318 mm irrigation and 291 kg/hm2 N, and the highest WUE was 18.69 kg/（hm2.mm） with 107 mm irrigation and 256 kg/hm2 N input. Crop yield and ET response to irrigation and N inputs followed a quadratic and a line function, respectively. The optimal coupling domain was determined using the elas- ticity index （El） and its expression in the water-N dimensions, and was represented by an ellipse, such that the global maximum WUE （WUErnax） and Ymax values corresponded to the left and right end points of the long axis, respectively. Considering the aim to get the greatest profit in practice, the optimal coupling domain was represented by the lower half of the ellipse, with the Yma~ and WUE^ax on the two end points of the long axis. Overall, we found that the total amount of irrigation for winter wheat should not exceed 324 ram. In addition, our optimal coupling domain visually reflects the optimal range of water and N inputs for the maximum winter wheat yield on the Loess Plateau, and it may also provide a useful reference for identifying appropriate water and N inputs in agricultural applications.

Optimizing control of a two-span rotor system with magnetorheological fluid dampers

A control system aims at vibration reduction in a two-span rotor system with two shear mode magnetorheological （MRF） dampers is designed. A finite element model of the MRF damper- rotor system is built and used to analyze the rotor vibration characteristics. Based on Hooke and Jeeves algorithm and the numerical simulation analysis, an optimal appropriate controller is proposed and designed. Experimental results show that rotor vibration caused by unbalance is well controlled （ first critical speed region 37% , second critical speed region 42% ）. To reflect advantages of optimi- zing strategy presented and validate the intelligent optimization control technology, detailed experi- ments were developed on a two-span rotor-vibration-control platform. The influence on accuracy, rapidity and stability of optimizing control for rotor vibration are analyzed. It provides a powerful technical support for the extension and application in target and control for shafting vibration.

Parameters Optimizing of Ammonia-Recovery System in the Modified Equipment of Flax Fiber

A detailed analysis of operational process and principle of ammonia-recovery system in the modified equipment of flax fiber,which will be applied to parameters optimizing of the ammoniarecovery system as a foundational principle,is presented. According to the principle,an ammonia compressor,whose working conditions are based on key operational parameters of the whole ammoniarecovery system, is the mainly energy-consumption part of ammonia-recovery system in the modified equipment of flax fiber. A generally mathematical model based on work efficiency of an ammonia compressor is founded,which is available to rate effective work and energy consumption of the ammonia compressor. The optimum operation-efficiency of the ammonia compressor is chosen as the goal to analyze and calculate the key operational parameters of the ammonia-recovery system. In the above analyzing and calculating,a mathematical model on ammonia flowing from the reactor to the register 1 is developed,in order to provide further understanding of the principle of an ammonia-recovery system. At the meantime,the ammonia flow regime in the pipeline and the process of ammonia inflation and deflation from the reactor to the register 1 are taken separately into account in the model. An iterative method is for obtaining parametric solutions of the mathematical model on ammonia flowing from the reactor to the register 1 and the key operational parameters of the ammoniarecovery system. A parametric analysis is put forward to complete showing the ammonia velocity or the state of the reactor and the register 1. The key optimized parameters will be achieved in term of the minimum efficiency after comparing the work efficiencies of an ammonia compressor at different working conditions.

RESEARCH ON OPTIMIZING THE MERGING RESULTS OF MULTIPLE INDEPENDENT RETRIEVAL SYSTEMS BY A DISCRETE PARTICLE SWARM OPTIMIZATION

The result merging for multiple Independent Resource Retrieval Systems (IRRSs), which is a key component in developing a meta-search engine, is a difficult problem that still not effectively solved. Most of the existing result merging methods, usually suffered a great influence from the usefulness weight of different IRRS results and overlap rate among them. In this paper, we proposed a scheme that being capable of coalescing and optimizing a group of existing multi-sources-retrieval merging results effectively by Discrete Particle Swarm Optimization (DPSO). The experimental results show that the DPSO, not only can overall outperform all the other result merging algorithms it employed, but also has better adaptability in application for unnecessarily taking into account different IRRS's usefulness weight and their overlap rate with respect to a concrete query. Compared to other result merging algorithms it employed, the DPSO's recognition precision can increase nearly 24.6%, while the precision standard deviation for different queries can decrease about 68.3%.

An Optimized System of Random Forest Model by Global Harmony Search with Generalized Opposition-Based Learning for Forecasting TBM Advance Rate

As massive underground projects have become popular in dense urban cities,a problem has arisen:which model predicts the best for Tunnel Boring Machine(TBM)performance in these tunneling projects?However,performance level of TBMs in complex geological conditions is still a great challenge for practitioners and researchers.On the other hand,a reliable and accurate prediction of TBM performance is essential to planning an applicable tunnel construction schedule.The performance of TBM is very difficult to estimate due to various geotechnical and geological factors and machine specifications.The previously-proposed intelligent techniques in this field are mostly based on a single or base model with a low level of accuracy.Hence,this study aims to introduce a hybrid randomforest(RF)technique optimized by global harmony search with generalized oppositionbased learning(GOGHS)for forecasting TBM advance rate(AR).Optimizing the RF hyper-parameters in terms of,e.g.,tree number and maximum tree depth is the main objective of using the GOGHS-RF model.In the modelling of this study,a comprehensive databasewith themost influential parameters onTBMtogetherwithTBM AR were used as input and output variables,respectively.To examine the capability and power of the GOGHSRF model,three more hybrid models of particle swarm optimization-RF,genetic algorithm-RF and artificial bee colony-RF were also constructed to forecast TBM AR.Evaluation of the developed models was performed by calculating several performance indices,including determination coefficient(R2),root-mean-square-error(RMSE),and mean-absolute-percentage-error(MAPE).The results showed that theGOGHS-RF is a more accurate technique for estimatingTBMAR compared to the other applied models.The newly-developedGOGHS-RFmodel enjoyed R2=0.9937 and 0.9844,respectively,for train and test stages,which are higher than a pre-developed RF.Also,the importance of the input parameters was interpreted through the SHapley Additive exPlanations(SHAP)method,and it was found that thrust force per cutter is the most important variable on TBMAR.The GOGHS-RF model can be used in mechanized tunnel projects for predicting and checking performance.

CCOC：An Optimizing Compiling System for Concurrent C Supporting Inter－process Code Analysis and Optimization

At present, there are some static code analyses and optimizations that can be applied to Concurrent C programs to improve their performance or verify their logical correctness. These analyses and optimizations are inter-process. In order to make their implementation easy, we propose a new method to construct an optimizing compiling system CCOC for Concurrent C. CCOC supports inter-process code analysis and optimization to Concurrent C programs and does not affect the system's portability and separate compilation of source programs. We also discuss some implementation details of CCOC briefly.

The Networked Manufacturing Resources Optimizing Configuration System and Its Partners Selection Method

The three levels optimizing strategy is put forward for the networked manufacturing resources optimizing configuration, namely, the optimizing of a logical manufacturing process, the optimizing of simulation-based integration of process planning and scheduling, and the optimizing of networked production scheduling. Then, the web services-based architecture of networked manufacturing resources optimizing configuration is brought forward. Finally, the key algorithm of the networked manufacturing resources optimizing configuration is discussed, namely, the two phases manufacturing partners selection method, which including the group technology-based manufacturing resources pre-configuration and the genetic algorithm-based executable manufacturing process optimizing.

Optimizing high-coordination shell of Co-based single-atom catalysts for efficient ORR and zinc-air batteries

Atom-level modulation of the coordination environment for single-atom catalysts(SACs)is considered as an effective strategy for elevating the catalytic performance.For the MNxsite,breaking the symmetrical geometry and charge distribution by introducing relatively weak electronegative atoms into the first/second shell is an efficient way,but it remains challenging for elucidating the underlying mechanism of interaction.Herein,a practical strategy was reported to rationally design single cobalt atoms coordinated with both phosphorus and nitrogen atoms in a hierarchically porous carbon derived from metal-organic frameworks.X-ray absorption spectrum reveals that atomically dispersed Co sites are coordinated with four N atoms in the first shell and varying numbers of P atoms in the second shell(denoted as Co-N/P-C).The prepared catalyst exhibits excellent oxygen reduction reaction(ORR)activity as well as zinc-air battery performance.The introduction of P atoms in the Co-SACs weakens the interaction between Co and N,significantly promoting the adsorption process of ^(*)OOH,resulting in the acceleration of reaction kinetics and reduction of thermodynamic barrier,responsible for the increased intrinsic activity.Our discovery provides insights into an ultimate design of single-atom catalysts with adjustable electrocatalytic activities for efficient electrochemical energy conversion.

Advanced Optimized Anomaly Detection System for IoT Cyberattacks Using Artificial Intelligence

While emerging technologies such as the Internet of Things(IoT)have many benefits,they also pose considerable security challenges that require innovative solutions,including those based on artificial intelligence(AI),given that these techniques are increasingly being used by malicious actors to compromise IoT systems.Although an ample body of research focusing on conventional AI methods exists,there is a paucity of studies related to advanced statistical and optimization approaches aimed at enhancing security measures.To contribute to this nascent research stream,a novel AI-driven security system denoted as“AI2AI”is presented in this work.AI2AI employs AI techniques to enhance the performance and optimize security mechanisms within the IoT framework.We also introduce the Genetic Algorithm Anomaly Detection and Prevention Deep Neural Networks(GAADPSDNN)sys-tem that can be implemented to effectively identify,detect,and prevent cyberattacks targeting IoT devices.Notably,this system demonstrates adaptability to both federated and centralized learning environments,accommodating a wide array of IoT devices.Our evaluation of the GAADPSDNN system using the recently complied WUSTL-IIoT and Edge-IIoT datasets underscores its efficacy.Achieving an impressive overall accuracy of 98.18%on the Edge-IIoT dataset,the GAADPSDNN outperforms the standard deep neural network(DNN)classifier with 94.11%accuracy.Furthermore,with the proposed enhancements,the accuracy of the unoptimized random forest classifier(80.89%)is improved to 93.51%,while the overall accuracy(98.18%)surpasses the results(93.91%,94.67%,94.94%,and 94.96%)achieved when alternative systems based on diverse optimization techniques and the same dataset are employed.The proposed optimization techniques increase the effectiveness of the anomaly detection system by efficiently achieving high accuracy and reducing the computational load on IoT devices through the adaptive selection of active features.

Study on Optimizing Thermal Energy Storage Capacity for Heat Supply System

Thermal energy storage (TES) can increase the energetic efficiencies and, in many cases, the exergetic efficiencies of thermal energy systems. Steam boiler plant with a violently fluctuating load is a typical example when a steam accumulator is added to it. While the conparatively big first cost constitutes a barrier to the wide use of TES, the cost will notably be reduced through minimizing the necessary thermal capacity of it JThe structure and illustrations are given for the computer program designed for performing the optimization. This'program was applied to an existing boiler plant equipped with a steam accumulator. The results show that there would have been a big reduction in the necessary capacity, if the design of this steam accumulator had been optimized. Four conclusions have been reached.

A Stochastic Strategy for Optimizing the Cost of Systematic Screening of Some Diseases in a Situation of Insufficient Equipment

In this work, we show that when there is insufficient equipment for detecting a disease whose prevalence is t% in a sub-population of size N, it is optimal to divide the N samples into n groups of size r each and then, the value allows systematic screening of all N individuals by performing less than N tests (In this expression, represents the floor function1 of x ∈ R). Based on this result and on certain functions of the R software, we subsequently propose a probabilistic strategy capable of optimizing the screening tests under certain conditions.

Optimizing wind farm layout for enhanced electricity extraction using a new hybrid PSO-ANN method

With the growing need for renewable energy,wind farms are playing an important role in generating clean power from wind resources.The best wind turbine architecture in a wind farm has a major influence on the energy extraction efficiency.This paper describes a unique strategy for optimizing wind turbine locations on a wind farm that combines the capabilities of particle swarm optimization(PSO)and artificial neural networks(ANNs).The PSO method was used to explore the solution space and develop preliminary turbine layouts,and the ANN model was used to fine-tune the placements based on the predicted energy generation.The proposed hybrid technique seeks to increase energy output while considering site-specific wind patterns and topographical limits.The efficacy and superiority of the hybrid PSO-ANN methodology are proved through comprehensive simulations and comparisons with existing approaches,giving exciting prospects for developing more efficient and sustainable wind farms.The integration of ANNs and PSO in our methodology is of paramount importance because it leverages the complementary strengths of both techniques.Furthermore,this novel methodology harnesses historical data through ANNs to identify optimal turbine positions that align with the wind speed and direction and enhance energy extraction efficiency.A notable increase in power generation is observed across various scenarios.The percentage increase in the power generation ranged from approximately 7.7%to 11.1%.Owing to its versatility and adaptability to site-specific conditions,the hybrid model offers promising prospects for advancing the field of wind farm layout optimization and contributing to a greener and more sustainable energy future.

Distributed Minimum-Energy Containment Control of Continuous-Time Multi-Agent Systems by Inverse Optimal Control

Dear Editor,This letter focuses on the distributed optimal containment control of continuous-time multi-agent systems(CTMASs)with respect to the minimum-energy performance index over fixed topology.To achieve this,we firstly investigate the optimal containment control problem using the inverse optimal control method,where all states of followers asymptotically converge to the convex hull spanned by the leaders while some quadratic performance indexes get minimized.A sufficient condition for existence of the distributed optimal containment control protocol is derived.By introducing the parametric algebraic Riccati equation(PARE),it is strictly proved that the global performance index can be used to approximate the standard minimumenergy performance index as the parameters tends to infinity.In consequence,the standard minimum-energy cooperative containment control can be solved by local steady state feedback protocols.

Fixed-Time Cluster Optimization for Multi-Agent Systems Based on Piecewise Power-Law Design

Dear Editor,This letter focuses on the fixed-time(FXT)cluster optimization problem of first-order multi-agent systems(FOMASs)in an undirected network,in which the optimization objective is the sum of the objective functions of all clusters.A novel piecewise power-law control protocol with cooperative-competition relations is proposed.Furthermore,a sufficient condition is obtained to ensure that the FOMASs achieve the cluster consensus within an FXT.

Digital Twin Technology of Human-Machine Integration in Cross-Belt Sorting System

The Chinese express delivery industry processes nearly 110 billion items in 2022,averaging an annual growth rate of 200%.Among the various types of sorting systems used for handling express items,cross-belt sorting systems stand out as the most crucial.However,despite their high degree of automation,the workload for operators has intensified owing to the surging volume of express items.In the era of Industry 5.0,it is imperative to adopt new technologies that not only enhance worker welfare but also improve the efficiency of cross-belt systems.Striking a balance between efficiency in handling express items and operator well-being is challenging.Digital twin technology offers a promising solution in this respect.A realization method of a human-machine integrated digital twin is proposed in this study,enabling the interaction of biological human bodies,virtual human bodies,virtual equipment,and logistics equipment in a closed loop,thus setting an operating framework.Key technologies in the proposed framework include a collection of heterogeneous data from multiple sources,construction of the relationship between operator fatigue and operation efficiency based on physiological measurements,virtual model construction,and an online optimization module based on real-time simulation.The feasibility of the proposed method was verified in an express distribution center.

Long-term operation optimization of circulating cooling water systems under fouling conditions

Fouling caused by excess metal ions in hard water can negatively impact the performance of the circulating cooling water system(CCWS)by depositing ions on the heat exchanger's surface.Currently,the operation optimization of CCWS often prioritizes short-term flow velocity optimization for minimizing power consumption,without considering fouling.However,low flow velocity promotes fouling.Therefore,it's crucial to balance fouling and energy/water conservation for optimal CCWS long-term operation.This study proposes a mixed-integer nonlinear programming(MINLP)model to achieve this goal.The model considers fouling in the pipeline,dynamic concentration cycle,and variable frequency drive to optimize the synergy between heat transfer,pressure drop,and fouling.By optimizing the concentration cycle of the CCWS,water conservation and fouling control can be achieved.The model can obtain the optimal operating parameters for different operation intervals,including the number of pumps,frequency,and valve local resistance coefficient.Sensitivity experiments on cycle and environmental temperature reveal that as the cycle increases,the marginal benefits of energy/water conservation decrease.In periods with minimal impact on fouling rate,energy/water conservation can be achieved by increasing the cycle while maintaining a low fouling rate.Overall,the proposed model has significant energy/water saving effects and can comprehensively optimize the CCWS through its incorporation of fouling and cycle optimization.

A Survey on Type-3 Fuzzy Logic Systems and Their Control Applications

In this paper,we offer a review of type-3 fuzzy logic systems and their applications in control.The main objective of this work is to observe and analyze in detail the applications in the control area using type-3 fuzzy logic systems.In this case,we review their most important applications in control and other related topics with type-3 fuzzy systems.Intelligent algorithms have been receiving increasing attention in control and for this reason a review in this area is important.This paper reviews the main applications that make use of Intelligent Computing methods.Specifically,type-3 fuzzy logic systems.The aim of this research is to be able to appreciate,in detail,the applications in control systems and to point out the scientific trends in the use of Intelligent Computing techniques.This is done with the construction and visualization of bibliometric networks,developed with VosViewer Software,which it is a free Java-based program,mainly intended to be used for analyzing and visualizing bibliometric networks.With this tool,we can create maps of publications,authors,or journals based on a co-citation network or construct maps of keywords,countries based on a co-occurrence networks,research groups,etc.