The Coupled Operational Systems:A Linear Optimisation Review

The purpose of this review is to summarise the existing literature on the operational systems as to explain the current state of understanding on the coupled operational systems.The review only considers the linear optimisation of the operational systems.Traditionally,the operational systems are classified as decoupled,tightly coupled,and loosely coupled.Lately,the coupled operational systems were classified as systems of time-sensitive and time-insensitive operational cycle,systems employing one mix and different mixes of factors of production,and systems of single-linear,single-linear-fractional,and multi-linear objective.These new classifications extend the knowledge about the linear optimisation of the coupled operational systems and reveal new objective-improving models and new state-of-the-art methodologies never discussed before.Business areas affected by these extensions include product assembly lines,cooperative farming,gas/oil reservoir development,maintenance service throughout multiple facilities,construction via different locations,flights traffic control in aviation,game reserves,and tramp shipping in maritime cargo transport.

Computational Experiments for Complex Social Systems:Integrated Design of Experiment System

Powered by advanced information industry and intelligent technology,more and more complex systems are exhibiting characteristics of the cyber-physical-social systems(CPSS).And human factors have become crucial in the operations of complex social systems.Traditional mechanical analysis and social simulations alone are powerless for analyzing complex social systems.Against this backdrop,computational experiments have emerged as a new method for quantitative analysis of complex social systems by combining social simulation(e.g.,ABM),complexity science,and domain knowledge.However,in the process of applying computational experiments,the construction of experiment system not only considers a large number of artificial society models,but also involves a large amount of data and knowledge.As a result,how to integrate various data,model and knowledge to achieve a running experiment system has become a key challenge.This paper proposes an integrated design framework of computational experiment system,which is composed of four parts:generation of digital subject,generation of digital object,design of operation engine,and construction of experiment system.Finally,this paper outlines a typical case study of coal mine emergency management to verify the validity of the proposed framework.

Analysis on Operational Safety and Efficiency of FAO System in Urban Rail Transit

This paper discusses two urgent problems that need to be solved in fully automatic operation(FAO)for urban rail transit.The first is the analysis of safety in FAO,while another is the analysis of efficiency in FAO.Firstly,this paper establishes an operational safety evaluation index system from the perspective of operation for the unique or typical risk sources of the FAO system,and uses the analytic hierarchy process(AHP)to evaluate the indicators,analyzes various factors that affect the safe operation of FAO,and provides safety management recommendations for FAO lines operation to maintain the FAO system specifically.Secondly,taking the Yanfang Line as an example,this paper uses OpenTrack software to analyze the efficiency of FAO operation,and conducts simulation calculations for key links such as the mainline tracking interval,train entry and exit,and return limit interval.The fault impact surface of the FAO trains is simulated and discussed to verify whether FAO can meet the long-term operation requirements of Yanfang Line.Finally,this paper compares the simulation data of FAO on the Yanfang Line with manual operation(MO)to verify the advantages of FAO and guide the engineering construction of subsequent fully automated driving lines.

Study of Axial Vibration of a Motor-Compressor System Using Operational Modal Analysis

A case study of excessive vibration on a motor-compressor system is presented in this paper.After barely two months of operation,the reciprocating compressor motor’s routine monitoring revealed excessive axial vibration amplitude.For this reason,the Operational Modal Analysis(OMA)was carried out in order to identify the pri-mary cause.According to the investigation,one of the harmonic components which was 18 times the motor’s running speed matched with a resonance frequency of 112 Hz.According to OMA study,the motor was vibrating in torsional motion because the compressor’s load had stimulated the entire motor-compressor unit at this reso-nance frequency.The analysis also demonstrates the bulging effect of the motor shaft’s axial vibration on the motor’s endplate.

A Planning Method for Operational Test of UAV Swarm Based on Mission Reliability

The unmanned aerial vehicle(UAV)swarm plays an increasingly important role in the modern battlefield,and the UAV swarm operational test is a vital means to validate the combat effectiveness of the UAV swarm.Due to the high cost and long duration of operational tests,it is essential to plan the test in advance.To solve the problem of planning UAV swarm operational test,this study considers the multi-stage feature of a UAV swarm mission,composed of launch,flight and combat stages,and proposes a method to find test plans that can maximize mission reliability.Therefore,a multi-stage mission reliability model for a UAV swarm is proposed to ensure successful implementation of the mission.A multi-objective integer optimization method that considers both mission reliability and cost is then formulated to obtain the optimal test plans.This study first constructs a mission reliability model for the UAV swarm in the combat stage.Then,the launch stage and flight stage are integrated to develop a complete PMS(Phased Mission Systems)reliability model.Finally,the Binary Decision Diagrams(BDD)and Multi Objective Quantum Particle Swarm Optimization(MOQPSO)methods are proposed to solve the model.The optimal plans considering both reliability and cost are obtained.The proposed model supports the planning of UAV swarm operational tests and represents a meaningful exploration of UAV swarm test planning.

Improving the operational stability of perovskite solar cells with cesium-doped graphene oxide interlayer

Perovskite solar cells(PSCs)have made great advances in terms of power conversion efficiency(PCE),yet their subpar stability continues to hinder their commercialization.The interface between the perovskite layer and the charge-carrier transporting layers plays a crucial role in undermining the stability of PSCs.In this work,we propose a strategy to stabilize high-performance PSCs with PCE over 23%by introducing a cesium-doped graphene oxide(GO-Cs)as an interlayer between the perovskite and hole-transporting material.The GO-Cs treated PSCs exhibit excellent operational stability with a projected T80(the time where the device PCE reduces to 80%of its initial value)of 2143 h of operation at the maximum powering point under one sun illumination.

Operational optimization of copper flotation process based on the weighted Gaussian process regression and index-oriented adaptive differential evolution algorithm

Concentrate copper grade(CCG)is one of the important production indicators of copper flotation processes,and keeping the CCG at the set value is of great significance to the economic benefit of copper flotation industrial processes.This paper addresses the fluctuation problem of CCG through an operational optimization method.Firstly,a density-based affinity propagationalgorithm is proposed so that more ideal working condition categories can be obtained for the complex raw ore properties.Next,a Bayesian network(BN)is applied to explore the relationship between the operational variables and the CCG.Based on the analysis results of BN,a weighted Gaussian process regression model is constructed to predict the CCG that a higher prediction accuracy can be obtained.To ensure the predicted CCG is close to the set value with a smaller magnitude of the operation adjustments and a smaller uncertainty of the prediction results,an index-oriented adaptive differential evolution(IOADE)algorithm is proposed,and the convergence performance of IOADE is superior to the traditional differential evolution and adaptive differential evolution methods.Finally,the effectiveness and feasibility of the proposed methods are verified by the experiments on a copper flotation industrial process.

A general Boolean semantic modelling approach for complex and intelligent industrial systems in the framework of DES

Discrete event system(DES)models promote system engineering,including system design,verification,and assessment.The advancement in manufacturing technology has endowed us to fabricate complex industrial systems.Consequently,the adoption of advanced modeling methodologies adept at handling complexity and scalability is imperative.Moreover,industrial systems are no longer quiescent,thus the intelligent operations of the systems should be dynamically specified in the model.In this paper,the composition of the subsystem behaviors is studied to generate the complexity and scalability of the global system model,and a Boolean semantic specifying algorithm is proposed for generating dynamic intelligent operations in the model.In traditional modeling approaches,the change or addition of specifications always necessitates the complete resubmission of the system model,a resource-consuming and error-prone process.Compared with traditional approaches,our approach has three remarkable advantages:(i)an established Boolean semantic can be fitful for all kinds of systems;(ii)there is no need to resubmit the system model whenever there is a change or addition of the operations;(iii)multiple specifying tasks can be easily achieved by continuously adding a new semantic.Thus,this general modeling approach has wide potential for future complex and intelligent industrial systems.

Operational requirements analysis method based on question answering of WEKG

The weapon and equipment operational requirement analysis(WEORA) is a necessary condition to win a future war,among which the acquisition of knowledge about weapons and equipment is a great challenge. The main challenge is that the existing weapons and equipment data fails to carry out structured knowledge representation, and knowledge navigation based on natural language cannot efficiently support the WEORA. To solve above problem, this research proposes a method based on question answering(QA) of weapons and equipment knowledge graph(WEKG) to construct and navigate the knowledge related to weapons and equipment in the WEORA. This method firstly constructs the WEKG, and builds a neutral network-based QA system over the WEKG by means of semantic parsing for knowledge navigation. Finally, the method is evaluated and a chatbot on the QA system is developed for the WEORA. Our proposed method has good performance in the accuracy and efficiency of searching target knowledge, and can well assist the WEORA.

Optimal scheduling of a township integrated-energy system using the adjustable heat-electricity ratio model

With the expansion and implementation of rural revitalization strategies,there is a constant need for new energy sources for the construction of new townships.Consequently,integrated energy systems with the interconnection and interaction of multiple energy sources are developing rapidly.Biomass energy,a renewable green energy source with low pollution and wide distribution,has significant application potential in integrated energy systems.Considering the application of biomass energy in townships,this study established an integrated biomass energy system and proposed a model to optimize its operation.Lowest economic cost and highest clean energy utilization rate were considered as the objective functions.In addition,a plan was suggested to adjust the heat-electricity ratio based on the characteristics of the combined heat and power of the biomass.Finally,a simulation analysis conducted for a town in China was discussed,demonstrating that the construction of a township integrated-energy system and the use of biomass can significantly reduce operating costs and improve the energy utilization rate.Moreover,by adjusting the heat-electricity ratio,the economic cost was further reduced by 6.70%,whereas the clean energy utilization rate was increased by 5.14%.

A lightweight symmetric image encryption cryptosystem in wavelet domain based on an improved sine map

In the era of big data,the number of images transmitted over the public channel increases exponentially.As a result,it is crucial to devise the efficient and highly secure encryption method to safeguard the sensitive image.In this paper,an improved sine map(ISM)possessing a larger chaotic region,more complex chaotic behavior and greater unpredictability is proposed and extensively tested.Drawing upon the strengths of ISM,we introduce a lightweight symmetric image encryption cryptosystem in wavelet domain(WDLIC).The WDLIC employs selective encryption to strike a satisfactory balance between security and speed.Initially,only the low-frequency-low-frequency component is chosen to encrypt utilizing classic permutation and diffusion.Then leveraging the statistical properties in wavelet domain,Gaussianization operation which opens the minds of encrypting image information in wavelet domain is first proposed and employed to all sub-bands.Simulations and theoretical analysis demonstrate the high speed and the remarkable effectiveness of WDLIC.

Double quantum images encryption scheme based on chaotic system

This paper explores a double quantum images representation(DNEQR)model that allows for simultaneous storage of two digital images in a quantum superposition state.Additionally,a new type of two-dimensional hyperchaotic system based on sine and logistic maps is investigated,offering a wider parameter space and better chaotic behavior compared to the sine and logistic maps.Based on the DNEQR model and the hyperchaotic system,a double quantum images encryption algorithm is proposed.Firstly,two classical plaintext images are transformed into quantum states using the DNEQR model.Then,the proposed hyperchaotic system is employed to iteratively generate pseudo-random sequences.These chaotic sequences are utilized to perform pixel value and position operations on the quantum image,resulting in changes to both pixel values and positions.Finally,the ciphertext image can be obtained by qubit-level diffusion using two XOR operations between the position-permutated image and the pseudo-random sequences.The corresponding quantum circuits are also given.Experimental results demonstrate that the proposed scheme ensures the security of the images during transmission,improves the encryption efficiency,and enhances anti-interference and anti-attack capabilities.

Optimizing near-carbon-free nuclear energy systems:advances in reactor operation digital twin through hybrid machine learning algorithms for parameter identification and state estimation

Accurate and efficient online parameter identification and state estimation are crucial for leveraging digital twin simulations to optimize the operation of near-carbon-free nuclear energy systems.In previous studies,we developed a reactor operation digital twin(RODT).However,non-differentiabilities and discontinuities arise when employing machine learning-based surrogate forward models,challenging traditional gradient-based inverse methods and their variants.This study investigated deterministic and metaheuristic algorithms and developed hybrid algorithms to address these issues.An efficient modular RODT software framework that incorporates these methods into its post-evaluation module is presented for comprehensive comparison.The methods were rigorously assessed based on convergence profiles,stability with respect to noise,and computational performance.The numerical results show that the hybrid KNNLHS algorithm excels in real-time online applications,balancing accuracy and efficiency with a prediction error rate of only 1%and processing times of less than 0.1 s.Contrastingly,algorithms such as FSA,DE,and ADE,although slightly slower(approximately 1 s),demonstrated higher accuracy with a 0.3%relative L_2 error,which advances RODT methodologies to harness machine learning and system modeling for improved reactor monitoring,systematic diagnosis of off-normal events,and lifetime management strategies.The developed modular software and novel optimization methods presented offer pathways to realize the full potential of RODT for transforming energy engineering practices.

A coordinated operation method of wind-PV-hydrogenstorage multi-agent energy system

Wind-photovoltaic(PV)-hydrogen-storage multi-agent energy systems are expected to play an important role in promoting renewable power utilization and decarbonization.In this study,a coordinated operation method was proposed for a wind-PVhydrogen-storage multi-agent energy system.First,a coordinated operation model was formulated for each agent considering peer-to-peer power trading.Second,a coordinated operation interactive framework for a multi-agent energy system was proposed based on the theory of the alternating direction method of multipliers.Third,a distributed interactive algorithm was proposed to protect the privacy of each agent and solve coordinated operation strategies.Finally,the effectiveness of the proposed coordinated operation method was tested on multi-agent energy systems with different structures,and the operational revenues of the wind power,PV,hydrogen,and energy storage agents of the proposed coordinated operation model were improved by approximately 59.19%,233.28%,16.75%,and 145.56%,respectively,compared with the independent operation model.

Review of Fault-tolerant Control for Motor Inverter Failure with Operational Quality Considered

In recent years,motor drive systems have garnered increasing attention due to their high efficiency and superior control performance.This is especially apparent in aerospace,marine propulsion,and electric vehicles,where high performance,efficiency,and reliability are crucial.The ability of the drive system to maintain long-term fault-tolerant control(FTC)operation after a failure is essential.The likelihood of inverter failures surpasses that of other components in the drive system,highlighting its critical importance.Long-term FTC operation ensures the system retains its fundamental functions until safe repairs or replacements can be made.The focus of developing a FTC strategy has shifted from basic FTC operations to enhancing the post-fault quality to accommodate the realities of prolonged operation post-failure.This paper primarily investigates FTC strategies for inverter failures in various motor drive systems over the past decade.These strategies are categorized into three types based on post-fault operational quality:rescue,remedy,and reestablishment.The paper discusses each typical control strategy and its research focus,the strengths and weaknesses of various algorithms,and recent advancements in FTC.Finally,this review summarizes effective FTC techniques for inverter failures in motor drive systems and suggests directions for future research.

Image encryption algorithm based on multiple chaotic systems and improved Joseph block scrambling

With the rapid development of digital information technology,images are increasingly used in various fields.To ensure the security of image data,prevent unauthorized tampering and leakage,maintain personal privacy,and protect intellectual property rights,this study proposes an innovative color image encryption algorithm.Initially,the Mersenne Twister algorithm is utilized to generate high-quality pseudo-random numbers,establishing a robust basis for subsequent operations.Subsequently,two distinct chaotic systems,the autonomous non-Hamiltonian chaotic system and the tentlogistic-cosine chaotic mapping,are employed to produce chaotic random sequences.These chaotic sequences are used to control the encoding and decoding process of the DNA,effectively scrambling the image pixels.Furthermore,the complexity of the encryption process is enhanced through improved Joseph block scrambling.Thorough experimental verification,research,and analysis,the average value of the information entropy test data reaches as high as 7.999.Additionally,the average value of the number of pixels change rate(NPCR)test data is 99.6101%,which closely approaches the ideal value of 99.6094%.This algorithm not only guarantees image quality but also substantially raises the difficulty of decryption.

PERIODIC SYSTEMS WITH TIME DEPENDENT MAXIMAL MONOTONE OPERATORS

We consider a first order periodic system in R^(N),involving a time dependent maximal monotone operator which need not have a full domain and a multivalued perturbation.We prove the existence theorems for both the convex and nonconvex problems.We also show the existence of extremal periodic solutions and provide a strong relaxation theorem.Finally,we provide an application to nonlinear periodic control systems.

Market Operation of Energy Storage System in Smart Grid:A Review

As a flexible resource,energy storage plays an increasingly significant role in stabilizing and supporting the power system,while providing auxiliary services.Still,the current high demand for energy storage contrasts with the fuzzy lack of market-oriented mechanisms for energy storage,the principle of market-oriented operation has not been embodied,and there is no unified and systematic analytical framework for the business model.However,the dispatch management model of energy storage in actual power system operation is not clear.Still,the specific scheduling process and energy storage strategy on the source-load-network side could be more specific,and there needs to be a greater understanding of the collaborative scheduling process of the multilevel scheduling center.On this basis,this paper reviews the energy storage operation model and market-based incentive mechanism,For different functional types and installation locations of energy storage within the power system,the operational models and existing policies for energy storage participation in the market that are adapted to multiple operating states are summarized.From the point of view of the actual scheduling and operation management of energy storage in China,an energy storage regulation and operation management model based on“national,provincial,and local”multilevel coordination is proposed,as well as key technologies in the interactive scenarios of source-load,network and storage.

Clinical effect of operative vs nonoperative treatment on humeral shaft fractures:Systematic review and meta-analysis of clinical trials

BACKGROUND Whether operation is superior to non-operation for humeral shaft fracture remains debatable.We hypothesized that operation could decrease the nonunion and reintervention rates and increase the functional outcomes.AIM To compare the clinical efficacy between operative and nonoperative approaches for humeral shaft fractures.METHODS We searched the PubMed,Web of Science,ScienceDirect,and Cochrane databases from 1990 to December 2023 for clinical trials and cohort studies comparing the effects of operative and conservative methods on humeral shaft fractures.Two investigators independently extracted data from the eligible studies,and the other two assessed the methodological quality of each study.The quality of the included studies was assessed using the Cochrane risk bias or Newcastle-Ottawa Scale.The nonunion,reintervention and the overall complications and functional scores were pooled and analyzed using Review Manager software(version 5.3).RESULTS A total of four randomized control trials and 13 cohort studies were included,with 1285 and 1346 patients in the operative and nonoperative groups,respectively.Patients in the operative group were treated with a plate or nail,whereas those in the conservative group were managed with splint or functional bracing.Four studies were assessed as having a high risk of bias,and the other 13 were of a low risk of bias according to the Newcastle-Ottawa Scale or Cochrane risk bias tool.The operative group had a significantly decreased rate of nonunion[odds ratio(OR)0.30;95%CI:0.23 to 0.40,reintervention(OR:0.33;95%CI:0.24 to 0.47),and overall complications(OR:0.62;95%CI:0.49 to 0.78)].The pooled effect of the Disabilities of Arm,Shoulder,and Hand score showed a significant difference at 3[mean difference(MD)-8.26;95%CI:-13.60 to-2.92],6(MD:-6.72;95%CI:-11.34 to-2.10),and 12 months(MD:-2.55;95%CI:-4.36 to-0.74).The pooled effect of Visual Analog Scale scores and the Constant-Murley score did not significantly differ between the two groups.CONCLUSION This systematic review and meta-analysis revealed a trend of rapid functional recovery and decreased rates of nonunion and reintervention after operation for humeral shaft fracture compared to conservative treatment.

Research and design of an expert diagnosis system for rail vehicle driven by data mechanism models

Purpose-The aim of this work is to research and design an expert diagnosis system for rail vehicle driven by data mechanism models.Design/methodology/approach-The expert diagnosis system utilizes statistical and deep learning methods to model the real-time status and historical data features of rail vehicle.Based on data mechanism models,it predicts the lifespan of key components,evaluates the health status of the vehicle and achieves intelligent monitoring and diagnosis of rail vehicle.Findings-The actual operation effect of this system shows that it has improved the intelligent level of the rail vehicle monitoring system,which helps operators to monitor the operation of vehicle online,predict potential risks and faults of vehicle and ensure the smooth and safe operation of vehicle.Originality/value-This system improves the efficiency of rail vehicle operation,scheduling and maintenance through intelligent monitoring and diagnosis of rail vehicle.