A Novel Framework to Construct S-Box Quantum Circuits Using System Modeling: Application to 4-Bit S-Boxes

Quantum computers accelerate many algorithms based on the superposition principle of quantum mechanics.The Grover algorithm provides significant performance to malicious users attacking symmetric key systems.Since the performance of attacks using quantum computers depends on the efficiency of the quantum circuit of the encryption algorithms,research research on the implementation of quantum circuits is essential.This paper presents a new framework to construct quantum circuits of substitution boxes(S-boxes)using system modeling.We model the quantum circuits of S-boxes using two layers:Toffoli and linear layers.We generate vector spaces based on the values of qubits used in the linear layers and apply them to find quantum circuits.The framework finds the circuit bymatching elements of vector spaces generated fromthe input and output of a given S-box,using the forward search or themeet-in-the-middle strategy.We developed a tool to apply this framework to 4-bit S-boxes.While the 4-bit S-box quantum circuit construction tool LIGHTER-R only finds circuits that can be implemented with four qubits,the proposed tool achieves the circuits with five qubits.The proposed tool can find quantum circuits of 4-bit odd permutations based on the controlled NOT,NOT,and Toffoli gates,whereas LIGHTER-R is unable to perform this task in the same environment.We expect this technique to become a critical step toward optimizing S-box quantum circuits.

System modeling and dynamic analysis of ISG hybrid power shafting

A system model is established to analyze the dynamic performance of an integrated starter and generator （ISG） hybrid power shafting. The model couples the electromechanical coupling shaft dynamics, the bearing hydrodynamic lubrication and the engine block stiffness. The model is com- pared with the model based on ADAMS or the model neglecting the bearing hydrodynamics. The bearing eccentricity and the oil film pressure have been calculated under different hybrid conditions or at the different motor power levels. It＇ s found that the bearing hydrodynamics decreases the cal- culation results of the bearing peak load. Changes of the hybrid conditions or the motor power have no significant effect on the main bearing, but have impact on the motor bearing. A hybrid power sys- tem composed of a 1.6 L engine and a 45 kW ISG motor can operate safely.

A systematic review of system modeling and control strategy of proton exchange membrane fuel cell

The proton exchange membrane fuel cell,as a novel energy device,exhibits a wide array of potential applications.This paper offers a comprehensive review and discussion of modeling and control strategies for fuel cell systems.It commences with a concise introduction to the structure and principles of fuel cells.Subsequently,it outlines modeling approaches for various fuel cell subsystems,encompassing the fuel cell stack,air supply system,hydrogen supply system,thermal management system,and water management system.Following this,it conducts a comparative analysis and discussion of prevalent control strategies for the aforementioned subsystems.Lastly,the paper outlines future research trends and directions in the modeling and control strategies of fuel cells.The aim of this paper is to provide ideas and inspirations for the design and management of membrane fuel cell systems from control aspects.

Refinement modeling and verification of secure operating systems for communication in digital twins

In traditional digital twin communication system testing,we can apply test cases as completely as possible in order to ensure the correctness of the system implementation,and even then,there is no guarantee that the digital twin communication system implementation is completely correct.Formal verification is currently recognized as a method to ensure the correctness of software system for communication in digital twins because it uses rigorous mathematical methods to verify the correctness of systems for communication in digital twins and can effectively help system designers determine whether the system is designed and implemented correctly.In this paper,we use the interactive theorem proving tool Isabelle/HOL to construct the formal model of the X86 architecture,and to model the related assembly instructions.The verification result shows that the system states obtained after the operations of relevant assembly instructions is consistent with the expected states,indicating that the system meets the design expectations.

Mathematical Modeling and Control Algorithm Development for Bidirectional Power Flow in CCS-CNT System

As the demand for more efficient and adaptable power distribution systems intensifies, especially in rural areas, innovative solutions like the Capacitor-Coupled Substation with a Controllable Network Transformer (CCS-CNT) are becoming increasingly critical. Traditional power distribution networks, often limited by unidirectional flow capabilities and inflexibility, struggle to meet the complex demands of modern energy systems. The CCS-CNT system offers a transformative approach by enabling bidirectional power flow between high-voltage transmission lines and local distribution networks, a feature that is essential for integrating renewable energy sources and ensuring reliable electrification in underserved regions. This paper presents a detailed mathematical representation of power flow within the CCS-CNT system, emphasizing the control of both active and reactive power through the adjustment of voltage levels and phase angles. A control algorithm is developed to dynamically manage power flow, ensuring optimal performance by minimizing losses and maintaining voltage stability across the network. The proposed CCS-CNT system demonstrates significant potential in enhancing the efficiency and reliability of power distribution, making it particularly suited for rural electrification and other applications where traditional methods fall short. The findings underscore the system's capability to adapt to varying operational conditions, offering a robust solution for modern power distribution challenges.

Mass Balance Modeling for Electric Arc Furnace and Ladle Furnace System in Steelmaking Facility in Turkey

In the electric arc furnace （EAF） steel production processes, scrap steel is principally used as a raw material instead of iron ore. In the steelmaking process with EAF, scrap is first melted in the furnace and then the desired chemical composition of the steel can be obtained in a special furnace such as ladle furnace （LF）. This kind of furnace process is used for the secondary refining of alloy steel. LF furnace offers strong heating fluxes and enables precise temperature control, thereby allowing for the addition of desired amounts of various alloying elements. It also provides outstanding desulfurization at high-temperature treatment by reducing molten steel fluxes and removing deoxidation products. Elemental analysis with mass balance modeling is important to know the precise amount of required alloys for the LF input with respect to scrap composition. In present study, chemical reactions with mass conservation law in EAF and LF were modeled altogether as a whole system and chemical compositions of the final steel alloy output can be obtained precisely according to different scrap compositions, alloying elements ratios, and other input amounts. Besides, it was found that the mass efficiency for iron element in the system is 95.93%. These efficiencies are calculated for all input elements as 8. 45% for C, 30.31% for Si, 46.36% for Mn, 30.64% for P, 41.96% for S, and 69.79% for Cr, etc. These efficiencies provide valuable ideas about the amount of the input materials that are vanished or combusted for 100 kg of each of the input materials in the EAF and LF system.

THEMODELINGOFATCSYSTEMSBASEDONPETRINET

In this paper a modeling method of ATC system is developed by using object Petri net. The formalized definition of the senior Petri net is given and illustrated by a practical example.

Feature selection for energy system modeling: Identification of relevant time series information

Heuristic or clustering based time series aggregation methods are often used to reduce temporal complexity of energy system models by selecting representative days.However,these methods potentially neglect relevant information of time series(e.g.,distribution parameters).To identify relevant time series parameters,feature selection algorithms can be applied.The present research contributes by(a)developing a new feature selection approach based on clustering,nested modeling and regression(CNR)which is designed for applications requiring high selectivity and using different data sets,(b)comparing and evaluating CNR with feature selection methods available from the literature(e.g.,LASSO)and(c)identifying relevant information of the time series applied in energy system models,in particular those of demand,photovoltaic and wind.Results show that CNR achieves on average up to 101%lower mean absolute errors when methods are directly compared.Thus,CNR better identifies relevant information when the number of selected features is restricted.The disadvantage of CNR,however,is its high computational effort.A potential remedy to counter this is the combination with another method(e.g.,as pre-feature selection).In terms of relevant information,energy systems including photovoltaic are mainly characterized by the correlation between demand and photovoltaic time series as well as the range and the 35%quantile of demand.When energy systems include wind power,the minimum and mean of wind as well as the correlation between demand and wind time series are relevant characteristics.The implications of these findings are discussed.

A Modeling Method Applied to Fault Diagnosis for Constant Linear System

The paper presents a new modeling method applied to fault diagnosis for constant linear closed-loop system by taking the impulse response series as the system model, and provides the calculation process of the method and output of model. The high frequency part of the pulse series, in the method, is reversed so as not to lose the frequency information of the pulse series in its transfer function. On the other hand, the method can also avoid the disadvantage that the learning results of neural network are uncertain every time. In the last part, the application with random disturbance of digital simulation and practical system shows that the modeling method is high accurate and suitable to be applied in fault diagnosis area.

Modeling of Agile Intelligent Manufacturing-oriented Production Scheduling System

Agile intelligent manufacturing is one of the new manufacturing paradigms that adapt to the fierce globalizing market competition and meet the survival needs of the enterprises, in which the management and control of the production system have surpassed the scope of individual enterprise and embodied some new features including complexity, dynamicity, distributivity, and compatibility. The agile intelligent manufacturing paradigm calls for a production scheduling system that can support the cooperation among various production sectors, the distribution of various resources to achieve rational organization, scheduling and management of production activities. This paper uses multi-agents technology to build an agile intelligent manufacturing-oriented production scheduling system. Using the hybrid modeling method, the resources and functions of production system are encapsulated, and the agent-based production system model is established. A production scheduling-oriented multi-agents architecture is constructed and a multi-agents reference model is given in this paper.

The Fuzzy Modeling Algorithm for Complex Systems Based on Stochastic Neural Network

A fuzzy modeling method for complex systems is studied. The notation of general stochastic neural network (GSNN) is presented and a new modeling method is given based on the combination of the modified Takagi and Sugeno's (MTS) fuzzy model and one-order GSNN. Using expectation-maximization(EM) algorithm, parameter estimation and model selection procedures are given. It avoids the shortcomings brought by other methods such as BP algorithm, when the number of parameters is large, BP algorithm is still difficult to apply directly without fine tuning and subjective tinkering. Finally, the simulated example demonstrates the effectiveness.

Identification of Type of a Fault in Distribution System Using Shallow Neural Network with Distributed Generation

A distributed generation system(DG)has several benefits over a traditional centralized power system.However,the protection area in the case of the distributed generator requires special attention as it encounters stability loss,failure re-closure,fluctuations in voltage,etc.And thereby,it demands immediate attention in identifying the location&type of a fault without delay especially when occurred in a small,distributed generation system,as it would adversely affect the overall system and its operation.In the past,several methods were proposed for classification and localisation of a fault in a distributed generation system.Many of those methods were accurate in identifying location,but the accuracy in identifying the type of fault was not up to the acceptable mark.The proposed work here uses a shallow artificial neural network(sANN)model for identifying a particular type of fault that could happen in a specific distribution network when used in conjunction with distributed generators.Firstly,a distribution network consisting of two similar distributed generators(DG1 and DG2),one grid,and a 100 Km distribution line is modeled.Thereafter,different voltages and currents corresponding to various faults(line to line,line to ground)at different locations are tabulated,resulting in a matrix of 500×18 inputs.Secondly,the sANN is formulated for identifying the types of faults in the system in which the above-obtained data is used to train,validate,and test the neural network.The overall result shows an unprecedented almost zero percent error in identifying the type of the faults.

Modeling Method of Multi-Energy Systems Based on LSTM Algorithm

The development of the Energy Internet has improved the efficiency of energy utilization and promoted sustainable development of power and energy systems.The multi-energy system modeling considering the dynamic process of transmission line is one of the key research points of Energy Internet operation control.Through the energy circuit theory,the lumped parameter model of natural gas pipelines is built and the dynamic characteristic parameters under the control instruction are extracted.Combined with dynamic characteristic parameters,the long short-term memory(LSTM)neural network is designed to fit the natural gas pipeline dynamic process into discrete linear time-varying(LTV)equations.Combined with the equations,an energy hub method is used to build a control model of industrial parks with multi-energy distribution system.Using the rolling optimal control strategy given in this paper,the model is solved by the Matlab-Yalmip solver and rolling control instructions of each energy conversion unit are obtained.Finally,the case study demonstrates that the LSTM neural network-based modeling method presented in this paper can accurately fit the dynamic process of a natural gas pipeline system.The rolling control model of the multi-energy system can improve the efficiency of energy utilization,exhibit the transmission line status constraints during the optimization control process and improve reliability of the multi-energy system operation.

Enhancing the Efficiency of Multi-Electrolyzer Clusters with Lye Mixer:Topology Design and Control Strategy

The rise in hydrogen production powered by renewable energy is driving the field toward the adoption of systems comprising multiple alkaline water electrolyzers.These setups present various operational modes:independent operation and multi-electrolyzer parallelization,each with distinct advantages and challenges.This study introduces an innovative configuration that incorporates a mutual lye mixer among electrolyzers,establishing a weakly coupled system that combines the advantages of two modes.This approach enables efficient heat utilization for faster hot-startup and maintains heat conservation post-lye interconnection,while preserving the option for independent operation after decoupling.A specialized thermal exchange model is developed for this topology,according to the dynamics of the lye mixer.The study further details startup procedures and proposes optimized control strategies tailored to this structural design.Waste heat from the caustic fully heats up the multiple electrolyzers connected to the lye mixing system,enabling a rapid hot start to enhance the system’s ability to track renewable energy.A control strategy is established to reduce heat loss and increase startup speed,and the optimal valve openings of the diverter valve and the manifold valve are determined.Simulation results indicate a considerable enhancement in operational efficiency,marked by an 18.28%improvement in startup speed and a 6.11%reduction in startup energy consumption inmulti-electrolyzer cluster systems,particularlywhen the systems are synchronized with photovoltaic energy sources.The findings represent a significant stride toward efficient and sustainable hydrogen production,offering a promising path for large-scale integration of renewable energy.

Mitigation of Transients in Capacitor Coupled Substations Using Traditional RLC Filter Techniques

This article presents an extensive examination and modeling of Capacitor Coupled Substations (CCS), noting some of their inherent constraints. The underlying implementation of a CCS is to supply electricity directly from high-voltage (HV) transmission lines to low-voltage (LV) consumers through coupling capacitors and is said to be cost-effective as compared to conventional distribution networks. However, the functionality of such substations is susceptible to various transient phenomena, including ferroresonance and overvoltage occurrences. To address these challenges, the study uses simulations to evaluate the effectiveness of conventional resistor-inductor-capacitor (RLC) filter in mitigating hazardous overvoltage resulting from transients. The proposed methodology entails using standard RLC filter to suppress transients and its associated overvoltage risks. Through a series of MATLAB/Simulink simulations, the research emphasizes the practical effectiveness of this technique. The study examines the impact of transients under varied operational scenarios, including no-load switching conditions, temporary short-circuits, and load on/off events. The primary aim of the article is to assess the viability of using an established technology to manage system instabilities upon the energization of a CCS under no-load circumstances or in case of a short-circuit fault occurring on the primary side of the CCS distribution transformer. The findings underscore the effectiveness of conventional RLC filters in suppressing transients induced by the CCS no-load switching.

Modeling and simulation of quadrotor UAV based on Simscape

In order to speed up and simplify the design of the quadrotor unmanned aerial vehicle(UAV)and carry out experimental simulation and verification of relevant control algorithms,this paper analyzed the system dynamics model of the mechanical structure and flight principle of the quadrotor aircraft,and used the Newton-Euler method to derive the non-linear dynamic equations.Aiming at improving the modeling accuracy and system integrity of the quadrotor,the physical system modeling was combined with the CAD software and the Matlab/Simscape toolbox.The three-dimensional quadrotor solid model built by CAD software was imported into the Simscape simulation platform to construct the body and power system model of the quadrotor.Based on this,the control algorithm designed by Simulink was added to the simulation platform to facilitate the experiment verification and parameter tuning.The simulation results show that the designed aircraft can achieve hover and tracking well and meet the control performance requirements of the system.

Research on a New Hybrid System of a Tyre Crane Using Supercapacitors

Research on a hybrid system of a crane is a focus which considers environmental protection and energy saving. A new environmental protection and energy saving hybrid system of tyre crane, which utilizes supercapacitors as the energy store device, is presented. Analyzing the principle of supercapacitors, the model of the crane＇s hybrid system is set up in this paper, and the model of main blocks are established. Through simulation analyzing, the energy saving result of the new hybrid system is obtained, and the good application value of the new hybrid system is explained.

A multi-input and multi-output design on automotive engine management system

Lookup table is widely used in automotive industry for the design of engine control units(ECU).Together with a proportional-integral controller,a feed-forward and feedback control scheme is often adopted for automotive engine management system(EMS).Usually,an ECU has a structure of multi-input and single-output(MISO).Therefore,if there are multiple objectives proposed in EMS,there would be corresponding numbers of ECUs that need to be designed.In this situation,huge efforts and time were spent on calibration.In this work,a multi-input and multi-out(MIMO) approach based on model predictive control(MPC) was presented for the automatic cruise system of automotive engine.The results show that the tracking of engine speed command and the regulation of air/fuel ratio(AFR) can be achieved simultaneously under the new scheme.The mean absolute error(MAE) for engine speed control is 0.037,and the MAE for air fuel ratio is 0.069.

Modeling of Multi-Freedom Ship Motions in Irregular Waves with Fuzzy Neural Networks

In this paper, the neural network technology is combined with the fuzzy set theory to model the wave-induced ship motions in irregular seas. This combination makes possible the handling of a non-linear dynamic system with insufficient input information. The numerical results from the strip theory are used to train the networks and to demonstrate the validity of the proposed procedure.

Nonlinear modeling of PEMFC based on neural networks identification

The proton exchange membrane generation technology is highly efficient and clean, and is considered as the most hopeful “green” power technology. The operating principles of proton exchange membrane fuel cell (PEMFC) system involve thermodynamics, electrochemistry, hydrodynamics and mass transfer theory, which comprise a complex nonlinear system, for which it is difficult to establish a mathematical model. This paper first simply analyzes the necessity of the PEMFC generation technology, then introduces the generating principle from four aspects: electrode, single cell, stack, system; and then uses the approach and self-study ability of artificial neural network to build the model of nonlinear system, and adapts the Leven- berg-Marquardt BP (LMBP) to build the electric characteristic model of PEMFC. The model uses experimental data as training specimens, on the condition the system is provided enough hydrogen. Considering the flow velocity of air (or oxygen) and the cell operational temperature as inputs, the cell voltage and current density as the outputs and establishing the electric characteristic model of PEMFC according to the different cell temperatures. The voltage-current output curves of model has some guidance effect for improving the cell performance, and provide basic data for optimizing cell performance that have practical significance.