Dynamic Parameter Adjustment Research Articles

Parameters Optimization of Decoy-State Phase-Matching Quantum Key Distribution Based on the Nature-inspired Algorithms

Abstract Phase-Matching quantum key distribution (PM-QKD) has achieved significant results in practical applications; however, real-time communication necessitates the dynamic adjustment and optimization of key parameters during the communication process. In this paper, we predict the parameters of PM-QKD using Nature-inspired Algorithms (NIAs), with results obtained from an Exhaustive Traversal Algorithm (ETA) serving as the benchmark. We mainly study the parameter optimization effects of the two NIAs which are Ant Colony Optimization (ACO) and Genetic Algorithm (GA). Meanwhile the configuration of the inherent parameters of these algorithms in decoy-state PM-QKD is discussed. The simulation results indicate that the parameters obtained by ACO exhibits superior convergence and stability, while the results of the GA are relatively scattered. Nevertheless, over 97% of the key rates predicted by both algorithms are highly consistent with the optimal key rate, with the relative error of key rates remaining below 10%. Furthermore, NIAs not only keep power consumption below 8W, but also require three orders of magnitude less computing time than ETA.

Research on performance curve prediction of centrifugal pump based on improved whale optimization algorithm and characteristic deformation method

This article proposes an improved hybrid method combining whale optimization algorithm (IMWOA) and the characteristic deformation method (CDM) to predict the performance curve of centrifugal pumps. The traditional whale optimization algorithm (WOA) is enhanced by introducing chaos mapping, adaptive weight, nonlinear convergence factor strategies, and mutation concept from the genetic algorithm. These improvements make IMWOA significantly superior to the traditional WOA in the basic criterion test functions. To address the limitations of sample set size and differences in data characteristics in practical applications, this paper also introduces CDM strategies, including dynamic parameter adjustment strategy and mixed standardization strategy. Comparative analysis shows that the IMWOA-CDM method achieves better results in various performance evaluation indicators. The final experimental results validated the high accuracy and reliability of the IMWOA-CDM method in predicting the performance curve of centrifugal pumps, with a maximum absolute error of 1.21 m for head and 1.08% for efficiency.

Hybrid PLM and ADRC Control for Sensorless Induction Motor Drive with Nine-Level Converter Employing SVPWM

This paper outlines the design of a predictive controller combined with an active disturbance rejection control (ADRC)-type controller to enhance the dynamic performance of the induction motor powered by a nine-level converter. The predictive control law proposed is derived from the Poisson Laguerre model, based on predictive control. The motor is controlled using indirect field-oriented control, both with and without a speed sensor. For speed estimation, the Luenberger observer of order 4 is used. The predictive method utilized allows for the dynamic adjustment of control parameters based on those of the induction motor. The paper aims to eliminate internal and external disturbances and reduce total harmonic distortion (THD) through the use of a Nine-level cascaded H-bridge inverter. Space vector pulse width modulation (SVPWM) signals are generated using the logic of hexagon decomposition. The SVPWM method operates by decomposing higher-level hexagons into multiple two-level hexagons. The Poisson-Laguerre model (PLM) is also compared with the ADRC and the proportional-integral (PI) control. Simulations with MATLAB/SIMULINK software for an induction motor are performed to test the performance of each controller and the validity of the observer.

Prediction of typical gas components in cigarette smoke based on transformer

Abstract Harm reduction of cigarette products has become one of the primary objectives of the entire tobacco industry. The prediction of typical gas components (TGC) and dynamic adjustment of process parameters are crucial for cigarette coke reduction and harm reduction. In this study, a deep learning (DL)-assisted TGC prediction framework is proposed to predict CO and NI simultaneously. Firstly, a large number of experimental and simulation parameters of cigarette process are collected as the data base for subsequent model construction. Then, the feature importance analysis of the process parameters was carried out by combining the mechanism of the combustion process through the mutual information method. Finally, DL models based on Multilayer Perceptron, One-Dimension Convolutional Neural Network and Transformer (TF) was developed as data-driven surrogate models to establish the mapping relationship between process parameters and TGC. The results show that TF model generalizes best and can predict TGC quickly and accurately with R2 over 0.99. This work will provide a valuable predictive and decision-making tool for cigarette harm reduction.

Dynamic prediction and optimization of tunneling parameters with high reliability based on a hybrid intelligent algorithm

In this paper, a hybrid intelligent framework comprising Bayesian optimization (BO), gradient boosting with categorical features (CatBoost) and the nondominated sorting genetic algorithm-III (NSGA-III) was proposed to support multiobjective optimization of shield construction parameters without large sample datasets, improve the shield performance, and ensure reliable and interpretable results. First, with the use of the specific tunneling energy, advancing speed and cutter wear as objective functions, a BO-CatBoost prediction model for shield construction parameters and various objectives was constructed, and the key influencing factors were identified via the SHapley Additive exPlanations (SHAP) method. Then, a BO-CatBoost-NSGA-III model was developed to obtain Pareto solutions under different scenarios involving the adjustment of the key influencing factors. Finally, adopting the Wuhan Metro as the background, the accuracy, stability, and generalizability of the constructed algorithm were verified. The results indicated that (1) the developed BO-CatBoost algorithm is superior to 9 other algorithms. The R2 values of the proposed approach were 0.976 and 0.901–0.976 on the test set. (2) The developed BO-CatBoost-NSGA-III algorithm could be used to obtain Pareto solutions under different scenarios via the adjustment of the key influencing factors with the SHAP method, and the optimal solutions could facilitate improvements in the advancing speed, specific tunneling energy and cutter wear of 3.45 %, 6.09 %, and 0.52 %, respectively, with an overall average reliability of 90.5 %. (3) By comparing various prediction algorithms, optimization schemes of different objectives and geological conditions, the accuracy, stability, and generalizability of the constructed algorithm were verified. The developed BO-CatBoost-NSGA-III framework could enable dynamic adjustment of shield construction parameters for decision-making purposes in the event of conflicting shield construction objectives and exhibits generality.

Visualization of electromagnetic fields in a circular waveguide using Mathematica

This paper employs Mathematica for visualizing electromagnetic fields (EMF) in circular waveguides, effectively addressing pedagogical challenges related to theoretical abstraction and computational complexity. We first derive the exact solutions for guided modes, then summarize the steps for visualizing EMF using Mathematica, propose educational strategies, and showcase simulation results. The Mathematica code is provided, allowing for dynamic parameter adjustment and real-time field change observation. This approach significantly enhances undergraduate students’ understanding of electromagnetism through engaging visual and interactive learning experiences.

A Survey on Manta Ray Foraging Optimization Algorithm of Variants and Applications

This review paper delves into the original MRFO and its variants, focusing on single-objective algorithms including but not limited to hybrid algorithms, learning strategies, multiple populations and dynamic parameter adjustment, highlighting the improvements made to enhance the algorithm's efficiency in global optimization, accelerate convergence rates, and improve its capacity to evade local optima. MRFO has emerged as an effective tool for solving complex optimization problems across various domains, including energy optimization, biomedical field, engineering problems, and others. A comprehensive analysis of applications of MRFO in different fields is provided, emphasizing its adaptability and efficacy. The paper concludes with a discussion on the challenges faced by MRFO and potential future research directions, aiming to consolidate the current research status and guide future investigations.

Novel Hybrid Crayfish Optimization Algorithm and Self-Adaptive Differential Evolution for Solving Complex Optimization Problems

This study presents the Hybrid COASaDE Optimizer, a novel combination of the Crayfish Optimization Algorithm (COA) and Self-adaptive Differential Evolution (SaDE), designed to address complex optimization challenges and solve engineering design problems. The hybrid approach leverages COA’s efficient exploration mechanisms, inspired by crayfish behaviour, with the symmetry of SaDE’s adaptive exploitation capabilities, characterized by its dynamic parameter adjustment. The balance between these two phases represents a symmetrical relationship wherein both components contribute equally and complementary to the algorithm’s overall performance. This symmetry in design enables the Hybrid COASaDE to maintain consistent and robust performance across a diverse range of optimization problems. Experimental evaluations were conducted using CEC2022 and CEC2017 benchmark functions, demonstrating COASaDE’s superior performance compared to state-of-the-art optimization algorithms. The results and statistical analyses confirm the robustness and efficiency of the Hybrid COASaDE in finding optimal solutions. Furthermore, the applicability of the Hybrid COASaDE was validated through several engineering design problems, where COASaDE outperformed other optimizers in achieving the optimal solution.

Optimizing volumetric modulated arc therapy prostate planning using an automated Fine-Tuning process through dynamic adjustment of optimization parameters

In radiotherapy treatment planning, optimization is essential for achieving the most favorable plan by adjusting optimization criteria. This study introduced an innovative approach to automatically fine-tune optimization parameters for volumetric modulated arc therapy prostate planning, ensuring all constraints were met. A knowledge-based planning model was invoked, and the fine-tuning process was applied through an in-house developed script. Among 25 prostate plans, this fine-tuning increased the number of plans meeting all constraints from 10/25 to 22/25, with a reduction in mean monitor units per gray without increasing plan’s complexity. This automation improved efficiency by saving time and resources in treatment planning.

Modified snake optimizer based multi-level thresholding for color image segmentation of agricultural diseases

Image segmentation is crucial for early identification and diagnosis of crop diseases in agriculture. It helps identify disease areas and characteristics, laying the groundwork for disease diagnosis and treatment. However, color image thresholding segmentation method faces challenges in finding the optimal threshold as the number of thresholds increases, which affects segmentation quality. In this paper, a modified snake optimizer (MSO) is proposed to address the color image thresholding segmentation problem using Kapur’s entropy as the objective function. MSO incorporates a dynamic adaptive parameter adjustment method. The improved global position update formula introduces guidance from the optimal individual and disturbance from random individuals, effectively achieving a balance between global and local search capabilities. A dynamic parameter adjustment method is added to accelerate convergence speed during snake movement to food. Lévy flight is introduced to the Flight mode to help the algorithm escape local extremums. A balancing strategy is implemented in the Mating mode, incorporating guidance from the optimal individual and information exchange between sub-populations, enabling balanced exploration and local exploitation capabilities. The hill-climbing jump operation for the optimal individual is added to help the algorithm overcome local stagnation. The proposed MSO is evaluated on CEC 2017 test functions and color test images, and the obtained results are compared with other segmentation methods and other intelligent optimization algorithms. The results demonstrate that MSO exhibits stable and excellent performance in both complex global optimization problems and color image thresholding segmentation problems. Finally, MSO is applied to segment rice disease images, and the results reveal significantly better segmentation performance compared to other algorithms. MSO shows great potential in solving the thresholding segmentation problem for agricultural disease images, thereby assisting in the accurate identification, diagnosis, and treatment of agricultural diseases and insect pests.

Research on fuzzy adaptive GPC of average coolant temperature based on regulation of boric acid concentration

In order to keep the balance of power distribution in the core of a nuclear reactor, the method of adjusting the boric acid concentration to regulate the average temperature of the coolant has been adopted to track the demands of power changes in the core. However, the chemical reaction process of regulating the average coolant temperature by adjusting the boric acid concentration is too slow to better meet the demands of power changes. In view of this challenge, this paper proposes an innovative fuzzy adaptive incremental generalized predictive control strategy to optimize the process of regulating the coolant temperature. This strategy employs a dynamic adjustment of control parameters, specifically through the application of incremental factors γ, which is calibrated using a fuzzy logic-based algorithm to enhance system responsiveness and robustness in real-time. The strategy aims to improve the efficiency and responsiveness of the nuclear reactor control mechanism and further enhance the safety and stability of nuclear power generation.Firstly, the system state space equations are established based on the boric acid concentration equation and the nuclear reactor kinetics. Secondly, the step factors β and incremental factors γ are used in improved-GPC to improve control real-time and robustness. The step factor β speeds up the rolling optimization, and the incremental factor γ is adjusted based on the fuzzy algorithm according to the online recognition degree of the system. Finally, the 1000 MW Pressurized Water Reactor is modeled and simulated. The results show that improved-GPC has a shorter regulation time and a stronger ability to cope with the model mismatch in this system than other controllers.

Study on the Fast Search Planning Problem of Lost Targets for Maritime Emergency Response Based on an Improved Adaptive Immunogenetic Algorithm.

This study investigates the problem of rapid search planning for moving targets in maritime emergencies using an improved adaptive immune genetic algorithm. Given the complexity and uncertainty inherent in searching for moving targets in maritime emergency situations, a task planning method based on the improved adaptive immunogenetic algorithm (IAIGA) is proposed to enhance search efficiency and accuracy. This method utilizes a priori information to construct the potential regions of the target and the distribution probability within each region. It establishes a "prediction-scheduling" search strategy model, planning a rapid search task for disconnected targets based on overlapping probability through the IAIGA. By incorporating an immune mechanism, the algorithm enhances its global search capability and robustness. Additionally, the adaptive strategy enables dynamic adjustment of the algorithm's parameters to accommodate varying search scenarios. The experimental results demonstrate that the proposed IAIGA significantly outperforms traditional methods, providing higher search speeds and more accurate search results in the context of maritime emergency response. These findings offer effective technical support for maritime emergency operations.

Neural network model predictive control of core power of Qinshan nuclear power plant based on reinforcement learning

The power change of Pressurized water reactors (PWRs) is a highly complex nonlinear coupling process, which requires the power controller to have a high nonlinear control capability. Since the traditional model predictive control (MPC) algorithm has the problems of insufficient robustness and adaptivity, this study proposes a neural network-based neural network model predictive control method based on model predictive control and optimizes its control parameters in real time by using the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm. The designed control method is validated on the simulation model of the Qinshan nuclear power plant (NPP). Simulation results show that the proposed control method can realize effective control of power under a variety of power changing conditions and dynamic adjustment of control parameters.

Adaptive Impedance Control for Teleoperation with Event-Triggered Controller

Uncertainty in the master–slave model is one of the primary factors affecting the transparency of teleoperation systems, and congestion in the master–slave communication network also greatly influences the performance of the teleoperation system. This paper proposes a combined framework of adaptive and impedance control to address the uncertainty in the master–slave model and achieve smooth operation at the slave end. Building upon this linear model, an event-triggered mechanism is designed using Lyapunov functions, with dynamic online adjustment of the triggering threshold parameters. Following the completion of the aforementioned research, control objectives are established to validate the performance of the teleoperation control system proposed in this paper. Finally, simulation verification is conducted in the Matlab/Simulink environment.

A master-slave teleoperation system control model based on model predictive control with fast dynamic response

Robot technology is advancing with constantly expanding applications. Teleoperation system demonstrates its potential to extend human capabilities in unstructured and hazardous environments as an important branch of robotics. However, there are still issues involving insufficient position tracking accuracy and dynamic response capabilities. This article proposes a master-slave teleoperation system control model based on model predictive control (MPC) with a real-time dynamic adaptive parameter adjustment algorithm. We establish a model based on the integral barrier Lyapunov function and construct a dynamic optimization model based on MPC. Then we carry out rolling optimization and feedback correction at each moment to feed the predicted values generated by MPC back to correct the actual tracking trajectories in real-time. Additionally, we utilize an adaptive neural network and robust term compensation to adjust dynamic parameters and eliminate the effects of time delays, external disturbances, and model uncertainties. Theoretical simulation experiments are conducted on the Simulink platform, analyzing position tracking error and dynamic response capabilities of the master-slave robot respectively to verify the proposed model. The results indicate a reduction in the position tracking errors of the master with 10% and slave with 11%, while also showing an improvement in their dynamic response capabilities. The speed error ranges of the master and slave are shortened with 6% and 7% respectively.

Dynamic Gaussian mutation beetle swarm optimization method for large-scale weapon target assignment problems

The weapon target assignment is a crucial issue for firepower resources optimization in modern warfare. Such a problem is complicated, multi-constrained, strongly nonlinear, NP-complete and the existing studies did not consider the suitability between different weapons and targets. In this paper, a novel weapon target assignment model is established that involves the weapon-target suitability and is closer to the real combat scenarios. Then, in view of that the conventional weapon target assignment methods are difficult to be applied in the large-scale problems efficiently, this work proposes a dynamic Gaussian mutation beetle swarm optimization algorithm with rule-based chaotic initialization. With the assistance of the dynamic parameter adjustment strategies and Gaussian mutation, the improved algorithm has fast convergence speed and high convergence accuracy, and it can solve the weapon target assignment problems with excellent optimization capabilities. Besides, the rule-based chaotic initialization strategy is embedded in this algorithm to generate high-quality population with better diversity. Finally, two comparative simulation cases of different initialization methods and algorithms for solving the large-scale weapon target assignment problems are designed. The results demonstrate that the proposed approach can provide more superior assignment schemes than its competitors with enhanced efficiency.

Locomotion Control of Cyborg Insects by Charge-Balanced Biphasic Electrical Stimulation.

The integration of electronic stimulation devices with insects in the context of cyborg insect systems has great application potential, particularly in the fields of environmental monitoring, urban surveillance, and rescue missions. Despite considerable advantages compared to the current robot technology, including flexibility, durability, and low energy consumption, this integration faces certain challenges related to the potential risk of charge accumulation caused by prolonged and repetitive electrical stimulations. To address these challenges, this study proposes a universal system for remote signal output control using infrared signals. The proposed system integrates high-precision digital-to-analog converters capable of generating customized waveform electrical stimulation signals within defined ranges. This enhances the accuracy of locomotion control in cyborg insects while maintaining real-time control and dynamic parameter adjustment. The proposed system is verified by experiments. The experimental results show that the signals generated by the proposed system have a success rate of over 76.25% in controlling the turning locomotion of cyborg insects, which is higher than previously reported results. In addition, the charge-balanced characteristics of these signals can minimize muscle tissue damage, thus substantially enhancing control repeatability. This study provides a comprehensive solution for the remote control and monitoring of cyborg insects, whose flexibility and adaptability can meet various application and experimental requirements. The results presented in this study lay a robust foundation for further advancement of various technologies, particularly those related to cyborg insect locomotion control systems and wireless control mechanisms for cyborg insects.

Intelligent Optimization Method and Network Security Analysis for Power System Active Control

Abstract Optimizing the active control of the power system and improving the stability of the system in the face of cyber-attacks are necessary to secure the power supply and achieve energy saving and emission reduction. The article proposes an improved granular computing method applied to power system active control, which includes chaotic initialization, dynamic parameter adjustment, and a fast search strategy. And also provides two strategies for defense against FDIA and DDoS attacks. The minimum cost of the IGrC algorithm in 5, 10 and 30 unit systems are 43115.67$, 1017569.51$ and 10170321$, and it has good convergence and robustness. The F1 value of the vertical and horizontal prediction algorithm used is more than 90% in all environments, and the optimal marginal cost of this paper’s algorithm is 44.8357 regardless of whether or not it is facing DDoS attacks. Therefore, the active control optimization method and attack defense strategy proposed in this paper have practical application effects.

РАЗРАБОТКА И ТЕСТИРОВАНИЕ АДАПТИВНЫХ СИСТЕМ РЕЛЕЙНОЙ ЗАЩИТЫ

The article is devoted to the development and testing of adaptive relay protection systems that ensure reliable and fast shutdown of emergency sections in power grids. The prin-ciples of adaptive algorithms, the use of real-time network state data for dynamic adjustment of protection parameters, as well as artificial intelligence methods for analyzing and predicting faults are considered. The system architecture is presented, including data collection modules, control elements, and communication interfaces. The effectiveness of the proposed approaches is assessed based on digital modeling and field tests, which allows demonstrating high accuracy, a decrease in the number of false alarms, and an increase in network stability.

Recent advances and challenges in gas injection techniques for enhanced oil recovery

Gas injection techniques have emerged as a pivotal method in Enhanced Oil Recovery (EOR), aimed at maximizing the extraction of oil from mature reservoirs. Recent advances in this domain have significantly improved the efficiency and effectiveness of gas injection methods, contributing to increased recovery rates and extended reservoir lifespans. This reviews the latest technological innovations and persistent challenges associated with gas injection techniques in EOR. One of the primary advancements in gas injection is the development of more sophisticated gas compositions, including the use of CO₂, nitrogen, and hydrocarbon gases. These gases have been optimized to improve miscibility with crude oil, thereby enhancing displacement efficiency and increasing oil recovery. Moreover, advancements in monitoring and modeling technologies, such as real-time reservoir simulation and 4D seismic monitoring, have enabled more precise control and assessment of gas injection processes, ensuring optimal gas utilization and minimizing environmental impact. Innovations in smart well technologies and downhole monitoring systems have further enhanced the ability to manage and optimize gas injection operations. These technologies provide continuous data on reservoir conditions, enabling dynamic adjustment of injection parameters to maximize recovery. Additionally, the integration of machine learning and artificial intelligence in EOR processes has facilitated predictive analytics, improving decision-making and operational efficiency. Despite these advancements, several challenges persist. The primary challenge lies in the high operational costs and economic feasibility of gas injection projects, particularly in volatile oil markets. Ensuring the long-term stability and effectiveness of injected gases in heterogeneous reservoirs also presents significant technical hurdles. Moreover, environmental concerns related to the use of CO₂ and other gases necessitate the development of more sustainable and eco-friendly injection methods. Addressing these challenges requires ongoing research and development, as well as collaboration between industry stakeholders, to refine gas injection techniques and mitigate associated risks. The future of gas injection in EOR looks promising, with continued innovation poised to overcome existing barriers and drive more efficient and sustainable oil recovery methods. In conclusion, while recent advances in gas injection techniques for EOR have demonstrated considerable potential, overcoming the economic, technical, and environmental challenges remains crucial for the widespread adoption and success of these methods.