Adaptive Coefficients Research Articles

Multi-Objective Optimization Strategy for Commercial Vehicle Permanent Magnet Water Pump Motor Based on Improved Sparrow Algorithm

In order to achieve multi-objective optimization for a permanent magnet water pump motor in heavy commercial vehicles, we propose a strategy based on response-surface methodology and the improved sparrow algorithm (CGE-SSA). Firstly, the output capacity of the pump during actual operation was tested with an experimental bench to determine the design parameters of the motor, and then its modeling was completed using Ansys Maxwell 2022r2 software. Secondly, the response-surface model was established by taking the parameters of permanent magnet width, rib width, and slot width as optimization parameters and the output torque (Ta), torque ripple (Tr), and back electromotive force (EMF) amplitude as optimization objectives. Meanwhile, three methods—namely, circular sinusoidal chaotic mapping, improved golden sinusoidal strategy, and adaptive weight coefficients—were used to improve the convergence speed and accuracy of the sparrow search algorithm (SSA). Finally, the multi-objective optimization of the permanent magnet synchronous motor was completed using the improved sparrow algorithm. A comparative analysis of the motor’s output before and after optimization showed that the torque pulsation and reverse electromotive force of the motor were significantly improved after optimization.

The effect of intersection vehicle scheduling based on the service system of sensors and intelligent traffic road information

With the high-speed development of the automobile industry, the number of domestic vehicles has increased significantly, which has posed a great challenge to the efficiency of road access. To fully utilize the intersection scheduling function of the intelligent transportation system, an optimization of the stranded traffic system based on LoRa is studied. Then, the greedy strategy and adaptive coefficient are used for optimization to obtain an improved genetic algorithm (GA), which is used to verify the intersection scheduling function. In addition, comparative experiments are conducted with standard GA, adaptive genetic algorithm (AGA), and hybrid genetic algorithm (HGA). The AGA optimizes the GA by adapting genetic parameters, while the HGA combines GA with a simulated annealing algorithm. The results showed that comparing the improved GA with GA, AGA, and HGA, the improved GA reduced the average value of individual extremum by 50.36%, 47.51%, and 37.16%, respectively. Compared with other mainstream algorithms, the improved GA reduced the number of stranded vehicles at intersections to 0 during traffic light timing. This indicates that the improved GA has higher and better performance and scheduling advantages in the intersection scheduling of intelligent transportation road service systems. The research aims to provide new ideas for improving traffic efficiency in intelligent transportation systems and promoting the informatization development of modern traffic management.

A short-circuit protection scheme for active distribution networks based on improved adaptive current reliability coefficient

Abstract Addressing the issue of misoperation in traditional three-zone current protection caused by the assisted, drawdown, or reverse current due to the large-scale integration of distributed generation (DG) into distribution networks (DNs), an adaptive current short-circuit protection scheme based on local information is proposed for active DNs (ADNs). Firstly, the adaptive current protection reliability coefficients are improved for the setting of protection zones I and II of the lines in the ANDs. On the DG side, an improved reliability coefficient is constructed using an e-exponential function. Subsequently, the distance between the fault point and the busbar is calculated by analyzing the relation between the positive-sequence voltage and positive-sequence current at the measuring point when faults occur at various locations. Based on this distance parameter, the reliability coefficient is adjusted in real time. Finally, the modified adjustable reliability coefficient, along with the online-calculated equivalent impedance and equivalent electromotive force, is used to coordinate the setting of short-circuit protection zones I and II of the lines in the ADNs. Simulation results demonstrate that, compared with traditional adaptive current protection schemes, this proposed scheme is independent of the location of short-circuit fault points, the grid-connected capacity, or the number of DGs, exhibiting better selectivity and reliability.

Event-triggered bipartite consensus to heterogeneous multiagent systems under DoS attacks: A fully distributed method

This paper studies event-triggered bipartite output consensus problem of heterogeneous multiagent systems under denial-of-service (DoS) attacks. A novel dynamic event-triggered scheme (DETS) is proposed, which, by introducing an extra dynamic function with time-varying coefficients into triggering conditions, can guarantee strictly positive minimum inter-event intervals no matter DoS attacks occur or not. An event-based resilient compensator with adaptive coupling coefficients is then designed to estimate leader's state, and a hybrid model with jump dynamics is constructed that can incorporate the estimation error, DETS, and DoS attacks, and is useful for convergence analysis. Then, a fully distributed observer-based control protocol is designed to regulate the bipartite output consensus. The main advantages of the proposed method include: 1) global information is not needed to implement the event-based control protocol; 2) strictly positive inter-event intervals are guaranteed even under DoS attacks. Finally, a numerical example is presented to testify the main results.

New Multi-Channel VSMFxLMS Algorithm for Vibration Reduction of Gear Systems

At present, the active control of gear vibration mostly relies on existing algorithms. In order to achieve effective vibration reduction of the gear system, particularly during the vibration process, this paper proposes a multi-channel VSMFxLMS algorithm based on the FxLMS algorithm. This novel approach takes into account the time-varying nature of the vibration signal during gear vibration. Adaptive filter power coefficients are updated in a skip-tongue variable-step manner using momentum factors. Firstly, the paper establishes the dynamics model of the gear system and analyzes the nonlinear dynamic characteristics of the system. It then examines the vibration damping effect of the FxLMS algorithm and analyzes its performance under different gear system motion states, considering different step lengths and momentum factors. Lastly, the proposed VSMFxLMS algorithm is compared with the FxLMS algorithm, highlighting the superiority of the former. Overall, this research highlights the potential of a multi-channel VSMFxLMS algorithm in reducing vibrations in gear systems. The study optimizes the performance of gear systems while using advanced control strategies.

Investigation of thermal comfort and preferred temperatures among rural elderly in Weihai, China: Considering metabolic rate effects

As the population ages, the thermal environment indoors for the elderly has garnered extensive attention. However, current research on thermal comfort for the elderly is not comprehensive, particularly for rural elderly individuals who experience unique thermal perceptions and adaptation behaviors due to physical changes. Moreover, metabolic rates, which are often overlooked in thermal comfort studies for the elderly, play a crucial role. A year-long survey of thermal comfort was conducted in the rural areas of Weihai, a coastal city in China's cold region, collecting 203 questionnaires. Three different values of metabolic rates (recommended values, estimated values, and values calculated using Basal Metabolic Rate and Physical Activity Ratio) were analyzed. The results demonstrate that the Basal Metabolic Rate and Physical Activity Ratio method provides predictions of thermal sensation that more closely align with actual thermal sensations. The adaptive coefficient of the aPMV model was found to be −0.38 in colder environments and 0.272 in warmer ones. Additionally, using PMV regression, TSV regression, and Griffiths' method, we calculated the local elderly's comfort temperatures to be 18.9 °C (15.43–22.57 °C), 19.3 °C (15.16–22.75 °C), and 21.63 °C (21.34–23.49 °C) respectively. This study introduces a straightforward method for calculating metabolic rates in elderly individuals, offering new insights for research on thermal comfort in the elderly and expanding the database for thermal comfort studies. It lays a foundation for future design and standardization of housing for rural elderly populations.

Adaptive-coefficient finite difference frequency domain method for time fractional diffusive-viscous wave equation arising in geophysics

The diffusive-viscous wave (DVW) equation is a widely used model to describe frequency dependent attenuation of seismic wave in fluid-saturated porous medium. In this paper taking power law frequency dependent attenuation into account, we first introduce a modified DVW equation (time fractional DVW equation) in which the first order temporal derivative of viscous term is replaced with a fractional order temporal derivative. In consideration of that most of the existing numerical simulations for seismic wave equations are based on time domain methods and truncation with some specific boundary conditions, we incorporate the absorbing boundary condition as complex-frequency-shifted (CFS) perfectly matched layer (PML) into the time fractional DVW equation, and then develop an adaptive-coefficient (AC) finite difference frequency domain (FDFD) method for numerical simulation. The corresponding analytical solution for homogeneous time fractional DVW equation is provided for model validation, and the effectiveness of the developed AC FDFD method is verified by some numerical examples including the homogeneous model and the layered model. Numerical results show that AC FDFD method is more accurate than the traditional 2nd-order FDFD method for numerical modeling of time fractional DVW equation with CFS PML absorbing boundary condition, while requiring similar computational costs.

QoS-Guaranteed Adaptive Power Reflection Coefficient for Self-Powered Cooperative Ambient Backscatter Communication

QoS-Guaranteed Adaptive Power Reflection Coefficient for Self-Powered Cooperative Ambient Backscatter Communication

Online Tree-Structure-Constrained RPCA for Background Subtraction of X-ray Coronary Angiography Images

Background and objectiveBackground subtraction of X-ray coronary angiograms (XCA) can significantly improve the diagnosis and treatment of coronary vessel diseases. The XCA background is complex and dynamic due to structures with different intensities and independent motion patterns, making XCA background subtraction challenging. MethodsThe current work proposes an online tree-structure-constrained robust PCA (OTS-RPCA) method to subtract the XCA background. A morphological closing operation is used as a pre-processing step to remove large-scale structures like the spine, chest and diaphragm. In the following, the XCA sequence is decomposed into three different subspaces: low-rank background, residual dynamic background and vascular foreground. A tree-structured norm is introduced and applied to the vascular submatrix to guarantee the vessel spatial coherency. Moreover, the residual dynamic background is separately extracted to remove noise and motion artifacts from the vascular foreground. The proposed algorithm also employs an adaptive regularization coefficient that tracks the vessel area changes in the XCA frames. ResultsThe proposed method is evaluated on two datasets of real clinical and synthetic low-contrast XCA sequences of 38 patients using the global and local contrast-to-noise ratio (CNR) and structural similarity index (SSIM) criteria. For the real XCA dataset, the average values of global CNR, local CNR and SSIM are 6.27, 3.07 and 0.97, while these values over the synthetic low-contrast dataset are obtained as 5.15, 2.69 and 0.94, respectively. The implemented quantitative and qualitative experiments verify the superiority of the proposed method over seven selected state-of-the-art methods in increasing the coronary vessel contrast and preserving the vessel structure. ConclusionsThe proposed OTS-RPCA background subtraction method accurately subtracts backgrounds from XCA images. Our method might provide the basis for reducing the contrast agent dose and the number of needed injections in coronary interventions.

A novel hybrid algorithm based on improved marine predators algorithm and equilibrium optimizer for parameter extraction of solar photovoltaic models

In the field of solar photovoltaic (PV) systems, the accurate and reliable extraction of parameters from PV models is crucial for effective simulation, evaluation, and control. Although various optimization algorithms have been widely used for parameter extraction in solar PV systems, the accuracy and reliability of the parameters extracted by these methods usually fall short of the expected standards. To address these shortcomings, a novel hybrid algorithm that combines the improved marine predators algorithm (MPA) with the equilibrium optimizer (EO), named IMPAEO, is proposed. In the IMPAEO, an elite opposition-based learning strategy is introduced to expand the exploration range of the algorithm to enhance population diversity; an adaptive weight coefficient is employed to improve the updating rule of the MPA, facilitating a balance between global exploration and local exploitation; the EO operator is integrated to enhance the performance of the MPA in the local exploitation phase to improve the solution quality and convergence speed; and the linear population size reduction (LPSR) technique is introduced to dynamically reduce the population size and improve the search performance. The effectiveness of the proposed IMPAEO is validated using the CEC2017 benchmark test suite, which is also applied to extract parameters of the single diode model, the double diode model, and three different PV modules. Comparative analysis with other algorithms demonstrates that the IMPAEO exhibits significant advantages in terms of solution quality, convergence speed, and algorithm stability. Consequently, the proposed IMPAEO not only proves to be efficient and reliable in parameter extraction for solar PV models but also offers a promising alternative in this field.

A coordination control between energy storage based DVR and wind turbine for continuous fault ride‐through

AbstractContinuous fault ride‐through (CFRT) issues often arise in wind power systems. CFRT results in continuous voltage fluctuations which is characterized by “initially decreasing and then increasing” and can significantly disrupt the normal operation of wind power systems. To mitigate CFRT related problems, one approach is the utilization of energy storage (ES) based dynamic voltage restorers (DVRs). Nevertheless, the prohibitive costs and substantial ES requirements hamper practical implementation. In this article, a control scheme incorporating adaptive mode switching and coordinated control is proposed. First, the adaptive mode switching control leverages the advantages of two DVR compensation methods to reduce the injected voltage amplitude. The mode switching is activated by the DC link voltage and utilizes the PQR transformation to unlock the phase‐locked loop (PLL). Subsequently, an adaptive coordination coefficient is introduced, which considers the margin of ES regulation power and rotor inertia, to maintain power balance during CFRT. This proposed control strategy not only enables wind turbines to seamlessly ride through continuous faults without disconnecting from the grid but also leads to a reduction in the ES compensation requirement. To validate the effectiveness of the proposed approach, simulations based on Simulink and hardware‐in‐loop (HIL) experiments are conducted.

A Reinforced Whale Optimization Algorithm for Solving Mathematical Optimization Problems.

The whale optimization algorithm has several advantages, such as simple operation, few control parameters, and a strong ability to jump out of the local optimum, and has been used to solve various practical optimization problems. In order to improve its convergence speed and solution quality, a reinforced whale optimization algorithm (RWOA) was designed. Firstly, an opposition-based learning strategy is used to generate other optima based on the best optimal solution found during the algorithm's iteration, which can increase the diversity of the optimal solution and accelerate the convergence speed. Secondly, a dynamic adaptive coefficient is introduced in the two stages of prey and bubble net, which can balance exploration and exploitation. Finally, a kind of individual information-reinforced mechanism is utilized during the encircling prey stage to improve the solution quality. The performance of the RWOA is validated using 23 benchmark test functions, 29 CEC-2017 test functions, and 12 CEC-2022 test functions. Experiment results demonstrate that the RWOA exhibits better convergence accuracy and algorithm stability than the WOA on 20 benchmark test functions, 21 CEC-2017 test functions, and 8 CEC-2022 test functions, separately. Wilcoxon's rank sum test shows that there are significant statistical differences between the RWOA and other algorithms.

SimpleTrackV2: Rethinking the Timing Characteristics for Multi-Object Tracking.

Multi-object tracking tasks aim to assign unique trajectory codes to targets in video frames. Most detection-based tracking methods use Kalman filtering algorithms for trajectory prediction, directly utilizing associated target features for trajectory updates. However, this approach often fails, with camera jitter and transient target loss in real-world scenarios. This paper rethinks state prediction and fusion based on target temporal features to address these issues and proposes the SimpleTrackV2 algorithm, building on the previously designed SimpleTrack. Firstly, to address the poor prediction performance of linear motion models in complex scenes, we designed a target state prediction algorithm called LSTM-MP, based on long short-term memory (LSTM). This algorithm encodes the target's historical motion information using LSTM and decodes it with a multilayer perceptron (MLP) to achieve target state prediction. Secondly, to mitigate the effect of occlusion on target state saliency, we designed a spatiotemporal attention-based target appearance feature fusion (TSA-FF) target state fusion algorithm based on the attention mechanism. TSA-FF calculates adaptive fusion coefficients to enhance target state fusion, thereby improving the accuracy of subsequent data association. To demonstrate the effectiveness of the proposed method, we compared SimpleTrackV2 with the baseline model SimpleTrack on the MOT17 dataset. We also conducted ablation experiments on TSA-FF and LSTM-MP for SimpleTrackV2, exploring the optimal number of fusion frames and the impact of different loss functions on model performance. The experimental results show that SimpleTrackV2 handles camera jitter and target occlusion better, achieving improvements of 1.6%, 3.2%, and 6.1% in MOTA, IDF1, and HOTA, respectively, compared to the SimpleTrack algorithm.

Polymeric silk fibroin hydrogel as a conductive and multifunctional adhesive for durable skin and epidermal electronics.

Silk fibroin (SF)-based hydrogels are promising multifunctional adhesive candidates for real-world applications in tissue engineering, implantable bioelectronics, artificial muscles, and artificial skin. However, developing conductive SF-based hydrogels that are suitable for the micro-physiological environment and maintain their physical and chemical properties over long periods of use remains challenging. Herein, we developed an ion-conductive SF hydrogel composed of glycidyl methacrylate silk fibroin (SilMA) and bioionic liquid choline acylate (ChoA) polymer chains, together with the modification of acrylated thymine (ThyA) and adenine (AdeA) functional groups. The resulting polymeric ion-conductive SF composite hydrogel demonstrated high bioactivity, strong adhesion strength, good mechanical compliance, and stretchability. The formed hydrogel network of ChoA chains can coordinate with the ionic strength in the micro-physiological environment while maintaining the adaptive coefficient of expansion and stable mechanical properties. These features help to form a stable ion-conducting channel for the hydrogel. Additionally, the hydrogel network modified with AdeA and ThyA, can provide a strong adhesion to the surface of a variety of substrates, including wet tissue through abundant hydrogen bonding. The biocompatible and ionic conductive SF composite hydrogels can be easily prepared and incorporated into flexible skin or epidermal sensing devices. Therefore, our polymeric SF-based hydrogel has great potential and wide application to be an important component of many flexible electronic devices for personalized healthcare.

An Adaptive Droop Control for Power Sharing Between Three Phase Parallel Inverter in Autonomous Microgrid

The development of an adaptive droop controller is for power sharing between three-phase parallel inverters. Traditionally, droop controllers have been employed in conventional power systems to allocate the load among generators with varying capacities. Our approach involves the utilization of an adaptive control technique. This method is designed for a setup involving three-phase parallel inverters linked to an AC microgrid, to enhance the proportional distribution of active power despite changes in feeder network characteristics and loads. The proposed control mechanism automatically tunes the frequency droop parameter to address mismatch frequencies. The system includes two sets of three-phase inverters regulated by a combination of P-F droop control, an enhanced droop control loop, and PI controllers. The adaptive droop coefficient control is capable of addressing problems where power distribution inaccuracies arise due to different line loads as well as different inverter capacities.

A short-term wind speed prediction method based on the IDBO-BPNN

Wind energy is known for its uncertainty and volatility, necessitating accurate wind speed prediction for stable wind farm operations. To enhance wind speed prediction accuracy, this study proposes a BP neural network (BPNN) short-term wind speed prediction model based on the Improved Dung Beetle Optimization (IDBO) algorithm. Addressing the issue of local optimization and reduced accuracy in the BPNN optimized by the Dung Beetle Optimization (DBO) algorithm, the circle chaotic mapping is utilized for population initialization to achieve a more uniform initial distribution. The improved sine-cosine algorithm, triangle wandering strategy, and adaptive weight coefficient are then employed to optimize dung beetle positions, balancing global exploration and local development capabilities and improving the algorithm’s search performance. Finally, the improved DBO algorithm optimizes the weights and thresholds of the BPNN, and the IDBO-BPNN prediction model was constructed. Simulation experiments were conducted based on wind speed data from a wind farm in Ohio, USA. The IDBO-BPNN model was compared with other prediction models, and error evaluation indexes were introduced to evaluate the experimental results. The findings demonstrate that the suggested model yields the most accurate predictions and achieves the optimal error evaluation indexes. MAE, MSE, RMSE, NSE and R2 of dataset 1 are 0.42247, 0.28775, 0.53642, 88.8785%, 89.161%, those of dataset 2 are 0.28283, 0.14952, 0.38668, 85.7383%, 86.577%, and those of dataset 3 are 0.45406, 0.39268, 0.62664, 84.3859%, 84.931%. In particular, compared with BPNN model, the five evaluation indexes of the IDBO-BPNN model promoted by 41.53%, 57.38%, 34.71%, 24.91%, and 11.44%, respectively in dataset 3. Therefore, that the proposed IDBO-BPNN model exhibits higher accuracy in short-term wind speed prediction, indicating its feasibility and superiority in the realm of wind energy.

Adaptive complex coefficient‐filtered extended states observer type three‐phase enhanced phase‐locked loop

AbstractThis paper proposes an adaptive complex coefficient filtered (CCF) full order extended state observer (ESO) type three phase enhanced phase locked loop (EPLL), which is employed in matrix reactance frequency converter (MRFC) for rotating vectors measurement estimation. EPLL phase detector was analysed first to track not only the rad frequency and transient phase, but also the amplitude of the rotating vectors. Full order ESO can filter out more high frequency disturbance, so it can track frequency and phase more accurately than proportion and integration algorithm. CCF modules were analysed in theory to attenuate the disturbance within two times of the EPLL's cut‐off frequency. The whole adaptive CCF‐ESO‐EPLL design, including stability and parameter tuning were then interpreted. The proposed CCF‐ESO‐EPLL adapts the estimated frequency with CCF to attenuate the MRFC switching non‐linear disturbance as well as unknown disturbance by the full order ESO. The MRFC topology necessary measured vectors, as well as the input side reactive power adjustment capacity, were discussed. The theoretical conclusion has been verified on a MRFC prototype. The proposed CCF‐ESO‐EPLL has less parameter tuning, less computation resource requirement, better steady and dynamic performance, and outstanding disturbance robustness.

Performance analysis of unconstrained partitioned-block frequency-domain adaptive filters in under-modeling scenarios

The unconstrained partitioned-block frequency-domain adaptive filter (PBFDAF) offers superior computational efficiency over its constrained counterpart. However, the correlation matrix governing the natural modes of the unconstrained PBFDAF is not full rank. Consequently, the mean coefficient behavior of the algorithm depends on the initialization of adaptive coefficients and the Wiener solution is non-unique. To address the above problems, a new theoretical model for the deficient-length unconstrained PBFDAF is proposed by constructing a modified filter weight vector within a system identification framework. Specifically, we analyze the transient and steady-state convergence behavior. Our analysis reveals that modified weight vector is independent of its initialization in the steady state. The deficient-length unconstrained PBFDAF converges to a unique Wiener solution, which does not match the true impulse response of the unknown plant. However, the unconstrained PBFDAF can recover more coefficients of the parameter vector of the unknown system than the constrained PBFDAF in certain cases. Also, the modified filter coefficient yields better mean square deviation (MSD) performance than previously assumed. The presented alternative performance analysis provides new insight into convergence properties of the deficient-length unconstrained PBFDAF. Simulations validate the analysis based on the proposed theoretical model.

Strong Single Frequency Jamming Detection Method based on Adaptive Equalization Coefficients

Abstract Serious jamming effect on the communication signal maybe comes from the result of single-frequency jamming which could concentrate energy applying into the transmitted spectra. Usually, the communication signal is covered by the jamming effect. In order to effectively detect the existence of single frequency interference signal or not, the paper presents a frequency domain abnormal phenomena detection method based on adaptive equalization coefficients. The proposed method directly transforms the calculated equalization coefficients into the frequency domain. Then, the existence of strong single-frequency interference signals can be directly detected online by observing the characteristics of the frequency domain. The simulation results show that this method has a good detection effect on strong single frequency jamming. The research shows that the mentioned detection method and the related anomaly recognition technology based on equalization coefficients can be used for jamming detection, and can provide a new technical approach for jamming detection.

Enhancing IoT (Internet of Things) feature selection: A two-stage approach via an improved whale optimization algorithm

Feature selection is a critical task for optimizing system performance and reducing computational overhead in the context of Internet of Things (IoT) applications. This paper presents a two-stage feature selection approach specifically designed for IoT scenarios. In the first stage, a variety of feature dimensionality reduction techniques are employed to significantly reduce the dimensionality of the original feature set by more than 50%. This process results in the creation of a highly effective feature subset, which serves as a solid foundation for subsequent feature selection in the second stage. In the second stage, feature selection is performed on the feature subset by an evolutionary algorithm to obtain high accuracy. Notably, we propose an improved whale optimization algorithm (WOA-HA), which incorporates several improvement factors such as a chaotic Hénon map mechanism (HMM), adaptive coefficient vector (ACV), and a binary operator. To assess the effectiveness of our approach, we compare the performance of WOA-HA with other evolutionary algorithms in terms of feature selection outcomes. Through extensive experiments, our proposed approach achieves an average accuracy of up to 95.5% on Aalto IoT dataset and 98.8% on RT-IoT 2022 dataset, respectively. Meanwhile, the average number of selected features reduced by about 82.5% on Aalto IoT dataset and about 62.3% in the RT-IoT 2022 dataset, respectively. Our proposed approach consistently outperforms other methods and achieves the best performance on most datasets with higher accuracy and fewer features.