Adaptive Frequency Research Articles

Low voltage user power internet of things monitoring system based on LoRa wireless technology

The operational efficiency of the current smart grid system is seriously affected by the stability of the operating system, and Internet of Things technology has good applicability in power grid information perception. This study uses LoRa technology to construct a monitoring system for the electric energy Internet of Things. Additionally, an optimization model based on a particle swarm optimization algorithm and backpropagation neural network for optimizing base station positioning and channel quality evaluation is proposed. In addition, a multi-channel adaptive frequency hopping technology has been developed. The experimental results showed that the adaptive frequency hopping technology of the system could complete frequency switching within 2 min, which was more efficient than the traditional sampling and statistical technology that took 4 min. In terms of coverage, the research method had a coverage radius of 25 km, which was superior to other communication technologies such as NB IoT and ZigBee. In terms of data transmission success rate, the research method achieved 98.11%, significantly higher than Sigfox’s 90.02%. In addition, the system had a latency of only 150ms and low power consumption. In summary, the PSO-BP LoRa model proposed in the study has high application value in smart grids and industrial environments, providing technical support for wide-area, low-power, and high-stability Internet of Things monitoring systems.

Substitution load revisited: a high proportion of deaths can be selective.

Haldane's Dilemma refers to the concern that the need for many "selective deaths" to complete a substitution (i.e. selective sweep) creates a speed limit to adaptation. However, discussion of this concern has been marked by confusion, especially with respect to the term "substitution load". Here we distinguish different historical lines of reasoning, and identify one, focused on finite reproductive excess and the proportion of deaths that are "selective" (i.e. causally contribute to adaptive allele frequency changes), that has not yet been fully addressed. We develop this into a more general theoretical model that can apply to populations with any life history, even those for which a generation or even an individual are not well defined. The actual speed of adaptive evolution is coupled to the proportion of deaths that are selective. The degree to which reproductive excess enables a high proportion of selective deaths depends on the details of when selection takes place relative to density regulation, and there is therefore no general expression for a speed limit. To make these concepts concrete, we estimate both reproductive excess, and the proportion of deaths that are selective, from a dataset measuring survival of 517 different genotypes of Arabidopsis thaliana grown in eight different environmental conditions. In this dataset, a much higher proportion of deaths contribute to adaptation, in all environmental conditions, than the 10% cap that was anticipated as substantially restricting adaptation during historical discussions of speed limits.

Chondroitin Sulfate and Proteinoids in Neuron Models.

This study examines the relationship between chondroitin sulfate, proteinoids, and computational neuron models, with a specific emphasis on the Izhikevich neuron model. We investigate the effect of chondroitin sulfate-proteinoid complexes on the behavior and dynamics of simulated neurons. Through the use of computational simulations, we provide evidence that these biomolecular components have the power to regulate the responsiveness of neurons, the patterns of their firing, and the ability of their synapses to change within the Izhikevich architecture. The findings suggest that the interactions between chondroitin sulfate and proteinoid cause notable alterations in the dynamics of membrane potential and the timing of spikes. We detect adjustments in the features of neuronal responses, such as shifts in the thresholds for firing, alterations in spike frequency adaptation, and changes to bursting patterns. The findings indicate that chondroitin sulfate and proteinoids may have a role in precisely adjusting neuronal information processing and network behavior. This study offers valuable information about the complex connection between the many components of the extracellular matrix, protein-based structures, and the functioning of neurons. In addition, our analysis of the proteinoid-chondroitine system using game theory uncovers a significant Prisoner's Dilemma scenario. The system's inclination toward defection, due to the appeal of cheating and the significant penalty for cooperation, with a mean voltage of -9.19 mV, indicates that defective behaviors may prevail in the long term dynamics of these neuronal interactions.

Learning and Phase Tracking by Frequency and Weight Adaptation for Coupled Networks of Kuramoto Oscillators

Learning and Phase Tracking by Frequency and Weight Adaptation for Coupled Networks of Kuramoto Oscillators

Virtual inertia and damping‐based cascaded control approach for enhancing load frequency control in low‐inertia multi‐area power systems

AbstractThis paper introduces a comprehensive cascaded controller to enhance transient and dynamic stability in load frequency control for low‐inertia multi‐area power systems (LIMAPSs) integrating solar photovoltaic and wind turbine sources. The proposed method combines a composite energy storage‐based virtual synchronous generator with a novel cascaded controller employing an adaptive neuro‐fuzzy inference system (ANFIS) and a fractional‐order proportional–integral with a fractional‐order proportional‐tilt‐integral (FOPI‐FOPTI) control strategy. Firstly, a dynamic model of a thermal power plant incorporating photovoltaic, wind turbine, and virtual synchronous generator is developed. Subsequently, the ANFIS‐FOPI‐FOPTI controller parameters, optimized via the whale optimization algorithm, provide adaptive and robust frequency stability control under disturbances. Finally, extensive simulations demonstrate the proposed approach's effectiveness in enhancing the transient and dynamic stability of LIMAPSs. The simulations are conducted on two‐area power systems and a modified IEEE 10‐generator 39‐bus system, considering disturbances like stochastic load‐generation. Comparative analysis with existing controllers shows the proposed cascaded controller effectively addresses load frequency control challenges in LIMAPSs. It outperforms other control approaches in terms of settling time, overshoot, and specified objective functions, with the lowest integral time absolute error values recorded: 0.1202 for the two‐area system and 0.480 for the modified IEEE 39‐bus four‐area power system.

Multi-rate sampled-data observer-based networked load frequency control for multi-area interconnected power systems under multiple cyber-attacks

This paper addresses the problem of adaptive event-triggered load frequency control (LFC) for multi-rate sampling multi-area interconnected power systems with integrated renewable energy. Different network channels can be vulnerable to multiple cyber-attacks, including deception attacks and DoS attacks. First, a matching mechanism is constructed to fuse the multi-rate sampled data, and a set of observers based on multi-rate sampled data is designed to independently estimate the states of the individual power systems. A networked sampled-data PI controller is then developed by introducing a new synchronous sampling scheme and an adaptive event-triggered mechanism. A co-design approach is proposed that integrates decentralized PI controllers, observers, and adaptive event-triggered mechanisms to achieve exponential mean-square stability with a given H∞ performance level for the networked LFC system in the presence of multiple cyber-attacks and multi-rate sampling. Finally, the effectiveness of the proposed method is verified through an example of an interconnected power system.

Adaptive Frequency Coupling Suppression Strategy for VSCs and Its Stability Region Analysis

Adaptive Frequency Coupling Suppression Strategy for VSCs and Its Stability Region Analysis

Dynamic event‐triggered‐based adaptive frequency control of microgrids under cyber‐attacks via adaptive dynamic programming

AbstractThe increasing penetration of renewable energy sources (RES) and the development of the cyber‐physical microgrids (CPMs) make greater demands for frequency control of microgrids. The common approach for frequency control is controlling the micro‐turbine to compensate for frequency deviations, with energy storage systems serving as an auxiliary approach. This article proposes an online adaptive frequency control method to control the governor and energy storage to realize the frequency recovery of microgrid, subject to the external unknown disturbances caused by wind turbine, power load and false data injection (FDI) attacks. First, the non‐zero sum (NZS) games of the considered system are modeled in this work, where the unknown disturbances are also taken into account. For the sake of estimating the unknown disturbances, a disturbance observer (DOB) for the microgrid system is introduced. Then, on the basis of the estimated results of the applied DOB, the disturbance compensation input is derived to offset the interference of the unknown disturbance. Meanwhile, the adaptive dynamic programming (ADP) approach is employed to derive the adaptive optimal control input for the NZS games of microgrid system. Besides, the dynamic event‐triggered (DET) control is introduced, reducing the occupation of computing resources. By utilizing the Lyapunov's method, the stability of the closed‐loop system, the convergence of the estimation weight, the estimation disturbances and the system state are guaranteed. The effectiveness of the proposed method is ultimately verified by the simulation results.

Spike frequency adaptation in primate lateral prefrontal cortex neurons results from interplay between intrinsic properties and circuit dynamics.

Cortical neurons in brain slices display intrinsic spike frequency adaptation (I-SFA) to constant current inputs, while extracellular recordings show extrinsic SFA (E-SFA) during sustained visual stimulation. Inferring how I-SFA contributes to E-SFA during behavior is challenging due to the isolated nature of slice recordings. To address this, we recorded macaque lateral prefrontal cortex (LPFC) neurons invivo during a visually guided saccade task and invitro in brain slices. Broad-spiking (BS) putative pyramidal cells and narrow-spiking (NS) putative inhibitory interneurons exhibit both E-SFA and I-SFA. Developing a data-driven hybrid circuit model comprising NS model neurons receiving BS input reveals that NS model neurons exhibit longer SFA than observed invivo; however, adding feedforward inhibition corrects this in a manner dependent on I-SFA. Identification of this circuit motif shaping E-SFA in LPFC highlights the roles of both intrinsic and network mechanisms in neural activity underlying behavior.

Mobile SoC Power Optimization: Redefining Performance with Machine Learning Techniques

Power optimization is a critical challenge in System-on-chip (SoC) design due to the growing demand for high-performance mobile devices with extended battery life. This paper explores novel applications of machine learning (ML) technologies to readdress the question of power optimization strategies in mobile SoCs. In order to balance performance and power consumption in real time, ML takes advantage of predictive modeling, dynamic power management, and energy efficient workload scheduling. This study makes key contributions to integrating ML models into SoC architectures, including a novel ML-driven adaptive voltage scaling and frequency tuning workflow that demonstrates the potential for significant power savings, as well as empirical analysis. Given our findings, we believe that ML offers a transformative potential for the realization of sustainable and efficient mobile computing solutions.

Improved Controller for Interlinking Converter in Hybrid AC/DC Microgrids

The AC/DC hybrid microgrids are a feasible solution to provide both AC and DC power to electrical equipment. However, the control problem to maintain voltage and frequency stability in AC/DC hybrid microgrids is essential. This paper proposes a control method for the converter to maintain voltage and frequency stability for AC/DC hybrid microgrids. The power converter will operate bidirectionally to transfer power back and forth between AC and DC subgrids in the AC/DC hybrid microgrid operating in standalone mode. The proposed control method not only controls the bidirectional power flow between AC and DC subgrids to stabilize voltage and frequency as well as balance active power and reactive power. In addition, the proposed method can restore voltage and frequency for the microgrid in the event of sudden load surges or power failures in AC and DC subgrids. This method is established based on the relationship between active power and frequency, because active power is present in both AC and DC subgrids, while frequency is only present in AC subgrid. However, in AC subgrid, frequency depends on active power, while in DC subgrid, DC bus voltage depends on active power. Therefore, the proposed method is designed based on adaptive frequency shifting to adjust the power flow exchange between the two subgrids in order to maintain the stability of the frequency and voltage of the buses. The suitability and feasibility of the method are demonstrated by simulating the AC/DC hybrid microgrid using Matlab/simulink software.

Neural networks with optimized single-neuron adaptation uncover biologically plausible regularization.

Neurons in the brain have rich and adaptive input-output properties. Features such as heterogeneous f-I curves and spike frequency adaptation are known to place single neurons in optimal coding regimes when facing changing stimuli. Yet, it is still unclear how brain circuits exploit single-neuron flexibility, and how network-level requirements may have shaped such cellular function. To answer this question, a multi-scaled approach is needed where the computations of single neurons and neural circuits must be considered as a complete system. In this work, we use artificial neural networks to systematically investigate single-neuron input-output adaptive mechanisms, optimized in an end-to-end fashion. Throughout the optimization process, each neuron has the liberty to modify its nonlinear activation function parametrized to mimic f-I curves of biological neurons, either by learning an individual static function or via a learned and shared adaptation mechanism to modify activation functions in real-time during a task. We find that such adaptive networks show much-improved robustness to noise and changes in input statistics. Using tools from dynamical systems theory, we analyze the role of these emergent single-neuron properties and argue that neural diversity and adaptation play an active regularization role, enabling neural circuits to optimally propagate information across time. Finally, we outline similarities between these optimized solutions and known coding strategies found in biological neurons, such as gain scaling and fractional order differentiation/integration.

Adaptive switching frequency PWM method of SiC inverters for EMI mitigation

Adaptive switching frequency PWM method of SiC inverters for EMI mitigation

Dual-mode virtual synchronous generator adaptive frequency characteristic control

Dual-mode virtual synchronous generator adaptive frequency characteristic control

Adaptive Secondary Frequency Regulation Strategy for Energy Storage Based on Dynamic Primary Frequency Regulation

Adaptive Secondary Frequency Regulation Strategy for Energy Storage Based on Dynamic Primary Frequency Regulation

Bearing Fault Feature Extraction via Adaptive Frequency Matching and Coupling Analysis

Bearing Fault Feature Extraction via Adaptive Frequency Matching and Coupling Analysis

High-stability adaptive frequency comparison method based on fuzzy area characteristics

To meet the high-accuracy requirement of time and frequency measurement and its synchronization in complex environment, a high- stability adaptive frequency comparison method based on fuzzy area characteristics is proposed in this paper. First, the frequency of the measured signal is measured by using FPGA and pulse-filling frequency measurement technology to obtain the frequency coarse value of the measured signal. Second, a communication protocol between FPGA (Field Programmable Gate Array) and DDS (Direct Digital Synthesizer) is established to enable DDS to recognize the information from FPGA. Third, the frequency coarse value of the measured signal is sent to FPGA and converted into the corresponding “frequency word” by DDS. Finally, according to the communication protocol between FPGA and DDS, an adaptive frequency standard signal with the same frequency coarse value as the measured signal is generated by controlling the DDS with the “frequency word”. Then there is a small relative frequency deviation between the frequency of the measured signal and the frequency of the adaptive frequency standard signal. The experiment results show that the frequency stability based on the proposed method can reach 1.76 × 10-13 /s, and the accuracy of the frequency synchronization is better than 5 Hz.

Structural Damage Early Warning Method of Quayside Container Crane Based on Fuzzy Entropy Ratio Variation Deviation.

Real-time monitoring and early warning of structures are essential for assessing structural health and ensuring safety maintenance. To improve the timeliness of early warnings for structural abnormal states in quayside container cranes (QCCs) with incomplete damage data, a structural abnormal state early warning method based on fuzzy entropy ratio variation deviation (FERVD) is proposed. First, monitoring data are subjected to dual-tree complex wavelet transform (DTCWT). The adaptive frequency bands obtained from the decomposition, combined with fuzzy entropy (FE), are used to extract response signal features and construct the FERVD warning indicator. Based on this indicator, dynamic thresholds for early warning are established to differentiate between structural health states and various damage conditions. Secondly, a finite element model of structure for QCCs is developed. By simulating damage at various locations and severities through the stiffness reduction of different elements, a comprehensive structural simulation monitoring dataset is generated. The efficacy of the proposed early warning method is validated through numerical experiments and engineering case studies. The numerical results demonstrate that the proposed method effectively distinguishes between different damage conditions and provides timely warnings for various damage states. Furthermore, engineering case analysis shows that when the structure is in a healthy state, the FERVD values at different monitoring points fluctuate within the threshold range, indicating the applicability of the proposed method in the structural health monitoring (SHM) of QCCs.

Frequency-spatial interaction network for gaze estimation

Gaze estimation is a fundamental task in the field of computer vision, which determines the direction a person is looking at. With advancements in Convolutional Neural Networks (CNNs) and the availability of large-scale datasets, appearance-based models have made significant progress. Nonetheless, CNNs exhibit limitations in extracting global information from features, resulting in a constraint on gaze estimation performance. Inspired by the properties of the Fourier transform in signal processing, we propose the Frequency-Spatial Interaction network for Gaze estimation (FSIGaze), which integrates residual modules and Frequency-Spatial Synergistic (FSS) modules. To be specific, its FSS module is a dual-branch structure with a spatial branch and a frequency branch. The frequency branch employs Fast Fourier Transformation to transfer a latent representation to the frequency domain and applies adaptive frequency filter to achieve an image-size receptive field. The spatial branch, on the other hand, can extract local detailed features. Acknowledging the synergistic benefits of global and local information in gaze estimation, we introduce a Dual-domain Interaction Block (DIB) to enhance the capability of the model. Furthermore, we implement a multi-task learning strategy, incorporating eye region detection as an auxiliary task to refine facial features. Extensive experiments demonstrate that our model surpasses other state-of-the-art gaze estimation models on three three-dimensional (3D) datasets and delivers competitive results on two two-dimensional (2D) datasets.

EDMF: A New Benchmark for Multi-Focus Images with the Challenge of Exposure Difference.

The goal of the multi-focus image fusion (MFIF) task is to merge images with different focus areas into a single clear image. In real world scenarios, in addition to varying focus attributes, there are also exposure differences between multi-source images, which is an important but often overlooked issue. To address this drawback and improve the development of the MFIF task, a new image fusion dataset is introduced called EDMF. Compared with the existing public MFIF datasets, it contains more images with exposure differences, which is more challenging and has a numerical advantage. Specifically, EDMF contains 1000 pairs of color images captured in real-world scenes, with some pairs exhibiting significant exposure difference. These images are captured using smartphones, encompassing diverse scenes and lighting conditions. Additionally, in this paper, a baseline method is also proposed, which is an improved version of memory unit-based unsupervised learning. By incorporating multiple adaptive memory units and spatial frequency information, the network is guided to focus on learning features from in-focus areas. This approach enables the network to effectively learn focus features during training, resulting in clear fused images that align with human visual perception. Experimental results demonstrate the effectiveness of the proposed method in handling exposure difference, achieving excellent fusion results in various complex scenes.