Search Results (49)

Excitatory–inhibitory (E-I) local circuits play a central role in synaptic plasticity and neural coding, yet their multilayer learning dynamics remain poorly understood. We constructed a multilayer feedforward spiking neural network with intra-layer E-I connectivity, using Izhikevich neurons to model regular spiking (RS) and fast spiking (FS) cells, and examined cooperative learning under excitatory and inhibitory spike-timing-dependent plasticity (eSTDP and iSTDP). FS-mediated lateral inhibition alleviates the long-term depression bias arising from RS firing rate adaptation via winner-take-all competition, promoting heterogeneous E→E weight differentiation while preserving mean synaptic strength. A $12\times 12$ parameter grid scan shows that iSTDP expands the stable learning region in the E-I parameter space and reveals a sustained cooperative co-evolution of eSTDP and iSTDP during training. For sparse coding, RS adaptation is the primary driver of Lifetime Sparseness, with FS inhibition acting as a cooperative enhancer; the network exhibits low sparseness at the input layer, a rapid increase at the second layer, and a stable plateau in deeper layers. For temporal pattern learning, the selectivity index $d^{\prime }$ improved substantially after training, reaching approximately 1.90 times that of the FS-absent condition; both interval sensitivity and pattern generalization tests confirmed that this advantage is robust across biologically plausible inter-group delays and preserved under small temporal jitter. Mutual information analysis reveals a consistent tendency for intra-layer FS circuits to maintain higher stimulus-related information across deep layers, consistent with FS-mediated suppression of non-specific responses. These findings provide computational evidence, within the scope of the present model, for understanding cortical E-I cooperative plasticity and inform design principles for neuromorphic systems with adaptive inhibitory regulation. Full article

Stable control of the main combustion chamber temperature is critical for pollutant emission compliance, energy recovery, and equipment longevity in municipal solid waste incineration (MSWI). However, the response delays from manipulated variables such as primary air, secondary air, and feed rate to the furnace temperature span from seconds to tens of minutes, and a uniform-delay assumption is inadequate to characterize the true response lag. Moreover, without an action-smoothing constraint, optimizers tend to produce abrupt control commands that destabilize the temperature trajectory. Using real industrial distributed control system (DCS) data from a full-scale grate furnace, this paper develops a prediction–decision collaborative control framework. In the prediction module, mutual information (MI) is used to identify the optimal delay of each manipulated variable separately, and the time-aligned manipulated variables together with a low-order autoregressive component serve as input to XGBoost and yield a prediction RMSE of 6.85 °C with an $R^2$ of 0.9845. In the decision module, a normalized smoothing penalty is incorporated into the fitness function of particle swarm optimization (PSO) to constrain the step-to-step variation in manipulated variables. Offline predictor-in-the-loop simulation on the test set shows that, compared with a multi-loop PID controller, the proposed method reduces the standard deviation of the furnace temperature tracking error by about 35% (from 5.80 °C to 3.80 °C), and lowers the mean tracking error to 3.65 °C while improving actuator smoothness over both unconstrained PSO and a genetic algorithm. The framework provides a collaborative-control design for pre-deployment evaluation of data-driven controllers in MSWI operation. Full article

Physical-layer key generation (PLKG) is a technique that produces symmetric encryption keys by exploiting the inherent characteristics of wireless channels. It offers advantages including high physical-layer security, elimination of pre-shared keys, dynamic upgradability, and resistance to quantum attacks, making PLKG a promising security solution for next-generation (6G) networks. However, satellite communication channels exhibit high dynamics and long propagation delays. Characteristics such as large Doppler shifts, short coherence times, and orbital predictability pose severe challenges to PLKG, including reciprocity degradation, low key generation rate (KGR), and susceptibility to channel-prediction attacks. This work proposes a delay-Doppler domain time-hopping key generation scheme (KE-DD-TH) based on Orthogonal Time Frequency Space (OTFS) modulation for high-speed links between Low-Earth-Orbit (LEO)/Medium-Earth-Orbit (MEO) satellites and ground terminals in Ka/Ku bands. The scheme performs non-uniform sampling on the DD domain grid of OTFS symbols using an ephemeris-driven pseudo-random time-hopping sequence generated by cascaded linear feedback shift registers (LFSRs) and a nonlinear matrix transformation. Both legitimate parties estimate the channel only at time-hopping instants and multiply two adjacent estimates to construct an “equivalent channel” matrix, yielding a random source with high entropy, high reciprocity, and low predictability. The eavesdropper’s key disagreement rate (KDR) remains close to 0.5 under all signal-to-noise ratio (SNR) conditions, corresponding to the ideal random-guessing baseline. This indicates that Eve obtains negligible mutual information, i.e., $I(K_A;K_E)\approx 0$. By contrast, the conventional KE-DD scheme allows Eve’s KDR to degrade to 0.014 at 30 dB SNR, indicating near-complete key recovery. The generated keys pass all 12 randomness tests of the NIST SP 800-22 statistical test suite. Full article

Impulsive noise significantly degrades the performance of power line communication (PLC) systems due to their non-Gaussian amplitude distribution, burst clustering, and inherent temporal dependence. Conventional statistical and spectral models often describe marginal behavior but do not fully account for the underlying temporal organization of such noise processes. This paper introduces an ensemble-based non-linear dynamical framework for the short-window characterization of impulsive PLC noise using delay-embedded phase-space reconstruction (PSR). Rather than relying on extended stationary recordings, the analysis is conducted across multiple independent short-duration acquisition windows obtained from indoor low-voltage networks. For each realization, the delay parameter is selected using average mutual information, and the embedding dimension is determined through the false nearest neighbors (FNN) criterion. The reconstructed trajectories are then examined using correlation dimension estimation, largest Lyapunov exponent analysis, and recurrence quantification measures. The resulting non-linear descriptors reveal structured phase-space organization and low-dimensional dynamical characteristics that are not readily observable in the original time-domain representation. In addition, these findings show that short-window PLC data preserve meaningful dynamical characteristics and support the use of non-linear geometric descriptors for impulsive PLC noise analysis and future mitigation approaches. Full article

This article proposes a dynamic causal inference framework that integrates theoretical analysis, numerical simulation, and industrial data mining to address the root-cause tracing problem of time-delay effects in strip thickness and shape quality during hot rolling. First, we analyze the key process parameters, equipment states, and material characteristics influencing geometric quality and clarify their dynamic interaction mechanisms. Second, a delay-correlation matrix calculation method based on Dynamic Time Warping (DTW) and Mutual Information (MI) is developed to handle temporal misalignment in multi-source industrial signals and quantify the strength of delayed correlations. Furthermore, a transformer-based information gain approximation mechanism is designed to replace traditional explicit probability modeling and learn dynamic information-flow relationships among variables in a data-driven manner. Experimental verification on real production data demonstrates that the proposed framework can accurately identify time-delay causal pathways, providing an interpretable and engineering-feasible solution for quality control under complex operating conditions. Full article

Accurate observations of Ocean Surface Wind Speed (OSWS) are vital for predicting extreme weather and understanding ocean–atmosphere interactions. However, spaceborne sensors (e.g., ASCAT, SMAP) often experience data loss due to harsh weather and instrument malfunctions. Existing inpainting methods often rely on reanalysis data that is released with delays, which restricts their real-time capability. Additionally, deep-learning-based methods, such as Transformers, face challenges due to their high computational complexity. To address these challenges, we present the Multimodal Wind Speed Inpainting Dataset (MWSID), which integrates 12 auxiliary forecasting variables to support real-time OSWS inpainting. Based on MWSID, we propose the MMamba framework, combining the Multimodal Feature Extraction module, which uses mutual information (MI) theory to optimize feature selection, and the OSWS Reconstruction module, which employs Attention-Mamba-2 within a Residual-in-Residual-Dense architecture for efficient OSWS inpainting. Experiments show that MMamba outperforms MambaIR (state-of-the-art) with an RMSE of 0.5481 m/s and an SSIM of 0.9820, significantly reducing RMSE by 21.10% over Kriging and 8.22% over MambaIR in high-winds (>15 m/s). We further introduce MMamba-L, a lightweight 0.22M-parameter variant suitable for resource-limited devices. These contributions make MMamba and MWSID powerful tools for OSWS inpainting, benefiting extreme weather prediction and oceanographic research. Full article

Accurate grid frequency prediction is essential for maintaining the stability and reliability of power systems. However, the complex dynamic characteristics of grid frequency and the nonlinear correlations among massive time series data make it challenging for traditional time series prediction methods to balance efficiency and accuracy. In this paper, we propose a Dynamic Significance–Correlation Weighting (D-SCW) method, which generates dynamic weight coefficients that evolve over time. This is achieved by constructing a joint screening mechanism of feature time series correlation analysis and statistical significance test, combined with the LightGBM gradient-boosting decision tree (GBDT) framework; accordingly, high-precision prediction of grid frequency time series data is realized. To verify the effectiveness of the D-SCW method, this study conducts comparative experiments on two actual grid operation datasets (including typical scenarios with wind/photovoltaic (PV) installations, accounting for 5–35% of the grid); additionally, the Spearman’s rank correlation coefficient method, mutual information (MI), Lasso regression, and the feature screening method of recursive feature elimination (RFE) are selected as the baseline control; root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) are adopted as assessment indicators. The results show that the D-SCW-LightGBM framework reduces the root mean squared error (RMSE) by 5.2% to 10.4% and shortens the dynamic response delay by 52% compared with the benchmark method in high renewable penetration scenarios, confirming its effectiveness in both prediction accuracy and computational efficiency. Full article

To address the conflicting demands of the energy crisis, environmental pollution, and economic growth, the electric vehicle (EV) industry has expanded rapidly, facilitating the widespread adoption of power batteries. This paper investigates the use of chaos theory and machine learning for predicting the remaining useful life (RUL) of lithium-ion batteries. Firstly, the mutual information method determines the time delay of the monitoring sequence, while the improved false nearest neighbor method (Cao algorithm) establishes the embedding dimension, yielding the phase space reconstruction parameters. Secondly, the maximum Lyapunov exponent identifies the chaotic properties of the capacity decay time series, and a prediction dataset is constructed based on phase space reconstruction theory. Finally, leveraging the chaotic time-series features, a support vector machine (SVM) model is developed for lithium-ion battery RUL prediction. The algorithm is subsequently validated through simulation using the NASA battery dataset. The results demonstrate that the proposed method achieves high predictive accuracy and stability, providing reliable RUL estimates for the battery management system (BMS). Full article

Traditional methods for urban power grid design have struggled to meet the demands of multi-energy integration and high resilience scenarios due to issues such as delayed updates of terminology and semantic ambiguity. Current techniques for constructing domain-specific lexicons face challenges like the insufficient coverage of specialized vocabulary and imprecise synonym mining, which restrict the semantic parsing capabilities of intelligent design systems. To address these challenges, this study proposes a framework for constructing a domain-specific lexicon for urban power grid design based on Large Language Models (LLMs). The aim is to enhance the accuracy and practicality of the lexicon through multi-level term extraction and synonym expansion. Initially, a structured corpus covering national and industry standards in the field of power was constructed. An improved Term Frequency–Inverse Document Frequency (TF-IDF) algorithm, combined with mutual information and adjacency entropy filtering mechanisms, was utilized to extract high-quality seed vocabulary from 3426 candidate terms. Leveraging LLMs, multi-level prompt templates were designed to guide synonym mining, incorporating a self-correction mechanism for semantic verification to mitigate errors caused by model hallucinations. This approach successfully built a domain-specific lexicon comprising 3426 core seed words and 10,745 synonyms. The average cosine similarity of synonym pairs reached 0.86, and expert validation confirmed an accuracy rate of 89.3%; text classification experiments showed that integrating the domain-specific dictionary improved the classifier’s F1-score by 9.2%, demonstrating the effectiveness of the method. This research innovatively constructs a high-precision terminology dictionary in the field of power design for the first time through embedding domain-driven constraints and validation workflows, solving the problems of insufficient coverage and imprecise expansion of traditional methods, and supporting the development of semantically intelligent systems for smart urban power grid design, with significant practical application value. Full article

The recent rapid deployment of low-Earth-orbit (LEO) broadband constellations has positioned these systems as expected emerging navigation sources, thereby driving research interest in integrated navigation and communication (INAC) technologies. Existing INAC waveforms face various challenges in LEO environments, including limited ranging accuracy due to high mutual interference (MI) between signal components, a heavy signal processing burden for navigation users, or degraded data transmission reliability. We propose an INAC waveform named delay–Doppler block division multiplexing (DDBDM) in this work. MI is effectively reduced by modulating pseudo-random noise (PRN) codes and data separately on orthogonal delay–Doppler (DD) blocks. Navigation and communication signals in DDBDM can be separated in the frequency band, which allows the user to receive only the bandwidth occupied by the navigation subcarriers, reducing the signal processing overhead. Moreover, data transmission in the DD domain exhibits a low bit error rate in high-mobility channels, which enables fast and reliable navigation augmentation information for users. Simulation results demonstrate that DDBDM offers superior navigation performance and data transmission reliability compared to existing INAC schemes. The proposed waveform enhances the performance of the LEO INAC system and effectively extends the position, navigation, and timing (PNT) service capability. Full article

The integration of Internet Protocol version 6 over Low-Power Wireless Personal Area Networks (6LoWPANs) provided IP technologies within wireless sensor networks that dramatically increased the Internet of Things (IoT). Therefore, to facilitate efficient mobility management for resource-constrained IP-based sensor nodes, the Proxy Mobile IPv6 (PMIPv6) standard has been introduced to reduce communication overhead. However, the standard has addressed security and mobility authentication challenges in 6LoWPANs, although recent solutions have yet to focus much on facilitating secure group handovers. Considering these issues, a Secure and Lightweight Group Mobility Authentication Scheme (SL_GAS) is proposed for 6LoWPAN’s highly constrained sensor nodes. SL_GAS innovatively utilizes one-time alias identities, temporary IDs, tickets, and an aggregated MAC with tags to ensure mutual authentication while maintaining sensor anonymity, providing a balanced security and privacy approach. SL_GAS’s robustness against a variety of security threats is validated through formal automated verification using the Scyther tool alongside SVO logic, while an informal analysis demonstrates its resilience to known attacks. Comparative analysis with existing schemes highlights SL_GAS’s advantages in reducing signal cost, transmission delay, communication, and computation overhead. SL_GAS stands out for its combination of security, privacy, and efficiency, making it a promising approach for enhancing IoT connectivity in resource-constrained settings. Full article

This study examines the impact of the Kahramanmaraş Earthquake on four key sectors of Borsa Istanbul: Basic Metal, Insurance, Non-Metallic Mineral Products, and Wholesale and Retail Trade using neutral delayed fractional differential equations. Employing the Chebyshev collocation method, we numerically solved the neutral delayed fractional differential equations with initial conditions scaled by each sector’s log difference standard deviation to accurately reflect market volatility. Fractional orders were derived from the Hurst exponent, and time delays were identified using average mutual information, autocorrelation function, and partial autocorrelation function methods. The results reveal significant changes post-earthquake, including reduced market persistence and increased volatility in the Basic Metal and Insurance sectors, contrasted by enhanced stability in the Non-Metallic Mineral Products sector. Neutral delayed fractional differential equations demonstrated superior performance over traditional models by effectively capturing memory and delay effects. This work underscores the efficacy of neutral delayed fractional differential equations in modeling financial resilience amid external shocks. Full article

The traction motor realizes the mutual conversion of electrical energy and mechanical energy during the train traction and braking process and is a key component of high-speed trains. The normal operation of the motor is directly related to the safety of high-speed train operation. Changes in temperature signals can reflect faults in the traction motor. By analyzing the internal and external influencing factors of temperature signals, a multi-factor prediction model for traction motors is established based on the maximal information coefficient and improved support vector regression. In this model, highly relevant features selections are performed based on time-delayed sequences and the maximal information coefficient. Using the adaptive particle swarm algorithm to optimize the improved support vector regression algorithm can enhance its accuracy and efficiency. Furthermore, using the K-nearest neighbor algorithm for error prediction will yield more accurate results. By comparing the $RMSE$, $MBE$, $MAE$, and other evaluation metrics of different algorithms under various working conditions, the results show that the prediction method proposed in this paper performs well across different working conditions. This method demonstrates greater adaptability to varying conditions and is more suitable for applications involving high-speed trains. Full article

In this study, the topological properties of the shallow seismicity occurring in the area around the Lai Chau hydropower plant (Vietnam) are investigated by using visibility graph (VG) analysis, a well-known method to convert time series into networks or graphs. The relationship between the seismicity and reservoir water level was analyzed using Interlayer Mutual Information (IMI) and the Frobenius norm, both applied to the corresponding VG networks. IMI was used to assess the correlation between the two variables, while the Frobenius norm was employed to estimate the time delay between them. The total seismicity, which resulted in an $M\ge 0.8$ with a b-value of 0.86, is characterized by a $k-M$ slope of ≈9.1. Analyzing the variation of the seismological and topological parameters of the seismicity relative to the distance from the center of the Lai Chau reservoir revealed the following features: (1) the b-value fluctuates around a mean value of 1.21 at distances of up to 10–11 km, while, for distances larger than 25–30 km, it tends to the value of 0.86; (2) the maximum IMI between the monthly number of earthquakes and the monthly mean water level occurs at a distance of 9–11 km, showing a distance evolution similar to that of the b-value; (3) at these distances from the center of the reservoir, the time lag between the earthquake monthly counts and the monthly water level mean is 9–10 months; (4) the relationship between the b-value and the $k-M$ slope suggests that the $k-M$ slope depends on the number of earthquakes within a 22 km radius from the center of the dam. Our study’s findings offer new insights into the complex dynamics of seismicity occurring around reservoirs. Full article

Mobile robots play an important role in the industrial Internet of Things (IIoT); they need effective mutual communication between the cloud and themselves when they move in a factory. By using the sensor nodes existing in the IIoT environment as relays, mobile robots and the cloud can communicate through multiple hops. However, the mobility and delay sensitivity of mobile robots bring new challenges. In this paper, we propose a dynamic cooperative transmission algorithm with mutual information accumulation to cope with these two challenges. By using rateless coding, nodes can reduce the delay caused by retransmission under poor channel conditions. With the help of mutual information accumulation, nodes can accumulate information faster and reduce delay. We propose a two-step dynamic algorithm, which can obtain the current routing path with low time complexity. The simulation results show that our algorithm is better than the existing heuristic algorithm in terms of delay. Full article