Search Results (24)

Short-term wind speed forecasting plays a critical role in enabling the reliable integration of renewable energy and supporting the sustainable operation of power systems. However, traditional dual-frequency decomposition methods oversimplify wind speed dynamics by separating them into only high-frequency disturbances and low-frequency trends, making it difficult to capture intermediate-frequency transitional dynamics. Additionally, single models struggle to adapt to multi-scale temporal features, limiting forecasting performance. To address these issues, this paper proposes a tri-band frequency-aware heterogeneous expert collaboration framework. First, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) is employed for signal denoising, followed by Particle Swarm Optimization-Time Varying Filtering-based Empirical Mode Decomposition (PSO-TVF-EMD) for multi-scale signal disentanglement. Then, Permutation Entropy (PE) is used to construct a tri-band structure consisting of high-, intermediate-, and low-frequency components. A frequency-aware expert routing mechanism assigns Bayesian Optimization Long Short-Term Memory (BO-LSTM), an improved Markov model, and Auto-Regressive Integrated Moving Average (ARIMA) to the corresponding frequency bands. Finally, a reliability-aware cooperative aggregation strategy integrates predictions from multiple experts. Experimental results show that representative baseline models, including BO-LSTM, Markov, ARIMA, Gated Recurrent Unit (GRU) and Convolutional Neural Network Long Short-Term Memory (CNN-LSTM), achieve MAE values ranging from 0.308 to 0.429, while the proposed framework reduces the Mean Absolute Error (MAE) to 0.193 and Root Mean Square Error (RMSE) to 0.274, with a Mean Absolute Percentage Error (MAPE) of 7.35% and R² of 0.927. Compared with the dual-frequency decomposition scheme (MAE = 0.266), the proposed tri-band framework achieves an average improvement of approximately 28.1%. The results suggest that explicitly modeling intermediate-frequency dynamics and aligning model inductive biases with multi-scale signal characteristics can effectively enhance short-term wind speed forecasting performance. Full article

The rapid expansion of Internet of Things (IoT) infrastructures in smart city environments has increased the demand for reliable intrusion detection systems (IDS). However, many existing studies rely on single-dataset evaluations and inconsistent experimental settings, which can lead to overly optimistic performance estimates. In this study, we propose a standardized benchmarking framework for evaluating artificial intelligence-based IDS across heterogeneous IoT datasets, including CIC-IoT 2023, BoT-IoT, and N-BaIoT. Multiple classical machine learning and deep learning models are evaluated under a unified preprocessing pipeline and a consistent evaluation protocol. A hybrid CNN–BiLSTM–Attention architecture is also implemented as a reference model within this framework. While several models achieve near-perfect performance under intra-dataset evaluation, cross-dataset experiments reveal substantial performance degradation and unstable metric behavior under distribution shifts. These results highlight the limitations of dataset-specific optimization and emphasize the necessity of cross-dataset validation for realistic IoT intrusion detection evaluation. All experiments are conducted under a binary intrusion detection setting (benign vs. attack) to enable consistent comparison across datasets. Consequently, the reported results reflect binary detection performance and do not capture attack-type discrimination. Full article

As IoT systems complexity grows, transparent and trustworthy machine-learning intrusion detection systems are crucial. Post hoc explainable AI methods, such as SHAP and LIME, are the most widely used ways to explain how models work, but the degree to which these methods are robust to adversarial conditioning is understudied. In this paper, we propose to create a unified system of evaluating explanation fidelity by using three metrics: sparsity, completeness, and robustness based on minimally distorting DeepFool input perturbations. Our study benchmarks SHAP and LIME across three datasets (BoT-IoT, Edge-IIoT, and N-BaIoT) using four classifiers: CNN, DNN, LSTM, and RF. Our results demonstrate a consistent trade-off: SHAP achieves stronger feature alignment and higher completeness under attack, whereas LIME exhibits greater rank stability in terms of top-k feature overlap. However, LIME also produces more spurious attributions and offers less explanatory power than SHAP, especially in the presence of synthetic features. Our findings reveal that high model accuracy does not guarantee that the provided explanation is also high-fidelity. This investigation highlights the necessity for robustness-aware XAI in cybersecurity and provides reproducible parameters to guide the adoption of XAI in adversarial environments. Full article

Reliable anomaly early warning for hydropower station sump wells remains challenging due to the strong nonlinearity of water level dynamics and the limited adaptability of conventional fixed-threshold alarms. Here, we present a hybrid deep learning framework—termed CNN–Attention–LSTM–BO—that fuses multi-scale local feature extraction, adaptive temporal weighting, and sequential dependency modeling within a unified architecture, with all critical hyperparameters tuned via Bayesian optimization. A four-dimensional input representation is first constructed from the raw water level signal and its first- and second-order differences together with the drainage pump operating state, capturing both trend and transient information. One-dimensional convolutions at multiple kernel scales encode short-range fluctuation patterns, a Bahdanau-style temporal attention layer selectively amplifies informative time steps, and a stacked LSTM propagates long-horizon risk dependencies. At the decision stage, a dual dynamic thresholding scheme couples an improved $3\sigma$ criterion with kernel density estimation (KDE) to partition the smoothed risk score into three graded alert levels (normal/warning/critical), replacing the binary alarm paradigm. Experiments on the SWaT benchmark yield an average area under the ROC curve (AUC) of 0.9246, an average Accuracy of 0.8812, and a best single-well false alarm rate (FAR) of 3.21% (Well-4), with an average FAR of 8.97% across three wells, outperforming both traditional limit-value alarms and ablated variants lacking CNN or attention modules. Full article

Accurate prediction of a ship’s Estimated Time of Arrival (ETA) is of great significance for port scheduling, logistics management, and navigation safety. Traditional ETA prediction approaches often rely on manual experience for parameter tuning, which tends to be inefficient and susceptible to subjective factors. To address this issue and improve prediction accuracy, this study proposes a hybrid modeling framework, integrating Bayesian Optimization (BO), Convolutional Neural Networks (CNNs), and Long Short-Term Memory (LSTM) networks. In this approach, Automatic Identification System (AIS) data is leveraged to predict the total voyage duration before departure, thereby deriving the vessel’s ETA. The model, referred to as BO-CNN-LSTM, utilizes BO for automatic hyperparameter tuning, employs CNN for extracting local features, and applies LSTM network to capture temporal dependencies. The model is developed using a dataset of 32,972 distinct voyage records, among which 23,947 are retained as valid samples after data cleaning. Pearson correlation analysis is conducted to select key input variables, including navigation speed, ship type, sailing distance, and deadweight tonnage. Additionally, sailing distance is processed using the Ramer–Douglas–Peucker algorithm. Experimental evaluation indicates that the BO-CNN-LSTM model achieves a coefficient of determination of 0.987, along with a mean absolute error and root mean square error of 6.078 and 8.730, respectively. These results significantly outperform comparison models such as CNN, LSTM, CNN-LSTM, random forest, AdaBoost, and Elman neural networks. Overall, this study validates the effectiveness and superiority of the proposed BO-CNN-LSTM model in ship ETA prediction, providing an efficient and effective prediction solution for intelligent maritime transportation systems. Full article

Accurate prediction of vibration responses in hydraulic structures during flood discharge is essential for ensuring safe and stable operation. This study develops a hybrid deep learning model that combines Convolutional Neural Networks (CNN), Bidirectional Long Short-Term Memory (BiLSTM), and a Channel Attention (CA) mechanism, optimized through Bayesian Optimization (BO), to predict dam gantry crane beam displacements. Time-lagged Pearson correlation and Maximum Information Coefficient (MIC) are applied to select the informative input features. The CNN-BiLSTM-CA model captures both spatial patterns and temporal dependencies in vibration signals. BO tunes model hyperparameters, while Partial Dependence (PD) analysis provides insight into how these parameters affect prediction accuracy. The model is validated using vibration data from an arch dam in Southwest China during flood discharge. Results show that CNN parameters have a greater impact on prediction accuracy than BiLSTM parameters, underscoring the importance of spatial feature extraction. Ablation studies confirm each component’s contribution. Compared with existing methods, the proposed model achieves superior accuracy with a Root Mean Square Error (RMSE) of 5.49, Mean Absolute Error (MAE) of 4.34, and correlation coefficient (R) of 99.42%. This framework provides a reliable and interpretable tool for predicting structural vibrations in hydraulic engineering under complex discharge conditions. Full article

Clouds are a key factor affecting solar radiation, and their dynamic variations directly cause uncertainty and fluctuations in photovoltaic (PV) power output. To improve PV power prediction accuracy, this paper proposes an enhanced short-term photovoltaic power forecasting approach based on a hybrid neural network architecture using features extracted from satellite cloud images. First, a dual-layer image fusion method is developed for satellite cloud images from different wavelengths and spectral bands, effectively improving fusion accuracy. Second, texture descriptors derived from the Gray-Level Co-occurrence Matrix and multiscale information obtained via the wavelet transform are employed for feature extraction from fused images. Combined with a residual network (ResNet), an optical flow method, as well as an LSTM-based temporal modeling module, multidimensional features of the predicted cloud images are obtained. An improved Bayesian optimization (IBO) algorithm is then employed to derive the optimal fused features, thereby improving the matching between cloud image features and PV power. Third, an enhanced hybrid architecture integrating a convolutional neural network and long short-term memory units with a multi-head self-attention mechanism is developed. Numerical weather prediction (NWP) meteorological features are incorporated, and a tilted irradiance model is introduced to calculate the solar irradiance received by PV modules for use in near-term photovoltaic power forecasting. Finally, measurements collected at a photovoltaic power plant located in Hebei Province are used to validate the proposed method. The results show that, relative to the SA-CNN-MSA-LSTM and BO-CNN-LSTM models, the developed approach lowers the RMSE to an extent of 22.56% and 4.32%, while decreasing the MAE by 24.84% and 5.91%, respectively. Overall, the proposed model accurately captures the characteristics of predicted cloud images and effectively improves PV power prediction accuracy. Full article

The Rate of Penetration (ROP), as a core indicator for evaluating drilling efficiency, holds significant importance for optimizing drilling parameter configurations, enhancing drilling efficiency, and reducing operational costs. To address the limitations of existing ROP prediction models—such as difficulties in modeling, solution complexity, and inefficient utilization of field big data—this paper proposes a Bayesian-Optimized LSTM-based ROP prediction model with fused feature selection (BO-LSTM-FS). The model innovatively introduces a sequential-cross-validation fused feature selection framework, which organically integrates Pearson correlation analysis, variance filtering, and mutual information, and incorporates a forward search strategy for final validation. Building on this, the Bayesian optimization algorithm is employed for systematic global optimization of the key hyperparameters of the LSTM neural network. Experimental results demonstrate that the BO-LSTM-FS model achieves significant performance improvements compared to traditional Backpropagation (BP) neural networks, standard LSTM neural networks, and CNN-LSTM models: Mean Absolute Error (MAE) is reduced by 48.0%, 29.3%, and 23.5%, respectively; Root Mean Square Error (RMSE) by 45.5%, 38.5%, and 32.2%, respectively; Mean Absolute Percentage Error (MAPE) by 47.8%, 29.4%, and 22.6%, respectively; and the Coefficient of Determination (R²) is increased by 8.6%, 4.4%, and 3.0%, respectively. The model exhibits high prediction accuracy, fast convergence speed, and strong generalization capability, providing a scientific reference for improving the Rate of Penetration in practical drilling operations. Full article

Mine-induced surface subsidence threatens infrastructure and can trigger cascading geohazards, so accurate and computationally efficient monitoring and forecasting are essential for early warning. We integrate Persistent Scatterer InSAR (PS-InSAR) time series with a Bayesian-optimized CNN–LSTM designed for spatiotemporal prediction. The CNN extracts spatial deformation patterns, the LSTM models temporal dependence, and Bayesian optimization selects the architecture, training hyperparameters, and the most informative exogenous drivers. Groundwater level and backfilling intensity are encoded as multichannel inputs. Endpoint anchoring with affine calibration aligns the historical series and the forward projections. PS-InSAR indicates a maximum subsidence rate of 85.6 mm yr⁻¹, and the estimates are corroborated against nearby leveling benchmarks and FLAC^3D simulations. Cross-site comparisons show acceleration followed by deceleration after backfilling and groundwater recovery, which is consistent with geological engineering conditions. A symmetry-aware preprocessing step exploits axial regularities of the deformation field through mirroring augmentation and documents symmetry-breaking hotspots linked to geological heterogeneity. These choices improve generalization to shifted and oscillatory patterns in both the spatial CNN and the temporal LSTM branches. Short-term forecasts from the BO–CNN–LSTM indicate subsequent stabilization with localized rebound, highlighting its practical value for operational planning and risk mitigation. The framework combines automated hyperparameter search with physically consistent objectives, reduces manual tuning, enhances reproducibility and generalizability, and provides a transferable quantitative workflow for forecasting mine-induced deformation in complex goaf systems. Full article

We implement, optimize, and compare the performance of deep learning models in forecasting prices of crude oil markets, namely West Texas Intermediate (WTI) and Brent. We focus on deep learning models as these are state-of-the-art forecasting systems for complex and nonlinear time series. In this regard, we implement convolutional neural networks (CNNs), long short-term memory (LSTM), and gated recurrent units (GRUs). Classical recurrent neural networks (RNNs) are chosen as the baseline artificial neural networks. We contribute to the literature by examining the effect of fine-tuning of the parameters of the predictive systems by means of Bayesian optimization (BO) on their performance. Also, to check the robustness of the optimized models, they are trained and tested on daily, weekly, and monthly data. The assessment of forecasting performance is based on three different metrics including the root mean of squared errors (RMSE), mean absolute deviation (MAD), and mean absolute percentage error (MAPE). The simulation results show that the GRU-BO and RNN-BO are respectively the best systems to predict prices of BRENT and WTI. In addition, the simulation results show that BO enhances the accuracy of the predictive models. The results obtained would help oil producers, suppliers, traders, and investors to implement the appropriate prediction system for each market to improve accuracy and generate profits for each time horizon. Full article

Crude oil price forecasting is an important task in energy management and storage. In this regard, deep learning has been applied in the literature to generate accurate forecasts. The main purpose of this study is to design an ensemble prediction system based on various deep learning systems. Specifically, in the first stage of our proposed ensemble system, convolutional neural networks (CNNs), long short-term memory networks (LSTMs), bidirectional LSTM (BiLSTM), gated recurrent units (GRUs), bidirectional GRU (BiGRU), and deep feedforward neural networks (DFFNNs) are used as individual predictive systems to predict crude oil prices. Their respective parameters are fine-tuned by Bayesian optimization (BO). In the second stage, forecasts from the previous stage are all weighted by using the sequential least squares programming (SLSQP) algorithm. The standard tree-based ensemble models, namely, extreme gradient boosting (XGBoost) and random forest (RT), are implemented as baseline models. The main findings can be summarized as follows. First, the proposed ensemble system outperforms the individual CNN, LSTM, BiLSTM, GRU, BiGRU, and DFFNN. Second, it outperforms the standard XGBoost and RT models. Governments and policymakers can use these models to design more effective energy policies and better manage supply in fluctuating markets. For investors, improved predictions of price trends present opportunities for strategic investments, reducing risk while maximizing returns in the energy market. Full article

The growing demand for energy efficiency and sustainability in smart buildings necessitates advanced AI-driven methods for adaptive control and predictive maintenance. This study explores the application of Spiking Neural Networks (SNNs) to event-driven processing, real-time anomaly detection, and edge computing-based optimization in building automation. In contrast to conventional deep learning models, SNNs provide low-power, high-efficiency computation by mimicking biological neural processes, making them particularly suitable for real-time, edge-deployed decision-making. The proposed SNN based on Reward-Modulated Spike-Timing-Dependent Plasticity (STDP) and Bayesian Optimization (BO) integrates occupancy and ambient condition monitoring to dynamically manage assets such as appliances while simultaneously identifying anomalies for predictive maintenance. Experimental evaluations show that our BO-STDP-SNN framework achieves notable reductions in both energy consumption by 27.8% and power requirements by 70%, while delivering superior accuracy in anomaly detection compared with CNN, RNN, and LSTM based baselines. These results demonstrate the potential of SNNs to enhance the efficiency and resilience of smart building systems, reduce operational costs, and support long-term sustainability through low-latency, event-driven intelligence. Full article

This study investigates the structural health monitoring and stress prediction of large-span steel roof structures in airport terminals, focusing on the impact of temperature variations and the development of an advanced hybrid prediction model. A comprehensive monitoring system was designed and implemented to track key structural responses, including stress, displacement, and temperature, revealing significant correlations between thermal effects and structural behavior. To enhance predictive accuracy, a BO-CNN-LSTM hybrid model was proposed, integrating Bayesian optimization with convolutional and long short-term memory neural networks. The model demonstrated superior performance in capturing spatial–temporal stress patterns compared to traditional methods, providing a reliable tool for real-time structural assessment and early warning. The findings highlight the importance of temperature effects on structural integrity and offer practical insights for the health monitoring of large-span steel structures in complex environments. This study provides a reference for future research on structural health monitoring and performance assessment. Full article

Sensors and anti-interference technology have a complementary relationship. The anti-interference capability directly affects the measurement accuracy, reliability, and stability of sensors. In complex electromagnetic or natural environments, sensors are inevitably influenced by various interference sources. Effective anti-interference technology is the key to ensuring the normal operation of sensors, and suppressing interference signals is one of the key links to improving communication quality. This paper proposes a CNN-LSTM-based interference signal suppression algorithm, aiming to enhance the anti-interference capability of wireless communication systems through deep learning technology. The algorithm utilizes CNN to extract the spatial features of the signal and LSTM to capture the temporal dynamic characteristics of the signal, outputting a predicted signal to effectively suppress interference signals. The performance of the experimental simulation algorithm under different interference scenarios was evaluated and compared with three models: LSTM, BO-LSTM, and CNN-GRU. The results demonstrated that this algorithm had a small error and a high degree of regression fitting. Finally, the effectiveness of the algorithm was verified by using the signal propagation model based on ITU-R P.1546 and the publicly available noise datasets collected from the actual environment. The research shows that this algorithm can significantly suppress the influence of interference signals and environmental noise on useful signals, providing a basis for promoting the evolution of sensors towards higher reliability and robustness. Full article

The vibration and noise of friction pairs are severe when cutting titanium alloy with cemented carbide tools, and the surface micro-texture can significantly reduce noise and friction. Therefore, it is very important to clarify the correlation mechanism between friction noise and friction force for processing quality control. Consequently, investigating the underlying mechanisms that link friction noise and friction is of considerable importance. This study focuses on the friction and wear acoustic signals generated by micro-textured cemented carbide–titanium alloy. A friction testing platform specifically designed for the micro-textured cemented carbide grinding of titanium alloy has been established. Acoustic sensors are employed to capture the acoustic signals, while ultra-depth-of-field microscopy and scanning electron microscopy are utilized for surface analysis. A novel approach utilizing the dung beetle algorithm (DBO) is proposed to optimize the parameters of variational mode decomposition (VMD), which is subsequently combined with wavelet packet threshold denoising (WPT) to enhance the quality of the original signal. Continuous wavelet transform (CWT) is applied for time–frequency analysis, facilitating a discussion on the underlying mechanisms of micro-texture. Additionally, features are extracted from the time domain, frequency domain, wavelet packet, and entropy. The Relief-F algorithm is employed to identify 19 significant features, leading to the development of a hybrid model that integrates Bayesian optimization (BO) and Transformer-LSTM for predicting friction. Experimental results indicate that the model achieves an R² value of 0.9835, a root mean square error (RMSE) of 0.2271, a mean absolute error (MAE) of 0.1880, and a mean bias error (MBE) of 0.1410 on the test dataset. The predictive performance and stability of this model are markedly superior to those of the BO-LSTM, LSTM–Attention, and CNN–LSTM–Attention models. This research presents a robust methodology for predicting friction in the context of friction and wear of cemented carbide–titanium alloys. Full article