Search Results (92)

The curing process of a concrete sample has a significant influence on hydration and its strength. This means that inadequate curing conditions lead to a loss of concrete quality and negative consequences in structural engineering. In addition, different state-of-the-art (SOTA) curing surface treatments and hydration periods have a significant effect on durability. This paper introduces an innovative non-destructive method to detect the development of the hydration process under different treatment conditions. Hyperspectral imaging is a non-contact measurement technique that provides detailed information on hydration characteristics within an electromagnetic wavelength range. A comparative laboratory measurement was conducted on twelve concrete samples, subjected to three curing treatments and four curing surface treatments, over a hydration period from the 1st to the 56th day. Additionally, artificial neural networks and convolutional neural networks have achieved classification accuracies of 67.8% (hydration time), 83.3% (curing regime), and 87.6% (surface type), demonstrating the feasibility of using neural networks for hydration monitoring. In this study, the results revealed differences in near-infrared spectral signatures, representing the type of curing treatment, curing surface, and hydration time of the concrete. The dataset was classified and analyzed using neural networks. For each hydration treatment, three different models were developed to achieve better prediction performance for hyperspectral imaging analysis. This method demonstrated a high level of reliability in investigating curing surface treatments, curing treatments, and hydration time. A recommended method for using hyperspectral imaging to evaluate the cured quality of concrete will be developed in future research. Full article

The aim of this article is to critically and reliably assess the potential of current emotion recognition technologies for practical applications in human–computer interaction (HCI) systems. The study made use of two databases: one in English (RAVDESS) and another in Polish (EMO-BAJKA), both containing speech recordings expressing various emotions. The effectiveness of recognizing seven and eight different emotions was analyzed. A range of acoustic features, including energy features, mel-cepstral features, zero-crossing rate, fundamental frequency, and spectral features, were utilized to analyze the emotions in speech. Machine learning techniques such as convolutional neural networks (CNNs), long short-term memory (LSTM) networks, and support vector machines with a cubic kernel (cubic SVMs) were employed in the emotion classification task. The research findings indicated that the effective recognition of a broad spectrum of emotions in a subject-independent approach is limited. However, significantly better results were obtained in the classification of paired emotions, suggesting that emotion recognition technologies could be effectively used in specific applications where distinguishing between two particular emotional states is essential. To ensure a reliable and accurate assessment of the emotion recognition system, care was taken to divide the dataset in such a way that the training and testing data contained recordings of completely different individuals. The highest classification accuracies for pairs of emotions were achieved for Angry–Fearful (0.8), Angry–Happy (0.86), Angry–Neutral (1.0), Angry–Sad (1.0), Angry–Surprise (0.89), Disgust–Neutral (0.91), and Disgust–Sad (0.96) in the RAVDESS. In the EMO-BAJKA database, the highest classification accuracies for pairs of emotions were for Joy–Neutral (0.91), Surprise–Neutral (0.80), Surprise–Fear (0.91), and Neutral–Fear (0.91). Full article

With the proposal of carbon peaking and carbon neutrality goals, the proportion of distributed renewable energy generation in active distribution networks (ADNs) has been continuously increasing. While this has effectively reduced greenhouse gas emissions, it has also given rise to power quality issues such as excessive or insufficient voltage amplitudes. To effectively address this problem, this paper proposes a multi-resource coordinated dynamic reactive power–voltage coordination optimization method. Firstly, an improved Generative Convolutional Adversarial Network (GCAN) is used to generate typical wind and solar power output scenarios. Based on these generated typical scenarios, a voltage control model for ADNs is established with the objective of minimizing voltage fluctuations, fully exploiting the dynamic reactive power regulation resources within the ADN. In view of the non-convex and nonlinear characteristics of the model, an improved Gray Wolf Optimizer (GWO) algorithm is employed for model optimization and solution seeking. Finally, the effectiveness and feasibility of the proposed method are demonstrated through simulations using modified IEEE-33-bus and IEEE-69-bus test systems. Full article

With global efforts intensifying towards achieving carbon neutrality, accurately monitoring and managing energy consumption in industrial sectors has become critical. Non-Intrusive Load Monitoring (NILM) technology presents a cost-effective solution for industrial energy management by decomposing aggregate power data into individual device-level information without extensive hardware requirements. However, existing NILM methods primarily tailored for residential applications struggle to capture complex inter-device correlations and production-dependent load dynamics prevalent in industrial environments, such as cement plants. This paper proposes a novel sequence-to-sequence-based non-intrusive load disaggregation method that integrates Convolutional Neural Networks (CNN) and Transformer architectures, specifically addressing the challenges of multi-device load disaggregation in industrial settings. An innovative time–application attention mechanism was integrated to effectively model long-term temporal dependencies and the collaborative operational relationships between industrial devices. Additionally, global constraints—including consistency, smoothness, and sparsity—were introduced into the loss function to ensure power conservation, reduce noise, and achieve precise zero-power predictions for inactive equipment. The proposed method was validated on real-world power consumption data collected from a cement production facility. Experimental results indicate that the proposed method significantly outperforms traditional NILM approaches with average improvements of 4.98%, 3.70%, and 4.38% in terms of accuracy, recall, and F1-score, respectively. These findings underscore its superior robustness in noisy conditions and under device fault conditions, further affirming its applicability and potential for deployment in industrial settings. Full article

The growing energy demands and increasing environmental concerns in industrial manufacturing necessitate innovative solutions to reduce fuel consumption and lower carbon emissions. This paper presents Sustain AI, a multi-modal deep learning framework that integrates Convolutional Neural Networks (CNNs) for defect detection, Recurrent Neural Networks (RNNs) for predictive energy consumption modeling, and Reinforcement Learning (RL) for dynamic energy optimization to enhance industrial sustainability. The framework employs IoT-based real-time monitoring and AI-driven supply chain optimization to optimize energy use. Experimental results demonstrate that Sustain AI achieves an 18.75% reduction in industrial energy consumption and a 20% decrease in CO₂ emissions through AI-driven processes and scheduling optimizations. Additionally, waste heat recovery efficiency improved by 25%, and smart HVAC systems reduced energy waste by 18%. The CNN-based defect detection model enhanced material efficiency by increasing defect identification accuracy by 42.8%, leading to lower material waste and improved production efficiency. The proposed framework also ensures economic feasibility, with a 17.2% reduction in operational costs. Sustain AI is scalable, adaptable, and fully compatible with Industry 4.0 requirements, making it a viable solution for sustainable industrial practices. Future extensions include enhancing adaptive decision-making with deep RL techniques and incorporating blockchain-based traceability for secure and transparent energy management. These findings indicate that AI-powered industrial ecosystems can achieve carbon neutrality and enhanced energy efficiency through intelligent optimization strategies. Full article

Accurate estimation of battery state of health (SOH) is critical to the efficient operation of energy storage battery systems. Furthermore, precise SOH estimation methods can significantly reduce resource waste by extending the battery service life and optimizing retirement strategies, which is compatible with the sustainable development of energy systems under carbon neutrality goals. Conventional methods struggle to comprehensively characterize the health degradation properties of batteries. To address that limitation, this study proposes a data-driven model based on multi-feature analysis using a hybrid convolutional neural network and long short-term memory (CNN-LSTM) architecture, which synergistically extracts multi-dimensional degradation features to enhance SOH estimation accuracy. The framework begins by systematically collecting the voltage, current, and other parameters during charge–discharge cycles to construct a temporally resolved multi-dimensional feature matrix. A correlation analysis employing Pearson correlation coefficients subsequently identifies key health indicators strongly correlated with SOH degradation. At the same time, the K-means clustering method was adopted to identify and process the outliers of CALCE data, which ensures the high quality of data and the stability of the model. Then, CNN-LSTM hybrid neural network architecture was constructed. The experimental results demonstrated that the absolute value of MBE for the dataset provided by CALCE was less than 0.2%. The MAE was less than 0.3%, and the RMSE was less than 0.4%. Furthermore, the proposed method demonstrated a strong performance on the dataset provided by NASA PCoE. The experimental results indicated that the proposed method significantly reduced the estimation error of SOH across the entire battery lifecycle, and they fully verified the superiority and engineering applicability of the algorithm in battery SOH estimation. Full article

In recent years, balloon-borne threats carrying hazardous or explosive materials have emerged as a novel form of asymmetric terrorism, posing serious challenges to public safety. In response to this evolving threat, this study presents an AI-driven autonomous drone defense system capable of real-time detection, tracking, and neutralization of airborne hazards. The proposed framework integrates state-of-the-art deep learning models, including YOLO (You Only Look Once) for fast and accurate object detection, and convolutional neural networks (CNNs) for X-ray image analysis, enabling precise identification of hazardous payloads. This multi-stage system ensures safe interception and retrieval while minimizing the risk of secondary damage from debris dispersion. Moreover, a robust data collection and storage architecture supports continuous model improvement, ensuring scalability and adaptability for future counter-terrorism operations. As balloon-based threats represent a new and unconventional security risk, this research offers a practical and deployable solution. Beyond immediate applicability, the system also provides a foundational platform for the development of next-generation autonomous security infrastructures in both civilian and defense contexts. Full article

(1) Background: This paper intends to accomplish a comparative study and analysis regarding the multiclass classification of facial thermal images, i.e., in three classes corresponding to predefined emotional states (neutral, happy and sad). By carrying out a comparative analysis, the main goal of the paper consists in identifying a suitable algorithm from machine learning field, which has the highest accuracy (ACC). Two categories of images were used in the process, i.e., images with Gaussian noise and images with “salt and pepper” type noise that come from two built-in special databases. An augmentation process was applied to the initial raw images that led to the development of the two databases with added noise, as well as the subsequent augmentation of all images, i.e., rotation, reflection, translation and scaling. (2) Methods: The multiclass classification process was implemented through two subsets of methods, i.e., machine learning with random forest (RF), support vector machines (SVM) and k-nearest neighbor (KNN) algorithms and deep learning with the convolutional neural network (CNN) algorithm. (3) Results: The results obtained in this paper with the two subsets of methods belonging to the field of artificial intelligence (AI), together with the two categories of facial thermal images with added noise used as input, were very good, showing a classification accuracy of over 99% for the two categories of images, and the three corresponding classes for each. (4) Discussion: The augmented databases and the additional configurations of the implemented algorithms seems to have had a positive effect on the final classification results. Full article

(1) Terrestrial ecosystems are critical carbon sinks, and the accurate assessment of their carbon storage is vital for understanding global carbon cycles and formulating climate change mitigation strategies. (2) This study integrated vegetation indices, meteorological factors, land use data, soil/vegetation types, field sampling, and a convolutional neural network (CNN) model to estimate the carbon storage of terrestrial ecosystems in Guangdong Province. (3) Total carbon storage increased by 0.11 Pg from 2000 to 2021, with vegetation carbon gains (+0.19 Pg) offsetting soil carbon losses (−0.08 Pg), with the latter primarily being driven by reduced soil carbon in forest ecosystems. (4) Northern and eastern Guangdong exhibit high potential for enhancing carbon storage capacity, which is crucial for achieving regional carbon peaking and neutrality targets. Full article

Electroencephalography (EEG)-basedpersonal identification has gained significant attention, but fluctuations in emotional states often affect model accuracy. Previous studies suggest that multisensory stimuli, such as video and olfactory cues, can enhance emotional responses and improve EEG-based identification accuracy. This study proposes a novel deep learning-based model, CNN-BiLSTM-Residual Network (CBR-Net), for EEG-based identification and establishes a multisensory emotional EEG dataset with both video-only and olfactory-enhanced video stimulation. The model includes a convolutional neural network (CNN) for spatial feature extraction, Bi-LSTM for temporal modeling, residual connections, and a fully connected classification module. Experimental results show that olfactory-enhanced video stimulation significantly improves the emotional intensity of EEG signals, leading to better recognition accuracy. The CBR-Net model outperforms video-only stimulation, achieving the highest accuracy for negative emotions (96.59%), followed by neutral (94.25%) and positive emotions (95.42%). Ablation studies reveal that the Bi-LSTM module is crucial for neutral emotions, while CNN is more effective for positive emotions. Compared to traditional machine learning and existing deep learning models, CBR-Net demonstrates superior performance across all emotional states. In conclusion, CBR-Net enhances identity recognition accuracy and validates the advantages of multisensory stimuli in EEG signals. Full article

Stress is a natural response of the organism to challenging situations, but its accurate detection is challenging due to its subjective nature. This study proposes a model based on depth-separable convolutional neural networks (DSCNN) to analyze heart rate variability (HRV) and detect stress. Electrocardiogram (ECG) signals are pre-processed to remove noise and ensure data quality. The signals are then transformed into two-dimensional images using the continuous wavelet transform (CWT) to identify pattern recognition in the time–frequency domain. These representations are classified using the DSCNN model to determine the presence of stress. The methodology has been validated using the SWELL-KW dataset, achieving an accuracy of 99.9% by analyzing the variability in three states (neutral, time pressure, and interruptions) of the 25 samples in the experiment, scanning the acquired signal every 5 s for 45 min per state. The proposed approach is characterized by its ability to transform ECG signals into time–frequency representations by means of short duration sampling, achieving an accurate classification of stress states without the need for complex feature extraction processes. This model is an efficient and accurate tool for stress analysis from biomedical signals. Full article

In recent years, significant advancements have been made in the field of brain–computer interfaces (BCIs), particularly in the area of emotion recognition using EEG signals. The majority of earlier research in this field has missed the spatial–temporal characteristics of EEG signals, which are critical for accurate emotion recognition. In this study, a novel approach is presented for classifying emotions into three categories, positive, negative, and neutral, using a custom-collected dataset. The dataset used in this study was specifically collected for this purpose from 16 participants, comprising EEG recordings corresponding to the three emotional states induced by musical stimuli. A multi-class Common Spatial Pattern (MCCSP) technique was employed for the processing stage of the EEG signals. These processed signals were then fed into an ensemble model comprising three autoencoders with Convolutional Neural Network (CNN) layers. A classification accuracy of 99.44 ± 0.39% for the three emotional classes was achieved by the proposed method. This performance surpasses previous studies, demonstrating the effectiveness of the approach. The high accuracy indicates that the method could be a promising candidate for future BCI applications, providing a reliable means of emotion detection. Full article

As the core component of railways, the switch sliding baseplate has a bad operating environment, and its surface is prone to corrosion. Existing methods, including traditional methods, ultrasonic detection, and image processing, have difficulty in extracting corrosion features and being applied in practice. To solve the above problems, the Residual Neural Network 50 (ResNet50) model, a deep learning model, is introduced in this paper. To solve the problems of gradient explosion and weak corrosion in the model, a new fusion model, VGG-ResNet50-corrosion (VGGRES50_Corrosion), is proposed in this paper. First of all, for the problem that there is no public dataset, this study conducts a neutral salt spray corrosion test and collects the image features and corrosion depth parameters of skateboard corrosion in different time periods as the dataset to test the performance of the model. Then, corrosion thickness is introduced as a modified variable in the ResNet50 network, and a new network, VGGRES50_Corrosion, is introduced by blending the improved model with the Visual Geometry Group-16 (VGG16) network through a model fusion strategy. Finally, a model test and ultrasonic contrast test are designed to verify the performance of the model. In the model test, the recognition accuracy of the fusion model is 98.98% higher than that of other models, which effectively solves the shortcoming of the gradient explosion’s weak generalization ability under a small sample model. In the ultrasonic comparison experiment, the mean relative errors of this method and ultrasonic detection method are 4.08% and 46.41%, respectively, and the mean square errors are 1.86 h and 15.01 h, respectively. The prediction result of deep learning is better than that of ultrasonic piecewise linear fitting. It has been proved that VGGRES50_Corrosion can identify the degree of corrosion of slip switches more effectively, and it has great significance in improving the corrosion detection efficiency of slip switches. Full article

In the context of carbon neutrality and emission reduction goals, energy consumption optimization in the oil and gas industry is crucial for reducing carbon emissions and improving energy efficiency. As a key component in drilling operations, optimizing the energy consumption of drilling pumps has significant potential for energy savings. However, due to the complex and variable geological conditions, diverse operational parameters, and inherent nonlinear relationships in the drilling process, accurately predicting energy consumption presents considerable challenges. This study proposes a novel Long Short-Term Memory Attention model for precise prediction of drilling pump energy consumption. By integrating Long Short-Term Memory (LSTM) networks with the Attention mechanism, the model effectively captures complex nonlinear relationships and long-term dependencies in energy consumption data. Comparative experiments with traditional LSTM and Convolutional Neural Network (CNN) models demonstrate that the LSTM-Attention model outperforms these models across multiple evaluation metrics, significantly reducing prediction errors and enhancing robustness and adaptability. The proposed model achieved Mean Absolute Error (MAE) values ranging from 5.19 to 10.20 and R² values close to one (0.95 to 0.98) in four test scenarios, demonstrating excellent predictive performance under complex conditions. The high-precision prediction of drilling pump energy consumption based on this method can support energy optimization and provide guidance for field operations. Full article

With the global objectives of achieving a “carbon peak” and “carbon neutrality” along with the implementation of carbon reduction policies, China’s industrial structure has undergone significant adjustments, resulting in constraints on high-energy consumption and high-emission industries while promoting the rapid growth of green industries. Consequently, these changes have led to an increasingly complex power system structure and presented new challenges for electricity demand forecasting. To address this issue, this study proposes a 24-step multivariate time series short-term load forecasting algorithm model based on KNN data imputation and BiTCN bidirectional temporal convolutional networks combined with BiGRU bidirectional gated recurrent units and attention mechanism. The Kepler adaptive optimization algorithm (KOA) is employed for hyperparameter optimization to effectively enhance prediction accuracy. Furthermore, using real load data from a wind farm in Xinjiang as an example, this paper predicts the electricity load from 1 January to 30 December in 2019. Experimental results demonstrate that our comprehensive short-term load forecasting model exhibits lower prediction errors and superior performance compared to traditional methods, thus holding great value for practical applications. Full article