Search Results (1,860)

A neural network was developed using the Multilayer Perceptron (MLP) model to predict the darkness value of the night sky in rural areas. For data collection, a photometer was placed in three different rural locations in the province of Cáceres, Spain, recording darkness values over a period of 23 months. The recorded data were processed, debugged, and used as a training set (75%) and validation set (25%) in the development of an MLP capable of predicting the darkness level for a given date. The network had a single hidden layer of 10 neurons and hyperbolic activation function, obtaining a coefficient of determination (R²) of 0.85 and a mean absolute percentage error (MAPE) of 6.8%. The developed model could be employed in unpopulated rural areas for the promotion of sustainable astronomical tourism. Full article

The raw data from ground-based telescopic optical observations serve as a key foundation for the analysis and identification of optical scattering properties of space objects, providing an essential guarantee for object identification and state prediction efforts. In this paper, a spatial object optical characterization model based on Augmented Implicit Neural Representations (AINRs) is proposed. This model utilizes a neural implicit function to delineate the relationship between the geometric observation model and the apparent magnitude arising from sunlight reflected off the object’s surface. Combining the dual advantages of data-driven and physical-driven, a novel pre-training procedure method based on transfer learning is designed. Taking omnidirectional angle simulation data as the basic training dataset and further introducing it with real observational data from ground stations, the Multi-Layer Perceptron (MLP) parameters of the model undergo constant refinement. Pre-fitting experiments on the newly developed S−net, R−net, and F−net models are conducted with a quantitative analysis of errors and a comparative assessment of evaluation indexes. The experiment demonstrates that the proposed F−net model consistently maintains a prediction error for satellite surface magnitude values within 0.2 $m_V$, outperforming the other two models. Additionally, preliminary accomplishment of component-level recognition has been achieved, offering a potent analytical tool for on-orbit services. Full article

Emotion recognition through speech is a technique employed in various scenarios of Human–Computer Interaction (HCI). Existing approaches have achieved significant results; however, limitations persist, with the quantity and diversity of data being more notable when deep learning techniques are used. The lack of a standard in feature selection leads to continuous development and experimentation. Choosing and designing the appropriate network architecture constitutes another challenge. This study addresses the challenge of recognizing emotions in the human voice using deep learning techniques, proposing a comprehensive approach, and developing preprocessing and feature selection stages while constructing a dataset called EmoDSc as a result of combining several available databases. The synergy between spectral features and spectrogram images is investigated. Independently, the weighted accuracy obtained using only spectral features was 89%, while using only spectrogram images, the weighted accuracy reached 90%. These results, although surpassing previous research, highlight the strengths and limitations when operating in isolation. Based on this exploration, a neural network architecture composed of a CNN1D, a CNN2D, and an MLP that fuses spectral features and spectogram images is proposed. The model, supported by the unified dataset EmoDSc, demonstrates a remarkable accuracy of 96%. Full article

The convolutional neural network (CNN) has been widely applied in medical image segmentation due to its outstanding nonlinear expression ability. However, applications of CNN are often limited by the receptive field, preventing it from modeling global dependencies. The recently proposed transformer architecture, which uses a self-attention mechanism to model global context relationships, has achieved promising results. Swin-Unet is a Unet-like simple transformer semantic segmentation network that combines the dominant feature of both the transformer and Unet. Even so, Swin-Unet has some limitations, such as only learning single-scale contextual features, and it lacks inductive bias and effective multi-scale feature selection for processing local information. To solve these problems, the Residual Local induction bias-Unet (RL-Unet) algorithm is proposed in this paper. First, the algorithm introduces a local induction bias module into the RLSwin-Transformer module and changes the multi-layer perceptron (MLP) into a residual multi-layer perceptron (Res-MLP) module to model local and remote dependencies more effectively and reduce feature loss. Second, a new densely connected double up-sampling module is designed, which can further integrate multi-scale features and improve the segmentation accuracy of the target region. Third, a novel loss function is proposed that can significantly enhance the performance of multiple scales segmentation and the segmentation results for small targets. Finally, experiments were conducted using four datasets: Synapse, BraTS2021, ACDC, and BUSI. The results show that the performance of RL-Unet is better than that of Unet, Swin-Unet, R2U-Net, Attention-Unet, and other algorithms. Compared with them, RL-Unet produces significantly a lower Hausdorff Distance at 95% threshold (HD95) and comparable Dice Similarity Coefficient (DSC) results. Additionally, it exhibits higher accuracy in segmenting small targets. Full article

IoT applications revolutionize industries by enhancing operations, enabling data-driven decisions, and fostering innovation. This study explores the growing potential of IoT-based facial recognition for mobile devices, a technology rapidly advancing within the interconnected IoT landscape. The investigation proposes a framework called IoT-MFaceNet (Internet-of-Things-based face recognition using MobileNetV2 and FaceNet deep-learning) utilizing pre-existing deep-learning methods, employing the MobileNetV2 and FaceNet algorithms on both ImageNet and FaceNet databases. Additionally, an in-house database is compiled, capturing data from 50 individuals via a web camera and 10 subjects through a smartphone camera. Pre-processing of the in-house database involves face detection using OpenCV’s Haar Cascade, Dlib’s CNN Face Detector, and Mediapipe’s Face. The resulting system demonstrates high accuracy in real-time and operates efficiently on low-powered devices like the Raspberry Pi 400. The evaluation involves the use of the multilayer perceptron (MLP) and support vector machine (SVM) classifiers. The system primarily functions as a closed set identification system within a computer engineering department at the College of Engineering, Mustansiriyah University, Iraq, allowing access exclusively to department staff for the department rapporteur room. The proposed system undergoes successful testing, achieving a maximum accuracy rate of 99.976%. Full article

Three-dimensional point clouds, as an advanced imaging technique, enable researchers to capture plant traits more precisely and comprehensively. The task of plant segmentation is crucial in plant phenotyping, yet current methods face limitations in computational cost, accuracy, and high-throughput capabilities. Consequently, many researchers have adopted 3D point cloud technology for organ-level segmentation, extending beyond manual and 2D visual measurement methods. However, analyzing plant phenotypic traits using 3D point cloud technology is influenced by various factors such as data acquisition environment, sensors, research subjects, and model selection. Although the existing literature has summarized the application of this technology in plant phenotyping, there has been a lack of in-depth comparison and analysis at the algorithm model level. This paper evaluates the segmentation performance of various deep learning models on point clouds collected or generated under different scenarios. These methods include outdoor real planting scenarios and indoor controlled environments, employing both active and passive acquisition methods. Nine classical point cloud segmentation models were comprehensively evaluated: PointNet, PointNet++, PointMLP, DGCNN, PointCNN, PAConv, CurveNet, Point Transformer (PT), and Stratified Transformer (ST). The results indicate that ST achieved optimal performance across almost all environments and sensors, albeit at a significant computational cost. The transformer architecture for points has demonstrated considerable advantages over traditional feature extractors by accommodating features over longer ranges. Additionally, PAConv constructs weight matrices in a data-driven manner, enabling better adaptation to various scales of plant organs. Finally, a thorough analysis and discussion of the models were conducted from multiple perspectives, including model construction, data collection environments, and platforms. Full article

The mechanical properties of fissured sandstone will deteriorate under water–rock interaction. It is crucial to extract the precursor information of fissured sandstone instability under water–rock interaction. The potential of each acoustic emission (AE) parameter as a precursor for instability in the failure process of fissured sandstone was investigated in this study. An experimental dataset comprising 586 acoustic emission experiments was established, and subsequent classification training and testing were conducted using three machine learning (ML) models: AdaBoost, MLP, and Random Forest (RF). The primary parameters for identifying the instability risk state of fissured sandstone include acoustic emission ringing count, energy (mV·ms), centroid frequency, peak frequency, Rise Angle (RA), Average Frequency (AF), b value, and the natural/saturated state of fissured sandstone: state. To enhance data utilization, a 10-fold cross-validation method was employed during the model training process. The machine learning models were developed and designed to identify the instability risk of fissured sandstone under the natural and saturated states. The results demonstrated that the established RF model was capable of identifying fissured sandstone instability risks with an accuracy of 97.87%. Feature importance analysis revealed that state and b value exerted the most significant influence on identification results. The Spearman correlation coefficient was utilized to assess the correlation between input features. This study can provide technical support to identify the risk of instability of fissured sandstones under both natural and saturated water conditions. Based on the models developed in this study, it is possible to implement an early warning method for instability in fissured sandstone that meets realistic working conditions. Compared with the traditional empirical and formulaic methods, the machine learning method can more quickly process huge amounts of AE data and accurately identify the damage state of fissured sandstone. Full article

Given the critical role of true triaxial strength assessment in underground rock and soil engineering design and construction, this study explores sandstone true triaxial strength using data-driven machine learning approaches. Fourteen distinct sandstone true triaxial test datasets were collected from the existing literature and randomly divided into training (70%) and testing (30%) sets. A Multilayer Perceptron (MLP) model was developed with uniaxial compressive strength (UCS, ${\sigma }_c$), intermediate principal stress (${\sigma }_2$), and minimum principal stress (${\sigma }_3$) as inputs and maximum principal stress (${\sigma }_1$) at failure as the output. The model was optimized using the Harris hawks optimization (HHO) algorithm to fine-tune hyperparameters. By adjusting the model structure and activation function characteristics, the final model was made continuously differentiable, enhancing its potential for numerical analysis applications. Four HHO-MLP models with different activation functions were trained and validated on the training set. Based on the comparison of prediction accuracy and meridian plane analysis, an HHO-MLP model with high predictive accuracy and meridional behavior consistent with theoretical trends was selected. Compared to five traditional strength criteria (Drucker–Prager, Hoek–Brown, Mogi–Coulomb, modified Lade, and modified Weibols–Cook), the optimized HHO-MLP model demonstrated superior predictive performance on both training and testing datasets. It successfully captured the complete strength variation in principal stress space, showing smooth and continuous failure envelopes on the meridian and deviatoric planes. These results underscore the model’s ability to generalize across different stress conditions, highlighting its potential as a powerful tool for predicting the true triaxial strength of sandstone in geotechnical engineering applications. Full article

The colonoscopy is the foremost technique for detecting polyps, where accurate segmentation is crucial for effective diagnosis and surgical preparation. Nevertheless, contemporary deep learning-based methods for polyp segmentation face substantial hurdles due to the large amount of labeled data required. To address this, we introduce an innovative semi-supervised learning framework based on cross-pseudo supervision (CPS) and contrastive learning, termed Semi-supervised Polyp Segmentation (SemiPolypSeg), which requires only limited labeled data. First, a new segmentation architecture, the Hybrid Transformer–CNN Segmentation Network (HTCSNet), is proposed to enhance semantic representation and segmentation performance. HTCSNet features a parallel encoder combining transformers and convolutional neural networks, as well as an All-MLP decoder with skip connections to streamline feature fusion and enhance decoding efficiency. Next, the integration of CPS in SemiPolypSeg enforces output consistency across diverse perturbed datasets and models, guided by the consistency loss principle. Finally, patch-wise contrastive loss discerns feature disparities between positive and negative sample pairs as delineated by the projector. Comprehensive evaluation demonstrated our method’s superiority over existing state-of-the-art semi-supervised segmentation algorithms. Specifically, our method achieved Dice Similarity Coefficients (DSCs) of 89.68% and 90.62% on the Kvasir-SEG dataset with 15% and 30% labeled data, respectively, and 89.72% and 90.06% on the CVC-ClinicDB dataset with equivalent ratios. Full article

Improving the operational efficiency of water distribution networks (WDNs) is a subject that has been widely explored in the literature. Usually, a hydraulic model is used jointly with optimization methods, which require considerable computational effort, hindering real-time interventions. Surrogate models based on machine learning are being studied to estimate the hydraulic state of WDNs and reduce the processing time, and the results have been successful. In this paper, different feedforward artificial neural networks (FFNNs) of the multilayer perceptron (MPL) type were developed to estimate important hydraulic parameters that were applied to optimization algorithms, namely, (i) energy consumption; (ii) tank levels; (iii) pressure in consumption nodes; and (iv) minimum pressure. These parameters were chosen because they are frequently used in objective functions, minimizing energy consumption and leakage volume, as well in operational restrictions. The results showed that creating an individual MLP for each parameter can be a good strategy to improve MLP accuracy. Full article

This work proposes a cascade model incorporating Long–Short-Term Memory (LSTM) and Multi-Layer Perceptron (MLP), which offers a more reliable model to forecast short-term (hourly) and medium horizon (week) water demand. The MLP model integrates the previously forecasted demand with exogenous variables, functioning as a filter to enhance the accuracy of the LSTM estimation. The LSTM model estimates, utilizing a univariate approach, the hourly forecasting of water demand for the entire available dataset and the minimum night flow. The algorithm considers various time series sizes for each DMA and predicts the water demand values for each hour throughout the week. Having forecasted all timesteps with the LSTM, a virtual online process can be implemented to enhance forecasting quality. Full article

Renovation of buildings has become a major area of development for the construction industry. In the building construction sector, generating a precise and trustworthy cost estimate before building begins is the greatest challenge. Emphasizing the value of using ANN models to forecast the total cost of a building renovation project is the ultimate objective. As a result, building firms may be able to avoid financial losses as long as there is as little discrepancy between projected and actual costs for remodeling works in progress. To address the gap in the research, Greek contractors specializing in building renovations provided a sizable dataset of real project cost data. To build cost prediction ANNs, the collected data had to be organized, assessed, and appropriately encoded. The network was developed, trained, and tested using IBM SPSS Statistics software 28.0.0.0. The dependent variable is the final cost. The independent variables are initial cost, estimated completion time, actual completion time, delay time, initial and final demolition-drainage costs, cost of expenses, initial and final plumbing costs, initial and final heating costs, initial and final electrical costs, initial and final masonry costs, initial and final construction costs of plasterboard construction, initial and final cost of bathrooms, initial and final cost of flooring, initial and final cost of frames, initial and final cost of doors, initial and final cost of paint, and initial and final cost of kitchen construction. The first procedure that was employed was the radial basis function (RBF). The efficiency of the RBFNN model was evaluated and analyzed during training and testing, with up to 6% sum of squares error and nearly 0% relative error in the training sample, which accounted for roughly 70% of the total sample. The second procedure implemented was the method called the multi-layer perceptron (MLP). The efficiency of the MLPNN model was assessed and examined during training and testing; the training sample, which made up around 70% of the overall sample, had a relative error of 0–7% and a sum of squares error ranging from 1% to 5%, confirming specifically the efficacy of RBFNN in calculating the overall cost of renovations. Full article

Accurate short-term load forecasting is critical for enhancing the reliability and stability of regional smart energy systems. However, the inherent challenges posed by the substantial fluctuations and volatility in electricity load patterns necessitate the development of advanced forecasting techniques. In this study, a novel short-term load forecasting approach based on a two-stage feature extraction process and a hybrid inverted Transformer model is proposed. Initially, the Prophet method is employed to extract essential features such as trends, seasonality and holiday patterns from the original load dataset. Subsequently, variational mode decomposition (VMD) optimized by the IVY algorithm is utilized to extract significant periodic features from the residual component obtained by Prophet. The extracted features from both stages are then integrated to construct a comprehensive data matrix. This matrix is then inputted into a hybrid deep learning model that combines an inverted Transformer (iTransformer), temporal convolutional networks (TCNs) and a multilayer perceptron (MLP) for accurate short-term load forecasting. A thorough evaluation of the proposed method is conducted through four sets of comparative experiments using data collected from the Elia grid in Belgium. Experimental results illustrate the superior performance of the proposed approach, demonstrating high forecasting accuracy and robustness, highlighting its potential in ensuring the stable operation of regional smart energy systems. Full article

This study primarily investigates advanced object detection and time series prediction methods in image-based visual servoing systems, aiming to capture targets better and predict the motion trajectory of robotic arms in advance, thereby enhancing the system’s performance and reliability. The research first implements object detection on the VOC2007 dataset using the Detection Transformer (DETR) and achieves ideal detection scores. The particle swarm optimization algorithm and 3-5-3 polynomial interpolation methods were utilized for trajectory planning, creating a unique dataset through simulation. This dataset contains randomly generated trajectories within the workspace, fully simulating actual working conditions. Significantly, the Bidirectional Long Short-Term Memory (BILSTM) model was improved by substituting its traditional Multilayer Perceptron (MLP) components with Kolmogorov–Arnold Networks (KANs). KANs, inspired by the K-A theorem, improve the network representation ability by placing learnable activation functions on fixed node activation functions. By implementing KANs, the model enhances parameter efficiency and interpretability, thus addressing the typical challenges of MLPs, such as the high parameter count and lack of transparency. The experiments achieved favorable predictive results, indicating that the KAN not only reduces the complexity of the model but also improves learning efficiency and prediction accuracy in dynamic visual servoing environments. Finally, Gazebo software was used in ROS to model and simulate the robotic arm, verify the effectiveness of the algorithm, and achieve visual servoing. Full article

Oxygen vacancies play a crucial role in stabilizing the ferroelectric phase in hafnium (Hf) oxide-based thin films and in shaping the evolution of their ferroelectric properties. In this study, we directly manipulated the oxygen vacancy concentration in Hf_0.5Zr_0.5O_2−x (HZO) ferroelectric thin films in situ using oxygen plasma treatment. We scrutinized the variations in the ferroelectric properties of HZO films across different oxygen vacancy concentrations by integrating the findings from ferroelectric performance tests. Additionally, we elucidated the mechanism underlying the influence of oxygen vacancies on the coercive field and polarization properties of HZO ferroelectric films through the first-principles density functional theory (DFT) calculations. Finally, to study the impact of oxygen vacancies on the practical application of HZO ferroelectric synaptic devices, leveraging the plasticity of the ferroelectric polarization, we constructed a multilayer perceptron (MLP) network. We simulated its recognition accuracy and convergence speed under different oxygen vacancy concentrations in the MNIST recognition task. Full article