Search Results (691)

Quantum many-body systems are of great interest for many research areas, including physics, biology, and chemistry. However, their simulation is extremely challenging, due to the exponential growth of the Hilbert space with system size, making it exceedingly difficult to parameterize the wave functions of large systems by using exact methods. Neural networks and machine learning, in general, are a way to face this challenge. For instance, methods like tensor networks and neural quantum states are being investigated as promising tools to obtain the wave function of a quantum mechanical system. In this tutorial, we focus on a particularly promising class of deep learning algorithms. We explain how to construct a Physics-Informed Neural Network (PINN) able to solve the Schrödinger equation for a given potential, by finding its eigenvalues and eigenfunctions. This technique is unsupervised, and utilizes a novel computational method in a manner that is barely explored. PINNs are a deep learning method that exploit automatic differentiation to solve integro-differential equations in a mesh-free way. We show how to find both the ground and the excited states. The method discovers the states progressively by starting from the ground state. We explain how to introduce inductive biases in the loss to exploit further knowledge of the physical system. Such additional constraints allow for a faster and more accurate convergence. This technique can then be enhanced by a smart choice of collocation points in order to take advantage of the mesh-free nature of the PINN. The methods are made explicit by applying them to the infinite potential well and the particle in a ring, a challenging problem to be learned by an artificial intelligence agent due to the presence of complex-valued eigenfunctions and degenerate states Full article

In this paper, we propose a hybrid method for detecting fake accounts on Instagram by using the Binary Grey Wolf Optimization (BGWO) and Particle Swarm Optimization (PSO) algorithms. By combining these two algorithms, we aim to leverage their complementary strengths and enhance the overall optimization performance. We evaluate the proposed hybrid method using four classifiers: Artificial Neural Network (ANN), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Logistic Regression (LR). The dataset for the experiments contains 65,329 Instagram accounts. We extract features from each account, including profile information, posting behavior, and engagement metrics. The Binary Grey Wolf and Particle Swarm Optimizations, when combined to form a hybrid method (BGWOPSO), improved the performance in accurately detecting fake accounts on Instagram. Full article

In the entire wind turbine system, the blade acts as the central load-bearing element, with its stability and reliability being essential for the safe and effective operation of the wind power unit. Carbon fiber, known for its high strength-to-weight ratio, high modulus, and lightweight characteristics, is extensively utilized in blade manufacturing due to its superior attributes. Despite these advantages, carbon fiber composites are frequently subjected to cyclic loading, which often results in fatigue issues. The presence of internal manufacturing defects further intensifies these fatigue challenges. Considering this, the current study focuses on carbon fiber composites with multiple pre-existing cracks, conducting both static and fatigue experiments by varying the crack length, the angle between cracks, and the distance among them to understand their influence on the fatigue life under various conditions. Furthermore, this study leverages the advantages of Paris theory combined with the Extended Finite Element Method (XFEM) to simulate cracks of arbitrary shapes, introducing a fatigue simulation method for carbon fiber composite laminates with multiple cracks to analyze their fatigue characteristics. Concurrently, the Particle Swarm Optimization (PSO) algorithm is employed to determine the optimal weight configuration, and the Backpropagation neural network (BP) is used to train and adjust the weights and thresholds to minimize network errors. Building on this foundation, a surrogate model for predicting the fatigue life of carbon fiber composite laminates with multiple cracks under conditions of physical parameter uncertainty has been constructed, achieving modeling and assessment of fatigue reliability. This research offers theoretical insights and methodological guidance for the utilization of carbon fiber-reinforced composites in wind turbine blade applications. Full article

Reusable launch vehicles need to face complex and diverse environments during flight. The design of rocket recovery control law based on traditional deep reinforcement learning (DRL) makes it difficult to obtain a set of network architectures that can adapt to multiple scenarios and multi-parameter uncertainties, and the performance of deep reinforcement learning algorithm depends on manual trial and error of hyperparameters. To solve this problem, this paper proposes a self-learning control method for launch vehicle recovery based on neural architecture search (NAS), which decouples deep network structure search and reinforcement learning hyperparameter optimization. First, using network architecture search technology based on a multi-objective hybrid particle swarm optimization algorithm, the proximal policy optimization algorithm of deep network architecture is automatically designed, and the search space is lightweight design in the process. Secondly, in order to further improve the landing accuracy of the launch vehicle, the Bayesian optimization (BO) method is used to automatically optimize the hyperparameters of reinforcement learning, and the control law of the landing phase in the recovery process of the launch vehicle is obtained through training. Finally, the algorithm is transplanted to the rocket intelligent learning embedded platform for comparative testing to verify its online deployment capability. The simulation results show that the proposed method can satisfy the landing accuracy of the launch vehicle recovery mission, and the control effect is basically the same as the landing accuracy of the trained rocket model under the untrained condition of model parameter deviation and wind field interference, which verifies the generalization of the proposed method. Full article

The separation of walnut kernels and shells has long been regarded as a bottleneck, limiting processing efficiency, product quality, and industry advancement. In response to the challenges of improving separation accuracy and the inadequacy of existing equipment for meeting industry demands, this paper proposes an innovative walnut shell–kernel separation device based on machine vision technology. An experimental system was constructed, and key parameters were optimized to enhance its performance. The device comprises five main modules: material conveyance, image acquisition, control module, sorting module, and frame. Differential separation technology is used to convert the walnut material group into a stable particle flow, addressing the issue of missed selections due to material blockages. An enhanced YOLOv8n algorithm improves small object detection and interference resistance, enabling accurate identification of walnut kernels. The Box–Behnken Design and Artificial Neural Network prediction model was used to determine the optimal operating parameters for the device. Experimental results showed that effective differential separation was achieved when the dual-stage conveyor system operated at speeds of 0.2 m/s and 1 m/s. The improved I-YOLOv8n algorithm reached an accuracy of 98.8%. Using the neural network model, the optimal operational parameters were determined: an air pressure of 0.72 MPa, a jetting component angle of 10.16°, and a sorting height of 105.12 cm. Under these conditions, the device achieved an actual cleaning rate of 93.56%, demonstrating outstanding separation performance. Compared to traditional separation methods, this device offers significant advantages in terms of efficiency, quality, and ease of operation, providing new technological pathways and support for the automation and intelligent transformation of the walnut processing industry. In the future, the device is expected to undergo further improvements to meet broader market demand and serve as a reference for the separation of other agricultural products. Full article

In the field of studies on the “Neural Synapses” in the nervous system, its experts manually (or pseudo-automatically) detect the bio-molecule clusters (e.g., of proteins) in many TIRF (Total Internal Reflection Fluorescence) images of a fluorescent cell and analyze their static/dynamic behaviors. This paper proposes a novel method for the automatic detection of the bio-molecule clusters in a TIRF image of a fluorescent cell and conducts several experiments on its performance, e.g., mAP @ IoU (mean Average Precision @ Intersection over Union) and F1-score @ IoU, as an objective/quantitative means of evaluation. As a result, the best of the proposed methods achieved 0.695 as its mAP @ IoU = 0.5 and 0.250 as its F1-score @ IoU = 0.5 and would have to be improved, especially with respect to its recall @ IoU. But, the proposed method could automatically detect bio-molecule clusters that are not only circular and not always uniform in size, and it can output various histograms and heatmaps for novel deeper analyses of the automatically detected bio-molecule clusters, while the particles detected by the Mosaic Particle Tracker 2D/3D, which is one of the most conventional methods for experts, can be only circular and uniform in size. In addition, this paper defines and validates a novel similarity of automatically detected bio-molecule clusters between fluorescent cells, i.e., SimMolCC, and also shows some examples of SimMolCC-based applications. Full article

Single-well productivity is a crucial metric for assessing the effectiveness of petroleum reservoir development. The accurate prediction of productivity is essential for achieving the efficient and economical development of oil–gas reservoirs. Traditional productivity prediction methods (empirical formulae and numerical simulation) are limited to specific reservoir types. There are few influencing factors, and a large number of ideal assumptions are made for the assumed conditions when predicting productivity. The application scenario is ideal. However, in tight oil reservoirs, numerous factors affect productivity, and their interactions exhibit significant complexity. Continuing to use traditional reservoir productivity prediction methods may result in significant calculation errors and lead to economic losses in oilfield development. To enhance the accuracy of tight reservoir productivity predictions and achieve economical and efficient development, this paper investigates the tight reservoir in the WZ block of the Beibuwan area, considering the impact of geological and engineering factors on productivity; the random forest tree and Spearman correlation coefficient are used to analyze the main influencing factors of productivity. The back propagation neural network optimized by particle swarm optimization was employed to develop a productivity prediction model (PSO-BP model) for offshore deep and ultra-deep tight reservoirs. The actual test well data of the oilfield are substituted into this model. The analysis results of the example application yielded an RMSE of 0.032, an MAE of 1.209, and an R² value of 0.919. Compared with traditional productivity prediction methods, this study concludes that the model is both reasonable and practical. The calculation speed is faster and the calculation result is more accurate, which can greatly reduce productivity errors. The model constructed in this paper is well suited for predicting the productivity of tight reservoirs within the WZ block. It offers substantial guidance for predicting the productivity of similar reservoirs and supports the economical and efficient development of petroleum reservoirs. Full article

Remote sensing technology can be used to monitor changes in crop planting areas to guide agricultural production management and help achieve regional carbon neutrality. Agricultural UAV remote sensing technology is efficient, accurate, and flexible, which can quickly collect and transmit high-resolution data in real time to help precision agriculture management. It is widely used in crop monitoring, yield prediction, and irrigation management. However, the application of remote sensing technology faces challenges such as a high imbalance of land cover types, scarcity of labeled samples, and complex and changeable coverage types of long-term remote sensing images, which have brought great limitations to the monitoring of cultivated land cover changes. In order to solve the abovementioned problems, this paper proposed a multi-scale fusion network (MSFNet) model based on multi-scale input and feature fusion based on cultivated land time series images, and further combined MSFNet and Model Diagnostic Meta Learning (MAML) methods, using particle swarm optimization (PSO) to optimize the parameters of the neural network. The proposed method is applied to remote sensing of crops and tomatoes. The experimental results showed that the average accuracy, F1-score, and average IoU of the MSFNet model optimized by PSO + MAML (PSML) were 94.902%, 91.901%, and 90.557%, respectively. Compared with other schemes such as U-Net, PSPNet, and DeepLabv3+, this method has a better effect in solving the problem of complex ground objects and the scarcity of remote sensing image samples and provides technical support for the application of subsequent agricultural UAV remote sensing technology. The study found that the change in different crop planting areas was closely related to different climatic conditions and regional policies, which helps to guide the management of cultivated land use and provides technical support for the realization of regional carbon neutrality. Full article

The training phase of a deep learning neural network (DLNN) is a computationally demanding process, particularly for models comprising multiple layers of intermediate neurons.This paper presents a novel approach to accelerating DLNN training using the particle swarm optimisation (PSO) algorithm, which exploits the GPGPU architecture and the Apache Spark analytics engine for large-scale data processing tasks. PSO is a bio-inspired stochastic optimisation method whose objective is to iteratively enhance the solution to a (usually complex) problem by approximating a given objective. The expensive fitness evaluation and updating of particle positions can be supported more effectively by parallel processing. Nevertheless, the parallelisation of an efficient PSO is not a simple process due to the complexity of the computations performed on the swarm of particles and the iterative execution of the algorithm until a solution close to the objective with minimal error is achieved. In this study, two forms of parallelisation have been developed for the PSO algorithm, both of which are designed for execution in a distributed execution environment. The synchronous parallel PSO implementation guarantees consistency but may result in idle time due to global synchronisation. In contrast, the asynchronous parallel PSO approach reduces the necessity for global synchronization, thereby enhancing execution time and making it more appropriate for large datasets and distributed environments such as Apache Spark. The two variants of PSO have been implemented with the objective of distributing the computational load supported by the algorithm across the different executor nodes of the Spark cluster to effectively achieve coarse-grained parallelism. The result is a significant performance improvement over current sequential variants of PSO. Full article

The inevitability of faults arises due to prolonged exposure of photovoltaic (PV) power plants to intricate environmental conditions. Therefore, fault diagnosis of PV power plants is crucial to ensure the continuity and reliability of power generation. This paper proposes a fault diagnosis method that integrates PV power prediction and an exponentially weighted moving average (EWMA) control chart. This method predicts the PV power based on meteorological factors using the adaptive particle swarm algorithm-back propagation neural network (APSO-BPNN) model and takes its error from the actual value as a control quantity for the EWMA control chart. The EWMA control chart then monitors the error values to identify fault types. Finally, it is verified by comparison with the discrete rate (DR) analysis method. The results showed that the coefficient of determination of the prediction model of the proposed method reached 0.98. Although the DR analysis can evaluate the overall performance of the inverter and identify the faults, it often fails to point out the specific location of the faults accurately. In contrast, the EWMA control chart can monitor abnormal states such as open and short circuits and accurately locate the string where the fault occurs. Full article

Amidst the backdrop of rural revitalization and cultural renaissance, there is a surge in the construction demand for replica traditional vernacular dwellings. Traditional cost estimation methods struggle to meet the need for rapid and precise estimation due to the complexity inherent in their construction. To address this challenge, this study aims to enhance the accuracy and efficiency of cost estimation by innovatively developing an Adaptive Self-Explanatory Convolutional Neural Network (ASCNN) model, tailored to meet the specific cost estimation needs of replica traditional vernacular dwellings in the Huizhou region. The ASCNN model employs a Random Forest model to filter key features, inputs these into the CNN for cost estimation, and utilizes Particle Swarm Optimization (PSO) to optimize parameters, thereby improving predictive accuracy. The decision-making process of the model is thoroughly interpreted through SHAP value analysis, ensuring credibility and transparency. During the construction of the ASCNN model, this study collected and analyzed bidding control price data from 98 replica traditional vernacular dwellings. The empirical results demonstrate that the ASCNN model exhibits outstanding predictive performance on the test set, with a Root Mean Square Error (RMSE) of 9828.06 yuan, a Mean Absolute Percentage Error (MAPE) of 0.6%, and a Coefficient of Determination (R²) as high as 0.989, confirming the model’s high predictive accuracy and strong generalization capability. Through SHAP value analysis, this study further identifies key factors such as floor plan layout, roof area, and column material coefficient that are central to cost prediction. The ASCNN model proposed in this study not only significantly improves the accuracy of cost estimation for Huizhou replica traditional vernacular dwellings, but also enhances its transparency and credibility through model interpretation methods, providing a reliable basis for related investment decisions. The findings of this study also offer valuable references and insights for rapid and precise cost estimation of replica buildings in other regions worldwide. Full article

This article reviews innovative diagnostic approaches for diabetic retinopathy as the prevalence of diabetes mellitus and its complications continue to escalate. Novel techniques focus on early disease detection. Technological innovations, such as teleophthalmology, smartphone-based photography, artificial intelligence with deep learning, or widefield photography, can enhance diagnostic accuracy and accelerate the treatment. The review highlights teleophthalmology and handheld photography as promising solutions for remote eye care. These methods revolutionize diabetic retinopathy screening, offering cost-effective and accessible solutions. However, the use of these techniques may be limited by insurance coverage in certain world regions. Ultra-widefield photography offers a comprehensive view of up to 80.0% of the retina in a single image, compared to the 34.0% coverage of the traditional seven-field imaging protocol. It allows retinal imaging without pupil dilation, especially for individuals with compromised mydriasis. However, they also have drawbacks, including high costs, artifacts from eyelashes, eyelid margins, and peripheral distortion. Recent advances in artificial intelligence and machine learning, particularly through convolutional neural networks, are revolutionizing diabetic retinopathy diagnostics, enhancing screening efficiency and accuracy. FDA-approved Artificial Intelligence-powered devices such as LumineticsCore™, EyeArt, and AEYE Diagnostic Screening demonstrate high sensitivity and specificity in diabetic retinopathy detection. While Artificial Intelligence offers the potential to improve patient outcomes and reduce treatment costs, challenges such as dataset biases, high initial costs, and cybersecurity risks must be considered to ensure safety and efficiency. Nanotechnology advancements further enhance diagnosis, offering highly branched polyethyleneimine particles with fluorescein sodium (PEI-NHAc-FS) for better fluorescein angiography or vanadium oxide-based metabolic fingerprinting for early detection. Full article

In the continuously evolving landscape of novel smart control strategies, optimization techniques play a crucial role in achieving precise control of indoor air quality. This study aims to enhance indoor air quality by precisely regulating carbon dioxide (CO₂) levels through an optimized control system. Prioritizing fast response, short settling time, and minimal overshoot is essential to ensure accurate control. To achieve this goal, chaos optimization is applied. By using the global search capability of the chaos particle swarm optimization (CPSO) algorithm, the initial weights connecting the input layer to the hidden layer and the hidden layer to the output layer of the backpropagation neural network (BPNN) are continuously optimized. The optimized weights are then applied to the BPNN, which employs its self-learning capability to calculate the output error of each neuronal layer, progressing from the output layer backward. Based on these errors, the weights are adjusted accordingly, ultimately tuning the proportional–integral–derivative (PID) controller to its optimal parameters. When comparing simulation results, it is evident that, compared to the baseline method, the enhanced Chaos Particle Swarm Optimization Backpropagation Neural Network PID (CPSO-BPNN-PID) controller proposed in this study exhibits the shortest settling time, approximately 0.125 s, with a peak value of 1, a peak time of 0.2 s, and zero overshoot, demonstrating exceptional control performance. The novelty of this control algorithm lies in the integration of four distinct technologies—chaos optimization, particle swarm optimization (PSO), BPNN, and PID controller—into a novel controller for precise regulation of indoor CO₂ concentration. Full article

In this work, interval type-2 and type-3 fuzzy systems were designed, of Mamdani and Sugeno types, for time series prediction. The aggregation performed by the type-2 and type-3 fuzzy systems was carried out by using the results of an optimized ensemble neural network (ENN) obtained with the particle swarm optimization algorithm. The time series data that were used were of the Mexican stock exchange. The method finds the best prediction error. This method consists of the aggregation of the responses of the ENN with type-2 and type-3 fuzzy systems. In this case, the systems consist of five inputs and one output. Each input is made up of two membership functions and there are 32 possible fuzzy if-then rules. The simulation results show that the approach with type-2 and type-3 fuzzy systems provides a good prediction of the Mexican stock exchange. Statistical tests of the comparison of type-1, type-2, and type-3 fuzzy systems are also presented. Full article

Ultra-high-energy cosmic rays (UHECRs) are extremely rare energetic particles of ordinary matter in the Universe, traveling astronomical distances before reaching the Earth’s atmosphere. When primary cosmic rays interact with atmospheric nuclei, cascading extensive air showers (EASs) of secondary elementary particles are developed. Radio detectors have proven to be a reliable method for reconstructing the properties of EASs, such as the shower’s axis, its energy, and its maximum (${\mathrm{X}}_{\max }$). This aids in understanding fundamental astrophysical phenomena, like active galactic nuclei and gamma-ray bursts. Concurrently, data science has become indispensable in UHECR research. By applying statistical, computational, and deep learning methods to both real-world and simulated radio data, researchers can extract insights and make predictions. We introduce a convolutional neural network (CNN) architecture designed to classify simulated air shower events as either being generated by protons or by iron nuclei. The classification achieved a stable test error of 10%, with Accuracy and ${\mathrm{F}}_1$ scores of 0.9 and an MCC of 0.8. These metrics indicate strong prediction capability for UHECR’s nuclear composition, based on data that can be gathered by detectors at the world’s largest cosmic rays experiment on Earth, the Pierre Auger Observatory, which includes radio antennas, water Cherenkov detectors, and fluorescence telescopes. Full article