Complex Mapping Relationship Research Articles

Porosity prediction using a deep learning method based on bidirectional spatio-temporal neural network

Deep learning is one of the best machine learning algorithms for modeling complex mapping relationships between independent and dependent variables, and thus it can be viewed as an ideal approach to predict porosity. In this study, to overcome the deficiencies in current porosity prediction based on deep learning and improve the prediction accuracy, we proposed a deep learning model based on bidirectional temporal convolutional network (BTCN) and bidirectional long short-term memory (BLSTM) network, called bidirectional spatio-temporal neural network (BSTNN), to establish a porosity prediction model. First, the maximum information coefficient is used to analyze the correlation between well logs and porosity, which provides a basis for determining the inputs of the prediction model. Then, a hybrid network structure is constructed by using BTCN and BLSTM, in which BTCN goes to learn the bidirectional long sequence features and BLSTM goes to learn the variation trend and context information with depth, so the hybrid network structure can learn richer logging signal features. Finally, the extracted features are passed through the fully connected layer to output the porosity prediction results. Porosity prediction experiment are conducted by using the actual field data set. The results show that the proposed method has the lower prediction errors for the porosity modeling (RMSE = 0.368 and MAE = 0.260) compared to the benchmark models convolutional neural network (RMSE = 0.404 and MAE = 0.292) and long short-term memory network (RMSE = 0.418 and MAE = 0.298), which verifies the effectiveness of this prediction method.

Short-Term Power Load Forecasting Method Based on GRU-Transformer Combined Neural Network Model

Load Forecast (LF) is an important task in the planning, control and application of public power systems. Accurate Short Term Load Forecast (STLF) is the premise of safe and economical operation of a power system. In the research of short-term power load forecasting, machine learning and deep learning are the most popular methods at present, but there still exists a problem that the single and simple structure of power load forecasting model leads to low accuracy of load forecasting. In order to improve the accuracy of STLF, a Gated Cycle Unit (GRU)-Transformer combined neural network model is proposed. Transformer encoder structure is used as feature extractor to mine the complex mapping relationships between the input features and load. The advantage of self-attention mechanism is used to solve the problem of information loss of long sequences in short-term power load forecasting. At the same time, the multivariate time series model of GRU is used for model training. The experimental results on the power load data set of a certain region in southwest China and Panama City show that the proposed combined model prediction method has higher accuracy than those proposed in other literatures, which further proves its feasibility and superiority.

A strategy for high-entropy copper alloys composition design assisted by deep learning based on data reconstruction and network structure optimization

Complex mapping relationships between high-entropy alloy compositions and performances struggle to precisely elucidate by traditional machine learning models, hindering the accurate and efficient design of high-performance alloys. In this work, a novel alloy design strategy combined self-decision deep neural network, data reconstruction with network structure optimization was proposed, which effectively enhanced the model's ability to predict the mapping relationships between high-dimensional composition spaces and mechanical properties of alloy. Compared to other machine learning methods, significantly improves model prediction accuracy (hardness model: 97.98%; strength model: 94.40%). Through above model, the mechanical properties of 308 new (CuNiMn)-X high-entropy copper alloys was studied added other elements such as Al, Ti, Cr, and Fe, which designed and produced a novel (CuNiMn)60Al20Cr20 alloy with high hardness of 474 HV, high compressive strength of 2322 MPa, and high fracture strain of 25.20%. The primary factors influencing alloy performance and their respective thresholds were also quantitatively revealed (hardness: electronegativity deviation (0.02, 0.1) and nuclear charge deviation (0, 0.25); compressive strength: mean nuclear charge (25, 30) and mean atomic radius (134 p.m.)). These findings provide a new perspective and approach for the design and mechanism understanding of high-performance alloys with complex compositions.

Effect Evaluation of Staged Fracturing and Productivity Prediction of Horizontal Wells in Tight Reservoirs

In this paper, the effect evaluation and production prediction of staged fracturing for horizontal wells in tight reservoirs are studied. Firstly, the basic characteristics and value of horizontal wells in tight reservoirs are introduced, their geological characteristics, flow mechanism and permeability model are analyzed and the application of grey theory in effect analysis is discussed. Considering the problems of staged fracturing effect evaluation and the production prediction of horizontal wells in tight reservoirs, a BP neural network model based on deep learning is proposed. Due to the interference of multiple physical parameters and the complex functional relationship in the development of tight reservoir fracturing, the traditional prediction method has low accuracy and it is difficult to establish an accurate mapping relationship. In this paper, a BP neural network is used to simulate multivariable nonlinear mapping by modifying the model, and its advantages in solving the coupling relationship of complex functions are brought into play. A neural network model with fracturing parameters as input and oil and gas production as output is designed. Through the training and testing of data sets, the accuracy and applicability of the proposed model for effect evaluation and yield prediction are verified. The research results show that the model can fit the complex mapping relationship between fracturing information and production and provide an effective evaluation and prediction tool for the development of the staged fracturing of horizontal wells in tight reservoirs.

A Novel Inversion Method for Permeability Coefficients of Concrete Face Rockfill Dam Based on Sobol-IDBO-SVR Fusion Surrogate Model

The accurate and efficient inversion of permeability coefficients is significant for the scientific assessment of seepage safety in concrete face rockfill dams. In addressing the optimization challenge of permeability coefficients with few samples, multiple parameters, and strong nonlinearity, this paper proposes a novel intelligent inversion method based on the Sobol-IDBO-SVR fusion surrogate model. Firstly, the Sobol sequence sampling method is introduced to extract high-quality combined samples of permeability coefficients, and the equivalent continuum seepage model is utilized for the forward simulation to obtain the theoretical hydraulic heads at the seepage monitoring points. Subsequently, the support vector regression surrogate model is used to establish the complex mapping relationship between the permeability coefficients and hydraulic heads, and the convergence performance of the dung beetle optimization algorithm is effectively enhanced by fusing multiple strategies. On this basis, we successfully achieve the precise inversion of permeability coefficients driven by multi-intelligence technologies. The engineering application results show that the permeability coefficients determined based on the inversion of the Sobol-IDBO-SVR model can reasonably reflect the seepage characteristics of the concrete face rockfill dam. The maximum relative error between the measured and the inversion values of the hydraulic heads at each monitoring point is only 0.63%, indicating that the inversion accuracy meets the engineering requirements. The method proposed in this study may also provide a beneficial reference for similar parameter inversion problems in engineering projects such as bridges, embankments, and pumping stations.

Multisource monitoring data-driven slope stability prediction using ensemble learning techniques

Field monitoring data from multiple monitoring points often contain redundant and/or dirty data, which provide less value for slope safety evaluation. Existing slope safety evaluation methods suffer from dealing with large amounts of monitoring data, resulting in a significant waste of monitoring effort. Moreover, these methods cannot evaluate the value of information (VoI) of the monitoring data for slope performance prediction and may abuse dirty data. To address these issues, this study proposes a multisource monitoring data-driven slope stability prediction method based on ensemble learning techniques. The complex mapping relationship between slope stability and monitoring data is established using the ensemble learning algorithm, based on which the VoI of the monitoring data is evaluated and the optimization of the monitoring layout is analyzed. An undrained slope example with multiple monitoring points arranged at various locations on the slope is analyzed to demonstrate the effectiveness of the proposed method. The results show that the proposed method can effectively and efficiently utilize the massive monitoring data to predict the slope stability. It also can provide a versatile tool for optimizing the layout of monitoring points and saving the monitoring costs.

Non-volatile double-tunable vortex metalens design based on Sb2S3 using deep neural network and particle swarm optimization algorithm

A dual-tunable vortex metalens is proposed that leverages deep neural network (DNN) and particle swarm optimization algorithm (PSO), incorporating the use of the phase-change material Sb2S3, which enables simultaneous tunable of both the focal length of the focused vortex beam and the topological charge. The control of traditional vortex metalens relies on changing the polarisation state of the incident light; however, its control range is limited. We employed neural network algorithms to learn the complex mapping relationship between the unit structure and the phase, achieving a fast and accurate prediction of the vortex metalens's phase. Compared with traditional simulation methods, the application of this neural network significantly reduces the time consumption of the design process and avoids the need for re-simulation. Furthermore, our design transcends limitations associated with specific wavelengths and enables the rapid design of tunable metalens across the 1–1.5 μm wavelength range, offering flexibility for the development of dual-tunable metalens. Based on this scheme, we simulated vortex metalens with tunable focal length and topological charge, and the results showed that the simulation results were consistent with the theoretical predictions.

A materials informatics framework based on reduced‐order models for extracting structure–property linkages of additively manufactured continuous fiber‐reinforced polymer composites

AbstractThe innovative combination of additive manufacturing (AM) and continuous fiber‐reinforced polymer composites (CFRPCs) confers products with the dual advantages of integrated manufacturing and designability of properties, but lack an efficient and reliable method for property prediction. This study presents a materials informatics framework using reduced‐order models and machine learning (ML) to extract the structure–property (SP) linkages between microstructures and macroscopic elastic properties of AM‐CFRPCs. The initial step involves generating microstructural 2D cross‐sections and representative volume elements (RVEs) with random fiber and pore distributions based on the minimum potential method. Then, finite element (FE) calculations are performed on RVEs to obtain nine macroscopic elastic properties. Following that, the quantification and dimensionality reduction of the 2D cross‐sectional dataset are conducted separately using two‐point spatial correlations and principal component analysis (PCA). Finally, a Bayesian optimized composite kernel support vector regression (CK‐SVR) algorithm is used to effectively establish complex mapping relationships between the reduced‐order representations of the microstructures and the mechanical properties. Despite the reduced‐order dataset containing only 3–6 variables, the framework generates an interpretable model exhibiting excellent accuracy with all predicted R2 values surpassing 0.91. Therefore, this framework presents a prospective solution for expediting the design and optimization of AM‐CFRPCs.Highlights A materials informatics scheme is proposed to predict the 9 elastic properties of AM‐CFRPCs. Microstructures are quantified and dimensionally reduced by two‐point statistics and PCA. SP linkages are established between 2D cross‐sections and 3D macromechanical properties. Modified CK‐SVR exhibits higher prediction accuracy compared to conventional models.

Vehicle Driving Safety of Underground Interchanges Using a Driving Simulator and Data Mining Analysis

In the process of driving in an underground interchange, drivers are faced with many challenges, such as being in a closed space, visual changes alternating between light and dark conditions, complex road conditions in the confluence section, and dense signage, which directly affect the safety and comfort of drivers in an underground interchange. Thus, driving simulation, building information modeling (BIM), and data mining were used to analyze the impact of underground interchange safety facilities on driving safety and comfort. Acceleration disturbance and steering wheel comfort loss values were used to assist the comfort analysis. The CART algorithm, classification decision trees, and neural networks were used for data mining, which uses a dichotomous recursive partitioning technique where multiple layers of neurons are superimposed to fit and replace very complex nonlinear mapping relationships. Ten different scenarios were designed for comparison. Multiple linear regression combined with ANOVA was used to calculate the significance of the control variables for each scenario on the evaluation index. The results show that appropriately reducing the length of the deceleration section can improve driving comfort, setting reasonable reminder signs at the merge junction can improve driving safety, and an appropriate wall color can reduce speed oscillation. This study indicates that the placement of traffic safety facilities significantly influences the safety and comfort of driving in underground interchanges. This study may provide support for the optimization of the design of underground interchange construction and internal traffic safety facilities.

Can deep learning compensate for sparse shots in the imaging domain? A potential alternative for reducing the acquisition cost of seismic data

Dense shots can improve the fold of subsurface imaging points, which is essential for the resolution of imaging results. However, dense shots significantly increase the cost of data acquisition, which is one of the major bottlenecks faced by seismic exploration. To address this issue, we speculate whether it is possible to construct an effective method to optimize the image made by stacking sparse shots and then generate an imaging result similar to the image made by stacking dense shots. In other words, we explore the possibility of using an optimization method to replace the dense shots in migration imaging, which would likely reduce the acquisition cost of seismic data. Deep learning can establish a nonlinear and complex mapping relationship by using data-driven strategies. Inspired by this, we use the convolutional neural network to establish a novel mapping relationship from the sparse-shot image to the dense-shot image by constructing a suitable training data set and designing a self-guided attention network architecture. We refer to this mapping relationship as shot compensation. We use the 2D Sigsbee2b model and the 3D SEG advanced modeling model to demonstrate the potential application of shot compensation in reducing the acquisition cost of seismic data. Moreover, a real 2D marine seismic data set is used to evaluate the effectiveness of shot compensation. Experimental results on synthetic and real data indicate that this shot compensation method can improve the quality of sparse-shot images and that the improved imaging results are similar to their corresponding dense-shot images.

Transfer-Learning-Based Temperature Uncertainty Reduction Algorithm for Large Scale Oil Tank Ground Settlement Monitoring.

Sensors operating in open-air environments can be affected by various environmental factors. Specifically, ground settlement (GS) monitoring sensors installed in oil tanks are susceptible to non-uniform temperature fields caused by uneven sunshine exposure. This disparity in environmental conditions can lead to errors in sensor readings. To address this issue, this study aimed to analyze the impact of temperature on GS monitoring sensors and establish a mapping relationship between temperature uncertainty (fluctuations of measurement caused by temperature variation) and temperature variation. By collecting the temperature information and inferring the temperature uncertainty being introduced, this interference can be removed. However, it is crucial to note that in real-world complex scenarios, the relationship between temperature uncertainty and temperature variation is not always a constant positive correlation, which limits the data available for certain periods. Moreover, the limited availability of data presents a challenge when analyzing the complex mapping relationship. To overcome these challenges, a transfer-learning-based algorithm was introduced to develop a more accurate model for predicting temperature uncertainty based on temperature variation, even with limited data. Subsequently, a practical test was conducted to validate the proposed algorithm's performance. The results demonstrated that the algorithm outperformed a simple linear fitting model using the least squares method (LSM), achieving an improvement of up to 21.9%. This outcome highlights the algorithm's potential for enhancing the performance of GS sensors in daytime monitoring and contributing to the safe operation of oil tank facilities and infrastructure health monitoring.

Application research of machine manufacturing process monitoring based on neural network

In the process of mechanical manufacturing, parameters such as geometry, size and surface quality are important factors affecting product quality. The existing technology makes it difficult to conduct product monitoring and data monitoring during machine manufacturing. At the same time, the uncertainty of the manufacturing process also brings great challenges. Therefore, it is necessary to design corresponding monitoring schemes according to different situations. Therefore, neural network based machinery manufacturing process monitoring technology is of great significance. The neural network model established in this paper can automatically learn and establish complex mapping relationships, and can predict and detect anomalies according to historical data. In addition, deep learning technology can be used to carry out adaptive feature learning on a large amount of monitoring data, and complete efficient training and real-time decision-making in a distributed environment. Compared with the common mechanical manufacturing process, neural network monitoring has a lower loss rate and error rate. The neural network based machinery manufacturing process monitoring technology will become an important direction of the future development of machinery manufacturing industry, and will have great practical application value in improving production efficiency, reducing production cost and ensuring product quality.

A novel method for online prediction of the remaining useful life of rolling bearings based on wavelet power spectrogram and Transformer structure

The vibration signal characteristics of rolling bearings are closely related to the performance decay process, predicting the remaining useful life (RUL) of rolling bearings by vibration signals can effectively prevent the occurrence of bearing failures. In this paper, a deep learning-based method for rolling bearing RUL prediction is proposed. The convolutional neural network (CNN), which is more effective in extracting local information, is combined with the Transformer structure, which is specialized in extracting global information, to deeply explore the complex mapping relationship between signal features in wavelet power spectrogram and bearing RUL. Meanwhile, the method of detecting the first prediction time of rolling bearings based on 3 criteria is improved. The proposed method is validated with the XJTU-SY rolling element bearing accelerated life test datasets, as well as compared with other methods to prove its superiority. The results show that the proposed method can effectively extract bearing degradation information and realize the accurate prediction of rolling bearing RUL. The performance-improved rolling bearing RUL prediction model is highly robust and generalizable, which applies to other mechanical parts performance prediction and can be realized for practical applications in industrial fields.

A modeling method of wide random forest multi-output soft sensor with attention mechanism for quality prediction of complex industrial processes

Complex industrial production processes often involve multiple product quality indicators that are interrelated. There exists a complex nonlinear mapping relationship between the operational input feature variables and multiple output target quality variables, making it difficult to accurately model through first-principle models. In order to fully capture the complex relationship between measurable variables and difficult-to-measure quality variables, and achieve accurate prediction of multiple output variables to meet the needs of practical industrial sites, this paper proposes a broad random forest-based multi-output soft sensor modeling method based on the idea of attention mechanism derived from the concept of broad learning systems. This method comprehensively considers the dynamic impact of different feature variables on the target quality indicators in actual production processes. The attention mechanism assists the soft sensor model in capturing contextual information better when dealing with long sequences, with a focus on the relevant parts related to the current task. Additionally, the interpretable random forest algorithm is employed as the weight estimator for the basic feature learning unit of Broad-based learning, enabling regression modeling of multiple target quality variables. The use of Broad-based random forest improves the model’s learning ability, interpretability, and generalization capability. To validate the reliability of the proposed method, it was applied to real industrial cases. The results demonstrated that the multi-output quality variable prediction performance of the proposed soft sensor outperforms existing soft sensors in terms of prediction accuracy. This indicates promising industrial application prospects for the proposed method.

Application of Deep Neural Network with Frequency Domain Filtering in the Field of Intrusion Detection

In the field of intrusion detection, existing deep learning algorithms have limited capability to effectively represent network data features, making it challenging to model the complex mapping relationship between network data and attack behavior. This limitation, in turn, impacts the detection accuracy of intrusion detection systems. To address this issue and further enhance detection accuracy, this paper proposes an algorithm called the Fourier Neural Network (FNN). The core of FNN consists of a Deep Fourier Neural Network Block (DFNNB), which is composed of a Hadamard Neural Network (HNN) and a Fourier Neural Network Layer (FNNL). In a DFNNB, the HNN is responsible for sampling the network intrusion data samples in different time domain spaces. The FNNL, on the other hand, performs a Fourier transform on the samples outputted by the HNN and maps them to the frequency domain space, followed by a filtering process. Finally, the data processed by filtering are transformed back to the time domain space for subsequent feature extraction work by the DFNNB. Additionally, to enhance the algorithm’s detection accuracy and filter out noise signals, this paper also introduces a High-energy Filtering Process (HFP), which eliminates noise signals from the data signal and reduces interference on the final detection result. Due to the ability of FNN to process network data in both the time domain space and the frequency domain space, it possesses a stronger capability in expressing data features. Finally, this paper conducts performance evaluations on the KDD Cup99, NSL-KDD, UNSW-NB15, and CICIDS2017 datasets. The results demonstrate that the proposed FNN-based IDS model achieves higher detection rates, lower false alarm rates, and better detection performance than classical deep learning and machine learning methods.

Data-driven algorithm based on the scaled boundary finite element method and deep learning for the identification of multiple cracks in massive structures

Structural defect identification is a vital aspect of structural health monitoring used to assess the safety of engineering structures. However, quantitatively determining the dimensions of structural defects is often difficult. Therefore, this study presents an innovative data-driven algorithm that combines the scaled boundary finite element method (SBFEM) and a deep learning framework based on a dilated causal convolutional neural network (CNN) to identify crack-like defects in large-scale structures. The SBFEM is used to simulate different crack-like defects. Mesh generation is significantly simplified by a simple procedure that requires only changing the scale centre at the crack tip and the positions of the nodes at the crack opening. This minimises remeshing and enables simple generation of sufficient data to train the neural network. In addition, an absorbing boundary model based on Rayleigh damping is used to avoid computing the entire model when simulating wave propagation in massive structures. To ensure that sequential data remain ordered and to obtain a large receptive field without increasing the complexity of the neural network, a dilated causal CNN is employed in the deep learning framework. Therefore, more historical information is captured, and the complex mapping relationship between the echo signal and the crack information is efficiently learnt. The proposed model can accurately identify the number, location, and depth of cracks in massive structures. Moreover, it is robust to noise, which is demonstrated via numerical examples. Therefore, the proposed algorithm provides valuable insight into the detection and diagnosis of structural defects, which can ultimately improve the safety of engineering structures.

Inversion analysis of rock mass permeability coefficient of dam engineering based on particle swarm optimization and support vector machine: A case study

In order to efficiently and reasonably determine the permeability coefficient of engineering rock mass, the two key problems of complex seepage field simulation and objective function solution in permeability coefficient inversion are studied and improved. Firstly, a 3D refined seepage numerical model is established considering the main fault fracture zones and dam seepage control system. Secondly, combined with the support vector machine (SVM) algorithm, the complex mapping relationship between permeability coefficient of each rock layer and impervious structure and piezometer head is constructed. Finally, the particle swarm optimization (PSO) algorithm is introduced, and then the efficient optimization of permeability coefficient inversion of engineering rock mass and impervious structure is realized based on PSO-SVM. The case study of LJH arch dam shows that the inversion calculation framework based on PSO-SVM not only completes the rapid proxy calculation of the numerical model of complex seepage field, but also realizes the accurate optimization of the inversion objective function. The simulation results under the final inversion parameters reproduce the variation characteristics of the piezometric tube water level of each typical measuring point under the change of reservoir water level. In addition, with the correlation analysis between the permeability coefficient and the measured value of the corresponding piezometer, the parameter inversion method is endowed with new application value. This study is of great significance for clarifying the seepage characteristics of complex rock mass and improving the inversion efficiency of dam seepage monitoring data.

Coordinated control of multiple converters in model-free AC/DC distribution networks based on reinforcement learning

Taking into account the challenges of obtaining accurate physical parameters and uncertainties arising from the integration of a large number of sources and loads, this paper proposes a real-time voltage control method for AC/DC distribution networks. The method utilizes model-free generation and coordinated control of multiple converters, and employs a combination of agent modeling and multi-agent soft actor critic (MASAC) techniques for modeling and solving the problem. Firstly, a complex nonlinear mapping relationship between bus power and voltage is established by training an power-voltage model, to address the issue of obtaining physical parameters in AC/DC distribution networks. Next, a Markov decision process is established for the voltage control problem, with multiple intelligent agents distributed to control the active and reactive power at each converter, in response to the uncertainties of photovoltaic (PV) and load variations. Using the MASAC method, a centralized training strategy and decentralized execution policy are implemented to achieve distributed control of the converters, with each converter making optimal decisions based on its local observation state. Finally, the proposed method is verified by numerical simulations, demonstrating its sound effectiveness and generalization ability.

Application of ensemble model in capacity prediction of the CCFST columns under axial and eccentric loading

Understanding the load-carrying capacity of circular concrete-filled steel tube (CCFST) columns is crucial for designing CCFST structures. However, traditional empirical formulas often yield inconsistent results for the same scenario, causing confusion for decision makers. Additionally, simple regression analysis is unable to accurately predict the complex mapping relationship between input and output variables. To address these limitations, this paper proposes an ensemble model that incorporates multiple input features, such as component geometry and material properties, to predict CCFST load capacity. The model is trained and tested on two datasets comprising 1305 tests on CCFST columns under concentric loading and 499 tests under eccentric loading. The results demonstrate that the proposed ensemble model outperforms conventional support vector regression and random forest models in terms of the determination coefficient (R2) and error metrics (MAE, RMSE, and MAPE). Moreover, a feature analysis based on the Shapley additive interpretation (SHAP) technique indicates that column diameter is the most critical factor affecting compressive strength. Other important factors include tube thickness, yield strength of steel tube, and concrete compressive strength, all of which have a positive effect on load capacity. Conversely, an increase in column length or eccentricity leads to a decrease in load capacity. These findings can provide useful insights and guidance for the design of CCFST columns.

A 3D Structure Mapping-Based Efficient Topology Optimization Framework

Abstract It is well known that the computational cost of classic topology optimization (TO) methods increases rapidly with the size of the design problem because of the high-dimensional numerical simulation required at each iteration. Recently, the technical route of replacing the TO process with artificial neural network (ANN) models has gained popularity. These ANN models, once trained, can rapidly produce an optimized design solution for a given design specification. However, the complex mapping relationship between design specifications and corresponding optimized structures presents challenges in the construction of neural networks with good generalizability. In this paper, we propose a new design framework that uses deep learning techniques to accelerate the TO process. Specifically, we present an efficient topology optimization (ETO) framework in which structure update at each iteration is conducted on a coarse scale and a structure mapping neural network (SMapNN) is constructed to map the updated coarse structure to its corresponding fine structure. As such, fine-scale numerical simulations are replaced by coarse-scale simulations, thereby greatly reducing the computational cost. In addition, fragmentation and padding strategies are used to improve the trainability and adaptability of SMapNN, leading to a better generalizability. The efficiency and accuracy of the proposed ETO framework are verified using both benchmark and complex design tasks. It has been shown that with the SMapNN, TO designs of millions of elements can be completed within a few minutes on a personal computer.