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This research paper focuses on comparing two Machine Learning (ML) approaches—Time Series Analysis and a Feature-Based Prediction—for predicting traffic accident counts. Spanning the years 2005 to 2016 in Paris, the study leverages a comprehensive dataset that includes attributes such as weather conditions. Following dataset preprocessing and partitioning into training and test sets, the research systematically develops and evaluates these two contrasting ML modeling approaches. Furthermore, the study extends its comparison by evaluating the performance of various ML models, emphasizing the power of the proposed Neural Network (NN) model. Metrics such as Mean Absolute Percentage Error (MAPE) are employed to assess and contrast the predictive capabilities of each model. The results not only showcase the effectiveness of the neural network model but also elucidate its superiority over alternative ML approaches, particularly in the context of traffic accident prediction. [ABSTRACT FROM AUTHOR]

Mushrooms are fungi that play significant roles in ecosystems, food, and medicine. However, distinguishing toxic mushrooms from non-toxic ones is challenging, as their visual characteristics can be misleading. Deep learning, a subset of artificial intelligence, offers a promising approach to detecting and classifying mushrooms based on their toxicity. By training deep neural networks on datasets of labelled mushroom images, it becomes possible to create models capable of accurately differentiating between toxic and non-toxic mushrooms. Mushrooms are fungi that play significant roles in ecosystems, food, and medicine. However, manually distinguishing toxic mushrooms from non-toxic ones is a challenging and time-consuming task. This research work is to help the local farmers and exporters to identify the difference between the toxic and the edible mushroom which will eventually boost production and will directly have an impact on the economic growth in India. This paper compared different deep learning algorithms such as Support Vector Machine (SVM), Resnet50, YOLO V5 and Alexnet, which have been found to be the most useful in classifying the mushrooms effectively as toxic and non-toxic respectively. The patterns in the images of the mushrooms have been predicted through the above classifiers. The validation techniques show SVM to be the most effective with an accuracy of 83%. [ABSTRACT FROM AUTHOR]

Construction companies estimate project costs at the beginning of the project; however, many factors impact the final project cost. Estimate at Completion (EAC) is a critical approach for estimating the final cost based on actual project performance. This paper aims to improve EAC predictions by integrating Adaptive Neuro-Fuzzy Inference Systems (ANFIS) and Artificial Neural Network (ANN) with Archimedes Optimization Algorithm (AOA). The integration of the input optimization algorithm aims to optimize the input features and explore the factors that significantly affect EAC. Using 306 data points from 13 construction projects in Taiwan between 2000 and 2007, this paper developed hybrid models and found a significant improvement in EAC estimation compared to ANN and ANFIS. • Four models (two single and two hybrid) were developed for EAC predictions. • Single models were developed using Adaptive Neuro-Fuzzy Inference Systems (ANFIS) and Artificial Neural Networks (ANN). • Hybrid models were developed by integrating the Archimedes Optimization Algorithm (AOA) into ANN and ANFIS. • AOA selects key parameters, improving accuracy with fewer input parameters. • The AOA-ANN model outperforms the other models with the minimum number of input parameters. [ABSTRACT FROM AUTHOR]

The inherent nonlinear and memory-dependent input-output characteristics of piezoelectric actuators pose challenges to the precision of piezoelectric positioning systems. In order to solve this problem, this paper firstly transforms the Jiles-Atherton (JA) model into a neural network structure, designs the Jiles-Atherton neural network (JANN), and combines JANN with nonlinear autoregressive exogenous input (NARX) neural network. A hybrid JA-NARX neural network model is proposed for the first time. This model has the advantages of simple structure, high modeling accuracy, and good interpretability. The effectiveness of the proposed JA-NARX neural network model is validated through a series of experiments, specifically assessing its capacity to accurately capture rate-dependent and asymmetric hysteresis characteristics. The results show that although the proposed neural network model has fewer layers and relatively simple structure, it can realize the high-precision modeling of piezoelectric hysteresis dynamics at a lower computational cost. The experimental data shows that, under the excitation of 60 Hz input signal, the model's PV error only accounts for 0.82% of the full scale range, and the modeling performance is far superior to other models. [Display omitted] • A shallow neural network based on Jiles-Atherton differential-equation model is proposed and constructed for the first time. • An innovative JA-NARX hybrid neural network model is proposed by combining Jiles-Atherton with NARX models. • The JA-NARX hybrid neural network model features simple structure, high modelling accuracy, strong interpretability. • The proposed JA-NARX model was rigorously validated through experiments, effectively describing complex dynamic hysteresis. [ABSTRACT FROM AUTHOR]

• For different types of electricity theft has obvious characteristics. • The proposed method can detect specific types of electricity theft. • Real-time electricity theft detection without historical data. Electricity theft detection is very important for the economic benefits of power companies and the effectiveness of safe operation of power systems. At present, the traditional power theft detection method can only identify whether the user has power theft, but cannot perform rapid and accurate inspections for various types of power theft users. Aiming at the characteristics of medium-voltage users with large power consumption and regular power consumption, this paper proposed a method for detecting the type of power theft in medium-voltage distribution lines based on robust regression and convolutional neural network. Firstly, considering the existence of abnormal data due to factors such as communication delay interruption, a robust regression algorithm is used to reduce its impact and improve the accuracy of regression analysis. Secondly, the correction coefficient and error term of each user obtained by regression are taken as the characteristics of user stealing electricity, and input into the convolutional neural network model for training to complete the identification of stealing electricity type. Finally, the method is verified by simulation and measured data. The results show that under different disturbance conditions, the proposed method can accurately identify different types of power stealing behaviors, which can better assist on-site investigation, narrow the investigation scope and improve the verification rate. [ABSTRACT FROM AUTHOR]

Heart rate variability (HRV) is a vital sign with the potential to predict stress and various diseases, including heart attack and arrhythmia. Typically, hospitals utilize electrocardiogram (ECG) devices to capture the heart's bioelectrical signals, which are then used to calculate HRV values. However, this method is costly and inconvenient due to the requirement for stable connections to the body. In recent years, photoplethysmography (PPG) sensors, which collect reflective light signals, have gained attention as a cost-effective alternative for measuring heart health. However, accurately estimating HRV using PPG signals remains a challenging task due to the inherent sensitivity of PPG sensors. To address the challenges, this paper presents an on-device, low-cost machine learning-based system that aims to achieve high-accuracy HRV estimation in real-time. Firstly, we propose a novel unified performance and resource-aware neural network (UP-RaNN) search method that leverages grid search techniques to identify a neural network model that can deliver both high HRV accuracy and smooth operation on resource-limited devices. Secondly, we design a real-time HRV monitoring system using a resource-limited, ultra-low-power microcontroller unit (MCU). This system utilizes the neural network model obtained through the UP-RaNN to provide HRV readings from PPG data in real-time. Thirdly, we evaluate the proposed UP-RaNN method and the real-time HRV monitoring system by comparing its performance to state-of-the-art studies. Moreover, the system is enhanced with adaptive reconfiguration capability, enabling it to improve energy efficiency and adapt to varying demands during runtime. The results demonstrate that when deployed on an MSP430FR5994 development board running at 8 MHz, the trained deep neural network model obtained through our proposed UP-RaNN achieves HRV estimation in just 0.3 s per inference. Additionally, the model exhibits a better mean absolute percentage error (∼ 5.8%) than the state-of-the-art HRV estimation methods using PPG, while significantly reducing model complexity and computational time. [Display omitted] • The UP-RaNN method drastically decreases the search space considering the hardware. • A real-time HRV monitoring system is designed for embedded devices. • Adaptive runtime reconfiguration can accommodate various performance demands. [ABSTRACT FROM AUTHOR]

Monte Carlo Tree Search (MCTS) is a pronounced empirical search algorithm for agent decision-making, especially when enhanced by Deep Learning (DL), in mastering board games that were once thought to be unconquerable. However, it does not appear to be as equally successful in the domain of real-time video games, where the simulation time limit for exploration is a crucial factor, since they are generally designed to be played by human users and hence require a significant amount of resources for simulation. We in this paper propose a surrogate-assisted MCTS approach, specifically targeting commercial real-time video games by approximating the result of gameplay with a deep-learning-based surrogate model. The key contribution of our work is that we designed a modified MCTS for video games that are both commercial and processed in real-time. Since commercial video games include considerably more complex and dynamic gameplays to satisfy their market consumers, as opposed to their non-commercial analogs, our work can be regarded as having challenged the domain unattempted by precedent studies. We validated the performance of our method by conducting a comparative experiment with other algorithms, including the traditional MCTS, under the environment of a commercial real-time video game. [Display omitted] • A Monte Carlo tree search (MCTS) method applicable to commercial real-time video games is proposed. • The method integrates MCTS with a deep neural network structure to function efficiently in a real-time environment. • Performance verification of the proposed surrogate-assisted MCTS is done via two commercial real-time video games with distinct features. • Our surrogate model can be trained with a comparatively little amount of computational resources in real-time video games. [ABSTRACT FROM AUTHOR]

Soil water retention curves (SWRC) are constitutive relationships that are critical for predicting the hydro-mechanical behaviour of unsaturated soil in a wide range of applications in geoenvironmental and geotechnical engineering, hydrology, and agricultural and soil science. Directly measuring SWRC can be costly and time-consuming. Recent research has shown that machine learning, especially artificial neural networks (ANNs), can effectively predict SWRC using easy-to-measure soil properties as inputs. However, significant questions remain about prediction accuracy and what are the most effective input parameters to use, and only shallow neural networks with one hidden layer have been developed in the past for this purpose. In this study, effectiveness of deep neural networks (DNNs) - ANNs with more than one hidden layer - for SWRC prediction is explored for a range of models with different combinations that require easy-to-measure input parameters. Specifically, the study investigates whether switching from a shallow neural network (SNN) to a deep neural network (DNN) can enhance the accuracy of SWRC predictions for these models. Significant differences are found in seven out of thirteen cases when shifting from SNN to DNN. It was also found that models including soil texture, augmented by either dry density or soil porosity as input parameters, perform well. Specific gravity, soil depth, and organic content, on the other hand, do not add to the predictive capability of these models. Findings of this paper can help improve SWRC prediction using machine learning and can lead to more accurate modelling of unsaturated soil behaviour. [ABSTRACT FROM AUTHOR]

The main purpose of the present paper is to investigate the failure pressure and the remaining life of the corroded offshore pipelines utilizing code-based methods such as ASME B31G, modified ASME B31G, DNV RP-F101 and FFS Level-1. Besides, the results of these approaches are compared with the responses of Finite Element Method (FEM) using Artificial Neural Network (ANN) model. To this purpose, the probability distribution of the corrosion defect length and depth as well as the corrosion rate values are obtained from an In-Line Inspection (ILI) survey of an offshore pipeline project in the Persian Gulf. In addition, a 3D verified numerical model of a corroded pipeline is constructed to conduct the numerical analyses. Using the numerical analysis data, a Multi-Layer Perceptron (MLP) model is created to estimate the failure pressure of the pipeline with arbitrary defect dimensions. Moreover, the remaining life of the corroded pipeline is quantified in order to prevent the catastrophic consequences of the pipeline failure. The results show that the MLP model is capable of estimating the failure pressure of the corroded offshore pipeline with quite acceptable accuracy compared with the mentioned code-based methods. Furthermore, the calculated failure pressure of the corroded pipeline can be sorted in ascending order as: (1) FFS-Level 1 (2) ASME modified B31G (3 & 4) ASME B31G & DNV RP-F101 (5) FEM + MLP. In fact, the failure pressure and the remaining life calculated by DNV RP-F101 are closer to the FEM + MLP results for the defects with small and medium dimensions. Diversely, the DNV RP-F101 method underestimates the failure pressure of the corroded pipeline when the defect dimensions become larger. • The failure pressure of corroded offshore pipelines is investigated by different code-based methods such as ASME B31G and DNV RP-F101. • A combination of FEM along with ANN model is utilized. • The remaining life of corroded offshore pipelines is estimated. • The results of DNV RP-F101 are closer to the FEM and ANN for the defects with small and medium dimensions. • The results of ASME B31G are more exact for relatively large sized corrosion defects. [ABSTRACT FROM AUTHOR]

The effective load prediction capability of Energy Aggregation Service Providers is fundamental to their participation in the electricity market and a crucial factor in maximizing their market profits. However, a major challenge we face is that the data required for prediction is typically scattered across individual energy entities, and due to concerns regarding data security and privacy, these entities are often unwilling to share their data. This issue hampers the operational efficiency of Energy Aggregation Service Providers and also impedes the learning and analysis of relevant energy supplier operational patterns. To address this problem, this paper proposes a Federated Learning-based load prediction method for Energy Aggregation Service Providers, allowing them to share information without accessing the actual datasets, thereby obtaining accurate and effective load prediction capabilities. Firstly, an artificial neural network with multidimensional environmental feature selection is established based on task requirements. Then, a weighted average strategy is employed to jointly train the artificial neural network, bridging the gap between individual energy entities and environmental features. Finally, the trained model features are sent to the server of the energy aggregation service provider for fitting. Through a case study using partial data from a large-scale energy aggregation service provider in a southern city of China, the results demonstrate the effectiveness of the proposed method in improving overall operational efficiency and average prediction accuracy. [ABSTRACT FROM AUTHOR]

To predict the dynamic response of high-speed trains (HSTs) passing through cable-stayed bridges (CSBs), this paper proposed a prediction framework based on the genetic algorithm (GA) and the neural network. Firstly, an HST-CSB coupled dynamic model was established using the rigid-flexible coupling method. Subsequently, a fused Temporal Convolutional Network and Long Short-Term Memory (TCN-LSTM) neural network model was developed, and its hyperparameters were optimized using the GA. The dataset was created based on the HST-CSB coupled dynamic model for training and testing the model. Additionally, the generalization capability of the model after adopting different track deformations was discussed. The main results were as follows: Comparative analysis with various neural network models revealed that the GA-TCN-LSTM model exhibited the most accurate prediction results across different train speeds. The significant temperature fluctuations of the bridge led to a drastic deterioration of the track smoothness, resulting in decreased predictive performance of the neural network models. The GA-TCN-LSTM model could optimize the hyperparameters, demonstrating good robustness and generalization capabilities. The predictive performance of the model improved when using the German low-interference spectrum, attributed to the greater irregularities in the German low-interference spectrum, resulting in larger train dynamic responses and stronger feature characteristics in the training data for the neural network. Different neural network models exhibited varying adaptability to track irregularities, with the GA-TCN-LSTM model demonstrating high accuracy in predicting dynamic responses under different train speeds. • A coupled dynamic model of high-speed train and cable-stayed bridges is established. • A model combining Temporal Convolutional Networks and Long Short-Term Memory network is established. • The genetic algorithm is employed to optimize the hyperparameters to mitigate the impact of them on model. • The generalization capability of the model under different conditions is discussed the performance. [ABSTRACT FROM AUTHOR]

• Thick shields and numerous penetrations in nuclear facilities necessitate advanced variance reduction techniques for accurate. • Proposed a groundbreaking variance reduction technique employing source biasing with the assistance of a deep neural network. • The method leverages iterative neural network training to predict source contributions to a tally. • Achieved a higher Figure of Merit (FOM), than the analogous case, in a few iterations. Nuclear analysis of fusion facilities, especially in the context of ITER, is complex due to the need for precise modeling of complex geometry and radiation sources. Monte Carlo (MC) codes, such as MCNP, are used in this context due to their high precision and capability to deal with these cases. To speed up calculations, variance reduction (VR) techniques are crucial for carrying out the simulations in reasonable times. Particularly, the cases where only a small source phase space contributes significantly to the tally must be optimized by an exhaustive sampling of this phase space. This paper introduces a novel VR method for optimizing the calculation. This method obtains the histories that contribute most to the tally and uses this information for a neural network (NN) to sample the source´s phase space. Here we present a preliminary implementation of the method to study its viability in complex cases. The method's efficacy is demonstrated in a representative source-geometry configuration, significantly reducing computational time for the tally convergence. The promising results suggest applicability to more intricate ITER-like scenarios, prompting further development. [ABSTRACT FROM AUTHOR]

Solid oxide fuel cell system is widely acknowledged as the leading alternative energy generation system in the field. Due to their high efficiency, low emissions, low noise, and various other advantages, solid oxide fuel cell systems are being considered for use in automobiles as a replacement for traditional internal combustion engines. However, prolonged operation and abnormal shutdowns can lead to performance degradation, which affects the efficiency, stability, and lifespan of stack. In the context of prolonged operation, considering the fluctuations in stack performance parameters and balance of plant, several regression models based on voltage parameters are established to accurately predict changes in stack performance. The results reveal that the genetic algorithm optimized backpropagation neural network model is highly sensitive in predicting the system performance degradation. Further analysis reveals that abnormal shutdowns can cause system performance fluctuations. As a result, the number and duration of shutdowns are incorporated into genetic algorithm optimized backpropagation neural network model for analysis. This model exhibits the best performance degradation evolution prediction compared to the long short-term memory and particle swarm optimization backpropagation neural network methods. This finding is important for monitoring and controlling the operational status of stack. [Display omitted] • This paper develops multiple models to predict performance degradation. • The models consider prolonged operation and abnormal shutdowns. • The proposed method reduces the prediction error by 68.47%. [ABSTRACT FROM AUTHOR]

• A machine learning-based multiscale method is presented to accelerate composite multiscale analysis. • Multiscale analysis of spatially varying microstructures is undertaken without the assumption of global periodicity. • The complex microstructure solving and stress homogenization processes are eliminated by the BPNN model. Multiscale computing for heterogeneous materials provides a powerful and fidelity approach to handling situations where a suitable macroscopic constitutive model is unavailable. However, there have been very few instances in the industrial sector where concurrent multiscale modeling techniques have been adopted, primarily because they require significant computational resources. In particular, when dealing with large numbers of representative volume elements (RVEs) with different microstructure morphologies throughout the macrostructure. In this paper, the novelty lies in providing an artificial neural network (ANN) model-based multiscale approach for accelerating multiscale analysis of the elliptical inclusion-reinforced composites with spatially varying microstructures, which give up the assumption of global periodicity. To tackle this challenge, firstly, a series of RVEs with different microstructure morphologies characterized by inclusion orientations and aspect ratios, under different load conditions, are simulated to generate training data. Second, the back propagation neural network (BPNN) is trained offline using data acquired from the RVE simulation for learning the underlying and possibly RVE morphologies. The trained BPNN replaces the complex microscopic mechanical solution problems over each macroscopic integration point at each iteration, to update the macroscopic stress-strain responses and it significantly reduces the computational cost of multiscale simulations. Then, the trained neural network is implemented on ABAQUS via user-defined materials (UMAT) and its effectiveness is verified by comparison with the classical multiscale finite element method (FE2) method. Finally, the outstanding computational capability of the proposed approach is further demonstrated through two numerical examples. [Display omitted] [ABSTRACT FROM AUTHOR]

As an emerging digital product, artificial intelligence models face the risk of being modified. Malicious tampering will severely damage model functions, which is different from normal modifications. In addition, tampering localization for targeted repair can effectively reduce the cost. Therefore, it is crucial to achieve model content authentication and locate the tampering location. We proposed a novel semi-fragile neural network watermarking method in this paper to address these issues. Specifically, with the precondition of maintaining model performance, we proposed a method that generates a set of semi-fragile samples for a model to achieve the content authentication and tampering localization. The experiment results show that the content authentication of the model can be achieved by analyzing the output results of the model for the semi-fragile samples. When the model is processed normally, the output results are consistent with the expected label, while when the model is maliciously tampered with, the model produces unstable output. Furthermore, the tamper localization of the model can be further achieved through the information hidden in the semi-fragile samples, resulting in an average accuracy of more than 99.42%. In addition, our method is also effective for other deep neural networks. [ABSTRACT FROM AUTHOR]

Railway in-train forces are an essential element in assessing multiple aspects of rolling stocks. Conventional methods for obtaining the forces can be time-consuming and require significant investment in manpower and domain expertise, while only gathering the force data for specific service conditions one at a time. However, automatic train operation (ATO) systems can measure real-time information for trains and tracks by on-board and trackside devices, which could provide an opportunity for in-train forces monitoring. This paper presents a data-driven approach that uses ATO-measured data and a neural network model to monitor in-train forces under service conditions. To develop this approach, longitudinal train dynamics simulations (LTSs) for a freight train were conducted to establish the relationship between ATO measurements and in-train forces on specific couplers, which was embedded in a large amount of training data. After that, a specially developed self-attention-based causal convolutional neural network (SA-CNN) was employed to learn the underlying relationship and estimate the in-train force histories considering temporal dependencies. The comparative evaluation between the SA-CNN against four alternative neural network models revealed that the SA-CNN exhibits a slightly higher level of accuracy. Furthermore, the generalisation capability of the well-trained SA-CNN model was confirmed by numerical LTSs under four different service conditions. The results indicated that the data-driven approach has superior compatibility for arbitrarily combined inputs with significantly reduced computational time compared to LTSs. This approach holds the potential for achieving reliable in-situ monitoring of railway in-train forces, which is beneficial to both in-train force-related research and industrial applications. [ABSTRACT FROM AUTHOR]