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Safety and passengers' comfort affect directly the reliability and availability of trains. Therefore, appropriate monitoring of the variables influencing those two concepts is mandatory for good operation and maintenance costs reduction. Moreover, operators and rail manufacturers are evolving to a condition-based maintenance strategy (CBM). For this purpose, machine learning is being used to predict early main component's failures, avoiding a loss of reliability or availability during commercial operation. From the passengers' point of view, punctuality and comfortability are two of the most relevant factors when evaluating a railway transport service. Comfort is mainly based on the availability of all services in the train, but it is also related to the accelerations received through the car body to the passengers. More precisely, the lateral car body acceleration (Ayc variable in this paper) is a key variable related to comfort perception and it is therefore important to be monitored and controlled. Rolling, suspension, and damping elements must be kept in perfect conditions during operation to achieve a smooth train movement. In this paper, an optimized model based on Long Short Term Memory networks (LSTM) is designed and trained to predict lateral car body accelerations using only the train accelerometers and with no additional sensoring for this specific experiment. This model will be compared with artificial neural networks (ANN) models to evaluate the relevance of time dependency for this purpose. The optimal time frame to group data is a key characteristic of the neural network training process and is also determined in this paper. The model is trained with data corresponding to the correct dynamic behavior of the train. To test the reliability of this method, a real case of excessive train movement is tested. In these situations, an alert could be sent to the operator or the maintainer for a not-programmed maintenance inspection at the depot (predictive maintenance), avoiding future passenger complaints for the loss of comfort or, in extreme cases, speed limitations due to safety reasons for excessive accelerations. The main contributions of this work are the following: - A LSTM-based neural network, predicting lateral car body accelerations achieving better results than a plain ANN for this dataset: 0.0335 m/s2 MAE and a 12% gain on extreme axles. - An optimization of the time frame to group the measurements in this algorithm that is considering as an input the time variable: 5 s. - A real use case of the model in which monitoring the deviation would have helped to take an early maintenance adjustment on the suspension or damping elements to prevent the loss of comfort. - The p-value used to reduce the uncertainty of the results obtained in the real use case is reaching over 0.95 in the coaches with good dynamic behavior and less than 0.15 in the coaches where a dynamic problem related to comfort loss is detected. [ABSTRACT FROM AUTHOR]

The critical heat flux (CHF) is an important parameter determining the heat transfer capability of nuclear reactors. Therefore, prediction of CHF with accuracy and correct understanding is essential for the design and safety analysis of nuclear power plants (NPPs) and industrial boiling systems. This paper focuses on the CHF prediction using the Gaussian process regression (GPR), a machine learning (ML) technique for modeling nonlinear systems. The covariance kernel is a critical component of GPR modeling because it contains assumptions about the function we want to know and defines the dependence and similarity between the data. In this study, the prediction of CHF is investigated during training and prediction in two different approaches: isotropic and anisotropic covariance kernels. Besides, we evaluate the influence of various isotropic and anisotropic kernels on the accuracy of CHF prediction and quantification of uncertainty encoded in the posterior variances in each of the approaches. The use of anisotropic kernels, considering different shape parameters in line with different data dimensions, results in higher accuracy and better generalization. In addition, compared to other ML tools such as support vector regression (SVR) and artificial neural networks (ANNs), the GPR with anisotropic kernels has much better performance. The evaluation criteria involving root mean square error (RMSE) and mean absolute percentage error (MAPE) in anisotropic kernels are significantly improved compared to isotropic kernels. For example, in squared exponential and rational quadratic kernels, RMSE and MAPE values improved by {65.81%, 67.28%} and {61.97%, 60.12%} values, respectively. The results of the GPR method with the anisotropic kernels show the relative superiority of this method compared to SVR and ANN. The lowest RMSE and MAPE values of all three methods were compared, and these values have improved in GPR compared to SVR by 96.01% and 94.25% and compared to ANN by 84.40% and 85.51%, respectively. Finally, some valuable ideas and practical findings will be offered based on numerical simulations and comparisons between isotropic and anisotropic kernel assumptions. Since the approach of using anisotropic kernels has high computational time despite its high flexibility, we will address this issue in future studies and also use this approach for other nuclear engineering problems. [Display omitted] • A flexible GPR structure is presented. • We compared isotropic & anisotropic kernels for critical heat flux prediction. • Anisotropic model outperforms isotropic in critical heat flux prediction. • Anisotropic GPR outperforms SVR & ANN for critical heat flux prediction. • Broader applicability to other CHF predictions in nuclear engineering applications. [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 critical heat flux (CHF) is an important parameter determining the heat transfer capability of nuclear reactors. Therefore, prediction of CHF with accuracy and correct understanding is essential for the design and safety analysis of nuclear power plants (NPPs) and industrial boiling systems. This paper focuses on the CHF prediction using the Gaussian process regression (GPR), a machine learning (ML) technique for modeling nonlinear systems. The covariance kernel is a critical component of GPR modeling because it contains assumptions about the function we want to know and defines the dependence and similarity between the data. In this study, the prediction of CHF is investigated during training and prediction in two different approaches: isotropic and anisotropic covariance kernels. Besides, we evaluate the influence of various isotropic and anisotropic kernels on the accuracy of CHF prediction and quantification of uncertainty encoded in the posterior variances in each of the approaches. The use of anisotropic kernels, considering different shape parameters in line with different data dimensions, results in higher accuracy and better generalization. In addition, compared to other ML tools such as support vector regression (SVR) and artificial neural networks (ANNs), the GPR with anisotropic kernels has much better performance. The evaluation criteria involving root mean square error (RMSE) and mean absolute percentage error (MAPE) in anisotropic kernels are significantly improved compared to isotropic kernels. For example, in squared exponential and rational quadratic kernels, RMSE and MAPE values improved by {65.81%, 67.28%} and {61.97%, 60.12%} values, respectively. The results of the GPR method with the anisotropic kernels show the relative superiority of this method compared to SVR and ANN. The lowest RMSE and MAPE values of all three methods were compared, and these values have improved in GPR compared to SVR by 96.01% and 94.25% and compared to ANN by 84.40% and 85.51%, respectively. Finally, some valuable ideas and practical findings will be offered based on numerical simulations and comparisons between isotropic and anisotropic kernel assumptions. Since the approach of using anisotropic kernels has high computational time despite its high flexibility, we will address this issue in future studies and also use this approach for other nuclear engineering problems. [Display omitted] • A flexible GPR structure is presented. • We compared isotropic & anisotropic kernels for critical heat flux prediction. • Anisotropic model outperforms isotropic in critical heat flux prediction. • Anisotropic GPR outperforms SVR & ANN for critical heat flux prediction. • Broader applicability to other CHF predictions in nuclear engineering applications. [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]

Stainless steel has many advantages when used in structures, however, the initial cost is high. Hence, it is essential to develop reliable and accurate design methods that can optimize the material. As novel, reliable soft computation methods, machine learning provided more accurate predictions than analytical formulae and solved highly complex problems. The present study aims to develop machine learning models to predict the cross-section resistance of circular hollow section stainless steel stub column. A parametric study is conducted by varying the diameter, thickness, length, and mechanical properties of the column. This database is used to train, validate, and test machine learning models, Artificial Neural Network (ANN), Decision Trees for Regression (DTR), Gene Expression Programming (GEP) and Support Vector Machine Regression (SVMR). Thereafter, results are compared with finite element models and Eurocode 3 (EC3) to assess their accuracy. It was concluded that the EC3 models provided conservative predictions with an average Predicted-to-Actual ratio of 0.698 and Root Mean Square Error (RMSE) of 437.3. The machine learning models presented the highest level of accuracy. However, the SVMR model based on RBF kernel presented a better performance than the ANN, GEP and DTR machine learning models, and RMSE value for SVMR, ANN, GEP and DTR is 22.6, 31.6, 152.84 and 29.07, respectively. The GEP leads to the lowest level of accuracy among the other three machine learning models, yet, it is more accurate than EC3. The machine learning models were implemented in a user-friendly tool, which can be used for design purposes. • The paper studies behaviour of stainless steel CHS stub columns. • ANN, SVR, GEP and DTR machine learning algorithms are developed and validated. • Machine learning-based design approach for predicting the capacity is proposed. • A comparison with EC3 is presented to assess the performance of the models. [ABSTRACT FROM AUTHOR]

Developing a high-performing traffic incident-duration prediction model is considered a key component for evaluating the impact of these incidents on the roadway network. Various research studies have developed robust incident-duration prediction models. Still, they have faced many issues in providing an accurate prediction result due to the countless data modeling issues, such as complex correlations, highly skewed data distributions, heteroscedasticity, and outliers. This paper investigates the impact of feature optimization (FO) - a relatively new term encompassing two already-known topics: feature engineering (FE) and feature selection (FS) techniques - on the performance of several machine learning models developed for predicting incident durations. The models developed included multivariate linear regression, decision trees, support vector regressors, K-Nearest Neighbors regression, ensembles, and artificial neural networks. Various FO techniques have been used for each model to derive the massive traffic incidents dataset and repeat the prediction process. Our results show that the proposed filtering, wrapper, and embedded FS techniques can successfully reduce the number of features without sacrificing the prediction performance. Using log-normal transformation to deal with continuous data skewness, min-max normalization to deal with data variability, and principal component analysis (PCA) to reform the dataset into a smaller independent feature subset, FE techniques can enhance the accuracy of incident duration estimation over the assessed ML models. The best-performing FE technique was the PCA since performance improvements were observed across all developed ML models. The best-performing FS technique was the Recursive Feature Elimination, outperforming other tested techniques in reducing model complexity while maintaining model accuracy. [ABSTRACT FROM AUTHOR]

State estimation is a critical process in modern power system control centers that operate under strict real-time requirements. Among its key post-processing components are bad data detection and identification, with the latter being more complex and potentially compromising the system's real-time performance. This paper proposes a novel component in the state estimation process called Anomaly Detection and Identification Module (ADIM), whose goal is to detect anomalies, or erroneous data points, prior to the state estimation process. Our work presents a deep learning-based method that exhibits a commendable level of accuracy in detecting anomalies within a continuous stream of measurements. ADIM's capabilities are demonstrated through comprehensive experiments on a set of test cases that effectively encompass various network configurations, transformer types, and load scenarios. It is shown that ADIM can detect and identify anomalies effectively, significantly reducing the need for the bad data detection component and improving overall state estimation responsiveness. Our work lays the foundation for developing a detection- and identification-based anomaly system for power system measurements. [ABSTRACT FROM AUTHOR]

This paper addresses how physical knowledge can improve machine learning in process control. A data-driven tracking control framework using physics-informed neural networks (PINNs) and deep reinforcement learning (DRL) is proposed for dynamical systems, which is particularly important when iterations or repetitions of data collection experiments are limited or even not allowed. An indirect control approach is followed to accomplish this. First, a new process model is identified using PINNs trained from a model representing the experimental data collected from the plant. Then, the DRL-based control agent utilizes the identified PINN model to deliver an offline policy. We evaluate our framework for data-driven tracking control of nonisothermal CSTR, considering measurement noise and stochastic dynamics for closed-loop control. Our approach performed comparably to an NMPC without requiring a model to predict the process dynamics. • Physical knowledge was implemented to improve machine learning. • A indirect data-driven control approach was developed. • Physics-informed neural networks and deep reinforcement learning are combined. • The developed framework was tested against nonlinear model predictive control. [ABSTRACT FROM AUTHOR]

One of the most important features of a reliable power system is its capability to supply the demand continuously. This continuous supply has been maintained by the transient stability of the system against large disturbances. The study of this type of stability is examined through different indicators. One of the common indicators to evaluate the transient stability of the power system is the well-known Critical Clearing Time (CCT) index. Conventional methods for calculating CCT have presented good accuracy, however, their computational cost is very high which makes them not suitable for real-time applications and real large-scale networks. Considering their ability to feature extraction of big data, deep neural networks can be utilized as reliable tools to cover these deficiencies. In this regard, to cover the shortcomings of the conventional methods, this paper proposes a method based on deep Convolutional Neural Networks (CNN) to estimate the CCT index in real-time power system applications. Moreover, to analyze a realistic case, nonlinear limiters of the excitation systems which have a considerable effect on transient stability index are considered in this study. Thanks to the using of several deep layers and the comprehensive established database, the accuracy of proposed method is appropriately high. Numerical studies on IEEE standard networks as well as a real case in Iran Power Grid (IPG) represents the advantages of the proposed method. • A deep-learning-based convolutional neural network is designed to estimate CCT index of power system. • Nonlinear limiters of the excitation system are taken into account. • A real case study in Iran grid is considered for simulation. • As a result, the transient stability of system is accurately determined in on-line manner. [ABSTRACT FROM AUTHOR]

In this paper, an optimized backpropagation neural network (BPNN) prediction models have been proposed to estimate the concentration of titanium dioxide (TiO 2) nanomaterials required for the removal of Acid Black 24 dye, a type of textile dye, from industrial wastewater. The research gap addressed in this study lies in the investigation of various optimization algorithms for BPNN models and their profound impact on the accuracy of nanomaterial concentration predictions, thus contributing to the advancement of nanotechnology-based wastewater treatment research and its practical applications in industrial settings. Although several studies have predicted the amount of nanomaterial needed to degrade the dyes in industrial wastewater using BPNN, few have investigated the effects of optimized BPNN and how greatly the amount of degradation can vary. Therefore, six different optimization algorithms, including genetic algorithms (GAs), ant colony optimization (ACO), particle swarm optimization (PSO), evaluation strategy (ES), biogeography-based optimization (BBO), and population-based incremental learning algorithm (PBIL), are applied to optimize and evaluate the BPNN models. Experimental data obtained from chemical laboratory experiments are used to train and test the models, with different experiments conducted for various factors like TiO 2 , H 2 SO 4 , NaIO 4 , K 2 S 2 O 8 , dye, and silver nitrate. The performance of the optimized BPNN models is assessed using metrics such as mean squared error (MSE), root means squared error (RMSE), and R-squared (R2). The results demonstrate the effectiveness of the proposed models, with ACO yielding the best outcomes across all models. The R2 values for the TiO 2 , H 2 SO 4 , NaIO 4 , and K 2 S 2 O 8 models after optimization indicate perfect predictions, whereas the R2 values before optimization ranged from 0.888 to 0.984. PSO performs well for most models except for K 2 S 2 O 8 , GA improves most models except for the silver nitrate model, ES enhances only the Dye model, and PBIL does not affect the TiO 2 model. Overall, the optimized BPNN models exhibit impressive accuracy in estimating the TiO 2 concentration required for removing Acid Black 24 dye from industrial wastewater. [ABSTRACT FROM AUTHOR]

Cut-Test technique employs visual inspection of interior coloration, compartmentalization, and defects of beans for effective classification of cocoa beans. However, due to its subjective nature and natural variations in visual perception, it is intrinsically limited, resulting in disparities in verdicts, imprecision, discordance, and time-consuming and labor-intensive classification procedures. Machine Learning (ML) techniques have been proposed to address these challenges with significant results, but there is still a need for improvement. In this paper, we propose a color and texture extraction technique for image representation, as well as a generalized, less complex Neural Network model, to help improve the performance of machine classification of Cut-Test cocoa beans. A total of 1400 beans were classified into 14 grades. Experimental results on the equal cocoa cut-test dataset, which is the standard publicly available cut-test dataset, show that the novel extraction method combined with the developed Artificial Neural Networks provides a more homogeneous classification rate for all grades, obtaining 85.36%, 85%, 83%, and 83% for accuracy, precision, recall, and F1 measure, respectively. The proposed model outperforms other ML models, such as Support Vector Machines, Decision Trees, Random Forests, and Naïve Bayes, on the same dataset. Additionally, the proposed ANN model demonstrates relatively better generalization when compared with earlier work by Santos on the same dataset. The proposed techniques in this work are robust on the cut-test dataset and can serve as an accurate Computer-Aided Diagnostic tool for cocoa bean classification. • Classification of fourteen grades of fermented cocoa beans using machine learning, image processing, and computer vision techniques. • Novel color and texture extraction technique, efficient in extracting valuable features for cocoa beans classification. • Proposed regularized neural network model with high accuracy, precision and recall for fermentation-grade classification. • An alternative to the manual cut-test technique which employs visual inspection of interior coloration. • The model is fit for use in various cocoa and chocolate industries to minimize human involvement in classification tasks. [ABSTRACT FROM AUTHOR]

Process mining focuses on the analysis of event log data to build various process analytical capabilities. Predictive process analytics has emerged as one of such key capabilities and it uses machine learning techniques to construct process prediction models. In recent years, deep neural networks have gained increasing interest in process prediction since they can handle multi-dimensional sequential inputs with minimal information loss. However, they are considered black-box models and existing studies in explaining deep neural network-based process predictions rely on only event-level features for explanation. In this paper, we propose a new approach for generating explanations for process outcome predictions at multiple levels. The approach is underpinned by three different prediction models: a transparent model for generating global explanations based on case-level features, an attention-based deep neural network for generating local explanations based on event-level features, and a novel eXplainable Dual-learning Deep network (XD 2 -net) for generating local explanations based on case-level features. Using three publicly available datasets, we have tested the applicability of the approach and further examined the multi-level explanations generated by the approach through an elaborate case study. Unlike others, the design of our approach promotes the idea of leveraging the complementary capabilities of different models and utilizing their strengths, rather than focusing on model performance competition. This will contribute towards generating more comprehensive explanations that meet the needs of different end users and purposes in the future. [ABSTRACT FROM AUTHOR]

The importance of studying double-diffusive fluid flows along with the significance of non-Newtonian fluids have been well recognized in the fluid dynamics field for scientific and practical purposes. However, and given the ever-rising complexity of such non-linear flows, the conventional simulation methods are confronted with problems related to accuracy and required computation time and resources. As a result, the Machine Learning approach qualifies as a promising solution to said limitations. The present study investigates the application of Machine Learning models to study double-diffusive natural convection in rectangular cavities filled with non-Newtonian fluids. The flow is governed by four dimensionless parameters, namely: thermal Rayleigh number R a T , Lewis number L e , buoyancy ratio N , and power-law behavior index n , employed as input features. To precisely model fluid flow, three characteristics are predicted: flow intensity | ψ c | , average Nusselt number N u ¯ , and average Sherwood number S h ¯ using four machine learning models: Artificial Neural Networks (ANN), Random Forests (RF), Gradient Boosted Decision Trees (GBDT), and Extreme Gradient Boosting (XGBoost). The analysis of investigated models' fine-tuned architectures using various tools confirms the non-linear complexity of the problem and allows to explore and discuss in details the inner-workings of each model. The ANN predicts the test data with R 2 = 0. 9999 , R 2 = 0. 9996 , and R 2 = 0. 9999 followed by XGBoost with R 2 = 0. 9979 , R 2 = 0. 9802 , and R 2 = 0. 9979 , and that for | ψ c | , N u ¯ , and S h ¯ , respectively. The study shows the strong and correlated effects of R a T and n on the flow characteristics. The models' generalization is further examined using fluids with power-law behavior indexes outside the learning range. The present paper validates the choice of Machine Learning approach as a promising solution to model non-Newtonian double-diffusive fluid flows and encourages future works in this direction. [ABSTRACT FROM AUTHOR]

Production rescheduling is one of the most challenging problems in production management, in which some parameters, such as customer demand and job processing time, are subject to uncertainty during the planning horizon. This paper develops a scheduling and rescheduling method for the simultaneous lot-sizing and job shop scheduling problem, considering sequence-dependent setup times and capacity constraints. The objective is to find a trade-off between safety levels and production costs focusing on schedulability and optimality as the two most important performance indicators in the face of uncertainty. Therefore, a new adjustable formulation based on satisfiability modulo theories has been developed to tackle the demand and processing time uncertainty. Then, a combination of neural networks and a K-means based heuristic was applied to calibrate the model by determining the profitable value of the safety levels as a strategy to increase the robustness of the schedule. We also developed a Monte Carlo simulation to assess the performance of the proposed algorithm and compare it with other approaches addressed in the literature. The computational results show that the proposed algorithm is efficient and promising in protecting the schedulability of the model, taking the optimality criterion into account. • A rescheduling approach is proposed for a dynamic lot-sizing and scheduling problem. • A new formulation is presented under satisfiability modulo theories (SMT). • Two safety parameters are employed to deal with the uncertainties. • A data-driven algorithm is developed to estimate the safety parameters. [ABSTRACT FROM AUTHOR]

The artificial intelligence (AI) revolution offers significant opportunities to capitalise on the growth of digitalisation and has the potential to enable the 'system of systems' approach required in increasingly complex infrastructure systems. This paper reviews the extent to which research in economic infrastructure sectors has engaged with fields of AI, to investigate the specific AI methods chosen and the purposes to which they have been applied both within and across sectors. Machine learning is found to dominate the research in this field, with methods such as artificial neural networks, support vector machines, and random forests among the most popular. The automated reasoning technique of fuzzy logic has also seen widespread use, due to its ability to incorporate uncertainties in input variables. Across the infrastructure sectors of energy, water and wastewater, transport, and telecommunications, the main purposes to which AI has been applied are network provision, forecasting, routing, maintenance and security, and network quality management. The data-driven nature of AI offers significant flexibility, and work has been conducted across a range of network sizes and at different temporal and geographic scales. However, there remains a lack of integration of planning and policy concerns, such as stakeholder engagement and quantitative feasibility assessment, and the majority of research focuses on a specific type of infrastructure, with an absence of work beyond individual economic sectors. To enable solutions to be implemented into real-world infrastructure systems, research will need to move away from a siloed perspective and adopt a more interdisciplinary perspective that considers the increasing interconnectedness of these systems. • Literature on the use of AI in infrastructure sectors is reviewed. • Types of AI, trends across sectors and purposes within infrastructure are identified. • Findings have been evaluated using an analysis framework. • The future of AI and the 'Survey PaperInternet of Things' in infrastructure are discussed. [ABSTRACT FROM AUTHOR]

Froth Flotation is one of the fundamental processes used in iron ore attainment. It involves various physical–chemical reactions, and its multivariate control is not trivial. Traditionally, plant operators get quality measurements from laboratory analysis of sampled material, which can take hours to be published. In this paper, we present alternatives that eliminate both the necessity to periodically sample material as well as the delay associated with laboratory analysis: a predictive machine learning model working as a soft sensor. Two different classes of models were developed, exploring both deep and shallow approaches of machine learning, with a deep neural network architecture constructed for the particular task at hand. All models yielded accurate predictions, with results favoring the deep neural network when a larger volume of data is available. [ABSTRACT FROM AUTHOR]

Rapid fire progression, such as flashover, has been one of the leading causes for firefighter deaths and injuries in residential building environments. Due to long computational time of and the required prior knowledge about the fire scene, existing models cannot be used to predict the potential occurrence of flashover in practical firefighting applications. In this paper, a scene-agnostic model (FlashNet) is proposed to predict flashover based on limited heat detector temperature information up to 150 °C. FlashNet utilizes spatial temporal graph convolutional neural networks to effectively learn features from the limited temperature information and to tackle building structure variations. The proposed model is benchmarked against five different state-of-the-art flashover prediction models. Results show that FlashNet outperforms the existing flashover prediction models and it can reliably predict flashover 30 s preceding its occurrence with an overall accuracy of about 92.1%. Ablation study is carried out to examine the effectiveness of different key model components and geometric average adjacency matrix. The research outcomes from this study are expected to enhance firefighters' situational awareness in the fire scene, protecting them from hazardous fire environments and to pave the way for the development of data-driven prediction systems. [ABSTRACT FROM AUTHOR]

Driving behavior recognition has attracted wide attention as it can act as an important reference input of many vehicle intelligent control systems. In this paper, a real-time recognition of driver's longitudinal driving behavior is investigated by proposing a hybrid adaptive pattern recognition method. Firstly, a framework of integrated behavior recognition model is established consisting of two sub models to cluster and label the sample data, respectively. Secondly, a new fast high-stability clustering method is proposed to solve the problem of clustering samples with non-negligible noise. And the test results show the clustering process can be completed within a short time lower than 0.1 s with different number of cluster centers. Then, support vector machine and artificial neural network are employed and trained to construct a heuristic self-labeling approach to label the clustered sample automatically in real time with high accuracy (92.9%) under an experimental driving cycle generated from our real-vehicle test bench. Subsequently, the two established modules are integrated and offline trained by 96 thousand historical data, and employed to a 400-second-long online application under a smoothly-varying driving condition, and three further applications under extreme driving cycles with about 1000 s, respectively. Simulation results show that the proposed integrated model is capable of eliminating the interference of noise, and has a relatively stable performance of recognition for different driving cycles with different driving behaviors (92.7% of overall performance for commonly cycles, and greater than 91.2% for extreme cycles), enhancing the capacity for online application. [ABSTRACT FROM AUTHOR]

This paper proposes a Deep Reinforcement Learning (DRL) algorithm that expands the Random Neural Network (RNN) Reinforcement Learning (RL) method to include the previous learnings entirely from previous rewards, rather than only the actual one. The Random Neural Network weighs are updated with the current reward and the previous values, including time and memory. This addition makes DRL slower to make decisions, although it also increases its performance in some experiments. Several configurations to introduce DRL, such as sampling rate and memory duration, are also proposed and analysed in this article. The proposed DRL algorithm is included in a decision process that predicts trends: upward, downward and equal market directions in addition to values. Experimental results based on market prices demonstrate that the addition of Deep Learning to the Reinforcement Learning algorithm increases its performance slightly in some experiments; however, it also increases its computational cost. In random environments such as the stock market, it is preferable to make decisions based on the previous values (short memory) rather than historical records (long memory). [ABSTRACT FROM AUTHOR]

The purpose of feeder-level energy disaggregation is to decouple the net load measured at the feeder-head into various components. This technology is vital for power system utilities since increased visibility of controllable loads enables the realization of demand-side management strategies. However, energy disaggregation at the feeder level is difficult to realize since the high penetration of embedded generation masks the actual demand and different loads are highly aggregated. In this paper, the solar energy at the grid supply point is separated from the net load at first via either an unsupervised upscaling method or the supervised gradient boosting regression tree (GBRT) method. To deal with the uncertainty of the load components, the probabilistic energy disaggregation models based on multi-quantile recurrent neural network model (multi-quantile long short-term memory (MQ-LSTM) model and multi-quantile gated recurrent unit (MQ-GRU) model) are proposed to disaggregate the demand load into thermostatically controlled loads (TCLs), non-thermostatically controlled loads (non-TCLs), and non-controllable loads. A variety of relevant information, including feeder measurements, meteorological measurements, calendar information, is adopted as the input features of the model. Instead of providing point prediction, the probabilistic model estimates the conditional quantiles and provides prediction intervals. A comprehensive case study is implemented to compare the proposed model with other state-of-the-art models (multi-quantile convolutional neural network (MQ-CNN), quantile gradient boosting regression tree (Q-GBRT), Quantile Light gradient boosting machine (Q-LGB)) from training time, reliability, sharpness, and overall performance aspects. The result shows that the MQ-LSTM can estimate reliable and sharp Prediction Intervals for target load components. And it shows the best performance among all algorithms with the shortest training time. Finally, a transfer learning algorithm is proposed to overcome the difficulty to obtain enough training data, and the model is pre-trained via synthetic data generated from a public database and then tested on the local dataset. The result confirms that the proposed energy disaggregation model is transferable and can be applied to other feeders easily. • Feeder level energy disaggregation system to separate demand into sub-components. • A multi-quantile LSTM model that estimates the conditional quantiles. • A solar energy separation model to detect the behind-the-meter PV generation. • A transfer learning model to apply a pre-trained model to other location. [ABSTRACT FROM AUTHOR]