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Over the past decades, deep learning techniques have attracted increased attention from various research and industrial domains aligned with the development of Industry Internet-of-Things(IIoT). Specifically, with the advantage of data-driven methods, industrial organizations are seeking novel proactive strategies supported by analytic models to guarantee the quality of their production by observing degradation or predicting failure ahead of the occurrence of the component or asset. Predictive strategies are expected to promise the influence of unnecessary maintenance interruptions and mitigate the consequence of that, hence, extending the remaining useful life of products. This paper conducts a survey of the utilization of deep learning technologies on engineering applications where they provide satisfactory solutions with respect to specific data types or input signals. 106 primary papers are reviewed on deep learning–driven approaches which mainly explore five of the most popular architectures in the application of predictive maintenance. The main content of this paper summarizes the common advantages of each architecture and, accordingly, points out their limitations, as well as describes the application scopes of fully connected deep neural networks, convolutional neural networks, stacked autoencoders, deep belief networks, and deep recurrent neural networks. Based on the technique discussion for each of them, we intend to provide a comprehensive understanding and guidance of the appropriate usage of deep learning architectures to devise an effective predictive maintenance strategy for the scientific and industrial developers whose expertise lies in the prior domain knowledge of multi-source isomerization data. Moreover, the main content demonstrated the summarization of the decisive factor by which the incremental stages of the approaches were determined, fundamentally including the dataset specification, feature extraction, and the integration of deep learning approaches. [ABSTRACT FROM AUTHOR]

With the increasing complexity of the power grid, the precision of the thermal power units' execution of automatic generation control (AGC) commands is gradually increasing the impact on the online regulation of the power grid. The deviation between the actual output of thermal power units and the AGC command of the grid will not only affect the consumption of new energy output, but also endanger the safe operation of the grid. This paper introduces "deep learning" technology to solve the problem. Firstly, an AGC command execution effect identification and confidence evaluation algorithm (ACEEI-CEA) is proposed. The algorithm builds a neural network model to accurately predict the unit output and the confidence evaluation of the prediction results. Next, a high-dimensional input preprocessing strategy based on variational autoencoder (VAE) is proposed to reduce the dimensionality of the model input attributes, improving the convergence and accuracy of the model. Finally, an AGC optimal command fast inversion solution method (AOCFISM) is designed. This method transforms the unit output deviation problem into an objective optimisation problem. And improve the efficiency of solving the optimal AGC command value by constraining the unit command value. The calculation results show that the error of the prediction results of the model proposed in this paper is 5% lower than that of the traditional neural network. The difference between the output value of the optimal AGC command and the expected output value obtained is less than 0.5 MV, which can support AGC online decision-making.© 2017 Elsevier Inc. All rights reserved. • An AGC command execution effect identification and confidence evaluation algorithm (ACEEI-CEA) is proposed. • Results show the ACEEI-CEA performs well in command prediction compared to the state-of-the-art methods. • ACEEI-CEA achieves confidence assessment of predicted values. • The pre-processing strategy based on variational autoencoder shortens the training time of the model. • The AGC optimal command fast inversion solution method (AOCFISM) can obtain the AGC optimal command value. [ABSTRACT FROM AUTHOR]

This paper proposes a real-time state of health (SOH) estimation model based on a deep learning (DL) framework. The proposed model is a combination of two different architectures; a one-dimensional convolutional neural network (1D-CNN) and a bidirectional gated recurrent unit (BiGRU). The hybrid CNN-BiGRU uses the 1D CNN layers to extract pertinent features from input data and then relies on the BiGRU layers for sequence learning in both directions. To account for all SOH indicators, the proposed approach uses the current, voltage, and temperature measurements, which are readily obtainable from the electric vehicle's battery management system (BMS). This prevents the complex and time-consuming feature extraction used in most related papers. Since the hyperparameters have a significant impact on the performance of neural network models, a Bayesian Optimization (BO) technique based on the Gaussian Process (GP) was considered to tune the CNN-BiGRU model hyperparameters. Accordingly, the objective function was able to converge to a low Mean Squared Error (MSE) of 1.2 × 10 − 5 in just 19 iterations. Afterward, to verify the accuracy of the optimized model, a Lithium-ion battery dataset with several discharge profiles provided by the National Aeronautics and Space Administration (NASA) was used. The obtained results demonstrated the accuracy and robustness of the proposed method compared to other commonly used models. The CNN-BiGRU model yielded a Mean Absolute Error (MAE) of 2.080% and a root-mean-square error (RMSE) of 2.516% in the case of the battery set #C, referring to a set with 70 cycles already used at 24 °C. Additionally, the End of life (EOL) indicator error of zero cycles for the same data. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs), particularly quadrotors, have seen a surge in applications ranging from surveillance to delivery services. However, autonomous control remains a challenge due to the complexity of their dynamics and the need for real-time responsiveness. This paper addresses the significant problem of autonomous quadrotor control by introducing a novel learning method that integrates a deep neural network with advanced control techniques. Novelty of the work is distribution of different learning tasks in different layers of learning and combining the total learning system in a applicable UAV. The purpose is to enhance the quadrotor's ability to perform complex maneuvers autonomously, which is crucial for expanding its application scope. The proposed method begins with the acquisition of direction control using a fractional order proportional–integral–derivative (FOPID) controller. The learning parameters are refined through a fuzzy method, ensuring adaptability and precision. For the optimization of the FOPID parameters, we apply two stochastic algorithms: Genetic Algorithm and Particle Swarm Optimization, comparing their efficacy. Our approach successfully teaches the quadrotor to generate and control a flight path autonomously. The second stage of learning involves proximal policy optimization, enabling the execution of intricate maneuvers. The experimental setup includes a quadrotor on a test stand with three degrees of freedom, where we validate the method through rigorous simulation and physical tests. The results demonstrate that our method not only teaches the quadrotor complex maneuvers but also ensures robust and precise execution, marking a significant advancement in UAV control systems. The implications of this research extend to improving UAV reliability for critical applications, potentially transforming the landscape of autonomous aerial operations. [ABSTRACT FROM AUTHOR]

Rail surface defect detection is an essential part of railroad maintenance to prevent safety accidents. Recently, deep learning-based defect detection algorithms have shown impressive detection performance. However, the performance improvement from deep neural networks is based on a strong assumption that the training images have the same data distribution as the test images. This requirement is unrealistic in practical railroad surface defect detection. The appearance of rails varies significantly in different railroad sections due to changes in working conditions and maintenance status. In this paper, we propose a novel uncertainty-inspired unsupervised domain adaptation framework (UIUDA) to improve the generalization of deep learning models on data with distribution differences. Specifically, domain adversarial learning is used to align the feature distributions of training and test data. To mitigate the negative transfer caused by global alignment, we design a locally aligned domain adaptation strategy. In addition, self-training is introduced to refine the decision boundaries of the model in the test data. In this process, we propose an uncertainty-inspired evaluation method to remove noisy pseudo labels. We conducted domain adaptation experiments with RSDD and NEU-RSDD as the source domain and FRSD dataset as the target domain. The results show that UIUDA can effectively improve the generalization ability of the model, and the mIoU metric of defect segmentation is 80.17%. [ABSTRACT FROM AUTHOR]

Electricity demand prediction is crucial to ensure the operational safety and cost-efficient operation of the power system. Electricity demand has predominantly been predicted deterministically, while uncertainty analysis has been usually overlooked. To address this research gap, an integrated Neural Facebook Prophet (NFBP) model and Gaussian Kernel Density Estimation (KDE) model is proposed in this paper, as a way to obtain point and interval predictions of electricity demand, quantifying this way the uncertainty in the predictions. First, historical lagged data, created by utilizing the Partial Auto-correlation Function and Mutual Information Test, is applied to train a prediction model based on NFBP, Deep Learning (DL) as well as Statistical Models. Second, the model Prediction Errors (PE) are derived from the difference between actual and predicted values. A splitting strategy based on the mean and standard deviation of PE is proposed. Finally, electricity demand prediction intervals are obtained by applying Gaussian KDE on split PE. To verify the effectiveness of the proposed model, simulation studies are carried out for three prediction horizons on freely available datasets for the Bulimba sub-station in Southeast Queensland, Australia. Compared with DL models (Long-Short Term Memory Network and Deep Neural Network), the Root Mean Square Error of the NFBP model was reduced by 6.1% and 11.3% for 0.5-hr ahead, 22.7% and 26.3% for 6-hr ahead, and 31.8% and 29.9% for daily prediction. In addition, the Prediction Interval normalized Interval width is smaller in magnitude for the proposed NFBP-KDE model compared to other DL and Statistical models. [Display omitted] • A novel method is proposed for interval prediction of electricity demand. • Kernel density estimation with Gaussian function is applied to obtain interval prediction based on point prediction error. • Parameter of KDE distribution is optimized by minimizing Mean Integrated Square Error. • Deterministic and probabilistic metrics verify the superiority of the proposed prediction model. [ABSTRACT FROM AUTHOR]

The minimization of milling wear from prolonged contact between a tool and a workpiece is crucial in modern manufacturing, where high precision, efficiency, and productivity are required. Therefore, many researchers have developed systems for monitoring milling wear in real-time. However, while the adoption of multiple sensor signals can enhance the training effectiveness of the model, the complex setup process, data extraction, and feature analysis make it challenging to practically apply it for wear monitoring on production lines. Therefore, this study integrates various machining parameters of machine tools and a single type of sensor signal to extract features related to tool wear. These features are utilized as training data for an artificial neural network (ANN) model. The paper proposes a tool wear prediction ANN model based on automatic hyperparameter tuning and transfer learning, incorporating the architecture of Inception, DenseNet, and Layer Normalization. The network structure enhances the training efficiency for target features by expanding and reusing features from a single sensor's data. The combination of automatic hyperparameter tuning with a general optimizer optimizes the ANN architecture to achieve the best tool wear prediction model. Lastly, the proposed model is compared with other models using metrics including Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Coefficient of Determination (R 2) to evaluate the model performance. In the results, the proposed model shows the best performance among all the comparing models in all three metrics. The MAE is 0.03527, RMSE is 0.05135, and the R 2 is 0.9611. Compared to the common models such as Random Forest, the MAE and RMSE can be reduced by 43.2%, and 43.5% respectively, and a 9.5% R 2 performance increase. • The deep learning model is used for extended feature extraction for the proposed network for transfer learning. • This study proposes a fully connected layers structure which is similar to DenseNet with Layer Normalization at the end of each layer of dense connection. • The model optimization method is used to automatically adjust and optimize hyperparameters. • The experimental results show the proposed model which is capable of predicting tool wear in real-time by feeding the processing parameters has higher prediction accuracy than the other algorithms. [ABSTRACT FROM AUTHOR]

The use of Unmanned Aerial Vehicles (UAVs) has become increasingly popular in recent years, leading to a surge in research on this topic that is widely represented in the literature. They would be examined in accordance with the requirements of the sensors they house, their operating limits, and the output information they give. Since perception, planning, localization, and control are the major tasks of UAVs, an outstanding problem solver "Deep learning" is recently used in these systems. It is ideally suited for UAV applications because of its great capacity for learning representations from the complicated data received in actual situations. This paper provides an extensive review and in-depth analysis of current advancements in UAVs that have applications with DNN, as well as a quick summary of research and development during the previous ten years. In terms of UAVs flight monitoring, remote sensing and vision capability, and energy modeling, this survey provides a roadmap for understanding the sequential development of sophisticated UAVs by reviewing 173 retrieved papers. For this purpose, first of all, the main titles of the studies carried out for UAV applications were gathered under a taxonomy, and then information about the studies carried out in recent years related to these main research fields was given. In the last part of the study, attention was drawn to the still challenging points, such as autonomous fault detection, path planning, and onboard event detection, which are anticipated to be the future trends involving UAV applications. • An up-to-date and comprehensive overview on UAV sensor applications with DNNs. • A taxonomy is given for the researchers to easily examine the studies. • To easily examine the data sets, information about them is summerized. • Information about DNNs architectures end evaluation method is given. • A total of 173 studies, with 117 articles, and 54 conferences proceedings are surveyed. [ABSTRACT FROM AUTHOR]

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]

Due to the inevitable light absorption and scattering, underwater images always suffer from severe quality degradation, leading to significant performance decline for various maritime engineering-related applications. In recent years, deep neural networks (DNNs) have been proven to achieve high-quality enhancement of underwater images, which aims to extract and learn abstract features to exhibit superior performance. However, existing solutions often stack multilayer processing units to enrich features, which not only significantly burdens computational load but also encounters difficulties in fully utilizing and interacting between features at different depths. To address the above problems, this paper presents a cost-effective underwater image enhancement network via cascaded feature extraction, termed CFENet. Specifically, we develop a cost-effective cascaded structure for sufficient feature extraction and multiscale establishment of pixel-level long-range dependencies. In addition, we construct a dual-branch structure for effective feature fusion, so that the detailed texture and semantic information in the image can be simultaneously enhanced. Extensive experiments reveal the superiority of our proposed CFENet in both underwater image enhancement effects and computational complexity. Sufficient ablation study is conducted to demonstrate the effectiveness of each component in the network. [ABSTRACT FROM AUTHOR]

Owing to the development of deep learning, indoor personnel detection methods based on deep neural networks have been extensively investigated in recent years. However, more complex and deeper network structures may consume more computational resources, which seriously limits the deployment of large-scale deep neural networks on lightweight devices. In view of this, a lightweight anchor-free one-level feature indoor personnel detection method based on transformer (LAOF-IPDT) is proposed in this paper, which is deployed on two embedded devices and achieves good detection accuracy. In the feature extraction backbone, an enhanced cross-stage partial ghost convolution block with ghost convolution and channel shuffle is designed to extract shallow features. Additionally, to obtain more comprehensive global features in the high-level semantic structure, an embedded vision transformer cross-stage partial block is constructed by embedding a lightweight mobile-friendly vision transformer. For the path-aggregation neck, a lightweight feature pyramid network is proposed, which integrates multi-scale feature maps to obtain richer one-level feature representations. Subsequently, a dilated convolution group block is applied to expand the one-level feature receptive field and detection is accomplished using a one-level feature map. For the detection head, an anchor-free mechanism is applied to reduce the hyper-parameter interference of anchor boxes. Extensive experiments on four datasets indicated that the LAOF-IPDT outperforms other lightweight networks in terms of accuracy, speed, model parameters, and model size. For example, the frames per second of the LAOF-IPDT are 8.39 and 14.67 on CPU devices and Jetson Nano devices, respectively, and the mean average precision is 84.49%. [ABSTRACT FROM AUTHOR]

This paper introduces a novel parallel deep neural network for fault diagnosis of drilling pumps. It integrates the Convolutional Block Attention Module with the AlexNet and synchronizes with the Anomaly Transformer model to delve meticulously into both the time and time-frequency domains of signals. The method prioritizes the singular extraction and subsequent amalgamation of features, facilitating a detailed view of diagnostic data and mitigating the risk of interference and overfitting. The integration of the anomaly attention of the Anomaly Transformer with the features of the Convolutional Block Attention Module results in a distinctive dual attention mechanism that is critical to the methodology. This mechanism emphasizes essential features in both the time domain and the time-frequency domain, improving the accuracy of fault diagnosis. Verification with on-site data underscores the preeminence of the approach over existing models, signaling improved reliability and accuracy in diagnosing faults in drilling pumps. This meticulous approach offers promising advances in the study and application of fault diagnosis in energy equipment, demonstrating increased efficiency and refined accuracy. • A novel Parallel Deep Neural Network-Based Fault Diagnosis Framework is proposed. • Time and time-frequency domains of signals are extracted and fused for Fault Diagnosis. • Integrating the Transformer with CBAM to consider the essential features of signals. [ABSTRACT FROM AUTHOR]

Despite massive development in aerial robotics, precise and autonomous landing in various conditions is still challenging. This process is affected by many factors, such as terrain shape, weather conditions, and the presence of obstacles. This paper describes a deep learning-accelerated image processing pipeline for accurate detection and relative pose estimation of the UAV with respect to the landing pad. Moreover, the system provides increased safety and robustness by implementing human presence detection and error estimation for both landing target detection and pose computation. Human presence and landing pad location are performed by estimating the presence probability via segmentation. This is followed by the landing pad keypoints' location regression algorithm, which, in addition to coordinates, provides the uncertainty of presence for each defined landing pad landmark. To perform the aforementioned tasks, a set of lightweight neural network models was selected and evaluated. The resulting measurements of the system's performance and accuracy are presented for each component individually and for the whole processing pipeline. The measurements are performed using onboard embedded UAV hardware and confirm that the method can provide accurate, low-latency feedback information for safe landing support. • An approach to vision-based detection of landing target for autonomous UAV missions. • Near real-time performance of pipeline is confirmed on modern embedded GPU devices. • Increased safety and robustness by human presence detection and error estimation. • Pipeline's accuracy as measured using the OptiTrack system confirms its capability. [ABSTRACT FROM AUTHOR]

Network representation learning based on graph auto-encoder has been the mainstream technique for learning embeddings in low-dimensional space. Graph auto-encoders learn the network representations by minimizing the loss between the generated and actual samples. However, limited information on the contextual structure of nodes is captured during the generation of samples. Furthermore, the dimensionality of the features is altered in the process of feature aggregation, resulting in impaired feature information in the network. In order to solve these two problems, normalized auto-encoder based on biased walk for network representation learning is proposed. This model can combine the advantages of shallow neural network and deep neural network to solve these two problems, and contains two key design components. One is the biased walk regularization module, a regularization term of shallow neural network, which captured broader and deeper context structure information of nodes. The other is the normalized regularization module of features, a regularization term of deep neural network, which smoothed the feature difference of nodes, and thus improved the feature information aggregation capability of convolution layer. This paper conducted experiments on three datasets Cora, CiteSeer and PubMed and compared them with other state-of-the-art network representation learning models. The results show that the proposed model can improve the link prediction results by 0.7% on AUC and 0.9% on AP to the state-of-the-art algorithms. In addition, the ablation experiments are conducted for two key design components, which can demonstrate the necessity of the proposed two regularization terms. [Display omitted] • A novel network representation learning model on graph auto-encoder is proposed. • The biased walk regularization module learns a wider range of contextual structural information. • The normalized regularization module improves the feature information aggregation capability. [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]

Image super resolution, which obtains high resolution output from a corresponding low resolution image, has been challenging due to inefficiencies in establishing complex high dimensional mapping for massive raw data. Single image super resolution can dramatically improve performance compared with current algorithms due to the proliferation of deep learning systems. However, convolutional kernels in deep neural networks are locally connected to the input feature maps, whereas features only interact with their local neighbors. Mutual interference between local features without considering global features causes blurring and staircase effects. This paper proposes an end-to-end single image super resolution model by simultaneously separating high and low frequency features and learning adaptive local and global features to effectively reconstruct the high resolution image by minimizing the loss of edges and texture information. The image frequency decomposition module with an attention block emphasizes self-representative low frequency features to separate high and low frequency features. The bidirectional global and local feature exchange module extracts global and local features from the separated network and fuses each feature to improve performance. Quantitative and qualitative analyses for the proposed frequency adaptive network validated that the proposed method is stable and robust against blurring and staircase effects by separating texture and the structure into adaptive and shared networks. [ABSTRACT FROM AUTHOR]

Activation functions play an important role in deep learning models by introducing non-linearity to the output of a neuron, enabling the network to learn complex patterns and non-linear relationships in data and make predictions on more complex tasks. Deep learning models' most commonly used activation functions are Purelin, Sigmoid, Tansig, Rectified Linear Unit (ReLU), and Exponential Linear Unit (ELU), which exhibit limitations such as non-differentiability, vanishing gradients, and neuron inactivity with negative values. These functions are typically defined over a finite range, and their outputs are integers or real numbers. Using fractional calculus in designing activation functions for neural networks has shown promise in improving the performance of deep learning models in specific applications. These activation functions can capture more complex non-linearities than traditional integer-order activation functions, improving performance on tasks such as image classification and time series prediction. This paper focuses on deriving and testing linear and non-linear fractional-order forms of activation functions and their variants. The linear activation function includes Purelin. In contrast, the non-linear activation functions are Binary Step, Sigmoid, Tansig, ReLU, ELU, Gaussian Error Linear Unit (GELU), Hexpo, and their variants. Besides, the standard formula has been implemented and used in developing the fractional-order linear activation function. Furthermore, various expansion series, such as Euler and Maclaurin, have been used to design non-linear fractional-order activation functions and their variants. The single- and multi-layer fractional-order neural network models have been developed using the designed fractional-order activation functions. The simulation study uses developed fractional-order neural network models for predicting the Texas wind turbine systems' generated power. The performance of single and multi-layer fractional-order neural network models has been evaluated by changing the activation functions in the hidden layer while keeping the Purelin function constant at the output layer. Experiments on neural network models demonstrate that the designed fractional-order activation functions outperform traditional functions like Sigmoid, Tansig, ReLU, ELU, and their variants, effectively addressing limitations. • Activation functions are derived into fractional functions using fractional calculus. • The fractional neural network models have been made by adopting derived functions. • A Case study has been done on the Texas wind turbine with the models to predict the power. • The models' performance has been evaluated using the fractional functions. [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]

This paper proposes a spatiotemporal graph neural network capable of effective representation learning of the spatiotemporal interrelationships and interdependencies of in-situ observation data from multiple locations for multivariate multi-step ahead time-series forecasting. The propose model is largely composed of graph learning, spatial encoder, and temporal decoder, and ablation studies on variants of the three modules and comparative experiments with state-of-the-art deep neural networks for sequence modeling were also performed extensively. The proposed model showed improved predictability than conventional numerical model-based approaches or state-of-the-art models by applying consecutive multi-step ahead time-series prediction of sea surface temperature at multiple locations along the coast. For more rigorous performance evaluation, not only the overall performance of the test data, but also the performance of extreme cases included in the test data based on historical records were separately assessed. The prediction rationales were also presented through quantified relative contributions between neighbor locations using the trained adjacency matrix obtained through graph learning. The results showed that it is well consistent with the ocean physics and geographical domain knowledge, demonstrating the feasibility and reliability of the proposed method. Therefore, the proposed method shows sufficient potential to be used as a scientific tool for decision-making in extreme events such as marine heat waves or for operational ocean forecasting. [ABSTRACT FROM AUTHOR]

Recently, facial expression recognition (FER) methods have achieved significant progress. However, FER is still challenged by factors such as uneven illumination and low-quality expression images. Exploring the potential of facial expression features to achieve robust FER is particularly important. Inspired by the Transformer's excellent performance in modeling long-range dependencies and in vision tasks, this paper proposes a Transformer Block Enhancement Module (TBEM) for enhancing the feature representation of facial expressions. The proposed module contains a Channel Enhancement (CE) block and a Spatial Enhancement (SE) block. The CE block can adaptively enhance the expression features on the channel dimension by effectively leveraging the channel dependency information, while the SE block enhances the expression features on the spatial dimension by integrating the spatial dependency information. TBEM can output a more robust expression representation by combining CE and SE. The proposed artificial intelligence learning module greatly improves the recognition accuracy of FER engineering tasks. To further illustrate the application of TBEM in real-world FER engineering, three engineering problems are used for verification. Extensive experiments demonstrate that the proposed method improves FER performance by focusing on more accurate decisional features and can be easily embedded into regular convolutional neural network models to help them improve the accuracy on FER tasks by about 2.64%–3.03%. The results show the proposed method achieves accuracies of 90.57% on FERPlus, 89.41% on RAFDB basic and 68.43% on RAFDB compound, respectively. The proposed method also provides a meaningful reference for further research on applying Transformer to other tasks. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel deep learning approach for predicting saddle points in stochastic two-player zero-sum games. Our method combines neurodynamic optimization and deep neural networks. First, we model the stochastic two-player zero-sum game as an ordinary differential equation (ODE) system using neurodynamic optimization. Second, we develop a neural network to approximate the solution to the ODE system, which includes the saddle point prediction for the game problem. Third, we introduce a specialized algorithm for training the neural network to enhance the accuracy of the saddle point prediction. Our experiments demonstrate that our model outperforms existing approaches, yielding faster convergence and more accurate saddle point predictions. [ABSTRACT FROM AUTHOR]

Accurate multi-node load forecasting is the key to the safe, reliable, and economical operation of the power system. However, the dynamic nature of load and the coupling nature of networks are difficult to extract, making consistent and accurate forecasting of node load rather difficult. In this regard, this paper proposes a soft sharing multi-task deep learning method for multi-node load forecasting in the power system. It has the following aspects: (1) Considering the coupling characteristics of the node network, a multi-modal feature module, based on the inception strategy and gated temporal convolutional network (GTCN), is firstly designed to explore the coupling features implied in the node load data. (2) A novel multi-objective neural network model is proposed to achieve simultaneous prediction of multi-node load by integrating the multi-modal feature module and gated recurrent unit (GRU). For sharing the learning information of sub-networks, this paper uses the soft sharing mechanism to capture load features, which can better optimize the prediction task for each node load simultaneously. Load data from the New Zealand distribution network and AEMO are used to compare the proposed model's performance in various scenarios using regression metrics such as mean absolute percentage error (MAPE), Weighted Mean Accuracy (WMA), root mean squared logarithmic error (RMSLE), and Diebold–Mariano (DM). The simulation results show that the proposed method can explore the spatial–temporal coupling characteristics in multi-node load data. Compared with existing state-of-the-art multi-node load prediction methods, our proposed method's MAPE decrease 17.04% and 3.92% in Non-aggregation and Aggregation situations. • A novel deep multi-task learning method is proposed for the multi-node forecasting problem. • An IGTCN module is designed to extract the coupling features from different node loads. • The multi-task deep neural network exhibits the accuracy of multi-node load forecasting. [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]

In this fast-moving digital era, millions of images are added to repositories every millisecond. These images are context-rich images with ample underlying data that are extracted and analyzed by deep neural networks for innumerable research purposes. Significant breakthroughs in deep neural networks are harvesting multi-fold benefits and reaching higher accuracy and prediction rates. Though there is vertical progress in deep learning models there is always a threat to security which disintegrates the strength of deep neural networks. Archived images that an adversary manipulates will still look indistinguishable to the user. Such manipulations called adversarial attacks that trick deep learning models into making wrong predictions are on the rise nowadays. This paper reviews the varied types of adversarial attacks shedding light on their origin, their progressive advancement in multidimensions, along with their applications and challenges. [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]

In this paper, a deep learning algorithm based on Deep Galerkin method (DGM) is presented for the approximate solution of the generalized Burgers–Huxley equation (gBHE), and generalized Huxley's equation (gHE). In this method, a deep neural network (DNN) is used for approximating the solution without generating mesh grid, which satisfies the differential operator, boundary and initial conditions. DNN is trained on randomly selected batches of time and space points, thus helping to avoid forming a mesh. Adam optimizer is used for optimizing the parameters of the DNN. Further, the convergence of the cost function and convergence of the neural network to the exact solution is demonstrated. This method shows very encouraging results which have been compared with recent methods such as: A fourth order improved numerical scheme (FDS4), Adomain-decomposition method (ADM), Modified cubic B-spline differential quadrature method (MCB-DQM), Variational iteration method (VIM), and others. [ABSTRACT FROM AUTHOR]

There are two types of voice conversion methods: statistical and deep learning-based. Although statistical methods can train with limited data, they face challenges, including spectral oversmoothing and time-domain discontinuity. On the other hand, extensively researched deep learning-based methods rely primarily on massive amounts of data, which limits their practical applicability. Given that voice conversion is an engineering problem with limited training data, it is crucial to develop techniques that can produce satisfactory results in terms of quality and similarity in the absence of a large amount of data. This paper proposes a voice conversion model based on stochastic variational deep kernel learning (SVDKL), which works with limited training data. The model allows the use of both the deep neural network's expressive capability and the high flexibility of the Gaussian process, which is a Bayesian and non-parametric method. The model utilizes a cascade of a deep neural network and a conventional kernel as the covariance function, which enables it to estimate non-smooth and more complex functions. Furthermore, the model's sparse variational Gaussian process solves the scalability problem of exact inference and enables the learning of a global mapping function for the entire acoustic space. One of the most important aspects of the proposed scheme is that the model parameters are trained using marginal likelihood optimization, which takes into account both data fitting and model complexity. Considering model complexity reduces the training data by increasing the robustness to overfitting. To evaluate the proposed scheme, we examined the model's performance with as little as approximately 80 s of training data. The results indicated that our method obtains a higher mean opinion score, smaller spectral distortion, and better preference tests than the state-of-the-art limited data methods. [ABSTRACT FROM AUTHOR]

Early damage identification and continuous system monitoring save dramatically maintenance costs and increase the lifespan of priceless structures. Convolutional neural networks (CNNs) have attracted the attention of the structural health monitoring (SHM) community in recent years due to their great potential for identifying underlying data patterns. However, employing two-dimensional convolutional layers in a CNN necessitates the use of strong computing resources. Therefore, based on the present state-of-the-art technical solutions, a two-dimensional CNN is not suitable for real-time SHM applications with stand-alone processing units. One-dimensional convolutional networks (1D-CNN) have recently been employed in Ultrasonic Guided Wave-based (UGW-based) damage detection to address the aforementioned disadvantage. In this paper, a methodology for damage assessment at three levels – detection, localization, and characterization – based on 1D-CNN is put forward. Furthermore, the sequence length of the time-domain signals is significantly shortened by the application of a novel approach for processing them. Additionally, it is shown to what extend this method can improve the distinguishability between datapoints obtained from various damage scenarios. Consequently, by reducing the dimensionality of the problem, the proposed approach significantly reduces the memory usage of the classification algorithm. Experimental measurements as well as Numerical simulations, in which various damage scenarios such as corrosion, circular hole and cracks have been considered, are carried out to evaluate the efficacy of the proposed algorithm. It is shown that the suggested approach has benefits in terms of true classification rate of instances (above 93 percent for detection, localization, and characterization), computing time, in-situ monitoring, and noise resilience. [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]

Deep convolutional neural networks have made unprecedented achievements in image super-resolution (SR) and dominated the field due to their remarkable performance. When the degradation pattern of the test images is inconsistent with the training images, it leads to poor model performance. For example, the degradation could happen after a dimensional stretching. In this case, the most common method is to take blurry, noise, and low-resolution (LR) images and reconstructs SR images by degradation estimation. However, the SR results for this method are highly dependent on the estimation accuracy. To overcome the difficulty with the degradation estimation, this paper designs a degradation representation attention network (DRAN) for image SR. In which, we propose the use of a simple Siamese representation learning to extract the degradation information from various LR images. Specifically, DRAN distinguishes degradation information instead of performing degradation estimation, which can greatly reduce the difficulty. In other words, DRAN can avoid pixel-level operations, transform degradation computation problems into degradation classification problems and flexibly process LR images through degradation representation learning. Finally, DRAN also introduces a channel attention mechanism to enhance the performance of SR. Experimental results show that the proposed scheme can distinguish different degradation modes and obtain accurate degradation information. Meanwhile, experiments on synthetic and real images show that the DRAN achieves remarkable performance on blind SR tasks with good visual effects. [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]