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With the rapid development of semiconductor technology, the reduction in device operating voltage and threshold voltage has made integrated circuits more susceptible to the effects of particle radiation. Moreover, as process sizes decrease, the impact of charge sharing effects becomes increasingly severe, with soft errors caused by single event effects becoming one of the main causes of circuit failures. Therefore, the study of sensitivity evaluation methods for integrated circuits is of great significance for promoting the optimization of integrated circuit design, improving single event effect experimental methods, and enhancing the irradiation reliability of integrated circuits. In this paper, we first established a device model for the charge sharing effect and simulated it under reasonable conditions. Based on the simulation results, we then built a neural network model to predict the charge amounts in primary and secondary devices. We also propose a comprehensive automated method for calculating soft errors in unit circuits and validated it through TCAD simulations, achieving an error margin of 2.8–4.3%. This demonstrated the accuracy and effectiveness of the method we propose. [ABSTRACT FROM AUTHOR]

Recently, data-driven approaches have become the dominant solution for prediction problems in agricultural industries. Several deep learning models have been applied to crop yield prediction in smart farming. In this paper, we proposed an efficient hybrid deep learning model that coordinates the outcomes of a classification model and a regression model in deep learning via the shared layers to predict the rice crop yield. Three statistical analyses on the features, including Pearson correlation coefficients (PCC), Shapley additive explanations (SHAP), and recursive feature elimination with cross-validation (RFECV), are proposed to select the most relevant ones for the predictive goal to reduce the model training time. The data preprocessing normalizes the features of the collected data into specific ranges of values and then reformats them into a three-dimensional matrix. As a result, the root-mean-square error (RMSE) of the proposed model in rice yield prediction has achieved 344.56 and an R-squared of 0.64. The overall performance of the proposed model is better than the other deep learning models, such as the multi-parametric deep neural networks (MDNNs) (i.e., RMSE = 370.80, R-squared = 0.59) and the artificial neural networks (ANNs) (i.e., RMSE = 550.03, R-squared = 0.09). The proposed model has demonstrated significant improvement in the predictive results of distinguishing high yield from low yield with 90% accuracy and 94% F1 score. [ABSTRACT FROM AUTHOR]

The construction sector in Greece has been developing radically in the field of building renovations. The foremost problem for projects in the building construction industry is producing an accurate and reliable cost estimate at the onset of construction. The artificial neural network (AΝΝ) approach, using data available at the early stages of the project, can help resolve or prevent any kind of difficulty that could make the successful completion of a building less likely. ANNs have been highly efficient in gaining results which could prevent the failure of building constructions projects. The ultimate goal is to highlight the usefulness of the adoption of ANNs models to predict the final cost of a building renovation project. Thus, construction companies could avoid financial failure, provided that the gap between cost prediction and final cost for renovation projects is minimized. This paper presents an artificial neural network (ANN) approach for predicting renovation costs in Greek construction projects. The study, based on a comprehensive literature review and real renovation data from construction companies, employs IBM SPSS Statistics software to build, train, and test the ANN model. The model, which uses initial cost, estimated time, and initial demolition cost as inputs, is based on the radial basis function procedure. The model presents high performance with up to 2% sum of squares error and near zero relative error, demonstrating the ANN's effectiveness in estimating total renovation costs. [ABSTRACT FROM AUTHOR]

Real-time detection of workers is crucial in construction safety management. Deep learning-based detecting methods are valuable, but always challenged by the possibility of target missing or identity errors under complex scenarios. To address these limitations, previous research depended on re-training for new models or datasets, which are prohibitively time-consuming and incur high computing demands. However, we demonstrate that the better detecting model might not rely on more re-training of weights; instead, a training-free model can achieve even better performance by integrating head information. In this paper, a new head-detecting branch (55 MB) is added to the Keypoint Region-based Convolutional Network (Keypoint R-CNN, 226 MB) without altering its original weights, allowing for a less occluded head to aid in body detection. We also deployed motion information and anthropometric data through a post-processing module to calculate movement relationships. This study achieved an identity F1-score (IDF1) of 97.609%, recall (Rcll) of 98.173%, precision (Prcn) of 97.052%, and accuracy of 95.329% as a state-of-the-art (SOTA) method for worker detection. This exploration breaks the inertial attitudes of re-training dependency and accelerates the application of universal models, in addition to reducing the computational difficulty for most construction sites, especially in scenarios with an insufficient graphics processing unit (GPU). More importantly, this study can address occlusion challenges effectively in the worker detection field, making it of practical significance. [ABSTRACT FROM AUTHOR]

When new rail stations or lines are planned, long-term planning for decades to come is required. The short-term passenger flow prediction is no longer of practical significance, as it only takes a few factors that affect passenger flow into consideration. To overcome this problem, we propose several long-term factors affecting the passenger flow of rail transit in this paper. We also create a visual analysis of these factors using ArcGIS and construct a long-term passenger flow prediction model for rail transit based on a class neural network using an SPSS Modeler. After optimizing relevant parameters, the prediction accuracy reaches 94.6%. We compare the results with other models and find that the neural network model has a good performance in predicting long-term rail transit passenger flow. Finally, the factors affecting passenger flow are ranked in terms of importance. It is found that among these factors, bicycles available for connection have the biggest influence on the passenger flow of rail stations. [ABSTRACT FROM AUTHOR]

The main goal of this work is to combine the usage of the numerical homogenization technique for determining the effective properties of representative volume elements with artificial neural networks. The effective properties are defined according to the classical laminate theory. The purpose is to create and train a rapid surrogate model for the quick calculation of the mechanical properties of hollow concrete slabs. First, the homogenization algorithm was implemented, which determines membrane, bending and transverse shearing properties of a given parametrized hollow-core precast slab reinforced with steel bars. The algorithm uses the finite element mesh but does not require a formal solution of the finite element method problem. Second, the learning and training artificial intelligence framework was created and fed with a dataset obtained by optimal Latin hypercube sampling. In the study, a multilayer perceptron type of artificial neural network was used. This allows for obtaining rapid calculations of the effective properties of a particular hollow-core precast slab by using a surrogate model. In the paper, it has been proven that such a model, obtained via complex numerical calculations, gives a very accurate estimation of the properties and can be used in many practical tasks, such as optimization problems or computer-aided design decisions. Above all, the efficient setup of the artificial neural network has been sought and presented. [ABSTRACT FROM AUTHOR]

This paper presents the application of self-supervised deep contrastive learning in clustering signals detected in the wideband RF spectrum, presented in the form of spectrograms. Radio clustering is a method of searching for similar signals within the analyzed part of the radio spectrum. Typically, it is based on one or several specific parameters processed from the signal in a given channel. The authors propose a slightly different, innovative approach; thanks to the self-supervised learning of neural networks, there is no need to define specific parameters, and the feature vector, enabling comparison of Euclidean distances between signals, is generated by a deep neural network trained using a contrastive loss function on a dataset containing different radio modulations. The authors describe self-supervised solutions based on contrastive learning and the methods of signal segmentation and augmentation. The training process utilizes a custom database and the Resnet-50 network with a contrastive cost function. Radio clustering is used for autonomous spectrum analysis across wide frequency ranges and enables, among other things, the detection of tactical radio stations operating with widely dispersed frequency-hopping or a significant reduction in computational power required for real-time analysis of a large number of radio signals. [ABSTRACT FROM AUTHOR]

In the era of Industry 3.0, product fault detection systems became important auxiliary systems for factories. These systems efficiently monitor product quality, and as such, substantial amounts of capital were invested in their development. However, with the arrival of Industry 4.0, high-volume low-mix production modes are gradually being replaced by low-volume high-mix production modes, reducing the applicability of existing systems. The extent of investment has prompted factories to seek upgrades to tailor existing systems to suit new production modes. In this paper, we propose an approach to upgrading based on the concept of transfer learning. The key elements are (1) using a framework with a basic model and an add-on model rather than fine-tuning parameters and (2) designing a radial basis function deep neural network (RBF-DNN) to extract important features to construct the basic and add-on models. The effectiveness of the proposed approach is verified using real-world data from a spring factory. [ABSTRACT FROM AUTHOR]

Pore pressure prediction is a critical parameter in petroleum engineering and is essential for safe drilling operations and wellbore stability. However, traditional methods for pore pressure prediction, such as empirical correlations, require selecting appropriate input parameters and may not capture the complex relationships between these parameters and the pore pressure. In contrast, artificial neural networks (ANNs) can learn complex relationships between inputs and outputs from data. This paper presents a new empirical correlation for predicting pore pressure using ANNs. The proposed method uses 42 datasets of well log data, including temperature, porosity, and water saturation, to train ANNs for pore pressure prediction. The trained model, with the Bayesian regularization backpropagation function, predicts the pore pressure with an average absolute percentage error (AAPE) and correlation coefficient (R) of 4.22% and 0.875, respectively. The trained ANN is then used to develop a new empirical correlation that relates pore pressure to the input parameters considering the weights and biases of the optimized ANN model. To validate the proposed correlation, it is applied to a blind dataset, where the model successfully predicts the pore pressure with an AAPE of 5.44% and R of 0.957. The results show that the proposed correlation provides accurate and reliable predictions of pore pressure. The proposed method provides a robust and accurate approach for predicting pore pressure in petroleum engineering applications, which can be used to improve drilling safety and wellbore stability. [ABSTRACT FROM AUTHOR]

The production of tight gas wells decreases rapidly, and the traditional method is difficult to accurately predict the production of tight gas wells. At present, intelligent algorithms based on big data have been applied in oil and gas well production prediction, but there are still some technical problems. For example, the traditional error back propagation neural network (BP) still has the problem of finding the local optimal value, resulting in low prediction accuracy. In order to solve this problem, this paper establishes the output prediction method of BP neural network optimized with the sparrow search algorithm (SSA), and optimizes the hyperparameters of BP network such as activation function, training function, hidden layer, and node number based on examples, and constructs a high-precision SSA-BP neural network model. Data from 20 tight gas wells, the SSA-BP neural network model, Hongyuan model, and Arps model are predicted and compared. The results indicate that when the proportion of the predicted data is 20%, the SSA-BP model predicts an average absolute mean percentage error of 20.16%. When the proportion of forecast data is 10% of the total data, the SSA-BP algorithm has high accuracy and high stability. When the proportion of predicted data is 10%, the mean absolute average percentage error is 3.97%, which provides a new method for tight gas well productivity prediction. [ABSTRACT FROM AUTHOR]

To aid cocrystal screening, a deep forest-based cocrystal prediction model was developed in this study using data from the Cambridge Structural Database (CSD). The positive samples in the experiment came from the CSD. The negative samples were partly from the failure records in other papers, and some were randomly generated according to specific rules, resulting in a total of 8576 pairs. Compared with the models of traditional machine learning methods and simple deep neural networks models, the deep forest model has better performance and faster training speed. The accuracy is about 95% on the test set. Febuxostat cocrystal screening was also tested to verify the validity of the model. Our model correctly predicted the formation of cocrystal. It shows that our model is practically useful in practice. [ABSTRACT FROM AUTHOR]

In the past decade, artificial neural networks (ANNs) have been widely employed to address many problems. Despite their powerful problem-solving capabilities, ANNs are susceptible to a significant risk of stagnation in local minima due to using backpropagation algorithms based on gradient descent (GD) for optimal solution searching. In this paper, we introduce an enhanced version of the reptile search algorithm (IRSA), which operates in conjunction with an ANN to mitigate these limitations. By substituting GD with IRSA within an ANN, the network gains the ability to escape local minima, leading to improved prediction outcomes. To demonstrate the efficacy of IRSA in enhancing ANN's performance, a numerical model of the Nam O Bridge is utilized. This model is updated to closely reflect actual structural conditions. Consequently, damage scenarios for single-element and multielement damage within the bridge structure are developed. The results confirm that ANNIRSA offers greater accuracy than traditional ANNs and ANNRSAs in predicting structural damage. [ABSTRACT FROM AUTHOR]

The issue contains five papers, all relevant to the core intersectional topic of AI and complex systems. This Special Issue, titled "Artificial Intelligence and Complex Systems", was created with the goal of collecting the latest research, both foundational and application-orientated, at the intersection of AI and complex systems, ranging across real-world domains as varied as education and fraud detection. The discussion in the paper also suggests that context is fundamentally necessary to understand complex systems using AI methodologies. [Extracted from the article]

A fuel cell is an energy conversion device that utilizes hydrogen energy through an electrochemical reaction. Despite their many advantages, such as high efficiency, zero emissions, and fast startup, fuel cells have not yet been fully commercialized due to deficiencies in service life, cost, and performance. Efficient evaluation methods for performance and service life are critical for the design and optimization of fuel cells. The purpose of this paper was to review the application of common machine learning algorithms in fuel cells. The significance and status of machine learning applications in fuel cells are briefly described. Common machine learning algorithms, such as artificial neural networks, support vector machines, and random forests are introduced, and their applications in fuel cell performance prediction and optimization are comprehensively elaborated. The review revealed that machine learning algorithms can be successfully used for performance prediction, service life prediction, and fault diagnosis in fuel cells, with good accuracy in solving nonlinear problems. Combined with optimization algorithms, machine learning models can further carry out the optimization of design and operating parameters to achieve multiple optimization goals with good accuracy and efficiency. It is expected that this review paper could help the reader comprehend the state of the art of machine learning applications in fuel fuels and shed light on further development directions in fuel cell research. [ABSTRACT FROM AUTHOR]

Digital signature technology is essential for ensuring the authenticity and unforgeability of transactions in a private blockchain framework. In some scenarios, transactions require verification from multiple parties, each of whom needs to authenticate different parts of the transaction. To address this issue, researchers have developed multi-party ECDSA (Elliptic Curve Digital Signature Algorithm) signature schemes. However, these schemes either need to consider the authentication of different parts of the transaction or generate an aggregated signature. This paper proposes a novel solution that combines functional signatures and multi-party ECDSA signatures to create a multi-party functional signature for private blockchains. Compared to previous constructions, the proposed scheme ensures that each part of the transaction is verified. Furthermore, when the aggregate signature of the entire transaction cannot be verified, this scheme identifies the specific part of the transaction for which the signature authentication fails instead of rejecting the entire transaction. This paper uses a smart contract to securely deploy the proposed scheme and authenticate the f in functional signatures. The constructed scheme also provides security under the existential unforgeability of the ECDSA signature, even if n − 1 parties are corrupted, assuming a total of n parties. The scheme of this paper successfully conducted experiments on a personal computer, with three users taking approximately 343 ms, six users taking 552 ms, and nine users taking 791 ms. [ABSTRACT FROM AUTHOR]

This research paper presents a novel digital color image encryption approach that ensures high-level security while remaining simple and efficient. The proposed method utilizes a composite key r and x of 128-bits to create a small in-dimension private key (a chaotic map), which is then resized to match the color matrix dimension. The proposed method is uncomplicated and can be applied to any image without any modification. Image quality, sensitivity analysis, security analysis, correlation analysis, quality analysis, speed analysis, and attack robustness analysis are conducted to prove the efficiency and security aspects of the proposed method. The speed analysis shows that the proposed method improves the performance of image cryptography by minimizing encryption–decryption time and maximizing the throughput of the process of color cryptography. The results demonstrate that the proposed method provides better throughput than existing methods. Overall, this research paper provides a new approach to digital color image encryption that is highly secure, efficient, and applicable to various images. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel attack on image encryption for privacy-preserving deep neural networks (DNNs). Although several encryption schemes have been proposed for privacy-preserving DNNs, existing cipher-text-only attacks (COAs) have succeeded in restoring visual information from encrypted images. Image encryption using the Vision Transformer (ViT) is known to be robust against existing COAs due to the operations of block scrambling and pixel shuffling, which permute divided blocks and pixels in an encrypted image. However, the correlation between blocks in the encrypted image can still be exploited for reconstruction. Therefore, in this paper, a novel jigsaw puzzle solver-based attack that utilizes block correlation is proposed to restore visual information from encrypted images. In the experiments, we evaluated the security of image encryption for privacy-preserving deep neural networks using both conventional and proposed COAs. The experimental results demonstrate that the proposed attack is able to restore almost all visual information from images encrypted for being applied to ViTs. [ABSTRACT FROM AUTHOR]

In this paper, we present a new approach to improve tabular datasets by applying the lottery ticket hypothesis to tabular neural networks. Prior approaches were required to train the original large-sized model to find these lottery tickets. In this paper we eliminate the need to train the original model and discover lottery tickets using networks a fraction of the model's size. Moreover, we show that we can remove up to 95% of the training dataset to discover lottery tickets, while still maintaining similar accuracy. The approach uses a genetic algorithm (GA) to train candidate pruned models by encoding the nodes of the original model for selection measured by performance and weight metrics. We found that the search process does not require a large portion of the training data, but when the final pruned model is selected it can be retrained on the full dataset, even if it is often not required. We propose a lottery sample hypothesis similar to the lottery ticket hypotheses where a subsample of lottery samples of the training set can train a model with equivalent performance to the original dataset. We show that the combination of finding lottery samples alongside lottery tickets can allow for faster searches and greater accuracy. [ABSTRACT FROM AUTHOR]

Sea surface temperature (SST) is crucial in ocean research and marine activities. It makes predicting SST of paramount importance. While SST is highly affected by different oceanic, atmospheric, and climatic parameters, few papers have investigated time-series SST prediction based on multiple features. This paper utilized multi features of air pressure, water temperature, wind direction, and wind speed for time-series hourly SST prediction using deep neural networks of convolutional neural network (CNN), long short-term memory (LSTM), and CNN–LSTM. Models were trained and validated by different epochs, and feature importance was evaluated by the leave-one-feature-out method. Air pressure and water temperature were significantly more important than wind direction and wind speed. Accordingly, feature selection is an essential step for time-series SST prediction. Findings also revealed that all models performed well with low prediction errors, and increasing the epochs did not necessarily improve the modeling. While all models were similarly practical, CNN was considered the most suitable as its training speed was several times faster than the other two models. With all this, the low variance of time-series data helped models make accurate predictions, and the proposed method may have higher errors while working with more variant features. [ABSTRACT FROM AUTHOR]

Mobile phones, laptops, computers, digital watches, and digital calculators are some of the most used products in our daily life. In the background, to make these gadgets work as per our desire, there are many simple components necessary for electronics to function, such as resistors, capacitors, and inductors, which are three basic circuit elements. The Memristor is one such component. This paper provides simulation results of the memristor circuit and its V-I characteristics at different functions as an input signal. A well-trained ANN is able to recognize images with higher precision. To enhance the properties such as accuracy, precision, and efficiency in recognition, memristor characteristics are introduced to the neural network, however, older devices experience some non-linearity issues, causing conductance-tuning problems. At the same time, to be used in some advanceable applications, ANN requires a huge amount of vector-matrix multiplication based on in-depth network expansion. An ionic floating gate (IFG) device with the characteristics of a memristive device can solve these problems. This work proposes a fully connected ANN using the IFG model, and the simulation results of the IFG model are given as synapses in deep learning. We use algorithms such as the gradient-descent model, forward and backward propagation for network building, and weight setting in neural networks to enhance their ability to recognize images. A well-trained network is formed by tuning those memristive devices to an optimized state. The synaptic memory obtained from the IFG device will be used in other deep neural networks to increase recognition accuracy. To be an activation function in the neural network, sigmoid functions were used but later replaced by the ReLu function to avoid vanishing gradients. This paper shows how images were recognized by their front, top, and side views. [ABSTRACT FROM AUTHOR]

In recent years, the increasing popularity of smart mobile devices has made the interaction between devices and users, particularly through voice interaction, more crucial. By enabling smart devices to better understand users' emotional states through voice data, it becomes possible to provide more personalized services. This paper proposes a novel machine learning model for speech emotion recognition called CLDNN, which combines convolutional neural networks (CNN), long short-term memory neural networks (LSTM), and deep neural networks (DNN). To design a system that closely resembles the human auditory system in recognizing audio signals, this article uses the Mel-frequency cepstral coefficients (MFCCs) of audio data as the input of the machine learning model. First, the MFCCs of the voice signal are extracted as the input of the model. Local feature learning blocks (LFLBs) composed of one-dimensional CNNs are employed to calculate the feature values of the data. As audio signals are time-series data, the resulting feature values from LFLBs are then fed into the LSTM layer to enhance learning on the time-series level. Finally, fully connected layers are used for classification and prediction. The experimental evaluation of the proposed model utilizes three databases: RAVDESS, EMO-DB, and IEMOCAP. The results demonstrate that the LSTM model effectively models the features extracted from the 1D CNN due to the time-series characteristics of speech signals. Additionally, the data augmentation method applied in this paper proves beneficial in improving the recognition accuracy and stability of the systems for different databases. Furthermore, according to the experimental results, the proposed system achieves superior recognition rates compared to related research in speech emotion recognition. [ABSTRACT FROM AUTHOR]

Short-term load forecasting (STLF) is critical for the energy industry. Accurate predictions of future electricity demand are necessary to ensure power systems' reliable and efficient operation. Various STLF models have been proposed in recent years, each with strengths and weaknesses. This paper comprehensively reviews some STLF models, including time series, artificial neural networks (ANNs), regression-based, and hybrid models. It first introduces the fundamental concepts and challenges of STLF, then discusses each model class's main features and assumptions. The paper compares the models in terms of their accuracy, robustness, computational efficiency, scalability, and adaptability and identifies each approach's advantages and limitations. Although this study suggests that ANNs and hybrid models may be the most promising ways to achieve accurate and reliable STLF, additional research is required to handle multiple input features, manage massive data sets, and adjust to shifting energy conditions. [ABSTRACT FROM AUTHOR]

The properties and behavior of soils depend on many factors. The interaction of individual factors is difficult to determine by traditional statistical methods due to their interdependence. The paper presents a procedure of creating an artificial neural network (ANN) model to determine the value of the damping ratio (D) of clay soils. The main purpose of this paper is to compare the appropriateness of ANN model application with empirical formulas described in the literature. The ANN model was developed using a series of laboratory tests of the damping ratio performed in the Resonance Column. Predicted values of the damping ratio of clay soils obtained from the ANN model are characterized by high convergence (coefficient of determination R2 = 0.976). In comparison with other published empirical formulas, the ANN model showed an improvement in the prediction accuracy. What is more, ANN models proved to be more flexible compared to formulas and relationships with a predetermined structure, and they were well suited to modeling the complex behavior of most geotechnical engineering materials, which, by their very nature, exhibit extreme variability. In conclusion, ANNs have the potential to predict the damping ratio (D) of clay soils and can do much better than traditional statistical techniques. [ABSTRACT FROM AUTHOR]

Digital technology development caused the digital transformation of the economy and society. E-commerce, the most widespread among digital innovations, reached a significant share, particularly during the COVID-19 pandemic, impacting economic growth. The progress of digital technologies and the evolution of e-commerce can contribute to the more sustainable development of organizations and worldwide economies. This paper analyzed the influences of digital transformation and e-commerce on GDP and sustainable development. The study used the Eurostat database to gather the research variables for the EU countries. The paper used artificial neural networks and cluster analysis to reveal the significant influence of digital transformation and e-commerce on GDP and sustainable organizational development. Countries with a low level of digital transformation and e-commerce should propel these activities to increase economic performance sustainably. [ABSTRACT FROM AUTHOR]

The accurate semantic segmentation of point cloud data is the basis for their application in the inspection of extra high-voltage transmission lines (EHVTL). As deep learning evolves, point-wise-based deep neural networks have shown great potential for the semantic segmentation of EHVTL point clouds. However, EHVTL point cloud data are characterized by a large data volume and significant class imbalance. Therefore, the down-sampling method and point cloud feature extraction method used in current point-wise-based deep neural networks hardly meet the needs of computational accuracy and efficiency. In this paper, we proposed a two-step down-sampling method and a point cloud feature extraction method based on local feature aggregation of the point clouds after down-sampling in each layer of the model (LFAPAD). We then established a deep neural network named PowerLine-Net for the semantic segmentation of the EHVTL point clouds. Furthermore, in order to test and analyze the performance of PowerLine-Net, we constructed a point cloud dataset for the EHVTL scenes. Using this dataset and the Semantic3D dataset, we implemented network parameter testing, semantic segmentation, and an accuracy comparison of different networks based on PowerLine-Net. The results illustrate that the semantic segmentation model proposed in this paper has a high computational efficiency and accuracy in the semantic segmentation of EHVTL point clouds. Compared with conventional deep neural networks, including PointCNN, KPConv, SPG, PointNet++, and RandLA-Net, PowerLine-Net also achieves a higher accuracy in the semantic segmentation of EHVTL point clouds. Moreover, based on the results predicted by PowerLine-Net, the risk point detection for EHVTL point clouds has been achieved, which demonstrates the important value of this network in practical applications. In addition, as shown by the results of Semantic3D, PowerLine-Net also achieves a high segmentation accuracy, which proves its powerful capability and wide applicability in semantic segmentation for the point clouds of large-scale scenes. [ABSTRACT FROM AUTHOR]

The deep groove ball bearing is one of the most important components of the rotary motion system and is the research subject in this paper. After factory assembly, new ball bearings need to pass quality control. The conventional approach relies on measuring the vibration amplitudes for each unit and sorting them into classes according to the vibration level. In this paper, based on experimental research, models are created to predict the vibration class and analyze the dynamic behavior of new ball bearings. The models are based on artificial neural networks. A feedforward multilayer perceptron (MLP) was applied, and a backpropagation learning algorithm was used. A specific method of training groups of artificial neural networks was applied, where each network provided an answer to the input within the group, and the final answer was the mean value of the answers of all networks in the group. The models achieved a prediction accuracy of over 90%. The main aim of the research was to construct models that are able to predict the vibration class of a new ball bearing based on the geometric parameters of the bearing rings. The models are also applied to analyze the influence of surface roughness of the raceways and the internal radial clearance on bearing vibrations. [ABSTRACT FROM AUTHOR]

Knee osteoarthritis is a challenging problem affecting many adults around the world. There are currently no medications that cure knee osteoarthritis. The only way to control the progression of knee osteoarthritis is early detection. Currently, X-ray imaging is a central technique used for the prediction of osteoarthritis. However, the manual X-ray technique is prone to errors due to the lack of expertise of radiologists. Recent studies have described the use of automated systems based on machine learning for the effective prediction of osteoarthritis from X-ray images. However, most of these techniques still need to achieve higher predictive accuracy to detect osteoarthritis at an early stage. This paper suggests a method with higher predictive accuracy that can be employed in the real world for the early detection of knee osteoarthritis. In this paper, we suggest the use of transfer learning models based on sequential convolutional neural networks (CNNs), Visual Geometry Group 16 (VGG-16), and Residual Neural Network 50 (ResNet-50) for the early detection of osteoarthritis from knee X-ray images. In our analysis, we found that all the suggested models achieved a higher level of predictive accuracy, greater than 90%, in detecting osteoarthritis. However, the best-performing model was the pretrained VGG-16 model, which achieved a training accuracy of 99% and a testing accuracy of 92%. [ABSTRACT FROM AUTHOR]

Nitrogen content is a crucial index for crop growth diagnosis and the exact estimation of nitrogen content is of great significance for grasping crop growth status in real-time. This paper takes winter wheat as the study object and the precision agriculture demonstration area of the Jiaozuo Academy of Agricultural and Forestry Sciences in Henan Province as the research area. The hyperspectral reflectance data of the wheat canopy in different growth periods are obtained with the ASD ground object hyperspectral instrument, and the original canopy spectral data are preprocessed by fractional differential and continuous wavelet transform; then, the vegetation indices are established, correlation analysis with nitrogen content is conducted, and the fractional differential spectra are selected; finally, based on the wavelet energy coefficient and the vegetation indices with strong correlations, the methods of support vector machine (SVM), ridge regression, stepwise regression, Gaussian process regression (GPR), and the BP neural network are used to construct the estimation model for nitrogen content in wheat at different growth stages. By adopting the  R 2  and root mean square error (RMSE) indices, the best nitrogen content estimation model at every growth stage is selected. The overall analysis of the nitrogen content estimation effect indicated that for the four growth periods, the maximum modeling and validation  R 2  of the nitrogen content estimation models of the SVM, ridge regression, stepwise regression, GPR, and BP neural network models reached 0.95 and 0.93, the average reached 0.76 and 0.71, and the overall estimation effect was good. The average values of the modeling and validation  R 2  of the nitrogen content estimation model at the flag picking stage were 0.85 and 0.81, respectively, which were 37.10% and 44.64%, 1.19% and 3.85%, and 14.86% and 17.39% higher than those at the jointing stage, flowering stage, and filling stage, respectively. Therefore, the model of the flag picking stage has higher estimation accuracy and a better estimation effect on the nitrogen content. For the different growth stages, the optimal estimation models of nitrogen content were different. Among them, continuous wavelet transform combined with the BP neural network model can be the most effective method for estimating the N content in wheat at the flagging stage. The paper provides an effective method for estimating the nitrogen content in wheat and a new idea for crop growth monitoring. [ABSTRACT FROM AUTHOR]

Pathologies in concrete structures, such as cracks, splintering, efflorescence, corrosion spots, and exposed steel bars, can be visually evidenced on the concrete surface. This paper proposes a method for automatically detecting these pathologies from images of the concrete structure. The proposed method uses deep neural networks to detect pathologies in these images. This method results in time savings and error reduction. The paper presents results in detecting the pathologies from wide-angle images containing the overall structure and also for the specific pathology identification task for cropped images of the region of the pathology. Identifying pathologies in cropped images, the classification task could be performed with 99.4% accuracy using cross-validation and classifying cracks. Wide images containing no, one, or several pathologies in the same image, the case of pathology detection, could be analyzed with the YOLO network to identify five pathology classes. The results for detection with YOLO were measured with mAP, mean Average Precision, for five classes of concrete pathology, reaching 11.80% for fissure, 19.22% for fragmentation, 5.62% for efflorescence, 27.24% for exposed bar, and 24.44% for corrosion. Pathology identification in concrete photos can be optimized using deep learning. [ABSTRACT FROM AUTHOR]

To boost the operational performance of a non-aqueous DES electrolyte-based vanadium-iron redox flow battery (RFB), our previous work proposed a double-layer porous electrode spliced by carbon paper and graphite felt. However, this electrode's architecture still needs to be further optimized under different operational conditions. Hence, this paper proposes a multi-layer artificial neural network (ANN) model to predict the relationship between vanadium-iron RFB's performance and double-layer electrode structural characteristics. A training dataset of ANN is generated by three-dimensional finite-element numerical simulations of the galvanostatic discharging process. In addition, a genetic algorithm (GA) is coupled to an ANN regression training process for optimizing the model parameters to elevate the accuracy of ANN prediction. The novelty of this work lies in this modified optimal method of a double-layer electrode for non-aqueous RFB driven by a machine learning (ML) model coupled with GA. The comparative result shows that the ML model reaches a satisfactory predictive accuracy, and the mean square error of this model is lower than other popular ML regression models. Based on the known region of operating conditions, the obtained results prove that this well-trained ML algorithm can be used to estimate whether a double-layer electrode should be applied to a non-aqueous vanadium-iron RFB and determine an appropriate thickness ratio for this double-layer electrode. [ABSTRACT FROM AUTHOR]

As deep neural networks (DNNs) are widely used in the field of remote sensing image recognition, there is a model security issue that cannot be ignored. DNNs have been shown to be vulnerable to small perturbations in a large number of studies in the past, and this security risk naturally exists in remote sensing object detection models based on DNNs. The complexity of remote sensing object detection models makes it difficult to implement adversarial attacks on them, resulting in the current lack of systematic research on adversarial examples in the field of remote sensing image recognition. In order to better deal with the adversarial threats that remote sensing image recognition models may confront and to provide an effective means for evaluating the robustness of the models, this paper takes the adversarial examples for remote sensing image recognition as the research goal and systematically studies vanishing attacks against a remote sensing image object detection model. To solve the problem of difficult attack implementation on remote sensing image object detection, adversarial attack adaptation methods based on interpolation scaling and patch perturbation stacking are proposed in this paper, which realizes the adaptation of classical attack algorithms. We propose a hot restart perturbation update strategy and the joint attack of the first and second stages of the two-stage remote sensing object detection model is realized through the design of the attack loss function. For the problem of the modification cost of global pixel attack being too large, a local pixel attack algorithm based on sensitive pixel location is proposed in this paper. By searching the location of the sensitive pixels and constructing the mask of attack area, good local pixel attack effect is achieved. Experimental results show that the average pixel modification rate of the proposed attack method decreases to less than 4% and the vanishing rate can still be maintained above 80%, which effectively achieves the balance between attack effect and attack cost. [ABSTRACT FROM AUTHOR]

Energy systems around the world are going through tremendous transformations, mainly driven by carbon footprint reductions and related policy imperatives and low-carbon technological development. These transformations pose unprecedented technical challenges to the energy sector, but they also bring opportunities for energy systems to develop, adapt, and evolve. With rising complexity and increased digitalization, there has been significant growth in the amount of data in the power/energy sector (data ranging from power grid to household levels). Utilization of this large data (or "big data"), along with the use of proper data analytics, will allow for useful insights to be drawn that will help energy systems to deliver an increased amount of technical, operational, economic, and environmental benefits. This paper reviews various categories of data available in the current and future energy systems and the potential benefits of utilizing those data categories in energy system planning and operation. This paper also discusses the Big Data Analytics (BDA) that can be used to process/analyze the data and extract useful information that can be integrated and used in energy systems. More specifically, this paper discusses typical applications of BDA in energy systems, including how BDA can be used to resolve the critical issues faced by the current and future energy network operations and how BDA contributes to the development of smarter and more flexible energy systems. Combining data characterization and analysis methods, this review paper presents BDA as a powerful tool for making electrical energy systems smarter, more responsive, and more resilient to changes in operations. [ABSTRACT FROM AUTHOR]

In this paper, a set of best practice data sharing guidelines for wind turbine fault detection model evaluation is developed, which can help practitioners overcome the main challenges of digitalisation. Digitalisation is one of the key drivers for reducing costs and risks over the whole wind energy project life cycle. One of the largest challenges in successfully implementing digitalisation is the lack of data sharing and collaboration between organisations in the sector. In order to overcome this challenge, a new collaboration framework called WeDoWind was developed in recent work. The main innovation of this framework is the way it creates tangible incentives to motivate and empower different types of people from all over the world to share data and knowledge in practice. In this present paper, the challenges related to comparing and evaluating different SCADA-data-based wind turbine fault detection models are investigated by carrying out a new case study, the "WinJi Gearbox Fault Detection Challenge", based on the WeDoWind framework. A total of six new solutions were submitted to the challenge, and a comparison and evaluation of the results show that, in general, some of the approaches (Particle Swarm Optimisation algorithm for constructing health indicators, performance monitoring using Deep Neural Networks, Combined Ward Hierarchical Clustering and Novelty Detection with Local Outlier Factor and Time-to-failure prediction using Random Forest Regression) appear to exhibit high potential to reach the goals of the Challenge. However, there are a number of concrete things that would have to have been done by the Challenge providers and the Challenge moderators in order to ensure success. This includes enabling access to more details of the different failure types, access to multiple data sets from more wind turbines experiencing gearbox failure, provision of a model or rule relating fault detection times or a remaining useful lifetime to the estimated costs for repairs, replacements and inspections, provision of a clear strategy for training and test periods in advance, as well as provision of a pre-defined template or requirements for the results. These learning outcomes are used directly to define a set of best practice data sharing guidelines for wind turbine fault detection model evaluation. The guidelines can be used by researchers in the sector in order to improve model evaluation and data sharing in the future. [ABSTRACT FROM AUTHOR]

This paper presents a model for determining fuzzy evaluations of partial indicators of the availability of continuous systems at coal open pits using a neuro-fuzzy inference system. The system itself is a combination of fuzzy logic and artificial neural networks. The system availability is divided into partial indicators. By combining the fuzzy logic and artificial neural networks, a model is obtained that has the ability to learn and uses expert judgment for that learning. This paper deals with the ECC system (bucket wheel excavator-conveyor-crushing plant) of the open pit Drmno-Kostolac, which operates within the Electric Power Company of Serbia. The advantage of a model of this type is that it does not rely on the historical experiences of experts and usual predicted values for the fuzzy evaluation of partial indicators, which are based on the assumption that similar systems affect availability in a similar way. The fuzzy evaluation of partial indicators is based on historical data for the specific system for which the model was created. As such, it can more accurately predict continuous systems availability on the basis of expert evaluations in the appropriate time period. Another advantage of this model is that the availability is estimated on a quarterly basis, which gives a more accurate view because it uses a smaller time period with more similar characteristics and, thus, includes certain external influences which are related to the quarterly meteorological conditions. [ABSTRACT FROM AUTHOR]

Simple Summary: This paper proposes a deep learning-based skin cancer classification network (DSCC_Net) that is based on a convolutional neural network (CNN) and implemented on three publicly available benchmark datasets (ISIC 2020, HAM10000, and DermIS). The proposed DSCC_Net obtained a 99.43% AUC, along with a 94.17% accuracy, a recall of 93.76%, a precision of 94.28%, and an F1-score of 93.93% in categorizing the four distinct types of skin cancer diseases. The accuracies of ResNet-152, Vgg-19, MobileNet, and Vgg-16, EfficientNet-B0, and Inception-V3 are 89.68%, 92.51%, 91.46%, 89.12%, 89.46%, and 91.82%, respectively. The results showed that the proposed DSCC_Net model performs better as compared to baseline models, thus offering significant support to dermatologists and health experts to diagnose skin cancer. Skin cancer is one of the most lethal kinds of human illness. In the present state of the health care system, skin cancer identification is a time-consuming procedure and if it is not diagnosed initially then it can be threatening to human life. To attain a high prospect of complete recovery, early detection of skin cancer is crucial. In the last several years, the application of deep learning (DL) algorithms for the detection of skin cancer has grown in popularity. Based on a DL model, this work intended to build a multi-classification technique for diagnosing skin cancers such as melanoma (MEL), basal cell carcinoma (BCC), squamous cell carcinoma (SCC), and melanocytic nevi (MN). In this paper, we have proposed a novel model, a deep learning-based skin cancer classification network (DSCC_Net) that is based on a convolutional neural network (CNN), and evaluated it on three publicly available benchmark datasets (i.e., ISIC 2020, HAM10000, and DermIS). For the skin cancer diagnosis, the classification performance of the proposed DSCC_Net model is compared with six baseline deep networks, including ResNet-152, Vgg-16, Vgg-19, Inception-V3, EfficientNet-B0, and MobileNet. In addition, we used SMOTE Tomek to handle the minority classes issue that exists in this dataset. The proposed DSCC_Net obtained a 99.43% AUC, along with a 94.17%, accuracy, a recall of 93.76%, a precision of 94.28%, and an F1-score of 93.93% in categorizing the four distinct types of skin cancer diseases. The rates of accuracy for ResNet-152, Vgg-19, MobileNet, Vgg-16, EfficientNet-B0, and Inception-V3 are 89.32%, 91.68%, 92.51%, 91.12%, 89.46% and 91.82%, respectively. The results showed that our proposed DSCC_Net model performs better as compared to baseline models, thus offering significant support to dermatologists and health experts to diagnose skin cancer. [ABSTRACT FROM AUTHOR]

General circulation models (GCMs) run at regional resolution or at a continental scale. Therefore, these results cannot be used directly for local temperatures and precipitation prediction. Downscaling techniques are required to calibrate GCMs. Statistical downscaling models (SDSM) are the most widely used for bias correction of GCMs. However, few studies have compared SDSM with multi-layer perceptron artificial neural networks and in most of these studies, results indicate that SDSM outperform other approaches. This paper investigates an alternative architecture of neural networks, namely the long-short-term memory (LSTM), to forecast two critical climate variables, namely temperature and precipitation, with an application to five climate gauging stations in the Lake Chad Basin. Lake Chad is a data scarce area which has been impacted by severe drought, where water resources have been influenced by climate change and recent agricultural expansion. SDSM was used as the benchmark in this paper for temperature and precipitation downscaling for monthly time–scales weather prediction, using grid resolution GCM output at a 5 degrees latitude × 5 degrees longitude global grid. Three performance indicators were used in this study, namely the root mean square error (RMSE), to measure the sensitivity of the model to outliers, the mean absolute percentage error (MAPE), to estimate the overall performance of the predictions, as well as the Nash Sutcliffe Efficiency (NSE), which is a standard measure used in the field of climate forecasting. Results on the validation set for SDSM and test set for LSTM indicated that LSTM produced better accuracy on average compared to SDSM. For precipitation forecasting, the average RMSE and MAPE for LSTM were 33.21 mm and 24.82% respectively, while the average RMSE and MAPE for SDSM were 53.32 mm and 34.62% respectively. In terms of three year ahead minimum temperature forecasts, LSTM presents an average RMSE of 4.96 degree celsius and an average MAPE of 27.16%, while SDSM presents an average RMSE of 8.58 degree celsius and an average MAPE of 12.83%. For maximum temperatures forecast, LSTM presents an average RMSE of 4.27 degree celsius and an average MAPE of 11.09 percent, while SDSM presents an average RMSE of 9.93 degree celsius and an average RMSE of 12.07%. Given the results, LSTM may be a suitable alternative approach to downscale global climate simulation models' output, to improve water management and long-term temperature and precipitations forecasting at local level. [ABSTRACT FROM AUTHOR]

Long series time forecasting has become a popular research direction in recent years, due to the ability to predict weather changes, traffic conditions and so on. This paper provides a comprehensive discussion of long series time forecasting techniques and their applications, using the Informer algorithm model as a framework. Specifically, we examine sequential time prediction models published in the last two years, including the tightly coupled convolutional transformer (TCCT) algorithm, Autoformer algorithm, FEDformer algorithm, Pyraformer algorithm, and Triformer algorithm. Researchers have made significant improvements to the attention mechanism and Informer algorithm model architecture in these different neural network models, resulting in recent approaches such as wavelet enhancement structure, auto-correlation mechanism, and depth decomposition architecture. In addition to the above, attention algorithms and many models show potential and possibility in mechanical vibration prediction. In recent state-of-the-art studies, researchers have used the Informer algorithm model as an experimental control, and it can be seen that the algorithm model itself has research value. The informer algorithm model performs relatively well on various data sets and has become a more typical algorithm model for time series forecasting, and its model value is worthy of in-depth exploration and research. This paper discusses the structures and innovations of five representative models, including Informer, and reviews the performance of different neural network structures. The advantages and disadvantages of each model are discussed and compared, and finally, the future research direction of long series time forecasting is discussed. [ABSTRACT FROM AUTHOR]

This paper focuses on image and video content analysis of handball scenes and applying deep learning methods for detecting and tracking the players and recognizing their activities. Handball is a team sport of two teams played indoors with the ball with well-defined goals and rules. The game is dynamic, with fourteen players moving quickly throughout the field in different directions, changing positions and roles from defensive to offensive, and performing different techniques and actions. Such dynamic team sports present challenging and demanding scenarios for both the object detector and the tracking algorithms and other computer vision tasks, such as action recognition and localization, with much room for improvement of existing algorithms. The aim of the paper is to explore the computer vision-based solutions for recognizing player actions that can be applied in unconstrained handball scenes with no additional sensors and with modest requirements, allowing a broader adoption of computer vision applications in both professional and amateur settings. This paper presents semi-manual creation of custom handball action dataset based on automatic player detection and tracking, and models for handball action recognition and localization using Inflated 3D Networks (I3D). For the task of player and ball detection, different configurations of You Only Look Once (YOLO) and Mask Region-Based Convolutional Neural Network (Mask R-CNN) models fine-tuned on custom handball datasets are compared to original YOLOv7 model to select the best detector that will be used for tracking-by-detection algorithms. For the player tracking, DeepSORT and Bag of tricks for SORT (BoT SORT) algorithms with Mask R-CNN and YOLO detectors were tested and compared. For the task of action recognition, I3D multi-class model and ensemble of binary I3D models are trained with different input frame lengths and frame selection strategies, and the best solution is proposed for handball action recognition. The obtained action recognition models perform well on the test set with nine handball action classes, with average F1 measures of 0.69 and 0.75 for ensemble and multi-class classifiers, respectively. They can be used to index handball videos to facilitate retrieval automatically. Finally, some open issues, challenges in applying deep learning methods in such a dynamic sports environment, and direction for future development will be discussed. [ABSTRACT FROM AUTHOR]

Deep learning (DL) and convolutional neural networks (CNNs) have achieved state-of-the-art performance in many medical image analysis tasks. Histopathological images contain valuable information that can be used to diagnose diseases and create treatment plans. Therefore, the application of DL for the classification of histological images is a rapidly expanding field of research. The popularity of CNNs has led to a rapid growth in the number of works related to CNNs in histopathology. This paper aims to provide a clear overview for better navigation. In this paper, recent DL-based classification studies in histopathology using strongly annotated data have been reviewed. All the works have been categorized from two points of view. First, the studies have been categorized into three groups according to the training approach and model construction: 1. fine-tuning of pre-trained networks for one-stage classification, 2. training networks from scratch for one-stage classification, and 3. multi-stage classification. Second, the papers summarized in this study cover a wide range of applications (e.g., breast, lung, colon, brain, kidney). To help navigate through the studies, the classification of reviewed works into tissue classification, tissue grading, and biomarker identification was used. [ABSTRACT FROM AUTHOR]

Nowdays, DNNs (Deep Neural Networks) are widely used in the field of DDoS attack detection. However, designing a good DNN architecture relies on the designer's experience and requires considerable work. In this paper, a GA (genetic algorithm) is used to automatically generate the DNN architecture for DDoS detection to minimize human intervention in the design process. Furthermore, given the complexity of contemporary networks and the diversity of DDoS attacks, the objective of this paper is to generate a DNN model that boasts superior performance, real-time capability, and generalization ability to tackle intricate network scenarios. This paper presents a fitness function that guarantees the best model generated possesses a specific level of real-time capability. Additionally, the proposed method employs multiple datasets to joint models generated, thereby enhancing the model's generalization performance. This paper conducts several experiments to validate the viability of the proposed method. Firstly, the best model generated with one dataset is compared with existing DNN models on the CICDDoS2019 dataset. The experimental results indicate that the model generated with one dataset has higher precision and F1-score than the existing DNN models. Secondly, model generation experiments are conducted on the CICIDS2017 and CICIDS2018 datasets, and the best model generated still performs well. Finally, this paper conducts comparative experiments on multiple datasets using the best model generated with six datasets and the best model generated by existing methods. The experimental results demonstrate that the best model generated with six datasets has better generalization ability and real-time capability. [ABSTRACT FROM AUTHOR]

Precise ventilation rate estimation of a naturally ventilated livestock building can benefit the control of the indoor environment. Machine learning has become a useful technique in many research fields and might be applied to ventilation rate prediction. This paper developed a machine-learning model for ventilation rate prediction from batch computational fluid dynamics (CFD) simulation results. By comparing deep neural networks (DNN), support vector regression (SVR), and random forest (RF), the best machine learning algorithm was selected. By comparing the modeling scheme of direct single-output (ventilation rate) and indirect multiple-output (predict averaged air velocities normal to the openings, then calculate the ventilation rate), the performances of the machine learning models widely applied in ventilation rate prediction were evaluated. In addition, this paper further evaluated the impact of adding indoor air velocity measurement in ventilation rate prediction. The results showed that the modeling performance of the DNN algorithm (Mean Absolute Percentage Error (MAPE) = 20.1%) was better than those of the SVR (MAPE = 23.2%) and RF algorithm (MAPE = 21.0%). The scheme of multiple-output performed better (MAPE < 8%) than the single-output scheme (MAPE = 20.1%), where MAPE was the mean absolute percentage error. Additionally, the comparison of modeling schemes with different inputs showed that the predictive accuracy could be improved by adding indoor velocities to the inputs. The MAPE decreased from 7.7% in the scheme without indoor velocity to 4.4% in the scheme with one indoor velocity, and 3.1% in the scheme with two indoor velocities. The location of the additional air velocity affected the accuracy of the predictive model, with the ones at the bottom layer performing better in the prediction than those at the top layer. This study enables a real-time and accurate prediction of the ventilation rate of a barn and provides a recommendation for optimal indoor sensor placement. [ABSTRACT FROM AUTHOR]

The diagnosis of different pathologies and stages of cancer using whole histopathology slide images (WSI) is the gold standard for determining the degree of tissue metastasis. The use of deep learning systems in the field of medical images, especially histopathology images, is becoming increasingly important. The training and optimization of deep neural network models involve fine-tuning parameters and hyperparameters such as learning rate, batch size (BS), and boost to improve the performance of the model in task-specific applications. Tuning hyperparameters is a major challenge in designing deep neural network models, having a large impact on the performance. This paper analyzes how the parameters and hyperparameters of a deep learning architecture affect the classification of colorectal cancer (CRC) histopathology images using the well-known VGG19 model. This paper also discusses the pre-processing of these images, such as the use of color normalization and stretching transformations on the data set. Among these hyperparameters, the most important neural network hyperparameter is the learning rate (LR). In this paper, different strategies for the optimization of LR are analyzed (both static and dynamic) and a new experiment based on the variation of LR is proposed (the relevance of dynamic strategies over fixed LR is highlighted), after each layer of the neural network together with decreasing variations according to the epochs. The results obtained are very remarkable, obtaining in the simulation an accurate system that achieves 96.4% accuracy on test images (for nine different tissue classes) using the triangular-cyclic learning rate. [ABSTRACT FROM AUTHOR]

This paper proposes a model capable of predicting fatal occurrences in aviation events such as accidents and incidents, using as inputs the human factors that contributed to each incident, together with information about the flight. This is important because aviation demands have increased over the years; while safety standards are very rigorous, managing risk and preventing failures due to human factors, thereby further increasing safety, requires models capable of predicting potential failures or risky situations. The database for this paper's model was provided by the Aviation Safety Network (ASN). Correlations between leading causes of incident and the human element are proposed, using the Human Factors Analysis Classification System (HFACS). A classification model system is proposed, with the database preprocessed for the use of machine learning techniques. For modeling, two supervised learning algorithms, Random Forest (RF) and Artificial Neural Networks (ANN), and the semi-supervised Active Learning (AL) are considered. Their respective structures are optimized applying hyperparameter analysis to improve the model. The best predictive model, obtained with RF, was able to achieve an accuracy of 90%, macro F1 of 87%, and a recall of 86%, outperforming ANN models, with a lower ability to predict fatal accidents. These performances are expected to assist decision makers in planning actions to avoid human factors that may cause aviation incidents, and to direct efforts to the more important areas. [ABSTRACT FROM AUTHOR]

In recent years, artificial neural networks have been widely used in the fault diagnosis of rolling bearings. To realize real-time diagnosis with high accuracy of the fault of a rolling bearing, in this paper, a bearing fault diagnosis model was designed based on the combination of VMD and ANN, which ensures a higher fault prediction accuracy with less computational time. This paper works from two aspects, including fault feature extraction and neural network structural parameter optimization to obtain an ANN bearing fault diagnosis model with high performance. The raw vibration signals of 10 fault types were divided into training, verification and testing datasets by the random step increment slip method. The variational mode decomposition method was used to decompose the raw vibration signal into several intrinsic mode functions. A new definition of the energy of each intrinsic mode function based on discrete Fourier transform and information entropy method were used as the input for the artificial neural network. Furthermore, the structural parameters of the artificial neural network were designed to obtain a high-performance neural network. The artificial neural network used in this paper had three hidden layers and 13 neurons in each hidden layer. Compared with several machine and deep learning algorithms, the artificial neural network can better fulfill the classification task of rolling bearing fault types with a mean prediction accuracy of 99.3% and computation time of 2.4 s based on a small training dataset. [ABSTRACT FROM AUTHOR]

The collection of reliable data is an important and time-consuming part of the life cycle inventory (LCI) phase. Automation of individual steps can help to obtain a higher volume of or more realistic data. The aim of this paper is to survey the current state of automation potential in the scientific literature published between 2008 and 2021, with a focus on LCI in the area of process engineering. The results show that automation was most frequently found in the context of process simulation (via interfaces between software), for LCI database usage (e.g., via using ontologies for linking data) and molecular structure models (via machine learning processes such as artificial neural networks), which were also the categories where the highest level of maturity of the models was reached. No further usage could be observed in the areas of automation techniques for exploiting plant data, scientific literature, process calculation, stoichiometry and proxy data. The open science practice of sharing programming codes, software or other newly created resources was only followed in 20% of cases, uncertainty evaluation was only included in 10 out of 30 papers and only 30% of the developed methods were used in further publication, always including at least one of the first authors. For these reasons, we recommend encouraging exchange in the LCA community and in interdisciplinary settings to foster long-term sustainable development of new automation methodologies supporting data generation. [ABSTRACT FROM AUTHOR]

Intracranial hemorrhage (ICH) is a serious medical condition that necessitates a prompt and exhaustive medical diagnosis. This paper presents a multi-label ICH classification issue with six different types of hemorrhages, namely epidural (EPD), intraparenchymal (ITP), intraventricular (ITV), subarachnoid (SBC), subdural (SBD), and Some. A patient may experience numerous hemorrhages at the same time in some situations. A CT scan of a patient's skull is used to detect and classify the type of ICH hemorrhage(s) present. First, our model determines whether there is a hemorrhage or not; if there is a hemorrhage, the model attempts to identify the type of hemorrhage(s). In this paper, we present a hybrid deep learning approach that combines convolutional neural network (CNN) and Long-Short Term Memory (LSTM) approaches (Conv-LSTM). In addition, to propose viable solutions for the problem, we used a Systematic Windowing technique with a Conv-LSTM. To ensure the efficacy of the proposed model, experiments are conducted on the RSNA dataset. The suggested model provides higher sensitivity (93.87%), specificity (96.45%), precision (95.21%), and accuracy (95.14%). In addition, the obtained F1 score results outperform existing deep neural network-based algorithms. [ABSTRACT FROM AUTHOR]

A chilled water system transports cooling functionality from refrigerators to users via heating, ventilation, and air conditioning (HVAC) systems. This paper investigated an optimal control strategy to regulate the volume flow rate of each user branch in a chilled water system, considering the minimum resistance operation to reduce energy consumption. An artificial neural network (ANN) was adopted to establish the nonlinear relationship between the volume flow rate of each user branch, pump frequency, and valve opening of each user branch. An optimal control strategy for a chilled water HVAC system is proposed in this article, according to the pump-valve combined control (PVCC) principle and an ANN model, i.e., pump-valve combined control using an artificial neural network model (PVCC-ANN). A series of tests were conducted to collect data to train the ANN model and analyze the performance of the PVCC-ANN in an experimental chilled water system. The results show that the trained ANN model has good prediction performance. A minimum resistance operation can be achieved to control the volume flow rate of each user branch independently by using the PVCC-ANN model. Moreover, the proposed PVCC-ANN method shows good energy-saving performance in chilled water systems, which can be attributed to the minimum resistance operation. Taking the energy consumption of the pump's constant frequency operation as the reference, the energy saving rate using the PVCC-ANN is between 14.3% and 58.6% under 10 operating conditions, as reported in this paper. [ABSTRACT FROM AUTHOR]

Increasing connection rates of rooftop photovoltaic (PV) systems to electricity distribution networks has become a major concern for the distribution network service providers (DNSPs) due to the inability of existing network infrastructure to accommodate high levels of PV penetration while maintaining voltage regulation and other operational requirements. The solution to this dilemma is to undertake a hosting capacity (HC) study to identify the maximum penetration limit of rooftop PV generation and take necessary actions to enhance the HC of the network. This paper presents a comprehensive review of two topics: HC assessment strategies and reinforcement learning (RL)-based coordinated voltage control schemes. In this paper, the RL-based coordinated voltage control schemes are identified as a means to enhance the HC of electricity distribution networks. RL-based algorithms have been widely used in many power system applications in recent years due to their precise, efficient and model-free decision-making capabilities. A large portion of this paper is dedicated to reviewing RL concepts and recently published literature on RL-based coordinated voltage control schemes. A non-exhaustive classification of RL algorithms for voltage control is presented and key RL parameters for the voltage control problem are identified. Furthermore, critical challenges and risk factors of adopting RL-based methods for coordinated voltage control are discussed. [ABSTRACT FROM AUTHOR]

This paper proposes facial expression recognition (FER) with the wild data set. In particular, this paper chiefly deals with two issues, occlusion and intra-similarity problems. The attention mechanism enables one to use the most relevant areas of facial images for specific expressions, and the triplet loss function solves the intra-similarity problem that sometimes fails to aggregate the same expression from different faces and vice versa. The proposed approach for the FER is robust to occlusion, and it uses a spatial transformer network (STN) with an attention mechanism to utilize specific facial region that dominantly contributes (or that is the most relevant) to particular facial expressions, e.g., anger, contempt, disgust, fear, joy, sadness, and surprise. In addition, the STN model is connected to the triplet loss function to improve the recognition rate which outperforms the existing approaches that employ cross-entropy or other approaches using only deep neural networks or classical methods. The triplet loss module alleviates limitations of the intra-similarity problem, leading to further improvement of the classification. Experimental results are provided to substantiate the proposed approach for FER, and the result outperforms the recognition rate in more practical cases, e.g., occlusion. The quantitative result provides FER results with more than 2.09 % higher accuracy compared to the existing FER results in CK+ data sets and 0.48 % higher than the accuracy of the results with the modified ResNet model in the FER2013 data set. [ABSTRACT FROM AUTHOR]

How to predict precipitation accurately and efficiently is the key and difficult problem in the field of weather forecasting. At present, we can obtain accurate meteorological data through many high-precision weather sensors and use them to forecast precipitation. However, the common numerical weather forecasting methods and radar echo extrapolation methods have insurmountable defects. Based on some common characteristics of meteorological data, this paper proposes a Pred-SF model for precipitation prediction in target areas. The model focuses on the combination of multiple meteorological modal data to carry out self-cyclic prediction and a step-by-step prediction structure. The model divides the precipitation prediction into two steps. In the first step, the spatial encoding structure and PredRNN-V2 network are used to construct the autoregressive spatio-temporal prediction network for the multi-modal data, and the preliminary predicted value of the multi-modal data is generated frame by frame. In the second step, the spatial information fusion network is used to further extract and fuse the spatial characteristics of the preliminary predicted value and, finally, output the predicted precipitation value of the target region. In this paper, ERA5 multi-meteorological mode data and GPM precipitation measurement data are used for testing to predict the continuous precipitation of a specific area for 4 h. The experimental results show that Pred-SF has strong precipitation prediction ability. Some comparative experiments were also set up for comparison to demonstrate the advantages of the combined prediction method of multi-modal data and the stepwise prediction method of Pred-SF. [ABSTRACT FROM AUTHOR]