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This paper presents a neural network predictive control scheme for studying the coagulation process of wastewater treatment in a paper mill. A multi-layer back-propagation neural network is employed to model the nonlinear relationships between the removal rates of pollutants and the chemical dosages, in order to adapt the system to a variety of operating conditions and acquire a more flexible learning ability. The system includes a neural network emulator of the reaction process, a neural network controller, and an optimization procedure based on a performance function that is used to identify desired control inputs. The gradient descent algorithm method is used to realize the optimization procedure. The results indicate that reasonable forecasting and control performances have been achieved through the developed system. [Copyright &y& Elsevier]

Artificial intelligence (AI)-driven fault diagnosis methods are crucial for ensuring rotating machinery's safety and effective operation. The success of most current methods relies on the assumption that sufficient high-quality labeled datasets can be obtained for model training. However, in real-world industrial scenarios, obtaining such datasets is difficult or nearly impossible, thereby hindering the practical implementation of these methods. The integration of virtual modeling and transfer learning offers a powerful approach to meet the above challenge. Abundant virtual data of different fault categories can be acquired in the virtual space with highly flexible and at a low cost, and transfer learning can enhance the practical utility of these virtual data for contributing to the construction of diagnosis models. Therefore, this paper proposes a digital twin-driven partial domain fault diagnosis method based on unlabeled physical data and labeled virtual data. First, a virtual model of rotating machinery is built to generate labeled virtual fault data with enough fault types. Then, an adversarial transfer learning network is developed to leverage the effective knowledge from the virtual and physical data. Meanwhile, a weighting learning module is introduced to reduce the negative effect caused by the redundant fault categories in the virtual space. Finally, the proposed digital twin-driven transfer learning network is trained with the labeled virtual data and unlabeled physical data. Experiments on a light truck transmission system demonstrate that the proposed method achieves satisfactory diagnostic performance even without labeled physical fault data, contributing to the advancement of AI engineering applications. [ABSTRACT FROM AUTHOR]

A new document management system is proposed in this paper. Its kernel is based on a new set of neuro-fuzzy systems of the ART family: FasArt and RFasArt. The first one, FasArt, is used to support a simple Optical Character Recognition (OCR) that inherits fine properties of ART architectures, such as fast and incremental learning, stability and modularity. On the other hand, RFasArt is a new recurrent version of FasArt which efficiently exploits contextual information in the task of logical labeling. The proposed system is extensively tested in two real-world applications, i.e. E-mail of printed business letter and digital library of scientific papers. Experimental results show logical labeling and OCR rates over 90%. The proposed system is better compared to a previous system proposed by the group, where instead of using contextual information in an integrated way, a postprocessing Viterbi-based model was employed. [Copyright &y& Elsevier]

The existing broiler health monitoring technology has problems such as low automation, unstable monitoring results, and low practical value, making it difficult to provide timely and reliable broiler health monitoring results. The broiler sound signal can provide feedback on their health. A widely validated and correct experience is to analyze the frequency of coughs in a segment of broiler sound signal to determine the health of the broiler group. Based on this, in this paper, the authors proposed a new broiler health monitoring technology based on sound detection. The broiler health monitoring problem is cleverly transformed into a multi-classification problem, which can be solved by identifying the sound types in broiler sound signals. Specifically, the audio signal collection system was designed to complete signal collection and preliminary signal filtering. Wiener filtering was used for deep signal filtering. The 60-dimensional sound features with good performance from three aspects, time-frequency domain, Mel-Frequency Cepstral Coefficients, and sparse representation were extracted, and a preliminary data set was created. Min-max normalization was used to align the numerical distribution of the data set, and a high-quality data set was created. Multi-classification models based on different classification algorithms and neural networks were trained, and the best-performing Random Forest was obtained, thus parameter optimization was carried out, and the optimal multi-classification model was obtained, achieving a classification accuracy of 91.14%. The visualization platform was built to process the classification results of the multi-classification model, completing majority voting processing and cough rate calculation, thereby achieving broiler health monitoring. In addition, the definitions of cough rate and prediction accuracy were newly proposed. A large number of experiments have verified the feasibility of the broiler health monitoring technology proposed in this paper, with an average prediction accuracy of 98.97% achieved. • Newly propose a complete broiler health monitoring technology based on sound detection. • Transform the broiler health monitoring problem into the sound type identification problem. • Newly propose an index of cough rate to evaluate the health of broiler groups. • Newly propose a data quality improvement scheme. • Obtain the highest prediction accuracy of broiler health monitoring in this field, currently. [ABSTRACT FROM AUTHOR]

Federated Learning (FL) improves the performance of the training phase of machine learning procedures by distributing the model training to a set of clients and recombining the final models in a server. All clients share the same model, each with a subset of the complete dataset, addressing size issues or privacy concerns. However, having a central server generates a bottleneck and weakens the failure tolerance in truly distributed environments. This work follows the line of applying consensus for FL as a no-centralized approach. Moreover, the paper presents a fully distributed consensus in MAS (multi-agent system) modeling and a new asynchronous consensus in MAS (multi-agent system). The paper also includes some descriptions and tests for implementing such learning algorithms in an actual agent platform, along with simulation results obtained in a case study about electrical production in Australian wind farms. [ABSTRACT FROM AUTHOR]

Artificial intelligence has advanced the applications of sensor-based human motion capture and recognition technology in various engineering fields, such as human–robot collaboration and health monitoring. Deep learning methods can achieve satisfactory recognition results when provided with sufficient labeled data. However, labeling a large dataset is expensive and time-consuming in practical applications. To address this issue, this paper proposes a deep convolutional transformer-based contrastive self-supervised (DCTCSS) model under the bootstrap your own latent (BYOL) framework. The DCTCSS model aims to achieve reliable activity recognition using only a small amount of labeled data. Firstly, a deep convolutional transformer (DCT) model is proposed as the backbone of DCTCSS model, to learn high-level feature representations from unlabeled data in pre-training period. Subsequently, a simple linear classifier is trained with supervised fine-tuning using a limited amount of labeled data to recognize activities. In addition, this paper experimentally formulates a random data augmentation strategy to increase the diversity of input data. The performance of the DCTCSS model is evaluated and compared with several state-of-the-art algorithms on three datasets widely used in daily life, medical monitoring, and intelligent manufacturing. Experimental results show that the DCTCSS model achieves mean F1 scores of 95.64%, 88.39%, and 98.40% on the UCI-HAR, Skoda, and Mhealth datasets, respectively, using only 10% of the labeled data. These results outperform both supervised and unsupervised baseline models. Consequently, the DCTCSS model demonstrates its effectiveness in reducing the dependence on large amounts of labeled data while still achieving competitive activity recognition performance. [ABSTRACT FROM AUTHOR]

Small and medium-sized enterprises (SMEs) play a vital role in economic and social development. Among them, scientific and technological innovation ability and investment choice ability are the key factors to evaluate the competitiveness of SMEs. Aiming at the capability evaluation of SMEs, this paper designs a multi-attribute decision-making (MADM) method with interval type-2 trapezoidal fuzzy information measure, which is driven by trigonometric function. Interval type-2 trapezoidal fuzzy numbers (IT2TrFNs) help us to model fuzzy information. Firstly, this paper discusses the three main concepts of entropy, similarity and cross-entropy, and introduces their properties in IT2TrFNs. Secondly, the information measurement formulas related to IT2TrFNs are constructed by using trigonometric functions: IT2TrF trigonometric information entropy, IT2TrF trigonometric similarity measure and IT2TrF trigonometric cross-entropy. They are used to measure the ambiguity and similarity of decision information. Then, taking into account the interdependence between the different attributes, we use entropy and cross-entropy to determine the unknown attribute weights. IT2TrF trigonometric similarity measure is utilized to determine the optimal alternative. Finally, the numerical example is given to evaluate the scientific and technological innovation ability and investment choice ability of SMEs. The feasibility and effectiveness of the proposed MADM method are verified by comparative analysis. • Axiomatic definitions of information measures of IT2TrFS are introduced. • Trigonometric information measure formulas for IT2TrFS are constructed. • The relationship among the information measures is discussed. • A MADM method is developed. • Two examples are given to illustrate the behavior of the proposed method. [ABSTRACT FROM AUTHOR]

Reinforcement learning-based methods have addressed production scheduling problems with flexible processing constraints. However, delayed rewards arise due to the dynamic arrival of jobs and transportation constraints between two successive operations. The flow time of operations can only be determined after processing due to the possibility that the solution for job sequencing may change if new operations are inserted in dynamic environments. Job sequencing is often overlooked in single-agent-based scheduling methods. The lack of information sharing between multiple agents necessitates that researchers manually design reward functions to fit the relationship between optimization objectives and rewards, thereby reducing the accuracy of the learned policies. Thus, this paper proposes a multi-agent-based scheduling optimization framework that facilitates collaboration between the agents of both machines and jobs to address dynamic flexible job-shop scheduling problems (DFJSP) with transportation time constraints. Then, this paper formulates the Partial Observation Markov Decision Process and constructs a reward-sharing mechanism to tackle the delayed reward issue and facilitate policy learning. Finally, we develop an improved multi-agent dueling double deep Q network algorithm to optimize scheduling policy during long-term training. The results show that, compared with the state-of-the-art methods, the proposed method efficiently shortens the weighted flow time under the trained and unseen scenarios. Additionally, the case study results demonstrate its efficiency and responsiveness. It indicates that the proposed method efficiently addresses production scheduling problems with complex constraints, including the insertion of jobs, transportation time constraints, and flexible processing routes. [ABSTRACT FROM AUTHOR]

Carbon Fiber Reinforced Polymer (CFRP) laminates, prized for their lightweight and high stiffness, are extensively used in aerospace and maritime applications. Bolted joints play a crucial role in connecting these laminates. However, manufacturing variations arise during the assembly process, impacting performance due to material-related factors. Predicting the assembly stress fields of Carbon Fiber Reinforced Polymer bolted joints is of great significance in design optimization, manufacturing process control, and structural health monitoring. The currently prevalent finite element analysis methods incur extremely high computational costs, failing to meet the requirements for real-time prediction of the assembly and multiparametric design of composite bolted joints. Proposing a methodological framework for rapidly predicting the assembly physical field is necessary. This paper introduces a stress prediction framework to enhance analysis and aid material parameter design. The framework is inspired by image processing and artificial intelligence drawing by analogizing the computed physical field results to the generated images. Therefore, the Bolted Tightening Generative Adversarial Network (BT-GAN), a cascaded generative model, is proposed in this paper to predict stress fields of the composite bolted joints during assembly. The model starts with data augmentation of the stress filed results from the finite element analysis in a super-resolution network, which realizes an integral interpolation mapping from coarse-grid to fine-grid results. Then, the results of the data enhancement are fed into the subsequent conditional generative adversarial network for learning. Similar to the text-guided image generation approach, the network learns to understand the physical mapping relationships between different parameters and assembly stress fields. Moreover, the network achieves higher accuracy in stress field prediction by extraction the understanding of multi-scale features through the skip connection and the attention mechanism. This method effectively learns the physical mapping relationship between multiple parameters and the stress field, applying a graph generation approach to end-to-end predictions of the field. Compared to the results of finite element analysis from the coarse-grid, the Structure Similarity Index Measure (SSIM) of the cascaded generative network proposed in this paper has been improved from 0.584 to 0.962 and the Peak Signal-to-Noise Ratio (PSNR) metric has been increased from 17.3 dB to 58.2 dB. What's more, the mean relative error on the maximum values of the stress field has reached 6.9%. The trained model takes only 6.1s to complete a single prediction, significantly improving the prediction efficiency compared with finite element analysis. It is compared with the other networks commonly used for physical field prediction and shows improvement in the metrics proposed in the article. By constructing such an end-to-end stress field prediction framework during assembly, efficient forecasting for the assembly of composite bolted joints can be achieved. This is advantageous for the digital twin modeling of the assembly lines and the effective control of assembly quality, providing a powerful tool for assembly design and analysis. [Display omitted] [ABSTRACT FROM AUTHOR]

In Turkey, the transportation industry's greenhouse gas (GHG) emissions increased by 147.1% between 1990 and 2019. Today, this transportation industry (i.e., freight and passenger) is among the significant contributors to greenhouse gas emissions in Turkey's megacities. Moreover, 65.43% of short-distance trips between home to work and home to school have been made by private automobiles in Istanbul and increasing concerns about environmental pollution have led practitioners to seek practical, robust, and effective solutions to reduce GHG emissions. Shared electric scooters have rapidly become popular for end-users and practitioners in megacities, depending on their valuable advantages. However, the rapid spread of micro-mobility, characterized by e-scooters, has also raised questions about this system's sustainability, suitability, and applicability. Thus, there are some critical and noteworthy gaps in this issue. This study investigates the factors affecting the suitable e-scooter selection for a sustainable urban transport system. Besides, it aims to develop a methodological framework for assessing the available e-scooter alternatives. For this purpose, a novel negotiation approach, a new form of the Delphi technique, was developed with the help of Fermatean fuzzy sets to identify the influential criteria. Also, the current paper presents a consensus-based MULTIMOORA (Multiple Objective Optimization on the basis of Ratio Analysis plus Full Multiplicative Form) decision-making model based on Fermatean fuzzy sets to address the appraisal problem concerning e-scooter selection. The current paper indicated that economic measures such as acquisition price and upkeep costs affect the e-scooter selection processes. In addition, an optimization model based on cross-entropy and dispersion measures is utilized to compute criteria weights. It highlighted that the costs of e-scooters are still high, and operators consider these criteria instead of the technical and operational features of the e-scooters. Finally, the validity check executed to test the robustness and trustworthiness of the model affirms the model's firmness and trustworthiness. [ABSTRACT FROM AUTHOR]

Heterogeneity and uncertainty in a composite microstructure lead to either computational bottlenecks if modeled rigorously or to solution inaccuracies in the stress field and failure predictions if approximated. Although methods suitable for analyzing arbitrary and non-linear microstructures exist, their computational cost makes them impractical to use in large-scale structural analysis. Surrogate models or Reduced Order Models (ROMs) commonly enhance efficiencies but are typically calibrated with a single microstructure. Homogenization methods, such as the Mori–Tanaka method, offer rapid homogenization for a wide range of constituent properties. However, simplifying assumptions, like stress and strain averaging in phases, render the consideration of both deterministic and stochastic variations in microstructure infeasible. This paper illustrates a transformer neural network architecture that captures the knowledge of various microstructures and constituents, enabling it to function as a computationally efficient homogenization surrogate model. Given an image or an abstraction of an arbitrary composite microstructure of linearly elastic fibers in an elastoplastic matrix, the transformer network predicts the history-dependent, non-linear, and homogenized stress–strain response. Two methods for encoding microstructure features were tested: calculating two-point statistics using Principal Component Analysis (PCA) for dimensionality reduction and employing an autoencoder with a Convolutional Neural Network (CNN). Both methods accurately predict the homogenized material response. The developed transformer neural network offers an efficient means for microstructure-to-property translation, generalizable and extendable to a variety of microstructures. The paper describes the network architecture, training and testing data generation, and performance under cycling and random loadings. [ABSTRACT FROM AUTHOR]

The reliability of bolt connections significantly impacts the operational state and lifespan of industrial equipment. Vision-based noncontact methods exhibit high efficiency in bolt loosening detection. However, limited image features hinder measurement accuracy. To improve bolt loosening detection performance, this paper proposes a novel deep learning backbone, the high-resolution cross-scale transformer, to extract high precision keypoints for bolt three-dimensional model construction. Simultaneously, a monocular vision measurement model is established to get the bolt exposed length and evaluate the connection loosening state. The proposed backbone hybridizes the advantages of high-resolution architecture and transformer, realizing global information aggregation and fine-grained image details. A simplified module, dual-scale multi-head self-attention, is designed to reduce the computational redundancy caused by the implementation of high-resolution multi-branch architecture. In the experiment section, the high-resolution cross-scale transformer outperforms other keypoint detection baselines, achieving the top one performance with 91.6 average precision and 84.9 average recall. The monocular vision measurement model realizes a 0.053 mm error with a 0.028 mm standard deviation, satisfying the industrial implementation requirement. Additionally, the model is tested on different industrial situations and an additional outside dataset, indicating the model's robustness and actual environment adaptability. • A monocular vision measurement method for bolt loosening detection is proposed. • It is the first attempt to introduce the transformer mechanism in bolt keypoint detection. • A new keypoint detection backbone is proposed in this paper for bolt feature extraction. • The 3D exposed length of bolts is calculated with a monocular vision system. • The bolt loosening detection method is validated. [ABSTRACT FROM AUTHOR]

Since 2000, the field of machine scheduling—an integral part of computer science and operations research—has seen significant advancements. This paper explores the dynamic progression of machine scheduling, offering a detailed overview of its past advancements, current practices, and future directions. Anchoring the research in robust data analysis and statistical methodologies, the paper reveals the subtle yet impactful changes that have characterized the field in the last two decades. It examines the prominence of various scheduling problems, identifies leading research journals, and highlights international contributions and collaborations, thereby offering a thorough guide to the machine scheduling ecosystem. The study delves into specific problem characteristics and assesses performance criteria and solution methods to provide an in-depth view of the field's multifaceted nature. Ultimately, this paper captures the essence of machine scheduling's evolution and suggests new paths for exploration. The insights gained contribute significantly to academic discussions and equip practitioners with a comprehensive understanding of the dynamic landscape of machine scheduling. [ABSTRACT FROM AUTHOR]

This paper delivers an exhaustive analysis of the fusion of multi-sensor technologies, including traditional sensors such as cameras, Light Detection and Ranging(LiDAR), Radio Detection and Ranging(RADAR), and ultrasonic sensors, with Artificial Intelligence(AI) powered methodologies in obstacle detection for Autonomous Vehicles(AVs). With the growing momentum in AVs adoption, a heightened need exists for versatile and resilient obstacle detection systems. Our research delves into study of literatures, where proposed approaches assimilate data from this diverse sensor suite, integrated through Deep Learning(DL) techniques, to refine AV performance. Recent advancements and prevailing challenges within the domain are thoroughly examined, with particular focus on the integration of sensor fusion techniques, the facilitation of real-time processing via edge and fog computing, and the implementation of advanced artificial intelligence architectures, including Convolutional Neural Networks(CNNs), Recurrent Neural Networks(RNNs), and Generative Adversarial Networks(GANs), to enhance data interpretation efficacy. In conclusion, the paper underscores the critical contribution of multi-sensor arrays and deep learning in enhancing the safety and reliability of autonomous vehicles, offering significant perspectives for future research and technological progress. [ABSTRACT FROM AUTHOR]

Current failure prediction methods of bearings have less uncertainty analysis with interpretability, less correlation analysis between degradation characteristics and prediction error. Moreover, there are multiple degradation stages in entire life cycle and prediction performance cannot meet practical demands. Therefore, this paper proposes a new approach for failure prediction of bearings. The change point detection method achieves multi-stage division of degradation data. The improved hybrid deep forest with best dissimilarity sequence (BDS) is studied and a new pretrained algorithm with pruning operation is developed. The convergence theorem is proved. A novel multi-stage failure prediction algorithm based on improved hybrid deep forest, hypothesis testing and interpretability analysis, is developed to get better prediction result. The time complexity of proposed algorithm is analyzed. The datasets of NASA and FEMTO-ST institute are utilized and experimental results show that: 1) Our approach with model interpretability has better prediction performances than support vector machine (SVR), recurrent neural network (RNN), long short-term memory (LSTM), and deep forest (DF); 2) The non-normal distribution characteristics, monotonic degradation trend and effect size of multiple stages are analyzed based on hypothesis testing methods; 3) The positive and inverse relation analysis achieves the correlation interpretability between multi-stage degradation characteristics and failure prediction results. • EWMA, CUSUM, and K-means clustering are used to obtain real change points for multi-stage division. • An improved hybrid deep forest model with BDS is presented to improve prediction performance. • This paper proposes a new pretrained algorithm to achieve a better tradeoff between accuracy and runtime cost. • A multi-stage failure prediction algorithm with model interpretability is developed. • Hypothesis testing and correlation analysis are utilized to enhance the interpretability of degradation characteristics. [ABSTRACT FROM AUTHOR]

Multi-target stance detection is the detection of the stance of multiple targets in text. Currently, most multi-target stance detection methods only detect the stance of two targets individually and do not make the two targets complement each other to take full advantage of the relevant semantic information between the two targets. In this paper, we propose a comparative learning based stance agreement detection framework. We applied contrastive learning to stance agreement detection, it enabled the model to learn more information about the features of the target and to strengthen the links between the semantic information of the targets so that they assist each other in stance detection. In addition, we fine-tuned a new model as our encoder to more fully exploit the semantic information between hidden contexts. We also apply joint training as a multi-task learning approach, allowing models to share domain-specific information based on the dataset. By comparing different methods, experimental results show that our method achieves state-of-the-art results on multi-target benchmark datasets. In the concluding sections of our paper, we conducted error analysis experiments on the proposed methodology, elucidating its inherent limitations and furnishing invaluable insights conducive to future enhancements. [ABSTRACT FROM AUTHOR]