Search

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]

This study investigates the penetration game strategy of the high dynamic vehicle against high-velocity interceptors in environments with multiple static no-fly zones. The primary issue addressed is the deficiency in control precision and the inadequacy of control margin under conditions of complex multi-constraint coupling. Firstly, an enhanced artificial potential field method is devised for the lateral penetration guidance strategy of high dynamic vehicles, which includes a predictive repulsion potential field, a buffer zone and new potential field functions. This approach not only averts trajectory oscillations caused by heading judgment ambiguity in the tangent direction of the obstacle area, but also significantly mitigates the inherent conflict between obstacle avoidance and target reachability. Secondly, considering the potential failure of the lateral penetration guidance strategy due to the high-velocity maneuvering of interceptors and detection sensor errors of the high dynamic vehicle, this paper initially designs a Kalman filter to denoise the detection information and provide a single-step optimal estimate. Subsequently, a multi-step state predictor based on the Transformer network is proposed, which obtains its future multi-step early warning information from the denoised detection historical data and refines it based on three-dimensional geometry knowledge. Then, the combination of the filtered estimate and the refined early warning information substantially enhances the success rate of the high dynamic vehicle in game confrontations with the high-velocity interceptors. Lastly, the numerical simulations are conducted to verify the effectiveness and performance of the penetration game guidance strategy. • This paper proposes a penetration game strategy for high dynamic vehicles in complex confrontation environments. • The proposed guidance method addresses the issues of insufficient control margin and inadequate control accuracy in the guidance systems. • An enhanced artificial potential field method is used to develop the lateral guidance strategy for high dynamic vehicles. • This paper develops a model-free predictor that combines a Kalman filter with a Transformer to handle time-series flight trajectories. [ABSTRACT FROM AUTHOR]

Natural gas pipeline leakage is a common safety hazard, which can have a great impact on the economy and the environment. This paper proposed a novel manifold learning-enabled feature extraction method for natural gas pipeline leakage diagnosis. Firstly, the natural gas pipeline working condition signal is decomposed and denoised by Variational mode decomposition (VMD). Secondly, the denoised pipeline signals were constructed into a form expressed by the Symmetric positive definite matrix (SPD) using the VMD reconstruction technique, and the geodesic distance measurement method was applied to the SPD matrix to make the data located on the SPD manifold. Then feature extraction is carried out by Local linear embedding (LLE) method based on asymmetric distance. Finally, pattern recognition of the features extracted in this paper by Support vector machine (SVM) can achieve 100% recognition accuracy. By enabling faster and more accurate leak detection, the method minimizes gas loss, as well as mitigating the environmental risks caused by this potent greenhouse gas. [ABSTRACT FROM AUTHOR]

Visual localization is the front-end part of visual simultaneous localization and mapping (VSLAM), and also the basis of environmental perception. Accurate visual localization can improve the map construction of complex dynamic environment, which can make robots and other carriers intelligent. To solve the visual localization problem in complex dynamic environment and overcome the localization error caused by the interference of moving targets, this paper proposes an efficient point and line feature combination method to locate the key features of static and dynamic regions. Firstly, the method uses batch frames to solve the motion compensation in dynamic environment, and optimizes the re-projection error of batch frames to locate low-speed and high-speed moving targets. Then a method of dividing the key feature regions of moving objects is proposed, and the dynamic gradient descent function is introduced to detect the point features of the key feature regions of moving objects. Finally, the edge of the dynamic target is expanded, and the key part of the static region is located by line features. The comparison results show that the point and line feature combination method proposed in this paper can be effectively applied to low-speed and high-speed dynamic scenes, and can accurately locate dynamic objects, with fast real-time performance. [ABSTRACT FROM AUTHOR]

The existing learning-based image dehazing methods usually adopt the encoder–decoder architecture with convolutional neural networks to estimate latent haze-free images from hazy images. However, the limited receptive field of convolutional neural networks may not effectively capture structure-level information, causing the model to be unable to the haze density. To solve this problem, this paper proposes a bi-decoder structure with a dense non-pooling encoder to enhance the structural features that are closely related to the haze density. Compared with conventional methods, the main advantage of our method is the integration of an additional coarse decoder in the encoder–decoder architecture, where a hybrid feature convolution (HFC) block is utilized to enlarge the receptive field to extract the structure of the image. Besides the difference in the network, the inputs of the fine and coarse decoders are non-pooling and pooling respectively. Moreover, a multi-scale feature attention (MSFA) module is proposed to selectively enhance the haze-relevant feature outputs of fine and coarse decoders. Experimental results on synthetic and real-world datasets demonstrate that the proposed method outperforms most state-of-the-art methods in terms of image quality and quantitative metrics. Especially in the NH-HAZE dataset, its PSNR exceeds other methods by more than 2.13 dB. In the end, this paper applies this dehazing technology to object detection. The code of this paper and data are available online at https://github.com/Qiaoyu-K/Bi-Decoder-Dehazing. [ABSTRACT FROM AUTHOR]

Image super-resolution reconstruction (SR) is an important ill-posed problem in low-level vision, which aims to reconstruct high-resolution images from low-resolution images. Although current state-of-the-art methods exhibit impressive performance, their recovery of image detail information and edge information is still unsatisfactory. To address this problem, this paper proposes a shifted window Transformer (Swin Transformer) with an efficient channel attention network (S-ECAN), which combines the attention based on convolutional neural networks and the self-attention of the Swin Transformer to combine the advantages of both and focuses on learning high-frequency features of images. In addition, to solve the problem of Convolutional Neural Network (CNN) based channel attention consumes a large number of parameters to achieve good performance, this paper proposes the Efficient Channel Attention Block (ECAB), which only involves a handful of parameters while bringing clear performance gain. Extensive experimental validation shows that the proposed model can recover more high-frequency details and texture information. The model is validated on Set5, Set14, B100, Urban100, and Manga109 datasets, where it outperforms the state-of-the-art methods by 0.03–0.13 dB, 0.04–0.09 dB, 0.01–0.06 dB, 0.13–0.20 dB, and 0.06–0.17 dB respectively in terms of objective metrics. Ultimately, the substantial performance gains and enhanced visual results over prior arts validate the effectiveness and competitiveness of our proposed approach, which achieves an improved performance-complexity trade-off. [Display omitted] [ABSTRACT FROM AUTHOR]

Computer vision technology has attracted significant interest in the condition monitoring (CM) community due to its potential to automate visual inspection and analysis of structures and components. By facilitating the processing and interpretation of visual information, including images and video data, computer vision holds promise for CM applications. However, it is essential to distinguish computer vision from non-contact CM techniques regarding their underlying principles and methods. While computer vision enables non-contact, remote monitoring, and condition assessment with minimal disruption to daily operations, it is distinct from non-contact CM techniques, which utilize various sensors to assess the condition of assets without physical contact or interference. Building upon the potential of computer vision technology, this survey paper presents a comprehensive overview of the current state-of-the-art CM methods based on computer vision and deep learning (DL) techniques, focusing on their application in monitoring synthetic fiber ropes (SFRs). SFRs are a viable alternative to steel wire ropes for underwater equipment and cranes that handle heavy loads. This is due to their high resistance to frictional wear, high tensile strength, lightweight, and flexibility. New materials, technologies, and processes for CM are being developed to meet the growing demand for SFRs. The paper explores ongoing research in applications that monitor the wear and aging of materials, as well as estimate their remaining useful life. The survey briefly discusses the traditional non-destructive testing and machine learning (ML) methods for CM applications. More importantly, DL-based methods, including supervised, unsupervised, semi-supervised, and self-supervised methods, are discussed in detail, together with the use of deep generative models and the recently developed diffusion models in the generation of synthetic datasets. Furthermore, the paper addresses the difficulties present in DL-based CM applications, including the scarcity of labeled data and the complexity and variety of the models used. The article ends by discussing the benefits of employing DL-based visual methods to understand SFR degradation processes, particularly in monitoring and maintenance. • This paper surveys visual data-based DL techniques for CM of SFRs. • Reviews defect detection DL models: supervised, unsupervised, semi, and self supervised. • Describes DGMs and diffusion models for generating synthetic data. • Presents TL techniques using pre-trained models for CM tasks. • Addresses challenges and opportunities for vision-based CM applications. [ABSTRACT FROM AUTHOR]

The Tactile Internet of Things (TIoT) is transforming the landscape of real-time applications by enabling haptic interactions and immersive experiences. This paper explores the potential of TIoT applications in critical sectors such as healthcare and manufacturing, emphasizing the necessity of ultra-reliable, low-latency communication. Conventional network infrastructures fall short of meeting these demands, necessitating innovative solutions such as Network Slicing (NS) to customize the network according to user activities. One of the key challenges addressed in this research is the allocation of resources for tactile data, which requires specialized solutions to prevent performance degradation in shared environments. Additionally, the paper proposes a solution that includes in-content awareness, enabling precise resource allocation based on the user's intent and requirements. Dynamic resource scaling, proactive resource allocation, and optimized bandwidth usage are essential components of the proposed architecture, guaranteeing responsive and efficient user experiences. Furthermore, the research introduces an end-to-end network slicing (NS) solution, emphasizing the importance of considering all components of the TIoT chain to prevent bottlenecks. Machine learning plays a crucial role in translating TIoT service profiles into specific requirements that are in line with the evolving needs of TIoT. To overcome the limitations of deep learning (DL), federated learning (FL) emerges as a groundbreaking approach, enabling collaborative model training without compromising data privacy. The paper explores the potential of FL and addresses its limitations within a centralized framework. It advocates for a novel architecture that integrates blockchain technology, Software-Defined Networking (SDN), Network Function Virtualization (NFV), and Multi-Access Edge Computing (MEC) to enhance FL in TIoT applications. The study investigates the performance of lightweight deep learning methods used as local models in federated learning for TIoT applications. The research also analyzes various FL algorithms from different perspectives, considering various local models contributing to the global model. Additionally, the study evaluates how the selected FL algorithms and DL local models collaborate, providing valuable insights into the performance and efficiency of the proposed architecture. These advancements aim to revolutionize the applications of TIoT and usher in a new era of intelligent, context-aware, and efficient communication in 6G networks. [ABSTRACT FROM AUTHOR]

Agent negotiation in multi-agent systems has been extensively studied, focusing on both theoretical and applied research. However, a limited number of studies have considered proposing an offer-generating strategy for agents to propose offers during the negotiation process in the multiple-negotiation situation where interdependency exist between a mixture of discrete issues and continuous issues across different negotiations. Especially, considering the above common real-life situation, there is little work of proposing such a strategy which is able to generate an approximately Pareto optimal solution. To address such challenges, this paper targets at multiple-negotiation scenarios involving interdependency between mixed types of issues across different negotiations. The contributions of this paper are threefold. Firstly, this paper addresses the research gap in mixed-type of issues in multiple negotiations. Secondly, the paper introduces a formalized negotiation model for multiple-negotiation scenarios, addressing both discrete and continuous issues, enabling automatic agents to obtain goal-aligned offers effectively. Thirdly, this paper introduces a Hybrid of PSO (Particle Swarm Optimization) and GA (Genetic Algorithm) Algorithm (i.e., named as HPGA in this paper) as an offer-generating strategy to assist agents in achieving approximately Pareto optimization in multiple-negotiation scenarios. To support those claims, this paper presents an overall modeling framework, introduces the proposed offer-generation strategy, conducts a series of experiments to demonstrate the superiority of the proposed approach in this paper, and presents two realistic case studies. Overall, this research expands upon existing studies in agent-based negotiation by addressing the overlooked aspects of mixed types of issues and issue interdependency across multiple negotiations. The proposed modeling approach and offer-generation strategy contribute to the advancement of negotiation techniques in multi-agent systems. • Fill the research gap of multi-negotiations with mixed-types of issues. • Propose a unified modeling for multi-issue negotiations scenario. • Propose an algorithm named HPGA as the offer-generating strategy. • Help agents to achieve approximately Pareto optimal by applying the proposed method. [ABSTRACT FROM AUTHOR]

In response to the challenges of low clarity and insufficient training samples in underwater concrete crack detection, this paper proposes an improved unsupervised learning method for the underwater concrete crack image enhancement (increase image quality) and augmentation (increase in number of images). Detecting structural defects underwater is vital for ensuring the proper functioning of underwater structures. However, the harsh underwater environment often leads to low-resolution images of concrete cracks, which in turn diminishes detection accuracy. Additionally, the challenges associated with underwater image collection make it difficult to gather an ample number of samples for training deep learning models to effectively detect these defects. Therefore, this paper proposes an unsupervised learning model that can simultaneously enhance and augment underwater concrete crack images in order to achieve better detection results. For the enhancement of underwater concrete crack images, the proposed method significantly improves the recognizability of images in turbid water environments and exhibits significant superiority compared to other similar methods, the values of the three evaluation indicators decreased by 45.2%, 40.4%, and 69.1%, respectively. Regarding the augmentation of underwater concrete crack images, the proposed method can translate images from clear water and waterless environments to muddy water environments. Compared to other methods, improved image quality by at least 61.2%, the proposed method generates images with better authenticity. This validates that the proposed cross domain translation strategy can simultaneously enhancing and augmenting underwater concrete crack images. • An unsupervised learning model is used to improving quality number of underwater carack images. • The residual network is used to extract local features of low-resolution crack images. • The Swin Transformer is used to obtain local and global features of high-resolution crack images. [ABSTRACT FROM AUTHOR]

With the rapid development of smart cities, urban infrastructure systems produce massive data that reflect their real-time operational conditions. These data provide insights for system monitoring and operation, and many existing studies develop various machine learning methods to understand recurrent system conditions. However, the extreme operational conditions, which could cause system failures, are not well explored. Importantly, methods for the recurrent conditions may not be suitable for modeling the failures. To fill this gap, this paper proposes a novel task of failure prediction, which aims to predict system failures before they happen. To solve this task, a generalized model that integrates survival analysis and the temporal convolutional networks, which is called TCNSurv in this paper, is developed to predict the distribution of system failure time. The model mainly contains three components: a data processing module, a time series module, and a survival analysis module. Specifically, the time series module employs Temporal Convolutional Networks to enable the modeling of temporal dependencies in time series data, and the survival analysis module explicitly formulates the probability of system failures. The proposed model is validated on three real-world datasets: vibration, traffic, and electricity, and results show that the developed model outperforms state-of-the-art regression-based models, survival analysis-based models, as well as integrated models. The research outcomes could help to understand the failure patterns of urban infrastructure systems and to develop early warning systems for smart cities. [ABSTRACT FROM AUTHOR]

Despite the widespread application of data-centric techniques in Geotechnical Engineering, there is a rising need for building trust in the artificial intelligence (AI)-driven safety assessment of road embankments due to its so-called "black-box" nature. In addition, from the lens of limit equilibrium approaches, e.g., Bishop, Fellenius, Janbu and Morgenstern–Price, and finite element method, it is essential to carefully examine the interplay of both topological and physical/mechanical properties during the safety factor (FoS) predictions. First, aside from having conventional geotechnical inputs for soil in core and foundation and the height of embankments, this paper codifies geometric features innovatively. The number of slope types with different ratios including 1:1, 1.5:1 and 2:1 as well as the number of berms is introduced. Second, a pool of 19 machine learning (ML) techniques is effortlessly trained on the dataset using an automated ML (AutoML) pipeline to identify the most optimized ML algorithm. Finally, to achieve post-hoc interpretability for the internal mechanism of the input–output relationship unbiasedly, a game-theory-based explainable AI (XAI) method called Shapley additive explanations (SHAP) values is applied. SHAP-aided importance analysis provides human-interpretable insights and indicates height, California bearing ratio, slope type 2:1 and cohesion as the most influential parameters. Exclusively, analyzing hazardous embankments by classifying main and joint contributors exhibits a complex and highly variable influence on the FoS. This paper harnesses the power of XAI tools to enhance reliability and transparency for the rapid FoS prediction of slopes. It targets geotechnical researchers, practitioners, decision-makers, and the general public for the first time. • Shedding light on the AI-aided slope stability analysis with SHAP values. • Coupling AutoML and XAI for reliable and easy-to-use FoS prediction of embankments. • Defining novel geometric parameters to capture their impact on geotechnical ones. • Categorizing unstable embankments from the global and local XAI perspectives. • Analyzing hazardous embankments by classifying main and joint contributors. [ABSTRACT FROM AUTHOR]

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]

Multi-Attribute Group Decision-Making (MAGDM) problems have become more common, with interval-valued hesitant fuzzy set (IVHFS) being found to be suitable for describing some complex fuzzy information. This paper first determined the additional relationships between generalized interval-valued hesitant fuzzy weighted averaging (GIVHFWA) operators and generalized interval-valued hesitant fuzzy weighted geometric (GIVHFWG) operators, and proposed mean and variance for a sequence of interval-valued hesitant fuzzy elements (IVHFEs). This paper then developed an outranking approach for MAGDM based on these operators to solve a consensus selection problem. In the first stage, which was based on the k-means clustering method for IVHFEs with feedback strategy taking both local and global consensus into consideration and a new consensus measure derived from the proposed variance measure, a compromised consensus was determined for each group involved in the decision. In the second stage, which was based on a probabilistic interval-valued hesitant fuzzy outranking method, the optimal alternative was determined based on the consensus information from the first stage. A case study on the enterprise credit risk assessment was given to illustrate the viability of the proposed method, which was then also compared with other current methods to demonstrate its greater flexibility and potential value. [ABSTRACT FROM AUTHOR]

Global airport perception levels vary due to natural geographical factors and economic development disparities. Understanding these differences is crucial for assessing regional airport development and its correlation with geographical patterns. However, there are limited methods available to effectively comprehend these disparities. To address this issue, this paper proposes a Salience, Disturbance, and Geographic-knowledge (SDG) approach for the cognitive analysis of global large-scale airport perception differences. Salience is assessed using a two-class deep learning model to evaluate the prominence of known airports. Disturbance is evaluated using an object detection model to measure background interference in large-scale airport perception. Geographic-knowledge analysis considers the correlation between regional airports and their surrounding geographic environment. The results rank perception difficulties for 17 regions worldwide, with Tajikistan exhibiting the highest difficulty at 0.922, while the Jiangsu–Zhejiang–Shanghai region in China has the lowest at 0.102. We also performed correlation analyses to validate the effectiveness of our model. To our knowledge, this paper pioneers the cognitive analysis of target perception difficulty differences across multiple global regions. • A unified model assesses airport perceived difficulty globally. • Main factors affecting regional salience differences are identified. • Factors are quantified for various downstream target calculation frameworks. [ABSTRACT FROM AUTHOR]

Parallel Surrogate-Based Optimization (PSBO) is an efficient approach to deal with black-box time-consuming objective functions. According to the available computational budget to solve a given problem, three classes of algorithms are investigated and opposed in this paper: Bayesian Optimization Algorithms (BOAs), Surrogate-Assisted Evolutionary Algorithms (SAEAs) and Surrogate-free Evolutionary Algorithms (EAs). A large set of benchmark functions and engineering applications are considered with various computational budgets. In this paper, we come up with guidelines for the choice between the three categories. According to the computational expensiveness of the objective functions and the number of processing cores, we identify a threshold from which SAEAs should be preferred to BOAs. Based on this threshold, we derive a new hybrid Bayesian/Evolutionary algorithm that allows one to tackle a wide range of problems without prior knowledge of their characteristics. [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]

Quality function deployment (QFD) is an effective method to convert customer requirements (CRs) into design requirements (DRs) by constructing house of quality (HOQ). With the rapid growth of the e-commerce market, it is a new challenge to utilize the available online reviews to facilitate the implementation of QFD. Therefore, this paper proposes a novel QFD model from the perspective of group decision making (GDM) and social network analysis (SNA), then applies the proposed model to product design under Chinese e-commerce scene. Firstly, this paper extracts CRs from online reviews on e-commerce platforms, and the initial HOQs can be constructed. Then a bilateral negotiation GDM method based on SNA is carried out to generate a consensus-based HOQ, and therefore the final priorities of DRs can be obtained. Finally, a case study is provided to illustrate the applicability, and some discussions and comparative analysis are also conducted. The result indicates that the proposed method can generate effective and stable results for QFD implementation in real-world e-commerce scenario. [ABSTRACT FROM AUTHOR]

In recent years, decomposition-based combination models have been widely used in wind power prediction. This type of method decomposes the highly volatile wind power into some relatively smooth subsequences, which reduces the difficulty of modeling. However, this might use information from future data in advance, creating the illusion of high prediction accuracy. Therefore, this paper proposes a wind power ultra-short-term prediction model based on fixed scale dual mode decomposition (FSDMD) and deep learning networks. First, the wind power series after fixed scale blocking is decomposed using ensemble empirical mode decomposition (EEMD), and use the improved variational mode decomposition (VMD) based on Spearman rank order correlation coefficient (SROCC) to decompose the obtained high-frequency components twice. Then, the appropriate mode components were selected by calculating the SROCC and experimental analysis, and combined with the convolutional neural network (CNN) and the bidirectional long short-term memory (BiLSTM) network to train the model. Finally, the historical data of wind turbines in a wind farm in Northwest China is used for example verification, and the comparison with other models in the two scenarios of sufficient and insufficient features. The results show that the proposed FSDMD–CNN–BiLSTM model has high prediction accuracy in both scenarios. Especially in the scenario of insufficient features, compared with CNN-BiLSTM model, RMSE, MAE and MAPE are reduced by 8.20,14.24 and 0.15, respectively. In addition, this paper verifies that mode decomposition can improve the performance of prediction model without using future features, which provides ideas for solving similar problems. [ABSTRACT FROM AUTHOR]

This paper proposes a new approach for reconstructing high-resolution images from low-resolution inputs using Denoising Diffusion Probabilistic Models (DDPMs). Existing DDPMs, while promising, face two issues: one is detail discrepancies due to the uncertain degradation factors in low-resolution images, the other is slow sampling speeds. To address these, a multimodal approach based on reversible guidance and cyclical knowledge distillation (MRKD) is introduced. This method is based on the concept where prior and posterior probabilities can assist in comprehending and predicting future events from available data and information. In the MRKD method, text and image information are separately encoded, and novel constraints are applied on prior and posterior distributions, optimizing the detailed features of the reconstructed image. In addition, due to the uncertainty of degradation factors in low-resolution images, a 'one-to-many' mapping issue arises in single-image super-resolution tasks. In response to this, the paper redefines constraints on the posterior distribution using the log-likelihood. Specifically, the Bayesian transformation of the input and output of the observation model is employed to effectively guide the diffusion process. To boost the slow sampling speed of DDPM, a cyclical knowledge distillation strategy is proposed, allowing iterative transfer of learned parameters from a high-step DDPM to a low-step model, thereby accelerating the sampling process while preserving image quality. The experimental results demonstrate that these strategies enable the model to effectively comprehend the high-level semantics and contextual information within images. Additionally, they address challenges associated with mode collapse, the loss of high-frequency details, and the complexities of long-tail data. [ABSTRACT FROM AUTHOR]