Network Of Processes Research Articles

Boolean operators and neural networks

We compute the homogeneous ideals of varieties, in a projective space of tensors, associated to different choices of the Boolean operators that describe the decision process in small neural networks. We prove that, starting with networks with three nodes, the varieties associated to different Boolean operators are all distinct.

Physics-informed reinforcement learning for probabilistic wind power forecasting under extreme events

With wind power penetration increases, accurate and reliable wind power forecasting is becoming gradually critical, and data driven model is a promising solution to implement this task. However, limited by deficient data samples under extreme conditions, scarcity of feature measurements fails to meet the number of training samples required, making the forecasting model exhibits low adaptability. This paper proposes a physics-informed reinforcement learning based method for probabilistic forecasting. Analytical physical expression of wind power output under extreme event is established to construct the error evaluation function for abrupt feature. Deep deterministic policy gradient-based quantile fitting model is then proposed, with abrupt feature embedded as the auxiliary input data for neural network. On this basis, parameter training technique under small data set is proposed to deal with the effect of extreme conditions on correction process of network. It extracts key historical transitions from experience replay pool to establish effective training samples, and model parameters are updated through the small-batch learning strategy to minimize long-term error feedback. Test result on the practical cold wave event of wind farms in China shows effectiveness of the proposed method.

Training feedforward neural networks with Bayesian hyper-heuristics

The process of training feedforward neural networks (FFNNs) can benefit from an automated process where the best heuristic to train the network is sought out automatically by means of a high-level probabilistic-based heuristic. This research introduces a novel population-based Bayesian hyper-heuristic (BHH) that is used to train feedforward neural networks (FFNNs). The performance of the BHH is compared to that of ten popular low-level heuristics, each with different search behaviours. The chosen heuristic pool consists of classic gradient-based heuristics as well as meta-heuristics (MHs). The empirical process is executed on fourteen datasets consisting of classification and regression problems with varying characteristics. The BHH is shown to be able to train FFNNs well and provide an automated method for finding the best heuristic to train the FFNNs at various stages of the training process.

A modified physics-informed neural network to fatigue life prediction of deck-rib double-side welded joints

Deck-rib welded joint is an important part of orthotropic steel decks, which are susceptible to fatigue cracks under various load cycles. Therefore, it is essential to use efficient predicting methods to assess the fatigue performance of deck-rib welded joints with new geometry and form in order to ensure a reliable operation of the bridge. A modified physics-informed neural network (PINN) is proposed for the fatigue life prediction of the novel deck-rib double-side welded joints. The modified PINN incorporates implicit physical model as an activation function into the hidden neurons, and explicit physical constraints into the loss function. Additionally, the influence of deck thickness is integrated using limited test sample data based on the observational approach. The 62 experimental data were taken from the literature to compare the performance of the artificial neural network (ANN), traditional PINN and the modified PINN. The results demonstrate that the modified PINN enhances the learning process of the neural network through the joint learning of physical knowledge by activation function and loss function. Furthermore, the fatigue life prediction of the deck-rib double-side welded joints under the modified PINN exhibits physical consistency and accuracy when compared with the traditional ANN and PINN.

Task-Importance-Oriented Task Selection and Allocation Scheme for Mobile Crowdsensing

In Mobile Crowdsensing (MCS), sensing tasks have different impacts and contributions to the whole system or specific targets, so the importance of the tasks is different. Since resources for performing tasks are usually limited, prioritizing the allocation of resources to more important tasks can ensure that key data or information can be collected promptly and accurately, thus improving overall efficiency and performance. Therefore, it is very important to consider the importance of tasks in the task selection and allocation of MCS. In this paper, a task queue is established, the importance of tasks, the ability of participants to perform tasks, and the stability of the task queue are considered, and a novel task selection and allocation scheme (TSAS) in the MCS system is designed. This scheme introduces the Lyapunov optimization method, which can be used to dynamically keep the task queue stable, balance the execution ability of participants and the system load, and perform more important tasks in different system states, even when the participants are limited. In addition, the Double Deep Q-Network (DDQN) method is introduced to improve on the traditional solution of the Lyapunov optimization problem, so this scheme has a certain predictive ability and foresight on the impact of future system states. This paper also proposes action-masking and iterative training methods for the MCS system, which can accelerate the training process of the neural network in the DDQN and improve the training effect. Experiments show that the TSAS based on the Lyapunov optimization method and DDQN performs better than other algorithms, considering the long-term stability of the queue, the number and importance of tasks to be executed, and the congestion degree of tasks.

Retina Blood Vessels Segmentation and Classification with the Multi-featured Approach.

Segmenting retinal blood vessels poses a significant challenge due to the irregularities inherent in small vessels. The complexity arises from the intricate task of effectively merging features at multiple levels, coupled with potential spatial information loss during successive down-sampling steps. This particularly affects the identification of small and faintly contrasting vessels. To address these challenges, we present a model tailored for automated arterial and venous (A/V) classification, complementing blood vessel segmentation. This paper presents an advanced methodology for segmenting and classifying retinal vessels using a series of sophisticated pre-processing and feature extraction techniques. The ensemble filter approach, incorporating Bilateral and Laplacian edge detectors, enhances image contrast and preserves edges. The proposed algorithm further refines the image by generating an orientation map. During the vessel extraction step, a complete convolution network processes the input image to create a detailed vessel map, enhanced by attention operations that improve modeling perception and resilience. The encoder extracts semantic features, while the Attention Module refines blood vessel depiction, resulting in highly accurate segmentation outcomes. The model was verified using the STARE dataset, which includes 400 images; the DRIVE dataset with 40 images; the HRF dataset with 45 images; and the INSPIRE-AVR dataset containing 40 images. The proposed model demonstrated superior performance across all datasets, achieving an accuracy of 97.5% on the DRIVE dataset, 99.25% on the STARE dataset, 98.33% on the INSPIREAVR dataset, and 98.67% on the HRF dataset. These results highlight the method's effectiveness in accurately segmenting and classifying retinal vessels.

Enhanced recognition of insulator defects on power transmission lines via proposal-based detection model with integrated improvement methods

Deep learning-driven transmission line inspection is a critical area for smart power grid development. Despite advances in deep learning for insulator defect detection, challenges remain in model robustness and adaptability for the varying real-world adaptability, especially for insignificant defects in complex backgrounds. This study presents a comprehensive improvement strategy for detecting insulators and cross-scale broken defects on transmission lines, employing a proposal-based detection model. The model introduces a holistic pipeline of improved methods, including backbone modification, anchor box scale recalibration, and improvements in Region of Interest (RoI) downsampling alignment and Intersection over Union (IoU) loss function. Various backbone networks, including convolutional network (ConvNet) and Vision Transformer (ViT) structures, are constructed and integrated with attention modules, specifically designed to amplify the perception of insulators and defective regions. The geometric scale of anchor boxes is reconstructed using a developed clustering algorithm, considering the elongated characteristics of insulator strings to improve the adaptability of anchor boxes. Bilinear interpolation is utilized to mitigate spatial misalignment issues during the downsampling process of Region Proposal Network (RPN)-based proposals. The experimental results indicate that the improved models with the Swin Transformer (Swin-T) backbone framework achieve the mean Average Precision (mAP)@0.5 of 88.42% and mAP@0.7 of 60.52%, with a defect recall rate of 81.94%. Additionally, the improved IoU loss function contributes to the performance of the model at higher IoU thresholds. The results of this study contribute to the further development of defect detection frameworks for power vision applications.

Improved YOLOv8 Algorithm for Water Surface Object Detection.

To address the issues of decreased detection accuracy, false detections, and missed detections caused by scale differences between near and distant targets and environmental factors (such as lighting and water waves) in surface target detection tasks for uncrewed vessels, the YOLOv8-MSS algorithm is proposed to be used to optimize the detection of water surface targets. By adding a small target detection head, the model becomes more sensitive and accurate in recognizing small targets. To reduce noise interference caused by complex water surface environments during the downsampling process in the backbone network, C2f_MLCA is used to enhance the robustness and stability of the model. The lightweight model SENetV2 is employed in the neck component to improve the model's performance in detecting small targets and its anti-interference capability. The SIoU loss function enhances detection accuracy and bounding box regression precision through shape awareness and geometric information integration. Experiments on the publicly available dataset FloW-Img show that the improved algorithm achieves an mAP@0.5 of 87.9% and an mAP@0.5:0.95 of 47.6%, which are improvements of 5% and 2.6%, respectively, compared to the original model.

Mega Research Infrastructure as a Driver for High-quality Development and Innovation: Promoting Scientific Cooperation and Interdisciplinarity

Mega research infrastructures, as megaprojects, play a crucial role in promoting high-quality development, particularly in relation to innovation. They contribute to the development of disciplinary layouts and scientific collaborations. Additionally, during the operational phase of a infrastructure, maximizing efficiency and ensuring sustainable development become key aspects of project management. The operation of mega research infrastructures involves increasing levels of interdisciplinarity, interagency collaboration, and cross-regional cooperation. Therefore, it is essential to further clarify the characteristics of scientific collaboration within these facilities and understand the current status and evolving trends of disciplinary and collaborative networks. This work serves as a fundamental and necessary endeavor. In this study, we employ social network analysis methods to investigate the Shanghai Synchrotron Radiation Facility as a case study. We analyze the network structure and evolution of its collaborative and disciplinary networks from 2009 to 2022. The findings reveal the enormous potential and value of mega research infrastructures in promoting scientific research cooperation and development. They emphasize the need for increased attention and support for instrument scientists by facility managers, as well as the importance of expanding collaboration areas and formats to enrich the range of disciplines involved. The contribution of this study lies in its systematic analysis of the evolutionary process of collaborative and disciplinary networks within mega research infrastructures. It reveals the scientific value of these facilities in driving scientific research cooperation and interdisciplinary integration, providing a scientific basis for project management and policy formulation in the operational phase of mega research infrastructures. The research will align the strategic development direction of mega research infrastructures with the requirements of high-quality development and deepen the foundational role of mega research infrastructures in supporting high-quality development.

Clarity in Wireless Quantum Communication: Exploring Explainable AI for Signal Modulation Classification

The advent of artificial intelligence (AI) and edge computing technologies has ushered in a new era for automatic modulation classification (AMC), with a particular focus on deep learning (DL)-based approaches. These AI-based AMC systems, deployed at edge devices, have shown remarkable advancements in recognizing and classifying a wide range of wireless signals. However, a significant challenge in this domain lies in the lack of adequate explanations for the models employed, which, in turn, hampers model accuracy and training efficiency. Consequently, the practical applications and further enhancements of these models are constrained. To address this limitation, researchers have started to explore interpretable methods for the technical analysis of DL-based AMC systems. In this paper, we delve into the subject, leveraging recent developments in interpretable methods, to provide a comprehensive review of applicable techniques and to outline the existing research challenges in the field of interpretable AMC. Moreover, we introduce a novel interpretable AI-based AMC framework designed to augment the interpretability of AMC outcomes. This framework achieves this by elucidating the contribution of wireless signal features to the training process of DL networks. In addition, we explore the potential impact of quantum computing on AMC systems. Quantum computing, with its ability to process complex computations at unprecedented speeds, offers a promising avenue for enhancing the efficiency and accuracy of AMC. By integrating quantum algorithms with AI-based AMC frameworks, it is possible to significantly improve the performance of wireless signal classification, especially in environments with high signal variability and noise. Our experimental results demonstrate that the proposed approach possesses the capacity to unravel the classification mechanisms hidden within opaque auto-modulated classification networks. Furthermore, it exhibits the potential to facilitate the training of networks on edge devices with reduced energy consumption and enhanced classification accuracy, thereby addressing critical challenges in the field of wireless signal classification. Additionally, the integration of quantum computing techniques holds the promise of further advancing the capabilities of AMC systems, paving the way for more robust and efficient wireless communication technologies.

NITRINET: A predictive model for nitrification in reclaimed water distribution in pressurised irrigation networks

In this work, a new model (NITRINET) to simulate nitrification processes in pressurised irrigation distribution networks fed with reclaimed water is developed and applied to the Tintín Irrigation District distribution network as case study (Montilla, southern Spain). The importance of this model relies on the fact that the nutrient chemical composition of reclaimed water varies spatially along the network. The water quality analyses carried out in the Irrigation District have shown that nitrate concentrations increase along the distribution network in contrast to the reduction observed in the ammoniacal forms. This confirms that nitrification processes occur inside the pipes. Thus, the model combines the hydraulic simulation of the distribution network and the modelling of nitrification processes to predict the concentration of ammoniacal nitrogen (NH4+-N) and nitric nitrogen (NO3--N) in water arriving at farms. This model is of great applicability to the irrigation sector, given that to carry out precision fertigation strategies and optimise the amount of fertiliser applied, it is necessary to determine the concentration of nutrients present in water arriving at farms. The nutrients that reclaimed water already carries must be considered when planning fertilisation. This allows for a reduction in the amount of fertiliser applied to the soil, which has a positive impact both on the environment and on farmers’ incomes. Simulations performed with NITRINET have shown promising results, predicting the concentration of NH4+-N and NO3--N in irrigation water with mean absolute errors of 1.49 mg·L−1 and 1.25 mg·L−1, respectively.

Identification of challenges to Industry 5.0 adoption in Indian manufacturing firms: an emerging economy perspective

ABSTRACT This study aims to identify a comprehensive list of challenges and empirically evaluate their impact on Industry 5.0 adoption, particularly in developing countries in Indian manufacturing firms. Challenges were classified into four significant categories: human, environmental, legal and ethical, and financial through an extensive literature review conducted and experts’ inputs. Multi-criteria decision-making (MCDM) is a method for analyzing complex issues by assessing multiple alternatives based on specific criteria. The analytic hierarchy Process (AHP) aids in selecting suitable options, while the analytical network processes (ANP) technique determines relevant criteria. A sensitivity analysis was carried out to validate the research findings. The most preeminent challenges were identified as human critical thinking, human-machine teams, incorporation of ethics, investment in Industry 5.0 technologies, human well-being perspective, over-dependency on technology, and personalization and hyper-customization 5.0, respectively, in the adoption of Industry 5.0. The comprehensive challenges proposed in the study will assist decision-makers, practitioners, and managers in determining fair benchmarking strategies to precipitate Industry 5.0. This research helps to gauge manufacturing firms’ readiness index to adopt Industry 5.0, provides a distinct perspective, and foregrounds the significance of research to assist the industry in its long-term to anticipate Production 6.0.

Entropy-based guidance of deep neural networks for accelerated convergence and improved performance

Neural networks have dramatically increased our capacity to learn from large, high-dimensional datasets across innumerable disciplines. However, their decisions are not easily interpretable, their computational costs are high, and building and training them are not straightforward processes. To add structure to these efforts, we derive new mathematical results to efficiently measure the changes in entropy as fully-connected and convolutional neural networks process data. By measuring the change in entropy as networks process data effectively, patterns critical to a well-performing network can be visualized and identified. Entropy-based loss terms are developed to improve dense and convolutional model accuracy and efficiency by promoting the ideal entropy patterns. Experiments in image compression, image classification, and image segmentation on benchmark datasets demonstrate these losses guide neural networks to learn rich latent data representations in fewer dimensions, converge in fewer training epochs, and achieve higher accuracy.

Probabilistic Discrete‐Time Models for Spreading Processes in Complex Networks: A Review

AbstractResearch into network dynamics of spreading processes typically employs both discrete and continuous time methodologies. Although each approach offers distinct insights, integrating them can be challenging, particularly when maintaining coherence across different time scales. This review focuses on the Microscopic Markov Chain Approach (MMCA), a probabilistic f ramework originally designed for epidemic modeling. MMCA uses discrete dynamics to compute the probabilities of individuals transitioning between epidemiological states. By treating each time step—usually a day—as a discrete event, the approach captures multiple concurrent changes within this time frame. The approach allows to estimate the likelihood of individuals or populations being in specific states, which correspond to distinct epidemiological compartments. This review synthesizes key findings from the application of this approach, providing a comprehensive overview of its utility in understanding epidemic spread.

BP Neural Network-Enhanced System for Employment and Mental Health Support for College Students

This paper employs BP Neural Network (BPNN) theory to evaluate innovation and entrepreneurship education in universities. It utilizes students' evaluation indexes as input vectors and determines the number of hidden layer neurons. Experimental results serve as output vectors. The BPNN method proves reasonable and feasible for vocational education course evaluation, exhibiting a 14.96% higher accuracy than traditional genetic algorithms. The paper discusses the model, configuration, characteristics, training process, algorithm enhancement, and limitations of neural networks, followed by an introduction to genetic algorithms. Through analysis of principles, basic operations, and common operators, it establishes a theoretical foundation for subsequent discussions.

Relational hyperevent models for the coevolution of coauthoring and citation networks

Abstract The development of appropriate statistical models has lagged behind the ambitions of empirical studies analysing large scientific networks—systems of publications connected by citations and authorship. Extant research typically focuses on either paper citation networks or author collaboration networks. However, these networks involve both direct relationships, as well as broader dependencies between references linked by multiple citation paths. In this work, we extend recently developed relational hyperevent models to analyse networks characterized by complex dependencies across multiple network modes. We introduce new covariates to represent theoretically relevant and empirically plausible mixed-mode network configurations. This model specification allows testing hypotheses that recognize the polyadic nature of publication data, while accounting for multiple dependencies linking authors and references of current and prior papers. We implement the model using open-source software to analyse publicly available data on a large scientific network. Our findings reveal a tendency for subsets of papers to be cocited, indicating that the impact of these papers may be partly due to endogenous network processes. More broadly, the analysis shows that models accounting for both the hyperedge structure of publication events and the interconnections between authors and references significantly enhance our understanding of the mechanisms driving scientific production and impact.

Automatic segmentation of intraluminal thrombosis of abdominal aortic aneurysms from CT angiography using a mixed-scale-driven multiview perception network (M2Net) model

Intraluminal thrombosis (ILT) plays a critical role in the progression of abdominal aortic aneurysms (AAA). Understanding the role of ILT can improve the evaluation and management of AAAs. However, compared with highly developed automatic vessel lumen segmentation methods, ILT segmentation is challenging. Angiographic contrast agents can enhance the vessel lumen but cannot improve boundary delineation of the ILT regions; the lack of intrinsic contrast in the ILT structure significantly limits the accurate segmentation of ILT. Additionally, ILT is not evenly distributed within AAAs; its sparsity and scattered distributions in the imaging data pose challenges to the learning process of neural networks. Thus, we propose a multiview fusion approach, allowing us to obtain high-quality ILT delineation from computed tomography angiography (CTA) data. Our multiview fusion network is named Mixed-scale-driven Multiview Perception Network (M2Net), and it consists of two major steps. Following image preprocessing, the 2D mixed-scale ZoomNet segments ILT from each orthogonal view (i.e., Axial, Sagittal, and Coronal views) to enhance the prior information. Then, the proposed context-aware volume integration network (CVIN) effectively fuses the multiview results.Using contrast-enhanced computed tomography angiography (CTA) data from human subjects with AAAs, we evaluated the proposed M2Net. A quantitative analysis shows that the proposed deep-learning M2Net model achieved superior performance (e.g., DICE scores of 0.88 with a sensitivity of 0.92, respectively) compared with other state-of-the-art deep-learning models. In closing, the proposed M2Net model can provide high-quality delineation of ILT in an automated fashion and has the potential to be translated into the clinical workflow.

A Multi-Agent Reinforcement Learning-Based Task-Offloading Strategy in a Blockchain-Enabled Edge Computing Network

In recent years, many mobile edge computing network solutions have enhanced data privacy and security and built a trusted network mechanism by introducing blockchain technology. However, this also complicates the task-offloading problem of blockchain-enabled mobile edge computing, and traditional evolutionary learning and single-agent reinforcement learning algorithms are difficult to solve effectively. In this paper, we propose a blockchain-enabled mobile edge computing task-offloading strategy based on multi-agent reinforcement learning. First, we innovatively propose a blockchain-enabled mobile edge computing task-offloading model by comprehensively considering optimization objectives such as task execution energy consumption, processing delay, user privacy metrics, and blockchain incentive rewards. Then, we propose a deep reinforcement learning algorithm based on multiple agents sharing a global memory pool using the actor–critic architecture, which enables each agent to acquire the experience of another agent during the training process to enhance the collaborative capability among agents and overall performance. In addition, we adopt attenuatable Gaussian noise into the action space selection process in the actor network to avoid falling into the local optimum. Finally, experiments show that this scheme’s comprehensive cost calculation performance is enhanced by more than 10% compared with other multi-agent reinforcement learning algorithms. In addition, Gaussian random noise-based action space selection and a global memory pool improve the performance by 38.36% and 43.59%, respectively.

Influence of structural properties and connectivity of initial fracture network on incipient karst genesis

A karst system is generally difficult to characterize especially regarding its conduits pattern and morphology. Even though speleology allows to map the geometry of cave systems, a large part of karst conduit networks remains unreachable underground. In this study, we use a reactive transport model that simulates dissolution processes in fracture networks to investigate the effect of discrete fracture network (DFN) properties (i.e., anisotropy, connectivity, fracture intersection types) on incipient karst conduit geometry under different boundary conditions (constant hydraulic head versus constant flow rate conditions, directional versus concentrated recharge conditions). We focused on single fracture (with/without cementation) and ladder like fracture networks which are common on limestone. Results show that fracture network properties, play a major role in the final geometry and topology of the incipient karst conduit patterns. Also, the presence of cement on a single fracture favors tortuous and curvilinear wormholes, while the anisotropy of the fracture network favors the formation of anisotropic incipient karst geometries. Finally, the type of hydraulic BC remains a major factor that governs dissolution processes and thus incipient karst conduit geometry.

Research on robot tracking force control algorithm based on neural networks

Purpose This study aims to propose a force control algorithm based on neural networks, which enables a robot to follow a changing reference force trajectory when in contact with human skin while maintaining a stable tracking force. Design/methodology/approach Aiming at the challenge of robots having difficulty tracking changing force trajectories in skin contact scenarios, a single neuron algorithm adaptive proportional – integral – derivative online compensation is used based on traditional impedance control. At the same time, to better adapt to changes in the skin contact environment, a gated recurrent unit (GRU) network is used to model and predict skin elasticity coefficients, thus adjusting to the uncertainty of skin environments. Findings In two robot–skin interaction experiments, compared with the traditional impedance control and robot force control algorithm based on the radial basis function model and iterative algorithm, the maximum absolute force error, the average absolute force error and the standard deviation of the force error are all decreased. Research limitations/implications As the training process of the GRU network is currently conducted offline, the focus in the subsequent phase is to refine the network to facilitate real-time computation of the algorithm. Practical implications This algorithm can be applied to robot massage, robot B-ultrasound and other robot-assisted treatment scenarios. Originality/value As the proposed approach obtains effective force tracking during robot–skin contact and is verified by the experiment, this approach can be used in robot–skin contact scenarios to enhance the accuracy of force application by a robot.