Adaptive Network Model Research Articles

PODB: A learning-based polarimetric object detection benchmark for road scenes in adverse weather conditions

Due to its insensitivity to light intensity and the capability to capture multidimensional information, polarimetric imaging technology has been proven to have advantages over traditional intensity-based imaging techniques for object detection tasks in adverse environmental conditions, particularly in road traffic scenarios. Recently, with the rapid development of artificial intelligence technology, deep learning (DL)-powered object detection techniques can further enhance recognition accuracy and algorithm robustness. This improvement is made possible by the ability of DL technology to leverage large datasets and extract deeper levels of target-specific features. However, constructing large-scale polarimetric datasets poses challenges as obtaining polarimetric information requires multiple captures of intensity images. In other words, the workload is several times higher compared to traditional imaging techniques. To address the current scarcity of polarimetric datasets and evaluate the practical performance of various algorithms on polarimetric datasets, this paper proposes a Polarimetric Object Detection Benchmark (PODB) dataset. The PODB provides an integrated quality evaluation framework for DL-based object detection algorithms in complex road scenes by incorporating polarimetric imaging. Besides, we conducted extensive object detection experiments using the PODB, which allowed for a comprehensive validation and performance evaluation of effective benchmark algorithms. Furthermore, a physics-based multi-scale image fusion cascaded object detection neural network model is proposed. By combining the multidimensional information provided by polarized images with an adaptive learning multi-decision object detection neural network model, the recognition accuracy of complex road scenes in adverse weather conditions has been improved by approximately 10%. Additionally, we anticipate that PODB will serve as an effective platform for evaluating and comparing the performance of object detection algorithms, as well as providing researchers with a baseline for future studies in developing new DL-based methods.

An adaptive network model for AI-assisted monitoring and management of neonatal respiratory distress

This article presents the use of second-order adaptive network models of hospital teams consisting of doctors and nurses, interacting together. A variety of scenarios are modelled and simulated, in relation with respiratory distress of a neonate, along with the integration of an AI-Coach for monitoring and support of such teams and of organizational learning. The research highlights the benefits of introducing a virtual AI-Coach in a hospital setting. The practical application setting revolves around a medical team responsible for managing neonates with respiratory distress. In this setting an AI-Coach act as an additional team member, to ensure correct execution of medical procedure. Through simulation experiments, the adaptive network models demonstrate that the AI-Coach not only aids in maintaining correct medical procedure execution but also facilitates organizational learning, leading to significant improvements in procedure adherence and error reduction during neonatal care.

Adaptive Network Model for Assisting People with Disabilities through Crowd Monitoring and Control.

Here, we present an effective application of adaptive cooperative networks, namely assisting disables in navigating in a crowd in a pandemic or emergency situation. To achieve this, we model crowd movement and introduce a cooperative learning approach to enable cooperation and self-organization of the crowd members with impaired health or on wheelchairs to ensure their safe movement in the crowd. Here, it is assumed that the movement path and the varying locations of the other crowd members can be estimated by each agent. Therefore, the network nodes (agents) should continuously reorganize themselves by varying their speeds and distances from each other, from the surrounding walls, and from obstacles within a predefined limit. It is also demonstrated how the available wireless trackers such as AirTags can be used for this purpose. The model effectiveness is examined with respect to the real-time changes in environmental parameters and its efficacy is verified.

A GCN-based adaptive generative adversarial network model for short-term wind speed scenario prediction

Wind prediction is of great significance for wind energy utilization due to the stochastic nature of wind. To effectively facilitate various downstream decision-making tasks such as wind turbine control, predictive wind Scenario Generation (SG), which is capable of providing a set of deterministic instantiated wind prediction results, plays a critical role. In this paper, a novel Graph neural networks-based Adaptive Predictive Generative Adversarial Network (GAPGAN) model is proposed for accurate prediction of short-term future scenarios of a wind field. In GAPGAN, the original multivariate time series data are first reconstructed into the form of a graph, and spatiotemporal features are then extracted using Graph Convolutional Networks (GCNs). Next, a predictive generative adversarial network (PGAN) framework is proposed, which could generate different outputs corresponding to given historical observations as conditions. Finally, an adaptive PGAN training mechanism is introduced to stabilize the training process, and the best SG model is selected based on the proposed comprehensive evaluation system. Based on wind speed data collected from 11 wind turbines, computational experiments validate that the GAPGAN outperforms five benchmarking models in terms of point prediction accuracy, shape similarity, uncertainty prediction quality, and prediction scenario diversity.

Adaptive neuro-fuzzy based hybrid classification model for emotion recognition from EEG signals

Emotion recognition using physiological signals has gained significant attention in recent years due to its potential applications in various domains, such as healthcare and entertainment. EEG signals have been particularly useful in emotion recognition due to their non-invasive nature and high temporal resolution. However, the development of accurate and efficient algorithms for emotion classification using EEG signals remains a challenging task. This paper proposes a novel hybrid algorithm for emotion classification based on EEG signals, which combines multiple adaptive network models and probabilistic neural networks. The research aims to improve the recognition accuracy of three and four emotions, which has been a challenge for existing approaches. The proposed model consists of N adaptively neuro-fuzzy inference system (ANFIS) classifiers designed in parallel, in which N is the number of emotion classes. The selected features with the most appropriate distribution for classification are given as input vectors to the ANFIS structures, and the system is trained. The outputs of these trained ANFIS models are combined to create a feature vector, which provides the inputs for adaptive networks, and the system is trained to acquire the emotional recognition output. The performance of the proposed model has been evaluated for classification on well-known emotion benchmark datasets, including DEAP and Feeling Emotions. The study results indicate that the model achieves an accuracy rate of 73.49% on the DEAP datasets and 95.97% on the Feeling Emotions datasets. These results demonstrate that the proposed model efficiently recognizes emotions and exhibits a promising classification performance.

A smart video analytical framework for sarcasm detection using novel adaptive fusion network and SarcasNet-99 model

A smart video analytical framework for sarcasm detection using novel adaptive fusion network and SarcasNet-99 model

Near-infrared quality monitoring modeling with multi-scale CNN and temperature adaptive correction

Considering the impact of temperature on near-infrared spectroscopy (NIRS), a temperature adaptive correction neural network model called TI-CNN has been developed. This model takes into account the temperature influence. First, a multi-scale CNN model is established to extract the global features of NIRS. Then, by establishing the quantitative relationship between the sample and the modeling error of the neural network, the influence of temperature on the spectral features. Based on the degree of influence, the weight is adaptively adjusted to quantify the effect of temperature on near-infrared spectral features. Finally, to verify the model performance, EPO-PLSR, OSC-PLSR, CNN, and TA-CNN were used for comparison on the test set. The results showed that traditional temperature correction models performed poorly, with a coefficient of determination R2< 0.80. However, the neural network models based on multi-scale CNN, R2> 0.80. Of these, the TI-CNN based on multi-scale CNN and temperature adaptive correction, demonstrated the best performance with R2> 0.95. Therefore, the method proposed in this paper demonstrates superior performance compared to existing methods. It utilizes a multi-scale CNN to extract near-infrared spectral features and also incorporates temperature to adaptively correct the spectral features, resulting in superior model performance across various temperature conditions.

Enhancing pressure gradient prediction in multi-phase flow through diverse well geometries of North American shale gas fields using deep learning

To ensure the efficient operation of production wells and to avoid flow assurance issues, accurately predicting pressure gradients, Δp/ΔL, in wells is crucial. This study explores the relationship between Δp/ΔL and production well geometries. The data from 29,047 wells across five shale gas basins is utilized: Bakken, Barnett, British Columbia, Marcellus, and Delaware. These basins have distinct shale gas reservoir properties and diverse well geometries. Each well's geometry was characterized by ten parameters, and k-medoids clustering was applied to identify geometry patterns. However, this method overlooked minor geometric variations impacting Δp/ΔL. An adaptive factorization network model, a deep learning approach, was employed to address this. This model demonstrated significant predictive accuracy, as evidenced by high determination coefficients, R2, comparing actual and predicted Δp/ΔL in test data (overall 0.904; Bakken 0.840; Barnett 0.848; BC 0.844; Marcellus 0.872; Loving-Delaware 0.914; Reeves-Delaware 0.880). The method's reliability in forecasting Δp/ΔL is supported by its R2 values of ∼0.8 and the mean absolute percentage error of ∼5 %, indicating its applicability in newly developing basins. This methodology offers reliable Δp/ΔL predictions for all possible well geometry scenarios in shale gas fields but also eliminates the need for additional multi-phase flow simulations, enhancing design and production plan.

Adaptive Inference Pathway-Gated Neural Network Model for Digital Predistortion With Varying Transmission Configurations

Adaptive Inference Pathway-Gated Neural Network Model for Digital Predistortion With Varying Transmission Configurations

Efficient Predefined-Time Adaptive Neural Networks for Computing Time-Varying Tensor Moore-Penrose Inverse.

This article proposes predefined-time adaptive neural network (PTANN) and event-triggered PTANN (ET-PTANN) models to efficiently compute the time-varying tensor Moore-Penrose (MP) inverse. The PTANN model incorporates a novel adaptive parameter and activation function, enabling it to achieve strongly predefined-time convergence. Unlike traditional time-varying parameters that increase over time, the adaptive parameter is proportional to the error norm, thereby better allocating computational resources and improving efficiency. To further enhance efficiency, the ET-PTANN model combines an event trigger with the evolution formula, resulting in the adjustment of step size and reduction of computation frequency compared to the PTANN model. By conducting mathematical derivations, the article derives the upper bound of convergence time for the proposed neural network models and determines the minimum execution interval for the event trigger. A simulation example demonstrates that the PTANN and ET-PTANN models outperform other related neural network models in terms of computational efficiency and convergence rate. Finally, the practicality of the PTANN and ET-PTANN models is demonstrated through their application for mobile sound source localization.

An Adaptive Evolutionary Neural Network Model for Load Management in Smart Grid Environment

An Adaptive Evolutionary Neural Network Model for Load Management in Smart Grid Environment

When the design of climate policy meets public acceptance: An adaptive multiplex network model

The socio-political processes that influence the acceptance of climate policies play a crucial role in shaping mitigation strategies. In this paper, we explore the interplay between social and political dynamics and their impact on climate policy support. Using a simplified model of the social and political system, we aim to uncover ways to enhance public support for climate change mitigation measures. Several factors come into play when considering policy support, including social norms, self-efficacy, social learning, and income. By examining climate mitigation policies and accounting for shifting and inherent preferences, we shed light on how individuals contribute to processes of social change. Through simulations, we find that even minimal peer pressure has a positive and significant impact on individuals' inclination towards green behaviours, regardless of whether regressive or progressive policies are implemented. Additionally, assuming uniform self-efficacy across society leads to an overestimation of society's acceptance of green policies. Our results highlight the importance of nurturing existing skills or developing new ones. Finally, our findings reveal that regional heterogeneity matters for climate policy acceptance.

RETRACTED: Yang et al. An Adaptive Hierarchical Network Model for Studying the Structure of Economic Network. Entropy 2022, 24, 702.

This journal retracts the article, 'An Adaptive Hierarchical Network Model for Studying the Structure of Economic Network' [...].

Content-based adaptive network model to analyze the effect of graphene oxide on cement hydration process and its main hydration products

Graphene oxide nanomaterials can significantly alter and enhance the microstructure of cementitious materials, thus improving their mechanical properties and durability and bringing about a variety of functionalities of cementitious materials. In this paper, the role of graphene oxide (GO) on cement hydration process and its important hydration products, calcium hydroxide (CH) and hydrated calcium silicate gel (C-S H), was investigated under an adaptive network model. Various GOs were characterized analytically using high atomic force microscopy and transmission electron microscopy techniques, and both the hydration process and the concentration of their hydration products were measured in freshly mixed aqueous mortars with various GO admixtures using heat of hydration, X-ray diffraction studies and thermal reanalysis tests.

MSHGN: Multi-scenario adaptive hierarchical spatial graph convolution network for GPU utilization prediction in heterogeneous GPU clusters

Accurately predicting GPU utilization is crucial for effectively managing heterogeneous GPU clusters, yet existing prediction methods are tailored to homogeneous clusters or ignore the unique characteristics of heterogeneous ones. To address this problem, we propose the Multi-Scenarios Adaptive Hierarchical Spatial Graph Convolution Network (MSHGN) model. This model leverages the hierarchical relationships among users, tasks, and machines to construct multiple scenarios' undirected graphs and uses Graph Convolution Neural (GCN) to capture the spatial dependency relationships between the computational resources consumed during execution. The MSHGN model accurately predicts the future GPU utilization rates of each machine in a heterogeneous GPU cluster, achieving an RMSE of only 0.0101, an MAE of 0.0072, and an MAPE of 0.0641. We evaluate the MSHGN model on a real-world Alibaba dataset collected from a heterogeneous GPU cluster and find that it achieves superior accuracy and robustness in predicting resource utilization, outperforming other baseline models.

A Fuzzy Adaptive Zeroing Neural Network Model With Event-Triggered Control for Time-Varying Matrix Inversion

Time-varying matrix inversion (TVMI) is a basic mathematical problem, which is widely involved in many scientific fields. In this paper, an event-triggered control fuzzy adaptive zeroing neural network (ETC-FAZNN) model is proposed for solving the TVMI problem, where the fuzzy adaptive convergence parameter (FACP) is got by the redesigned fuzzy logic system, which makes the ETC-FAZNN model adaptive. Meanwhile, the event-triggered control is introduced to control the update of the FACP, which improves the calculation speed of the ETC-FAZNN model. Moreover, a novel activation function called segmented predefined-time activation function (SptAF) is put forward in this paper to improve the convergence and robustness of the ETC-FAZNN model. Theoretical analysis and simulation experiments reveal that the ETC-FAZNN model can realize stability, predefined-time convergence, robustness, and adaptability performances in solving the TVMI problem.

Provisioning a cross-domain recommender system using an adaptive adversarial network model

Provisioning a cross-domain recommender system using an adaptive adversarial network model

Neural Network Optimization and Implications of High and Low Gradient Results in Training Vanilla and Hybrid Adaptive Neural Network Models for Effective Signal Power Loss Prediction

Neural Network Optimization and Implications of High and Low Gradient Results in Training Vanilla and Hybrid Adaptive Neural Network Models for Effective Signal Power Loss Prediction

Machining surface roughness detection by adaptive deep fusion capsule network with low illumination and noise robustness

Abstract Surface roughness measurement is of great importance for monitoring machining quality. To address the performance degradation of deep models caused by low illumination and ambient noise, this study proposes a non-contact surface roughness detection method based on an adaptive deep fusion capsule network (ADFCNet) model. Firstly, principal competent analysis-based color image enhancement is employed to augment initial surface images collected from normal illumination. Then, consisting of a deep multi-model fusion for high-level feature representation and a capsule classifier for roughness recognition, the ADFCNet model is designed to detect roughness grades by using workpiece surface images. The key hyperparameters of the model are automatically determined by a genetic algorithm. Finally, laboratory and factory experiments under low illumination are carried out to validate the effectiveness and superiority of the proposed method. Experimental results demonstrate that the proposed method has strong low-illumination and noise robustness and generalization capability, indicating a better application prospect in actual scenarios.

Higher-order adaptive dynamical system modeling of the role of epigenetics in anxiety disorders

In this paper, a fifth-order adaptive self-modelling network model is introduced to describe epigenetic involvement in the development of anxiety disorders and its regulation by a possible epigenetics-based therapeutic method. Multiple orders of adaptivity are used in the model to depict the development process, where a higher pathway of any order of adaptivity adapts characteristics of pathways in lower orders and acts as a form of control. These orders of adaptivity and their interlevel interaction were modelled as a higher-order adaptive dynamical system according to the self-modelling network modelling principle. The model was inspired by the structure of the relevant human biological and neurological processes. In addition to modelling the development of an anxiety disorder, also the possibility of an epigenetics-based therapy is suggested and computationally analyzed in this paper.