Original Model Research Articles

Data-driven high-order rogue waves and parameters discovery for Gardner equation using deep learning approach

In this paper, an adaptive mix training physics-informed neural networks (A-MTPINNs) model is proposed to study the high-order rogue waves of the famous Gardner equation (GE). Through a series of numerical experiments, we can conclude that this A-MTPINNs model can not only recover the dynamic behavior of the high-order rogue waves of the GE well, but also can guarantee higher learning ability and prediction accuracy than the original PINNs model, and the prediction accuracy is improved by two orders of magnitude. By testing that the A-MTPINNs model can maintain high robustness under different noises for the data-driven solutions of the GE. Furthermore, this model also has good performance for the inverse problem of the Gardner equation by a data-driven discovery approach and also remains robust in noise experiments.

Predictive Performance of a Gentamicin Pharmacokinetic Model in Term Neonates with Perinatal Asphyxia Undergoing Controlled Therapeutic Hypothermia.

Model validation procedures are crucial when population pharmacokinetic (PK) models are used to develop dosing algorithms and to perform model-informed precision dosing. We have previously published a population PK model describing the PK of gentamicin in term neonates with perinatal asphyxia during controlled therapeutic hypothermia (TH), which showed altered gentamicin clearance during the hypothermic phase dependent on gestational age and weight. In this study, the predictive performance and generalizability of this model were assessed using an independent data set of neonates with perinatal asphyxia undergoing controlled TH. The external data set contained a subset of neonates included in the prospective observational multicenter PharmaCool Study. Predictive performance was assessed by visually inspecting observed-versus-predicted concentration plots and calculating bias and precision. In addition, simulation-based diagnostics, model refitting, and bootstrap analyses were performed. The external data set included 323 gentamicin concentrations of 39 neonates. Both the model-building and external data set included neonates from multiple centers. The original gentamicin PK model predicted the observed gentamicin concentrations with adequate accuracy and precision during all phases of controlled TH. Model appropriateness was confirmed with prediction-corrected visual predictive checks and normalized prediction distribution error analyses. Model refitting to the merged data set (n = 86 neonates with 935 samples) showed accurate estimation of PK parameters. The results of this external validation study justify the generalizability of the gentamicin dosing recommendations made in the original study for neonates with perinatal asphyxia undergoing controlled TH (5 mg/kg every 36 or 24 h with gestational age 36-41 and 42 wk, respectively) and its applicability in model-informed precision dosing.

Visual Re-Initialization Model Development Methodology for Solving Problems Regarding Metaheuristic Algorithm-Based MPPT Applications

Metaheuristic algorithms are particularly useful for maximum power point tracking (MPPT) applications, because they can adapt to changes in operating conditions and effectively handle partial shading conditions. However, metaheuristic algorithms also have some limitations that need to be addressed to make them suitable for MPPT applications. The problems associated with metaheuristic algorithm-based MPPT applications include being trapped in local optima, slow convergence speed, shading condition variability, computational complexity and robustness. These problems lead to reduced efficiency in MPPT applications. In the literature, the solution of the aforementioned problems is partially addressed and some of them are solved via an additional irradiation sensor. The motivation of this study is to develop a control algorithm that covers all problems that have been partially solved in the literature and includes an original re-initialization modeling method in accordance with visual programing concept, without using any additional radiation sensor. The proposed control algorithm has the flexibility to be easily adapted to other metaheuristic algorithms and does not require any radiation sensors. The re-initialization model created via Matlab/Simulink and “Embedded Coder Support Package for TI C2000 Processors” allows easy tracking of the global maximum power point (GMPP) by detecting variable radiation conditions. The proposed model was implemented on the Cuckoo Search Algorithm (CSA) and verified through experimental studies carried out with a TI-TMS320f28069 microcontroller and PV emulator. The experimental results confirm that issue 1 is solved with 100%, issue 2 is solved with 99.5%, issue 3 is solved with 99.84%, and issue 4 is solved with 100% MPPT efficiency.

Integrated Interactive Control of Distribution Systems with Multi-Building Microgrids Based on Game Theory

In the transactional processes within a multi-building microgrid system, it is imperative to safeguard stakeholders’ interests and ensure stable, economically efficient operation. Therefore, this paper proposes an integrated interactive control of distribution systems with multi-building microgrids based on game theory. Initially, an interactive framework encompassing superior power grids, distribution network operators, and multi-building microgrids is proposed. This framework establishes a master–slave game model between distribution network operators and multi-building microgrids. Additionally, it introduces the concept of Soft Open Points between building microgrids to enhance system operational safety while also reducing economic costs for distribution network operators. Subsequently, to maintain solution accuracy and optimality, it employs linearization and cone relaxation methods to transform the original model into a mixed-integer second-order cone programming model. Furthermore, it enhances an iterative search method and the price mechanism based on supply and demand ratios. The revised price mechanism serves to boost the participation of building microgrid users in energy regulation, while the iterative search method effectively resolves the equilibrium point of interest among all game participants. Finally, a simulation analysis is conducted on an IEEE-33 test system, and the effectiveness of the proposed strategy is verified by comparing three schemes.

A novel framework to evaluate urban-rural coordinated development: A case study in Shanxi Province, China

The coexistences of urban diseases and rural decline threaten the achievements of Sustainable Development Goals (SDGs). China, experiencing rapid urbanization recently, is facing the prominent issues of unbalanced and inadequate development. Accordingly, the Chinese government has launched new-type urbanization (NU) and rural vitalization (RV) strategies. After illustrating the coupling mechanism of NU and RV strategies, a novel framework that combines a multidimensional development index (MDI) and the modified coupling degree model (MCDM) is proposed to delineate urban-rural coordinated development. Taking Shanxi Province as an example, multi-source data are incorporated into NU and RV evaluation subsystems. MDI is introduced to evaluate the comprehensive performance of NU and RV, which overcomes the restriction of ordinary linear weighting method that assumes the independence of elements. MCDM is developed to estimate coordinated relationships of urban and rural development. Results show that only two cities are in the extreme coupling stage according to MCDM, which demonstrates it can effectively differentiate the degree of urban-rural integration, whereas the original model potentially overestimates the coupling degree. The effectiveness of the framework is validated in our empirical analysis, which can be applied to evaluate the performance and coordination among multiple subsystems in other fields.

Prediction of the mechanical properties of TPMS structures based on Back propagation neural network

Triply Periodic Minimal Surface (TPMS) has the characteristics of high porosity, a highly interconnected network, and a smooth surface, making it an ideal candidate for bone tissue engineering applications. However, due to the complex relationship between multiple parameters of the TPMS structure and mechanical properties, it is a challenging task to optimize the properties of TPMS structures with different parameters. In this study, a Back-Propagation Neural Network (BPNN) was utilized to construct the relationship between TPMS parameters. Its mechanical performance and the TPMS structure were optimized using the BPNN. Results indicated that after training the correlation coefficient (R) between the BPNN prediction and the experimental results is 0.955475, it shows that our BPNN model has an adequate accuracy in describing the TPMS structures properties. Result of TPMS structure optimization shows that after optimization the yield strength of Hybridized Gyroid-Diamond Structure (HGDS) is 6.20 MPa, which is increased by 102.61% when compared with the original Hybridized Gyroid-Diamond Structure (3.06 MPa). Result of topological morphology indicates the effective bearing area of the optimized model was increased by 12.92% compared with the original model, which ascribe the increase in yield strength of the optimization model.

Oscillatory Behavior of the Solutions for a Parkinson’s Disease Model with Discrete and Distributed Delays

In this paper, the oscillatory behavior of the solutions for a Parkinson’s disease model with discrete and distributed delays is discussed. The distributed delay terms can be changed to new functions such that the original model is equivalent to a system in which it only has discrete delays. Using Taylor’s expansion, the system can be linearized at the equilibrium to obtain both the linearized part and the nonlinearized part. One can see that the nonlinearized part is a disturbed term of the system. Therefore, the instability of the linearized system implies the instability of the whole system. If a system is unstable for a small delay, then the instability of this system will be maintained as the delay increased. By analyzing the linearized system at the smallest delay, some sufficient conditions to guarantee the existence of oscillatory solutions for a delayed Parkinson’s disease system can be obtained. It is found that under suitable conditions on the parameters, time delay affects the stability of the system. The present method does not need to consider a bifurcating equation. Some numerical simulations are provided to illustrate the theoretical result.

Psychometric Properties and Validity of the Screen for Child Anxiety Related Emotional Disorders: Parent Version (SCARED-P) in an Early Childhood Sample.

The Screen for Child Anxiety Related Emotional Disorders: Parent Version (SCARED-P) was originally developed for use in middle childhood and adolescence. The present study examined the psychometric properties and validity of the SCARED-P in an early childhood sample (predominantly aged 4-7 years). The 41-item version of the SCARED-P was administered to the parents of 233 children (mean age = 6.31 years, SD = 1.08; females = 34.3%). Confirmatory factor analysis provided mixed support for the original five-factor model of the SCARED-P. The SCARED-P demonstrated good to excellent internal consistency (total α = .94, subscale α = .68-.89), and good construct validity with the Child Behavior Checklist, Strengths and Difficulties Questionnaire, and the Developmental and Well-being Assessment. These findings indicate overall initial support for the SCARED-P's utility as a measure of anxiety in early childhood, but further psychometric and validation studies are needed in larger community-based samples of young children.

Numerical approximation of a hybrid Poisson-jump Ait-Sahalia-type interest rate model with delay

. While the original Ait-Sahalia interest rate model has been found to be of considerable use for describing the time-series evolution of interest rates, it may not possess adequate specifications to explain the responses of interest rates to empirical phenomena such as volatility skew-smile effect, jumps, market regulatory lapses, economic crises, financial clashes, and political instability, among others collectively. In this article, we propose a modified version of this model by incorporating additional features to help collectively describe these empirical phenomena adequately. Since the proposed model does not have a closed-form formula, we construct new techniques of the truncated EM method to study it and justify the method within a Monte Carlo framework to compute some financial quantities.

A lightweight fault diagnosis model for planetary gearbox using domain adaptation and model compression

This article proposes a novel lightweight attention spatiotemporal joint distribution adaptation network fault diagnosis model to address the key challenges of domain transfer and high model complexity in traditional methods. The novelty lies in 1. Using model compression techniques to reduce the complexity of the network model and improve its computational efficiency; 2. Introducing new domain adaptation and adversarial methods to solve the domain transfer problem. The effectiveness of the proposed model is verified through a transfer experiment of planetary gearbox vibration data. The experimental results show that the proposed model reduces the parameters and computational complexity to 18 % and 15 % of the original model, respectively, and has a diagnostic accuracy of over 98 % in cross-condition transfer tasks, and still maintains an accuracy of over 88 % even under high noise levels. This indicates that the proposed model is an efficient and accurate fault diagnosis model.

Automatic detection of standing dead trees based on improved YOLOv7 from airborne remote sensing imagery.

Detecting and localizing standing dead trees (SDTs) is crucial for effective forest management and conservation. Due to challenges posed by mountainous terrain and road conditions, conducting a swift and comprehensive survey of SDTs through traditional manual inventory methods is considerably difficult. In recent years, advancements in deep learning and remote sensing technology have facilitated real-time and efficient detection of dead trees. Nevertheless, challenges persist in identifying individual dead trees in airborne remote sensing images, attributed to factors such as small target size, mutual occlusion and complex backgrounds. These aspects collectively contribute to the increased difficulty of detecting dead trees at a single-tree scale. To address this issue, the paper introduces an improved You Only Look Once version 7 (YOLOv7) model that incorporates the Simple Parameter-Free Attention Module (SimAM), an unparameterized attention mechanism. This improvement aims to enhance the network's feature extraction capabilities and increase the model's sensitivity to small target dead trees. To validate the superiority of SimAM_YOLOv7, we compared it with four widely adopted attention mechanisms. Additionally, a method to enhance model robustness is presented, involving the replacement of the Complete Intersection over Union (CIoU) loss in the original YOLOv7 model with the Wise-IoU (WIoU) loss function. Following these, we evaluated detection accuracy using a self-developed dataset of SDTs in forests. The results indicate that the improved YOLOv7 model can effectively identify dead trees in airborne remote sensing images, achieving precision, recall and mAP@0.5 values of 94.31%, 93.13% and 98.03%, respectively. These values are 3.67%, 2.28% and 1.56% higher than those of the original YOLOv7 model. This improvement model provides a convenient solution for forest management.

Research on surface defect detection algorithm of pipeline weld based on YOLOv7

Aiming at the problems of low target detection accuracy and high leakage rate of the current traditional weld surface defect detection methods and existing detection models, an improved YOLOv7 pipeline weld surface defect detection model is proposed to improve detection results. In the improved model, a Le-HorBlock module is designed, and it is introduced into the back of fourth CBS module of the backbone network, which preserves the characteristics of high-order information by realizing second-order spatial interaction, thus enhancing the ability of the network to extract features in weld defect images. The coordinate attention (CoordAtt) block is introduced to enhance the representation ability of target features, suppress interference. The CIoU loss function in YOLOv7 network model is replaced by the SIoU, so as to optimize the loss function, reduce the freedom of the loss function, and accelerate convergence. And a new large-scale pipeline weld surface defect dataset containing 2000 images of pipeline welds with weld defects is used in the proposed model. In the experimental comparison, the improved YOLOv7 network model has greatly improved the missed detection rate compared with the original network. The experimental results show that the improved YOLOv7 network model mAP@80.5 can reach 78.6%, which is 15.9% higher than the original model, and the detection effect is better than the original network and other classical target detection networks.

Blowup analysis for a quasi-exact 1D model of 3D Euler and Navier–Stokes

We study the singularity formation of a quasi-exact 1D model proposed by Hou and Li (2008 Commun. Pure Appl. Math. 61 661–97). This model is based on an approximation of the axisymmetric Navier–Stokes equations in the r direction. The solution of the 1D model can be used to construct an exact solution of the original 3D Euler and Navier–Stokes equations if the initial angular velocity, angular vorticity, and angular stream function are linear in r. This model shares many intrinsic properties similar to those of the 3D Euler and Navier–Stokes equations. It captures the competition between advection and vortex stretching as in the 1D De Gregorio (De Gregorio 1990 J. Stat. Phys. 59 1251–63; De Gregorio 1996 Math. Methods Appl. Sci. 19 1233–55) model. We show that the inviscid model with weakened advection and smooth initial data or the original 1D model with Hölder continuous data develops a self-similar blowup. We also show that the viscous model with weakened advection and smooth initial data develops a finite time blowup. To obtain sharp estimates for the nonlocal terms, we perform an exact computation for the low-frequency Fourier modes and extract damping in leading order estimates for the high-frequency modes using singularly weighted norms in the energy estimates. The analysis for the viscous case is more subtle since the viscous terms produce some instability if we just use singular weights. We establish the blowup analysis for the viscous model by carefully designing an energy norm that combines a singularly weighted energy norm and a sum of high-order Sobolev norms.

Detection of Small Lesions on Grape Leaves Based on Improved YOLOv7

The precise detection of small lesions on grape leaves is beneficial for early detection of diseases. In response to the high missed detection rate of small target diseases on grape leaves, this paper adds a new prediction branch and combines an improved channel attention mechanism and an improved E-ELAN (Extended-Efficient Long-range Attention Network) to propose an improved algorithm for the YOLOv7 (You Only Look Once version 7) model. Firstly, to address the issue of low resolution for small targets, a new detection head is added to detect smaller targets. Secondly, in order to increase the feature extraction ability of E-ELAN components in YOLOv7 for small targets, the asymmetric convolution is introduced into E-ELAN to replace the original 3 × 3 convolution in E-ELAN network to achieve multi-scale feature extraction. Then, to address the issue of insufficient extraction of information from small targets in YOLOv7, a channel attention mechanism was introduced and improved to enhance the network’s sensitivity to small-scale targets. Finally, the CIoU (Complete Intersection over Union) in the original YOLOv7 network model was replaced with SIoU (Structured Intersection over Union) to optimize the loss function and enhance the network’s localization ability. In order to verify the effectiveness of the improved YOLOv7 algorithm, three common grape leaf diseases were selected as detection objects to create a dataset for experiments. The results show that the average accuracy of the algorithm proposed in this paper is 2.7% higher than the original YOLOv7 algorithm, reaching 93.5%.

A machine vision method for the evaluation of ship-to-ship collision risk

The development of ship technology and information technology has been driving the continuous improvement of ship intelligence, with safety being an inevitable requirement in the shipping industry. A machine vision-based ship collision warning method is proposed for high monitoring system cost and limited information acquisition in safety design of autonomous ship navigation. The method combines machine learning with image algorithms. Firstly, the backbone of YOLOv7 detector is replaced by EfficientFormerV2 network to achieve model lightweight while ensuring detection accuracy. Public datasets SeaShips, Flow and self-made ship pictures are combined, and the network is trained on this dataset. StrongSORT is used for target tracking. Secondly, a data fusion algorithm is introduced to determine the target point at the bow-bottom of the ship based on the time-varying attitude of the camera and the time-series features of the bounding boxes. Ship navigation trajectory estimation is performed using image algorithms. Finally, a collision evaluation model is established to calculate the collision risk index. Experimental results demonstrate that the improved YOLOv7 network maintains similar mAP.5 and Recall compared to the original model, while reducing the parameters by 31.2 % and GFLOPs by 58.4 %. The accuracy of target ship trajectory estimation is high, with MAE values below 1.5 % and RMSE values below 2 % in experiments. In ship collision warning experiments, the proposed method accurately identifies navigating vessels, estimates the trajectories, and provides timely warnings for imminent collision accidents. Compared to traditional ship collision warning methods, this paper offers a more intelligent and lightweight solution.

PAM-FOG Net: A Lightweight Weed Detection Model Deployed on Smart Weeding Robots

Visual target detection based on deep learning with high computing power devices has been successful, but the performance in intelligent agriculture with edge devices has not been prominent. Specifically, the existing model architecture and optimization methods are not well-suited to low-power edge devices, the agricultural tasks such as weed detection require high accuracy, short inference latency, and low cost. Although there are automated tuning methods available, the search space is extremely large, using existing models for compression and optimization greatly wastes tuning resources. In this article, we propose a lightweight PAM-FOG net based on weed distribution and projection mapping. More significantly, we propose a novel model compression optimization method to fit our model. Compared with other models, PAM-FOG net runs on smart weeding robots supported by edge devices, and achieves superior accuracy and high frame rate. We effectively balance model size, performance and inference speed, reducing the original model size by nearly 50%, power consumption by 26%, and improving the frame rate by 40%. It shows the effectiveness of our model architecture and optimization method, which provides a reference for the future development of deep learning in intelligent agriculture.

Unleashing the power of AI in detecting metal surface defects: an optimized YOLOv7-tiny model approach.

The detection of surface defects on metal products during the production process is crucial for ensuring high-quality products. These defects also lead to significant losses in the high-tech industry. To address the issues of slow detection speed and low accuracy in traditional metal surface defect detection, an improved algorithm based on the YOLOv7-tiny model is proposed. Firstly, to enhance the feature extraction and fusion capabilities of the model, the depth aware convolution module (DAC) is introduced to replace all ELAN-T modules in the network. Secondly, the AWFP-Add module is added after the Concat module in the network's Head section to strengthen the network's ability to adaptively distinguish the importance of different features. Finally, in order to expedite model convergence and alleviate the problem of imbalanced positive and negative samples in the study, a new loss function called Focal-SIoU is used to replace the original model's CIoU loss function. To validate the effectiveness of the proposed model, two industrial metal surface defect datasets, GC10-DET and NEU-DET, were employed in our experiments. Experimental results demonstrate that the improved algorithm achieved detection frame rates exceeding 100 fps on both datasets. Furthermore, the enhanced model achieved an mAP of 81% on the GC10-DET dataset and 80.1% on the NEU-DET dataset. Compared to the original YOLOv7-tiny algorithm, this represents an increase in mAP of nearly 11% and 9.2%, respectively. Moreover, when compared to other novel algorithms, our improved model demonstrated enhanced detection accuracy and significantly improved detection speed. These results collectively indicate that our proposed enhanced model effectively fulfills the industry's demand for rapid and efficient detection and recognition of metal surface defects.

An iterative method for updating finite element models with connectivity constraints

It is well known that the analytical matrices arising from the discretization of distributed parameter systems using the finite element technique are usually symmetric and banded. How to preserve the coefficient matrices of the updated model being of the same band structure is an important yet difficult challenge for model updating in structural dynamics. In this paper, an iterative method for updating mass, damping and stiffness matrices simultaneously based on partial modal measured data is provided. By the method, the optimal updated matrices can be obtained within finite iteration steps by choosing a special kind of initial matrix triplet. The proposed approach not only preserves the physical connectivity of the original model, but also the updated model reproduces the measured modal data, which can be utilized for various finite element model updating problems. Numerical examples confirm the effectiveness of the introduced method.

Gender Identification of Chinese Mitten Crab Juveniles Based on Improved Faster R-CNN

The identification of gender in Chinese mitten crab juveniles is a critical prerequisite for the automatic classification of these crab juveniles. Aiming at the problem that crab juveniles are of different sizes and relatively small, with unclear male and female characteristics and complex background environment, an algorithm C-SwinFaster for identifying the gender of Chinese mitten crab juveniles based on improved Faster R-CNN was proposed. This algorithm introduces Swin Transformer as the backbone network and an improved Path Aggregation Feature Pyramid Network (PAFPN) in the neck to obtain multi-scale high-level semantic feature maps, thereby improving the gender recognition accuracy of Chinese mitten crab male and female juveniles. Then, a self-attention mechanism is introduced into the region of interest pooling network (ROI Pooling) to enhance the model’s attention to the classification features of male and female crab juveniles and reduce background interference on the detection results. Additionally, we introduce an improved non-maximum suppression algorithm, termed Softer-NMS. This algorithm refines the process of determining precise target candidate boxes by modulating the confidence level, thereby enhancing detection accuracy. Finally, the focal loss function is introduced to train the model, reducing the weight of simple samples during the training process, and allowing the model to focus more on samples that are difficult to distinguish. Experimental results demonstrate that the enhanced C-SwinFaster algorithm significantly improves the identification accuracy of male and female Chinese mitten crab juveniles. The mean average precision (mAP) of this algorithm reaches 98.45%, marking a 10.33 percentage point increase over the original model. This algorithm has a good effect on the gender recognition of Chinese mitten crab juveniles and can provide technical support for the automatic classification of Chinese mitten crab juveniles.

An Efficient and Lightweight Detection Model for Forest Smoke Recognition

Massive wildfires have become more frequent, seriously threatening the Earth’s ecosystems and human societies. Recognizing smoke from forest fires is critical to extinguishing them at an early stage. However, edge devices have low computational accuracy and suboptimal real-time performance. This limits model inference and deployment. In this paper, we establish a forest smoke database and propose a model for efficient and lightweight forest smoke detection based on YOLOv8. Firstly, to improve the feature fusion capability in forest smoke detection, we fuse a simple yet efficient weighted feature fusion network into the neck of YOLOv8. This also greatly optimizes the number of parameters and computational load of the model. Then, the simple and parametric-free attention mechanism (SimAM) is introduced to address the problem of forest smoke dataset images that may contain complex background and environmental disturbances. The detection accuracy of the model is improved, and no additional parameters are introduced. Finally, we introduce focal modulation to increase the attention to the hard-to-detect smoke and improve the running speed of the model. The experimental results show that the mean average precision of the improved model is 90.1%, which is 3% higher than the original model. The number of parameters and the computational complexity of the model are 7.79 MB and 25.6 GFLOPs (giga floating-point operations per second), respectively, which are 30.07% and 10.49% less than those of the unimproved YOLOv8s. This model is significantly better than other mainstream models in the self-built forest smoke detection dataset, and it also has great potential in practical application scenarios.