Problem Of Accuracy Research Articles

Hybrid Strategy Improved Beetle Antennae Search Algorithm and Application

The multi-dimensional optimization of mechanisms is a typical optimization problem encountered in mechanical design. Herein, the Hybrid strategy improved Beetle Antennae Search (HSBAS) algorithm is proposed to solve the multi-dimensional optimization problems encountered in structural design. To solve the problems of local optimization and low accuracy of the high-dimensional solution of the Beetle Antennae Search (BAS) algorithm, the algorithm adopts the adaptive step strategy, multi-directional exploration strategy, and Lens Opposition-Based Learning strategy, significantly reducing the probability of the algorithm falling into the local optimum and improving its global search capability. Comparative experiments of the improved algorithm are carried out by selecting eleven benchmark test functions. HSBAS can reach 1 × 10−22 accuracy from the optimal value when dealing with low-dimensional functions. It can also obtain 1 × 10−2 accuracy when dealing with high-dimensional functions, significantly improving the algorithm’s capability. According to Friedman’s ranking test result, HSBAS ranks first, which proves that HSBAS is superior to the other three algorithms. The HSBAS algorithm is further used to optimize the design of the altitude compensation module of the gravity compensation device for solar wings, controlling the fluctuation of bearing capacity within 0.25%, which shows that the algorithm can be used as an effective tool for engineering structural optimization problems.

The Effectiveness of Repeated Reading and Supported Cloze Procedure to Improve Beginning Reading Skills in Elementary School Slow Learner Students

Literacy plays a very important role in the academic performance and overall development of elementary school students. However, many elementary school age children experience problems in reading, including fluency, accuracy and reading comprehension. This affects the learning process and causes poor academic results. The initial reading ability of 4th grade elementary school slow learner students at MI Salafiyah Mahbubiyah still has not received special treatment to improve their reading ability. Improving initial reading skills in slow learner students must be done in the right way, one of which is repeated reading and supported cloze procedures. To overcome this problem, the intervention was carried out using the repeated reading method and supported cloze procedure. The aim of this research is to see the effectiveness of repeated reading and supported cloze procedures to improve the beginning reading abilities of slow learner students in grade 4 of elementary school. This intervention aims to improve early reading skills in elementary school children who experience slow learning. This research uses a single case experimental design, where the subjects are 4th grade elementary school students. The development of the subject's reading ability was measured using Gickling and Armstrong's checklist, namely Reading Proficiency and Error Checklists. The results of the research show that there is a difference after being given the repeated reading and supported cloze procedure technique and before being given the repeated reading and supported cloze procedure on the subject. This means that (Ha) is accepted and (Ho) is rejected. So, it can be concluded that the repeated reading and supported cloze procedure is effective in improving the initial reading skills of slow learner students in grade 4 of elementary school.

CSINet: Channel–Spatial Fusion Networks for Asymmetric Facial Expression Recognition

Occlusion or posture change of the face in natural scenes has typical asymmetry; however, an asymmetric face plays a key part in the lack of information available for facial expression recognition. To solve the problem of low accuracy of asymmetric facial expression recognition, this paper proposes a fusion of channel global features and a spatial local information expression recognition network called the “Channel–Spatial Integration Network” (CSINet). First, to extract the underlying detail information and deepen the network, the attention residual module with a redundant information filtering function is designed, and the backbone feature-extraction network is constituted by module stacking. Second, considering the loss of information in the local key area of face occlusion, the channel–spatial fusion structure is constructed, and the channel features and spatial features are combined to enhance the accuracy of occluded facial recognition. Finally, before the full connection layer, more local spatial information is embedded into the global channel information to capture the relationship between different channel–spatial targets, which improves the accuracy of feature expression. Experimental results on the natural scene facial expression data sets RAF-DB and FERPlus show that the recognition accuracies of the modeling approach proposed in this paper are 89.67% and 90.83%, which are 13.24% and 11.52% higher than that of the baseline network ResNet50, respectively. Compared with the latest facial expression recognition methods such as CVT, PACVT, etc., the method in this paper obtains better evaluation results of masked facial expression recognition, which provides certain theoretical and technical references for daily facial emotion analysis and human–computer interaction applications.

DMS-yolov8 A gesture recognition algorithm based on improved yolov81

Aiming at the problems of slow speed and low accuracy of traditional neural network systems for real-time gesture recognition in complex backgrounds., this paper proposes DMS-yolov8-a gesture recognition method to improve yolov8. This algorithm replaces the Bottleneck convolution module in the backbone network of yolov8 with variable row convolution DCNV2, and increases the feature convolution range without increasing the computation amount through a more flexible feeling field. in addition, the self-developed MPCA attention module is added after the feature output layer of the backbone layer, which improves the problem of recognizing the accuracy of difference gestures in complex backgrounds by effectively combining the feature information of the contextual framework, taking into account the multi-scale problem of the gestures in the image, this paper introduces the SPPFCSPS module, which realizes multi-feature fusion and improves real-time accuracy of detection. Finally, the model proposed in this paper is compared with other models, and the proposed DMS-yolov8 model achieves good results on both publicly available datasets and homemade datasets, with the average accuracy up to 97.4% and the average mAP value up to 96.3%, The improvements proposed in this paper are effectively validated.

A MWCMLAI-Net method for LAI inversion in maize and rice using GF-3 and Lutan radar data

ABSTRACT This study aimed at alleviating the problems of unsatisfactory inversion accuracy and weak model stability in LAI remote sensing quantitative inversion. The properties and complex scattering mechanism of SAR data specify the polarization combinations and frequencies. This paper proposes an improved water cloud model combined with a deep neural network (MWCMLAI-Net) for high-precision inversion. The polarized GF-3 (C-band) and Lutan (L-band) were used to investigate the potential of SAR images to estimate LAI, a strong indicator of crop productivity. The study selected xiangfu district in the eastern part of Kaifeng City, Henan Province, as the test area and investigated the LAI of maize and rice. The RV I Freeman Model, backward scattering coefficient extracted by the modified cloud and water model (MWCM), and LAI obtained by the inversion of the MWCM were used as the inputs, and the MWCMLAI-Net inversion of the LAI was constructed. The results showed that the model’s inverted LAI fitting accuracies of maize and rice for the three fertility periods were better than the other models, with R 2 above 0.8516 and RMSE below 0.3999 m2/m2. The addition of noise did not affect the results.

Improved YOLOv8 for B-scan image flaw detection of the heavy-haul railway

With the high speed and heavy duty of railway transportation, internal flaw detection of railway rails has become a hot issue. Existing rail flaw detection systems have problems of low detection accuracy and occasional missed flaw detection. In this paper, a high-precision flaw detection based on data augmentation and YOLOv8 improvement is proposed. Firstly, three data augmentation algorithms based on the characteristics of B-scan images are designed to enrich the dataset of rail flaws. Then, the small target detection layer and the cross-layer connectivity module are added to capture more information for small targets. Finally, the introduction of dynamic weights to coordinate attention can adjust the attentional weights and capture long-range information. The experimental results show that the mAP50 of the model after data enhancement and algorithm improvement is 97.9%, which is improved by 4.4% from the baseline model, and the frame per second is 64.52. The proposed method effectively detects many typical flaws, including the railhead flaw, rail jaw flaw, screw hole crack, and bottom flaw, which can provide technology supports for on-site maintenance staff.

Research on Stock Price Prediction and Quantitative Stock Picking Strategy Based on Deep Learning

With the continuous development of the domestic stock market and the continuous improvement of the financial system system, and at the same time, the domestic stock market gradually rises in the financial system, based on the prediction research of the domestic stock market will become more and more important. In order to solve the problems of low precision and poor accuracy of short-term stock price prediction, this paper selects the bi-directional long- and short-term memory network of attention mechanism (WOA-BiLSTM-Attenion) model under the whale optimization algorithm for stock price prediction. The modeling of bi-directional long- and short-term memory network with attention mechanism can reduce the loss of historical information and increase the influence of important information. On this basis, Whale Optimization Algorithm (WOA) is then used for hyperparameter selection to reduce human interference. The experimental results show that compared with BP, LSTM, BiLSTM, BiLSTM-Attention, the WOA-BiLSTM-Attenion model has a better effect on stock closing price prediction, with a value of 13.9446, and the value of 0.9477, which has a higher accuracy, with a view to providing certain reference for the prediction research in other fields.

A new fault diagnosis of rolling bearing on FFT image coding and L-CNN

To address the problems of low diagnostic accuracy and slow diagnostic speed of the convolutional neural network (CNN) fault diagnosis method in rolling bearing diagnosis, a new rolling bearing fault diagnosis method based on fast Fourier transform (FFT) image coding and lightweight-CNN (L-CNN) is proposed. The method is mainly divided into three stages: firstly, the original signal is reconstructed by noise reduction using a joint noise reduction method of complete ensemble empirical mode decomposition with adaptive noise, permutation entropy, and wavelet threshold denoise; then, the frequency spectra and phase spectra feature fusion data of the noise-reduced and reconstructed bearing vibration signals are obtained by FFT, the feature fusion data are encoded into a heat map, and the image coding data-set is fed into an improved L-CNN for fault diagnosis. Experiments were carried out using the Guangdong University of Petrochemical Technology bearing fault data-set and the Case Western Reserve University bearing fault data-set with diagnostic accuracies of 98.75% and 99%, respectively. The results demonstrate that the method can effectively classify bearing fault vibration signals with the advantages of a fast diagnosis, high accuracy, and good generalization ability.

PECT Composite Defect Detection Algorithm Based on DualGAN

To address the problems of insufficient accuracy and slow reconstruction speed of Planar Electrical Capacitance Tomography (PECT) detection of damaged specimens, a Dual Generative Adversarial Networks (DualGAN)-based PECT image defect detection method is proposed in this paper. The improved particle swarm algorithm with adaptive particle number and L2-norm is used to optimize the sensitivity field, combined with the parallel Landweber algorithm to solve the PECT inverse problem to obtain the dielectric constant distribution map. In the DualGAN network, the Unet generator utilizes an Adam-based local attention mechanism to adjust module weights, facilitating feature extraction and the generation of high-quality transformation images of the Landweber dielectric constant distribution. A PatchGAN discriminator is employed to distinguish between transformation images and real images, using the generated transformation images as target images. Experimental results demonstrate that the sensitivity field, enhanced by the improved particle swarm algorithm and L2-norm normalization, achieves better balance. Furthermore, the addition of a network transformation using the Adam-based local attention weight mechanism on the DualGAN network reduces artifacts in the reconstructed images, resulting in more accurate PECT reconstructions. The PECT image defect detection method, integrating DualGAN, an improved particle swarm optimization algorithm, and a local attention mechanism, has made significant strides in addressing challenges related to image reconstruction accuracy and speed. This technological advancement has enhanced the precision and efficiency of defect detection in carbon fiber composite materials, thereby fostering the broader utilization of planar capacitance tomography technology in industrial damage detection and material defect analysis.

Optimising Deep Neural Networks for Tumour Diagnosis Algorithms Based on Improved MRFO Algorithm

INTRODUCTION: Cancer has become one of the most prevalent diseases with the highest mortality rate in the world, and timely detection and early acceptance of medical therapeutic interventions are effective means of controlling the progression of cancer patients and improving their post-intervention outcomes.OBJECTIVES: To make the defects of incomplete features, low accuracy and low real-time performance of current tumour diagnosis methods.METHODS: This paper proposes a tumour diagnosis method based on the improved MRFO algorithm to improve the optimization process of DBN network parameters. Firstly, the diagnostic features are extracted by analysing the tumour diagnosis identification problem; then, the manta ray foraging optimization algorithm is improved by combining the good point set initialization strategy, the adaptive control parameter strategy and the distribution estimation strategy, and the tumour diagnostic model based on the improved manta ray foraging optimization algorithm to optimize the parameters of the depth confidence network is constructed; finally, the high accuracy and real-time performance of the proposed method are verified by the analysis of simulation experiments.RESULTS: The results show that the proposed method improves the accuracy of the diagnostic model.CONLUSION: Addresses the problem of poor accuracy and real-time availability of tumour diagnostic methods.

Fault Diagnosis Method of Box-Type Substation Based on Improved Conditional Tabular Generative Adversarial Network and AlexNet

To solve the problem of low diagnostic accuracy caused by the scarcity of fault samples and class imbalance in the fault diagnosis task of box-type substations, a fault diagnosis method based on self-attention improvement of conditional tabular generative adversarial network (CTGAN) and AlexNet was proposed. The self-attention mechanism is introduced into the generator of CTGAN to maintain the correlation between the indicators of the input data, and a large amounts of high-quality data are generated according to the small number of fault samples. The generated data are input into the AlexNet model for fault diagnosis. The experimental results demonstrate that compared with the SMOTE and CTGAN methods, the dataset generated by the self-attention-conditional tabular generative adversarial network (SA-CTGAN) model has better data relevance. The accuracy of fault diagnosis by the proposed method reaches 94.81%, which is improved by about 11% compared with the model trained on the original data.

Hydrogeological Investigation of a Goaf and Subsidence Area Based on a Ground-to-Air Transient Electromagnetic Sounding Method

Water hazards in coal mines have always been the main geological hazard that restricts the safety of coal mine production. The traditional electromagnetic method is difficult to accurately detect the hydrogeological conditions of underground goafs due to accuracy and interference problems. The ground-to-air transient electromagnetic method is an electromagnetic detection technology with strong adaptability to various terrains, a large detection depth, a wide coverage, a high resolution, and fast speed. This paper mainly applies the ground-to-air transient electromagnetic method to conduct a hydrogeological engineering geological survey in a mine subsidence area. By using this method effectively, changes in hydrogeological and engineering geological conditions in the mine subsidence area are identified and water filling conditions below section 310 and section 250 of the roadway are determined. This study provides accurate and reliable basic data support for rescue operations and post-disaster reconstruction efforts, while also offering valuable insights for similar exploration projects.

A Serial Multi-Scale Feature Fusion and Enhancement Network for Amur Tiger Re-Identification.

The Amur tiger is an important endangered species in the world, and its re-identification (re-ID) plays an important role in regional biodiversity assessment and wildlife resource statistics. This paper focuses on the task of Amur tiger re-ID based on visible light images from screenshots of surveillance videos or camera traps, aiming to solve the problem of low accuracy caused by camera perspective, noisy background noise, changes in motion posture, and deformation of Amur tiger body patterns during the re-ID process. To overcome this challenge, we propose a serial multi-scale feature fusion and enhancement re-ID network of Amur tiger for this task, in which global and local branches are constructed. Specifically, we design a global inverted pyramid multi-scale feature fusion method in the global branch to effectively fuse multi-scale global features and achieve high-level, fine-grained, and deep semantic feature preservation. We also design a local dual-domain attention feature enhancement method in the local branch, further enhancing local feature extraction and fusion by dividing local feature blocks. Based on the above model structure, we evaluated the effectiveness and feasibility of the model on the public dataset of the Amur Tiger Re-identification in the Wild (ATRW), and achieved good results on mAP, Rank-1, and Rank-5, demonstrating a certain competitiveness. In addition, since our proposed model does not require the introduction of additional expensive annotation information and does not incorporate other pre-training modules, it has important advantages such as strong transferability and simple training.

A medical image segmentation method for rectal tumors based on multi-scale feature retention and multiple attention mechanisms.

With the continuous development of deep learning algorithms in the field of medical images, models for medical image processing based on convolutional neural networks have made great progress. Since medical images of rectal tumors are characterized by specific morphological features and complex edges that differ from natural images, achieving good segmentation results often requires a higher level of enrichment through the utilization of semantic features. The efficiency of feature extraction and utilization has been improved to some extent through enhanced hardware arithmetic and deeper networks in most models. However, problems still exist with detail loss and difficulty in feature extraction, arising from the extraction of high-level semantic features in deep networks. In this work, a novel medical image segmentation model has been proposed for Magnetic Resonance Imaging (MRI) image segmentation of rectal tumors. The model constructs a backbone architecture based on the idea of jump-connected feature fusion and solves the problems of detail feature loss and low segmentation accuracy using three novel modules: Multi-scale Feature Retention (MFR), Multi-branch Cross-channel Attention (MCA), and Coordinate Attention (CA). Compared with existing methods, our proposed model is able to segment the tumor region more effectively, achieving 97.4% and 94.9% in Dice and mIoU metrics, respectively, exhibiting excellent segmentation performance and computational speed. Our proposed model has improved the accuracy of both lesion region and tumor edge segmentation. In particular, the determination of the lesion region can help doctors identify the tumor location in clinical diagnosis, and the accurate segmentation of the tumor edge can assist doctors in judging the necessity and feasibility of surgery.

CD-BTMSE: A Concept Drift detection model based on Bidirectional Temporal Convolutional Network and Multi-Stacking Ensemble learning

The existence of concept drift phenomenon seriously affects the quality of data, it is an urgent need to investigate accurate concept drift detection methods to improve the data quality. Concept drift detection is widely used in the application scenarios of anomaly detection, intrusion detection, fake reviews detection, etc. In recent years, although scholars have proposed many concept drift detection methods, these methods can only cope with a single type of concept drift and are unable to effectively deal with time-series data. In this paper, we propose a Concept Drift detection model based on Bidirectional Temporal convolutional network and Multi-Stacking Ensemble learning (namely CD-BTMSE). CD-BTMSE choose six suitable base learners to solve the problems of overfitting, weak generalization ability and poor robustness of existing ensemble learning-based concept drift detection models, it also innovatively utilizes the Bidirectional Temporal Convolutional Network (BiTCN) model to improve the detection accuracy of concept drift through considering the temporal characteristics of the data as well as the bidirectional semantics in the detection process; At the same time, it utilizes the multi-stacking ensemble learning model to solve the problem of low accuracy of concept drift detection caused by the relatively high generalization error rate and poor generalization ability of the existing ensemble learning-based methods. In addition, the negative log-likelihood loss function in BiTCN model is replaced by a Focal Loss to solve the problem of category inhomogeneity, and the ReLU activation function is replaced by LeakyReLU to solve the neuron ”death” and gradient problems. Extensive experiments on six public datasets show that the proposed CD-BTMSE model outperforms seven state-of-the-art concept drift detection models in terms of accuracy, precision, recall and F1-measure, and it also has better stable performance.

Advancing parabolic operators in thermodynamic MHD models II: Evaluating a Practical Time Step Limit for Unconditionally Stable Methods

Unconditionally stable time stepping schemes are useful and often practically necessary for advancing parabolic operators in multi-scale systems. However, serious accuracy problems may emerge when taking time steps that far exceed the explicit stability limits. In our previous work, we compared the accuracy and performance of advancing parabolic operators in a thermodynamic MHD model using an implicit method and an explicit super time-stepping (STS) method. We found that while the STS method outperformed the implicit one with overall good results, it was not able to damp oscillatory behavior in the solution efficiently, hindering its practical use. In this follow-up work, we evaluate an easy-to-implement method for selecting a practical time step limit (PTL) for unconditionally stable schemes. This time step is used to ‘cycle’ the operator-split thermal conduction and viscosity parabolic operators. We test the new time step with both an implicit and STS scheme for accuracy, performance, and scaling. We find that, for our test cases here, the PTL dramatically improves the STS solution, matching or improving the solution of the original implicit scheme, while retaining most of its performance and scaling advantages. The PTL shows promise to allow more accurate use of unconditionally stable schemes for parabolic operators and reliable use of STS methods.

Hardware-in-the-loop simulation test platform for UAV flight control system

The flight control system is the integrated command center and core component of the UAV system. Aiming at the problems of poor accuracy and low efficiency in the field measurement of flight control system parameters, combined with the configuration of the flight attitude sensor-vertical gyroscope, hardware-in-the-loop simulation technology, and integrated automatic test methods are adopted. The hardware-in-the-loop simulation test platform for the UAV flight control system composed of a low-cost, high-precision miniaturized UAV attitude calibration platform and a control piston deflection angle test device is designed. The main controller replaces the ground master control station of the UAV and directly sends remote control commands to the aircraft. By changing the attitude of the aircraft, and measuring the voltage of the control signal, the feedback signal, and the rudder angle, the comprehensive performance test of the parameters of the flight control system is realized. The performance test of the platform shows that the platform can meet the test requirements of angular position, system voltage, and control piston deflection angle.

Shore Power System Load Forecasting Model based on IDBO algorithm and PCA-BiLSTM Network

Shore power offers convenient and sustainable power services to ships docking in ports, playing a pivotal role in advancing environmental preservation and the effective utilization of resources. Shore power system loads are susceptible to numerous influencing factors. Therefore, it is crucial to identify a highly accurate load forecasting model to facilitate the judicious allocation of power resources. A short-term shore power system load forecasting model based on the improved dung beetle optimization algorithm (IDBO) and principal component analysis (PCA) in conjunction with the enhanced bidirectional long and short-term memory (BiLSTM) network is proposed in this paper. The IDBO algorithm improves the problem of low convergence accuracy and vulnerability to local optima in DBO. This is achieved by population initialization based on cubic chaotic mapping, amalgamating walrus optimization algorithm, and adaptive t-distribution perturbation strategy. The performance of the BiLSTM, IDBO-BiLSTM, and PCA-IDBO-BiLSTM shore power system load forecasting models are compared through case studies, revealing that the proposed PCA-IDBO-BiLSTM model demonstrates superior predictive capabilities.

Adaptive adjustment of brightness and blur of the camera for high precision internal parameter calibration

Camera calibration is a prerequisite for many computer vision tasks, such as visual measurement and visual localization. This paper proposes a new method for high-precision calibration of camera intrinsic parameters. By dynamically adjusting image brightness and blur to cope with changes in external lighting conditions and camera working distance, high-precision internal parameter measurement is achieved. This method solves the problems of the traditional camera intrinsic parameter calibration process being cumbersome and susceptible to changes in the placement of calibration boards and fluctuations in external lighting conditions. Firstly, an adaptive exposure adjustment algorithm is proposed to adjust the camera exposure value by statistically analyzing the average gray value of randomly selected rectangular regions on the calibration board, thereby solving the problem of the camera calibration accuracy being affected by overly bright or dark environments. Subsequently, an evaluation criterion and adaptive adjustment strategy for adjusting image blur are proposed. By extracting edge points of feature circles and obtaining the slope of the fitted curve, the relationship between edge sharpness and slope is utilized to adjust the image blur. Finally, the proposed method achieves high-precision semi-automatic calibration. Experimental results demonstrate that the proposed method has significant advantages in terms of correctness, robustness, and flexibility, with an average reprojection error of 0.0167.

Improved Position Sensorless Resonant Frequency Tracking Control Method for Linear Oscillatory Machine

To improve the reliability of control system and obtain the maximum output efficiency, it is very necessary to carry out the position sensorless piston stroke control and the resonance frequency tracking control for linear oscillatory machine (LOM) at the same time. The conventional resonant frequency tracking control method based on model reference adaptive system (MRAS) has the problem of low estimation accuracy caused by inaccurate model parameters. In order to obtain more accurate resonant frequency, an improved position sensorless resonant frequency tracking control method is proposed in this article. In this method, the adjustable model is constructed based on the mechanical dynamics equation of LOM to track the system resonant frequency. A hybrid terminal sliding-mode observer (HTSMO) is designed to replace the conventional reference model, which can obtain more accurate velocity reference signal for adjustable model by introducing the current error feedback link, thereby further improving the resonant frequency tracking accuracy. Moreover, a second-order generalized integrator (SOGI) is introduced to filter the estimated velocity signal of HTSMO. The filtered velocity signal is not only used for the adjustable model to track the resonant frequency, but also used to obtain the smooth piston stroke signal for the stroke closed-loop control. Comprehensive simulation and experimental results have proved the effectiveness and the superiority of the proposed method.