Abstract:

Abstract: To solve to the problems of long planning times and low success rates in automatic parking in constrainedparking lanes, a modified approach to path planning was proposed, which could improve the hybrid A∗algorithm.Firstly, the parking path was divided into two parts: a forward pose adjustment segment and a reverse parkingsegment. A linear-arc constrained optimization model was established to plan the pose adjustment segment, whilefinding a suitable starting point for reverse parking. Subsequently, collision risk cost was introduced as anadditional factor in the hybrid A∗algorithm. The node expansion process was improved by checking whether thevehicle contour intersects with obstacle lines, enhancing the real-time performance and safety of the reverse parkingsegment. Finally, a cost function was designed with criteria such as path length, smoothness, and deviation, takinginto account the vehicle′s kinematic constraints. A quadratic programming approach was used to smooth the initialpath, resulting in the final path. Simulation analysis was conducted using MATLAB to compare the modifiedalgorithm with the original algorithm. The results showed that, in constrained parking lanes, the improved algorithmcould produce a smooth, collision-free parking path with a reduced search time of 23. 8% compared with the hybridA∗algorithm. Additionally, the obtained path was safer and more suitable for tracking control.

Abstract: The variable steering transmission ratio was a crucial factor affecting the active safety and handling stability of vehicles. In order to enhance the steering characteristics of steering-by-wire vehicles on low-adhesion coefficient road surfaces, a variable gain transmission ratio that adapts to changes in road adhesion coefficient and vehicle speed was designed. A 2 DOF model was established for the vehicle, to analyze the factors influencing the yawrate gain, and to obtain the data relationship between the influencing factors and the gain through simulation. TheMin-Max normalization method was utilized to preprocess the data between the influencing factors and the yaw rategain, constructing a neural network dataset. Design a Snake Optimizer Backpropagation Neural Network ( SO-BP)was desighed and train to use the preprocessed dataset to dynamically acquire the variable yaw rate gain. A strategywas employed to combines the variable yaw rate gain with the lateral acceleration gain in proportion to design thevariable gain transmission ratio for electronic control steering. Simulink-CarSim was used to build a steer-by-wiresteering whole vehicle model. Compare and analyze the designed variable gain transmission ratio was analized andcompared with a traditional fixed gain transmission ratio under conditions of both high-adhesion coefficient road surfaces with a double lane change scenario and low-adhesion coefficient road surfaces with a step input scenario. Results indicated that with high-adhesion coefficient road conditions, the trajectory error of the two transmission ratiovehicles remained within 3%, while the variable gain transmission ratio vehicle reduced the peak steering wheel angle by 9. 1%. With low-adhesion coefficient road conditions, the variable gain transmission ratio vehicle showed a22. 3% reduction in steady-state yaw rate at low to moderate speeds and a 24. 6% reduction in peak yaw rate. Atmoderate to high speeds, the steady-state yaw rate decreased by 6. 6%, and the peak yaw rate decreased by10. 8%. The variable gain transmission ratio not only enhanced steering sensitivity on high-adhesion coefficient roadsurfaces, but also improved safety and maneuverability when driving on low-adhesion coefficient road surfaces.

Abstract: Traditional chest bitmap bullet hole detection technology was easily affected by light intensity and complex background in natural conditions. In order to solve the proplem an improved algorithm based on YOLOv8s wasdesigned in this study. Firstly, in order to avoid the impact of complex environment on the accuracy of bullet holerecognition, graph segmentation was introduced in the process of data set production to separate the backgroundfrom the chest bitmap. Secondly, in order to improve the detection ability of the model to the bullet hole, CBAMattention mechanism was introduced into C2f, and the recognition ability of the network to the target bullet hole wasimproved by giving different weights to the spatial and channel characteristics. In order to reduce the informationloss of bullet hole characteristics in the down sampling process and reduce the probability of missing bullet holedetection, the detection scale was increased to 160×160 small target output layer. Considering that the original convolutional layer was not sensitive to small targets, the SPD-Conv module was used to replace the original convolutional layer to extract more feature information to improve detection accuracy. Finally, the loss function of the bounding box was changed to WIoU to weaken the influence of the unbalanced number of positive and negative samplesand improve the regression accuracy of the prediction box. The experimental results on the self-made chest bitmapdata set showed that the accuracy rate P of the improved algorithm was 96. 9%, the recall rate R was 96. 4%, andthe average accuracy mAP50 was 98. 0%, which were improved by 8. 8 percentage points, 25. 4 percentage points,and 15. 3 percentage points respectively, compared with the original algorithm. The experimental results showedthat the improved YOLOv8s model had better performance in the detection of complex environment and dense bulletholes.

Abstract: In order to improve the running characteristics of the main bearing of the CT machine and ensure the stability of the medical imaging of the CT machine, the double-row angular contact ball turntable bearing developed by Luoyang LYC Bearing Co. , Ltd. was used as the research object for modeling and vibration characteristic analysis.Firstly, based on Hertz′s contact theory, the mechanical model of the bearing with the action of composite load wasestablished. Then the nonlinear dynamic differential equation of the bearing outer ring with 3 degrees of freedomwas established in combination with Newton′s law of motion. Finally the differential equation system was solved bythe fourth-order Runge-Kutta method to obtain the vibration characteristics of the bearing outer ring. On this basis,by analyzing the factors affecting the vibration characteristics of the bearing, such as the radius coefficient of curvature of the groove in the inner and outer rings of the bearing, the number of rolling elements, the clearance, thepreload, etc. , the optimal structural parameters to meet the low vibration of the main bearing of the CT machinewere determined. The results show that the vibration amplitude of the bearing decreases first and then increases withthe increase of the radius coefficient of curvature of the inner and outer ring grooves, which were controlled at0. 515 to 0. 525, respectively. The range of 0. 510 to 0. 525 was more conducive to reducing the vibration of thebearing. With the increase of the number of rolling elements, the vibration amplitude of the bearing also increases,but the change was stable, so the vibration of the bearing can be reduced by reducing the number of rolling elements under the premise of satisfying the design conditions. With the increase of the bearing clearance, the vibration amplitude of the bearing decreases first and then increases sharply, and the vibration reaches the minimum at18 μm. Appropriate axial load can effectively reduce the vibration amplitude of the bearing.

Abstract: In order to solve the problem of difficulty in improving the recognition accuray and the low robustness ofthe model caused by the lack of feature information provided by single mode data, a dynamic gesture recognitionstrategy based on multi-modal data fusion of machining operations for human-computer interaction was proposed.Firstly, the C3D network model was used to extract features from the depth image and color image modal data basedon the spatial and temporal dimensions of videos. Secondly, the recognition results of the two modal data were fusedaccording to the maximum principle at the decision-making level. Meanwhile, the Relu activation function used inthe original model was replaced by Mish activation function to optimize the gradient update effect. Finally, throughthree sets of comparative experiments, it was found that the average recognition accuracy of six dynamic gesturesreached 96. 8%. The results showed that the proposed method achieved the goal of high accuracy and high robustness of dynamic gesture recognition in machining operation, which would play a role in promoting the application ofhuman-computer interaction technology in actual production scenes.

Abstract: In view of the fact that it was difficult to solve the problem of irregular vibration of small and mediumsized branch pipes in nuclear power units with traditional vibration damping supports, a circular double three-waydynamic vibration absorber based on metal rubber material was designed in this study, with total mass of 0. 084 kg,and the rectangular spring and metal rubber were fixed between the mass and the tooling fixture. Firstly, thedynamic model of the double dynamic vibration absorber was constructed, and then the equivalent mass of the smallbranch was determined by the mass induction method, and the stiffness and damping ratio of the vibration absorberwere optimized by combining the fixed-point theory. The harmonic response simulation analysis of the small branchtube double dynamic vibration absorber system showed that the vibration absorption effect of the double dynamicvibration absorber in the X, Y and Z directions was greater than 95%. The excitation test was carried out on thesmall branch tube with or without the double dynamic vibration absorber, the sinusoidal excitation amplitude was5 N, and the frequency range was 0 to 150 Hz, and the data showed that the vibration absorption effect of the double dynamic vibration absorber for the main vibration direction of the small branch tube Y and Z reached more than95%, and the vibration absorption efficiency in the X direction reached more than 75%.

Abstract: To study the variation law of combustion instability of natural gas engine with lean-burn condition,experiments were carried out on a natural gas engine with six different excess air coefficients with the condition of25% load and 1 000 r / min. The frequency distributions of pmax , θpmaxand IMEP were studied by statistical analysismethod, and the correlation between each combustion characteristic parameter was also analyzed. Results showedthat with the increase of λ, the combustion instability of natural gas engine enhanced and the frequency distributionranges of pmaxand θpmaxincreased gradually, while the frequency distribution range of IMEP initially decreased andthen increased, and the difference between combustion cycles increased. In each correlation diagram of combustioncharacteristic parameters, the distribution range of points becme wider, where existed a linear relationship. Whenthe mixture concentration became thinner, the linear relationship weakened. The reasons for the combustion instability of natural gas engine with lean-burn conditions were analyzed, providing a theoretical basis for the optimization and control of lean-burn combustion stability of natural gas engine.

Abstract: In addressing the challenge of diminished intrusion detection accuracy resulting from the abundance ofredundant and irrelevant features in high-dimensional network data, an improved multi-factorial optimization bat algorithm ( IMFBA) was introduced for precise data feature selection, with the ultimate goal of improving network intrusion detection accuracy. Within the multi-factorial optimization framework, global and local feature selectiontasks were formulated. Information exchange between these tasks was facilitated by selection and vertical culturaltransmission operators, strategically designed based on the bat algorithm. The global feature selection task was accelerated in identifying optimal solution spaces, thereby enhancing the algorithm′s convergence speed and stability.By incorporating the reverse learning strategy and differential evolution into the bat algorithm, the initial solution selection stage and individual updating process were refined to address the absence of a mutation mechanism, fostering solution diversity and aiding the algorithm in escaping local optima. An adaptive parameter adjustment strategywas introduced, determining weightings for guiding individual updates based on potential optimal solution quality.This could mitigate the risk of knowledge negative transfer during multi-task feature selection, achieving a balancebetween global exploration and local exploitation. The feature subsets selected by IMFBA demonstrate classificationaccuracy of 95. 37% and 85. 14% on the KDD CUP 99 and NSL-KDD intrusion detection datasets, respectively.This reflected increased by 3. 01 percentage points and 9. 78 percentage points compared to the complete dataset.Experiment results confirm the efficacy of EMFBA in selecting higher-quality feature subsets and, consequently,enhancing network intrusion detection accuracy.

Abstract: Since clinically acquired standard 12-lead ECGs often contain noise, which could affects the accuracy ofthe ECG signal classification results, a noise reduction classification algorithm for ECGs based on a two-stagefeature extraction network was proposed. Firsty, in the spatial feature extraction stage, spatial features wereextracted from the input 12-lead standard ECG signal by a residual contraction network with a deeply coupled softthresholding denoising method. Secondly, in the temporal feature extraction stage, temporal features were extractedfrom the ECG signal by a combination of a long and short-term memory network and an attentional mechanism. Andultimately, the extracted spatial and temporal features were fused through the fully-connected network layer tooutput the probabilistic predictive distributions for the nine categories. In order to verify the effect of the proposedalgorithm, comparison experiments were conducted with other state-of-the-art classification algorithms of the sametype on the CPSC2018 dataset, and the experimental results showed that the proposed classification algorithm couldachieve an average F1 score of 0. 848 when classifying the nine categories of ECG signals, which was a much betterperformance in terms of various indicators. In addition, the experiment proved that the proposed algorithm alsocould outperform other mainstream networks in noise-containing data, which fully demonstrated the noise reductionclassification performance of the proposed algorithm for noise-containing ECG signals. And the algorithm can alsobe applied to other similar noise-containing physiological signals for analysis and processing.

Abstract: Abstract: Aiming at the limited studies researches on visual classification directly using image-induced EEG signals and low average accuracy of visual classification, a method combining convolutional neural networks ( CNN)and ensemble learning was designed to learn the visual feature representation related to EEG signals. By adding theK-max pooling method to the stackCNN network to solve the problem of information loss when extracting EEGfeatures, and combining with Bagging algorithm to enhance the generalization ability of the network, this methodwas called StackCNN-B. In order to verify the performance of StackCNN-B method in image classification, imageswere classified using deep residual network regression. The results of ablation experiments and comparativeexperiments with existing studies showed that the recognition accuracy of this method was high. The averageaccuracy in learning the visual feature representation of EEG signals was 99. 78%, and the average accuracy inimage classification was 96. 45%. Compared with the most advanced Bi-LSTM-AttGW method, the averageaccuracy was improved by 0. 28 percentage point and 2. 97 percentage point. The results verified that EEG signalscould effectively decode human brain activities related to visual recognition, proved the advantages of the proposedStackCNN-B model.

Abstract: Aiming at the problem of high altitude turbulence environment to the time-delay stable image acquisitionof UAV, an anti-shaking algorithm for aerial video was proposed for hovering shooting and moving shooting. Firstlyfrom the time-delay photography video captured the UAV camera, some video frames were extracted globally tocompare their histogram distributions. This comparison could identify whether the video contained active cameramotion or not, and help categorize the video accordingly. For videos with active camera motion, FAST cornerdetection and optical flow methods were used to extract and match feature points. The RANSAC algorithm couldremove all mismatched feature points, and estimate the camera′s motion trajectory. The resulting motion estimationparameters were then smoothed using Gaussian filtering, producing a stable camera motion trajectory. For videoswithout active camera motion, the first frame was divided into grids and feature points were extracted based onHarris matrix. Optical flow tracking was carried out on these feature points in subsequent frames. Reverse opticalflow and Harris matrix calculation were used to extract and match feature points, to increase the constraint of featurepoints. Finally, the retained feature points were used to estimate the stable transformation from subsequent framesto the first frame. Experimental results showed that the video classification module could correctly distinguishbetween the two types of videos. The algorithm was used to classify the video scene and stabilize the picture.Compared to other methods, this algorithm could improve the average peak signal-to-noise ratio of stabilized videoimages the most. For videos without active camera motion, the image could be absolutely stable, and the averagepeak signal-to-noise ratio of the image was increased by more than 39%, while the other two methods only by 10%to 12%.

Abstract: The globally central clustering algorithms, such as k-means and spectral clustering, often suffer from theproblem of local optima and difficulty in parameter setting with overlapping and adhesive clusters in the data distribution, which might greatly limits the effectiveness of globally central clustering algorithms in practicalapplications. To address this issue, a border-peeling inspired globally central clustering algorithm was proposed.Firstly, a one-step border peeling method was designed, which defines a locally distance-weighted density according to the reverse k-nearest neighbor relationships between sample points. The density value at the maximal point of thefirst-order difference of the density empirical distribution function was utilized as the threshold to divide the datasetinto boundary and core sets. Then, the traditional globally central clustering algorithms were embedded to clusterthe core set. Benefiting from the significant improvement in the overlapping of the core set, the embedding algorithms could converge to the true cluster centers easily. Finally, a boundary attraction algorithm was proposed,which could progressively amalgamate sample points from the boundary set, utilizing existing reverse k-nearestneighbor relationships, and commencing from the already categorized core set sample points. Compared with thecurrently iterative border peeling algorithms, the proposed algorithm had significant advantages in computational efficiency. There was no additional complex termination conditions but only direct performs boundary partitioning using a threshold. Furthermore, the global approach also exhibited stronger robustness local data densities were different. In the experimental phase, three synthetic datasets and six real-world datasets were used to evaluate the algorithm′s performance, parameter sensitivity, and time consumption, further validating the efficacy and practicalityof this algorithm.

Abstract: In the era of big data in response to the conflict between the reliability enhancement of user data in cloudserver storage and the strategy for removing duplicate data, a heterogeneous server data security deduplication method was proposed blockchain-based smart contracts. Leveraging the decentralized, tamper-proof, and publicly transparent characteristics of blockchain, as well as the automation capabilities of smart contracts, this method could achieve data storage security, reliability, and privacy protection. Specifically, the method combined secret sharingand blockchain smart contract technology to design a secure and efficient cloud storage data deduplication service.Moreover, by replacing the role of centralized third-party entities with blockchain and mitigating server heterogeneity through smart contract scripts, potential security risks were eliminated. Experimental results demonstrated that,under various scenarios with different file sizes and block quantities, the average computational overhead of thismethod was 65. 42% to 115. 77% lower than the comparative solutions, and the average storage overhead was7. 94% to 19. 50% lower. Additionally, for varying numbers of heterogeneous storage servers, this method exhibited significantly lower average computational and storage overhead, with reductions of 67. 27% to 177. 89% and34. 01% to 72. 89%, respectively. Therefore, the proposed approach could outperform two existing blockchainbased deduplication method in terms of security, computational and storage efficiency.

Abstract: Aiming at the problem that the weak isolation characteristic of containers easily makes them suffer fromco-resident and escape attacks, a dynamic scheduling strategy selection method for heterogeneous containers basedon signaling game was proposed. Firstly, the degree of container heterogeneity was quantified, and the set of heterogeneity was calculated by combining multi-dimensional indicators to provide the necessary parameters for accuratecalculation of attack and defense benefits. Then, considering the constant change of the attacker′s access degree tothe container information, a dynamic set of the attacker′s access degree to the container information was designed,and a multi-stage incomplete information signaling game model was constructed on this basis. Finally, an algorithmof dynamic scheduling strategy selection for heterogeneous containers was proposed to solve the optimization problemof multi-stage dynamic scheduling strategy. The experimental results showed that compared with the SmartSCRmethod, the average dynamic rotation overhead was reduced by 47. 3% and the average gain of the defender wasimproved by 14. 2%, and compared with the Stackelberg method, the average gain of the defender was improved by65. 73% while the average overhead of the dynamic rotation was basically the same.

Abstract: The introduction of fractional vegetation cover could improve the accuracy of soil moisture inversion model to some extent, but most studies estimated fractional vegetation cover based on normalized difference vegetationindex NDVI, and without in-depth study on the impact of vegetation coverage based on other vegetation indices onthe model. Therefore, taking the winter wheat planting area in Xiping County, Zhumadian City, Henan Province asthe experimental area, based on the UAV platform with high resolution and strong mobility, the multi-spectral andTIR imaging apparatus were equipped to carry out the soil moisture inversion research of winter wheat covered surface, and to explore the changes of model accuracy after introducing different fractional vegetation cover parameters, so as to make up for the limitations of soil moisture monitoring caused by the low resolution and poor timelinessof satellite remote sensing images. The two drought indices of temperature vegetation dryness index TVDI and perpendicular drought index PDI were combined with the parameters of fractional vegetation cover estimated throughseven vegetation indices, respectively, and seven soil moisture inversion models were constructed based on the random forest algorithm, and the spatial distribution of soil moisture in the experimental area was analyzed according tothe inversion results of the optimal model. At the same time, the soil moisture inversion model TP model, whichcould integrateed TVDI and PDI indices and without introducing fractional vegetation cover, was built as the controlgroup. The results showed that the R2of the TP model was 0. 606,0. 670, the root mean square error RMSE was0. 045、0. 041 for the depths of 0 to10 cm and >10 to 20 cm respectively. The accuracy of the seven models introducing fractional vegetation cover was improved to some extent compared with that of the TP model. Among them, theR2of the optimal model TP OSAVI was improved by 0. 143,0. 158, the RMSE was reduced by 0. 7 percentage points,0. 8 percentage points respectively, compared with the TP model. It showed that the introduction of fractional vegetation cover based on the drought indices could improve the accuracy of model inversion and different fractional vegetation cover had different effects on the accuracy of the model.

Abstract: In order to reduce the content of carcinogenic polycyclic aromatic hydrocarbons in coal asphalt and realize its green application, polyurethane monomer, trioxymethylene, polyethylene glycol, divinyl benzene and epoxyresin were respectively used to react with coal-tar pitch alone or mixed. The removal rate of benzo [ a ] pyrene( BaP) , a representative carcinogen in coal-tar pitch, was used as an evaluation index to analyze the effects of eachsingle modifier and composite modifier on BaP in coal-tar pitch. The results showed that all the selected modifierscould effectively reduce the mass fraction of BaP in coal-tar pitch, the order of detoxification effect of a single modifier in descending was polyurethane monomer, trioxymethylene, polyethylene glycol, divinyl benzene, epoxy resin;the optimal combination (mass fraction) of composite modifiers was 6% polyurethane monomer +10% trioxymethylene +8% polyethylene glycol, which reduction rate of BaP could reach 82. 16% with the optimal reaction conditions.

Abstract: The study was initiated for the consistent mass matrix of Euler beam element including shear deformation. The consistent mass matrix of uniform element was got separately for the uncoupled tension, torsion, and bending conditions, with the shear deformation included or not, based on the shape functions and the virtual work. Itwas shown that the inertia force along the axial direction was always ignored in the mass matrix derivation for thebending condition if the shear deformation was not included, so only the shape functions for vertical deformationwere needed for the bending condition. When the shear deformation was included, the inertia force along the axialdirection must be considered and the shape functions for the section rotation angle due to bending were also requiredbesides the complete shape functions for vertical deformation due to the bending and shear forces. For tapered Eulerelement, the theoretical expression for the consistent mass matrix would be quite complicated and a simple expression was proposed following an approximate strategy, matching the ending or average section areas or polar momentswith the elements in the mass matrix according to their positions. Additionally, the stiffness matrix could also bededuced on the foundation of the complete shape functions for vertical deformation and the shape functions for thesection rotation angle. This derivation procedure was different with the traditional manner superficially but theyshared the same principle essentially.

Abstract: Aiming at the seismic problem of underground utility tunnel, the three-core arch section underground utility tunnel between Genghe Substation and Bowen Substation in Zhengzhou City was taken as the research object,ANSYS Workbench software was used to establish a three-dimensional solid model, and carries out the first 6 ordermodal analysis. It was found that the utility tunnel had longitudinal vibration, vertical vibration, lateral vibrationand rotation. Based on the modal analysis, the displacement response, acceleration response and principal stressresponse of the utility tunnel with horizontal and vertical earthquakes were analyzed. The results showed that themaximum horizontal and vertical displacement peaks were -24. 222 mm and -8. 954 mm, respectively. The maximum acceleration peaks were 3 354. 2 mm / s2and 1 646. 0 mm / s2, respectively. The peak values of the first principal stress were 514. 7 kPa and 244. 15 kPa, respectively. The peak values of the third principal stress were -608. 15 kPa and -256. 71 kPa, respectively. It could be concluded that the horizontal earthquake played a leadingrole. Based on the transverse seismic dynamic time history analysis of the utility tunnel, the parameters such asspectral characteristics, structural material strength and structural depth were changed to analyze their influence onthe seismic performance of the utility tunnel. The utility tunnel structure studied was the most sensitive when theseismic wave spectrum characteristic was about 1. 1 Hz; the change of concrete strength had little effect on the dynamic response of the pipe gallery structure. With the increase of the buried depth of the utility tunnel, the surrounding soil pressure increased, and the internal force gradually increased.