Bearing mechanism of roof and rib support structure in automatically formed roadway and its support design method

Non-pillar mining technology with automatically formed roadway is a new mining method without coal pillar reservation and roadway excavation.The stability control of automatically formed roadway is the key to the successful application of the new method.In order to realize the stability control of the roadway surrounding rock,the mechanical model of the roof and rib support structure is established,and the influence mechanism of the automatically formed roadway parameters on the compound force is revealed.On this basis,the roof and rib support structure technology of confined lightweight concrete is proposed,and its mechanical tests under different eccentricity are carried out.The results show that the bearing capacity of confined lightweight concrete specimens is basically the same as that of ordinary confined concrete specimens.The bearing capacity of confined lightweight concrete specimens under different eccentricities is 1.95 times higher than those of U-shaped steel specimens.By comparing the test results with the theoretical calculated results of the confined concrete,the calculation method of the bearing capacity for the confined lightweight concrete structure is selected.The design method of confined lightweight concrete support structure is established,and is successfully applied in the extra-large mine,Ningtiaota Coal Mine,China.

Comparative study of model tests on automatically formed roadway and gob-side entry driving in deep coal mines

Automatically formed roadway(AFR)by roof cutting with bolt grouting(RCBG)is a new deep coal mining technology.By using this technology,the broken roadway roof is strengthened,and roof cutting is applied to cut off stress transfer between the roadway and gob to ensure the collapse of the overlying strata.The roadway is automatically formed owing to the broken expansion characteristics of the collapsed strata and mining pressure.Taking the Suncun Coal Mine as the engineering background,the control effect of this new technology on roadways was studied.To compare the law of stress evolution and the surrounding rock control mechanisms between AFR and traditional gob-side entry driving,a comparative study of geomechanical model tests on the above methods was carried out.The results showed that the new technology of AFR by RCBG effectively reduced the stress concentration of the roadway compared with gob-side entry driving.The side abutment pressure peak of the solid coal side was reduced by 24.3%,which showed an obvious pressure-releasing effect.Moreover,the position of the side abutment pressure peak was far from the solid coal side,making it more beneficial for roadway stability.The deformation of AFR surrounding rock was also smaller than the deformation of the gob-side entry driving by the overload test.The former was more beneficial for roadway stability than the latter under higher stress conditions.Field application tests showed that the new technology can effectively control roadway deformation.Moreover,the technology reduced roadway excavation and avoided resource waste caused by reserved coal pillars.

Percutaneous endoscopic gastrostomy under steadypressure automatically controlled endoscopy:First clinicalseries

AIM: To elucidate the safety of percutaneous endoscopic gastrostomy(PEG) under steady pressure automatically controlled endoscopy(SPACE) using carbon dioxide(CO_2).METHODS: Nine patients underwent PEG with a modified introducer method under conscious sedation. A T-tube was attached to the channel of an endoscope connected to an automatic surgical insufflator. The stomach was inflated under the SPACE system. The intragastric pressure was kept between 4-8 mmH g with a flow of CO_2 at 35 L/min. Median procedure time, intragastric pressure, median systolic blood pressure, partial pressure of CO_2, abdominal girth before and immediately after PEG, and free gas and small intestinal gas on abdominal X-ray before and after PEG were recorded. RESULTS: PEG was completed under stable pneumostomach in all patients, with a median procedural time of 22 min. Median intragastric pressure was 6.9 mmH g and median arterial CO_2 pressure before and after PEG was 42.1 and 45.5 Torr(NS). The median abdominal girth before and after PEG was 68.1 and 69.6 cm(NS). A mild free gas image after PEG was observed in two patients, and faint abdominal gas in the downstream bowel was documented in two patients.CONCLUSION: SPACE might enable standardized pneumostomach and modified introducer procedure of PEG.

Measurement of Stone Diameter with Three Sizes of Automatically Fixating Stone Baskets

Background: Complications after endoscopic retrieval of kidney and ureter stones are obviously related to the size of the stones as well as the experience of the surgeon and other factors. During the procedure it is sometimes difficult for surgeons to estimate stone size and therefore give prognostic advises. The visual perception of the stone size depends on the shape, colour, distance to the renoscope and dilatation of the ureter. This is the so-called binding problem, because shape, color and direction of motion are processed separately by different population of optical neurons. In order to establish a better prognostic rational especially for less experienced surgeons, we established an intra operative semi-quantitative measurement of the stone size supported by a stone basket. Materials and Methods: We modified the tipped, nitinol stone baskets from the company Urotech with diameters of 2.5, 3.0 and 4 FR. The handle of this basket has a spring mechanism, which automatically closes the basket and provides a predefined fixation force of the stones within the basket. On the handle we established a non-linear scale in mm by grabbing standardized balls or standardized screws. Result: The scales are nonlinear because of the nonlinear relation between the diameter of the stone and the distance of the slider. Also the scales differ in between the basket size, because of the different strain conditions due to the different wire sizes and materials or the spring and basket. Conclusion: This scale could be an important orientation for a surgeon during endourological procedures to estimate stone sizes. After further clinical experience a semi-quantitative visualization like green, yellow and red colors could help to predict potential complications due to large stone sizes. Finally it could bevery interesting for other disciplines like gastroenterology.

Automatically positioning microseismic sources in mining by the stereo tomographic method using full wavefields

For microseisimic monitoring it is difficult to determine wave modes and their propagation velocity. In this paper, we propose a new method for automatically inverting in real time the source characteristics of microseismic events in mine engineering without wave mode identification and velocities. Based on the wave equation in a spherical coordinate system, we derive a tomographic imaging equation and formulate a scanning parameter selection criterion by which the microseisimic event maximum energy and corresponding parameters can be determined. By determining the maximum energy positions inside a given risk district, we can indentify microseismic events inside or outside the risk districts. The synthetic and field examples demonstrate that the proposed tomographic imaging method can automatically position microseismic events by only knowing the risk district dimensions and range of velocities without identifying the wavefield modes and accurate velocities. Therefore, the new method utilizes the full wavefields to automatically monitor microseismic events.

A Method of Automatically Generating 2D Animation Intermediate Frames

This paper proposes the automatic generation of the middle frame and the middle frame automatic coloring method of two-dimensional animation process, users simply given starting key frames and end key frames, According to the algorithm proposed in this paper, the system can automatically generate all key frames that in the middle, and based on the starting key frame and termination of key frame color, the generated in the middle of the frame will been automatically chromatically. The experimental results show that, the automatic generation of intermediate frames and the middle frame automatic coloring method of two-dimensional animation is proposed in this paper production process can be successfully used in animation production, greatlyimproving the efficiency of animation.

Testing Forms in Web Applications Automatically

Forms enhance both the dynamic and interactive abilities of Web applications and the system complexity. And it is especially important to test forms completely and thoroughly. Therefore, this paper discusses how to carry out the form testing by different methods in the related testing phases. Namely, at first, automatically abstracting forms in the Web pages by parsing the HTML documents; then, ohtai ning the testing data with a certain strategies, such as by requirement specifications, by mining users＇ hefore input informarion or by recording meehanism; and next executing the testing actions automatically due to the well formed test cases; finally, a case study is given to illustrate the convenient and effective of these methods.

Automatically Mining Application Signatures for Lightweight Deep Packet Inspection

Automatic signature generation approaches have been widely applied in recent traffic classification.However,they are not suitable for LightWeight Deep Packet Inspection(LW_DPI) since their generated signatures are matched through a search of the entire application data.On the basis of LW_DPI schemes,we present two Hierarchical Clustering(HC) algorithms:HC_TCP and HC_UDP,which can generate byte signatures from TCP and UDP packet payloads respectively.In particular,HC_TCP and HC_ UDP can extract the positions of byte signatures in packet payloads.Further,in order to deal with the case in which byte signatures cannot be derived,we develop an algorithm for generating bit signatures.Compared with the LASER algorithm and Suffix Tree(ST)-based algorithm,the proposed algorithms are better in terms of both classification accuracy and speed.Moreover,the experimental results indicate that,as long as the application-protocol header exists,it is possible to automatically derive reliable and accurate signatures combined with their positions in packet payloads.

Algorithm of Dynamic Operation Process of Hydraulic Automatically Operated Canals with Constant-Downstream Level Gates

On the basis of analysis the governing process of downstream water level gates AVIO and AVIS, a mathematical model for simulation of dynamic operation process of hydraulically automated irrigation canals instalIed with AVIO and AVIS gates is presented, the main point of this rnathematical model is firstly applying a set of unsteady flow equations （St. Venant equations here） and treating the condition of gate movement as its dynamic boundary, and then deeoupling this interaction of gate movement with the change of canal flow. In this process, it is necessary to give the gateg open-loop transfer function whose input is water level deviation and output is gate discharge. The result of this simulation for a practical reach has shown it has satisfactory accuracy.

Method for Automatically Determining the Importance of Criteria in Multicriteria Decision-Making Problems

Multicriteria group decision-making problems （DMP） require criteria weights. Assigning weights of importance of the criteria Face Decision-maker （DM） means, in essence, a priori purpose variant of the winner. There are a number of problematic situations involving a large number of criteria： （1） problems where the evaluation of alternatives represent the degree of satisfaction of basic performance requirements object bidders. Matrix estimates with different low variability and a very large number of requirements （criteria）; （2） the use of cognitive maps for modeling problem situations. If the alternatives are considered not only divisible strategy （options impact on concepts）, matrix estimates accepts small size. If the task is allowed to use an alternative strategy mixtures fraction （e.g., 25% influences on the concept 1, 50% influences on the concept 2, 10% influences on the concept 3, etc.）, the matrix ratings also gaining greater dimension. It is clear that in such cases the appointment criteria weights DMP becomes a problem.

A Method for Automatically Establishing a Mathematical Model of Kinematic Analysis to a Planar Mechanism with a Multiple Joint and Prismatic Pair

A method for automatically establishing a mathematical model of kinematic analysis to a planar mechanism with multiple joint and prismatic pair is presented. The breadth （ or depth ） first search spanning tree can be obtained based on an adjacency matrix of the mechanism. Then the kinematic chain （or mechanism）＇s basic loops can be obtained. On the basis of these basic loops, a mathematical model of kinematic analysis can be established and solved automatically. In the sense of a calculative mechanism, structural analysis of the kinematic chain relates to the kinematic analysis of a mechanism. Thus, an effective way is supplied to the given mechanism＇s kinematic analysis for automatic modeling and solving, and a method is supplied to the structural type to optimize kinematic synthesis.

AutoQNN: An End-to-End Framework for Automatically Quantizing Neural Networks

Exploring the expected quantizing scheme with suitable mixed-precision policy is the key to compress deep neural networks(DNNs)in high efficiency and accuracy.This exploration implies heavy workloads for domain experts,and an automatic compression method is needed.However,the huge search space of the automatic method introduces plenty of computing budgets that make the automatic process challenging to be applied in real scenarios.In this paper,we propose an end-to-end framework named AutoQNN,for automatically quantizing different layers utilizing different schemes and bitwidths without any human labor.AutoQNN can seek desirable quantizing schemes and mixed-precision policies for mainstream DNN models efficiently by involving three techniques:quantizing scheme search(QSS),quantizing precision learning(QPL),and quantized architecture generation(QAG).QSS introduces five quantizing schemes and defines three new schemes as a candidate set for scheme search,and then uses the Differentiable Neural Architecture Search(DNAS)algorithm to seek the layer-or model-desired scheme from the set.QPL is the first method to learn mixed-precision policies by reparameterizing the bitwidths of quantizing schemes,to the best of our knowledge.QPL optimizes both classification loss and precision loss of DNNs efficiently and obtains the relatively optimal mixed-precision model within limited model size and memory footprint.QAG is designed to convert arbitrary architectures into corresponding quantized ones without manual intervention,to facilitate end-to-end neural network quantization.We have implemented AutoQNN and integrated it into Keras.Extensive experiments demonstrate that AutoQNN can consistently outperform state-of-the-art quantization.For 2-bit weight and activation of AlexNet and ResNet18,AutoQNN can achieve the accuracy results of 59.75%and 68.86%,respectively,and obtain accuracy improvements by up to 1.65%and 1.74%,respectively,compared with state-of-the-art methods.Especially,compared with the full-precision AlexNet and ResNet18,the 2-bit models only slightly incur accuracy degradation by 0.26%and 0.76%,respectively,which can fulfill practical application demands.

Research on a Monte Carlo global variance reduction method based on an automatic importance sampling method

Global variance reduction is a bottleneck in Monte Carlo shielding calculations.The global variance reduction problem requires that the statistical error of the entire space is uniform.This study proposed a grid-AIS method for the global variance reduction problem based on the AIS method,which was implemented in the Monte Carlo program MCShield.The proposed method was validated using the VENUS-Ⅲ international benchmark problem and a self-shielding calculation example.The results from the VENUS-Ⅲ benchmark problem showed that the grid-AIS method achieved a significant reduction in the variance of the statistical errors of the MESH grids,decreasing from 1.08×10^(-2) to 3.84×10^(-3),representing a 64.00% reduction.This demonstrates that the grid-AIS method is effective in addressing global issues.The results of the selfshielding calculation demonstrate that the grid-AIS method produced accurate computational results.Moreover,the grid-AIS method exhibited a computational efficiency approximately one order of magnitude higher than that of the AIS method and approximately two orders of magnitude higher than that of the conventional Monte Carlo method.

Path Planning and Tracking Control for Parking via Soft Actor-Critic Under Non-Ideal Scenarios

Parking in a small parking lot within limited space poses a difficult task. It often leads to deviations between the final parking posture and the target posture. These deviations can lead to partial occupancy of adjacent parking lots, which poses a safety threat to vehicles parked in these parking lots. However, previous studies have not addressed this issue. In this paper, we aim to evaluate the impact of parking deviation of existing vehicles next to the target parking lot(PDEVNTPL) on the automatic ego vehicle(AEV) parking, in terms of safety, comfort, accuracy, and efficiency of parking. A segmented parking training framework(SPTF) based on soft actor-critic(SAC) is proposed to improve parking performance. In the proposed method, the SAC algorithm incorporates strategy entropy into the objective function, to enable the AEV to learn parking strategies based on a more comprehensive understanding of the environment. Additionally, the SPTF simplifies complex parking tasks to maintain the high performance of deep reinforcement learning(DRL). The experimental results reveal that the PDEVNTPL has a detrimental influence on the AEV parking in terms of safety, accuracy, and comfort, leading to reductions of more than 27%, 54%, and 26%respectively. However, the SAC-based SPTF effectively mitigates this impact, resulting in a considerable increase in the parking success rate from 71% to 93%. Furthermore, the heading angle deviation is significantly reduced from 2.25 degrees to 0.43degrees.

An inverse analysis of fluid flow through granular media using differentiable lattice Boltzmann method

This study presents a method for the inverse analysis of fluid flow problems.The focus is put on accurately determining boundary conditions and characterizing the physical properties of granular media,such as permeability,and fluid components,like viscosity.The primary aim is to deduce either constant pressure head or pressure profiles,given the known velocity field at a steady-state flow through a conduit containing obstacles,including walls,spheres,and grains.The lattice Boltzmann method(LBM)combined with automatic differentiation(AD)(AD-LBM)is employed,with the help of the GPU-capable Taichi programming language.A lightweight tape is used to generate gradients for the entire LBM simulation,enabling end-to-end backpropagation.Our AD-LBM approach accurately estimates the boundary conditions for complex flow paths in porous media,leading to observed steady-state velocity fields and deriving macro-scale permeability and fluid viscosity.The method demonstrates significant advantages in terms of prediction accuracy and computational efficiency,making it a powerful tool for solving inverse fluid flow problems in various applications.

Automatic modulation recognition of radio fuzes using a DR2D-based adaptive denoising method and textural feature extraction

The identification of intercepted radio fuze modulation types is a prerequisite for decision-making in interference systems.However,the electromagnetic environment of modern battlefields is complex,and the signal-to-noise ratio(SNR)of such environments is usually low,which makes it difficult to implement accurate recognition of radio fuzes.To solve the above problem,a radio fuze automatic modulation recognition(AMR)method for low-SNR environments is proposed.First,an adaptive denoising algorithm based on data rearrangement and the two-dimensional(2D)fast Fourier transform(FFT)(DR2D)is used to reduce the noise of the intercepted radio fuze intermediate frequency(IF)signal.Then,the textural features of the denoised IF signal rearranged data matrix are extracted from the statistical indicator vectors of gray-level cooccurrence matrices(GLCMs),and support vector machines(SVMs)are used for classification.The DR2D-based adaptive denoising algorithm achieves an average correlation coefficient of more than 0.76 for ten fuze types under SNRs of-10 d B and above,which is higher than that of other typical algorithms.The trained SVM classification model achieves an average recognition accuracy of more than 96%on seven modulation types and recognition accuracies of more than 94%on each modulation type under SNRs of-12 d B and above,which represents a good AMR performance of radio fuzes under low SNRs.

A Fully Soft Bionic Grasping Device with the Properties of Segmental Bending Shape and Automatically Adjusting Grasping Range

In this paper,we propose a fully Soft Bionic Grasping Device(SBGD),which has advantages in automatically adjusting the grasping range,variable stiffness,and controllable bending shape.This device consists of soft gripper structures and a soft bionic bracket structure.We adopt the local thin-walled design in the soft gripper structures.This design improves the grippers’bending efficiency,and imitate human finger’s segmental bending function.In addition,this work also proposes a pneumatic soft bionic bracket structure,which not only can fix grippers,but also can automatically adjust the grasping space by imitating the human adjacent fingers’opening and closing movements.Due to the above advantages,the SBGD can grasp larger or smaller objects than the regular grasping devices.Particularly,to grasp small objects reliably,we further present a new Pinching Grasping(PG)method.The great performance of the fully SBGD is verified by experiments.This work will promote innovative development of the soft bionic grasping robots,and greatly meet the applications of dexterous grasping multi-size and multi-shape objects.

A High Resolution Convolutional Neural Network with Squeeze and Excitation Module for Automatic Modulation Classification

Automatic modulation classification(AMC) technology is one of the cutting-edge technologies in cognitive radio communications. AMC based on deep learning has recently attracted much attention due to its superior performances in classification accuracy and robustness. In this paper, we propose a novel, high resolution and multi-scale feature fusion convolutional neural network model with a squeeze-excitation block, referred to as HRSENet,to classify different kinds of modulation signals.The proposed model establishes a parallel computing mechanism of multi-resolution feature maps through the multi-layer convolution operation, which effectively reduces the information loss caused by downsampling convolution. Moreover, through dense skipconnecting at the same resolution and up-sampling or down-sampling connection at different resolutions, the low resolution representation of the deep feature maps and the high resolution representation of the shallow feature maps are simultaneously extracted and fully integrated, which is benificial to mine signal multilevel features. Finally, the feature squeeze and excitation module embedded in the decoder is used to adjust the response weights between channels, further improving classification accuracy of proposed model.The proposed HRSENet significantly outperforms existing methods in terms of classification accuracy on the public dataset “Over the Air” in signal-to-noise(SNR) ranging from-2dB to 20dB. The classification accuracy in the proposed model achieves 85.36% and97.30% at 4dB and 10dB, respectively, with the improvement by 9.71% and 5.82% compared to LWNet.Furthermore, the model also has a moderate computation complexity compared with several state-of-the-art methods.

Construction Activity Analysis of Workers Based on Human Posture Estimation Information

Identifying workers’construction activities or behaviors can enable managers to better monitor labor efficiency and construction progress.However,current activity analysis methods for construction workers rely solely on manual observations and recordings,which consumes considerable time and has high labor costs.Researchers have focused on monitoring on-site construction activities of workers.However,when multiple workers are working together,current research cannot accu rately and automatically identify the construction activity.This research proposes a deep learning framework for the automated analysis of the construction activities of multiple workers.In this framework,multiple deep neural network models are designed and used to complete worker key point extraction,worker tracking,and worker construction activity analysis.The designed framework was tested at an actual construction site,and activity recognition for multiple workers was performed,indicating the feasibility of the framework for the automated monitoring of work efficiency.

Automatic fovea detection and choroid segmentation for choroidal thickness assessment in optical coherence tomography

AIM:To develop an automated model for subfoveal choroidal thickness(SFCT)detection in optical coherence tomography(OCT)images,addressing manual fovea location and choroidal contour challenges.METHODS:Two procedures were proposed:defining the fovea and segmenting the choroid.Fovea localization from B-scan OCT image sequence with three-dimensional reconstruction(LocBscan-3D)predicted fovea location using central foveal depression features,and fovea localization from two-dimensional en-face OCT(LocEN-2D)used a mask region-based convolutional neural network(Mask R-CNN)model for optic disc detection,and determined the fovea location based on optic disc relative position.Choroid segmentation also employed Mask R-CNN.RESULTS:For 53 eyes in 28 healthy subjects,LocBscan-3D’s mean difference between manual and predicted fovea locations was 170.0μm,LocEN-2D yielded 675.9μm.LocEN-2D performed better in non-high myopia group(P=0.02).SFCT measurements from Mask R-CNN aligned with manual values.CONCLUSION:Our models accurately predict SFCT in OCT images.LocBscan-3D excels in precise fovea localization even with high myopia.LocEN-2D shows high detection rates but lower accuracy especially in the high myopia group.Combining both models offers a robust SFCT assessment approach,promising efficiency and accuracy for large-scale studies and clinical use.