Incipient faults are difficult to detect in plant-wide processes compared to conventional faults due to the lack of distinctive features.An incipient fault detection method based on multiple subspace weighted moving window principal component analysis(PCA)was proposed by shifting the detection perspective from the global to the local to improve the detection rate and sensitivity of incipient faults in plant-wide processes.Process variables were partitioned into different subspaces using a two-layer subspace partitioning method that combines process knowledge and data-driven approaches.A weighted moving window was used to increase the offset of incipient faults,while a local outlier factor(LOF)algorithm was introduced into PCA to further focus on the local features of the data to model fault detection in each subspace.The monitoring results in each subspace were fused with information by the Bayesian inference fusion method to obtain distributed monitoring results.The proposed method was validated by industrial examples,and the results showed that the method effectively improved the accuracy and detection speed of incipient fault detection in plant-wide processes.
This paper addresses the robust control scheme for electrohydraulic servo systems (EHSS) with system uncertainties by utilizing a novel extended sliding mode observer (ESMO). The state-space model of the EHSS is derived by considering system uncertainties. The ESMO with only the displacement feedback signal is employed to estimate both the system full-state and the unmodelled system uncertainties. What’s more, with estimation values from the ESMO, a robust control scheme (RCS) is addressed in detail for the EHSS by employing the multiple-surface sliding control (MSSC) architecture. In addition, three proper boundary layers are properly employed to suppress the chattering phenomenon in the control input so that the controller can achieve a smooth sliding manifold. Subsequently, a proper Lyapunov function is defined to prove the stability of the closed-loop. Comparative simulation and experimental results prove the efficiency of the proposed control methodology.
The influence of fine (ϕ0.11 mm) metallic Z-pin volume fraction and arrangement on the mechanical performance and failure behavior of the open-hole laminates compression was analyzed through open-hole compression test and parametric multi-scale finite element model. Discrete solid element was employed to represent Z-pins, and the 3D Hashin failure criterion was utilized to assess the initial in-plane damage. Then the unstable propagation of kink band was effectively simulated during structural failure. Results showed that the compressive strength of all Z-pinned open-hole laminates was lower than that of specimens without Z-pins. The bridging effect between Z-pins and laminates was enhanced with an increase in Z-pin volume fraction, resulting in increased compressive strength of Z-pinned open-hole laminates. The delaminated area around the hole was suppressed, leading to a maximum reduction of 67% in the damaged area. The variation of Z-pin arrangement did not significantly affect the compression strength of open-hole laminates under the same volume fraction. The maximum relative error between the finite element model simulated results of Z-pinned open-hole laminates and experimental results was 8.6%.
The optimization problem of multi-stage disaster response capacity of road transportation network was analyzed in order to reduce the cost of disaster response for road network and ensure the rapid connectivity of road network. A three-layer planning model for the selection of comprehensive pre-disaster emergency workstations and post-disaster road network recovery decisions was established. The differences in exhaustibility, transportation mode and recovery effect of emergency rescue equipments and logistics support resources were specially considered, and the interdependent relationship between the two was quantitatively modeled. An approximate optimal solution for the model was obtained by combining the bi-level genetic algorithm and the Frank-Wolfe algorithm. The research results show that the optimal decisions can respectively reduce the transportation cost of logistics support resources by 10.96% and the weighted recovery cost by 11.51% compared with the pre-disaster deployment decisions not considering the post-disaster recovery process and the decisions not considering the pre-disaster layout decision of logistics support resources. The quantity of logistics support resources and emergency rescue equipments layout jointly affect the recovery effect of the road network. The impact of increasing the quantity of emergency rescue equipments on the recovery effect will be overestimated if the interdependent relationship between the two is neglected.
Numerical simulation and on-site testing methods were used to analyze the diffusion law of dust and CO during the ventilation process of high-altitude tunnel construction by relying on the Mangkang Mountain Tunnel Project of the Sichuan Tibet Railway in order to analyze the diffusion law of pollutants during the ventilation process of high-altitude tunnel construction. Results show that dust mainly diffuses towards the outside of the tunnel in the form of wall-adhering flow, and it accumulates into ‘dust clusters’ during this process. Dust diffusion is mainly influenced by gravity and causes sedimentation. Excessive wind speed in the tunnel is not conducive to reducing dust mass concentration. CO migrates in the form of ‘air masses’ from the vicinity of the palm face to the entrance of the cave. The volume of CO air masses gradually expands during the migration process, and the mass concentration peak continuously decreases, gradually forming a ‘U-shaped’ distribution trend. The on-site test results of CO mass concentration basically accorded with the numerical simulation results. The CO mass concentration in the tunnel will increase as the altitude increases, and the time it takes for the CO mass concentration at the same location in the tunnel to meet the standard requirements will increase. A formula for calculating the correction coefficient of CO mass concentration during tunnel ventilation was derived based on altitude, which is a good supplement to the altitude correction coefficient of CO in the current specifications.
A new pre-stressed pipe pile foundation with enlarged spudcan was proposed. Different from the soil squeezing effect of pipe piles with uniform section, the expansion of the pile toe can not be regarded as an ideal sphere by considering the dimension of the spudcan is larger than that of the prefabricated pipe pile, but is much closer to an oblate spheroid (rotational ellipsoid). The construction squeezing displacement field around the new pile foundation with enlarged spudcan was analytically solved based on an oblate spheroid expansion source, and the volume deformation of the plastic region of soil around the pile was considered based on the cavity expansion theory. The calculation results of the oblate spheroid expansion strain path method were modified. The soil squeezing effect of pile foundation with enlarged spudcan was analyzed by employing the analytical solution. Results show that the surrounding soil can be roughly divided into three areas as near the ground surface, near the pile toe and near the pile shaft according to the distribution of the displacement field. The horizontal “squeezing” effect in the area near the pile shaft is slightly enhanced as the shape of the expansion source is more “flat”, while the “uplift” displacement in the area near the ground surface and the vertical displacement in the area near the pile toe are significantly reduced, which is very beneficial for the construction of the squeezing soil type piles.
A super-efficient SBM model including non-desired outputs was used to measure industrial environmental efficiency in 30 Chinese provinces from 2008 to 2020 in order to solve the problem of how industrial enterprises can pick appropriate green technology innovations to accomplish industrial green transformation under the background of strict environmental regulations. The efficiency was used to characterize the level of industrial green transformation. A panel threshold model was used to explore the mechanism of the impact of different green technology innovations on industrial green transformation under different environmental regulation intensities. Results show that China's industrial environmental efficiency fluctuates and rises from 2008 to 2020 as a whole, and the efficiency gap between regions shows a slightly decreasing trend. The environmental impacts of various green technology innovations significantly differ, among which process-oriented green technology innovations emphasizing on processes and products is the key to achieving industrial green transformation. The positive environmental effect of process-oriented green technology innovation increases, while the negative environmental effect of result-oriented green technology innovation decreases as environmental regulations become more stringent.
A method was proposed where an active phase shifter with acoustic power recovery was applied to actively control their cooling supplies there in response to the new requirement of variable cooling supplies in dual temperature zones in space exploration. The influence characteristics on the distribution of the cold finger acoustic power and the efficiency of the regenerator in the dual temperature zones were obtained by analyzing the influence of the piston movement characteristics of the phase shifter in the high-temperature zone on the impedance of the cold fingers in the dual-temperature zones. The cryocooler can actively supply the desired cooling powers (@80 K and 40 K), respectively. The numerical calculation results show that the phase difference of the phase shifter piston mainly influences the cooling capacity in the high-temperature zone. The amplitude and phase difference of the phase shifter piston significantly affects the cooling capacity in the low-temperature zone. The experimental results show that the cooling capacity in the 80 K temperature zone can be actively adjusted in the range of 9.2 W to 23.7 W with the active control of the phase shifter, and the cooling capacity in the 40 K temperature zone can be actively adjusted in the range of 3.2 W to 4.5 W.
Enhanced gas collection cover (EGCC) was proposed in order to achieve efficient gas collection from municipal solid waste landfills in China and eliminate environmental pollution and personal safety issues caused by the disorderly release of landfill gas. This facility from top to bottom includes a HDPE geomembrane, gas collection pipes and a soil regulating layer. The Air/W module in Geo-studio software was used to analyze the gas closure performance of the EGCC and relevant influencing factors. Results show that the inherent permeability of the soil regulating layer, the extraction pressure within the gas collection pipes, and the thickness of the landfilled waste are three main factors that affect the gas closure performance of the EGCC. The maximum pressure under the HDPE geomembrane increases with the decrease in the inherent permeability of the soil regulating layer, the increase in the extraction pressure within the gas collection pipes and the increase in the thickness of the landfilled waste. A formula for estimating the arrangement spacing of the gas collection pipes based on the soil regulating layer and the extraction pressure within the gas collection pipes was proposed. It is recommended that preference should be given to locally available medium sand or coarser soils as soil regulating layers. Xiaping Landfill in Shenzhen had achieved a 7-fold increase in landfill gas collection rate from 2014 to 2019 after adopting the EGCC combined with multiple gas extraction wells, with a landfill gas collection efficiency increasing from less than 30% to over 90%.
An open domain 3D model retrieval algorithm was proposed in order to meet the requirement of management and retrieval of massive new model data under the open domain. The semantic consistency of multi-modal information can be effectively used. The category information among unknown samples was explored with the help of unsupervised algorithm. Then the unknown class information was introduced into the parameter optimization process of the network model. The network model has better characterization and retrieval performance in the open domain condition. A hierarchical multi-modal information fusion model based on a Transformer structure was proposed, which could effectively remove the redundant information among the modalities and obtain a more robust model representation vector. Experiments were conducted on the dataset ModelNet40, and the experiments were compared with other typical algorithms. The proposed method outperformed all comparative methods in terms of mAP metrics, which verified the effectiveness of the method in terms of retrieval performance improvement.
The basic level of carbon dioxide volume fraction in the operating environment of highway tunnels and the relationship between carbon dioxide volume fraction and tunnel traffic flow state, plane alignment and cross-section geometric characteristics were analyzed based on the field measurement of five highway tunnels in Ningbo in order to explore the distribution characteristics of carbon dioxide in the operating environment of highway tunnels. The change of carbon dioxide volume fraction in highway tunnel operation environment with time was analyzed based on 4G remote intelligent continuous monitoring. The specific influence of tunnel length, alignment, traffic flow state and section geometric characteristics on the distribution of carbon dioxide was discussed by numerical simulation. Results showed that the volume fraction of carbon dioxide had obvious linear increasing characteristics along the longitudinal direction of the tunnel. Generally, the volume fraction of carbon dioxide at the exit of the tunnel was the highest, up to 691×10−6~1226×10−6, which was 2~4 times that of the general atmospheric environment level. Ventilation level, cross channel, broadband, line shape and length will increase the slope of linear growth and the degree of influence will decrease in turn under the same traffic volume. The cross-section distribution of carbon dioxide has an obvious diffusion phenomenon and gravity effect. The higher the wall height of the same section is, the lower the volume fraction is. The volume fraction of carbon dioxide on both sides of the straight tunnel is symmetrically distributed. The volume fraction inside the curve tunnel is significantly higher than that on the outside. The broadband has a certain buffer effect and the volume fraction on the broadband side is slightly lower than that on the other side. The cross channel has a certain complementary ventilation effect. The volume fraction of carbon dioxide has obvious time-varying characteristics and periodicity. The daily volume fraction extreme value appears at 8, 12 and 17 o’clock, and the weekly volume fraction extreme value appears at the weekend. The volume fraction change is significantly correlated with the traffic volume.
A road network extraction method based on a lightweight Transformer was proposed, named RoadViT aiming at some limitations of the existing methods, such as imprecise road region extraction and limited real-time performance. The MobileViT architecture which could mix convolutional neural networks and the Transformer was used to encode features in order to efficiently extract high-level context information. Then a pyramid decoder was proposed to implement the extraction and fusion of multi-scale features, and the probability distribution of pixel categories was generated. The Mosaic method was combined with multi-scale scaling and random cropping strategies to implement data enhancement, which could construct fine and various remote sensing images. A dynamic weighting loss function was proposed to mitigate the problem according to the imbalance between the road category and background category in urban remote sensing images. The experimental results show that RoadViT, with a number of parameters of only 1.25 × 106, can achieve an inference speed of up to 10 frames in a second on the Jetson TX2, and an accuracy of up to 57.0% on the CHN6-CUG dataset. The proposed method is an effective exploration of the lightweight Transformer in urban remote sensing images, which can achieve improved road extraction accuracy while maintaining the real-time performance of inference.
A lightweight Yolov5 garbage detection solution was proposed aiming at the issue of poor real-time performance in garbage detection classification on edge devices. The Stem module was introduced to enhance the model’s ability to extract features from input images. The C3 module of the backbone was improved to increase feature extraction capabilities. Depthwise separable convolution was used to replace the 3×3 downsampling convolutions in the network, achieving model lightweighting. The K-means++ algorithm was employed to recompute anchor box values for objects, enabling the model to better predict target box sizes during training. Experimental research and comparisons show that the improved model achieves a 0.8% increase in mAP_0.5 and a 3% increase in mAP_0.5:0.95, while reducing model parameters by 77.9% and improving inference speed by 21.9% compared with the Yolov5s model, significantly enhancing the detection performance of the model.
A new steam Carnot battery based on high-temperature and low-temperature phase change materials was proposed in order to analyze the new route of multi-energy complementation of integrated energy system in industrial parks. A thermodynamic cycle calculation model considering the equipment performance and mass flow rate was established. The effects of design parameters and multi-stage compression structure on the system heat pump coefficient, round-trip efficiency, power storage loss and efficiency of the heating were analyzed. The phase change temperature of low-temperature phase change material and the phase change temperature of high-temperature phase change material are the main factors affecting the performance of steam Carnot battery. The high cycle performance region of steam Carnot battery was obtained. The parameters and structure of the steam Carnot battery were optimized. Results showed that the round-trip efficiency could reach 56.96%, the coefficient of performance of the heat pump could reach 2.55, and the efficiency of the heating could reach 68.74%.
The PDM-YOLO model for accurate real-time obstacle detection in unmanned electric locomotives was proposed in order to address the problem of low accuracy of obstacle recognition in existing coal mine underground unmanned electric locomotives due to poor roadway environments. The ordinary convolution in the C3 module of the conventional YOLOv5 model was replaced with partial convolution to construct the C3_P feature extraction module, which effectively reduced the floating-point operations (FLOPs) and computational delay of the model. The improved decoupled head was used to decouple the prediction head of the conventional YOLOv5 model in order to improve the convergence speed of the model and the accuracy of obstacle recognition. The Mosaic data augmentation method was optimized to enrich the feature information of the training images and enhance the generalizability and robustness of the model. The experimental results showed that the mean average precision (mAP) of the PDM-YOLO model reached 96.3% and the average detection speed reached 109.2 frames per second on the self-built dataset. The detection accuracy of the PDM-YOLO model on the PASCAL VOC public dataset is higher than that of the existing mainstream YOLO series models.
A video object detection algorithm which was built upon the YOLOX-S single-stage detector based on mixed weighted reference-frame sampler and multi-level feature aggregation attention was proposed aiming at the problems of existing deep learning-based video object detection algorithms failing to simultaneously meet accuracy and efficiency requirements. Mixed weighted reference-frame sampler (MWRS) included weighted random sampling and local consecutive sampling to fully utilize effective global information and inter-frame local information. Multi-level feature aggregation attention (MFAA) module refined the classification features extracted by YOLOX-S based on self-attention mechanism, encouraging the network to learn richer feature information from multi-level features. The experimental results demonstrated that the proposed algorithm achieved an average precision AP50 of 77.8% on the ImageNet VID dataset with an average detection speed of 11.5 milliseconds per frame. The object classification and location performance are significantly better than that of YOLOX-S, indicating that the proposed algorithm achieves higher accuracy and faster detection speed.
A many-core parallel optimization scheme for large-point FFT was proposed according to the structural characteristics and programming specifications of the domestic Sunway 26010 processor, which was used in the Sunway Taihu Light supercomputer. The scheme was derived from the classic Cooley-Tukey FFT algorithm, and was accelerated in parallel by iteratively decomposing the one-dimensional large-point data into two-dimensional small-scale matrices. The "column-sharing, row-continuity" strategy was specially proposed in order to solve the problem of reading, writing, transposing and calculating of the "column FFT" of the matrix. The computing resources and transmission bandwidth of the many-core processor were fully utilized by reasonable data allocation, rearrangement and exchange combined with other optimization methods such as SIMD vectorization, twiddle factor optimization, double-buffering, register communication and stride transmission. The experimental results prove that the single core-group of 64 slave cores running parallel program can achieve a maximum speed-up of 65x and an average speed-up of more than 48x compared with the main core running the FFTW library.
A topology of asymmetric hybrid pole permanent magnet motor (AHPPMM) was proposed for the problem of high loss and high heat generation of conventional interior permanent magnet motor. The topology of the asymmetric hybrid pole permanent magnet motor was introduced. The electromagnetic characteristics and loss distribution characteristics of the two were compared and analyzed. The equivalent thermal conductivity was determined based on the features of the distributed winding structure of the AHPPMM, and the lumped parameter thermal model was constructed. Then a unidirectional magneto-thermal coupling model was established to calculate the temperature distribution of each motor component and verify the correctness of the thermal network model. A bi-directional magneto-thermal coupling model was established by considering the temperature influence on the permanent magnet material in order to compare and analyze the influence law of different current densities on the motor temperature rise. A prototype was fabricated and a temperature rise experiment platform was constructed. The effectiveness and rationality of the new topology were verified. The accuracy of the calculation results of the bi-directional magneto-thermal coupling method was validated.
A trajectory privacy protection mechanism based on clustering and deep learning (PPCDL) was proposed aiming at the problem of privacy leakage faced by users in the trajectory distribution of Internet of Vehicles. The trajectory space was divided into multiple regions using timestamps by considering the time factor in the trajectory in order to obtain the distribution points of trajectories within each region. Improved stable membership multi-peak clustering was performed on each region, and the privacy budget matrix was pre-allocated based on the trajectory density of each region. The time graph convolutional network model was utilized to extract spatiotemporal features from trajectory data for training and predicting the pre-allocated privacy budget matrix. The trajectory data was perturbed by adding the appropriate Laplace noise based on the prediction results before it was published. The theoretical analysis and experimental results show that PPCDL has lower time overhead and can predict the privacy budget more accurately compared with the comparison mechanism. Laplace noise can be added to the trajectory data in a reasonable manner by using PPCDL, which effectively improves the availability of the trajectory data.
A lightweight and efficient human pose estimation method with an enhanced priori skeleton structure was proposed to better utilize the unique distribution properties of human pose keypoints. The high-resolution network was used to preserve spatial location information better. The lightweight inverse residual module was employed to reduce the number of model parameters. The postural enhancement module was designed to strengthen the priori information of human pose and the connection between human pose keypoints using global spatial feature information and context information. The direction-enhanced convolution module was proposed to address the problem of missing spatial feature information of keypoints caused by blurred pixel positions and directional shifts of convolution kernel optimization when fusing multi-resolution feature images. The prior distribution of keypoints was combined by utilizing the properties of the horizontal and vertical directions of the keypoints on the torso. The experimental results demonstrate that the network can efficiently estimate human pose. The model achieves an average precision score of 78.4 on the COCO test-dev set and reduces the number of parameters by 17.4×106 compared with the benchmark network, balancing accuracy and efficiency.
