Intelligent Mining of Urban Ventilated Corridor Based on Digital Surface Model under the Guidance of K-Means

With the acceleration of urbanization, climate problems affecting human health and safe operation of cities have intensified, such as heat island effect, haze, and acid rain. Using high-resolution remote sensing mapping image data to design scientific and efficient algorithms to excavate and plan urban ventilation corridors and improve urban ventilation environment is an effective way to solve these problems. In this paper, we use unmanned aerial vehicle (UAV) tilt photography technology to obtain high-precision remote sensing image digital elevation model (DEM) and digital surface model (DSM) data, count the city’s dominant wind direction in each season using long-term meteorological data, and use building height to calculate the dominant wind direction. The projection algorithm calculates the windward area density of this dominant direction. Under the guidance of K-means, the binarized windward area density map is used to determine each area and boundary of potential ventilation corridors within the threshold range, and the length and angle of each area’s fitted elliptical long axis are calculated to extract the ventilation corridors that meet the criteria. On the basis of high-precision stereo remote sensing data from UAV, the paper uses image classification, segmentation, fitting, and fusion algorithms to intelligently mine potential urban ventilation corridors, and the effectiveness of the proposed method is demonstrated through a case study in Zhuji City, Zhejiang Province

Low storage space for compressive sensing: semi-tensor product approach

Abstract Random measurement matrices play a critical role in successful recovery with the compressive sensing (CS) framework. However, due to its randomly generated elements, these matrices require massive amounts of storage space to implement a random matrix in CS applications. To effectively reduce the storage space of the random measurement matrix for CS, we propose a random sampling approach for the CS framework based on the semi-tensor product (STP). The proposed approach generates a random measurement matrix, where the dimensions of the random measurement matrix are reduced to a quarter (or 1/16, 1/64, and even 1/256) of the number of dimensions, which are used for conventional CS. We then estimate the values of the sparse vector with a modified iteratively re-weighted least-squares (IRLS) algorithm. The results of numerical simulations showed that the proposed approach can reduce the storage space of a random matrix to at least a quarter while maintaining quality of reconstruction. All results confirmed that the proposed approach significantly influences the physical implementation of the CS in images, especially on embedded system and field programmable gate array (FPGA), where storage is limited

A Thermovisco-Hyperelastic Constitutive Model of NEPE Propellant Over a Large Range of Strain Rates

The uniaxial compressive mechanical curves of nitrate ester plasticized polyether (NEPE) propellant under different temperatures and strain rates have been obtained with a universal testing machine and modified split Hopkinson pressure bar (SHPB). The experimental results show that the mechanical properties of NEPE propellant are both rate dependent and temperature dependent. With decreasing temperature or increasing strain rate, the modulus and rigidity obviously increase. Based on the previous models proposed by Yang and Pouriayevali, we propose a modified viscohyperelastic constitutive model which can describe the mechanical response over a large range of strain rates. Then we add a rate-dependent temperature item into the modified model to make a thermovisco-hyperelastic constitutive model. By comparing the experimental results with the model, we find that the thermovisco-hyperelastic constitutive model can correctly describe the uniaxial compressive mechanical properties of NEPE propellant at different temperatures and over a large range of strain rates from the static state to 4500 s À1

Intelligent Mining of Urban Ventilated Corridor Based on Digital Surface Model under the Guidance of K-Means

With the acceleration of urbanization, climate problems affecting human health and safe operation of cities have intensified, such as heat island effect, haze, and acid rain. Using high-resolution remote sensing mapping image data to design scientific and efficient algorithms to excavate and plan urban ventilation corridors and improve urban ventilation environment is an effective way to solve these problems. In this paper, we use unmanned aerial vehicle (UAV) tilt photography technology to obtain high-precision remote sensing image digital elevation model (DEM) and digital surface model (DSM) data, count the city’s dominant wind direction in each season using long-term meteorological data, and use building height to calculate the dominant wind direction. The projection algorithm calculates the windward area density of this dominant direction. Under the guidance of K-means, the binarized windward area density map is used to determine each area and boundary of potential ventilation corridors within the threshold range, and the length and angle of each area’s fitted elliptical long axis are calculated to extract the ventilation corridors that meet the criteria. On the basis of high-precision stereo remote sensing data from UAV, the paper uses image classification, segmentation, fitting, and fusion algorithms to intelligently mine potential urban ventilation corridors, and the effectiveness of the proposed method is demonstrated through a case study in Zhuji City, Zhejiang Province

Research on Multiple-Axis Contour Error Suppression Method Based on Composite Layered Control

With the widespread application of multi-axis machining in the industrial manufacturing, aerospace, and military equipment sectors, the demand for machining ultra-precision components has been steadily increasing. Contour errors directly impact the quality of machined parts. In conventional multi-axis motion control systems based on cross-coupling, it is conventionally assumed that all individual axes are of equal significance during machine processing. However, in practical machining scenarios, diverse machining trajectories and accuracy requirements give rise to distinct control necessities for each axis. This complication leads to challenges in ensuring a consistent single-plane contour, thereby constraining the elevation of the overall contour accuracy. To address this issue, this study proposes a multi-axis contour error suppression method based on composite hierarchical control. The approach advocated in this paper initially ensures the precision of single-axis position control through the development of an advanced S-shaped function-based sliding-mode disturbance observer. Building on this foundation, the three-dimensional spatial contour is segregated into upper and lower layers. Subsequently, dedicated fuzzy PID cross-coupling controllers are devised for each layer. The experimental outcomes substantiate that in comparison to conventional cross-coupling control methods, the method introduced in this study, rooted in composite hierarchical control, not only guarantees the accuracy of single-plane contours but also further enhances the overall contour precision

Analysis of the skeleton of human movement for orthopedics tasks

Last time a few algorithm for human pose detection was developed. As rule, the basic element for pose description is skeleton. It is possible extract many important information from such object for orthopedics task. In this paper algorithm for automatic estimation of walking motion is proposed on base reconstruction human skeleton and definition of harmonic component of walking

Riding towards a sustainable future: an evaluation of bike sharing’s environmental benefits in Xiamen Island, China

In the pursuit of sustainable urbanization, Bike-Sharing Services (BSS) emerge as a pivotal instrument for promoting green, low-carbon transit. While BSS is often commended for its environmental benefits, we offer a more nuanced analysis that elucidates previously neglected aspects. Through the Dominant Travel Distance Model (DTDM), we evaluate the potential of BSS to replace other transportation modes for specific journey based on travel distance. Utilizing multiscale geographically weighted regression (MGWR), we illuminate the relationship between BSS’s environmental benefits and built-environment attributes. The life cycle analysis (LCA) quantifies greenhouse gas (GHG) emissions from production to operation, providing a deeper understanding of BSS’s environmental benefits. Notably, our study focuses on Xiamen Island, a Chinese “Type II large-sized city” (1–3 million population), contrasting with the predominantly studied “super large-sized cities” (over 10 million population). Our findings highlight: (1) A single BSS trip in Xiamen Island reduces GHG emissions by an average of 19.97 g CO2-eq, accumulating monthly savings of 144.477 t CO2-eq. (2) Areas in the southwest, northeast, and southeast of Xiamen Island, characterized by high population densities, register significant BSS environmental benefits. (3) At a global level, the stepwise regression model identifies five key built environment factors influencing BSS’s GHG mitigation. (4) Regionally, MGWR enhances model precision, indicating that these five factors function at diverse spatial scales, affecting BSS’s environmental benefits variably