In order to improve the movement accuracy of the robot, let the movement of the robot trajectory smoother, we design a third-order uniform acceleration / deceleration trajectory based on the Jerk value. Through the establishment of the mathematical model of the trajectory, combined with the logic control algorithm, constituted the core of the trajectory control. Finally, the StateFlow toolbox in Simulink as the carrier. Using the state module in StateFlow combined with the logic control conversion module, the third-order uniform acceleration and deceleration trajectory design is realized.
Abstract The Oil and Gas (O&G) industry is embracing modern and intelligent digital technologies such as big data analytics, cloud services, machine learning etc. to increase productivity, enhance operations safety, reduce operation cost and mitigate adverse environmental impact. Challenges that come with such an oil field digital transformation include, but are certainly not limited to: information explosion; isolated and incompatible data repositories; logistics for data exchange and communication; obsoleted processes; cost of support; and the lack of data security. In this paper, we introduce an elastically scalable cloud-based platform to provide big data service for the upstream oil and gas industry, with high reliability and high performance on real-time or near real-time services based on industry standards. First, we review the nature of big data within O&G, paying special attention to distributed fiber optic sensing technologies. We highlight the challenges and necessary system requirements to build effective and scalable downhole big data management and analytics. Secondly, we propose a cloud-based platform architecture for data management and analytics services. Finally, we will present multiple case studies and examples with our system as it is applied in the field. We demonstrate that a standardized data communication and security model enables high efficiency for data transmission, storage, management, sharing and processing in a highly secure environment. Using a standard big data framework and tools (e.g., Apache Hadoop, Spark and Kafka) together with machine learning techniques towards autonomous analysis of such data sources, we are able to process extremely large and complex datasets in an efficient way to provide real-time or near real-time data analytical service, including prescriptive and predictive analytics. The proposed integrated service comprises multiple main systems, such as a downhole data acquisition system; data exchange and management system; data processing and analytics system; as well as data visualization, event alerting and reporting system. With emerging fiber optic technologies, this system not only provides services using legacy O&G data such as static reservoir information, fluid characteristics, well log, well completion information, downhole sensing and surface monitoring data, but also incorporates distributed sensing data (DxS) such as distributed temperature sensing (DTS), distributed strain sensing (DSS) and distributed acoustic sensing (DAS) for continuous downhole measurements along the wellbore with very high spatial resolution. It is the addition of the fiber optic distributed sensing technology that has increased exponentially the volume of downhole data needed to be transmitted and securely managed.
The three-dimensional model of high-speed loading spindle is established by using ABAQUS's modeling module. A finite element analysis model of high-speed loading spindle was established by using spring element to simulate bearing boundary condition. The static and dynamic performance of the spindle structure with different specifications of the rectangular spline and the different diameter neck of axle are studied in depth, and the influence of different spindle span on the static and dynamic performance of the high-speed loading spindle is studied. Finally, the optimal structure of the high-speed loading spindle is obtained. The results provide a theoretical basis for improving the overall performance of the test-bed
ABSTRACT (1) Soil alkalinization and salinization represent a growing global challenge. Maize ( Zea mays L.), with its relatively low tolerance to salt and alkali, is increasingly vulnerable to saline‐alkali stress. Identifying maize genotypes that can withstand salinity and alkalinity is crucial to broaden the base of salt‐alkali‐tolerant maize germplasm. (2) In this study, we screened 65 maize germplasm resources for alkali stress using a 60 mM NaHCO 3 solution. We measured fifteen morphological and physiological indices, including seedling height, stem thickness, and leaf area. Various analytical methods—correlation analysis, principal component analysis, subordinate function analysis, cluster analysis, stepwise discriminant analysis, and ridge regression analysis—were used to assess the seedling alkali tolerance of these maize germplasm resources. The physiological indices of six tested maize varieties were analyzed in greater detail. (3) The findings revealed complex correlations among traits, particularly strong negative associations between conductivity and root traits such as length, volume, surface area, diameter, and number of branches. The 15 evaluation indices were reduced to 7 principal components, explaining 77.89% of the variance. By applying affiliation functions and weights, we derived a comprehensive evaluation of maize seedling alkali tolerance. Notably, three germplasms—Liang Yu 99, Bi Xiang 638, and Gan Xin 2818—demonstrated significant comprehensive seedling alkali tolerance. Cluster analysis grouped the 65 maize germplasm resources into four distinct categories (I, II, III, and IV). The results of the cluster analysis were confirmed by multiclass stepwise discriminant analysis, which achieved a correct classification rate of 92.3% for 60 maize genotypes regarding alkalinity tolerance. Using principal component and ridge regression analyses, we formulated a regression equation for alkali tolerance: D ‐value = −1.369 + 0.002 * relative root volume + 0.003 * relative number of root forks + 0.006 * relative chlorophyll SPAD + 0.005 * relative stem thickness + 0.005 * relative plant height + 0.001 * relative conductivity + 0.002 * relative dry weight of underground parts. Under sodium bicarbonate stress, morphological indices and germination rates were significantly reduced, germination was inhibited, photosynthetic pigment levels in maize leaves decreased to varying degrees, and the activities of peroxidase (POD), superoxide dismutase (SOD), and catalase (CAT) significantly increased. Alkali stress markedly enhanced the antioxidant enzyme activities in maize varieties, with alkali‐resistant varieties exhibiting a greater increase in antioxidant enzyme activities than alkali‐sensitive varieties under such stress. (4) By screening for alkali tolerance in maize seedlings, the identified alkali‐tolerant genotypes can be further utilized as suitable donor parents, thereby enhancing the use of alkali‐tolerant germplasm resources and providing theoretical guidance for maize cultivation in saline‐alkaline environments.
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