geometrical vision measurement in mechanical engineering

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Energy-Efficient Clustered Cell-Free Networking with Access Point Selection

Ultra-densely deploying access points (APs) to support the increasing data traffic would significantly escalate the cell-edge problem resulting from traditional cellular networks. By removing the cell boundaries and coordinating all APs for joint transmission, the cell-edge problem can be alleviated, which in turn leads to unaffordable system complexity and channel measurement overhead. A new scalable clustered cell-free network architecture has been proposed recently, under which the large-scale network is flexibly partitioned into a set of independent subnetworks operating parallelly. In this paper, we study the energy-efficient clustered cell-free networking problem with AP selection. Specifically, we propose a user-centric ratio-fixed AP-selection based clustering (UCR-ApSel) algorithm to form subnetworks dynamically. Following this, we analyze the average energy efficiency achieved with the proposed UCR-ApSel scheme theoretically and derive an effective closed-form upper-bound. Based on the analytical upper-bound expression, the optimal AP-selection ratio that maximizes the average energy efficiency is further derived as a simple explicit function of the total number of APs and the number of subnetworks. Simulation results demonstrate the effectiveness of the derived optimal AP-selection ratio and show that the proposed UCR-ApSel algorithm with the optimal AP-selection ratio achieves around 40% higher energy efficiency than the baselines. The analysis provides important insights to the design and optimization of future ultra-dense wireless communication systems

Uncertainty and Explainable Analysis of Machine Learning Model for Reconstruction of Sonic Slowness Logs

Logs are valuable information for oil and gas fields as they help to determine the lithology of the formations surrounding the borehole and the location and reserves of subsurface oil and gas reservoirs. However, important logs are often missing in horizontal or old wells, which poses a challenge in field applications. In this paper, we utilize data from the 2020 machine learning competition of the SPWLA, which aims to predict the missing compressional wave slowness and shear wave slowness logs using other logs in the same borehole. We employ the NGBoost algorithm to construct an Ensemble Learning model that can predicate the results as well as their uncertainty. Furthermore, we combine the SHAP method to investigate the interpretability of the machine learning model. We compare the performance of the NGBosst model with four other commonly used Ensemble Learning methods, including Random Forest, GBDT, XGBoost, LightGBM. The results show that the NGBoost model performs well in the testing set and can provide a probability distribution for the prediction results. In addition, the variance of the probability distribution of the predicted log can be used to justify the quality of the constructed log. Using the SHAP explainable machine learning model, we calculate the importance of each input log to the predicted results as well as the coupling relationship among input logs. Our findings reveal that the NGBoost model tends to provide greater slowness prediction results when the neutron porosity and gamma ray are large, which is consistent with the cognition of petrophysical models. Furthermore, the machine learning model can capture the influence of the changing borehole caliper on slowness, where the influence of borehole caliper on slowness is complex and not easy to establish a direct relationship. These findings are in line with the physical principle of borehole acoustics

Analysis of Seismic Response Characteristics of Cenozoic Igneous Facies and Hypothesis of Annular Eruption Pattern, Bohai Area-A Case Study

Based on the study of the igneous rocks of X structure, Bohai Bay Basin, the seismic response characteristics of the igneous facies in the entire area were described and summarized. Aimed at the special seismic response characteristics of the igneous rock in the study area, based on the three types of traditional eruption patterns, a new type of volcanic eruption pattern, annular eruption pattern, was propose for the first time. Annular eruption pattern meant that the volcanic conduit represented zonal distribution in the plane. The characteristics of annular eruption pattern and its impact on hydrocarbon accumulation were fully demonstrated. Annular eruption pattern was firstly proposed in the study field of igneous rocks, which can effectively guide the analysis of the risk and potential of the oil field

Location Reference Recognition from Texts: A Survey and Comparison

A vast amount of location information exists in unstructured texts, such as social media posts, news stories, scientific articles, web pages, travel blogs, and historical archives. Geoparsing refers to recognizing location references from texts and identifying their geospatial representations. While geoparsing can benefit many domains, a summary of its specific applications is still missing. Further, there is a lack of a comprehensive review and comparison of existing approaches for location reference recognition, which is the first and core step of geoparsing. To fill these research gaps, this review first summarizes seven typical application domains of geoparsing: geographic information retrieval, disaster management, disease surveillance, traffic management, spatial humanities, tourism management, and crime management. We then review existing approaches for location reference recognition by categorizing these approaches into four groups based on their underlying functional principle: rule-based, gazetteer matching–based, statistical learning-–based, and hybrid approaches. Next, we thoroughly evaluate the correctness and computational efficiency of the 27 most widely used approaches for location reference recognition based on 26 public datasets with different types of texts (e.g., social media posts and news stories) containing 39,736 location references worldwide. Results from this thorough evaluation can help inform future methodological developments and can help guide the selection of proper approaches based on application needs

Investigation of robust visual reaction and functional connectivity in the rat brain induced by rocuronium bromide with functional MRI

Functional magnetic resonance imaging (fMRI) has been used extensively to understand the brain function of a wide range of neurological and psychiatric disorders. When applied to animal studies, anesthesia is always used to reduce the movement of the animal and also reduce the impacts on the results of fMRI. Several awake models have been proposed by applying physical animal movement restrictions. However, restraining devices were designed for individual subject which limits the promotion of fMRI in awake animals. Here, a clinical muscle relaxant rocuronium bromide (RB) was introduced to restrain the animal in fMRI scanning time. The fMRI reactions of the animal induced with RB and the other two commonly used anesthesia protocols were investigated. The results of the fMRI showed that there were increased functional connectivity and well-round visual responses in the RB induced state. Furthermore, significant BOLD signal changes were found in the cortex and thalamus regions when the animal revived from isoflurane, which should be essential to further understand the effects of anesthesia on the brain. Keywords: Rocuronium bromide, isoflurane, animal anesthesia, fMRI, visual stimulation, resting stat

Aligned macroporous TiO2/chitosan/reduced graphene oxide (rGO) composites for photocatalytic applications

In this article ice templating is used to fabricate novel TiO2/chitosan/reduced graphene oxide (rGO) composites with a highly aligned macroporous structure for photocatalytic applications. The structure of the composites was readily tailored using the composite composition, for example the lamellar pore width decreased from 50–45 to 5–10 μm, while the lamellar thickness increased from 2–3 to 20–25 μm, with an increase of the TiO2 content from 45 to 77 vol%. Lamellar pore channels between the layers exhibited a more uniform distribution when the rGO content was 1.0 wt%. The increase in viscosity of the composites with high TiO2 contents led to the formation of smaller ice crystals and smaller lamellar pore sizes to enable the production of composite structures with improved mechanical strength. The TiO2/chitosan/rGO composites exhibited excellent photocatalytic degradation of methyl orange and the photocatalytic efficiency was optimized by control of the active material content and microstructure. The hybrid composites with 1.0 wt% rGO showed a degradation percentage of 97%, which makes these novel TiO2/chitosan/rGO freeze cast structures attractive materials as high performance and high strength substrates for photocatalytic degradation applications