Design of fl uid pipeline leakage signal acquisition system

Fluid, namely liquid and gas, is a necessary resource in national production and life, so fl uid plays a pivotal role in the progress of the whole society and the development of human civilization. If the fl uid leaks, and is not found and treated in time, it will cause huge material losses and waste of resources, what is more, it will cause irreversible environmental damage. This paper takes fl uid pipeline leakage detection as the main research object, the main research is as follows: According to the steady-state pipeline as the main analysis goal, C8051F040 single chip microcomputer as the main processor, DS18B20 temperature sensor, CEMPX221 pressure sensor, LWGY-C model turbine fl owmeter for data acquisition, and the collected data through the CAN interface, RS485 interface to the host computer

Deep convolutional neural networks for cardiovascular vulnerable plaque detection

In this paper, an accurate two-stage deep learning method is proposed to detect vulnerable plaques in ultrasonic images of cardiovascular. Firstly, a Fully Convonutional Neural Network (FCN) named U-Net is used to segment the original Intravascular Optical Coherence Tomography (IVOCT) cardiovascular images. We experiment on different threshold values to find the best threshold for removing noise and background in the original images. Secondly, a modified Faster RCNN is adopted to do precise detection. The modified Faster R-CNN utilize six-scale anchors (122,162,322,642,1282,2562) instead of the conventional one scale or three scale approaches. First, we present three problems in cardiovascular vulnerable plaque diagnosis, then we demonstrate how our method solve these problems. The proposed method in this paper apply deep convolutional neural networks to the whole diagnostic procedure. Test results show the Recall rate, Precision rate, IoU (Intersection-over-Union) rate and Total score are 0.94, 0.885, 0.913 and 0.913 respectively, higher than the 1st team of CCCV2017 Cardiovascular OCT Vulnerable Plaque Detection Challenge. AP of the designed Faster RCNN is 83.4%, higher than conventional approaches which use one-scale or three-scale anchors. These results demonstrate the superior performance of our proposed method and the power of deep learning approaches in diagnose cardiovascular vulnerable plaques

6G Enabled Advanced Transportation Systems

The 6th generation (6G) wireless communication network is envisaged to be able to change our lives drastically, including transportation. In this paper, two ways of interactions between 6G communication networks and transportation are introduced. With the new usage scenarios and capabilities 6G is going to support, passengers on all sorts of transportation systems will be able to get data more easily, even in the most remote areas on the planet. The quality of communication will also be improved significantly, thanks to the advanced capabilities of 6G. On top of providing seamless and ubiquitous connectivity to all forms of transportation, 6G will also transform the transportation systems to make them more intelligent, more efficient, and safer. Based on the latest research and standardization progresses, technical analysis on how 6G can empower advanced transportation systems are provided, as well as challenges and insights for a possible road ahead.Comment: Submitted to an open access journa

Learning Dynamic Tetrahedra for High-Quality Talking Head Synthesis

Recent works in implicit representations, such as Neural Radiance Fields (NeRF), have advanced the generation of realistic and animatable head avatars from video sequences. These implicit methods are still confronted by visual artifacts and jitters, since the lack of explicit geometric constraints poses a fundamental challenge in accurately modeling complex facial deformations. In this paper, we introduce Dynamic Tetrahedra (DynTet), a novel hybrid representation that encodes explicit dynamic meshes by neural networks to ensure geometric consistency across various motions and viewpoints. DynTet is parameterized by the coordinate-based networks which learn signed distance, deformation, and material texture, anchoring the training data into a predefined tetrahedra grid. Leveraging Marching Tetrahedra, DynTet efficiently decodes textured meshes with a consistent topology, enabling fast rendering through a differentiable rasterizer and supervision via a pixel loss. To enhance training efficiency, we incorporate classical 3D Morphable Models to facilitate geometry learning and define a canonical space for simplifying texture learning. These advantages are readily achievable owing to the effective geometric representation employed in DynTet. Compared with prior works, DynTet demonstrates significant improvements in fidelity, lip synchronization, and real-time performance according to various metrics. Beyond producing stable and visually appealing synthesis videos, our method also outputs the dynamic meshes which is promising to enable many emerging applications.Comment: CVPR 202

Online identification of lithium-ion battery model parameters with initial value uncertainty and measurement noise

Online parameter identification is essential for the accuracy of the battery equivalent circuit model (ECM). The traditional recursive least squares (RLS) method is easily biased with the noise disturbances from sensors, which degrades the modeling accuracy in practice. Meanwhile, the recursive total least squares (RTLS) method can deal with the noise interferences, but the parameter slowly converges to the reference with initial value uncertainty. To alleviate the above issues, this paper proposes a co-estimation framework utilizing the advantages of RLS and RTLS for a higher parameter identification performance of the battery ECM. RLS converges quickly by updating the parameters along the gradient of the cost function. RTLS is applied to attenuate the noise effect once the parameters have converged. Both simulation and experimental results prove that the proposed method has good accuracy, a fast convergence rate, and also robustness against noise corruption

In Situ Reconstruction of High‐Entropy Heterostructure Catalysts for Stable Oxygen Evolution Electrocatalysis under Industrial Conditions

info:eu-repo/semantics/publishedVersio

[68Ga]Ga-DOTA-FAPI-04 PET/MR in patients with acute myocardial infarction: potential role of predicting left ventricular remodeling.

PURPOSE To assess predictive value of 68Ga-labeled fibroblast activation protein inhibitor-04 ([68Ga]Ga-DOTA-FAPI-04) PET/MR for late left ventricular (LV) remodeling in patients with ST-segment elevated myocardial infarction (STEMI). METHODS Twenty-six patients with STEMI were included in the study. [68Ga]Ga-DOTA-FAPI-04 PET/MR was performed at baseline and at average 12 months after STEMI. LV remodeling was defined as >10% increase in LV end-systolic volume (LVESV) from baseline to 12 months. RESULTS The LV remodeling group demonstrated higher [68Ga]Ga-DOTA-FAPI-04 uptake volume (UV) at baseline than the non-LV remodeling group (p < 0.001). [68Ga]Ga-DOTA-FAPI-04 UV at baseline was a significant predictor (OR = 1.048, p = 0.011) for LV remodeling at 12 months after STEMI. Compared to clinical information, MR imaging and cardiac function parameters at baseline, [68Ga]Ga-DOTA-FAPI-04 UV demonstrated better predictive ability (AUC = 0.938, p < 0.001) for late LV remodeling, with sensitivity of 100.0% and specificity of 81.3%. CONCLUSIONS [68Ga]Ga-DOTA-FAPI-04 PET/MR is an effective tool to non-invasively quantify myocardial fibroblasts activation, and baseline [68Ga]Ga-DOTA-FAPI-04 UV may have potential predictive value for late LV remodeling

Structural Based Screening of Antiandrogen Targeting Activation Function-2 Binding Site

Androgen receptor (AR) plays a critical role in the development and progression of prostate cancer (PCa). Current antiandrogen therapies induce resistant mutations at the hormone binding pocket (HBP) that convert the activity of these agents from antagonist to agonist. Thus, there is a high unmet medical need for the development of novel antiandrogens which circumvent mutation-based resistance. Herein, through the analysis of AR structures with ligands binding to the activation function-2 (AF2) site, we built a combined pharmacophore model. In silico screening and the subsequent biological evaluation lead to the discovery of the novel lead compound IMB-A6 that binds to the AF2 site, which inhibits the activity of either wild-type (WT) or resistance mutated ARs. Our work demonstrates structure-based drug design is an efficient strategy to discover new antiandrogens, and provides a new class of small molecular antiandrogens for the development of novel treatment agents against PCa