Segmentation method of U-net sheet metal engineering drawing based on CBAM attention mechanism

In the manufacturing process of heavy industrial equipment, the specific unit in the welding diagram is first manually redrawn and then the corresponding sheet metal parts are cut, which is inefficient. To this end, this paper proposes a U-net-based method for the segmentation and extraction of specific units in welding engineering drawings. This method enables the cutting device to automatically segment specific graphic units according to visual information and automatically cut out sheet metal parts of corresponding shapes according to the segmentation results. This process is more efficient than traditional human-assisted cutting. Two weaknesses in the U-net network will lead to a decrease in segmentation performance: first, the focus on global semantic feature information is weak, and second, there is a large dimensional difference between shallow encoder features and deep decoder features. Based on the CBAM (Convolutional Block Attention Module) attention mechanism, this paper proposes a U-net jump structure model with an attention mechanism to improve the network's global semantic feature extraction ability. In addition, a U-net attention mechanism model with dual pooling convolution fusion is designed, the deep encoder's maximum pooling + convolution features and the shallow encoder's average pooling + convolution features are fused vertically to reduce the dimension difference between the shallow encoder and deep decoder. The dual-pool convolutional attention jump structure replaces the traditional U-net jump structure, which can effectively improve the specific unit segmentation performance of the welding engineering drawing. Using vgg16 as the backbone network, experiments have verified that the IoU, mAP, and Accu of our model in the welding engineering drawing dataset segmentation task are 84.72%, 86.84%, and 99.42%, respectively

Forced Oscillation Source Location via Multivariate Time Series Classification

Precisely locating low-frequency oscillation sources is the prerequisite of suppressing sustained oscillation, which is an essential guarantee for the secure and stable operation of power grids. Using synchrophasor measurements, a machine learning method is proposed to locate the source of forced oscillation in power systems. Rotor angle and active power of each power plant are utilized to construct multivariate time series (MTS). Applying Mahalanobis distance metric and dynamic time warping, the distance between MTS with different phases or lengths can be appropriately measured. The obtained distance metric, representing characteristics during the transient phase of forced oscillation under different disturbance sources, is used for offline classifier training and online matching to locate the disturbance source. Simulation results using the four-machine two-area system and IEEE 39-bus system indicate that the proposed location method can identify the power system forced oscillation source online with high accuracy.Comment: 5 pages, 3 figures. Accepted by 2018 IEEE/PES Transmission and Distribution Conferenc

CEN34 -- High-Mass YSO in M17 or Background Post-AGB Star?

We investigate the proposed high-mass young stellar object (YSO) candidate CEN34, thought to be associated with the star forming region M17. Its optical to near-infrared (550-2500 nm) spectrum reveals several photospheric absorption features, such as H{\alpha}, Ca triplet and CO bandheads but lacks any emission lines. The spectral features in the range 8375-8770{\AA} are used to constrain an effective temperature of 5250\pm250 (early-/mid-G) and a surface gravity of 2.0\pm0.3 (supergiant). The spectral energy distribution of CEN34 resembles the SED of a high-mass YSO or an evolved star. Moreover, the observed temperature and surface gravity are identical for high-mass YSOs and evolved stars. The radial velocity relative to LSR (V_LSR) of CEN34 as obtained from various photospheric lines is of the order of -60 km/s and thus distinct from the +25 km/s found for several OB stars in the cluster and for the associated molecular cloud. The SED modeling yields ~ 10^{-4} M_sun of circumstellar material which contributes only a tiny fraction to the total visual extinction (11 mag). In the case of a YSO, a dynamical ejection process is proposed to explain the V_LSR difference between CEN34 and M17. Additionally, to match the temperature and luminosity, we speculate that CEN34 had accumulated the bulk of its mass with accretion rate > 4x10^{-3} M_sun/yr in a very short time span (~ 10^3 yrs), and currently undergoes a phase of gravitational contraction without any further mass gain. However, all the aforementioned characteristics of CEN34 are compatible with an evolved star of 5-7 M_sun and an age of 50-100 Myrs, most likely a background post-AGB star with a distance between 2.0 kpc and 4.5 kpc. We consider the latter classification as the more likely interpretation. Further discrimination between the two possible scenarios should come from the more strict confinement of CEN34's distance.Comment: 8 pages, 8 figures, 2 tables; accepted by A&

Integrated Sensor Fault Diagnosis and Fault-Tolerant Control for Manipulator

In this paper, an integrated scheme including fault diagnosis and fault-tolerant controller design is proposed for the manipulator system with the sensor fault. Any constant fault or time-varying fault can be estimated by the fault diagnosis scheme based on the adaptive observer rapidly and accurately, and the designed parameters can be solved by the linear matrix inequality. Using the fault estimation information, a fault-tolerant controller combining the characteristics of the proportional differentiation control and the sliding model control is designed to trace the expected trajectory via the back-stepping method. Finally, the effectiveness of the above scheme is verified by the simulation results

Comparison study of constitutive models for overconsolidated clays

Widely distributed in natural deposits, the overconsolidated (OC) clays have attracted extensive experimental investigations on their mechanical behaviors, especially in the 1960s and 1970s. Based on these results, numerous constitutive models have also been established. These models generally fall into two categories: one based on the classical plasticity theory and the other the bounding surface (BS) plasticity theory, with the latter being more popular and successful. The BS concept and the subloading surface (SS) concept are the two major BS plasticity theories. The features of these two concepts and the representative models based on them are introduced, respectively. The unified hardening (UH) model for OC clays is also based on the BS plasticity theory but distinguishes itself from other models by the integration of the reference yield surface, unified hardening parameter, potential failure stress ratio, and transformed stress tensor. Modification is made to the Hvorslev envelop employed in the UH model to improve its capability of describing the behaviors of clays with extremely high overconsolidation ratio in this paper. The comparison among the BS model, SS model, and UH model is performed. Evidence shows that all these three models can characterize the fundamental behaviors of OC clays, such as the stress dilatancy, strain softening and attainment of the critical state. The UH model with the revised Hvorslev envelop has the fewest parameters which are identical to those of the modified Cam-Clay model

Fabrication of a microresonator-fiber assembly maintaining a high-quality factor by CO2 laser welding

We demonstrate fabrication of a microtoroid resonator of a high-quality (high-Q) factor using femtosecond laser three-dimensional (3D) micromachining. A fiber taper is reliably assembled to the microtoroid using CO2 laser welding. Specifically, we achieve a high Q-factor of 2.12*10^6 in the microresonator-fiber assembly by optimizing the contact position between the fiber taper and the microtoroid.Comment: 7 pages, 5 figure

A Chemical Study of Nine Star-forming Regions with Evidence of Infall Motion

The study of the physical and chemical properties of gas infall motion in the molecular clumps helps us understand the initial stages of star formation. We used the FTS wide-sideband mode of the IRAM 30-m telescope to observe nine infall sources with significant double peaked blue line profile. The observation frequency range are 83.7 - 91.5 GHz and 99.4 - 107.2 GHz. We have obtained numbers of molecular line data. Using XCLASS, a total of 7 to 27 different molecules and isotopic transition lines have been identified in these nine sources, including carbon chain molecules such as CCH, c-C3H2 and HC3N. According to the radiation transfer model, we estimated the rotation temperatures and column densities of these sources. Chemical simulations adopting a physical model of HMSFRs are used to fit the observed molecular abundances. The comparison shows that most sources are in the early HMPO stage, with the inner temperature around several ten K

Expression of cocaine- and amphetamine-regulated transcript (CART) in hen ovary

Cocaine- and amphetamine-regulated transcript (CART), discovered initially by via differential display RT-PCR analysis of brains of rats administered cocaine, is expressed mainly in central nervous system or neuronal origin cells, and is involved in a wide range of behaviors, such as regulation of food intake, energy homeostasis, and reproduction. The hens egg-laying rate mainly depends on the developmental status of follicles, expression of CART have not been identified from hen follicles, the regulatory mechanisms of CART biological activities are still unknown. The objective of this study was to characterize the mRNA expression of CART in hen follicular granulosa cells and determine CART peptide localization and regulatory role during follicular development

Mutual-Guided Dynamic Network for Image Fusion

Image fusion aims to generate a high-quality image from multiple images captured under varying conditions. The key problem of this task is to preserve complementary information while filtering out irrelevant information for the fused result. However, existing methods address this problem by leveraging static convolutional neural networks (CNNs), suffering two inherent limitations during feature extraction, i.e., being unable to handle spatial-variant contents and lacking guidance from multiple inputs. In this paper, we propose a novel mutual-guided dynamic network (MGDN) for image fusion, which allows for effective information utilization across different locations and inputs. Specifically, we design a mutual-guided dynamic filter (MGDF) for adaptive feature extraction, composed of a mutual-guided cross-attention (MGCA) module and a dynamic filter predictor, where the former incorporates additional guidance from different inputs and the latter generates spatial-variant kernels for different locations. In addition, we introduce a parallel feature fusion (PFF) module to effectively fuse local and global information of the extracted features. To further reduce the redundancy among the extracted features while simultaneously preserving their shared structural information, we devise a novel loss function that combines the minimization of normalized mutual information (NMI) with an estimated gradient mask. Experimental results on five benchmark datasets demonstrate that our proposed method outperforms existing methods on four image fusion tasks. The code and model are publicly available at: https://github.com/Guanys-dar/MGDN.Comment: ACMMM 2023 accepte