Effect of phosphorus content on interfacial heat transfer and film deposition behavior during the high-temperature simulation of strip casting

The interfacial wettability and heat transfer behavior are crucial in the strip casting of high phosphorus-containing steel.A hightemperature simulation of strip casting was conducted using the droplet solidification technique with the aims to reveal the effects of phosphorus content on interfacial wettability,deposited film,and interfacial heat transfer behavior.Results showed that when the phosphorus content increased from 0.014wt%to 0.406wt%,the mushy zone enlarged,the complete solidification temperature delayed from1518.3 to 1459.4℃,the final contact angle decreased from 118.4°to 102.8°,indicating improved interfacial contact,and the maximum heat flux increased from 6.9 to 9.2 MW/m2.Increasing the phosphorus content from 0.081wt%to 0.406wt%also accelerated the film deposition rate from 1.57 to 1.73μm per test,resulting in a thickened naturally deposited film with increased thermal resistance that advanced the transition point of heat transfer from the fifth experiment to the third experiment.

Paragraph Vector Representation Based on Word to Vector and CNN Learning

Document processing in natural language includes retrieval,sentiment analysis,theme extraction,etc.Classical methods for handling these tasks are based on models of probability,semantics and networks for machine learning.The probability model is loss of semantic information in essential,and it influences the processing accuracy.Machine learning approaches include supervised,unsupervised,and semi-supervised approaches,labeled corpora is necessary for semantics model and supervised learning.The method for achieving a reliably labeled corpus is done manually,it is costly and time-consuming because people have to read each document and annotate the label of each document.Recently,the continuous CBOW model is efficient for learning high-quality distributed vector representations,and it can capture a large number of precise syntactic and semantic word relationships,this model can be easily extended to learn paragraph vector,but it is not precise.Towards these problems,this paper is devoted to developing a new model for learning paragraph vector,we combine the CBOW model and CNNs to establish a new deep learning model.Experimental results show that paragraph vector generated by the new model is better than the paragraph vector generated by CBOW model in semantic relativeness and accuracy.

Investigates of substrate mingling ratio and organic loading rate of KOH pretreated corn stover and pig manure in batch and semi-continuous system:Anaerobic digestion performance and microbial characteristics

The effects of substrate mingling ratio(SMR)(1:1,1:2,1:3,3:1,and 2:1)and organic loading rate(OLR)(50-90 g total solids per liter per day)on anaerobic co-digestion performance and microbial characteristics were investigated for pig manure(PM)and pretreated/untreated corn stover in batch and semicontinuous anaerobic digestion(AD)system.The results showed that SMR and pretreatment affected co-digestion performance.The maximum cumulative methane yield of 428.5 ml·g^(-1)(based on volatile solids(VS))was obtained for PCP13,which was 35.7%and 40.0%higher than that of CSU and PM.In the first 5 days,the maximum methane yield improvement rate was 378.1%for PCP13.The daily methane yield per gram VS of PCP13 was 11.4%-18.5%higher than that of PC_(U)13.Clostridium_sensu_stricto_1,DMER64,and Bacteroides and Methanosaeta,Methanobacterium,and Methanospirillum had higher relative abundance at the genus level.Therefore,SMR and OLR are important factor affecting the AD process,and OLR can affect methane production through volatile fatty acids.

Learning accurate template matching with differentiable coarseto-fine correspondence refinement

Template matching is a fundamental task in computer vision and has been studied for decades.It plays an essential role in manufacturing industry for estimating the poses of different parts,facilitating downstream tasks such as robotic grasping.Existing methods fail when the template and source images have different modalities,cluttered backgrounds,or weak textures.They also rarely consider geometric transformations via homographies,which commonly exist even for planar industrial parts.To tackle the challenges,we propose an accurate template matching method based on differentiable coarse-tofine correspondence refinement.We use an edge-aware module to overcome the domain gap between the mask template and the grayscale image,allowing robust matching.An initial warp is estimated using coarse correspondences based on novel structure-aware information provided by transformers.This initial alignment is passed to a refinement network using references and aligned images to obtain sub-pixel level correspondences which are used to give the final geometric transformation.Extensive evaluation shows that our method to be significantly better than state-of-the-art methods and baselines,providing good generalization ability and visually plausible results even on unseen real data.

6DOF pose estimation of a 3D rigid object based on edge-enhanced point pair features

The point pair feature(PPF)is widely used for 6D pose estimation.In this paper,we propose an efficient 6D pose estimation method based on the PPF framework.We introduce a well-targeted down-sampling strategy that focuses on edge areas for efficient feature extraction for complex geometry.A pose hypothesis validation approach is proposed to resolve ambiguity due to symmetry by calculating the edge matching degree.We perform evaluations on two challenging datasets and one real-world collected dataset,demonstrating the superiority of our method for pose estimation for geometrically complex,occluded,symmetrical objects.We further validate our method by applying it to simulated punctures.

High-performance,low-power,and flexible ultraviolet photodetector based on crossed ZnO microwires p-n homojunction

Low-power, flexible, and integrated photodetectors have attracted increasing attention due to their potential applications of photosensing, astronomy, communications, wearable electronics, etc. Herein, the samples of ZnO microwires having p-type(Sb-doped ZnO, ZnO:Sb) and n-type(Ga-doped ZnO, ZnO:Ga) conduction properties were synthesized individually. Sequentially, a p-n homojunction vertical structure photodiode involving a single ZnO:Sb microwire crossed with a ZnO:Ga microwire, which can detect ultraviolet light signals, was constructed.

ARM3D:Attention-based relation module for indoor 3D object detection

Relation contexts have been proved to be useful for many challenging vision tasks.In the field of3D object detection,previous methods have been taking the advantage of context encoding,graph embedding,or explicit relation reasoning to extract relation contexts.However,there exist inevitably redundant relation contexts due to noisy or low-quality proposals.In fact,invalid relation contexts usually indicate underlying scene misunderstanding and ambiguity,which may,on the contrary,reduce the performance in complex scenes.Inspired by recent attention mechanism like Transformer,we propose a novel 3D attention-based relation module(ARM3D).It encompasses objectaware relation reasoning to extract pair-wise relation contexts among qualified proposals and an attention module to distribute attention weights towards different relation contexts.In this way,ARM3D can take full advantage of the useful relation contexts and filter those less relevant or even confusing contexts,which mitigates the ambiguity in detection.We have evaluated the effectiveness of ARM3D by plugging it into several state-of-the-art 3D object detectors and showing more accurate and robust detection results.Extensive experiments show the capability and generalization of ARM3D on 3D object detection.Our source code is available at https://github.com/lanlan96/ARM3D.

EFECL:Feature encoding enhancement with contrastive learning for indoor 3D object detection

Good proposal initials are critical for 3D object detection applications.However,due to the significant geometry variation of indoor scenes,incomplete and noisy proposals are inevitable in most cases.Mining feature information among these“bad”proposals may mislead the detection.Contrastive learning provides a feasible way for representing proposals,which can align complete and incomplete/noisy proposals in feature space.The aligned feature space can help us build robust 3D representation even if bad proposals are given.Therefore,we devise a new contrast learning framework for indoor 3D object detection,called EFECL,that learns robust 3D representations by contrastive learning of proposals on two different levels.Specifically,we optimize both instance-level and category-level contrasts to align features by capturing instance-specific characteristics and semantic-aware common patterns.Furthermore,we propose an enhanced feature aggregation module to extract more general and informative features for contrastive learning.Evaluations on ScanNet V2 and SUN RGB-D benchmarks demonstrate the generalizability and effectiveness of our method,and our method can achieve 12.3%and 7.3%improvements on both datasets over the benchmark alternatives.The code and models are publicly available at https://github.com/YaraDuan/EFECL.