Screening Algorithm Based on The Color Halftone Fluorescent Printing and Its Application in Packaging Design

Abstract：This paper analyzed the characteristics of colorless fluorescent ink and the existing color separation theory, so that colored additive method should be used in printing color pattern with colorless fluorescent ink as well as three-color screening separation type (red, green and blue). Considering the exhibition of the tone, this paper selected dot parallel screening method. At the same time, through comparing the properties of different dots, this paper adopted a special method of AM screening, using regular triangle as the basic dot model to a threshold matrix of AM screening. Finally, designing a screening algorithm which best suit the colorless fluorescent color printing through the obtained threshold matrix, as well as simulating the screening effects of color printing image in the MATLAB. The experimental results show that this method of screening in anti-counterfeit packaging field can copy better colorless fluorescent color pattern, so as to achieve the effects of color printing using colorless fluorescent ink, which will be widely used in outer package such as medicine, food, tobacco and beverage

Retrieval of All-Sky Land Surface Temperature Considering Penetration Effect Using Spaceborne Thermal and Microwave Radiometry

Thermal infrared (TIR) remote sensing (RS) has been widely adopted for monitoring land surface temperature (LST). However, its application has been limited to cloud-free conditions, resulting in a need for LST retrieval methods that combine microwave (MW) and TIR channels. This is especially crucial in areas frequently covered by clouds. One limitation of the current LST retrieval methods is the absence of considering the penetration effect (PE) of MW, which leads to great uncertainty in barren and sparsely vegetated areas. To address this issue, this study proposes a new perspective that considers the PE to merge the LST retrieved from MW and TIR channels. The soil temperature integral equation is simplified based on the soil temperature and water content profiles. Consequently, a PE-based model is developed to convert the effective soil temperature into LST and merge the LST estimated from passive MW observations with those from moderate resolution imaging spectroradiometer (MODIS) LST products. The model considering PE performs better than the method that does not consider PE, as demonstrated by higher $R$ and lower root-mean-square error (RMSE) values. The PE-based model is then applied to Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) data, and the estimated LST is found to fit well with the MODIS LST product ( $R$ = 0.91). Using this model, an all-sky LST is retrieved by merging passive MW observations and MODIS LST products. Validation of the model at eight ground-based stations over the Tibetan Plateau (TP) demonstrates its reasonable accuracy in both clear-sky and cloudy conditions.</p

Inductive Meta-path Learning for Schema-complex Heterogeneous Information Networks

Heterogeneous Information Networks (HINs) are information networks with multiple types of nodes and edges. The concept of meta-path, i.e., a sequence of entity types and relation types connecting two entities, is proposed to provide the meta-level explainable semantics for various HIN tasks. Traditionally, meta-paths are primarily used for schema-simple HINs, e.g., bibliographic networks with only a few entity types, where meta-paths are often enumerated with domain knowledge. However, the adoption of meta-paths for schema-complex HINs, such as knowledge bases (KBs) with hundreds of entity and relation types, has been limited due to the computational complexity associated with meta-path enumeration. Additionally, effectively assessing meta-paths requires enumerating relevant path instances, which adds further complexity to the meta-path learning process. To address these challenges, we propose SchemaWalk, an inductive meta-path learning framework for schema-complex HINs. We represent meta-paths with schema-level representations to support the learning of the scores of meta-paths for varying relations, mitigating the need of exhaustive path instance enumeration for each relation. Further, we design a reinforcement-learning based path-finding agent, which directly navigates the network schema (i.e., schema graph) to learn policies for establishing meta-paths with high coverage and confidence for multiple relations. Extensive experiments on real data sets demonstrate the effectiveness of our proposed paradigm

Experimental Study and CFD Modelling of Down-Reaching Flame Behaviors of Tank Fires with Large Ullage Heights

This paper is aimed at studying the down-reaching flame behaviors of tank fires with large ullage heights. Experiments were first conducted using a gas burner in a transparent quartz glass cylinder to simulate the large ullage and the experimental data was used to validate the computational fluid dynamics (CFD) model. Subsequently the effects of ullage height, fuel velocity and burner diameter on the flame behaviors were examined systematically. Both experimental and numerical results showed that, for lower fuel velocities, the down-reaching flame height (hdown) is restricted by the ullage height. As the fuel velocity continues to increase exceeding a critical value, independent of the ullage height, hdown starts to decrease. For a given fuel velocity, hdown increases with an increase of the burner diameter owing to enhanced air entrainment. A detailed analysis of the flow field and oxygen concentration inside the tank at the steady burning stage was also carried out. Based on the numerical results and dimensionless analysis, a piecewise function was proposed to predict the down-reaching flame height and validated against the experimental data

User Interests Modeling Based on Multi-source Personal Information Fusion and Semantic Reasoning

Abstract. User interests are usually distributed in different systems on the Web. Traditional user interest modeling methods are not designed for integrating and analyzing interests from multiple sources, hence, they are not very effective for obtaining comparatively complete description of user interests in the distributed environment. In addition, previous studies concentrate on the text level analysis of user interests, while semantic relationships among interests are not fully investigated. This might cause incomplete and incorrect understanding of the discovered interests, especially when interests are from multiple sources. In this paper, we propose an approach of user interest modeling based on multi-source personal information fusion and semantic reasoning. We give different fusion strategies for interest data from multiple sources. Further more, we investigate the semantic relationship between users&apos; explicit interests and implicit interests by reasoning through concept granularity. Semantic relatedness among interests are also briefly illustrated for information fusion. Illustrative examples based on multiple sources on the Web (e.g. microblog system Twitter, social network sites Facebook and LinkedIn, personal homepage, etc.) show that proposed approach is potentially effective

View-Disentangled Transformer for Brain Lesion Detection

Deep neural networks (DNNs) have been widely adopted in brain lesion detection and segmentation. However, locating small lesions in 2D MRI slices is challenging, and requires to balance between the granularity of 3D context aggregation and the computational complexity. In this paper, we propose a novel view-disentangled transformer to enhance the extraction of MRI features for more accurate tumour detection. First, the proposed transformer harvests long-range correlation among different positions in a 3D brain scan. Second, the transformer models a stack of slice features as multiple 2D views and enhance these features view-by-view, which approximately achieves the 3D correlation computing in an efficient way. Third, we deploy the proposed transformer module in a transformer backbone, which can effectively detect the 2D regions surrounding brain lesions. The experimental results show that our proposed view-disentangled transformer performs well for brain lesion detection on a challenging brain MRI dataset.Comment: International Symposium on Biomedical Imaging (ISBI) 2022, code: https://github.com/lhaof/ISBI-VDForme

Quantum interference between non-identical single particles

Quantum interference between identical single particles reveals the intrinsic quantum statistic nature of particles, which could not be interpreted through classical physics. Here, we demonstrate quantum interference between non-identical bosons using a generalized beam splitter based on a quantum memory. The Hong-Ou-Mandel type interference between single photons and single magnons with high visibility is demonstrated, and the crossover from the bosonic to fermionic quantum statistics is observed by tuning the beam splitter to be non-Hermitian. Moreover, multi-particle interference that simulates the behavior of three fermions by three input photons is realized. Our work extends the understanding of the quantum interference effects and demonstrates a versatile experimental platform for studying and engineering quantum statistics of particles.Comment: 6 pages, 4 figure

Epitaxial growth of high quality Mn3SnMn_3Sn thin films by pulsed laser deposition

Non-collinear antiferromagnet Weyl semimetal $Mn_3Sn$ have attracted great research interest recently. Although large anomalous Hall effect, anomalous Nernst effect and magneto-optical effect have been observed in $Mn_3Sn$, most studies are based on single crystals. So far, it is still challenging to grow high quality epitaxial $Mn_3Sn$ thin films with transport and optical properties comparable to their single crystal counterparts. Here, we report the structure, magneto-optical and transport properties of epitaxial $Mn_3Sn$ thin films fabricated by pulsed laser deposition (PLD). Highly oriented $Mn_{3+x}Sn_{1-x}$ (0001) and (11$\bar2$0) epitaxial films are successfully growth on single crystalline $Al_2O_3$ and MgO substrates. Large anomalous Hall effect (AHE) up to $\left| \Delta R_H\right|$=3.02 $\mu\Omega\cdot cm$, and longitudinal magneto-optical Kerr effect (LMOKE) with $\theta_K$ = 38.1 mdeg at 633 nm wavelength are measured at 300 K temperature, which are comparable to $Mn_3Sn$ single crystals. Our work demonstrates that high quality $Mn_3Sn$ epitaxial thin films can be fabricated by PLD, paving the way for future device applications

Loading Model and Mechanical Properties of Mature Broccoli (Brassica oleracea L. Var. Italica Plenck) Stems at Harvest

Stem cutting is the main process of broccoli harvesting, and the structure and mechanical properties of the stem significantly affect the cutting efficiency. In the current research, the structural characteristics and component contents of the broccoli stem are analyzed. Through different processing methods of stretching, compressing and bending, the aim is to obtain the parameters for mechanical properties of broccoli stem, and to provide basic data and reference for establishing visual models of broccoli stem. The test results show: The content of rind is highest in the middle of the stem, the content of xylem is highest in the bottom of the stem, and the content of pith is highest in the top of the stem. The densities of rind, xylem and pith of broccoli stem were 1056.1, 938.9 and 1009.9 kg&middot;m&minus;3, respectively. The elastic modulus of the rind of broccoli stem was 27.2~47.5 MPa, the elastic modulus of the xylem was 19.2~110.7 MPa, and the elastic modulus of the pith was 6.5~7.5 MPa. The compressive elastic modulus of the stem was 1.3~2 MPa. The bending strength of the broccoli stem was 6.9 MPa, and the bending modulus was 3.1 MPa. The mechanical model of broccoli stem established in this study provides a theoretical basis for cutting and other processes

A Score-Guided Regularization Strategy-Based Unsupervised Structural Damage Detection Method

It is critical to use scientific methods to track the performance degradation of in-service buildings over time and avoid accidents. In recent years, both supervised and unsupervised learning methods have yielded positive results in structural health monitoring (SHM). Supervised learning approaches require data from the entire structure and various damage scenarios for training. However, it is impractical to obtain adequate training data from various damage situations in service facilities. In addition, most known unsupervised approaches for training only take response data from the entire structure. In these situations, contaminated data containing both undamaged and damaged samples, typical in real-world applications, prevent the models from fitting undamaged data, resulting in performance loss. This work provides an unsupervised technique for detecting structural damage for the reasons stated above. This approach trains on contaminated data, with the anomaly score of the data serving as the model’s output. First, we devised a score-guided regularization approach for damage detection to expand the score difference between undamaged and damaged data. Then, multi-task learning is incorporated to make parameter adjustment easier. The experimental phase II of the SHM benchmark data and data from the Qatar University grandstand simulator are used to validate this strategy. The suggested algorithm has the most excellent mean AUC of 0.708 and 0.998 on the two datasets compared to the classical algorithm