Fracture Behavior of the Hot Rolled Strip’s Surface Scale Layer from Different Impacts Angles

In order to improve the energy efficiency of shot blasting impact descaling, a three-dimensional finite element impact descaling model was established. Based on the finite element model, the cracking behavior of the scale layer on hot rolled strip from different impacts angles was simulated. The results of finite element calculation and theoretical analysis show that: (1)Under the premise of constant velocity, the descaling area increases with the increase of impact angle, but the increasing rate tends to be moderate. (2)The depth of the impact tunnel and the residual compressive stress surface (-200 MPa) increase as the impact angle goes bigger. The ideal range of impact angle for shot blasting descaling should be 60°-75°.

A qualitative study of small-scale layer farms on a tropical island Malang Indonesia

The research was conducted at Malang Regency, East Java, Indonesia, with the research purpose of understanding the amount of small-scale income and characteristics generated by layer farmers. Data collection from February to March 2020 used the survey method. The total sample was 109 respondents determined by the purposive sampling method with the consideration of laying farmers with 2.000 – 7.666 birds for at least three years. The data collection consisted of primary and secondary data. Data were analyzed using descriptive analysis. The result showed that the success rate of the livestock business is inseparable from the characteristics of the farmers. Characteristics of respondents observed in the research included age, education, occupation, breeding experience, and the number of family members. The conclusion of this research is the livestock population was 7,667-15,333 birds, the age around 40–49 years, educational level was elementary school, occupation as farmers, the farming experience was 11-15 years and family member of farmers was 5 people.

Multimodal Image Fusion Method Based on Guided Filter

In the process of multimodal image fusion, how to improve the visual effect after the image fused, while taking into account the protection of energy and the extraction of details, has attracted more and more attention in recent years. Based on the research of visual saliency and the final action-level measurement of the base layer, a multimodal image fusion method based on a guided filter is proposed in this paper. Firstly, multi-scale decomposition of a guided filter is used to decompose the two source images into a small-scale layer, large-scale layer and base layer. The fusion rule of the maximum absolute value is adopted in the small-scale layer, the weight fusion rule based on regular visual parameters is adopted in the large-scale layer and the fusion rule based on activity-level measurement is adopted in the base layer. Finally, the fused three scales are laminated into the final fused image. The experimental results show that the proposed method can improve the image edge processing and visual effect in multimodal image fusion.

Infrared and Visible Image Fusion via Fast Approximate Bilateral Filter and Local Energy Characteristics

Image fusion is to effectively enhance the accuracy, stability, and comprehensiveness of information. Generally, infrared images lack enough background details to provide an accurate description of the target scene, while visible images are difficult to detect radiation under adverse conditions, such as low light. People hoped that the richness of image details can be improved by using effective fusion algorithms. In this paper, we propose an infrared and visible image fusion algorithm, aiming to overcome some common defects in the process of image fusion. Firstly, we use fast approximate bilateral filter to decompose the infrared image and visible image to obtain the small-scale layers, large-scale layer, and base layer. Then, the fused base layer is obtained based on local energy characteristics, which avoid information loss of traditional fusion rules. The fused small-scale layers are acquired by selecting the absolute maximum, and the fused large-scale layer is obtained by summation rule. Finally, the fused small-scale layers, large-scale layer, and base layer are merged to reconstruct the final fused image. Experimental results show that our method retains more detailed appearance information of the fused image and achieves good results in both qualitative and quantitative evaluations.

Proposing an Intelligent Dual-Energy Radiation-Based System for Metering Scale Layer Thickness in Oil Pipelines Containing an Annular Regime of Three-Phase Flow

Deposition of scale layers inside pipelines leads to many problems, e.g., reducing the internal diameter of pipelines, damage to drilling equipment because of corrosion, increasing energy consumption because of decreased efficiency of equipment, and shortened life, etc., in the petroleum industry. Gamma attenuation could be implemented as a non-invasive approach suitable for determining the mineral scale layer. In this paper, an intelligent system for metering the scale layer thickness independently of each phase’s volume fraction in an annular three-phase flow is presented. The approach is based on the use of a combination of an RBF neural network and a dual-energy radiation detection system. Photo peaks of 241Am and 133Ba registered in the two transmitted detectors, and scale-layer thickness of the pipe were considered as the network’s input and output, respectively. The architecture of the presented network was optimized using a trial-and-error method. The regression diagrams for the testing set were plotted, which demonstrate the precision of the system as well as correction. The MAE and RMSE of the presented system were 0.07 and 0.09, respectively. This novel metering system in three-phase flows could be a promising and practical tool in the oil, chemical, and petrochemical industries.

A TOOL FOR MACHINE LEARNING BASED DASYMETRIC MAPPING APPROACHES IN GRASS GIS

Socio-economic and demographic data is often released at the level of census administrative units. However, there is often a need for data available at a higher spatial resolution. Dasymetric mapping is an approach that can be used to disaggregate such data into finer levels of detail. It relies on the assumption that proxies available at a higher spatial resolution, along with knowledge of an area, can be used to produce weights in order to spatially reallocate the data to a finer scale layer. The power and efficiency of machine learning (ML) approaches can be taken advantage of when producing weighted layers for dasymetric mapping. Less advanced users, however, may find these approaches too complex. To encourage a wider uptake of such approaches, easy-to-use tools are necessary. GRASS GIS is a free and open-source GIS software that contains many modules for processing geographic data. The existing GRASS GIS add-on “v.area.weigh” already makes the dasymetric mapping approach more accessible, however users must provide their own weighted layer. This paper presents the development of a GRASS GIS add-on, “r.area.createweight”, which provides a simple and convenient tool to facilitate the implementation of a ML-based approach to produce weighted layers for dasymetric mapping. The tool will be available on the official GRASS GIS add-on repository to encourage a more widespread uptake of these approaches.

Application of Artificial Intelligence and Gamma Attenuation Techniques for Predicting Gas–Oil–Water Volume Fraction in Annular Regime of Three-Phase Flow Independent of Oil Pipeline’s Scale Layer

To the best knowledge of the authors, in former studies in the field of measuring volume fraction of gas, oil, and water components in a three-phase flow using gamma radiation technique, the existence of a scale layer has not been considered. The formed scale layer usually has a higher density in comparison to the fluid flow inside the oil pipeline, which can lead to high photon attenuation and, consequently, reduce the measuring precision of three-phase flow meter. The purpose of this study is to present an intelligent gamma radiation-based, nondestructive technique with the ability to measure volume fraction of gas, oil, and water components in the annular regime of a three-phase flow independent of the scale layer. Since, in this problem, there are several unknown parameters, such as gas, oil, and water components with different amounts and densities and scale layers with different thicknesses, it is not possible to measure the volume fraction using a conventional gamma radiation system. In this study, a system including a 241Am-133Ba dual energy source and two transmission detectors was used. The first detector was located diametrically in front of the source. For the second detector, at first, a sensitivity investigation was conducted in order to find the optimum position. The four extracted signals in both detectors (counts under photo peaks of both detectors) were used as inputs of neural network, and volume fractions of gas and oil components were utilized as the outputs. Using the proposed intelligent technique, volume fraction of each component was predicted independent of the barium sulfate scale layer, with a maximum MAE error of 3.66%.

Effects of a Clean Subcritical Degreasing System on Wool Fibers

It makes sense to use environmentally friendly methods of degreasing in fur-making process. In this study, subcritical n-pentane was used to degrease wool fibers. Thermogravimetric analysis (TGA) was used to observe and analyze the properties of the subcritical n-pentane degreased wool fibers. The results showed that the thermal stability of the fibers increased. Fourier Transform Infrared (FTIR) spectroscopy was used to analyze the structural changes of macromolecular chains in wool fibers. It was found that when the pressure was higher than 0.4 MPa, the wool fibers underwent a conformational change with the α-helix changing to β-folding. If the pressure was as high as 0.6 MPa, the disulfide bonds in the wool fibers scale layer appeared to break. X-ray powder diffraction experiment was used to study changes in wool fiber aggregation morphology. It was demonstrated that the crystalline zone of wool fibers changed and the fibers index grew, from 22.89% to 30.19%. Field emission scanning electron microscopy and ultra-depth of field microscopy was used to analyze changes in the surface morphology of wool fibers. The results suggested that after the treatment, the wool fibers were not damaged and the impurities on the wool surface were reduced.