Improved Design of Lifting Slide Rail System of Stone Processing Machine

In this research, improved design for the lifting slide rail system on stone processing machine has innovated in a few aspects, including the innovative design of the lifting rail, fixture to fix and connect with the hydraulic tappet, and the sliding block equipped with limit device etc. The improved design has been successfully applied to the stone processing machine to crush and process block and stone flake processing waste of different shapes, it turned out that the needle flake particle content and crushing index of the processed stone are in line with the national standard requirements, i.e. crushing index of 7.2%, reaching the technical requirements of Class I crushed stone. In another words, the improved design has significantly improved the processing efficiency and safety and reduced costs effectively; meanwhile, its handy operation and utilization has made it ready to promote in a wider range.

A Review on Rice Crop Disease Classification Using Computational Approach

Rice is a significant cereal crop across the world. In rice cultivation, different types of sowing methods are followed, and thus bring in issues regarding sampling collection. Climate, soil, water level, and a diversified variety of crop seeds (hybrid and traditional varieties) and the period of growth are some of the challenges. This survey mainly focuses on rice crop diseases which affect the parts namely leaves, stems, roots, and spikelet; it mainly focuses on leaf-based diseases. Existing methods for diagnosing leaf disease include statistical approaches, data mining, image processing, machine learning, and deep learning techniques. This review mainly addresses diseases of the rice crop, a framework to diagnose rice crop diseases, and computational approaches in Image Processing, Machine Learning, Deep Learning, and Convolutional Neural Networks. Based on performance indicators, interpretations were made for the following algorithms namely support vector machine (SVM), convolutional neural network (CNN), backpropagational neural network (BPNN), and feedforward neural network (FFNN).

Acquisition/Processing: Machine learning-based deblending: Dispersed source array data example

Machine learning (ML) has proven its value in the seismic industry with successful implementations in areas of seismic interpretation such as fault and salt dome detection and velocity picking. The field of seismic processing research also is shifting toward ML applications in areas such as tomography, demultiple, and interpolation. Here, a supervised ML deblending algorithm is illustrated on a dispersed source array (DSA) data example in which both high- and low-frequency vibrators were deployed simultaneously. Training data pairs of blended and corresponding unblended data were constructed from conventional (unblended) data from another survey. From this training data, the method can automatically learn a deblending operator that is used to deblend for both the low- and the high-frequency vibrators of the DSA data. The results obtained on the DSA data are encouraging and show that the ML deblending method can offer a good performing, less user-intensive alternative to existing deblending methods.

Increasing the effectiveness of the fiber separating machine by abrasive blasting of the teeth of the saw cylinder discs

Abrasive blasting of the side surfaces of the teeth of saw blades of a cotton processing machine by particles of black silicon carbide is proposed. The required processing quality is achieved by the formation of an effective microrelief on the treated surface. Keywords: saw blade, abrasive blasting, fiber separation, roughness, pressure, angle of attack, fiber. [email protected]