Application of a new feature extraction and optimization method to surface defect recognition of cold rolled strips

Considering that the surface defects of cold rolled strips are hard to be recognized by human eyes under high-speed circumstances, an automatic recognition technique was discussed. Spectrum images of defects can be got by fast Fourier transform （FFF） and sum of valid pixels （SVP）, and its optimized center region, which concentrates nearly all energies, are extracted as an original feature set. Using genetic algorithm to optimize the feature set, an optimized feature set with 51 features can be achieved. Using the optimized feature set as an input vector of neural networks, the recognition effects of LVQ neural networks have been studied. Experiment results show that the new method can get a higher classification rate and can settle the automatic recognition problem of surface defects on cold rolled strips ideally.

A Review on Intelligent Detection and Classification of Power Quality Disturbances:Trends,Methodologies,and Prospects

With increasing global concerns about clean energy in smart grids,the detection of power quality disturbances(PQDs)caused by energy instability is becoming more and more prominent.It is well acknowledged that the PQD effects on power grid equipment are destructive and hazardous,which causes irreversible damage to underlying electrical/electronic equipment of the concerned intelligent grids.In order to ensure safe and reliable equipment implementation,appropriate PQDdetection technologiesmust be adopted to avoid such adverse effects.This paper summarizes the newly proposed and traditional PQD detection techniques in order to give a quick start to new researchers in the related field,where specific scenarios and events for which each technique is applicable are also clearly presented.Finally,comments on the future evolution of PQD detection techniques are given.Unlike the published review articles,this paper focuses on the new techniques from the last five years while providing a brief recap on traditional PQD detection techniques so as to supply researchers with a systematic and state-of-the-art review for PQD detection.

Extraction,Purification,and Anti-Inflammatory Activity of Steroid Fraction from Physalis Alkekengi L.Var.Franchetii(Mast.)Makino

Objective: As a traditional medicinal plant listed in the Chinese Pharmacopeia, Physalis alkekengi L. var. franchetii(Mast.) Makino(PAF) has a long medicinal history and high economic value. PAF has immunomodulatory properties and can be used to treat acute lung injury and eczema.The aim of this study is to solve the problems of extraction and purification of active components from PAF. Materials and Methods: The solvent to be used for extraction and its concentration, the solid-to-liquid ratio, and extraction duration were investigated using a single-factor experiment.An orthogonal design(L_(9)[3^(4)]) was used to determine the optimum extraction conditions. After optimization, the sample’s concentrations and flow velocity, the eluents and their velocity, adsorption time, and the removed water volume were measured. The content of the five steroids in the sample was determined by high-performance liquid chromatography(HPLC). We also investigated the anti-inflammatory property of PAF calyxes before and after purification. Results: The optimum extraction and purification processes were determined by single-factor analysis.AB-8 was identified as the best macroporous adsorption resin for enrichment. After optimization, the average total steroid content was 71.83%,and the average recovery was 90% after purification. Among the five steroid components detected by HPLC, physalin F showed the highest content. Furthermore, the sample obtained after purification could significantly inhibit paw edema by egg whites induced. Conclusions: An environmentally-sustainable, efficient, and stable process was first optimized for enriching and purifying total steroids from PAF. The process has the potential for further development and utilization in the pharmaceutical industry.