Yang Xin-xin

Bio: Yang Xin-xin is an academic researcher from Henan University. The author has contributed to research in topics: Algal bloom & Image retrieval. The author has an hindex of 1, co-authored 2 publications receiving 8 citations.

Analysis of the Causes of Cyanobacteria Bloom: A Review

Abstract: Among water blooms, cyanobacteria bloom occurs over the widest range and is much more harmful than other blooms. Its occurrence in inland water bodies is affected by many factors, such as meteorology, hydrology, and human activities. Therefore, the study of the causes of cyanobacterial bloom has become a major focus of scholars. The China Knowledge Network Journal Database contains 143 papers from China and abroad from the years 2004 to 2019 that are relevant to the study of cyanobacteria bloom. We begin by analyzing keywords in these studies and creating a keyword distribution map which indicates the factors related to the blooms. Based on parameters such as the frequency of words appearing in the text, the full text of each of the 143 papers is analyzed to form a word cloud created by a program written in Python language. After irrelevant terms are eliminated, the word cloud map can reveal potential factors that were not identified by keywords alone. After completing this macro analysis, we examined approximately 100 related papers from the China Knowledge Network Journal Database and Web of Science Database published from 2014 to 2019. Finally, we summarize the main reasons for the outbreak of water blooms. The factors causing blooms can be divided into natural factors and human factors. Among the natural factors are illumination, water temperature and nutrient salt conditions. The human factors are generally related to large-scale water conservancy projects. This paper analyzes and summarizes these factors, and provides a reference to aid in the prevention and treatment of algal blooms. The information in the paper has a certain practical significance for the protection of water environments.

A content-based parallel image retrieval system

Abstract: As we all know, the content-based image retrieval (CBIR) is very time-consuming due to the extraction and matching of high dimensional and complex features. The traditional CBIR systems could not respond to a very large number of retrieval requests at mean time, which are submitted from the Internet. In this paper, we propose a content-based parallel image retrieval system to achieve high responding ability. Our system is developed on cluster architectures. It has several retrieval servers to supply the service of content-based image retrieval. Our system adopts the Browser/Server (B/S) mode. The users could visit our system though web pages. Our system uses the Symmetrical Color-Spatial Features (SCSF)[25] to represent the content of an image. The SCSF is effective and efficient for image matching because it is independent of image distortion such as rotation and flip as well as it increases the matching accuracy. The SCSF was organized by M-tree[16], which could speedup the searching procedure. Our experiments show that the image matching is quickly and efficiently with the use of SCSF. And with the support of several retrieval servers, the system could respond to many users at mean time. (Abstract)

Detection and Characterisation of Carboniferous Foraminifers for Content-based Retrieval from an Image Database

Abstract: Carboniferous Foraminifers are a specific type of microfossil which are manifest in plane sections of rock and are used by geologists for dating rock samples. The images contain a high degree of visual noise and currently must be interpreted by human experts. We are studying the classification problem in the context of intelligent image databases. Here we present a technique for automatic identification of microfossil structures and for classification of the structures according to which type of 3-D section they represent. This is achieved by using: (1) A specialized filter to detect local curves in the gray level image data; and (2) Hough transform processing of the resulting feature point vectors. An interesting aspect of our approach is that the processing of the features is not embedded in a program but is instead specified using a visual query language. This allows us to experiment quickly with different types of grouping criteria. The detection performance of our system is comparable with that of a trained geologist. We store the information obtained in a database together with the raw image data. The system can then present the user with only those images which contain structures of interest.

Photocatalysis-enhanced coagulation for removal of intracellular organic matter from Microcystis aeruginosa: efficiency and mechanism

Abstract: Photocatalysis is an efficient advanced oxidation process that provides a promising alternative for pollution control during algal blooms in lakes or water reservoirs. Previous research has mainly focused on the destruction of algal cells by photocatalysis, whereas the release and control of concomitant secretions specially intracellular organic matter (IOM) is undervalued. Herein, we studied the removal efficiency and mechanism of algal IOM extracted from Microcystis aeruginosa during stationary phase by the combined process of pre-photocatalysis with a novel composite of Bi2O3-TiO2/PAC (Bi-doped TiO2 nano-composites supported by powdered activated carbon) followed by enhanced coagulation with aluminum sulfate (AS) coagulant. Optimized photocatalysis and coagulation combined treatment (PC) was indicated to enhance IOM removal with 64%, 92% and 100% for DOC, absorbance at 254nm (UV254) and the optical density at 680nm (OD680) respectively. Pre-photocatalysis reduced the required AS coagulant dose and weakened the effect of solution pH. Photocatalysis was more inclined to remove humic substances and building blocks than high-molecular weight components, while coagulation alone displayed preference for high-molecular weight biopolymers and protein-like substance. Pre-photocatalysis was complementary with subsequent coagulation for efficient removal of protein-like substances and fractionation with medium molecular weight of 0.5-1kDa. The produced flocs revealed that the Bi2O3-TiO2/PAC adsorbing IOM acted as a flocculation core to improve coagulation performance. Besides, the combined PC process was proved to perform well to co-existing algal pollution without damaging cells integrity and enhanced algal cells removal from 26% by coagulation alone to 61%. Therefore, this combined treatment was an effective method for the removal of IOM of Microcystis aeruginosa and control cyanobacteria bloom.

Gradually focused fine-grained sketch-based image retrieval.

Abstract: This paper focuses on fine-grained image retrieval based on sketches. Sketches capture detailed information, but their highly abstract nature makes visual comparisons with images more difficult. In spite of the fact that the existing models take into account the fine-grained details, they can not accurately highlight the distinctive local features and ignore the correlation between features. To solve this problem, we design a gradually focused bilinear attention model to extract detailed information more effectively. Specifically, the attention model is to accurately focus on representative local positions, and then use the weighted bilinear coding to find more discriminative feature representations. Finally, the global triplet loss function is used to avoid oversampling or undersampling. The experimental results show that the proposed method outperforms the state-of-the-art sketch-based image retrieval methods.

Study on a novel omnidirectional ultrasonic cavitation removal system for Microcystis aeruginosa

Abstract: Microcystis aeruginosa, as a typical alga, produces microcystin with strong liver toxicity, seriously endangering the liver health of human and animals. Inhibiting the bloom of the Microcystis aeruginosa in lakes becomes a significant and meaningful work. Ultrasonic cavitation is currently considered to be the most environmentally friendly and effective method for the removal of Microcystis aeruginosa. However, the commercialized ultrasonic algae removal systems require multi-Langevin transducers to achieve omnidirectional ultrasonic irradiation due to the single irradiation direction of the Langevin transducer, resulting in the complex design and high energy consumption. To achieve a low-cost, simple structure, and high-efficiency algae removal system, a novel omnidirectional ultrasonic cavitation removal system for Microcystis aeruginosa is proposed. The proposed system is major composed of a novel omnidirectional ultrasonic transducer, which generates the omnidirectional ultrasonic irradiation by its shaking-head motion coupled by two orthogonal bending vibration modes. Modal simulation, sound field simulation, and cavitation bubble radius simulation are first carried out to optimize the geometric sizes of the proposed transducer and verify the correctness of the omnidirectional ultrasonic irradiation principle. Then the vibration characteristics of the transducer prototype are measured by vibration tests and impedance tests. Finally, the feasibility and effectiveness of the proposed omnidirectional ultrasonic removal system for Microcystis aeruginosa are evaluated through the algae removal experiments. The experimental results exhibit that the algal cells damaged by ultrasonic irradiation from the proposed system do not have the ability to self-repair. In addition, the algal removal rates reached 55.41% and 72.97% after 30 min of ultrasonic treatment when the corresponding ultrasonic densities are 0.014 W/mL and 0.021 W/mL, respectively. The proposed omnidirectional ultrasonic algae removal system significantly simplifies the configuration and reduces energy consumption, presenting the potential promise of algae removal and environmental protection.

Distributed Retrieval of Images using Particle Swarm Optimization and Hadoop

Abstract: based Image Retrieval (CBIR) is the problem of searching for digital images in large databases It is the vital application of computer vision techniques to the image retrieval problem One inherent problem associated with Content based Image Retrieval is the response time of the system to retrieve relevant result from the image database The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers The parallel processing of Hadoop can be leveraged to efficiently retrieve images with very less response time The proposed approach also avoids the semantic gap in image retrieval by utilizing automatic relevance feedback and meta-heuristic optimization Automatic relevance feedback is implemented using Latent Semantic Analysis, and Particle swarm optimization provides meta-heuristic based development The goal of proposed approach is to - cluster relevant images using meta-heuristics in less amount of time effectively