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Extraction of visual texture features of seabed sediments using an SVDD approach
Li, Yan,Liu, Shijie,Zhu, Puqiang,Yu, Jiancheng,Li, Shuo Pergamon Press 2017 Ocean engineering Vol.142 No.-
<P><B>Abstract</B></P> <P>Perception of the seabed environment is an important capability of autonomous underwater vehicles. This paper focuses on defining and extracting robust texture features from visual images that lead to useful and practical automated identification of the types of seabed sediments. The visual texture features are described by using a gray-level co-occurrence matrix (GLCM) and fractal dimension, after which an unsupervised learning method, self-organizing map (SOM), is adopted to evaluate the validity of features descriptors on three types of seabed sediments. Subsequently, a kernel-based approach that exhibits robustness versus low numbers of high-dimensional samples, named support vector domain description (SVDD), is applied to classify the types of seabed sediments. In comparison with state-of-the-art classifiers, the experimental results demonstrated the effectiveness of the SVDD on the classification of seabed sediments.</P> <P><B>Highlights</B></P> <P> <UL> <LI> The visual images of seabed sediments are characterized by the texture features which are extracted based on the GLCM and fractal theory. </LI> <LI> A multi-class classification strategy for seabed sediments is proposed by adding a distance measure after SVDD implementation. </LI> <LI> The experimental results demonstrate that the proposed classification strategy is feasible in recognizing the type of seabed sediments. </LI> </UL> </P>
An Ideal Two-dimensional Porous B4O2 as Anode Material for Enhancing Ion Storage Performance
Chen Li,Yangtong Luo,Zhangyan Wang,Chengyong Zhong,Shuo Li 대한금속·재료학회 2024 ELECTRONIC MATERIALS LETTERS Vol.20 No.3
The utilization of two-dimensional porous materials as anodes in ion batteries has garnered signifi cant interest within thefi eld of clean energy because of their fl exible architecture, high conductivity, rapid diff usion process and high specifi c ioncapacity. Herein, we developed a new metal-free 2D porous compound, namely, B 4 O 2 . The stability of the B 4 O 2 monolayerwas verifi ed through the ab-initio molecular dynamics simulations and phonon spectrum calculations. The results demonstratethat the adsorption of K, Na, and Li atoms onto the B 4 O 2 monolayer surface is remarkably stable, with all three speciesexhibiting a shared diff usion path. Specifi cally, we found that the adsorption of K atoms on the B 4 O 2 monolayer surpassesthat of Na and Li atoms, and the diff usion of K atoms occurs at a faster rate than Na and Li atoms on the same monolayersurface. The maximum theoretical specifi c capacity of K + , Na + and Li + is calculated to be 626.1 mAh/g. In addition, the B 4 O 2monolayer retains good electronic conductivity and electron activity during the atomic adsorption processes. Based on ourfi ndings, the B 4 O 2 monolayer exhibits signifi cant potential as anode material for ion batteries. This study paves the way fora novel approach in designing new 2D porous materials specifi cally tailored for energy storage and conversion applications.
Li Shuo,Wang Shucai,Cheng Fang,Xia Gaobing 보안공학연구지원센터 2015 International Journal of Smart Home Vol.9 No.8
This paper puts forward a quick-freezing method by putting the ultralow temperature medium into a negative pressure. Through the experiment, it indicated the speed and quality to refrigerating water under the condition of negative pressure. Based on that, it designed a quick-freezing device. Meanwhile this paper researched on how to quick-freeze products in bags by adopting this device. After being testified by the experiment, it proved that this device had improved the quick-freezing efficiency and the refrigerating effect.
Shuo Li,Lizhou Sun,Guohua Zhou,Huan Huang 한국생물공학회 2015 Biotechnology and Bioprocess Engineering Vol.20 No.6
Early detection of abnormal expression levels of cancer-related genes is crucial for reducing cancer mortality. Here, we describe a dye-free multiplex bioassay for simultaneous and quantitative analysis of the expression levels of multiple genes from one sample in a single assay, based on Sequence-tagged Multiplex ligationdependent probe Amplification (MLPA) coupled with pyrosequencing (termed as “SMAP”). Each pair of MLPA probes, containing a designed barcode, represents a gene of interest; thus, the use of various dyes to label different genes was avoided. The unique three-base barcode design on the probes, which can be decoded by pyrosequencing, enables individual quantification of the expression levels of six genes. Moreover, a new carryover contamination prevention system based on the use of restriction endonucleases was developed for PCR-based diagnostic screening assays. SMAP analysis revealed significant differences between the expression levels of CRC-related genes in the tumor tissues and normal tissues from a CRC patient. For PCR-based diagnostic screening assays, 0.5 U of the FokI restriction endonuclease was sufficient for the removal 0.01 pmol of PCR contamination. The ability to analyze the expression levels of a greater number of cancer-related genes would improve diagnostic sensitivity and efficacy. SMAP is amenable to the detection of an increased number of genes by lengthening the artificially designed barcodes; thus our method provides a promising means for cancer diagnostics and improving the treatment options available to cancer patients.
Performance Comparison Between Hama and Hadoop
Shuo Li,Baomin Xu 보안공학연구지원센터 2015 International Journal of Database Theory and Appli Vol.8 No.3
Massive scientific computations such as matrix, graph and network algorithms are very attractive when they come to modelling real-world data. Apache Hama is a pure BSP (Bulk Synchronous Parallel) distributed computing framework for massive scientific computations. In this paper, our experiments were conducted on a 4-node Hadoop cluster. We implement Monte Carlo algorithm of Pi in Hama and Hadoop under the same software and hardware environment. The experimental results show that Hama can achieve much higher performance than Hadoop in our testbed.
POSS/Polyurethane Hybrids and Nanocomposites: A Review on Preparation, Structure and Performance
( Shuo Diao ),( Li Xin Mao ),( Li Qun Zhang ),( Yi Qing Wang ) 한국고무학회 2015 엘라스토머 및 콤포지트 Vol.50 No.1
Polyhedral oligomeric silsesquioxane (POSS) is an important inorganic-organic hybrid material with a threedimensional structure. Polyurethane (PU) is a widely applied polymer that has versatile properties with the change of two phase structure. When POSS is incorporated into PU by physical or chemical methods, many properties can be greatly improved, such as mechanical properties, thermal stability, biodegradation resistance, and water resistance. This paper reviews the recent progress in preparation, structure, and performance of POSS-modified polyurethane from the viewpoint of physical blending and chemical modification.
Target unbiased meta-learning for graph classification
Li Ming,Zhu Shuo,Li Chunxu,Zhao Wencang 한국CDE학회 2021 Journal of computational design and engineering Vol.8 No.5
Even though numerous works focus on the few-shot learning issue by combining meta-learning, there are still limits to traditional graph classification problems. The antecedent algorithms directly extract features from the samples, and do not take into account the preference of the trained model to the previously “seen” targets. In order to overcome the aforementioned issues, an effective strategy with training an unbiased meta-learning algorithm was developed in this paper, which sorted out problems of target preference and few-shot under the meta-learning paradigm. First, the interactive attention extraction module as a supplement to feature extraction was employed, which improved the separability of feature vectors, reduced the preference of the model for a certain target, and remarkably improved the generalization ability of the model on the new task. Second, the graph neural network was used to fully mine the relationship between samples to constitute graph structures and complete image classification tasks at a node level, which greatly enhanced the accuracy of classification. A series of experimental studies were conducted to validate the proposed methodology, where the few-shot and semisupervised learning problem has been effectively solved. It also proved that our model has better accuracy than traditional classification methods on real-world datasets.
Shuo Li,Zhengrong Xiang,Hamid Reza Karimi 제어·로봇·시스템학회 2014 International Journal of Control, Automation, and Vol.12 No.4
This paper investigates the problem of fault detection observer design for positive switched systems with time-varying delay via delta operator approach. A new fault sensitivity measure, called l– index, is proposed. The l– fault detection observer design and multi-objective l– /l1 fault detection observer design problems are addressed. Based on the average dwell time approach and the piecewise co-positive type Lyapunov-Krasovskii functional method in delta domain, sufficient conditions for the existence of such two kinds of fault detection observers are firstly given, and then the design methods are presented. Finally, two examples are provided to show the effectiveness and the applicability of the proposed methods.