A Quantized Kernel Least Mean Square Scheme with Entropy-Guided Learning for Intelligent Data Analysis

Quantized kernel least mean square(QKLMS) algorithm is an effective nonlinear adaptive online learning algorithm with good performance in constraining the growth of network size through the use of quantization for input space. It can serve as a powerful tool to perform complex computing for network service and application. With the purpose of compressing the input to further improve learning performance, this article proposes a novel QKLMS with entropy-guided learning, called EQ-KLMS. Under the consecutive square entropy learning framework, the basic idea of entropy-guided learning technique is to measure the uncertainty of the input vectors used for QKLMS, and delete those data with larger uncertainty, which are insignificant or easy to cause learning errors. Then, the dataset is compressed. Consequently, by using square entropy, the learning performance of proposed EQ-KLMS is improved with high precision and low computational cost. The proposed EQ-KLMS is validated using a weather-related dataset, and the results demonstrate the desirable performance of our scheme.

Preliminary analysis of distribution and variation of perennialmonthly mean water masses in the Bohai Sea,the Huanghai Sea and the East China Sea

On the basis of perennial monthly mean temperature and salinity data, the classification of monthly water masses at the surface and the bottom in the Bohai Sea, the Huanghai Sea and the East China Sea, has been made by using the method of fuzzy cluster from the modified characteristic of water masses in the shallow water area. In this paper, the basic features, growth and decline patterns of water masses in relation to fishing grounds in the whole shelves of the Bohai Sea, the Huanghai Sea and the East China Sea are discussed with emphasis.

A Brief Analysis of Harold Pinter’s Use of Verbal and Visual Means in The Caretaker

This thesis focuses on the analysis of Harold Pinter’s use of verbal and visual means in The Caretaker. The verbal means are: the ambiguity of the meaning of language; the evasiveness of language; silence. The functions of visual means are: end-connection; theme-revelation; atmosphere-creation. A meticulous study of Pinter’s dramatic technique can enhance the understanding of the theme of the play.

Optimization of constitutive parameters of foundation soils k-means clustering analysis

The goal of this study was to optimize the constitutive parameters of foundation soils using a k-means algorithm with clustering analysis. A database was collected from unconfined compression tests, Proctor tests and grain distribution tests of soils taken from three different types of foundation pits： raft foundations, partial raft foundations and strip foundations. k-means algorithm with clustering analysis was applied to determine the most appropriate foundation type given the un- confined compression strengths and other parameters of the different soils.

Research on the spectral analysis and testmethod of period signals in monthly mean sea level

This paper summarizes the general methods,existing problems and their causes of the period analysis for the monthly mean sea level and points out that it is the key to the analysing period signals and forecasting the change trend of the monthly mean sea level that the periods of the signals are selected reasonably. As there are often many period signals in these series, nonlinear effects exist between pairs of period signals. In order to avoid the false periods that may be introduced due to the effects of side lobes and the periods with statistical phase significance coherence that may be introduced due to the effects of nonlinear effects and their restraint to other period signals, the maximum entropy spectral analysis and the corresponding significance period test may be performed repeatedly on the basis of the bispectrum analysis and meanwhile the most significant period component is filtered out by the least square filtering method, i. e., the method of the significance period analysis with mixed spectra modeled by a nonlinear system is adopted and the signal periods approaching the reality are selected one by one. The examples of the bispectrum analysis, the signal period analysis by mixed spectra and the fitting parameters for combined period components with linear trend in the time series of monthly mean sea level are given in this paper.

Generational Mean Analysis of Salt Tolerance during Osmotic Phase in Maize Seedling

This study details the nature and magnitude of the genetic effects associated with various quantitative characters (morphological and hydric relations) measured in maize seedlings during the osmotic phase of saline stress (100 mM NaCl). Three lines with differential behavior in salt stress: SC2 (tolerant), AFE (susceptible) and LP3 (moderately tolerant) were used to obtain contrasting crosses (SC2 × AFE) and (SC2 × LP3). An analysis of six generational means (P1, P2, F1, F2, BC1 and BC2) was applied for each cross. First a scaling test was applied and then a three and six-parameter genetic models were used to estimate various genetic components. In none of the traits studied there was evidence of adequacy to the three parameter model, which indicates important epistatic effects in genetic expression. The dominant genetic effects were greater than the additive ones for all the characters evaluated. LG showed positive and significant differences for [h] in both crosses, indicating the presence of hybrid vigor and its possible use in the improvement. Low value of [d] and high of [h] both significant in SC2 × AFE, indicates existence of genes dispersion between the parental lines. While, for the cross SC2 × LP3, the low and significant value of [d] and not significant value of [h], indicate greater genetic similarity. In the SC2 × LP3 cross, the negative interaction [l] confirms ambidirectional dominance, while for SC2 × LP3 the positive sign indicates directional dominance. The analysis of tolerance to salinity in the osmotic phase showed a complex polygenic inheritance for the traits used, determined by simple and interaction effects of different magnitudes and significance according to the cross considered.

Correlation Analysis between Mean Corpuscular Hemoglobin and Mean Corpuscular Volume for Thalassemia Screening in Large Population

Correlation analysis between the hematological parameters mean corpuscular hemoglobin (MCH) and mean corpuscular volume (MCV) for thalassemia screening in large population was discussed. A total of 4920 peripheral blood samples of reproductive age persons were collected from Guangdong province of China. The hematological parameters MCH and MCV values of samples were first measured, and then the DNA analyses for thalassemia were conducted. All samples were composed by 4463 non-thalassemia and 457 thalassemia, and among 457 thalassemia samples, 311 were α-thalassemia, 133 were β-thalassemia, and 13 were α & β-thalassemia. In accordance with non-thalassemia, thalassemia, α-thalassemia, β-thalassemia, α & β-thalassemia and the entire group itself, a total of six sample groups were divided. The corresponding correlation coefficients between the measured MCH and MCV values for the six sample groups were 0.880, 0.968, 0.966, 0.962, 0.980 and 0.965 respectively. For the thalassemia carriers, highly significant correlation between MCH and MCV were observed. The fitting equations between MCH and MCV values were also obtained. The results indicated that the feasibility for thalassemia screening using MCV or MCH independently as parameter, and provided suitable strategy to select parameters and models for thalassemia screening in large population.

Improving Queuing System Throughput Using Distributed Mean Value Analysis to Control Network Congestion

In this paper, we have used the distributed mean value analysis (DMVA) technique with the help of random observe property (ROP) and palm probabilities to improve the network queuing system throughput. In such networks, where finding the complete communication path from source to destination, especially when these nodes are not in the same region while sending data between two nodes. So, an algorithm is developed for single and multi-server centers which give more interesting and successful results. The network is designed by a closed queuing network model and we will use mean value analysis to determine the network throughput (b) for its different values. For certain chosen values of parameters involved in this model, we found that the maximum network throughput for β≥0.7?remains consistent in a single server case, while in multi-server case for β≥ 0.5?throughput surpass the Marko chain queuing system.

Integrated classification method of tight sandstone reservoir based on principal component analysise simulated annealing genetic algorithmefuzzy cluster means

In this research,an integrated classification method based on principal component analysis-simulated annealing genetic algorithm-fuzzy cluster means(PCA-SAGA-FCM)was proposed for the unsupervised classification of tight sandstone reservoirs which lack the prior information and core experiments.A variety of evaluation parameters were selected,including lithology characteristic parameters,poro-permeability quality characteristic parameters,engineering quality characteristic parameters,and pore structure characteristic parameters.The PCA was used to reduce the dimension of the evaluation pa-rameters,and the low-dimensional data was used as input.The unsupervised reservoir classification of tight sandstone reservoir was carried out by the SAGA-FCM,the characteristics of reservoir at different categories were analyzed and compared with the lithological profiles.The analysis results of numerical simulation and actual logging data show that:1)compared with FCM algorithm,SAGA-FCM has stronger stability and higher accuracy;2)the proposed method can cluster the reservoir flexibly and effectively according to the degree of membership;3)the results of reservoir integrated classification match well with the lithologic profle,which demonstrates the reliability of the classification method.

Polarimetric Meteorological Satellite Data Processing Software Classification Based on Principal Component Analysis and Improved K-Means Algorithm

With the increasing variety of application software of meteorological satellite ground system, how to provide reasonable hardware resources and improve the efficiency of software is paid more and more attention. In this paper, a set of software classification method based on software operating characteristics is proposed. The method uses software run-time resource consumption to describe the software running characteristics. Firstly, principal component analysis (PCA) is used to reduce the dimension of software running feature data and to interpret software characteristic information. Then the modified K-means algorithm was used to classify the meteorological data processing software. Finally, it combined with the results of principal component analysis to explain the significance of various types of integrated software operating characteristics. And it is used as the basis for optimizing the allocation of software hardware resources and improving the efficiency of software operation.

Strategic Focus and Business Model Organization： The Mean Field Analysis Approach

The objective of this research is to show a new methodology for modeling phenomena present in complex economic systems. The case study we analyzed is the adoption of open organization model among firms operating in a particular industry. A firm with an open system model creates and captures value taking advantage not only from the internal resource but also from external. The organization could approach to open model acquisition using different focus： external focus namely looking out of its boundary, acting and reacting to competitor innovation, costumers＇ changing, demand growth, or internal focus remaining inside its boundary improving its best capabilities ignoring what happened outside （Vagnani, Moran, ＆ Simoni, 2010）. The actors involved are firms, customers and suppliers linked together through a business to business model. The methodology is based on an Object-Oriented Analysis Field Model that allows to intuitively describe systems characterized by a large number of objects that interact, as in this case of a system composed by different organizational entities. The system simulation allows to analyze how the actors influence the acquisition and diffusion of the open organization model. This approach permits the generation of different classes of objects to represent all actors involved in the evolution of the system and to define the dynamics that determine their interaction. The solution of the model can be approximated using the Mean-Field analysis technique （Kurtz, 1978）, following the results proposed in Bobbio, Gribaudo, and Yelek （2008）. A qualitative result is illustrated in order to show the applicability of the proposed methodology and to emphasize its relevant features： flexible modeling approach, capacity of solving complex systems and output management facilities. The presented model is comprehensive and its scope is wide; it could be used to study the behavior of enterprises changing model in many different scenarios and situations. In future works quantitative results will be given, and different situations will be analyzed.

Composition Analysis and Identification of Ancient Glass Products Based on L1 Regularization Logistic Regression

In view of the composition analysis and identification of ancient glass products, L1 regularization, K-Means cluster analysis, elbow rule and other methods were comprehensively used to build logical regression, cluster analysis, hyper-parameter test and other models, and SPSS, Python and other tools were used to obtain the classification rules of glass products under different fluxes, sub classification under different chemical compositions, hyper-parameter K value test and rationality analysis. Research can provide theoretical support for the protection and restoration of ancient glass relics.

Meaning Construction of 2019 Beijing International Horticultural Exposition Emblem from the Perspective of Multimodal Discourse Analysis

The multimodal discourse has been playing a significant role in modern society since its springing-up from the 1990s.Based on the theoretical framework of Visual Grammar(VG)put forward by Kress&van Leeuwen(1996),this study,from the per⁃spective of multimodal discourse analysis,aims at exploring the meaning construction in the emblem of 2019 Beijing International Horticultural Exposition.It is found that an emblem can be interpreted with other modes such as visual image,color and pattern de⁃sign apart from verbal language,which will not only work to participate in the overall meaning construction with mutual interaction,but also analyze how sociocultural background,local customs and humanity spirit are reflected through these forms.

A Multimodal Discourse Analysis of Meaning Construction of Real Estate Posters

Posters have been one of the main research subjects in the Systemic-Functional Linguistics. Nowadays, posters are full of various types of semiotic modes with their wide applications of multimodality. This phenomenon is reflected in real estate posters as well, therefore, this thesis hopes to explore their essence of using multimodality and discussing meaning construction of the real estate posters.

Identification of Geochemical Anomaly by Multifractal Analysis

The separation of anomalies from geochemical background is an important part of data analysis because lack of such identifications might have profound influence on or even distort the final analysis results. In this article, 1 672 geochemical analytical data of 11 elements, including Cu, Mo, Ag, Sn, and others, from a region within Tibet, South China, are used as one example. Together with the traditional anomaly recognition method of using the iterative mean ±2σ local multifractality theory has been utilized to delineate the ranges of geochemical anomalies of the elements. To different degrees, on the basis of original data mapping, C-A fractal analysis and singularity exponents, Sn differs from the other 10 elements. Moreover, geochemical mapping results based on values of the multifractal asymmetry index for all elements delineate the highly anomalous area. Similar to other 10 elements, the anomalous areas of Sn delineated by the asymmetry index distribute along the main structure orientations. According to the asymmetry indexes, the 11 elements could be classified into 3 groups： （1） Ag and Au, （2） As-Sb-Cu-Pb-Zn-Mo, and （3） Sn-Bi-W. This parageneflc association of elements can be used to interpret possible origins of mineralization, which is in agreement with petrological analysis and field survey results.

Applying Discourse Analysis to Reading Course

阅读课是英语教学的主要手段，也是学生获取语言知识与掌握基本技能的主要途径。传统的阅读课讲授方法只注意了阅读材料的文字而忽视了语篇的作用。现在，教师要自觉地运用语篇分析法来分析语篇的交际功能。

sEMG Feature Analysis on Forearm Muscle Fatigue During Isometric Contractions

In order to detect and assess the muscle fatigue state with the surface electromyography(sEMG) characteristic parameters,this paper carried out a series of isometric contraction experiments to induce the fatigue on the forearm muscles from four subjects,and recorded the sEMG signals of the flexor carpi ulnaris.sEMG's median frequency(MDF) and mean frequency(MF) were extracted by short term Fourier transform(STFT),and the root mean square(RMS) of wavelet coefficients in the frequency band of 5—45 Hz was obtained by continuous wavelet transform(CWT).The results demonstrate that both MDF and MF show downward trends within 1 min; however,RMS shows an upward trend within the same time.The three parameters are closely correlated with absolute values of mean correlation coefficients greater than 0.8.It is suggested that the three parameters above can be used as reliable indicators to evaluate the level of muscle fatigue during isometric contractions.

Genetic analysis of maturity and flowering characteristics in maize(Zea mays L.)

Objective:To elucidate the pattern of inheritance and determine the relative magnitude of various genetic effects for maturity and flowering attributes in subtropical maize.Methods: Four white grain maize inbred lines from flint group of corn,two with late maturity and two with early maturity,were used.These contrasting inbred lines were crossed to form four crosses.Six generations(P_1,P_2,F_1,F_2,BC_1,and BC_2) were developed for each individual cross.These were evaluated in triplicate trial for two consecutive years.Results:Roth dominance gene action and epistatic interaction played major role in governing inheritance of days to pollen shedding, 50%silking,anlbesis silking interval and maturity.Conclusions:Preponderance of dominance gene action for these traits indicated their usefulness in hybrid programs of subtropical maize.