SA-Model:Multi-Feature Fusion Poetic Sentiment Analysis Based on a Hybrid Word Vector Model

Sentiment analysis in Chinese classical poetry has become a prominent topic in historical and cultural tracing,ancient literature research,etc.However,the existing research on sentiment analysis is relatively small.It does not effectively solve the problems such as the weak feature extraction ability of poetry text,which leads to the low performance of the model on sentiment analysis for Chinese classical poetry.In this research,we offer the SA-Model,a poetic sentiment analysis model.SA-Model firstly extracts text vector information and fuses it through Bidirectional encoder representation from transformers-Whole word masking-extension(BERT-wwmext)and Enhanced representation through knowledge integration(ERNIE)to enrich text vector information;Secondly,it incorporates numerous encoders to remove text features at multiple levels,thereby increasing text feature information,improving text semantics accuracy,and enhancing the model’s learning and generalization capabilities;finally,multi-feature fusion poetry sentiment analysis model is constructed.The feasibility and accuracy of the model are validated through the ancient poetry sentiment corpus.Compared with other baseline models,the experimental findings indicate that SA-Model may increase the accuracy of text semantics and hence improve the capability of poetry sentiment analysis.

Multi-Features Disease Analysis Based Smart Diagnosis for COVID-19

Coronavirus 2019(COVID-19)is the current global buzzword,putting the world at risk.The pandemic’s exponential expansion of infected COVID-19 patients has challenged the medical field’s resources,which are already few.Even established nations would not be in a perfect position to manage this epidemic correctly,leaving emerging countries and countries that have not yet begun to grow to address the problem.These problems can be solved by using machine learning models in a realistic way,such as by using computer-aided images during medical examinations.These models help predict the effects of the disease outbreak and help detect the effects in the coming days.In this paper,Multi-Features Decease Analysis(MFDA)is used with different ensemble classifiers to diagnose the disease’s impact with the help of Computed Tomography(CT)scan images.There are various features associated with chest CT images,which help know the possibility of an individual being affected and how COVID-19 will affect the persons suffering from pneumonia.The current study attempts to increase the precision of the diagnosis model by evaluating various feature sets and choosing the best combination for better results.The model’s performance is assessed using Receiver Operating Characteristic(ROC)curve,the Root Mean Square Error(RMSE),and the Confusion Matrix.It is observed from the resultant outcome that the performance of the proposed model has exhibited better efficient.

ESTIMATION METHOD FOR AIRCRAFT SIMILARITY BASED ON FUZZY THEORY AND GREY INCIDENCE ANALYSIS

An estimation method for aircraft similarity based on fuzzy theory and grey incidence analysis is presented. This estimation method is made up of the triangular fuzzy transforming model of linguistic variables and the method of grey incidence analysis. Nine feature attributes of aircraft are selected to estimate the similarity between the new aircraft and the existing aircraft. A new aircraft X and other six existing aircrafts are taken as examples. Analyses show that similarity estimation results obtained from the method are in accordance with practice.

Cross-Project Software Defect Prediction Based on SMOTE and Deep Canonical Correlation Analysis

Cross-Project Defect Prediction(CPDP)is a method that utilizes historical data from other source projects to train predictive models for defect prediction in the target project.However,existing CPDP methods only consider linear correlations between features(indicators)of the source and target projects.These models are not capable of evaluating non-linear correlations between features when they exist,for example,when there are differences in data distributions between the source and target projects.As a result,the performance of such CPDP models is compromised.In this paper,this paper proposes a novel CPDP method based on Synthetic Minority Oversampling Technique(SMOTE)and Deep Canonical Correlation Analysis(DCCA),referred to as S-DCCA.Canonical Correlation Analysis(CCA)is employed to address the issue of non-linear correlations between features of the source and target projects.S-DCCA extends CCA by incorporating the MlpNet model for feature extraction from the dataset.The redundant features are then eliminated by maximizing the correlated feature subset using the CCA loss function.Finally,cross-project defect prediction is achieved through the application of the SMOTE data sampling technique.Area Under Curve(AUC)and F1 scores(F1)are used as evaluation metrics.This paper conducted experiments on 27 projects from four public datasets to validate the proposed method.The results demonstrate that,on average,our method outperforms all baseline approaches by at least 1.2%in AUC and 5.5%in F1 score.This indicates that the proposed method exhibits favorable performance characteristics.

Multimode Process Fault Detection Using Local Neighborhood Similarity Analysis

Traditional data driven fault detection methods assume unimodal distribution of process data so that they often perform not well in chemical process with multiple operating modes. In order to monitor the multimode chemical process effectively, this paper presents a novel fault detection method based on local neighborhood similarity analysis(LNSA). In the proposed method, prior process knowledge is not required and only the multimode normal operation data are used to construct a reference dataset. For online monitoring of process state, LNSA applies moving window technique to obtain a current snapshot data window. Then neighborhood searching technique is used to acquire the corresponding local neighborhood data window from the reference dataset. Similarity analysis between snapshot and neighborhood data windows is performed, which includes the calculation of principal component analysis(PCA) similarity factor and distance similarity factor. The PCA similarity factor is to capture the change of data direction while the distance similarity factor is used for monitoring the shift of data center position. Based on these similarity factors, two monitoring statistics are built for multimode process fault detection. Finally a simulated continuous stirred tank system is used to demonstrate the effectiveness of the proposed method. The simulation results show that LNSA can detect multimode process changes effectively and performs better than traditional fault detection methods.

Rapid analysis of piperazine ferulate tablets by optic-fiber sensing technology and the similarity of ultraviolet spectra

A rapid analysis method of piperazine ferulate tablets by optic-fiber sensing technology with UV-vis absorption spectrum was established. Qualitative and quantitative data were obtained and compared by maximum and minimum wavelength, absorbance and contrast spectra. Similarity method was used to identify authenticity of drugs. The difference of contents measured by this method and UV determination method in China Pharmacopoeia showed no statistical significance (P40.05), while the similarity can be used as a parameter to identify the authenticity of drugs.

The Bio-Geographical Regions Division of Global Terrestrial Animal by Multivariate Similarity Clustering Analysis Method

A novel multivariate similarity clustering analysis (MSCA) approach was used to estimate a biogeographical division scheme for the global terrestrial fauna and was compared against other widely used clustering algorithms. The faunal dataset included almost all terrestrial and freshwater fauna, a total of 4631 families, 141,814 genera, and 1,334,834 species. Our findings demonstrated that suitable results were only obtained with the MSCA method, which was associated with distinct hierarchies, reasonable structuring, and furthermore, conformed to biogeographical criteria. A total of seven kingdoms and 20 sub-kingdoms were identified. We discovered that the clustering results for the higher and lower animals did not differ significantly, leading us to consider that the analysis result is convincing as the first zoogeographical division scheme for global all terrestrial animals.

Effect of Two Kinds of Similarity Factors on Principal Component Analysis Fault Detection in Air Conditioning Systems

Screening similar historical fault-free candidate data would greatly affect the effectiveness of fault detection results based on principal component analysis(PCA).In order to find out the candidate data,this study compares unweighted and weighted similarity factors(SFs),which measure the similarity of the principal component subspace corresponding to the first k main components of two datasets.The fault detection employs the principal component subspace corresponding to the current measured data and the historical fault-free data.From the historical fault-free database,the load parameters are employed to locate the candidate data similar to the current operating data.Fault detection method for air conditioning systems is based on principal component.The results show that the weighted principal component SF can improve the effects of the fault-free detection and the fault detection.Compared with the unweighted SF,the average fault-free detection rate of the weighted SF is 17.33%higher than that of the unweighted,and the average fault detection rate is 7.51%higher than unweighted.

Comparisons of Several Similarity Coefficients for Cluster Analysis of Hot Pepper Germplasm Resources

An appropriate genetic similarity coefficient is particularly important for accurately estimating the genetic similarity and phylogenetic relationship between individuals and evaluating the genetic diversity of populations. In this study, five genetic similarity coefficients were compared for analysis of phylogenetic relationship among 31 hot pepper inbred lines based on SRAP. The applicability of different genetic similarity coefficient was investigated by means of SRAP data of hot pepper inbred lines. According to the experimental results, the variation ranges of Nei ＆ Li, Jaceard, Sorensen, Simple matching and Yule coefficients were 0. 598 - 0. 973, 0. 427 - 0. 947, 0. 598 - 0. 973, 0.427 - 0. 947 and 0. 133 - 0. 997, respectively. Results of cluster analysis based on different similarity coefficients varied greatly. To be specific, clustering results based on Nei ＆ Li, Jaccard and Sorensen coefficients were consistent; clustering with Simple matching and Yule coef ficients led to consistent classification of category in different order and slightly different classification of subcategory. Comprehensively comparing the results of cluster analysis and the dendrograms of hot pepper inbred lines, Yule coefficient is suitable for SRAP analysis of hot pepper.

Dimensional and Mechanical Similarity Analysis of the Flow in Rotating Liquid Film Reactor

A rotating liquid film reactor (RLFR) is a device of two coaxial rotating conical cylinders with the inner cone rotating and the outer one stationary. A complete mathematical model for the flow between the conical cylinders is built and a dimensional analysis is carried out. It is proved that at each point of the flow field the dimensionless pressure and velocity of the flow are determined by parameters: Reynolds number (Re), aspect ratio (Γ), radius ratio (η) and wall inclination angle (α). Furthermore, a sufficient and a necessary condition are derived from mechanical similarity between RLFR and a manufacturing equipment geometrically similar to RLFR. Finally, a numerical simulation for the distribution of pressure and velocity is performed. The results may provide a theoretical basis for experiment method and numerical simulation of the flow in a RLFR-like device.

Similarity Analysis-based Component Standardization for Product Family Design

During the product family design, it is necessary to reduce the variety of components and share common components among many products. The major benefits are lessened design efforts and reduced costs. Therefore, this paper presents an approach to standardize components of a product family. Form feature modeling for components is discussed. Based on the similarity analysis, a step by step method to standardize the feature architectures of components is described. The algorithms for standardization are identified as well. A case for standardizing components of an auto-body family is used to demonstrate the validity of this approach.

Similarity/dissimilarity analysis of protein sequences using the spatial median as a descriptor

A novel 3-D graphical representation of protein sequence has been introduced. A right cone of a unit base and unit height has been selected to represent protein sequences on its surface. The twenty amino acids have been represented by 20 circles and all protein's residues have been represented by n lines on the cone's surface. All the spots which represent the protein's residues have been shown in the cone's top view. The spatial median of all the spots is used as a new descriptor of any protein sequence. This approach was applied on two short segments of protein of yeast Saccharomyces cerevisiae. The examination of the similarities/dissimilarities for the eight ND5 proteins and the six β-globin proteins illustrate the utility of our approach. A linear correlation and significance analysis have been provided to compare our results and the percentage sequence alignment identity.

Method for assessing the similarity and distance between curves/trials and its applications in gait analysis

In clinical assessment or sports exercise,it is common that a subject is required to repeat a specific per- formance so that a stable movement pattern is obtained and analysed.In practice,however,the trials done by a sub- ject vary more or less,depending on the psychological or physical conditions.Some of the trials can be used as rep- resentatives of the stable movement pattern,and some not.Therefore,there is a demand for a new method to identify which trials/curves are similar.The traditional methods used to assess curve similarity are not perfectly suitable for the case where there are only a few of trials available.This study proposes a similarity-distance coefficient to assess the similarity of curves/trials.A group of designed curves are used to validate the coefficient.The results show that given joint kinematic data during gait as examples,the proposed coefficient can be used to quantitatively evaluate the similarity of trials,and thus find which trials would be representative (s) for the gait investigated.The proposed method could be applied in various situations where repeat movements have to be measured and analysed.

Genome characterization of the Caprine arthritis-encephalitis virus in China:A retrospective genomic analysis of the earliest Chinese isolates

Caprine arthritis-encephalitis virus(CAEV) is an under-studied virus infecting caprines and ovines worldwide. Over the last four decades, CAEV has spread in China, obtaining genomic data on CAEV strains circulating in China is of importance for developing diagnostic methods and eradicating associated diseases. However, there is limited information on the genome, including characterizations, and the probable origin. This work aimed to characterize Chinese CAEV genomes and population structures. Five CAEV strains isolated from infected dairy goats between 1989and 1994 in Gansu, Guizhou, Shaanxi, Shandong and Sichuan provinces were cloned and sequenced. The Chinese CAEV had a 58–93% genome similarities to strains outside of China, and they belonged to subgenotype B1. The highest similarity levels(98.3–99.3%) were with two other Chinese strains, and they shared a 91.8–92.3% similarity with the strain Clements(GenBank accession no. NC_001463.1) from outside of China. The Chinese CAEV strains isolated from different provinces over five years were still highly homologous and contained unique ancestral population components,indicating that these Chinese strains had a common origin that differed from other known strains. Our results provide genomic data on circulating Chinese CAEV strains and will be useful for future epidemiological investigations and CAEV eradication programs.

Fuzzy cluster analysis of water mass in the western Taiwan Strait in spring 2019

The classification of the springtime water mass has an important influence on the hydrography,regional climate change and fishery in the Taiwan Strait.Based on 58 stations of CTD profiling data collected in the western and southwestern Taiwan Strait during the spring cruise of 2019,we analyze the spatial distributions of temperature(T)and salinity(S)in the investigation area.Then by using the fuzzy cluster method combined with the T-S similarity number,we classify the investigation area into 5 water masses:the Minzhe Coastal Water(MZCW),the Taiwan Strait Mixed Water(TSMW),the South China Sea Surface Water(SCSSW),the South China Sea Subsurface Water(SCSUW)and the Kuroshio Branch Water(KBW).The MZCW appears in the near surface layer along the western coast of Taiwan Strait,showing low-salinity(<32.0)tongues near the Minjiang River Estuary and the Xiamen Bay mouth.The TSMW covers most upper layer of the investigation area.The SCSSW is mainly distributed in the upper layer of the southwestern Taiwan Strait,beneath which is the SCSUW.The KBW is a high temperature(core value of 26.36℃)and high salinity(core value of 34.62)water mass located southeast of the Taiwan Bank and partially in the central Taiwan Strait.

The Deformation Analysis of the 3D Alignment Control Network Based on the Multiple Congruence Models

In the construction and maintenance of particle accelerators,all the accelerator elements should be installed in the same coordinate system,only in this way could the devices in the actual world be consistent with the design drawings.However,with the occurrence of the movements of the reinforced concrete cover plates at short notice or building deformations in the long term,the control points upon the engineering structure will be displaced,and the fitness between the subnetwork and the global control network may be irresponsible.Therefore,it is necessary to evaluate the deformations of the 3D alignment control network.Different from the extant investigations,in this paper,to characterize the deformations of the control network,all of the congruent models between the points measured in different epochs have been identified,and the congruence model with the most control points is considered as the primary or fundamental model,the remaining models are recognized as the additional ones.Furthermore,the discrepancies between the primary S-transformation parameters and the additional S-transformation parameters can reflect the relative movements of the additional congruence models.Both the iterative GCT method and the iterative combinatorial theory are proposed to detect multiple congruence models in the control network.Considering the actual work of the alignment,it is essential to identify the competitive models in the monitoring network,which can provide us a hint that,even the fitness between the subnetwork and the global control network is good,there are still deformations which may be ignored.The numerical experiments show that the suggested approaches can describe the deformation of the 3D alignment control network roundly.

MULTIRESOLUTION ANALYSIS, SELF-SIMILAR TILINGS AND HAAR WAVELETS ON THE HEISENBERG GROUP

In this article, the properties of multiresolution analysis and self-similar tilings on the Heisenberg group are studied. Moreover, we establish a theory to construct an orthonormal Haar wavelet base in L^2（H^d） by using self-similar tilings for the acceptable dilations on the Heisenberg group.

Clustering analysis algorithm for security supervising data based on semantic description in coal mines

In order to mine production and security information from security supervising data and to ensure security and safety involved in production and decision-making,a clustering analysis algorithm for security supervising data based on a semantic description in coal mines is studied.First,the semantic and numerical-based hybrid description method of security supervising data in coal mines is described.Secondly,the similarity measurement method of semantic and numerical data are separately given and a weight-based hybrid similarity measurement method for the security supervising data based on a semantic description in coal mines is presented.Thirdly,taking the hybrid similarity measurement method as the distance criteria and using a grid methodology for reference,an improved CURE clustering algorithm based on the grid is presented.Finally,the simulation results of a security supervising data set in coal mines validate the efficiency of the algorithm.

A Study of E-C and C-E Translation in the Light of Lexical Contrastive Analysis and Cultural Differences

The essay briefly illustrates E-C and C-E Translation strategies in the light of lexical similarity and differences of Lexical Contrastive Analysis, and explores lexical translation skills under the theory of Cultural Differences.

Group decision-making method based on entropy and experts cluster analysis

According to the aggregation method of experts＇ evaluation information in group decision-making,the existing methods of determining experts＇ weights based on cluster analysis take into account the expert＇s preferences and the consistency of expert＇s collating vectors,but they lack of the measure of information similarity.So it may occur that although the collating vector is similar to the group consensus,information uncertainty is great of a certain expert.However,it is clustered to a larger group and given a high weight.For this,a new aggregation method based on entropy and cluster analysis in group decision-making process is provided,in which the collating vectors are classified with information similarity coefficient,and the experts＇ weights are determined according to the result of classification,the entropy of collating vectors and the judgment matrix consistency.Finally,a numerical example shows that the method is feasible and effective.