The world economy is interrelated and changing so fast that china has to think globally to grow and prosper in order to have a closer relationship with the international market. In this process language is undoubtedly...The world economy is interrelated and changing so fast that china has to think globally to grow and prosper in order to have a closer relationship with the international market. In this process language is undoubtedly necessary and business English is one of the most important ones. Business English requires to be explicit, professional and accurate; therefore, its translation should also follow these principles. This paper tends to analyze the features of business English and demonstrate how to realize effective translation.展开更多
Since China introduced the reform and opening policy thirty years ago,the course of foreign economic trade has made wonderful achievements. It is obvious that,as a common-used language,Englishplays an important role i...Since China introduced the reform and opening policy thirty years ago,the course of foreign economic trade has made wonderful achievements. It is obvious that,as a common-used language,Englishplays an important role in it. After 13 years of arduous negotiation,China finally returned to the big family of the World Trade Organization as expected. So,as a communicationtool,Englishwill inevitably become more and more important. However,trade Englishas a variety of ESP ( Englishfor Special Purpose) has its own particular linguistic traits and characteristics. As a result,trade translation is a complicated course,which involves not only translation theoriesand practice but also business background knowledge and there must be some effective ways to follow for translation.展开更多
Chinese long distance binding is explored in terms of a feature orientated approach: a long distance anaphor with barren or impoverished φ features is obliged to acquires φ features (or phi features) from its adjace...Chinese long distance binding is explored in terms of a feature orientated approach: a long distance anaphor with barren or impoverished φ features is obliged to acquires φ features (or phi features) from its adjacent NPs in its upward movement at LF, and that this feature obtaining process, governed by rules that are summarized in terms of Feature Saturation Process (FSP), provides answers to long distance binding. Accordingly, binding is seen as an instance of a perfect match of features possessed by a saturated anaphor and an NP at LF. An anaphor is bound when it moves to INFL with its φ features matched with a feature functioning NP, whereas a middle way anaphor with unsaturated φ features is not bound. This approach satisfactorily explains the binding relations in sentences of long distance coreference and providing alternative answers to other issues of binding. It is further shown that the binding of Chinese reflexive ziji (自己)to its antecedent(s) results from a sequence of local dependency through movement.展开更多
The classification of pathological voice from healthy voice was studied based upon 27 acoustic features derived from a single sound signal of vowel /a:/. First, the feature space was transferred to reduce the data dim...The classification of pathological voice from healthy voice was studied based upon 27 acoustic features derived from a single sound signal of vowel /a:/. First, the feature space was transferred to reduce the data dimension by principle component analysis (PCA). Then the voice samples were classified according to the reduced PCA parameters by support vector machine (SVM) using radial basis function (RBF) as a kernel function. Meanwhile, by changing the ratio of opposite class samples, the accuracy under different features combinations was tested. Experimental data were provided by the voice database of Massachusetts Eye and Ear Infirmary (MEEI) in which 216 vowel /a:/ samples were collected from subjects of healthy and pathological cases, and tested with 5 fold cross-validation method. The result shows the positive rate of pathological voices was improved from 92% to 98% through the PCA method. STD, Fatr, Tasm, NHR, SEG, and PER are pathology sensitive features in illness detection. Using these sensitive features the accuracy of detection of pathological voice from healthy voice can reach 97%.展开更多
Dependence of conductance of corrugated graphene quantum dot(CGQD)on geometrical features includinglength,width,connection and edge is investigated by the first principles calculations.The results demonstrate that the...Dependence of conductance of corrugated graphene quantum dot(CGQD)on geometrical features includinglength,width,connection and edge is investigated by the first principles calculations.The results demonstrate that theconductance of CGQD with different geometrical features is different from each other.The positions and amplitudesof discrete levels in densities of states and transmission coefficients are sensitive to geometrical features.The I-Vcharacteristics of graphene are modified by size and edge,it is surprise the current does not change monotonously butoscillatory with length.And they are slight change for different connections.展开更多
Parkinson’s disease(PD)is a neurodegenerative disease in the central nervous system.Recently,more researches have been conducted in the determination of PD prediction which is really a challenging task.Due to the dis...Parkinson’s disease(PD)is a neurodegenerative disease in the central nervous system.Recently,more researches have been conducted in the determination of PD prediction which is really a challenging task.Due to the disorders in the central nervous system,the syndromes like off sleep,speech disorders,olfactory and autonomic dysfunction,sensory disorder symptoms will occur.The earliest diagnosing of PD is very challenging among the doctors community.There are techniques that are available in order to predict PD using symptoms and disorder measurement.It helps to save a million lives of future by early prediction.In this article,the early diagnosing of PD using machine learning techniques with feature selection is carried out.In the first stage,the data preprocessing is used for the preparation of Parkinson’s disease data.In the second stage,MFEA is used for extracting features.In the third stage,the feature selection is performed using multiple feature input with a principal component analysis(PCA)algorithm.Finally,a Darknet Convolutional Neural Network(DNetCNN)is used to classify the PD patients.The main advantage of using PCA-DNetCNN is that,it provides the best classification in the image dataset using YOLO.In addition to that,the results of various existing methods are compared and the proposed DNetCNN proves better accuracy,performance in detecting the PD at the initial stages.DNetCNN achieves 97.5%of accuracy in detecting PD as early.Besides,the other performance metrics are compared in the result evaluation and it is proved that the proposed model outperforms all the other existing models.展开更多
Active Shape Model （ASM） is a powerful statistical tool to extract the facial features of a face image under frontal view. It mainly relies on Principle Component Analysis （PCA） to statistically model the variabil...Active Shape Model （ASM） is a powerful statistical tool to extract the facial features of a face image under frontal view. It mainly relies on Principle Component Analysis （PCA） to statistically model the variability in the training set of example shapes. Independent Component Analysis （ICA） has been proven to be more efficient to extract face features than PCA. In this paper, we combine the PCA and ICA by the consecutive strategy to form a novel ASM. Firstly, an initial model, which shows the global shape variability in the training set, is generated by the PCA-based ASM. And then, the final shape model, which contains more local characters, is established by the ICA-based ASM. Experimental results verify that the accuracy of facial feature extraction is statistically significantly improved by applying the ICA modes after the PCA modes.展开更多
文摘The world economy is interrelated and changing so fast that china has to think globally to grow and prosper in order to have a closer relationship with the international market. In this process language is undoubtedly necessary and business English is one of the most important ones. Business English requires to be explicit, professional and accurate; therefore, its translation should also follow these principles. This paper tends to analyze the features of business English and demonstrate how to realize effective translation.
文摘Since China introduced the reform and opening policy thirty years ago,the course of foreign economic trade has made wonderful achievements. It is obvious that,as a common-used language,Englishplays an important role in it. After 13 years of arduous negotiation,China finally returned to the big family of the World Trade Organization as expected. So,as a communicationtool,Englishwill inevitably become more and more important. However,trade Englishas a variety of ESP ( Englishfor Special Purpose) has its own particular linguistic traits and characteristics. As a result,trade translation is a complicated course,which involves not only translation theoriesand practice but also business background knowledge and there must be some effective ways to follow for translation.
文摘Chinese long distance binding is explored in terms of a feature orientated approach: a long distance anaphor with barren or impoverished φ features is obliged to acquires φ features (or phi features) from its adjacent NPs in its upward movement at LF, and that this feature obtaining process, governed by rules that are summarized in terms of Feature Saturation Process (FSP), provides answers to long distance binding. Accordingly, binding is seen as an instance of a perfect match of features possessed by a saturated anaphor and an NP at LF. An anaphor is bound when it moves to INFL with its φ features matched with a feature functioning NP, whereas a middle way anaphor with unsaturated φ features is not bound. This approach satisfactorily explains the binding relations in sentences of long distance coreference and providing alternative answers to other issues of binding. It is further shown that the binding of Chinese reflexive ziji (自己)to its antecedent(s) results from a sequence of local dependency through movement.
文摘The classification of pathological voice from healthy voice was studied based upon 27 acoustic features derived from a single sound signal of vowel /a:/. First, the feature space was transferred to reduce the data dimension by principle component analysis (PCA). Then the voice samples were classified according to the reduced PCA parameters by support vector machine (SVM) using radial basis function (RBF) as a kernel function. Meanwhile, by changing the ratio of opposite class samples, the accuracy under different features combinations was tested. Experimental data were provided by the voice database of Massachusetts Eye and Ear Infirmary (MEEI) in which 216 vowel /a:/ samples were collected from subjects of healthy and pathological cases, and tested with 5 fold cross-validation method. The result shows the positive rate of pathological voices was improved from 92% to 98% through the PCA method. STD, Fatr, Tasm, NHR, SEG, and PER are pathology sensitive features in illness detection. Using these sensitive features the accuracy of detection of pathological voice from healthy voice can reach 97%.
文摘Dependence of conductance of corrugated graphene quantum dot(CGQD)on geometrical features includinglength,width,connection and edge is investigated by the first principles calculations.The results demonstrate that theconductance of CGQD with different geometrical features is different from each other.The positions and amplitudesof discrete levels in densities of states and transmission coefficients are sensitive to geometrical features.The I-Vcharacteristics of graphene are modified by size and edge,it is surprise the current does not change monotonously butoscillatory with length.And they are slight change for different connections.
文摘Parkinson’s disease(PD)is a neurodegenerative disease in the central nervous system.Recently,more researches have been conducted in the determination of PD prediction which is really a challenging task.Due to the disorders in the central nervous system,the syndromes like off sleep,speech disorders,olfactory and autonomic dysfunction,sensory disorder symptoms will occur.The earliest diagnosing of PD is very challenging among the doctors community.There are techniques that are available in order to predict PD using symptoms and disorder measurement.It helps to save a million lives of future by early prediction.In this article,the early diagnosing of PD using machine learning techniques with feature selection is carried out.In the first stage,the data preprocessing is used for the preparation of Parkinson’s disease data.In the second stage,MFEA is used for extracting features.In the third stage,the feature selection is performed using multiple feature input with a principal component analysis(PCA)algorithm.Finally,a Darknet Convolutional Neural Network(DNetCNN)is used to classify the PD patients.The main advantage of using PCA-DNetCNN is that,it provides the best classification in the image dataset using YOLO.In addition to that,the results of various existing methods are compared and the proposed DNetCNN proves better accuracy,performance in detecting the PD at the initial stages.DNetCNN achieves 97.5%of accuracy in detecting PD as early.Besides,the other performance metrics are compared in the result evaluation and it is proved that the proposed model outperforms all the other existing models.
文摘Active Shape Model （ASM） is a powerful statistical tool to extract the facial features of a face image under frontal view. It mainly relies on Principle Component Analysis （PCA） to statistically model the variability in the training set of example shapes. Independent Component Analysis （ICA） has been proven to be more efficient to extract face features than PCA. In this paper, we combine the PCA and ICA by the consecutive strategy to form a novel ASM. Firstly, an initial model, which shows the global shape variability in the training set, is generated by the PCA-based ASM. And then, the final shape model, which contains more local characters, is established by the ICA-based ASM. Experimental results verify that the accuracy of facial feature extraction is statistically significantly improved by applying the ICA modes after the PCA modes.
