Using an integrated feature set to generalize and justify the Chinese-to-English transferring rule of the 'ZHE' aspect

In machine translation(MT) practice,there is an urgent need for constructing a set of Chinese-to-English aspect transferring rules to define the transferring conditions.The integrated feature set was used to generalize and justify the Chinese-to-English transferring rule of the 'ZHE' aspect(ZHE Rule).A ZHE classification model was built in this study.The impacts of each set of temporal,lexical aspectual,and syntactic features,and their integrated impacts,on the accuracy of the ZHE Rule were tested.Over 600 misclassified corpus sentences were manually examined.A 10-fold cross-validation was used with a decision tree algorithm.The main results are:(1) The ZHE Rule was generalized and justified to have a higher accuracy under the two metrics:the precision rate and the areas under the receiver operating characteristic curve(AUC).(2) The temporal,lexical aspectual,and syntactic feature sets have an integrated contribution to the accuracy of the ZHE Rule.The syntactic and temporal features have an impact on ZHE aspect derivations,while the lexical aspectual features are not predictive of ZHE aspect derivation.(3) While associated with active verbs,the ZHE aspect can denote a perfective situation.This study suggests that the temporal and syntactic features are the predictive ZHE aspect classification features and that the ZHE Rule with an overall precision rate of 80.1% is accurate enough to be further explored in MT practice.The machine learning method,decision tree,can be applied to the automatic aspect transferring in MT research and aspectual interpretations in linguistic research.

Consistency of weighted feature set and polyspectral kernels in individual communication transmitter identification

This paper presents a method using support vector machine with polyspectral kernels for classification of individual transmitters.Then,the neighborhood-roughset-based weighted feature set is proposed.The experiments of the algorithms mentioned above indicate that they have consistency,which raises a new weighted kernel.The experiment shows that better classification rate can be achieved.

Geological Features,Mineralization Types and Metallogenic Setting of the Phlaythong Large Iron Deposit,Southern Laos

The Phlaythong large iron deposit in Shampasak of southern Laos,is located in the Kon Tum microblock （Fig.1A）,central-southern part of the Indo-China block,and the geographic coordinate of the central mining area is 14°43′04″ N and 106°07′02″ E.

Application of a new feature extraction and optimization method to surface defect recognition of cold rolled strips

Considering that the surface defects of cold rolled strips are hard to be recognized by human eyes under high-speed circumstances, an automatic recognition technique was discussed. Spectrum images of defects can be got by fast Fourier transform （FFF） and sum of valid pixels （SVP）, and its optimized center region, which concentrates nearly all energies, are extracted as an original feature set. Using genetic algorithm to optimize the feature set, an optimized feature set with 51 features can be achieved. Using the optimized feature set as an input vector of neural networks, the recognition effects of LVQ neural networks have been studied. Experiment results show that the new method can get a higher classification rate and can settle the automatic recognition problem of surface defects on cold rolled strips ideally.

Cobalt crust recognition based on kernel Fisher discriminant analysis and genetic algorithm in reverberation environment

Recognition of substrates in cobalt crust mining areas can improve mining efficiency.Aiming at the problem of unsatisfactory performance of using single feature to recognize the seabed material of the cobalt crust mining area,a method based on multiple-feature sets is proposed.Features of the target echoes are extracted by linear prediction method and wavelet analysis methods,and the linear prediction coefficient and linear prediction cepstrum coefficient are also extracted.Meanwhile,the characteristic matrices of modulus maxima,sub-band energy and multi-resolution singular spectrum entropy are obtained,respectively.The resulting features are subsequently compressed by kernel Fisher discriminant analysis(KFDA),the output features are selected using genetic algorithm(GA)to obtain optimal feature subsets,and recognition results of classifier are chosen as genetic fitness function.The advantages of this method are that it can describe the signal features more comprehensively and select the favorable features and remove the redundant features to the greatest extent.The experimental results show the better performance of the proposed method in comparison with only using KFDA or GA.

Dual-modal Physiological Feature Fusion-based Sleep Recognition Using CFS and RF Algorithm

Research has demonstrated a significant overlap between sleep issues and other medical conditions.In this paper,we consider mild difficulty in falling asleep(MDFA).Recognition of MDFA has the potential to assist in the provision of appropriate treatment plans for both sleep issues and related medical conditions.An issue in the diagnosis of MDFA lies in subjectivity.To address this issue,a decision support tool based on dual-modal physiological feature fusion which is able to automatically identify MDFA is proposed in this study.Special attention is given to the problem of how to extract candidate features and fuse dual-modal features.Following the identification of the optimal feature set,this study considers the correlations between each feature and class and evaluates correlations between the inter-modality features.Finally,the recognition accuracy was measured using 10-fold cross validation.The experimental results for our method demonstrate improved performance.The highest recognition rate of MDFA using the optimal feature set can reach 96.22%.Based on the results of current study,the authors will,in projected future research,develop a real-time MDFA recognition system.

Regional wind power forecasting model with NWP grid dataoptimized

Unlike the traditional fossil energy, wind, as the clean renewable energy, can reduce the emission of the greenhouse gas. To take full advantage of the environmental benefits of wind energy, wind power forecasting has to be studied to overcome the troubles brought by the variable nature of wind. Power forecasting for regional wind farm groups is the problem that many power system operators care about. The high-dimensional feature sets with redundant information are frequently encountered when dealing with this problem. In this paper, two kinds of feature set construction methods are proposed which can achieve the proper feature set either by selecting the subsets or by transforming the original variables with specific combinations. The former method selects the subset according to the criterion of minimal-redundancy-maximal-relevance (mRMR), while the latter does so based on the method of principal component analysis (PCA). A locally weighted learning method is also proposed to utilize the processed feature set to produce the power forecast results. The proposed model is simple and easy to use with parameters optimized automatically. Finally, a case study of 28 wind farms in East China is provided to verify the effectiveness of the proposed method.