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Traditional noise data processing methods have certain requirements for input data, and have a long running time. However, there are some problems of manual adjustment noise data of coal mine gas sensor such as less quantity, poor quality, inconsistent time and easy to be affected by environment, it is difficult to filter the noise data by traditional noise processing methods. For the above problems, a manual adjustment noise data processing method for coal mine gas sensor is proposed. The data average value is used to fill the missing value of concentration data of coal mine gas sensor; the feature set and sample set of concentration data of coal mine gas sensor are constructed by using multi time granularity; five curve fitting functions, namely Gaussian function, mixed Gaussian function, binomial function, trinomial function and piecewise binomial function, are used to fit manual adjustment noise data, and parameters of the fitting function are determined based on the least square method, and the optimal fitting function is obtained according to fitting effect; through analysis of manual adjustment noise data, it is concluded that the noise data is related to the slope, peak and difference of gas concentration before and after adjustment, according to these basic characteristics, the manual adjustment noise data is identified and deleted. The experimental results verify effectiveness of the method.

To address the problem of telecom customer value evaluation, this paper proposed a novel method based on dominance-based rough set approach. According to the telecom experts' priori-knowledge, the method labeled the value features and extracted the selected training customers' category from the current value and potential value two aspects. As a result, it got two customer value decision tables, one was current value decision table and the other was potential value decision table. After that, it used the maximum certainty of every class union as the corresponding variable threshold value to control the decision rule extraction program. The proposed method could combine prior knowledge of the domain and the characteristics of the data itself effectively. Owing to the amount of telecom customer data was huge and the labeled category cases were difficult to obtain, the active learning method based on neighbor entropy was adopted to select and label the training data, which could minimize the experts' workload and improve the quality of the selected training data. Through simulation experiments in the real field show that the proposed method can effectively evaluate the telecom customers' value. [ABSTRACT FROM AUTHOR]

Objective: To investigate the effects of Naoxinduotai capsule on the markers of prothrombotic state as plasminogen activator inhibitor-1 (PAI-1), von Willebrand factor (vWF) and alpha-granular membrane protein (CD62p) in essential hypertension patients of yang hyperactivity and blood stagnation., Method: The 62 essential hypertension patients of yang hyperactivity and blood stagnation were divided into Naoxinduotai capsule + perindopril group (treatment group, 30 subjects) and Perindopril group (control group, 32 subjects). Clinical symptoms, blood pressure and the blood plasma PAI-1, vWF, CD62p of the patients were observed. The blood plasma PAI-1, vWF and CD62p were measured by enzyme-linked immunosorbant assay (ELISA)., Result: After 8 weeks treatment, in treatment group the clinical symptoms became better, and there was a significant difference with control group (P < 0.05). Blood pressure was significantly degraded, but there was not a significant difference with control group. The blood plasma PAI-1, vWF, CD62p level was degraded ,and there were significant differences with control group in all the three makers (P < 0.05)., Conclusion: Naoxinduotai capsule can treat the essential hypertension patients of Yang hyperactivity and blood stagnation, and it has conspicuous advantages in improving the clinical symptoms and the makers of prothrombotic state.