Detection Situation and Improvement of Lightning Monitoring Network in Guizhou Province

Based on lightning location data of lightning monitoring network in Guizhou Province in recent eight years,the effective detection radius of a station and the effective detection range of lightning monitoring network in Guizhou Province were analyzed. The results show that the effective detection radius of a lightning monitoring sub-station in Guizhou Province is 160 km; some counties in the southwest,northwest and northeast of Guizhou were not detected. To improve the detector efficiency of lightning monitoring network in Guizhou Province,it is suggested that nine sub-stations should be built in Weining,Shuicheng,Qinglong,Pingtang,Rongjiang,Yuping,Songtao,Tongren and Renhuai,so that the effective detection efficiency will reach more than 95%.

Study Application of RADIUS Protocol on Ethernet

This paper presents how to apply the RADIUS (Remote Authentication Dial In User Service)protocol ,which is generally applied to dial-up network, to the authentication & charge of Broad Band accessing control system on Ethernet. It is provided that the Broad Band accessing control system included a self-designed communication protocol is used in communicating between an terminal user and Network Access Server .The interface module on the servers side and the Radius system is also given in this article.

Identification between Fimbristylis miliacea （L.） Vahl and Fimbristhlis stauntonii Debeaux et Franch. by CWT-FTIR-RBFNN

Horizontal attenuation total reflection-Fourier transform infrared spectroscopy （HATR-FTIR） is used to measure the FTIR of Fimbristylis miliacea （L.） Vahl seed and Fimbristhlis stauntonii Debeaux et Franch. seed. In order to extrude the difference between Fimbristylis miliacea （L.） Vahl seed and Fimbristhlis stauntonii Debeaux et Franch. seed, continuous wavelet transform （CWT） is used to decompose the FFIR of Fimbristylis miliacea （L.） Vahl seed and Fimbristhlis stauntonii Debeaux et Franch. seed. Three main scales are selected as the feature extracting space in the CWT domain. According to the distribution of FTIR of Fimbristylis miliacea （L.） Vahl seed and Fimbristhlis stauntonii Debeaux et Franch. seed, three feature regions are determined at every spectra band at selected three scales in the CWT domain. Thus nine feature parameters form the feature vector. The feature vector is input to the radial basis function neural network （RBFNN） to train so as to accurately classify the Fimbristylis miliacea （L.） Vahl seed and Fimbristhlis stauntonii Debeaux et Franch. seed. 110 couples of FI＇IR are used to train and test the proposed method, where 60 couples are used as training samples and 50 couples are used as testing samples. Experimental results show that the accurate recognition rate between Fimbristylis miliacea （L.） Vahl seed and Fimbristhlis stauntonii Debeaux et Franch. seed is respectively 96% and 98% by using the proposed method.