【導讀】活水平有了很大提高。各種高檔家電產(chǎn)品和貴重物品為許多家庭所擁有。然而一些不法分子也是越來越多。這點就是看到了大部分人防盜意識還不。夠強.造成偷盜現(xiàn)象屢見不鮮。因此，越來越多的居民家庭對財產(chǎn)安全問題。是用于一些大公司財政機構(gòu)。價格高昂,一般人們難以接受。方面發(fā)揮更加有效的作用。由于紅外線是不見光，有很強的隱蔽性和保密。性能穩(wěn)定等特點而受到廣大用戶和專業(yè)人士的歡迎。紅外報警器大多數(shù)采用國外的先進技術(shù),其功能也非常先進。括被動式熱釋電型紅外報警器,也即是本文將研究的產(chǎn)品。紅外防盜報警器對所防護的范圍應可直視，不能有障礙物；內(nèi)離開探測區(qū)域；報警聲，報警結(jié)束后5秒，若有人員觸發(fā)它，它將再次報警，周而復始。腳的輸入保持為低電平，從而封鎖觸發(fā)信號Vs。通或截止，繼電器吸合或釋放。損壞性故障包括性能全部失效和突然失效。生的誤報警必須提高產(chǎn)品的設計水平和工藝水平，在作系統(tǒng)設計的同時，件時要注意元件與元件之間的干擾。

　　

【正文】 with kimono relations. Searches for in inputs “the kimono” the search homepage, altogether the result is 26,917, the first 20 results all are and the kimono correlation homepage. In this search engine result mistake, is because the participle inaccurate author39。s understanding, the Google Chinese participle technology uses is an American name is called Chinese participle technology which Basis Technology ( pany provides, hundred degrees uses the participle technology which is oneself pany develops, searches for the use the participle technology which is the domestic magnanimous science and technology ( it can be seen, Chinese participle accuracy, has the quite big relations to the search engine result relevance and the accuracy. Chinese participle technology Chinese participle technology belongs to the natural language processing technology category, regarding a speech, the human may through own knowledge understand which are the words, which aren39。t the words, but how enables the puter also to understand? Its treating processes are the participle algorithm. The existing participle algorithm may divide into three big kinds: Based on character string matching participle method, based on understanding participle method and based on statistical participle method. 1st, based on character string matching participle method This method is called the mechanical participle method, it will be defers to certain strategy to wait the analysis the Chinese string “fully big” in the electronic dictionary entry to carry on with one matches, if found some string of character in the dictionary, then will match successfully (distinguishes a word).According to the scanning direction difference, the string matching participle method may divide into the forward match and the reversion match。 Situation first matches which according to the different length, may divide into biggest (longest) matches and smallest (shortest) matches。 According to whether unifies with the partofspeech tagging process, also may divide into the integrated method which the pure participle method and the participle and labelling monly used several mechanical participle method is as follows: 1) to biggest match law (from left to right direction)。 2) reversion biggest match law (from right to left direction)。 3) the least segmentations (causes word number which in each cuts to be smallest). Also may the above each method interbination, for example, be possible is unifying to the biggest match method and the reversion biggest match method the constitution bilateral matching a result of Chinese individual character Cheng Ci characteristic, the forward smallest match and the reversion smallest match very little uses general, the reversion match segmentation precision is higher than the forward match slightly, meets the different meanings phenomenon are also statistical result indicated that, the pure use to the biggest match error rate is 1/169, is using the error rate which the reversion matches most greatly is purely 1/ this kind of precision by far cannot meet the actual actual use participle system, all is the mechanical participle took one at the beginning of kind divides the method, but also must t hrough use each other language information further to enhance the segmentation the rate of accuracy. One method improves the scanning way, is called the characteristic scanning or the symbol segmentation, first in waits in the analysis string of character to distinguish and seg ments some to have the obvious characteristic word, takes the break point by these words, may divide into the original string of character the small string to e again the mechanical participle, thus reduced match error method is unifies the participle and part of speech labelling, uses the rich part of speech information to provide the help to the participle decisionmaking, and in turn carries on the examination, the adjustment in the labelling process to the participle result, thus enhances the segmentation enormously the rate of accuracy. Regarding the mechanical participle method, may establish a general model, has the specialty dissertation in this aspect, here does not make the detailed elaboration. 2nd, is through lets the puter simulation person based on the understanding participle method this participle method to the sentence the understanding, achieves the recognition word the basic thought is carries on the syntax, the semantic analysis while participle, processes the different meanings phenomenon using the syntax information and the semantic usually includes three parts: The participle subsystem, the syntax semantics subsystem, always controls the always controls the part under the coordination, the participle subsystem may obtain the related word, the sentence and so on the syntax and the semantic information es to the participle different meanings to carry on the judgment, namely it has simulated the human to the sentence the understanding participle method needs to use the massive language knowledge and the a result of Chinese language knowledge general, plexity, anizes with difficulty the form each language information which the machine may read direct ly, therefore also occupies at present based on the understanding participle system the experimental stage. 3rd, based on the statistical participle method formally, the word is the stable character bination, therefore in the context, the neighboring character simultaneously appears the number of times are more, more has the possibility to constitute a the character and the character neighboring altogether present frequency or the probability can good reflect Cheng Ci the confidence the neighboring altogether present each character bination frequency carry on the statistics to the language materials in, calculates them mutually the present two characters mutually the present