CAJ | 학술논문

物质的太赫兹光谱包含着非常丰富的物理和化学信息。它对化合物晶体具有高的灵敏度、单光子能量低等特点。但受到检测人员知识背景、背景噪声、识别算法精度等因素的影响，光谱样本识别准确率和效率较低。为了提高对太赫兹光谱的检测能力，提出应用基于凸组合核函数的 support vector machines （SVM ）对化合物的T Hz脉冲透射谱进行分类。在使用小波变换对数据进行滤波预处理之后，提取了传统波峰、波谷位置特征和term frequency-inverse document frequency （TF-IDF）最大间隔特征。TF-IDF方法使用信息论的原理确定每个采样点的权重，选择权重较大的点作为特征。针对太赫兹透射谱特征相似、维数较低带来的分类困难问题，构建基于凸组合核函数的SVM分类模型。并利用核评价的方法，通过高维非线性规划方程求解最优凸组合参数。当最优凸组合参数被确定时，构建分类模型进行分类和预测。相比较于单一核函数，凸组合核函数将透射谱特征与分类模型融合起来。对于不同的检测样本，数据经过凸组合核函数映射到高维空间后，特征具有更显著的区分度。使用不同的太赫兹透射谱样本进行分类实验，结果表明，分类准确率得到极大提高。
물질적태혁자광보포함착비상봉부적물리화화학신식。타대화합물정체구유고적령민도、단광자능량저등특점。단수도검측인원지식배경、배경조성、식별산법정도등인소적영향，광보양본식별준학솔화효솔교저。위료제고대태혁자광보적검측능력，제출응용기우철조합핵함수적 support vector machines （SVM ）대화합물적T Hz맥충투사보진행분류。재사용소파변환대수거진행려파예처리지후，제취료전통파봉、파곡위치특정화term frequency-inverse document frequency （TF-IDF）최대간격특정。TF-IDF방법사용신식론적원리학정매개채양점적권중，선택권중교대적점작위특정。침대태혁자투사보특정상사、유수교저대래적분류곤난문제，구건기우철조합핵함수적SVM분류모형。병이용핵평개적방법，통과고유비선성규화방정구해최우철조합삼수。당최우철조합삼수피학정시，구건분류모형진행분류화예측。상비교우단일핵함수，철조합핵함수장투사보특정여분류모형융합기래。대우불동적검측양본，수거경과철조합핵함수영사도고유공간후，특정구유경현저적구분도。사용불동적태혁자투사보양본진행분류실험，결과표명，분류준학솔득도겁대제고。
In the present paper ,support vector machine (SVM ) based on convex combination kernel function will be used for classification of T Hz pulse transmission spectra .Wavelet transform is used in data pre-processing .Peaks and valleys are regar-ded as location features of THz pulse transmission spectra ,which are injected into maximum interval features of term frequency-inverse document frequency (TF-IDF) .We can conclude weight of each sampling point from the information theory .The weight represents the possibility that sampling point becomes feature .According to the situation that different terahertz-transmission spectra are lack of obvious features ,we composed a SVM classification model based on convex combination kernel function . Evaluation function should be used as an evaluation method for obtaining the parameters of optimal convex combination to achieve a better accuracy .When the optimal parameter of kenal founction was determined ,we should compose the model for process of classification and prediction .Compared with the single kernel function ,the method can be combined with transmission spectro-scopic features with classification model iteratively .Thanks to the dimensional mapping process ,outstanding margin of features can be gained for the samples of different terahertz transmission spectrum .We carried out experiments using different samples The results demonstrated that the new approach is on par or superior in terms of accuracy and much better in feature fusion than SVM with single kernel function .