CAJ | 학술논문

针对水质异常监测问题，本文提出了一种基于计算机视觉技术和支持向量机相结合的生物式水质异常监测方法。首先通过计算机视觉获取可以反映水质状况的鱼类行为运动特征参数，对其进行预处理；然后建立样本集并获得基于 SVM 的水质异常监测模型；最后利用模型对未知水质下的鱼类行为特征参数分析评价，间接监测水质异常状况。鉴于支持向量机核函数类型和参数优化对模型优劣有重大影响，本文对不同类型的核函数进行实验对比，其次分别采用粒子群优化算法(PSO)、遗传算法(GA)以及网格搜索法(Grid Search)对参数进行优化选择。实验结果表明该方法可以快速有效的进行水质异常监测。
침대수질이상감측문제，본문제출료일충기우계산궤시각기술화지지향량궤상결합적생물식수질이상감측방법。수선통과계산궤시각획취가이반영수질상황적어류행위운동특정삼수，대기진행예처리；연후건립양본집병획득기우 SVM 적수질이상감측모형；최후이용모형대미지수질하적어류행위특정삼수분석평개，간접감측수질이상상황。감우지지향량궤핵함수류형화삼수우화대모형우렬유중대영향，본문대불동류형적핵함수진행실험대비，기차분별채용입자군우화산법(PSO)、유전산법(GA)이급망격수색법(Grid Search)대삼수진행우화선택。실험결과표명해방법가이쾌속유효적진행수질이상감측。
Aiming at the problem of water quality anomaly monitoring, a bio-monitoring method based on computer vision and support vector machine is proposed. First, fish behavior movement information is collected by computer vision. Then, establishing training sample set is used for obtaining water quality anomaly monitoring model. Finally, the model is utilized to analyze the fish data of unknown water quality. Kernel function type and parameter optimization have a significant impact on the model. The different types of kernel function experimental results are compared to choose the best kernel, and then Particle Swarm Optimization (PSO) algorithm and Genetic Algorithm (GA) and the Grid Search method (Grid Search) are used to optimize parameter. The experimental results show that the method can monitor the water quality quickly and efficiently.