Center for Advanced Life Cycle Engineering (CALCE), University of Maryland, College Park, MD, USA;
Department of Physics, National University of Singapore, Singapore, Singapore;
School of Mechanical and Chemical Engineering, University of Western Australia, Crawley, WA, Australia;
School of Mathematics and Statistics, University of Western Australia, Crawley, WA, Australia;
Department of IT Convergence, University of Ulsan, Ulsan, South Korea;
Center for Advanced Life Cycle Engineering (CALCE), University of Maryland, College Park, MD, USA;
Data-driven fault diagnosis; Density-based affinity measure; k-Nearest neighbor; Machine learning;
机译:基于无因次指标和K最近邻算法的旋转机械故障诊断方法
机译:多尺度故障分类框架使用内核主成分分析和k最近邻居进行化学过程系统
机译:基于互相关和k近邻的传输线故障检测与分类
机译:使用基于距离和密度的亲和力测量的数据驱动故障诊断顺序k最近邻分类方法
机译:绝缘栅双极晶体管(igbt)的故障检测和预测,使用k最近邻分类算法。
机译:碘地图的纹理分析及良邻邻良肺结核k最近邻分类的常规图像
机译:顺序K-近邻近邻模式识别可用于语音分类