Order-preserving submatrix （OPSM） has become important in modelling biologically meaningful subspace cluster, capturing the general tendency of gene expressions across a subset of conditions. With the advance of mic...Order-preserving submatrix （OPSM） has become important in modelling biologically meaningful subspace cluster, capturing the general tendency of gene expressions across a subset of conditions. With the advance of microarray and analysis techniques, big volume of gene expression datasets and OPSM mining results are produced. OPSM query can efficiently retrieve relevant OPSMs from the huge amount of OPSM datasets. However, improving OPSM query relevancy remains a difficult task in real life exploratory data analysis processing. First, it is hard to capture subjective interestingness aspects, e.g., the analyst＇s expectation given her/his domain knowledge. Second, when these expectations can be declaratively specified, it is still challenging to use them during the computational process of OPSM queries. With the best of our knowledge, existing methods mainly fo- cus on batch OPSM mining, while few works involve OPSM query. To solve the above problems, the paper proposes two constrained OPSM query methods, which exploit userdefined constraints to search relevant results from two kinds of indices introduced. In this paper, extensive experiments are conducted on real datasets, and experiment results demonstrate that the multi-dimension index （cIndex） and enumerating sequence index （esIndex） based queries have better performance than brute force search.展开更多
为了从具有周期性的纱线毛羽H值数据中提取有代表性的毛羽H值周期模式(即周期时间或周期数据长度上毛羽数据的变化),使用动态时间规整(dynamic time warping,DTW)算法识别毛羽H值的周期模式,同时使用局部暴力搜索和剪枝算法对DTW算法进...为了从具有周期性的纱线毛羽H值数据中提取有代表性的毛羽H值周期模式(即周期时间或周期数据长度上毛羽数据的变化),使用动态时间规整(dynamic time warping,DTW)算法识别毛羽H值的周期模式,同时使用局部暴力搜索和剪枝算法对DTW算法进行优化。从14台细纱机上采集棉纺与混纺纱试样,利用乌斯特条干仪测得的毛羽H值计算理论周期及任意两周期模式间的DTW距离。结果表明:当DTW距离矩阵中出现显著不同于其他周期模式的现象时,该设备可能存在异常或故障;在设定的试验条件下,不同品种纱线的理论周期和实际周期存在差异,平均相差0.48 m,由此可根据实际周期反向推导纱线每分钟的实际卷绕长度。展开更多
基金The authors thank the anonymous referees for their useful comments that greatly improved the quality of the paper. This work was supported in part by the National Basic Research Program 973 of China （2012CB316203）, the Natural Science Foundation of China （Grant Nos. 61033007, 61272121, 61332014, 61572367, 61332006, 61472321, and 61502390）, the National High Technology Research and Development Program 863 of China （2015AA015307）, the Fundational Research Funds for the Central Universities （3102015JSJ0011, 3102014JSJ0005, and 3102014JSJ0013）, and the Graduate Starting Seed Fund of Northwestern Polytechnical University （Z2012128）.
文摘Order-preserving submatrix （OPSM） has become important in modelling biologically meaningful subspace cluster, capturing the general tendency of gene expressions across a subset of conditions. With the advance of microarray and analysis techniques, big volume of gene expression datasets and OPSM mining results are produced. OPSM query can efficiently retrieve relevant OPSMs from the huge amount of OPSM datasets. However, improving OPSM query relevancy remains a difficult task in real life exploratory data analysis processing. First, it is hard to capture subjective interestingness aspects, e.g., the analyst＇s expectation given her/his domain knowledge. Second, when these expectations can be declaratively specified, it is still challenging to use them during the computational process of OPSM queries. With the best of our knowledge, existing methods mainly fo- cus on batch OPSM mining, while few works involve OPSM query. To solve the above problems, the paper proposes two constrained OPSM query methods, which exploit userdefined constraints to search relevant results from two kinds of indices introduced. In this paper, extensive experiments are conducted on real datasets, and experiment results demonstrate that the multi-dimension index （cIndex） and enumerating sequence index （esIndex） based queries have better performance than brute force search.
文摘为了从具有周期性的纱线毛羽H值数据中提取有代表性的毛羽H值周期模式(即周期时间或周期数据长度上毛羽数据的变化),使用动态时间规整(dynamic time warping,DTW)算法识别毛羽H值的周期模式,同时使用局部暴力搜索和剪枝算法对DTW算法进行优化。从14台细纱机上采集棉纺与混纺纱试样,利用乌斯特条干仪测得的毛羽H值计算理论周期及任意两周期模式间的DTW距离。结果表明:当DTW距离矩阵中出现显著不同于其他周期模式的现象时,该设备可能存在异常或故障;在设定的试验条件下,不同品种纱线的理论周期和实际周期存在差异,平均相差0.48 m,由此可根据实际周期反向推导纱线每分钟的实际卷绕长度。
