无效吸气努力(Ineffective Inspiratory Effort during Expiration,IEE)是使用机械通气抢救危重病人过程中最常见的一种人机不同步问题。针对该问题缺乏检测手段的现状,提出基于机械通气波形,采用K最邻近法(K-Nearest Neighbors,KNN)结...无效吸气努力(Ineffective Inspiratory Effort during Expiration,IEE)是使用机械通气抢救危重病人过程中最常见的一种人机不同步问题。针对该问题缺乏检测手段的现状,提出基于机械通气波形,采用K最邻近法(K-Nearest Neighbors,KNN)结合动态时间规整(Dynamic time warping,DTW)实现IEE检测。在临床采集的数据集上进行测试发现,基于DTW-KNN的方法得到96.5%的特异性和97.2%的灵敏度,优于传统的基于规则的方法和机器学习方法。研究表明,该方法有望用于临床IEE检测,提示医护人员调整呼吸机参数设置,改善病人与呼吸机的同步性,优化重症呼吸治疗。展开更多
Reverse k nearest neighbor （RNNk） is a generalization of the reverse nearest neighbor problem and receives increasing attention recently in the spatial data index and query. RNNk query is to retrieve all the data po...Reverse k nearest neighbor （RNNk） is a generalization of the reverse nearest neighbor problem and receives increasing attention recently in the spatial data index and query. RNNk query is to retrieve all the data points which use a query point as one of their k nearest neighbors. To answer the RNNk of queries efficiently, the properties of the Voronoi cell and the space-dividing regions are applied. The RNNk of the given point can be found without computing its nearest neighbors every time by using the rank Voronoi cell. With the elementary RNNk query result, the candidate data points of reverse nearest neighbors can he further limited by the approximation with sweepline and the partial extension of query region Q. The approximate minimum average distance （AMAD） can be calculated by the approximate RNNk without the restriction of k. Experimental results indicate the efficiency and the effectiveness of the algorithm and the approximate method in three varied data distribution spaces. The approximate query and the calculation method with the high precision and the accurate recall are obtained by filtrating data and pruning the search space.展开更多
Matrix principal component analysis （MatPCA）, as an effective feature extraction method, can deal with the matrix pattern and the vector pattern. However, like PCA, MatPCA does not use the class information of sampl...Matrix principal component analysis （MatPCA）, as an effective feature extraction method, can deal with the matrix pattern and the vector pattern. However, like PCA, MatPCA does not use the class information of samples. As a result, the extracted features cannot provide enough useful information for distinguishing pat- tern from one another, and further resulting in degradation of classification performance. To fullly use class in- formation of samples, a novel method, called the fuzzy within-class MatPCA （F-WMatPCA）is proposed. F-WMatPCA utilizes the fuzzy K-nearest neighbor method（FKNN） to fuzzify the class membership degrees of a training sample and then performs fuzzy MatPCA within these patterns having the same class label. Due to more class information is used in feature extraction, F-WMatPCA can intuitively improve the classification perfor- mance. Experimental results in face databases and some benchmark datasets show that F-WMatPCA is effective and competitive than MatPCA. The experimental analysis on face image databases indicates that F-WMatPCA im- proves the recognition accuracy and is more stable and robust in performing classification than the existing method of fuzzy-based F-Fisherfaces.展开更多
The problem of continuously monitoring multiple K-nearest neighbor (K-NN) queries with dynamic object and query dataset is valuable for many location-based applications. A practical method is to partition the data spa...The problem of continuously monitoring multiple K-nearest neighbor (K-NN) queries with dynamic object and query dataset is valuable for many location-based applications. A practical method is to partition the data space into grid cells, with both object and query table being indexed by this grid structure, while solving the problem by periodically joining cells of objects with queries having their influence regions intersecting the cells. In the worst case, all cells of objects will be accessed once. Object and query cache strategies are proposed to further reduce the I/O cost. With object cache strategy, queries remaining static in current processing cycle seldom need I/O cost, they can be returned quickly. The main I/O cost comes from moving queries, the query cache strategy is used to restrict their search-regions, which uses current results of queries in the main memory buffer. The queries can share not only the accessing of object pages, but also their influence regions. Theoretical analysis of the expected I/O cost is presented, with the I/O cost being about 40% that of the SEA-CNN method in the experiment results.展开更多
文摘无效吸气努力(Ineffective Inspiratory Effort during Expiration,IEE)是使用机械通气抢救危重病人过程中最常见的一种人机不同步问题。针对该问题缺乏检测手段的现状,提出基于机械通气波形,采用K最邻近法(K-Nearest Neighbors,KNN)结合动态时间规整(Dynamic time warping,DTW)实现IEE检测。在临床采集的数据集上进行测试发现,基于DTW-KNN的方法得到96.5%的特异性和97.2%的灵敏度,优于传统的基于规则的方法和机器学习方法。研究表明,该方法有望用于临床IEE检测,提示医护人员调整呼吸机参数设置,改善病人与呼吸机的同步性,优化重症呼吸治疗。
基金Supported by the National Natural Science Foundation of China (60673136)the Natural Science Foundation of Heilongjiang Province of China (F200601)~~
文摘Reverse k nearest neighbor （RNNk） is a generalization of the reverse nearest neighbor problem and receives increasing attention recently in the spatial data index and query. RNNk query is to retrieve all the data points which use a query point as one of their k nearest neighbors. To answer the RNNk of queries efficiently, the properties of the Voronoi cell and the space-dividing regions are applied. The RNNk of the given point can be found without computing its nearest neighbors every time by using the rank Voronoi cell. With the elementary RNNk query result, the candidate data points of reverse nearest neighbors can he further limited by the approximation with sweepline and the partial extension of query region Q. The approximate minimum average distance （AMAD） can be calculated by the approximate RNNk without the restriction of k. Experimental results indicate the efficiency and the effectiveness of the algorithm and the approximate method in three varied data distribution spaces. The approximate query and the calculation method with the high precision and the accurate recall are obtained by filtrating data and pruning the search space.
文摘Matrix principal component analysis （MatPCA）, as an effective feature extraction method, can deal with the matrix pattern and the vector pattern. However, like PCA, MatPCA does not use the class information of samples. As a result, the extracted features cannot provide enough useful information for distinguishing pat- tern from one another, and further resulting in degradation of classification performance. To fullly use class in- formation of samples, a novel method, called the fuzzy within-class MatPCA （F-WMatPCA）is proposed. F-WMatPCA utilizes the fuzzy K-nearest neighbor method（FKNN） to fuzzify the class membership degrees of a training sample and then performs fuzzy MatPCA within these patterns having the same class label. Due to more class information is used in feature extraction, F-WMatPCA can intuitively improve the classification perfor- mance. Experimental results in face databases and some benchmark datasets show that F-WMatPCA is effective and competitive than MatPCA. The experimental analysis on face image databases indicates that F-WMatPCA im- proves the recognition accuracy and is more stable and robust in performing classification than the existing method of fuzzy-based F-Fisherfaces.
基金Project (No.ABA048) supported by the Natural Science Foundationof Hubei Province,China
文摘The problem of continuously monitoring multiple K-nearest neighbor (K-NN) queries with dynamic object and query dataset is valuable for many location-based applications. A practical method is to partition the data space into grid cells, with both object and query table being indexed by this grid structure, while solving the problem by periodically joining cells of objects with queries having their influence regions intersecting the cells. In the worst case, all cells of objects will be accessed once. Object and query cache strategies are proposed to further reduce the I/O cost. With object cache strategy, queries remaining static in current processing cycle seldom need I/O cost, they can be returned quickly. The main I/O cost comes from moving queries, the query cache strategy is used to restrict their search-regions, which uses current results of queries in the main memory buffer. The queries can share not only the accessing of object pages, but also their influence regions. Theoretical analysis of the expected I/O cost is presented, with the I/O cost being about 40% that of the SEA-CNN method in the experiment results.
