The classic state methods for trajectory estimation in boost phase with multi-range-rate system include method of point-by-point manner and that of spline-model-based manner. Both are deficient in terms of model-appro...The classic state methods for trajectory estimation in boost phase with multi-range-rate system include method of point-by-point manner and that of spline-model-based manner. Both are deficient in terms of model-approximation accuracy and systematic error determination thus resulting in the estimation errors well beyond the requirements, especially, concerning the maneuvering trajectory. This article proposes a new high-precision estimation approach based on the residual error analysis. The residual error comprises three components, i. e. systematic error, model truncation error and random error. The approach realizes self-adaptive estimation of systematic errors in measurements following the theory of sparse representation of signals to minimize the low-frequency components of residual errors. By taking median- and high-frequency components as indexes, the spline model-approximation is improved by optimizing node sequence of the spline function and the weight selection for data fusion through iteration. Simulation has validated the performances of the proposed method.展开更多
The recent Polytope ARTMAP（PTAM） suggests that irregular polytopes are more flexible than the predefined category geometries to approximate the borders among the desired output predictions.However,category expansion...The recent Polytope ARTMAP（PTAM） suggests that irregular polytopes are more flexible than the predefined category geometries to approximate the borders among the desired output predictions.However,category expansion and adjustment steps without statistical information make PTAM not robust to noise and category overlap.In order to push the learning problem towards Structural Risk Minimization（SRM）,this paper proposes Hierarchical Polytope ARTMAP （HPTAM） to use a hierarchical structure with different levels,which are determined by the complexity of regions incorporating the input pattern.Besides,overlapping of simplexes from the same desired prediction is designed to reduce category proliferation.Although HPTAM is still inevitably sensible to noisy outliers in the presence of noise,main experimental results show that HPTAM can achieve a balance between representation error and approximation error,which ameliorates the overall generalization capabilities.展开更多
Supervised machine learning approaches are effective in text mining,but their success relies heavily on manually annotated corpora.However,there are limited numbers of annotated biomedical event corpora,and the availa...Supervised machine learning approaches are effective in text mining,but their success relies heavily on manually annotated corpora.However,there are limited numbers of annotated biomedical event corpora,and the available datasets contain insufficient examples for training classifiers;the common cure is to seek large amounts of training samples from unlabeled data,but such data sets often contain many mislabeled samples,which will degrade the performance of classifiers.Therefore,this study proposes a novel error data detection approach suitable for reducing noise in unlabeled biomedical event data.First,we construct the mislabeled dataset through error data analysis with the development dataset.The sample pairs’vector representations are then obtained by the means of sequence patterns and the joint model of convolutional neural network and long short-term memory recurrent neural network.Following this,the sample identification strategy is proposed,using error detection based on pair representation for unlabeled data.With the latter,the selected samples are added to enrich the training dataset and improve the classification performance.In the BioNLP Shared Task GENIA,the experiments results indicate that the proposed approach is competent in extract the biomedical event from biomedical literature.Our approach can effectively filter some noisy examples and build a satisfactory prediction model.展开更多
Purpose： The current development of patient safety reporting systems is criticized for loss of information and low data quality due to the lack of a uniformed domain knowledge base and text processing functionality. ...Purpose： The current development of patient safety reporting systems is criticized for loss of information and low data quality due to the lack of a uniformed domain knowledge base and text processing functionality. To improve patient safety reporting, the present paper suggests an ontological representation of patient safety knowledge. Design/methodology/approach： We propose a framework for constructing an ontological knowledge base of patient safety. The present paper describes our design, implementation,and evaluation of the ontology at its initial stage. Findings： We describe the design and initial outcomes of the ontology implementation. The evaluation results demonstrate the clinical validity of the ontology by a self-developed survey measurement. Research limitations： The proposed ontology was developed and evaluated using a small number of information sources. Presently, US data are used, but they are not essential for the ultimate structure of the ontology.Practical implications： The goal of improving patient safety can be aided through investigating patient safety reports and providing actionable knowledge to clinical practitioners.As such, constructing a domain specific ontology for patient safety reports serves as a cornerstone in information collection and text mining methods.Originality/value： The use of ontologies provides abstracted representation of semantic information and enables a wealth of applications in a reporting system. Therefore, constructing such a knowledge base is recognized as a high priority in health care.展开更多
The bearing fault information is often interfered or lost in the background noise after the vibration signal being transferred complicatedly, which will make it very difficult to extract fault features from the vibrat...The bearing fault information is often interfered or lost in the background noise after the vibration signal being transferred complicatedly, which will make it very difficult to extract fault features from the vibration signals. To avoid the problem in choosing and extracting the fault features in bearing fault diagnosing, a novelty fault diagnosis method based on sparse decomposition theory is proposed. Certain over-complete dictionaries are obtained by training, on which the bearing vibration signals corresponded to different states can be decomposed sparsely. The fault detection and state identification can be achieved based on the fact that the sparse representation errors of the signal on different dictionaries are different. The effects of the representation error threshold and the number of dictionary atoms used in signal decomposition to the fault diagnosis are analyzed. The effectiveness of the proposed method is validated with experimental bearing vibration signals.展开更多
基金National Natural Science Foundation of China(60604020)
文摘The classic state methods for trajectory estimation in boost phase with multi-range-rate system include method of point-by-point manner and that of spline-model-based manner. Both are deficient in terms of model-approximation accuracy and systematic error determination thus resulting in the estimation errors well beyond the requirements, especially, concerning the maneuvering trajectory. This article proposes a new high-precision estimation approach based on the residual error analysis. The residual error comprises three components, i. e. systematic error, model truncation error and random error. The approach realizes self-adaptive estimation of systematic errors in measurements following the theory of sparse representation of signals to minimize the low-frequency components of residual errors. By taking median- and high-frequency components as indexes, the spline model-approximation is improved by optimizing node sequence of the spline function and the weight selection for data fusion through iteration. Simulation has validated the performances of the proposed method.
基金Supported by the National Basic Research 973 Program of China under Grant No.2007CB311006.
文摘The recent Polytope ARTMAP（PTAM） suggests that irregular polytopes are more flexible than the predefined category geometries to approximate the borders among the desired output predictions.However,category expansion and adjustment steps without statistical information make PTAM not robust to noise and category overlap.In order to push the learning problem towards Structural Risk Minimization（SRM）,this paper proposes Hierarchical Polytope ARTMAP （HPTAM） to use a hierarchical structure with different levels,which are determined by the complexity of regions incorporating the input pattern.Besides,overlapping of simplexes from the same desired prediction is designed to reduce category proliferation.Although HPTAM is still inevitably sensible to noisy outliers in the presence of noise,main experimental results show that HPTAM can achieve a balance between representation error and approximation error,which ameliorates the overall generalization capabilities.
基金This work was supported by the National Natural Science Foundation of China(No.61672301)Jilin Provincial Science&Technology Development(20180101054JC)+1 种基金Science and Technology Innovation Guide Project of Inner Mongolia Autonomous Region of China(2017)Talent Development Fund of Jilin Province(2018).
文摘Supervised machine learning approaches are effective in text mining,but their success relies heavily on manually annotated corpora.However,there are limited numbers of annotated biomedical event corpora,and the available datasets contain insufficient examples for training classifiers;the common cure is to seek large amounts of training samples from unlabeled data,but such data sets often contain many mislabeled samples,which will degrade the performance of classifiers.Therefore,this study proposes a novel error data detection approach suitable for reducing noise in unlabeled biomedical event data.First,we construct the mislabeled dataset through error data analysis with the development dataset.The sample pairs’vector representations are then obtained by the means of sequence patterns and the joint model of convolutional neural network and long short-term memory recurrent neural network.Following this,the sample identification strategy is proposed,using error detection based on pair representation for unlabeled data.With the latter,the selected samples are added to enrich the training dataset and improve the classification performance.In the BioNLP Shared Task GENIA,the experiments results indicate that the proposed approach is competent in extract the biomedical event from biomedical literature.Our approach can effectively filter some noisy examples and build a satisfactory prediction model.
基金supported by a grant from AHRQ, 1R01HS022895a patient safety grant from the University of Texas system, #156374
文摘Purpose： The current development of patient safety reporting systems is criticized for loss of information and low data quality due to the lack of a uniformed domain knowledge base and text processing functionality. To improve patient safety reporting, the present paper suggests an ontological representation of patient safety knowledge. Design/methodology/approach： We propose a framework for constructing an ontological knowledge base of patient safety. The present paper describes our design, implementation,and evaluation of the ontology at its initial stage. Findings： We describe the design and initial outcomes of the ontology implementation. The evaluation results demonstrate the clinical validity of the ontology by a self-developed survey measurement. Research limitations： The proposed ontology was developed and evaluated using a small number of information sources. Presently, US data are used, but they are not essential for the ultimate structure of the ontology.Practical implications： The goal of improving patient safety can be aided through investigating patient safety reports and providing actionable knowledge to clinical practitioners.As such, constructing a domain specific ontology for patient safety reports serves as a cornerstone in information collection and text mining methods.Originality/value： The use of ontologies provides abstracted representation of semantic information and enables a wealth of applications in a reporting system. Therefore, constructing such a knowledge base is recognized as a high priority in health care.
基金Projects(51375484,51475463)supported by the National Natural Science Foundation of ChinaProject(kxk140301)supported by Interdisciplinary Joint Training Project for Doctoral Student of National University of Defense Technology,China
文摘The bearing fault information is often interfered or lost in the background noise after the vibration signal being transferred complicatedly, which will make it very difficult to extract fault features from the vibration signals. To avoid the problem in choosing and extracting the fault features in bearing fault diagnosing, a novelty fault diagnosis method based on sparse decomposition theory is proposed. Certain over-complete dictionaries are obtained by training, on which the bearing vibration signals corresponded to different states can be decomposed sparsely. The fault detection and state identification can be achieved based on the fact that the sparse representation errors of the signal on different dictionaries are different. The effects of the representation error threshold and the number of dictionary atoms used in signal decomposition to the fault diagnosis are analyzed. The effectiveness of the proposed method is validated with experimental bearing vibration signals.
