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A Support Data-Based Core-Set Selection Method for Signal Recognition
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作者 Yang Ying Zhu Lidong Cao Changjie 《China Communications》 SCIE CSCD 2024年第4期151-162,共12页
In recent years,deep learning-based signal recognition technology has gained attention and emerged as an important approach for safeguarding the electromagnetic environment.However,training deep learning-based classif... In recent years,deep learning-based signal recognition technology has gained attention and emerged as an important approach for safeguarding the electromagnetic environment.However,training deep learning-based classifiers on large signal datasets with redundant samples requires significant memory and high costs.This paper proposes a support databased core-set selection method(SD)for signal recognition,aiming to screen a representative subset that approximates the large signal dataset.Specifically,this subset can be identified by employing the labeled information during the early stages of model training,as some training samples are labeled as supporting data frequently.This support data is crucial for model training and can be found using a border sample selector.Simulation results demonstrate that the SD method minimizes the impact on model recognition performance while reducing the dataset size,and outperforms five other state-of-the-art core-set selection methods when the fraction of training sample kept is less than or equal to 0.3 on the RML2016.04C dataset or 0.5 on the RML22 dataset.The SD method is particularly helpful for signal recognition tasks with limited memory and computing resources. 展开更多
关键词 core-set selection deep learning model training signal recognition support data
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Real-Time Data Transmission with Data Carrier Support Value in Neighbor Strategic Collection in WSN
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作者 S.Ponnarasi T.Rajendran 《Computers, Materials & Continua》 SCIE EI 2023年第6期6039-6057,共19页
An efficient trust-aware secure routing and network strategy-based data collection scheme is presented in this paper to enhance the performance and security of wireless sensor networks during data collection.The metho... An efficient trust-aware secure routing and network strategy-based data collection scheme is presented in this paper to enhance the performance and security of wireless sensor networks during data collection.The method first discovers the routes between the data sensors and the sink node.Several factors are considered for each sensor node along the route,including energy,number of neighbours,previous transmissions,and energy depletion ratio.Considering all these variables,the Sink Reachable Support Measure and the Secure Communication Support Measure,the method evaluates two distinct measures.The method calculates the data carrier support value using these two metrics.A single route is chosen to collect data based on the value of data carrier support.It has contributed to the design of Secure Communication Support(SCS)Estimation.This has been measured according to the strategy of each hop of the route.The suggested method improves the security and efficacy of data collection in wireless sensor networks.The second stage uses the two-fish approach to build a trust model for secure data transfer.A sim-ulation exercise was conducted to evaluate the effectiveness of the suggested framework.Metrics,including PDR,end-to-end latency,and average residual energy,were assessed for the proposed model.The efficiency of the suggested route design serves as evidence for the average residual energy for the proposed framework. 展开更多
关键词 data carrier support data collection neighbor strategy secure routing wireless sensor network
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A Dual-Task Learning Approach for Bearing Anomaly Detection and State Evaluation of Safe Region
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作者 Yuhua Yin Zhiliang Liu +1 位作者 Bin Guo Mingjian Zuo 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2024年第1期229-241,共13页
Predictive maintenance has emerged as an effective tool for curbing maintenance costs,yet prevailing research predominantly concentrates on the abnormal phases.Within the ostensibly stable healthy phase,the reliance o... Predictive maintenance has emerged as an effective tool for curbing maintenance costs,yet prevailing research predominantly concentrates on the abnormal phases.Within the ostensibly stable healthy phase,the reliance on anomaly detection to preempt equipment malfunctions faces the challenge of sudden anomaly discernment.To address this challenge,this paper proposes a dual-task learning approach for bearing anomaly detection and state evaluation of safe regions.The proposed method transforms the execution of the two tasks into an optimization issue of the hypersphere center.By leveraging the monotonicity and distinguishability pertinent to the tasks as the foundation for optimization,it reconstructs the SVDD model to ensure equilibrium in the model’s performance across the two tasks.Subsequent experiments verify the proposed method’s effectiveness,which is interpreted from the perspectives of parameter adjustment and enveloping trade-offs.In the meantime,experimental results also show two deficiencies in anomaly detection accuracy and state evaluation metrics.Their theoretical analysis inspires us to focus on feature extraction and data collection to achieve improvements.The proposed method lays the foundation for realizing predictive maintenance in a healthy stage by improving condition awareness in safe regions. 展开更多
关键词 Bearing condition monitoring Anomaly detection Safe region Support vector data description
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Multi-mode process monitoring based on a novel weighted local standardization strategy and support vector data description 被引量:6
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作者 赵付洲 宋冰 侍洪波 《Journal of Central South University》 SCIE EI CAS CSCD 2016年第11期2896-2905,共10页
There are multiple operating modes in the real industrial process, and the collected data follow the complex multimodal distribution, so most traditional process monitoring methods are no longer applicable because the... There are multiple operating modes in the real industrial process, and the collected data follow the complex multimodal distribution, so most traditional process monitoring methods are no longer applicable because their presumptions are that sampled-data should obey the single Gaussian distribution or non-Gaussian distribution. In order to solve these problems, a novel weighted local standardization(WLS) strategy is proposed to standardize the multimodal data, which can eliminate the multi-mode characteristics of the collected data, and normalize them into unimodal data distribution. After detailed analysis of the raised data preprocessing strategy, a new algorithm using WLS strategy with support vector data description(SVDD) is put forward to apply for multi-mode monitoring process. Unlike the strategy of building multiple local models, the developed method only contains a model without the prior knowledge of multi-mode process. To demonstrate the proposed method's validity, it is applied to a numerical example and a Tennessee Eastman(TE) process. Finally, the simulation results show that the WLS strategy is very effective to standardize multimodal data, and the WLS-SVDD monitoring method has great advantages over the traditional SVDD and PCA combined with a local standardization strategy(LNS-PCA) in multi-mode process monitoring. 展开更多
关键词 multiple operating modes weighted local standardization support vector data description multi-mode monitoring
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Use of Data Mining to Support the Development of Knowledge Intensive CAD
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作者 K H Lau C Y Yip Alvin Wong 《厦门大学学报(自然科学版)》 CAS CSCD 北大核心 2002年第S1期201-,共1页
In order to compete in the global manufacturing mar ke t, agility is the only possible solution to response to the fragmented market se gments and frequently changed customer requirements. However, manufacturing agil ... In order to compete in the global manufacturing mar ke t, agility is the only possible solution to response to the fragmented market se gments and frequently changed customer requirements. However, manufacturing agil ity can only be attained through the deployment of knowledge. To embed knowledge into a CAD system to form a knowledge intensive CAD (KIC) system is one of way to enhance the design compatibility of a manufacturing company. The most difficu lt phase to develop a KIC system is to capitalize a huge amount of legacy data t o form a knowledge database. In the past, such capitalization process could only be done solely manually or semi-automatic. In this paper, a five step model fo r automatic design knowledge capitalization through the use of data mining is pr oposed whilst details of how to select, verify and performance benchmarking an a ppropriate data mining algorithm for a specific design task will also be discuss ed. A case study concerning the design of a plastic toaster casing was used as an illustration for the proposed methodology and it was found that the avera ge absolute error of the predictions for the most appropriate algorithm is withi n 17%. 展开更多
关键词 Use of data Mining to Support the Development of Knowledge Intensive CAD In KIC
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Improved QoS-Secure Routing in MANET Using Real-Time Regional ME Feature Approximation
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作者 Y.M.Mahaboob John G.Ravi 《Computer Systems Science & Engineering》 SCIE EI 2023年第9期3653-3666,共14页
Mobile Ad-hoc Network(MANET)routing problems are thoroughly studied several approaches are identified in support of MANET.Improve the Quality of Service(QoS)performance of MANET is achieving higher performance.To redu... Mobile Ad-hoc Network(MANET)routing problems are thoroughly studied several approaches are identified in support of MANET.Improve the Quality of Service(QoS)performance of MANET is achieving higher performance.To reduce this drawback,this paper proposes a new secure routing algorithm based on real-time partial ME(Mobility,energy)approximation.The routing method RRME(Real-time Regional Mobility Energy)divides the whole network into several parts,and each node’s various characteristics like mobility and energy are randomly selected neighbors accordingly.It is done in the path discovery phase,estimated to identify and remove malicious nodes.In addition,Trusted Forwarding Factor(TFF)calculates the various nodes based on historical records and other characteristics of multiple nodes.Similarly,the calculated QoS Support Factor(QoSSF)calculating by the Data Forwarding Support(DFS),Throughput Support(TS),and Lifetime Maximization Support(LMS)to any given path.One route was found to implement the path of maximizing MANET QoS based on QoSSF value.Hence the proposed technique produces the QoS based on real-time regional ME feature approximation.The proposed simulation implementation is done by the Network Simulator version 2(NS2)tool to produce better performance than other methods.It achieved a throughput performance had 98.5%and a routing performance had 98.2%. 展开更多
关键词 Mobile ad-hoc network(MANET) routing problem regional approximation secure routing QoS support factor trusted forwarding factor data forwarding support(DFS) mobility energy
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ESSENTIAL RELATIONSHIP BETWEEN DOMAIN-BASED ONE-CLASS CLASSIFIERS AND DENSITY ESTIMATION 被引量:2
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作者 陈斌 李斌 +1 位作者 冯爱民 潘志松 《Transactions of Nanjing University of Aeronautics and Astronautics》 EI 2008年第4期275-281,共7页
One-class support vector machine (OCSVM) and support vector data description (SVDD) are two main domain-based one-class (kernel) classifiers. To reveal their relationship with density estimation in the case of t... One-class support vector machine (OCSVM) and support vector data description (SVDD) are two main domain-based one-class (kernel) classifiers. To reveal their relationship with density estimation in the case of the Gaussian kernel, OCSVM and SVDD are firstly unified into the framework of kernel density estimation, and the essential relationship between them is explicitly revealed. Then the result proves that the density estimation induced by OCSVM or SVDD is in agreement with the true density. Meanwhile, it can also reduce the integrated squared error (ISE). Finally, experiments on several simulated datasets verify the revealed relationships. 展开更多
关键词 one-class support vector machine(OCSVM) support vector data description(SVDD) kernel density estimation
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Interactive early warning technique based on SVDD 被引量:6
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作者 Lin Jian~(1,2) Peng Minjing~(1,2) 1.School of Business Administration,South China Univ.of Technology,Guangzhou 510641,F.R.China 2.Systems Science & Technology Inst,Wuyi Univ.,Jiangmen 529020,P.R.China 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2007年第3期527-533,共7页
After reviewing current researches on early warning, it is found that “bad”data of some systems is not easy to obtain, which makes methods proposed by these researches unsuitable for monitored systems. An interactiv... After reviewing current researches on early warning, it is found that “bad”data of some systems is not easy to obtain, which makes methods proposed by these researches unsuitable for monitored systems. An interactive early warning technique based on SVDD (support vector data description) is proposed to adopt “good” data as samples to overcome the difficulty in obtaining the “bad” data. The process consists of two parts: (1) A hypersphere is fitted on “good” data using SVDD. If the data object are outside the hypersphere, it would be taken as “suspicious”; (2) A group of experts would decide whether the suspicious data is “bad” or “good”, early warning messages would be issued according to the decisions. And the detailed process of implementation is proposed. At last, an experiment based on data of a macroeconomic system is conducted to verify the proposed technique. 展开更多
关键词 interactive data mining early warning support vector data description group decision making.
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Rolling bearing performance degradation evaluation by VMD and embedding selection-based NPE 被引量:4
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作者 Tong Qingjun Hu Jianzhong +1 位作者 Jia Minping Xu Feiyun 《Journal of Southeast University(English Edition)》 EI CAS 2019年第4期408-416,共9页
In order to improve the incipient fault sensitivity and stability of degradation index in the rolling bearing performance degradation evaluation process,an embedding selection-based neighborhood preserving embedding(E... In order to improve the incipient fault sensitivity and stability of degradation index in the rolling bearing performance degradation evaluation process,an embedding selection-based neighborhood preserving embedding(ESNPE)method is proposed.Firstly,the acquired vibration signals are decomposed by variational mode decomposition(VMD),and the singular value and relative energy of each intrinsic mode function(IMF)are extracted to form a high-dimensional feature set.Then,the NPE manifold learning method is used to extract the embedded features in the feature space.Considering the problem that useful embedding information is easily suppressed in NPE,an embedding selection strategy is built based on the Spearman correlation coefficient.The effectiveness of embeddings is measured by the coefficient absolute value,and useful embeddings are preserved in the early stage of bearing degradation by using the first-order difference method.Finally,the degradation index is established using the support vector data description(SVDD)model and bearing performance degradation evaluation is achieved.The proposed method was tested with the whole life experiment data of a rolling bearing,and the result was compared with the feature extraction methods of traditional principal component analysis(PCA)and NPE.The results show that the proposed method is superior in improving the incipient fault sensitivity and stability of the degradation index. 展开更多
关键词 performance degradation evaluation variational mode decomposition(VMD) neighborhood preserving embedding(NPE) support vector data description(SVDD)
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Life prediction of ZPW-2000A track circuit equipment based on SVDD and gray prediction 被引量:2
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作者 WANG Rui-feng JIA Nan 《Journal of Measurement Science and Instrumentation》 CAS CSCD 2018年第4期373-379,共7页
Evaluation of the health state and prediction of the remaining life of the track circuit are important for the safe operation of the equipment of railway signal system.Based on support vector data description(SVDD)and... Evaluation of the health state and prediction of the remaining life of the track circuit are important for the safe operation of the equipment of railway signal system.Based on support vector data description(SVDD)and gray prediction,this paper illustrates a method of life prediction for ZPW-2000A track circuit,which combines entropy weight method,SVDD,Mahalanobis distance and negative conversion function to set up a health state assessment model.The model transforms multiple factors affecting the health state into a health index named H to reflect the health state of the equipment.According to H,the life prediction model of ZPW-2000A track circuit equipment is established by means of gray prediction so as to predict the trend of health state of the equipment.The certification of the example shows that the method can visually reflect the health state and effectively predict the remaining life of the equipment.It also provides a theoretical basis to further improve the maintenance and management for ZPW-2000A track circuit. 展开更多
关键词 track circuit health state assessment life prediction support vector data description(SVDD) gray prediction
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A Method of Shield Attitude Working Condition Classification 被引量:1
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作者 郭正刚 王奉涛 +1 位作者 孙伟 张旭 《Journal of Donghua University(English Edition)》 EI CAS 2012年第3期259-262,共4页
Aiming at solving shield attitude rectification failure problem,a method of shield working condition classification based on support vector data description( SVDD) was introduced. Shield attitude mechanics model conta... Aiming at solving shield attitude rectification failure problem,a method of shield working condition classification based on support vector data description( SVDD) was introduced. Shield attitude mechanics model containing priori knowledge was helpful to feature selection. SVDD handled the one class classification problem and a decision function for attitude rectification was proposed. Experimental results indicate that the method is able to accomplish the shield attitude working condition classification. 展开更多
关键词 SHIELD attitude rectification support vector data description ( SVDD) working condition classification
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Application of deep learning in image recognition and diagnosis of gastric cancer 被引量:1
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作者 Yu Li Da Zhou +1 位作者 Tao-Tao Liu Xi-Zhong Shen 《Artificial Intelligence in Gastrointestinal Endoscopy》 2021年第2期12-24,共13页
In recent years,artificial intelligence has been extensively applied in the diagnosis of gastric cancer based on medical imaging.In particular,using deep learning as one of the mainstream approaches in image processin... In recent years,artificial intelligence has been extensively applied in the diagnosis of gastric cancer based on medical imaging.In particular,using deep learning as one of the mainstream approaches in image processing has made remarkable progress.In this paper,we also provide a comprehensive literature survey using four electronic databases,PubMed,EMBASE,Web of Science,and Cochrane.The literature search is performed until November 2020.This article provides a summary of the existing algorithm of image recognition,reviews the available datasets used in gastric cancer diagnosis and the current trends in applications of deep learning theory in image recognition of gastric cancer.covers the theory of deep learning on endoscopic image recognition.We further evaluate the advantages and disadvantages of the current algorithms and summarize the characteristics of the existing image datasets,then combined with the latest progress in deep learning theory,and propose suggestions on the applications of optimization algorithms.Based on the existing research and application,the label,quantity,size,resolutions,and other aspects of the image dataset are also discussed.The future developments of this field are analyzed from two perspectives including algorithm optimization and data support,aiming to improve the diagnosis accuracy and reduce the risk of misdiagnosis. 展开更多
关键词 ENDOSCOPE Artificial intelligence Algorithm optimization data support
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Design of multisensory integration gripper system for Internet-based teleoperation
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作者 臧希喆 Cai Hegao 《High Technology Letters》 EI CAS 2007年第1期23-26,共4页
A layered architecture of muhisensory integration gripper system is first developed, which includes data acquisition layer, data processing layer and network interface layer. Then we propose a novel support-vector-mac... A layered architecture of muhisensory integration gripper system is first developed, which includes data acquisition layer, data processing layer and network interface layer. Then we propose a novel support-vector-machine-based data fusion algorithm and also design the gripper system by combining data fusion with CAN bus and CORBA technology, which provides the gripper system with outstanding characteristics such as modularization and intelligence. A multisensory integration gripper test bed is finally built on which a circuit board replacement job based on Internet-based teleoperation is achieved. The experimental results verify the validity of this gripper system design. 展开更多
关键词 teleoperation multisenso integration gripper data fusion support vector machine
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Formation of Hybrid Ring Structure of Cyanurate/Isocyanurate in the Reaction be-tween 2,4,6-Tris(4-Phenyl-Phenoxy)-1, 3,5-Triazine and Phenyl Glycidyl Ether
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作者 Daisuke Ohno Kazuya Zenyoji +2 位作者 Youji Kurihara Kazuyoshi Ueda Hitoshi Habuka 《International Journal of Organic Chemistry》 CAS 2016年第2期117-125,共9页
Reaction products of 2,4,6-tris(4-phenyl-phenoxy)-1,3,5-triazine derived from 4-phenylphenol cyanate ester and phenyl glycidyl ether were analyzed. In addition to an isocyanurate compound and an oxazolidone compound w... Reaction products of 2,4,6-tris(4-phenyl-phenoxy)-1,3,5-triazine derived from 4-phenylphenol cyanate ester and phenyl glycidyl ether were analyzed. In addition to an isocyanurate compound and an oxazolidone compound which were well known as reaction products of cyanate esters and epoxy resins, compounds with hybrid ring structure of cyanurate/isocyanurate were determined. Gibbs free energies of the compound having hybrid ring structure of cyanurate/isocyanurate with two isocyanurate moiety were found to be lower than that of the compound with cyanurate ring structure through calculations. Calculation data supported the existence of hybrid ring structure of cy-anurate/isocyanurate. It was revealed that isomerization from cyanurate to isocyanurate occurs via hybrid ring structure of cyanurate/isocyanurate in the reaction of aryl cyanurate and epoxy. 展开更多
关键词 Reaction products of 2 4 6-tris(4-phenyl-phenoxy)-1 3 5-triazine derived from 4-phenylphenol cya-nate ester and phenyl glycidyl ether were analyzed. In addition to an isocyanurate compound and an oxazolidone compound which were well known as reaction products of cyanate esters and epoxy resins compounds with hybrid ring structure of cyanurate/isocyanurate were determined. Gibbs free energies of the compound having hybrid ring structure of cyanurate/isocyanurate with two isocyanurate moiety were found to be lower than that of the compound with cyanurate ring struc-ture through calculations. Calculation data supported the existence of hybrid ring structure of cy-anurate/isocyanurate. It was revealed that isomerization from cyanurate to isocyanurate occurs via hybrid ring structure of cyanurate/isocyanurate in the reaction of aryl cyanurate and epoxy.
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Slice Data Based Support Generation Algorithm for Fused Deposition Modeling 被引量:10
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作者 黄小毛 叶春生 +1 位作者 莫健华 刘海涛 《Tsinghua Science and Technology》 SCIE EI CAS 2009年第S1期223-228,共6页
This paper presents a robust algorithm to generate support for fused deposition modeling (FDM). Since many flaws appear in most stereo lithography (STL) models, this algorithm utilizes slice data as input. A top-down ... This paper presents a robust algorithm to generate support for fused deposition modeling (FDM). Since many flaws appear in most stereo lithography (STL) models, this algorithm utilizes slice data as input. A top-down approach was used to calculate the support slice layer by layer. The generation algorithm was described in detail including the slice grouping, oriental bounding box (OBB) calculation, offsetting, and Boolean operations. Several cases are given to validate the efficiency and robustness of the procedure. The algorithm provides necessary support not only for hanging surface but also for hanging vertexes and edges with O(n) time complexity, where n is the number of layers. The algorithm fully utilizes the parts’ self-support ability and reduces support volume to the maximum extent. This slice data based algorithm has the same efficiency as the STL based algorithm but is more stable, which significantly enhances the robustness of the support generation process. 展开更多
关键词 rapid prototype manufacturing (RPM) support slice data fused deposition modeling
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Geostatistical approaches to refinement of digital elevation data
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作者 Jingxiong ZHANG Tao ZHU +1 位作者 Yunwei TANG Wangle ZHANG 《Geo-Spatial Information Science》 SCIE EI 2014年第4期181-189,共9页
Data refinement refers to the processes by which a dataset’s resolution,in particular,the spatial one,is refined,and is thus synonymous to spatial downscaling.Spatial resolution indicates measurement scale and can be... Data refinement refers to the processes by which a dataset’s resolution,in particular,the spatial one,is refined,and is thus synonymous to spatial downscaling.Spatial resolution indicates measurement scale and can be seen as an index for regular data support.As a type of change of scale,data refinement is useful for many scenarios where spatial scales of existing data,desired analyses,or specific applications need to be made commensurate and refined.As spatial data are related to certain data support,they can be conceived of as support-specific realizations of random fields,suggesting that multivariate geostatistics should be explored for refining datasets from their coarser-resolution versions to the finerresolution ones.In this paper,geostatistical methods for downscaling are described,and were implemented using GTOPO30 data and sampled Shuttle Radar Topography Mission data at a site in northwest China,with the latter’s majority grid cells used as surrogate reference data.It was found that proper structural modeling is important for achieving increased accuracy in data refinement;here,structural modeling can be done through proper decomposition of elevation fields into trends and residuals and thereafter.It was confirmed that effects of semantic differences on data refinement can be reduced through properly estimating and incorporating biases in local means. 展开更多
关键词 REFINEMENT elevation data data support variogram deconvolution semantic differences
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A defect recognition model for cross-section profile of hot-rolled strip based on deep learning
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作者 Tian-lun Li Wen-quan Sun +5 位作者 An-rui He Jian Shao Chao Liu Ai-bin Zhang Yi Qiang Xiang-hong Ma 《Journal of Iron and Steel Research International》 SCIE EI CAS CSCD 2023年第12期2436-2447,共12页
The cross-section profile is a key signal for evaluating hot-rolled strip quality,and ignoring its defects can easily lead to a final failure.The characteristics of complex curve,significant irregular fluctuation and ... The cross-section profile is a key signal for evaluating hot-rolled strip quality,and ignoring its defects can easily lead to a final failure.The characteristics of complex curve,significant irregular fluctuation and imperfect sample data make it a challenge of recognizing cross-section defects,and current industrial judgment methods rely excessively on human decision making.A novel stacked denoising autoencoders(SDAE)model optimized with support vector machine(SVM)theory was proposed for the recognition of cross-section defects.Firstly,interpolation filtering and principal component analysis were employed to linearly reduce the data dimensionality of the profile curve.Secondly,the deep learning algorithm SDAE was used layer by layer for greedy unsupervised feature learning,and its final layer of back-propagation neural network was replaced by SVM for supervised learning of the final features,and the final model SDAE_SVM was obtained by further optimizing the entire network parameters via error back-propagation.Finally,the curve mirroring and combination stitching methods were used as data augmentation for the training set,which dealt with the problem of sample imbalance in the original data set,and the accuracy of cross-section defect prediction was further improved.The approach was applied in a 1780-mm hot rolling line of a steel mill to achieve the automatic diagnosis and classification of defects in cross-section profile of hot-rolled strip,which helps to reduce flatness quality concerns in downstream processes. 展开更多
关键词 Hot-rolled strip cross section:Curve recognition Deep learning-Stacked denoising autoencoder Support vector machine Imperfect data
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Power transmission risk assessment considering component condition 被引量:4
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作者 Lei GUO Qiwei QIU +2 位作者 Jian LIU Yu ZHOU Linglei JIANG 《Journal of Modern Power Systems and Clean Energy》 SCIE EI 2014年第1期50-58,共9页
This paper proposes a new method for power transmission risk assessment considering historical failure statistics of transmission systems and operation failure risks of system components.Component failure risks are in... This paper proposes a new method for power transmission risk assessment considering historical failure statistics of transmission systems and operation failure risks of system components.Component failure risks are integrated into the new method based on operational condition assessment of components using the support vector data description(SVDD)approach.The traditional outage probability model of transmission lines has been modified to build a new framework for power transmission system risk assessment.The proposed SVDD approach can provide a suitable mechanism to map component assessment grades to failure risks based on probabilistic behaviors of power system failures.Under the new method,both up-todate component failure risks and traditional system risk indices can be processed with the proposed outage model.As a result,component failure probabilities are not only related to historical statistic data but also operational data of components,and derived risk indices can reflect current operational conditions of components.In simulation studies,the SVDD approach is employed to evaluate component conditions and link such conditions to failure rates using up-to-date component operational data,including both on-line and off-line data of components.The IEEE 24-bus RTS-1979 system is used to demonstrate that component operational conditions can greatly affect the overall transmission system failure risks. 展开更多
关键词 Risk assessment Component failure risk Outage probability Condition assessment Support vector data description
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Indicator and Multivariate Geostatistics for Spatial Prediction 被引量:1
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作者 ZHANG Jingxiong YAO Na 《Geo-Spatial Information Science》 2008年第4期243-246,共4页
There are various occasions where simple, ordinary, and universal kriging techniques may find themselves incapable of performing spatial prediction directly or efficiently. One type of application concerns quantificat... There are various occasions where simple, ordinary, and universal kriging techniques may find themselves incapable of performing spatial prediction directly or efficiently. One type of application concerns quantification of cumulative distribution function (CDF) or probability of occurrences of categorical variables over space. The other is related to optimal use of co-variation inherent to multiple regionalized variables as well as spatial correlation in spatial prediction. This paper extends geostatistics from the realm of kriging with uni-variate and continuous regionalized variables to the territory of indicator and multivariate kriging, where it is of ultimate importance to perform non-parametric estimation of probability distributions and spatial prediction based on co-regionalization and multiple data sources, respectively. 展开更多
关键词 auto- and cross-covariance indicator kriging CO-KRIGING data support BLOCK
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