With the expansion and extension of power grid scale, the impact of catastrophic weather on the safe operation of power grids is increasing, and strengthening the prediction and early warning of catastrophic weather p...
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With the expansion and extension of power grid scale, the impact of catastrophic weather on the safe operation of power grids is increasing, and strengthening the prediction and early warning of catastrophic weather processes, which have a serious impact on power grid production, can effectively improve the efficiency and ability of power grids to cope with catastrophic weather. In this paper, using multi-datasourcedata processing technology, the monitoring data of the transmission line micro-meteorological monitoring device is applied to the refinement numerical prediction calculation to improve the accuracy of the numerical weather forecast results in Henan province, and based on this research to establish the transmission line dance Prediction model, Predict the occurrence probability of transmission line dance under different meteorological environment conditions. Through the fine regional numerical prediction system and dynamic reduction scale technology, the hourly meteorological factor forecast of 1 km resolution in Henan Province in the next 3 days is predicted, the statistical revision of the forecast field of meteorological elements around the transmission line is established, and based on this, the prediction model of transmission line dance can greatly improve the prediction accuracy of transmission line dance, To form a more precise and accurate dance prediction results, to facilitate the power sector to carry out different time scales of transmission line dance early warning work.
With the popularization of smart devices, the demand for mobile communication is increasing. The pursuit of stronger signals and faster transmission rates make effective value assessment and rational allocation of res...
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With the popularization of smart devices, the demand for mobile communication is increasing. The pursuit of stronger signals and faster transmission rates make effective value assessment and rational allocation of resources more important. In this paper, mobile communication value assessment problem is converted into base station classification problem with data, the high-value base station densely distributed area is the high-value mobile communication area. We have given a new value partitioning solution based on machine learning classification model, and on the real test set has achieved far better than the traditional method.
The objective of our study was to present a multiple method design to examine users’ eco-driving behaviour while driving a battery electric vehicle in a critical range situation. We adapted an existing research desig...
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The objective of our study was to present a multiple method design to examine users’ eco-driving behaviour while driving a battery electric vehicle in a critical range situation. We adapted an existing research design and used a combination of users’ self-reported data (questionnaires and interviews) and driving data (data logger). A sample of 53 participants drove a standardized route on which they experienced a critical range situation (marginal remaining range). We showed that this research design is also suitable for motivating eco-driving strategies and examining eco-driving behaviour.
There are numerous search engines available in today's world to search and retrieve the required information. However retrieval of meaningful and appropriate formation as per the user requirement is always a chall...
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There are numerous search engines available in today's world to search and retrieve the required information. However retrieval of meaningful and appropriate formation as per the user requirement is always a challenging task. The foremost intention of any search engine is to provide the information with in a quick span of time. Since the nature of data available in World-Wide-Web shows heterogeneity in common and the sources of data are also distinct with each other, issues pertaining to schema structure and data representational are also there. In such circumstances, to eliminate inconsistencies and for enabling seamless integration of multiple data sources while retrieving web data, an efficient web search mechanism that fulfils the customer requirement is always needed. To enable the integration of multiple data sources while performing efficient retrieval of web data, an intelligent web search framework has been proposed in this paper. (C) 2016 Elsevier Ltd. All rights reserved.
Mashup is emerging as a promising software development method for allowing software developers to compose existing Web APIs to create new or value-added composite Web services. However, the rapid growth in the number ...
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ISBN:
(纸本)9783319269795;9783319269788
Mashup is emerging as a promising software development method for allowing software developers to compose existing Web APIs to create new or value-added composite Web services. However, the rapid growth in the number of available Mashup services makes it difficult for software developers to select a suitable Mashup service to satisfy their requirements. Even though clustering based Mashup discovery technique shows a promise of improving the quality of Mashup service discovery, Mashup service clustering with high accuracy for discovery of Mashup services is still a challenge problem. In this paper, we propose a novel Mashup service clustering method for Mashup service discovery with high accuracy by exploiting LDA topic model built from multiple data sources. It enables to infer topic probability distribution of Mashup services, which serves as a basis of computation of similarity of Mashup services. K-means and Agnes algorithm are used to perform Mashup service clustering in terms of their similarities. Compared with other service clustering approaches, experimental results show that our approach achieves significant improvement in terms of precision, recall and F-measure rate, which will improve Mashup service discovery.
Background: Systematic review (SR) of randomized controlled trials (RCT) is the gold standard for informing treatment choice. Decision analyses (DA) also play an important role in informing health care decisions. It i...
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Background: Systematic review (SR) of randomized controlled trials (RCT) is the gold standard for informing treatment choice. Decision analyses (DA) also play an important role in informing health care decisions. It is unknown how often the results of DA and matching SR of RCTs are in concordance. We assessed whether the results of DA are in concordance with SR of RCTs matched on patient population, intervention, control, and outcomes. Methods: We searched PubMed up to 2008 for DAs comparing at least two interventions followed by matching SRs of RCTs. data were extracted on patient population, intervention, control, and outcomes from DAs and matching SRs of RCTs. data extraction from DAs was done by one reviewer and from SR of RCTs by two independent reviewers. Results: We identified 28 DAs representing 37 comparisons for which we found matching SR of RCTs. Results of the DAs and SRs of RCTs were in concordance in 73% (27/37) of cases. The sensitivity analyses conducted in either DA or SR of RCTs did not impact the concordance. Use of single (4/37) versus multiple data source (33/37) in design of DA model was statistically significantly associated with concordance between DA and SR of RCTs. Conclusions: Our findings illustrate the high concordance of current DA models compared with SR of RCTs. It is shown previously that there is 50% concordance between DA and matching single RCT. Our study showing the concordance of 73% between DA and matching SR of RCTs underlines the importance of totality of evidence (i.e. SR of RCTs) in the design of DA models and in general medical decision-making.
The operating departments of power system use different information systems for work convenience. The differences in appliances, datasources, structures and maintenance personnel lead to the independence between data...
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ISBN:
(纸本)9783037856345
The operating departments of power system use different information systems for work convenience. The differences in appliances, datasources, structures and maintenance personnel lead to the independence between datasources as well as heavy workload of data exchange and maintenance. The simulation of different problems needs various tools while the establishment of grid model requires hard work. This paper, on the basis of function requirement analysis, made project design of integrative simulation platform, and developed a data graphical integrated modeling platform featuring the functions of steady state, electromechanical transient and electromagnetic transient with the ability of multi-sourcedata fusion by using the programming language C#. The platform can integrate data of PSDB, PSD-BPA and PMIS, automatically form PSASP and PSD-BPA power flow calculation data, electromechanical transient simulation data and generate ".psc" files for PSCAD\EMTDC electromagnetic transient simulation analysis according to the selected sectors and components on the graphical interfaces.
Background: Protein function determination is a key challenge in the post-genomic era. Experimental determination of protein functions is accurate, but time-consuming and resource-intensive. A cost-effective alternati...
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Background: Protein function determination is a key challenge in the post-genomic era. Experimental determination of protein functions is accurate, but time-consuming and resource-intensive. A cost-effective alternative is to use the known information about sequence, structure, and functional properties of genes and proteins to predict functions using statistical methods. In this paper, we describe the Multi-source k-Nearest Neighbor (MS-kNN) algorithm for function prediction, which finds k-nearest neighbors of a query protein based on different types of similarity measures and predicts its function by weighted averaging of its neighbors' functions. Specifically, we used 3 datasources to calculate the similarity scores: sequence similarity, protein-protein interactions, and gene expressions. Results: We report the results in the context of 2011 Critical Assessment of Function Annotation (CAFA). Prior to CAFA submission deadline, we evaluated our algorithm on 1,302 human test proteins that were represented in all 3 datasources. Using only the sequence similarity information, MS-kNN had term-based Area Under the Curve (AUC) accuracy of Gene Ontology (GO) molecular function predictions of 0.728 when 7,412 human training proteins were used, and 0.819 when 35,622 training proteins from multiple eukaryotic and prokaryotic organisms were used. By aggregating predictions from all three sources, the AUC was further improved to 0.848. Similar result was observed on prediction of GO biological processes. Testing on 595 proteins that were annotated after the CAFA submission deadline showed that overall MS-kNN accuracy was higher than that of baseline algorithms Gotcha and BLAST, which were based solely on sequence similarity information. Since only 10 of the 595 proteins were represented by all 3 datasources, and 66 by two datasources, the difference between 3-source and one-\source MS-kNN was rather small. Conclusions: Based on our results, we have several useful insights:
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