The artificial intelligence technique is used to generate a freeway incident response plan. The incident response framework based on rule-based reasoning, case-based reasoning and Bayesian networks reasoning is presen...The artificial intelligence technique is used to generate a freeway incident response plan. The incident response framework based on rule-based reasoning, case-based reasoning and Bayesian networks reasoning is presented. First, a freeway incident management system （RK-IMS） based on rule-based reasoning is developed and applied for incident management in the northern section of the Nanjing-Lianyunguang Freeway. Then, field data from the two-year long operations of the RK-IMS are analyzed. Representations of incident case structures and Bayesian networks（BNs） structures related to incident responses are deduced. Finally, the k-nearest neighbor （k-NN） algorithm is applied to calculate the similarities of the cases. The preplan generation and the control strategy by integrating the k-NN algorithm are also developed. The model is validated by using incident data of the year 2006 from the RK-IMS. The comparison results indicate that the proposed algorithm is accurate and reliable.展开更多
The application of support vector machines to forecasting problems is becoming popular, lately. Several comparisons between neural networks trained with error backpropagation and support vector machines have shown adv...The application of support vector machines to forecasting problems is becoming popular, lately. Several comparisons between neural networks trained with error backpropagation and support vector machines have shown advantage for the latter in different domains of application. However, some difficulties still deteriorate the performance of the support vector machines. The main one is related to the setting of the hyperparameters involved in their training. Techniques based on meta-heuristics have been employed to determine appropriate values for those hyperparameters. However, because of the high noneonvexity of this estimation problem, which makes the search for a good solution very hard, an approach based on Bayesian inference, called relevance vector machine, has been proposed more recently. The present paper aims at investigating the suitability of this new approach to the short-term load forecasting problem.展开更多
Although genome-wide association studies （GWAS） have successfully identified thousands of genomic loci associated with hun- dreds of complex traits in the past decade, the debate about such problems as missing herit...Although genome-wide association studies （GWAS） have successfully identified thousands of genomic loci associated with hun- dreds of complex traits in the past decade, the debate about such problems as missing heritabiUty and weak interpretability has been appealing for effective computational methods to facilitate the advanced analysis of the vast volume of existing and antici- pated genetic data. Towards this goal, gene-tevel integrative GWAS analysis with the assumption that genes associated with a phenotype tend to be enriched in biological gene sets or gene networks has recently attracted much attention, due to such advan- tages as straightforward interpretation, tess multiple testing burdens, and robustness across studies. However, existing methods in this category usually exploit non-tissue-specific gene networks and thus lack the ability to utilize informative tissue-specific characteristics. To overcome this limitation, we proposed a Bayesian approach called SIGNET （Simultaneously Inference of GeNEs and Tissues） to integrate GWAS data and multiple tissue-specific gene networks for the simultaneous inference of phenotype- associated genes and relevant tissues. Through extensive simulation studies, we showed the effectiveness of our method in find- ing both associated genes and relevant tissues for a phenotype. In applications to real GWAS data of 14 complex phenotypes, we demonstrated the power of our method in both deciphering genetic basis and discovering biological insights of a phenotype. With this understanding, we expect to see SIGNET as a valuable tool for integrative GWAS analysis, thereby boosting the preven- tion, diagnosis, and treatment of human inherited diseases and eventually facilitating precision medicine.展开更多
基金The Natural Science Foundation of Jiangsu Province(NoBK2008308)
文摘The artificial intelligence technique is used to generate a freeway incident response plan. The incident response framework based on rule-based reasoning, case-based reasoning and Bayesian networks reasoning is presented. First, a freeway incident management system （RK-IMS） based on rule-based reasoning is developed and applied for incident management in the northern section of the Nanjing-Lianyunguang Freeway. Then, field data from the two-year long operations of the RK-IMS are analyzed. Representations of incident case structures and Bayesian networks（BNs） structures related to incident responses are deduced. Finally, the k-nearest neighbor （k-NN） algorithm is applied to calculate the similarities of the cases. The preplan generation and the control strategy by integrating the k-NN algorithm are also developed. The model is validated by using incident data of the year 2006 from the RK-IMS. The comparison results indicate that the proposed algorithm is accurate and reliable.
文摘The application of support vector machines to forecasting problems is becoming popular, lately. Several comparisons between neural networks trained with error backpropagation and support vector machines have shown advantage for the latter in different domains of application. However, some difficulties still deteriorate the performance of the support vector machines. The main one is related to the setting of the hyperparameters involved in their training. Techniques based on meta-heuristics have been employed to determine appropriate values for those hyperparameters. However, because of the high noneonvexity of this estimation problem, which makes the search for a good solution very hard, an approach based on Bayesian inference, called relevance vector machine, has been proposed more recently. The present paper aims at investigating the suitability of this new approach to the short-term load forecasting problem.
文摘Although genome-wide association studies （GWAS） have successfully identified thousands of genomic loci associated with hun- dreds of complex traits in the past decade, the debate about such problems as missing heritabiUty and weak interpretability has been appealing for effective computational methods to facilitate the advanced analysis of the vast volume of existing and antici- pated genetic data. Towards this goal, gene-tevel integrative GWAS analysis with the assumption that genes associated with a phenotype tend to be enriched in biological gene sets or gene networks has recently attracted much attention, due to such advan- tages as straightforward interpretation, tess multiple testing burdens, and robustness across studies. However, existing methods in this category usually exploit non-tissue-specific gene networks and thus lack the ability to utilize informative tissue-specific characteristics. To overcome this limitation, we proposed a Bayesian approach called SIGNET （Simultaneously Inference of GeNEs and Tissues） to integrate GWAS data and multiple tissue-specific gene networks for the simultaneous inference of phenotype- associated genes and relevant tissues. Through extensive simulation studies, we showed the effectiveness of our method in find- ing both associated genes and relevant tissues for a phenotype. In applications to real GWAS data of 14 complex phenotypes, we demonstrated the power of our method in both deciphering genetic basis and discovering biological insights of a phenotype. With this understanding, we expect to see SIGNET as a valuable tool for integrative GWAS analysis, thereby boosting the preven- tion, diagnosis, and treatment of human inherited diseases and eventually facilitating precision medicine.
