Due to ever-growing soccer data collection approaches and progressing artificial intelligence(AI) methods, soccer analysis, evaluation, and decision-making have received increasing interest from not only the professio...Due to ever-growing soccer data collection approaches and progressing artificial intelligence(AI) methods, soccer analysis, evaluation, and decision-making have received increasing interest from not only the professional sports analytics realm but also the academic AI research community. AI brings gamechanging approaches for soccer analytics where soccer has been a typical benchmark for AI research. The combination has been an emerging topic. In this paper, soccer match analytics are taken as a complete observation-orientation-decision-action(OODA) loop.In addition, as in AI frameworks such as that for reinforcement learning, interacting with a virtual environment enables an evolving model. Therefore, both soccer analytics in the real world and virtual domains are discussed. With the intersection of the OODA loop and the real-virtual domains, available soccer data, including event and tracking data, and diverse orientation and decisionmaking models for both real-world and virtual soccer matches are comprehensively reviewed. Finally, some promising directions in this interdisciplinary area are pointed out. It is claimed that paradigms for both professional sports analytics and AI research could be combined. Moreover, it is quite promising to bridge the gap between the real and virtual domains for soccer match analysis and decision-making.展开更多
In the process of fault detection and classification,the operation mode usually drifts over time,which brings great challenges to the algorithms.Because traditional machine learning based fault classification cannot d...In the process of fault detection and classification,the operation mode usually drifts over time,which brings great challenges to the algorithms.Because traditional machine learning based fault classification cannot dynamically update the trained model according to the probability distribution of the testing dataset,the accuracy of these traditional methods usually drops significantly in the case of covariate shift.In this paper,an importance-weighted transfer learning method is proposed for fault classification in the nonlinear multi-mode industrial process.It effectively alters the drift between the training and testing dataset.Firstly,the mutual information method is utilized to perform feature selection on the original data,and a number of characteristic parameters associated with fault classification are selected according to their mutual information.Then,the importance-weighted least-squares probabilistic classifier(IWLSPC)is utilized for binary fault detection and multi-fault classification in covariate shift.Finally,the Tennessee Eastman(TE)benchmark is carried out to confirm the effectiveness of the proposed method.The experimental result shows that the covariate shift adaptation based on importance-weight sampling is superior to the traditional machine learning fault classification algorithms.Moreover,IWLSPC can not only be used for binary fault classification,but also can be applied to the multi-classification target in the process of fault diagnosis.展开更多
Objective: To study the sonographic features of the primary site of papillary thyroid microcarcinoma(PTMC) for the prediction of cervical lymph node metastasis during preoperative diagnosis.Methods: A total of 710 PTM...Objective: To study the sonographic features of the primary site of papillary thyroid microcarcinoma(PTMC) for the prediction of cervical lymph node metastasis during preoperative diagnosis.Methods: A total of 710 PTMC patients between 2013 and 2016 with a diagnosis of cervical lymph node metastases were reviewed.We analyzed the sonographic features of the PTMC primary site to predict ipsilateral or central lymph node metastases in univariate and multivariate models.The ratio of abutment/perimeter of the PTMC primary site was utilized to evaluate cervical lymph node status.Results: Regarding clinical characteristics, multifocality and extrathyroidal extension were associated with cervical lymph node involvement.In the multivariate regression model, calcification and the abutment/perimeter ratio of lesions were evaluated as independent factors in level Ⅵ, ipsilateral or skip cervical lymph node metastases.The cut-off value of the ratio of abutment/perimeter of the PTMC primary site(25%) was significantly correlated with cervical lymph node metastases(P = 0.000).Conclusions: Independent sonographic features, including lesion size, lesion location, calcification, and the ratio of abutment/perimeter of the primary site, were associated with cervical lymph node metastases in PTMC patients.展开更多
This paper presents a study on the improvement of wind field hindcasts for two typical tropical cyclones, i.e., Fanapi and Meranti, which occurred in 2010. The performance of the three existing models for the hindcast...This paper presents a study on the improvement of wind field hindcasts for two typical tropical cyclones, i.e., Fanapi and Meranti, which occurred in 2010. The performance of the three existing models for the hindcasting of cyclone wind fields is first examined, and then two modification methods are proposed to improve the hindcasted results. The first one is the superposition method, which superposes the wind field calculated from the parametric cyclone model on that obtained from the cross-calibrated multi-platform （CCMP） reanalysis data. The radius used for the superposition is based on an analysis of the minimum difference between the two wind fields. The other one is the direct modification method, which directly modifies the CCMP reanalysis data according to the ratio of the measured maximum wind speed to the reanalyzed value as well as the distance from the cyclone center. Using these two methods, the problem of underestimation of strong winds in reanalysis data can be overcome. Both methods show considerable improvements in the hindcasting of tropical cyclone wind fields, compared with the cyclone wind model and the reanalysis data.展开更多
基金supported by the National Key Research,Development Program of China (2020AAA0103404)the Beijing Nova Program (20220484077)the National Natural Science Foundation of China (62073323)。
文摘Due to ever-growing soccer data collection approaches and progressing artificial intelligence(AI) methods, soccer analysis, evaluation, and decision-making have received increasing interest from not only the professional sports analytics realm but also the academic AI research community. AI brings gamechanging approaches for soccer analytics where soccer has been a typical benchmark for AI research. The combination has been an emerging topic. In this paper, soccer match analytics are taken as a complete observation-orientation-decision-action(OODA) loop.In addition, as in AI frameworks such as that for reinforcement learning, interacting with a virtual environment enables an evolving model. Therefore, both soccer analytics in the real world and virtual domains are discussed. With the intersection of the OODA loop and the real-virtual domains, available soccer data, including event and tracking data, and diverse orientation and decisionmaking models for both real-world and virtual soccer matches are comprehensively reviewed. Finally, some promising directions in this interdisciplinary area are pointed out. It is claimed that paradigms for both professional sports analytics and AI research could be combined. Moreover, it is quite promising to bridge the gap between the real and virtual domains for soccer match analysis and decision-making.
文摘In the process of fault detection and classification,the operation mode usually drifts over time,which brings great challenges to the algorithms.Because traditional machine learning based fault classification cannot dynamically update the trained model according to the probability distribution of the testing dataset,the accuracy of these traditional methods usually drops significantly in the case of covariate shift.In this paper,an importance-weighted transfer learning method is proposed for fault classification in the nonlinear multi-mode industrial process.It effectively alters the drift between the training and testing dataset.Firstly,the mutual information method is utilized to perform feature selection on the original data,and a number of characteristic parameters associated with fault classification are selected according to their mutual information.Then,the importance-weighted least-squares probabilistic classifier(IWLSPC)is utilized for binary fault detection and multi-fault classification in covariate shift.Finally,the Tennessee Eastman(TE)benchmark is carried out to confirm the effectiveness of the proposed method.The experimental result shows that the covariate shift adaptation based on importance-weight sampling is superior to the traditional machine learning fault classification algorithms.Moreover,IWLSPC can not only be used for binary fault classification,but also can be applied to the multi-classification target in the process of fault diagnosis.
基金supported by grants from the National Natural Science Foundation of China(Grant No.81771852)
文摘Objective: To study the sonographic features of the primary site of papillary thyroid microcarcinoma(PTMC) for the prediction of cervical lymph node metastasis during preoperative diagnosis.Methods: A total of 710 PTMC patients between 2013 and 2016 with a diagnosis of cervical lymph node metastases were reviewed.We analyzed the sonographic features of the PTMC primary site to predict ipsilateral or central lymph node metastases in univariate and multivariate models.The ratio of abutment/perimeter of the PTMC primary site was utilized to evaluate cervical lymph node status.Results: Regarding clinical characteristics, multifocality and extrathyroidal extension were associated with cervical lymph node involvement.In the multivariate regression model, calcification and the abutment/perimeter ratio of lesions were evaluated as independent factors in level Ⅵ, ipsilateral or skip cervical lymph node metastases.The cut-off value of the ratio of abutment/perimeter of the PTMC primary site(25%) was significantly correlated with cervical lymph node metastases(P = 0.000).Conclusions: Independent sonographic features, including lesion size, lesion location, calcification, and the ratio of abutment/perimeter of the primary site, were associated with cervical lymph node metastases in PTMC patients.
基金supported by the National Natural Science Foundation of China(Grants No.51309092 and 51379072)the Special Fund for Public Welfare Industry of the Ministry of Water Resources of China(Grant No.201201045)+1 种基金the Natural Science Fund for Colleges and Universities in Jiangsu Province(Grant No.BK20130833)the Fundamental Research Funds for the Central Universities(Grants No.2015B16014 and 2013B03414)
文摘This paper presents a study on the improvement of wind field hindcasts for two typical tropical cyclones, i.e., Fanapi and Meranti, which occurred in 2010. The performance of the three existing models for the hindcasting of cyclone wind fields is first examined, and then two modification methods are proposed to improve the hindcasted results. The first one is the superposition method, which superposes the wind field calculated from the parametric cyclone model on that obtained from the cross-calibrated multi-platform （CCMP） reanalysis data. The radius used for the superposition is based on an analysis of the minimum difference between the two wind fields. The other one is the direct modification method, which directly modifies the CCMP reanalysis data according to the ratio of the measured maximum wind speed to the reanalyzed value as well as the distance from the cyclone center. Using these two methods, the problem of underestimation of strong winds in reanalysis data can be overcome. Both methods show considerable improvements in the hindcasting of tropical cyclone wind fields, compared with the cyclone wind model and the reanalysis data.
