How to obtain proper prior distribution is one of the most critical problems in Bayesian analysis. In many practical cases, the prior information often comes from different sources, and the prior distribution form cou...How to obtain proper prior distribution is one of the most critical problems in Bayesian analysis. In many practical cases, the prior information often comes from different sources, and the prior distribution form could be easily known in some certain way while the parameters are hard to determine. In this paper, based on the evidence theory, a new method is presented to fuse the information of multiple sources and determine the parameters of the prior distribution when the form is known. By taking the prior distributions which result from the information of multiple sources and converting them into corresponding mass functions which can be combined by Dempster-Shafer (D-S) method, we get the combined mass function and the representative points of the prior distribution. These points are used to fit with the given distribution form to determine the parameters of the prior distribution. And then the fused prior distribution is obtained and Bayesian analysis can be performed. How to convert the prior distributions into mass functions properly and get the representative points of the fused prior distribution is the central question we address in this paper. The simulation example shows that the proposed method is effective.展开更多
A novel broad concept of numbers was given out based upon the analyzing of numbers′ carrying rule. In international mathematics & computer science, all researches on numbers are only confined to same varying rule...A novel broad concept of numbers was given out based upon the analyzing of numbers′ carrying rule. In international mathematics & computer science, all researches on numbers are only confined to same varying rule of the FCN (fixed carrying numbers). The concept of VCN (variable carrying numbers) was presented, and some applied examples of practice were given out. So the engineering application of VCN for n-figures is wider than that of FCN in human society.展开更多
The rapid developments in the fields of telecommunication, sensor data, financial applications, analyzing of data streams, and so on, increase the rate of data arrival, among which the data mining technique is conside...The rapid developments in the fields of telecommunication, sensor data, financial applications, analyzing of data streams, and so on, increase the rate of data arrival, among which the data mining technique is considered a vital process. The data analysis process consists of different tasks, among which the data stream classification approaches face more challenges than the other commonly used techniques. Even though the classification is a continuous process, it requires a design that can adapt the classification model so as to adjust the concept change or the boundary change between the classes. Hence, we design a novel fuzzy classifier known as THRFuzzy to classify new incoming data streams. Rough set theory along with tangential holoentropy function helps in the designing the dynamic classification model. The classification approach uses kernel fuzzy c-means(FCM) clustering for the generation of the rules and tangential holoentropy function to update the membership function. The performance of the proposed THRFuzzy method is verified using three datasets, namely skin segmentation, localization, and breast cancer datasets, and the evaluated metrics, accuracy and time, comparing its performance with HRFuzzy and adaptive k-NN classifiers. The experimental results conclude that THRFuzzy classifier shows better classification results providing a maximum accuracy consuming a minimal time than the existing classifiers.展开更多
The recent advances in wireless communication technology enable high-speed vehicles to download data from roadside units(RSUs). However, the data download volume of individual vehicle is rather restricted due to high ...The recent advances in wireless communication technology enable high-speed vehicles to download data from roadside units(RSUs). However, the data download volume of individual vehicle is rather restricted due to high mobility and limited transmission range of vehicles, bringing users poor performance. To address this issue, we exploit the combination of both clustering and carry-and-forward schemes in this paper. Our scheme coordinates the cooperation of multiple infrastructures, cluster formation in the same direction and data forwarding of reverse vehicles to facilitate the target vehicle to download large-size content in dark areas. The process of data dissemination and achievable data download volume are then derived and analyzed theoretically. Finally, we conduct extensive simulations to verify the performance of the proposed scheme. Results show significant benefits of the proposed scheme in terms of increasing data download volume and throughput.展开更多
Monte Carlo Analysis has been an accepted method for circuit tolerance analysis, but the heavy computational complexity has always prevented its applications. Based on random set theory, this paper presents a simple a...Monte Carlo Analysis has been an accepted method for circuit tolerance analysis, but the heavy computational complexity has always prevented its applications. Based on random set theory, this paper presents a simple and flexible tolerance analysis method to estimate circuit yield. It is the alternative to Monte Carlo analysis, but reduces the number of calculations dramatically.展开更多
This research was aimed to evaluate the training program enhancing caring behaviors of new nurses by Kirkpatrick＇s four level for evaluation model： reaction, learning, behavior, and result of the program. The partic...This research was aimed to evaluate the training program enhancing caring behaviors of new nurses by Kirkpatrick＇s four level for evaluation model： reaction, learning, behavior, and result of the program. The participants were new nurses, preceptors of new nurses, administrators, patients and members of the patients＇ family cared by new nurses. The research instruments were： （1） five questionnaires toward program suitability, knowledge, attitude, caring expression, and result to organization. The mean, standard deviation and dependent sample t-test were used for data analysis; （2） guidelines for focus group discussion and semi-structural questionnaire analyzed by content analysis. The study revealed that： （1） the mean of suitability was at the very high level （X = 4.49, SD. = .30）; （2） the knowledge and attitude after training were significantly higher than before training at .000 level, [t =-21.65, p = .000 and t = -19.30, p = .000）; {3} caring behavior after training was significantly higher than before training at the .000 level; and {4} the result of the program was at the high level { X = 4.25, SD. = .17}, related to the result of semi-structured interview and focus group discussion. These evaluation research finding suggested that administrators can use for improving the preparation of any project and apply to evaluate other training programs, developing human resource system.展开更多
The article deals with the methodology of pseudorandom data analysis. As a mathematical tool for carrying out the research the extreme value theory was used that creates one of the directions in mathematical statistic...The article deals with the methodology of pseudorandom data analysis. As a mathematical tool for carrying out the research the extreme value theory was used that creates one of the directions in mathematical statistics, and is related to investigating the extreme deviations from the median values in probability distributions. Also, the methods for estimating unknown parameters and algorithm of random-number generation are discussed. The models of treatment the extreme values are constructed which are based on machine generated sample and approach is proposed for their future application for constructing forecasting models.展开更多
In order to overcome the limitations of traditional methods in uncertainty analysis, a modified Bayesian network(BN), which is called evidence network(EN), was proposed with evidence theory to handle epistemic uncerta...In order to overcome the limitations of traditional methods in uncertainty analysis, a modified Bayesian network(BN), which is called evidence network(EN), was proposed with evidence theory to handle epistemic uncertainty in probabilistic risk assessment(PRA). Fault trees(FTs) and event trees(ETs) were transformed into an EN which is used as a uniform framework to represent accident scenarios. Epistemic uncertainties of basic events in PRA were presented in evidence theory form and propagated through the network. A case study of a highway tunnel risk analysis was discussed to demonstrate the proposed approach. Frequencies of end states are obtained and expressed by belief and plausibility measures. The proposed approach addresses the uncertainties in experts' knowledge and can be easily applied to uncertainty analysis of FTs/ETs that have dependent events.展开更多
文摘How to obtain proper prior distribution is one of the most critical problems in Bayesian analysis. In many practical cases, the prior information often comes from different sources, and the prior distribution form could be easily known in some certain way while the parameters are hard to determine. In this paper, based on the evidence theory, a new method is presented to fuse the information of multiple sources and determine the parameters of the prior distribution when the form is known. By taking the prior distributions which result from the information of multiple sources and converting them into corresponding mass functions which can be combined by Dempster-Shafer (D-S) method, we get the combined mass function and the representative points of the prior distribution. These points are used to fit with the given distribution form to determine the parameters of the prior distribution. And then the fused prior distribution is obtained and Bayesian analysis can be performed. How to convert the prior distributions into mass functions properly and get the representative points of the fused prior distribution is the central question we address in this paper. The simulation example shows that the proposed method is effective.
基金This research is supported by the Nature Science Foundation of Fujian Province of China(item No.2006J0414foundation No.A0640015)
文摘A novel broad concept of numbers was given out based upon the analyzing of numbers′ carrying rule. In international mathematics & computer science, all researches on numbers are only confined to same varying rule of the FCN (fixed carrying numbers). The concept of VCN (variable carrying numbers) was presented, and some applied examples of practice were given out. So the engineering application of VCN for n-figures is wider than that of FCN in human society.
基金supported by proposal No.OSD/BCUD/392/197 Board of Colleges and University Development,Savitribai Phule Pune University,Pune
文摘The rapid developments in the fields of telecommunication, sensor data, financial applications, analyzing of data streams, and so on, increase the rate of data arrival, among which the data mining technique is considered a vital process. The data analysis process consists of different tasks, among which the data stream classification approaches face more challenges than the other commonly used techniques. Even though the classification is a continuous process, it requires a design that can adapt the classification model so as to adjust the concept change or the boundary change between the classes. Hence, we design a novel fuzzy classifier known as THRFuzzy to classify new incoming data streams. Rough set theory along with tangential holoentropy function helps in the designing the dynamic classification model. The classification approach uses kernel fuzzy c-means(FCM) clustering for the generation of the rules and tangential holoentropy function to update the membership function. The performance of the proposed THRFuzzy method is verified using three datasets, namely skin segmentation, localization, and breast cancer datasets, and the evaluated metrics, accuracy and time, comparing its performance with HRFuzzy and adaptive k-NN classifiers. The experimental results conclude that THRFuzzy classifier shows better classification results providing a maximum accuracy consuming a minimal time than the existing classifiers.
基金supported by the National Natural Science Foundation of China under Grant No.61571350Key Research and Development Program of Shaanxi(Contract No.2017KW-004,2017ZDXM-GY-022)the 111 Project(B08038)
文摘The recent advances in wireless communication technology enable high-speed vehicles to download data from roadside units(RSUs). However, the data download volume of individual vehicle is rather restricted due to high mobility and limited transmission range of vehicles, bringing users poor performance. To address this issue, we exploit the combination of both clustering and carry-and-forward schemes in this paper. Our scheme coordinates the cooperation of multiple infrastructures, cluster formation in the same direction and data forwarding of reverse vehicles to facilitate the target vehicle to download large-size content in dark areas. The process of data dissemination and achievable data download volume are then derived and analyzed theoretically. Finally, we conduct extensive simulations to verify the performance of the proposed scheme. Results show significant benefits of the proposed scheme in terms of increasing data download volume and throughput.
基金the National Natural Science Foundation of China (No.60772006, 60434020)the Zhejiang Natural Science Foundation (No.R106745, Y1080422).
文摘Monte Carlo Analysis has been an accepted method for circuit tolerance analysis, but the heavy computational complexity has always prevented its applications. Based on random set theory, this paper presents a simple and flexible tolerance analysis method to estimate circuit yield. It is the alternative to Monte Carlo analysis, but reduces the number of calculations dramatically.
文摘This research was aimed to evaluate the training program enhancing caring behaviors of new nurses by Kirkpatrick＇s four level for evaluation model： reaction, learning, behavior, and result of the program. The participants were new nurses, preceptors of new nurses, administrators, patients and members of the patients＇ family cared by new nurses. The research instruments were： （1） five questionnaires toward program suitability, knowledge, attitude, caring expression, and result to organization. The mean, standard deviation and dependent sample t-test were used for data analysis; （2） guidelines for focus group discussion and semi-structural questionnaire analyzed by content analysis. The study revealed that： （1） the mean of suitability was at the very high level （X = 4.49, SD. = .30）; （2） the knowledge and attitude after training were significantly higher than before training at .000 level, [t =-21.65, p = .000 and t = -19.30, p = .000）; {3} caring behavior after training was significantly higher than before training at the .000 level; and {4} the result of the program was at the high level { X = 4.25, SD. = .17}, related to the result of semi-structured interview and focus group discussion. These evaluation research finding suggested that administrators can use for improving the preparation of any project and apply to evaluate other training programs, developing human resource system.
文摘The article deals with the methodology of pseudorandom data analysis. As a mathematical tool for carrying out the research the extreme value theory was used that creates one of the directions in mathematical statistics, and is related to investigating the extreme deviations from the median values in probability distributions. Also, the methods for estimating unknown parameters and algorithm of random-number generation are discussed. The models of treatment the extreme values are constructed which are based on machine generated sample and approach is proposed for their future application for constructing forecasting models.
基金Project(71201170)supported by the National Natural Science Foundation of China
文摘In order to overcome the limitations of traditional methods in uncertainty analysis, a modified Bayesian network(BN), which is called evidence network(EN), was proposed with evidence theory to handle epistemic uncertainty in probabilistic risk assessment(PRA). Fault trees(FTs) and event trees(ETs) were transformed into an EN which is used as a uniform framework to represent accident scenarios. Epistemic uncertainties of basic events in PRA were presented in evidence theory form and propagated through the network. A case study of a highway tunnel risk analysis was discussed to demonstrate the proposed approach. Frequencies of end states are obtained and expressed by belief and plausibility measures. The proposed approach addresses the uncertainties in experts' knowledge and can be easily applied to uncertainty analysis of FTs/ETs that have dependent events.
