This paper integrates the maximum information principle with the Cell Transmission Model （CTM） to formulate the velocity distribution evolution of vehicle traffic flow. The proposed discrete traffic kinetic model us...This paper integrates the maximum information principle with the Cell Transmission Model （CTM） to formulate the velocity distribution evolution of vehicle traffic flow. The proposed discrete traffic kinetic model uses the cell transmission model to calculate the macroscopic variables of the vehicle transmission, and the maximum information principle to examine the velocity distribution in each cell. The velocity distribution based on maximum information principle is solved by the Lagrange multiplier method. The advantage of the proposed model is that it can simultaneously calculate the hydrodynamic variables and velocity distribution at the cell level. An example shows how the proposed model works. The proposed model is a hybrid traffic simulation model, which can be used to understand the self-organization phenomena in traffic flows and predict the traffic evolution.展开更多
Right randomly censored data with incomplete infor-mation are frequently met in practice.Although much study about right randomly censored data has been seen in the proportional hazards model,relatively little is know...Right randomly censored data with incomplete infor-mation are frequently met in practice.Although much study about right randomly censored data has been seen in the proportional hazards model,relatively little is known about the inference of regression parameters for right randomly censored data with in-complete information in such model.In particular,theoretical properties of the maximum likelihood estimator of the regression parameters have not been proven yet in that model.In this paper,we show the consistency and asymptotic normality of the maxi-mum likelihood estimator of unknown regression parameters.展开更多
Information based models for radiation emitted by a Black Body which passes through a scattering medium are analyzed. In the limit, when there is no scattering this model reverts to the Black Body Radiation Law. The a...Information based models for radiation emitted by a Black Body which passes through a scattering medium are analyzed. In the limit, when there is no scattering this model reverts to the Black Body Radiation Law. The advantage of this mathematical model is that it includes the effect of the scattering of the radiation between source and detector. In the case when the exact form of the scattering mechanism is not known a model using a single scattering parameter is derived. A simple version of this model is derived which is useful for analyzing large data.展开更多
篡改电表数据是一种典型的窃电行为。针对此类窃电行为,现有的检测方法需要标记好的数据集或额外的电力系统状态信息,这在现实中很难获得或即使获得也与实际值存在较大误差。因此,利用较低维度的数据来实现对此类窃电行为进行检测的方...篡改电表数据是一种典型的窃电行为。针对此类窃电行为,现有的检测方法需要标记好的数据集或额外的电力系统状态信息,这在现实中很难获得或即使获得也与实际值存在较大误差。因此,利用较低维度的数据来实现对此类窃电行为进行检测的方法亟待深入研究。创新性地结合最大互信息系数(maximum information coefficient,MIC)技术和基于密度峰值的快速聚类算法提出了一种新的融合检测方法。该方法利用最大互信息系数度量管理线损与用户特定行为之间的相关性,采用CFSFDP定位异常用电用户,适用性强,能够检测多种不同类型的窃电行为。最后利用爱尔兰智能电表数据集进行了算法验证,结果证明了该方法的良好性能。展开更多
Molecular management is a promising technology to face challenges in the refining industry, such as more stringent requirements for product oil and heavier crude oil, and to maximize the value of every molecule in pet...Molecular management is a promising technology to face challenges in the refining industry, such as more stringent requirements for product oil and heavier crude oil, and to maximize the value of every molecule in petroleum fractions. To achieve molecular management in refining processes, a novel model that is based on structure oriented lumping(SOL) and group contribution(GC) methods was proposed in this study. SOL method was applied to describe a petroleum fraction with structural increments, and GC method aimed to estimate molecular properties. The latter was achieved by associating rules between SOL structural increments and GC structures. A three-step reconstruction algorithm was developed to build a representative set of molecules from partial analytical data. First, structural distribution parameters were optimized with several properties. Then, a molecular library was created by using the optimized parameters. In the final step, maximum information entropy(MIE) method was applied to obtain a molecular fraction. Two industrial samples were used to validate the method, and the simulation results of the feedstock properties agreed well with the experimental data.展开更多
通过对最大信息熵原理(Maximum Information Entropy Principle,MIEP)中约束条件的新理解,进一步把经典的MIEP运用于边界彻底开放的情形,建立起拓展的MIEP。由此不同于现有的诸多前沿理论,提出了一个关于世界的信息过程本体论的论点,并...通过对最大信息熵原理(Maximum Information Entropy Principle,MIEP)中约束条件的新理解,进一步把经典的MIEP运用于边界彻底开放的情形,建立起拓展的MIEP。由此不同于现有的诸多前沿理论,提出了一个关于世界的信息过程本体论的论点,并导出了相应的动力学机制,把世界万象描述为一个从无限的深层次的"隐存在"到有限的"具象存在"尔后又回归无限的隐存在"之动态涌现和不断进化的过程,形成一种基于信息过程本体论的世界观,从信息主义的角度对宇宙万象的本质作了统一阐述。展开更多
We propose a method to improve positioning accuracy while reducing energy consumption in an indoor Wireless Local Area Network(WLAN) environment.First,we intelligently and jointly select the subset of Access Points(AP...We propose a method to improve positioning accuracy while reducing energy consumption in an indoor Wireless Local Area Network(WLAN) environment.First,we intelligently and jointly select the subset of Access Points(APs) used in positioning via Maximum Mutual Information(MMI) criterion.Second,we propose Orthogonal Locality Preserving Projection(OLPP) to reduce the redundancy among selected APs.OLPP effectively extracts the intrinsic location features in situations where previous linear signal projection techniques failed to do,while maintaining computational efficiency.Third,we show that the combination of AP selection and OLPP simultaneously exploits their complementary advantages while avoiding the drawbacks.Experimental results indicate that,compared with the widely used weighted K-nearest neighbor and maximum likelihood estimation method,the proposed method leads to 21.8%(0.49 m) positioning accuracy improvement,while decreasing the computation cost by 65.4%.展开更多
The purpose of this study was to measure the effect of maximum price information and contextual factors on people's bidding behaviors in a controlled Becker-DeGroot-Marschak(BDM)experimental auctions.354 responden...The purpose of this study was to measure the effect of maximum price information and contextual factors on people's bidding behaviors in a controlled Becker-DeGroot-Marschak(BDM)experimental auctions.354 respondents from three Asian countries(China,Cambodia and the Philippines)participated in this study.In each country,both households with piped water connection and households without piped water connection were investigated.The sample in each country was then randomly assigned to two groups:one group was provided with a maximum price of a water filter and the other group was not provided with the maximum price information.The results show that the treatment group with maximum price information had a higher actual willingness-to-pay than the control group without maximum price information,but they were not significantly different.Our results also indicate that contextual and socioeconomic factors did play a role in participants'bid results for the water filter.展开更多
Classifier learning methods commonly assume that the training data and the testing data are drawn from the same underlying distribution. However, in many practical situations, this assumption is violated. One examp...Classifier learning methods commonly assume that the training data and the testing data are drawn from the same underlying distribution. However, in many practical situations, this assumption is violated. One example is the practical action videos with complex background and the universal human action databases of Kangliga Tekniska Hogskolan （KTH）. When training data are very scarce, supervised learning is difficult. However, it will cost lots of human and material resources to establish a labeled video set which includes a large amount of videos with complex backgrounds. In this paper, we propose an action recognition framework which uses transfer boosting learning algorithm. By using this algorithm, we can train an action recognition model fitting for most practical situations just relaying on the universal action video dataset and a tiny set of action videos with complex background. And the experiment results show that the performance is improved.展开更多
Short-term load forecasting of regional distribution network is the key to the economic operation of smart distribution systems,which not only requires high accuracy and fast calculation speed,but also has a diversity...Short-term load forecasting of regional distribution network is the key to the economic operation of smart distribution systems,which not only requires high accuracy and fast calculation speed,but also has a diversity of influential factors and strong randomness.This paper proposes a short-term load forecasting model for regional distribution network combining the maximum information coefficient,factor analysis,gray wolf optimization,and generalized regression neural network(MIC-FA-GWO-GRNN).To screen and decrease the dimension of the multiple-input features of the short-term load forecasting model,MIC is first used to quantify the non-linear correlation between the load and input features,and to eliminate the ineffective features,and then FA is used to reduce the dimension of the screened input features on the premise of preserving the main information of input features.After that the high-precision short-term丨oad forecasting based on GWO-GRNN model is realized.GRNN is used to regressively analyze the input features after screening and dimension reduction,and the parameter of GRNN is optimized by using the GWO,which has strong global searching ability and fast convergence.Finally a case study of a regional distribution network in Tianjin,China verifies the accuracy and applicability of the proposed forecasting model.展开更多
To overcome the disadvantage of classical recognition model that cannot perform well enough when there are some noises or lost frames in expression image sequences, a novel model called fuzzy buried Markov model （FBM...To overcome the disadvantage of classical recognition model that cannot perform well enough when there are some noises or lost frames in expression image sequences, a novel model called fuzzy buried Markov model （FBMM） is presented in this paper. FBMM relaxes conditional independence assumptions for classical hidden Markov model （HMM） by adding the specific cross-observation dependencies between observation elements. Compared with buried Markov model （BMM）, FBMM utilizes cloud distribution to replace probability distribution to describe state transition and observation symbol generation and adopts maximum mutual information （MMI） method to replace maximum likelihood （ML） method to estimate parameters. Theoretical justifications and experimental results verify higher recognition rate and stronger robustness of facial expression recognition for image sequences based on FBMM than those of HMM and BMM.展开更多
基金Project supported by the National Natural Science Foundation of China(Grant No.71071024)the Hunan Provincial Natural Science Foundation(Grant No.12JJ2025)
文摘This paper integrates the maximum information principle with the Cell Transmission Model （CTM） to formulate the velocity distribution evolution of vehicle traffic flow. The proposed discrete traffic kinetic model uses the cell transmission model to calculate the macroscopic variables of the vehicle transmission, and the maximum information principle to examine the velocity distribution in each cell. The velocity distribution based on maximum information principle is solved by the Lagrange multiplier method. The advantage of the proposed model is that it can simultaneously calculate the hydrodynamic variables and velocity distribution at the cell level. An example shows how the proposed model works. The proposed model is a hybrid traffic simulation model, which can be used to understand the self-organization phenomena in traffic flows and predict the traffic evolution.
基金Supported by the National Natural Science Foundation of China (10771163)
文摘Right randomly censored data with incomplete infor-mation are frequently met in practice.Although much study about right randomly censored data has been seen in the proportional hazards model,relatively little is known about the inference of regression parameters for right randomly censored data with in-complete information in such model.In particular,theoretical properties of the maximum likelihood estimator of the regression parameters have not been proven yet in that model.In this paper,we show the consistency and asymptotic normality of the maxi-mum likelihood estimator of unknown regression parameters.
文摘Information based models for radiation emitted by a Black Body which passes through a scattering medium are analyzed. In the limit, when there is no scattering this model reverts to the Black Body Radiation Law. The advantage of this mathematical model is that it includes the effect of the scattering of the radiation between source and detector. In the case when the exact form of the scattering mechanism is not known a model using a single scattering parameter is derived. A simple version of this model is derived which is useful for analyzing large data.
文摘篡改电表数据是一种典型的窃电行为。针对此类窃电行为,现有的检测方法需要标记好的数据集或额外的电力系统状态信息,这在现实中很难获得或即使获得也与实际值存在较大误差。因此,利用较低维度的数据来实现对此类窃电行为进行检测的方法亟待深入研究。创新性地结合最大互信息系数(maximum information coefficient,MIC)技术和基于密度峰值的快速聚类算法提出了一种新的融合检测方法。该方法利用最大互信息系数度量管理线损与用户特定行为之间的相关性,采用CFSFDP定位异常用电用户,适用性强,能够检测多种不同类型的窃电行为。最后利用爱尔兰智能电表数据集进行了算法验证,结果证明了该方法的良好性能。
基金Supported by the National Natural Science Foundation of China(U1462206)
文摘Molecular management is a promising technology to face challenges in the refining industry, such as more stringent requirements for product oil and heavier crude oil, and to maximize the value of every molecule in petroleum fractions. To achieve molecular management in refining processes, a novel model that is based on structure oriented lumping(SOL) and group contribution(GC) methods was proposed in this study. SOL method was applied to describe a petroleum fraction with structural increments, and GC method aimed to estimate molecular properties. The latter was achieved by associating rules between SOL structural increments and GC structures. A three-step reconstruction algorithm was developed to build a representative set of molecules from partial analytical data. First, structural distribution parameters were optimized with several properties. Then, a molecular library was created by using the optimized parameters. In the final step, maximum information entropy(MIE) method was applied to obtain a molecular fraction. Two industrial samples were used to validate the method, and the simulation results of the feedstock properties agreed well with the experimental data.
文摘通过对最大信息熵原理(Maximum Information Entropy Principle,MIEP)中约束条件的新理解,进一步把经典的MIEP运用于边界彻底开放的情形,建立起拓展的MIEP。由此不同于现有的诸多前沿理论,提出了一个关于世界的信息过程本体论的论点,并导出了相应的动力学机制,把世界万象描述为一个从无限的深层次的"隐存在"到有限的"具象存在"尔后又回归无限的隐存在"之动态涌现和不断进化的过程,形成一种基于信息过程本体论的世界观,从信息主义的角度对宇宙万象的本质作了统一阐述。
基金the High-Tech Research and Development Program of China，the National Seience Foundation for Young Scientists of China，the China Postdoctoral Science Foundation funded project
文摘We propose a method to improve positioning accuracy while reducing energy consumption in an indoor Wireless Local Area Network(WLAN) environment.First,we intelligently and jointly select the subset of Access Points(APs) used in positioning via Maximum Mutual Information(MMI) criterion.Second,we propose Orthogonal Locality Preserving Projection(OLPP) to reduce the redundancy among selected APs.OLPP effectively extracts the intrinsic location features in situations where previous linear signal projection techniques failed to do,while maintaining computational efficiency.Third,we show that the combination of AP selection and OLPP simultaneously exploits their complementary advantages while avoiding the drawbacks.Experimental results indicate that,compared with the widely used weighted K-nearest neighbor and maximum likelihood estimation method,the proposed method leads to 21.8%(0.49 m) positioning accuracy improvement,while decreasing the computation cost by 65.4%.
基金Beijing university young talent program(YETP0246)
文摘The purpose of this study was to measure the effect of maximum price information and contextual factors on people's bidding behaviors in a controlled Becker-DeGroot-Marschak(BDM)experimental auctions.354 respondents from three Asian countries(China,Cambodia and the Philippines)participated in this study.In each country,both households with piped water connection and households without piped water connection were investigated.The sample in each country was then randomly assigned to two groups:one group was provided with a maximum price of a water filter and the other group was not provided with the maximum price information.The results show that the treatment group with maximum price information had a higher actual willingness-to-pay than the control group without maximum price information,but they were not significantly different.Our results also indicate that contextual and socioeconomic factors did play a role in participants'bid results for the water filter.
基金National Natural Science Foundation of China ( No.60873179)Shenzhen Municipal Science and Technology Planning Program for Basic Research, China ( No. JC200903180630A)Research Fund for the Doctoral Program of Higher Education of China (No.20090121110032)
文摘Classifier learning methods commonly assume that the training data and the testing data are drawn from the same underlying distribution. However, in many practical situations, this assumption is violated. One example is the practical action videos with complex background and the universal human action databases of Kangliga Tekniska Hogskolan （KTH）. When training data are very scarce, supervised learning is difficult. However, it will cost lots of human and material resources to establish a labeled video set which includes a large amount of videos with complex backgrounds. In this paper, we propose an action recognition framework which uses transfer boosting learning algorithm. By using this algorithm, we can train an action recognition model fitting for most practical situations just relaying on the universal action video dataset and a tiny set of action videos with complex background. And the experiment results show that the performance is improved.
基金supported by the National Key Research and Development Program of China(2017YFB0903300)Research Program of State Grid Corporation of China(SGTYHT/16-JS-198)the National Natural Science Foundation of China(51807134).
文摘Short-term load forecasting of regional distribution network is the key to the economic operation of smart distribution systems,which not only requires high accuracy and fast calculation speed,but also has a diversity of influential factors and strong randomness.This paper proposes a short-term load forecasting model for regional distribution network combining the maximum information coefficient,factor analysis,gray wolf optimization,and generalized regression neural network(MIC-FA-GWO-GRNN).To screen and decrease the dimension of the multiple-input features of the short-term load forecasting model,MIC is first used to quantify the non-linear correlation between the load and input features,and to eliminate the ineffective features,and then FA is used to reduce the dimension of the screened input features on the premise of preserving the main information of input features.After that the high-precision short-term丨oad forecasting based on GWO-GRNN model is realized.GRNN is used to regressively analyze the input features after screening and dimension reduction,and the parameter of GRNN is optimized by using the GWO,which has strong global searching ability and fast convergence.Finally a case study of a regional distribution network in Tianjin,China verifies the accuracy and applicability of the proposed forecasting model.
基金supported by the National Natural Science Foundation of China under Grant No. 60673190
文摘To overcome the disadvantage of classical recognition model that cannot perform well enough when there are some noises or lost frames in expression image sequences, a novel model called fuzzy buried Markov model （FBMM） is presented in this paper. FBMM relaxes conditional independence assumptions for classical hidden Markov model （HMM） by adding the specific cross-observation dependencies between observation elements. Compared with buried Markov model （BMM）, FBMM utilizes cloud distribution to replace probability distribution to describe state transition and observation symbol generation and adopts maximum mutual information （MMI） method to replace maximum likelihood （ML） method to estimate parameters. Theoretical justifications and experimental results verify higher recognition rate and stronger robustness of facial expression recognition for image sequences based on FBMM than those of HMM and BMM.
