Transit smart card data mining for passenger origin information extraction

The automated fare collection(AFC) system,also known as the transit smart card(SC) system,has gained more and more popularity among transit agencies worldwide.Compared with the conventional manual fare collection system,an AFC system has its inherent advantages in low labor cost and high efficiency for fare collection and transaction data archival.Although it is possible to collect highly valuable data from transit SC transactions,substantial efforts and methodologies are needed for extracting such data because most AFC systems are not initially designed for data collection.This is true especially for the Beijing AFC system,where a passenger’s boarding stop(origin) on a flat-rate bus is not recorded on the check-in scan.To extract passengers’ origin data from recorded SC transaction information,a Markov chain based Bayesian decision tree algorithm is developed in this study.Using the time invariance property of the Markov chain,the algorithm is further optimized and simplified to have a linear computational complexity.This algorithm is verified with transit vehicles equipped with global positioning system(GPS) data loggers.Our verification results demonstrated that the proposed algorithm is effective in extracting transit passengers’ origin information from SC transactions with a relatively high accuracy.Such transit origin data are highly valuable for transit system planning and route optimization.     

Convex relaxation for a 3D spatiotemporal segmentation model using the primal-dual method

A method based on 3D videos is proposed for multi-target segmentation and tracking with a moving viewing system. A spatiotemporal energy functional is built up to perform motion segmentation and estimation simultaneously. To overcome the limitation of the local minimum problem with the level set method, a convex relaxation method is applied to the 3D spatiotemporal segmentation model. The relaxed convex model is independent of the initial condition. A primal-dual algorithm is used to improve computational efficiency. Several indoor experiments show the validity of the proposed method.     

一种新的码片内多径时延估计方法

根据信道冲激响应的稀疏特性,提出了一种频域的时延估计压缩感知模型,将时延估计问题转化为基于欠采样数据的稀疏向量估计问题.利用离散傅里叶变换(Discrete Fourier transform,DFT)矩阵的子矩阵所满足的受限等距性(Restricted isometry property,RIP)以及信道冲激响应的稀疏特性充分降低了时延估计所需数据量的要求.分析了本文模型具有码片内多径分辨能力以及良好抗噪性能的原因,并与多信号分类(Multiple signal classification,MUSIC)和旋转不变技术的信号参数估计(Estimation of signal parameters via rotational invariance technique,ESPRIT)算法的时延估计性能进行仿真比较.仿真结果表明,本文提出的方法不需要预知多径的条数,对码片内多径时延具有较高的估计精度,其时延估计性能在特定条件下优于MUSIC和ESPRIT算法.     

一种基于高阶统计信息的信噪比估计改进算法

针对多进制正交幅度调制(Multiple quadrature amplitude modulation,MQAM)信号在低信噪比条件下估计精度不高的问题,提出了一种基于综合利用高阶统计信息的信噪比估计改进算法。根据所选高阶统计量最高阶数的不同,建立了3种信噪比与多种高阶统计量运算式之间的线性关系,利用全回归线性分析方法将3种线性关系转化为3种全回归模型,并求解模型系数。该算法充分利用了多种高阶统计量的有用信息,提高了信噪比估计精度。MQAM的仿真结果表明:在低信噪比条件下,该算法减小了信噪比估计误差,其估计性能明显优于传统的其他算法,且3种模型估计性能依次增加,可依据不同的信噪比要求对3种模型进行选取。     

基于正则总体最小二乘准则的OFDM系统信道估计算法

基于导频的最小二乘(LS)估计方法的优点是结构简单,容易实现,但对噪声和干扰敏感。通过对OFDM系统中由于信道的时变性产生的载波间干扰进行了分析,在此基础上提出正则总体最小二乘(TLS)信道估计算法。根据Tikhonov正则化原理,导出了总体最小二乘正则化解法的计算公式。与一般的LS信道估计相比,TLS方法同时考虑了信道噪声和信道时变特性,所以该算法不仅在一般的信道条件下具有良好的性能而且也适合快变信道条件下的OFDM系统。仿真结果表明,该算法具有很好的均方误差和误比特率性能。     

Probabilistic heuristic estimates

Though they constitute the major knowledge source in problem-solving systems, no unified theory of heuristics has emerged. Pearl [15] defines heuristics as criteria, methods, or principles for deciding which among several alternative courses of action promises to be the most effective in order to achieve some goal. The absence of a more precise definition has impeded our efforts to understand, utilize, and discover heuristics. Another consequence is that problem-solving techniques which rely on heuristic knowledge cannot be relied upon to act rationally — in the sense of the normative theory of rationality.To provide a sound basis for BPS, the Bayesian Problem-Solver, we have developed a simple formal theory of heuristics, which is general enough to subsume traditional heuristic functions as well as other forms of problem-solving knowledge, and to straddle disparate problem domains. Probabilistic heuristic estimates represent a probabilistic association of sensations with prior experience — specifically, a mapping from observations directly to subjective probabilities which enables the use of theoretically principled mechanisms for coherent inference and decision making during problem-solving. This paper discusses some of the implications of this theory, and describes its successful application in BPS.This research was made possible by support from Heuristicrats, the National Aeronautics and Space Administration, and the Rand Corporation.     

3D human pose estimation in motion based on multi-stage regression

3D human pose estimation in motion is a hot research direction in the field of computer vision. However, the performance of the algorithm is affected by the complexity of 3D spatial information, self-occlusion of human body, mapping uncertainty and other problems. In this paper, we propose a 3D human joint localization method based on multi-stage regression depth network and 2D to 3D point mapping algorithm. First of all, we use a single RGB image as the input, through the introduction of heatmap and multi-stage regression to constantly optimize the coordinates of human joint points. Then we input the 2D joint points into the mapping network for calculation, and get the coordinates of 3D human body joint points, and then to complete the 3D human body pose estimation task. The MPJPE of the algorithm in Human3.6 M dataset is 40.7. The evaluation of dataset shows that our method has obvious advantages.     

Bayesian network based general correspondence retrieval method for depth sensing with single-shot structured light

This study reinvestigated one of the most fundamental problems in structure light depth sensing field: correspondence retrieval of features between patterns and images. We formulate the global optimum correspondence retrieval by maximizing a conditional probability of correspondence given observed features, which is depicted by a Bayesian network. Different from traditional “code-only” based correspondence retrieval methods, the proposed Bayesian network based method exploits the positional correlations of correspondences of neighboring features, namely, the correspondences of poorly detected features are estimated with the aid of the correspondences of well detected features. The method performs especially well on challenging scenes with rich depth variations, abrupt depth changes, edges, etc. Experiments show that the proposed method increase the correspondence accuracy by about 40% on challenging scenes, compared with traditional “code-only” based correspondence retrieval methods.     

Analysis of oceanaut operating performance using an integrated Bayesian network aided by the fuzzy logic theory

Because the oceanaut plays a significant role in safety and capability during manned deep-diving scientific tasks, preventing oceanaut performance decline is of paramount importance. However, the factors responsible for oceanaut performance are almost entirely unexplored. To address the preceding issues, a quantitative method of fuzzy integrated Bayesian network (FIBN) was modeled within the limits of oceanaut operating procedures. To quantify the probabilities of the influencing factors, the probability of each node in the FIBN was calculated using integrated expert judgement, fuzzy logic theory, and Bayesian network. By considering a total of 28 factors related to oceanaut performance in the “Jiaolong” manned submersible, this study found that difficult sampling, long sampling times, cabin equipment failure, oceanaut physical decline, and declining decision-making ability are important factors that affect oceanaut performance. The FIBN proposed in our study fused the qualitative and quantitative methods and can be developed into a versatile tool for analysis of comprehensive systems that contain both static and dynamic factors.Relevance to industryThe results provide a powerful basis for the design of manned submersible and assignment of tasks to oceanauts, while the fuzzy integrated Bayesian network (FIBN) method proposed can be effectively applied to various quantitative assessment fields which direct researchers to deal with analysis problems of complex systems.