A semi-supervised social relationships inferred model based on mobile phone data

Exploring the relationships of humans is an important study in the mobile communication network. But the relationship prediction accuracy is not good enough when the number of known relationship labels (e.g., “friend” and “colleague”) is small, especially when the number of different relation classes are imbalanced in the mobile communication network. To deal with issues, we present a semi-supervised social relationships inferred model. This model can infer the relationships based on a large amount of unlabeled data or a small amount of labeled data. The model is a co-training style semi-supervised model which is combined with the support vector machine and naive Bayes. The final relationship labels are decided by the two classifiers. The proposed model is evaluated by a real mobile communication network dataset and the experiment results show that the model is effective in relationship mining, especially when the relationship network is in a stable state.     

利用测井资料确定页岩储层有机碳含量的方法优选——以焦石坝页岩气田为例

国内外利用测井资料计算页岩储层有机碳含量的方法众多,但各种计算方法都有其相应的适用性及计算精度的优劣。因此,探讨各种计算方法的适用性并最终确定页岩储层有机碳含量的区域性经验计算模型就显得尤为重要。为此,以四川盆地焦石坝地区上奥陶统五峰组—下志留统龙马溪组页岩储层为例,在继承和发展前人研究成果的基础上,结合区域地质特征,利用改进的ΔlgR技术、体积密度法、自然伽马能谱法、多元拟合法、体积模型法等分别建立了有机碳含量的计算模型,并利用岩心分析资料对模型进行对比优选,最终确立了体积密度法为区域性经验计算模型。实践证明:改进的ΔlgR技术和自然伽马能谱法计算精度相对较差;多元拟合法和体积密度法的计算成果均能满足精度要求,但体积密度法方法更为简单且适用范围更广。为进一步验证经验模型的普适性,应用该方法对焦石坝页岩气田重点井的总有机碳含量开展测井精细解释评价,并利用岩心实测资料进行精度分析,其有机碳含量测井解释符合率介于90.5%~91.0%。     

Multinational vehicle license plate detection in complex backgrounds

Many methods for multinational License Plate Detection (LPD) have been proposed in recent times but most of them are not sophisticated enough to handle complex backgrounds. Moreover, their ability to handle various environmental and illumination conditions has been limited and still needs improvement. In this paper, we propose a novel technique to detect license plates of vehicles regardless of their color, size, and content. As the rear vehicle lights are an essential part of any vehicle, we reduce the image processing area to eliminate the complex background by detecting the rear-lights as the license plates are in a certain range of these lights. Heuristic Energy Map (HEM) of the vertical edge information in the Region of Interest (ROI) is calculated and area with the dense edges is selected using a unique histogram approach which is considered to be the license plate. The proposed algorithm is tested on 855 images from various countries including China, Pakistan, Serbia, Italy and various states of America. Experimental results show that the proposed method is able to detect license plates 90.4% of times despite of complex backgrounds in 0.25 s on average that can achieve real time performance.     

Robust numerical approach to steady-state calibration of mean-value models

A numerically robust approach to steady-state calibration of nonlinear dynamic models is presented. The approach is based on explicit formulation of the constraints on validity of internal model signals by set of inequalities. The constrained optimization with feasible iterates guarantees that the model will never be evaluated with invalid internal signals. This overcomes numerical difficulties often encountered when dealing with highly nonlinear models. Because the approach uses a large number of slack variables, distributed least squares algorithm is proposed. The robustness of this approach is demonstrated on a steady-state calibration of turbocharged diesel engine model starting from grossly inaccurate initial estimates.     

Hierarchical Spatially Varying Coefficient Process Model

The spatially varying coefficient process model is a nonstationary approach to explaining spatial heterogen-eity by allowing coefficients to vary across space. In this article, we develop a methodology for generalizing this model to accommodate geographically hierarchical data. This article considers two-level hierarchical structures and allow for the coefficients of both low-level and high-level units to vary over space. We assume that the spatially varying low-level coefficients follow the multivariate Gaussian process, and the spatially varying high-level coefficients follow the multivariate simultaneous autoregressive model that we develop by extending the standard simultaneous autoregressive model to incorporate multivariate data. We apply the proposed model to transaction data of houses sold in 2014 in a part of the city of Los Angeles. The results show that the proposed model predicts housing prices and fits the data effectively.     

Data-driven approaches for emissions-minimized paths in urban areas

Concerns about air quality and global warming have led to numerous initiatives to reduce emissions. In general, emissions are proportional to the amount of fuel consumed, and the amount of fuel consumed is a function of speed, distance, acceleration, and weight of the vehicle. In urban areas, vehicles must often travel at the speed of traffic, and congestion can impact this speed particularly at certain times of day. Further, for any given time of day, the observations of speeds on an arc can exhibit significant variability. Because of the nonlinearity of emissions curves, optimizing emissions in an urban area requires explicit consideration of the variability in the speed of traffic on arcs in the network. We introduce a shortest path algorithm that incorporates sampling to both account for variability in travel speeds and to estimate arrival time distributions at nodes on a path. We also suggest a method for transforming speed data into time-dependent emissions values thus converting the problem into a time-dependent, but deterministic shortest path problem. Our results demonstrate the effectiveness of the proposed approaches in reducing emissions relative to the use of minimum distance and time-dependent paths. In this paper, we also identify some of the challenges associated with using large data sets.     

Urban navigation beyond shortest route: The case of safe paths

Advancements in mobile technology and computing have fostered the collection of a large number of civic datasets that capture the pulse of urban life. Furthermore, the open government and data initiative has led many local authorities to make these datasets publicly available, hoping to drive innovation that will further improve the quality of life for the city-dwellers. In this paper, we develop a novel application that utilizes crime data to provide safe urban navigation. Specifically, using crime data from Chicago and Philadelphia we develop a risk model for their street urban network, which allows us to estimate the relative probability of a crime on any road segment. Given such model we define two variants of the SafePaths problem where the goal is to find a short and low-risk path between a source and a destination location. Since both the length and the risk of the path are equally important but cannot be combined into a single objective, we approach the urban-navigation problem as a biobjective shortest path problem. Our algorithms aim to output a small set of paths that provide tradeoffs between distance and safety. Our experiments demonstrate the efficacy of our algorithms and their practical applicability.     

1∶2000成图比例尺数字航空摄影测量质量控制与保障

通过《太原市1∶2 000成图比例尺数字航空摄影测量》项目实践,以该项目中1∶2 000数字线划图制作为例,结合数据分层详细介绍了数字摄影测量中的质量控制与保障的措施,以供类似测量工作借鉴。     

Parameter estimation of permanent magnet stepper motors without mechanical sensors

The paper presents a new sensorless parameter identification method for permanent magnet stepper motors. Current sensors are assumed available, but mechanical sensors are not. Data is obtained with open-loop commands at multiple speeds. A new frame is proposed that presents advantages similar to the d–q frame, but without the need for a position sensor. The method exploits derived linear parameterizations and least-squares algorithms. In some cases, overparameterization is resolved using elimination theory. The parameters identified using the new procedure are found to be very close to those obtained with sensors. The approach is potentially applicable to other types of synchronous motors.     

Non‐minimum phase switched systems: HOSM‐based fault detection and fault identification via Volterra integral equation

In this paper, the problem of continuous and discrete state estimation for a class of linear switched systems with additive faults is studied. The class of systems under study can contain non‐minimum phase zeroes in some of their ‘operating modes’. The conditions for exact reconstruction of the discrete state are given using structural properties of the switched system. The state space is decomposed into the strongly observable part, the non‐strongly observable part, and the unobservable part, to analyze the effect of the unknown inputs. State observers based on high‐order sliding mode to exactly estimate the strongly observable part and Luenberger‐like observers to estimate the remaining parts are proposed. For the case when the exact estimation of the state cannot be achieved, the ultimate bounds on the estimation errors are provided. The proposed strategy includes a high‐order sliding‐mode‐based fault detection and a fault identification scheme via the solution of a Volterra integral equation. The feasibility of the proposed method is illustrated by simulations. Copyright © 2013 John Wiley & Sons, Ltd.