招聘网站的岗位分析系统设计与实现

随着高校扩招，应届毕业生人数逐年增长，就业压力不容小觑。针对就业难的问题，进行招聘网站数据分析，挖 掘出岗位所需技能，再结合学生在校时所学课程，二者对比，就能很好地给学生推荐合适的工作，并且给学校专业人才培养方 案中的岗位职业定位     

大数据时代大学生个人隐私保护问题研究

伴随着互联网+的风生水起,信息量暴增的大数据时代应运而来,"网络原住民"大学生不可避免、理所当然地处于大数据洪流之中,但也为其个人隐私保护带来了极大挑战。文章针对大数据带来的安全隐私问题,指出大学生个人隐私泄露的可能原因,提出可以采取的个人隐私保护措施,给出大数据时代隐私保护几种技术,期望能给相关人士提供有效参考。     

Smart work packaging-enabled constraint-free path re-planning for tower crane in prefabricated products assembly process

Lack of constraint-free crane path planning is one of the critical concerns in the dynamic on-site assembly process of prefabrication housing production (PHP). For decades, researchers and practitioners have endeavored to improve both the efficiency and safety of crane path planning from either static environment or re-planning the path when colliding with constraints or periodically updating the path in the dynamic environment. However, there is a lack of approach related to the in-depth exploration of the nature of dynamic constraints so as to assist the crane operators in making adaptive path re-planning decisions by categorizing and prioritizing constraints. To address this issue, this study develops the smart work packaging (SWP)-enabled constraints optimization service. This service embraces the core characteristics of SWP, including adaptivity, sociability, and autonomy to achieve autonomous initial path planning, networked constraints classification, and adaptive decisions on path re-planning. This service is simulated and verified in the BIM environment, and it is found that SWP-enabled constraints optimization service can generate the constraint-free path when it is necessary.     

基于模拟退火优化BP神经网络的变形监测数据预测方法

针对BP神经网络模型用于变形监测数据处理时容易陷入局部极小值并且收敛速度慢的问题，提出一种基于模拟退火(Simulated Annealing,SA)算法优化BP神经网络的建筑物变形预测方法，利用SA的全局寻优能力对BP神经网络的模型参数进行优化，使参数迭代过程始终保持较高的“温度”和“能量”，从而确保BP神经网络能够得到全局最优解的同时具备较高的预测精度和收敛速度。采用实际算例对所提SA-BP方法在低信噪比和小样本条件下的预测精度进行验证，结果表明所提方法相对于传统BP神经网络法和小波方法能够获得更高的预测精度，并且在小样本和低信噪比条件下优势更加明显。     

Incomplete analytic hierarchy process with minimum weighted ordinal violations

ABSTRACT

Incomplete pairwise comparison matrices offer a natural way of expressing preferences in decision-making processes. Although ordinal information is crucial, there is a bias in the literature: cardinal models dominate. Ordinal models usually yield nonunique solutions; therefore, an approach blending ordinal and cardinal information is needed. In this work, we consider two cascading problems: first, we compute ordinal preferences, maximizing an index that combines ordinal and cardinal information; then, we obtain a cardinal ranking by enforcing ordinal constraints. Notably, we provide a sufficient condition (that is likely to be satisfied in practical cases) for the first problem to admit a unique solution and we develop a provably polynomial-time algorithm to compute it. The effectiveness of the proposed method is analyzed and compared with respect to other approaches and criteria at the state of the art.     

Modeling and design of 3-D MPPT for ultra low power RF energy harvesters

Radio-Frequency (RF) energy harvesting must cope with the limited availability and high variability of the energy source. In this paper, the modeling of an RF harvester for ultra low power environments is presented. A mathematical model based on theoretical analysis is developed. The model demonstrates that the maximum transferred power point is located in a three-dimensional space defined by the input capacitance, the output voltage, and the load resistance of the rectifier circuit. Moreover, the mathematical model returns results in substantial agreement with the SPICE simulation results, while guaranteeing a remarkable reduction of the required computation time. Furthermore, the paper reports the implementation of a mixed signal system for the 3-D MPPT, to be embedded in an RF harvester, in a 65 nm CMOS technology. The circuit exhibits a simulated power consumption lower than 100 nW, making this solution suitable for ultra low power harvesting.     

基于BIM技术和有限元的检测与分析在PC结构中的应用研究

本文建立了PC结构施工全过程的BIM模型及有限元分析模型,对施工过程进行数值模拟与动态、静态监测,对装配式结构进行了结构性能评价,为结构可视化施工监测提供了技术支持。     

Data quality and uncertainty assessment methodology for pavement LCA

In performing pavement life cycle assessment (LCA), users are facing various reports of energy intensity coefficient (EIC) of pavement materials which differ considerably among data sources and therefore alter the LCA results significantly. Instead of selecting a certain EIC without or of little explanation for the current pavement LCA practices, this study proposed a methodology to build probability density function (PDF) for EIC based on available data-set and their qualities. Each data was first evaluated about the data quality indicator (DQI) through data quality pedigree matrix and converted to PDF in modified Beta distribution form. Three weighting methods, the DQI one, coefficient of variation (COV) one and analytical hierarchy process (AHP) one, were developed to assign weightings for different data. Monte Carlo simulation was run with the weighted PDF of each data as input to obtain the ultimate PDF for EIC. A case study to estimate the bitumen’s EIC with eight data samples were performed using the proposed methodology. It is found (1): the estimates by the proposed methodology is of higher reliability (lower COV) compared to any single data due to utilisation of information of the overall data samples; (2) the AHP weighting method is most favoured despite the results of the three weighting methods are close; (3) the central estimates of bitumen’s EIC are between5.4~5.8 MJ/kg. The proposed methodology is helpful in aiding calculating EICs for pavement materials and capturing uncertainties in LCA results in a statistical sense.