Study on internal stress damage detection in long-distance oil and gas pipelines via weak magnetic method

Weak magnetic stress detection is an important issue in oil–gas pipeline internal detection area. In order to verify the characteristics of weak magnetic stress internal detection signals, we built herein a magneto-mechanics equivalent model having a balanced magnetic field. First, we calculated the relationship between the stress and the weak magnetic signals; consequently, the analysis propagation laws of the weak magnetic signals with non-magnetic saturation were pointed out. Finally, the theoretical model was validated by a systematic experimental research. The analytical results show that a one-to-one linear link between the weak magnetic signals and the stress concentration is clear. Instead, the change of the weak magnetic signals with the liftoff is nonlinear, therefore we are proposing the Boltzmann liftoff correction factor whose degree of adaptability of the equivalent model can reach the value of 94%. It is possible to note that when the liftoff is in the approximate linear stage, the relevance ratio and the recognition rate of the magneto-mechanics curve show a high-quality. This conclusion is important in the engineering field for the set of the liftoff.     

Attitudinal choice models with applications in human decision making

A new family of attitudinal discrete choice models is proposed by considering the attitudinal character and the weight vector, both of which are specific to a decision maker (DM). Given the attribute values of different alternatives, the proposed models give varying choice probabilities, as per the DM's-specific attitudinal character and the weight vector. It is also shown that the conventional discrete choice models are the special cases of the proposed attitudinal models. The proposed choice models are also generalized through an additional parameter to add to their capabilities. An application on real data is included to demonstrate their usefulness in the real world.     

含齿形裂隙类岩石材料单轴压缩试验研究

为研究含齿形裂隙岩石在单轴压缩下的破坏特征及强度特性，制作了含不同裂隙倾角和起伏角的齿形裂隙类岩石材料试件，并采用岩石力学伺服试验机进行单轴压缩试验。试验结果表明：（1）试件主要产生拉伸、剪切和拉剪复合裂纹，且根据裂纹的扩展路径可划分为A型（拉伸破坏）、B型（剪切破坏）、C型（复合破坏）3种破坏模式，裂隙倾角对试件最终破坏模式影响显著；（2）当裂隙倾角较小时，试件应力—应变曲线为多峰曲线，随着裂隙倾角的增大，曲线呈单峰形式，表现为延性减弱，脆性增强，而裂隙倾角相同但起伏角不同的试件应力—应变曲线大致相同；（3）当裂隙起伏角相同时，试件当量峰值强度随裂隙倾角的增大呈先减小后增大的规律，且裂隙起伏角对试件当量峰值强度的影响小于裂隙倾角。     

Evaluation of dynamic modulus measurement for C/C-SiC composites at different temperatures

The determination of elastic properties at application temperature is fundamental for the design of fibre reinforced ceramic composite components. An attractive method to characterize the flexural modulus at room and high temperature under specific atmosphere is the nondestructive Resonant Frequency Damping Analysis (RFDA). The objective of this paper was to evaluate and validate the modulus measurement via RFDA for orthotropic C/C-SiC composites at the application temperature. At room temperature flexural moduli of C/C-SiC with 0/90° reinforcement were measured under quasi-static 4-point bending loads and compared with dynamic moduli measured via RFDA longitudinally to fibre direction. The dynamic modulus of C/C-SiC was then measured via RFDA up to 1250°C under flowing inert gas and showed an increase with temperature which fitted with literature values. The measured fundamental frequencies were finally compared to those resulting from numerical modal analyses. Dynamic and quasi-static flexural moduli are comparable and the numerical analyses proved that bending modes are correctly modeled by means of dynamic modulus measured via RFDA. The nondestructive RFDA as well as the numerical modeling approach are suitable for evaluation of C/C-SiC and may be transferred to other fibre reinforced ceramic composite materials.     

APP软件性能效率研究

性能效率是APP软件的重要质量属性，但目前缺乏APP软件性能效率的通用模型。分析了APP软件的性能特征，基于ISO/IEC 25010标准提出了APP软件的性能效率模型，定义了APP软件性能效率的子特性和度量指标。基于提出的APP软件性能效率模型，通过实验对APP软件的性能效率进行了度量及相关分析。     

甲苯+乙醇+1-癸基-3-甲基咪唑醋酸盐物系等压气液相平衡数据的测定及关联

在常压下测定了甲苯+乙醇+1-癸基-3-甲基咪唑醋酸盐([C_(10)MIM][OAc])三元物系的等压气液相平衡数据,使用Aspen软件中NRTL模型对三元气液相平衡数据进行关联,通过计算得到方程参数。实验结果表明,[C_(10)MIM][OAc]的加入对甲苯有显著的盐析效应,[C_(10)MIM][OAc]摩尔分数越大效果越明显,当[C_(10)MIM][OAc]摩尔分数达到0.15时甲苯-乙醇的共沸现象完全消失。由NRTL模型计算出[C_(10)MIM][OAc]打破甲苯-乙醇体系共沸的最小摩尔分数为0.139 7。[C_(10)MIM][OAc]可以用作分离甲苯-乙醇共沸体系的萃取剂。     

The location and sizing of urban freight loading/unloading lay-by areas

The volume of freight vehicles operating within city boundaries is steadily increasing, which contributes to road congestion, especially in urban areas with a parking deficit. The proper identification of the location and size of commercial parking lay-by areas, where delivery vehicles can park for a limited time to perform loading/unloading operations, can relieve road congestion. Compared to the existing literature, this paper presents an improved two-stage approach, which includes a mathematical programme and a simulation model for determining the location and sizing of lay-by areas. The robustness and soundness of the results from a methodological and practical point of view are discussed using an example application.     

Intelligent Kano classification of product features based on customer reviews

Product features can be classified into different categories based on customer opinions. The rapid development of artificial intelligence and machine learning paves the way toward computational analysis of customer reviews for opinion mining. This paper presents an Intelligent Kano framework to extract, quantify, and classify different product features based on customer reviews. The framework is enabled by a novel integration of multiple artificial intelligence and machine learning techniques such as sentiment analysis and anomaly detection. A case study is conducted to validate the framework’s effectiveness. Over 12,000 customer reviews on two coffee machines are analyzed for the classification.