陕西煤灰纱出现的特征性规律与防治

煤灰纱严重影响纺织产品质量,同时也是难以解决的技术问题。文章分析了煤灰纱出现的特征性规律,试图找到防治煤灰纱的措施,从而找到切实可行的技术解决途径。     

An adaptive local search algorithm for vehicle routing problem with simultaneous and mixed pickups and deliveries

The Vehicle Routing Problem with Simultaneous Pickup and Delivery (VRPSPD) is an extension to the classical Vehicle Routing Problem (VRP), where customers may both receive and send goods simultaneously. The Vehicle Routing Problem with Mixed Pickup and Delivery (VRPMPD) differs from the VRPSPD in that the customers may have either pickup or delivery demand. However, the solution approaches proposed for the VRPSPD can be directly applied to the VRPMPD. In this study, an adaptive local search solution approach is developed for both the VRPSPD and the VRPMPD, which hybridizes a Simulated Annealing inspired algorithm with Variable Neighborhood Descent. The algorithm uses an adaptive threshold function that makes the algorithm self-tuning. The proposed approach is tested on well-known VRPSPD and VRPMPD benchmark instances derived from the literature. The computational results indicate that the proposed algorithm is effective in solving the problems in reasonable computation time.     

Unsupervised regions based segmentation using object discovery

We present a new unsupervised algorithm to discovery and segment out common objects from multiple images. Compared with previous cosegmentation methods, our algorithm performs well even when the appearance variations in the foregrounds are more substantial than those in some areas of the backgrounds. Our algorithm mainly includes two parts: the foreground object discovery scheme and the iterative region allocation algorithm. Two terms, a region-saliency prior and a region-repeatness measure, are introduced in the foreground object discovery scheme to detect the foregrounds without any supervisory information. The iterative region allocation algorithm searches the optimal solution for the final segmentation with the constraints from a maximal spanning tree, and an effective color-based model is utilized during this process. The comparative experimental results show that the proposed algorithm matches or outperforms several previous methods on several standard datasets.     

A synergistic strategy for combining thesaurus-based and corpus-based approaches in building ontology for multilingual search engines

In this article we illustrate a methodology for building cross-language search engine. A synergistic approach between thesaurus-based approach and corpus-based approach is proposed. First, a bilingual ontology thesaurus is designed with respect to two languages: English and Spanish, where a simple bilingual listing of terms, phrases, concepts, and subconcepts is built. Second, term vector translation is used – a statistical multilingual text retrieval techniques that maps statistical information about term use between languages (Ontology co-learning). These techniques map sets of t f id f term weights from one language to another. We also applied a query translation method to retrieve multilingual documents with an expansion technique for phrasal translation. Finally, we present our findings.     

Synthesis,Characterization, and Catalytic Activity of Mn‐doped Perovskite Oxides for Three‐Way Catalysis

Mn‐doped La_0.8Sr_0.2CoO₃ perovskite oxides (La_0.8Sr_0.2Co_1–xMn_xO₃; x = 0, 0.1, 0.3, 0.5) were synthesized by a modified sol‐gel method. The phase‐pure oxides were obtained. CoO and carbonates were formed on the surface of La_0.8Sr_0.2CoO₃. With increasing doping content, these impurities were reduced while the stability of the perovskite structure was improved. The valence state of B‐site ions and the amount of absorbed oxygen were influenced by Mn doping. The catalytic activity of the perovskite catalysts was investigated for CO oxidation and simultaneous removal of CO, C₃H₈, and NO. For CO and NO removal, La_0.8Sr_0.2Co_0.9Mn_0.1O₃ exhibited the best performance. For C₃H₈ removal, the reactivity was promoted linearly with the doping content. The structure‐activity relationship is also discussed.     

Polarity Effect and Electromagnetic Radiation of Partial Discharge Accompanying Growth of Electrical Tree

We have investigated the characteristics of radiated electromagnetic (EM) waves from positive and negative partial discharges (PD) in epoxy resin and cross‐linked polyethylene. We found that there is a correlation among the EM level from PD, the positive PD current, and electrical trees. Therefore, the growth of an electrical tree produces a lot of positive PD. We have also investigated the characteristics of the frequency region of EM waves from PD in air, insulating oil, and liquid epoxy in addition to the above insulators. EM waves were detected in the frequency region of 40 MHz to 300 MHz from positive and negative PD in epoxy resin and cross‐linked polyethylene. EM waves were also detected in the frequency region of 40 MHz to 150 MHz from positive and negative PD in air. In the case of insulating oil and liquid epoxy, EM waves were detected in the frequency regions of 40 MHz to 150 MHz from positive PD, and 40 MHz to 250 MHz from negative PD. The frequency region differed depending on the material and the discharge polarity. Our investigation indicates that the cause is differences in electric field strength at the time of PD occurrence.     

Multi-objective optimization of shield construction parameters based on random forests and NSGA-II

Metro shield construction will inevitably cause changes in the stress and strain state of the surrounding soil, resulting in stratum deformation and surface settlement (SS), which will seriously endanger the safety of nearby buildings, roads and underground pipe networks. Therefore, in the design and construction stage, optimizing the shield construction parameters (SCP) is the key to reducing the SS rate and increasing the safe driving speed (DS). However, optimization of existing SCP are challenged by the need to construct a unified multiobjective model for optimization that are efficient, convenient, and widely applicable. This paper innovatively proposes a hybrid intelligence framework that combines random forest (RF) and non-dominant classification genetic algorithm II (NSGA-II), which overcomes the shortcomings of time-consuming and high cost for the establishment and verification of traditional prediction models. First, RF is used to rank the importance of 10 influencing factors, and the nonlinear mapping relationship between the main SCP and the two objectives is constructed as the fitness function of the NSGA-II algorithm. Second, a multiobjective optimization framework for RF-NSGA-II is established, based on which the optimal Pareto front is calculated, and reasonable optimized control ranges for the SCP are obtained. Finally, a case study in the Wuhan Rail Transit Line 6 project is examined. The results show that the SS is reduced by 12.5% and the DS is increased by 2.5% with the proposed framework. Meanwhile, the prediction results are compared with the back-propagation neural network (BPNN), support vector machine (SVM), and gradient boosting decision tree (GBDT). The findings indicate that the RF-NSGA-II framework can not only meet the requirements of SS and DS calculation, but also used as a support tool for real-time optimization and control of SCP.     

Failure load prediction and optimisation for adhesively bonded joints enabled by deep learning and fruit fly optimisation

Adhesively bonded joints have been extensively employed in the aeronautical and automotive industries to join thin-layer materials for developing lightweight components. To strengthen the structural integrity of joints, it is critical to estimate and improve joint failure loads effectually. To accomplish the aforementioned purpose, this paper presents a novel deep neural network (DNN) model-enabled approach, and a single lap joint (SLJ) design is used to support research development and validation. The approach is innovative in the following aspects: (i) the DNN model is reinforced with a transfer learning (TL) mechanism to realise an adaptive prediction on a new SLJ design, and the requirement to re-create new training samples and re-train the DNN model from scratch for the design can be alleviated; (ii) a fruit fly optimisation (FFO) algorithm featured with the parallel computing capability is incorporated into the approach to efficiently optimise joint parameters based on joint failure load predictions. Case studies were developed to validate the effectiveness of the approach. Experimental results demonstrate that, with this approach, the number of datasets and the computational time required to re-train the DNN model for a new SLJ design were significantly reduced by 92.00% and 99.57% respectively, and the joint failure load was substantially increased by 9.96%.     

Specification uncertainty in modeling internet adoption: A developing city case analysis

Internet adoption fosters economic growth and development. Specifying policy control drivers is particularly relevant for developing countries. However, there is no consensus on the most relevant variables. We explored 33.6 million potential models to identify the most important determinants of household internet adoption using stochastic search variable selection and socioeconomic data from Medellín, Colombia. We found that monthly income, the head of household education and voting, and having a computer and cable television at home are the most relevant variables.