超深溜井爆破一次成井技术实践

为解决溜井掘进效率低、安全性差等问题，以柿竹园多金属矿掘进出矿溜井为例，提出了爆破一次成井技术，根据矿山具体爆破条件，设计了布孔参数、装药结构和起爆顺序等。实践表明，爆破一次成井技术施工天井效果良好，溜井贯穿上下2个分段，与设计相符，相较于普通法施工天井，节约成本30.7%，节约时间58.3%，经济效益显著，施工周期短，且安全性好，值得在其他类似矿山推广应用。     

Use of conditioning time to investigate the mechanisms of interactions of selected fatty acids on hematite Part II laboratory investigations

It is the aim of this paper to examine the effects of conditioning time on the flotation of hematite using three technical grade fatty acid reagents as providing additional evidence on their mechanism of interaction with the hematite surface. Various mechanisms have been postulated as occurring as conditioning time is increased. Both physical (e.g. conditioning time and power input) and chemical (nature, dispersion and solubility of the adsorbing species) contribute to the mechanisms of attachment of collector. In this paper, the mechanism of attachment of oleate to hematite can be readily explained by chemisorption, but the mechanism of attachment of lauric acid appears to be physical adsorption at neutral pH. The flotation of hematite with a mixture of tallow-type fatty acids (palmitic, stearic and oleic acids) is very sensitive to conditioning time, and suggests that, even though flotation is very effective at short conditioning times, it is very susceptible to the presence of fines and their associated high surface areas. It is therefore obvious that both the physical and chemical conditions contribute to the mechanisms of adsorption of fatty acids on iron-containing oxide minerals and must be understood in order to optimise the flotation of these minerals in an industrial situation.     

Profit maximization of TSP through a hybrid algorithm

Here a new model of Traveling Salesman Problem (TSP) with uncertain parameters is formulated and solved using a hybrid algorithm. For this TSP, there are some fixed number of cities and the costs and time durations for traveling from one city to another are known. Here a Traveling Salesman (TS) visits and spends some time in each city for selling the company’s product. The return and expenditure at each city are dependent on the time spent by the TS at that city and these are given in functional forms of t. The total time limit for the entire tour is fixed and known. Now, the problem for the TS is to identify a tour program and also to determine the stay time at each city so that total profit out of the system is maximum. Here the model is solved by a hybrid method combining the Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO). The problem is divided into two subproblems where ACO and PSO are used successively iteratively in a generation using one’s result for the other. Numerical experiments are performed to illustrate the models. Some behavioral studies of the models and convergences of the proposed hybrid algorithm with respect to iteration numbers and cost matrix sizes are presented.     

Low‐order optimal regulation of parabolic PDEs with time‐dependent domain

Observer and optimal boundary control design for the objective of output tracking of a linear distributed parameter system given by a two‐dimensional (2‐D) parabolic partial differential equation with time‐varying domain is realized in this work. The transformation of boundary actuation to distributed control setting allows to represent the system's model in a standard evolutionary form. By exploring dynamical model evolution and generating data, a set of time‐varying empirical eigenfunctions that capture the dominant dynamics of the distributed system is found. This basis is used in Galerkin's method to accurately represent the distributed system as a finite‐dimensional plant in terms of a linear time‐varying system. This reduced‐order model enables synthesis of a linear optimal output tracking controller, as well as design of a state observer. Finally, numerical results are prepared for the optimal output tracking of a 2‐D model of the temperature distribution in Czochralski crystal growth process which has nontrivial geometry. © 2014 American Institute of Chemical Engineers AIChE J, 61: 494–502, 2015     

Toward the adaptation of component‐based architectures by model transformation: behind smart user interfaces

Graphical user interfaces are not always developed for remaining static. There are GUIs with the need of implementing some variability mechanisms. Component‐based GUIs are an ideal target for incorporating this kind of operations, because they can adapt their functionality at run‐time when their structure is updated by adding or removing components or by modifying the relationships between them. Mashup user interfaces are a good example of this type of GUI, and they allow to combine services through the assembly of graphical components. We intend to adapt component‐based user interfaces for obtaining smart user interfaces. With this goal, our proposal attempts to adapt abstract component‐based architectures by using model transformation. Our aim is to generate at run‐time a dynamic model transformation, because the rules describing their behavior are not pre‐set but are selected from a repository depending on the context. The proposal describes an adaptation schema based on model transformation providing a solution to this dynamic transformation. Context information is processed to select at run‐time a rule subset from a repository. Selected rules are used to generate, through a higher‐order transformation, the dynamic model transformation. This approach has been tested through a case study which applies different repositories to the same architecture and context. Moreover, a web tool has been developed for validation and demonstration of its applicability. The novelty of our proposal arises from the adaptation schema that creates a non pre‐set transformation, which enables the dynamic adaptation of component‐based architectures. Copyright © 2014 Copyright © 2014 John Wiley & Sons, Ltd.     

A New Time-Aware Collaborative Filtering Intelligent Recommendation System

Aiming at the problem that the traditional collaborative filtering recommendation algorithm does not fully consider the influence of correlation between projects on recommendation accuracy, this paper introduces project attribute fuzzy matrix, measures the project relevance through fuzzy clustering method, and classifies all project attributes. Then, the weight of the project relevance is introduced in the user similarity calculation, so that the nearest neighbor search is more accurate. In the prediction scoring section, considering the change of user interest with time, it is proposed to use the time weighting function to improve the influence of the time effect of the evaluation, so that the newer evaluation information in the system has a relatively large weight. The experimental results show that the improved algorithm improves the recommendation accuracy and improves the recommendation quality.     

Detection of correlation characteristics between financial time series based on multi-resolution analysis

Interactions between financial time series are complex and changeable in both time and frequency domains. To reveal the evolution characteristics of the time-varying relations between bivariate time series from a multi-resolution perspective, this study introduces an approach combining wavelet analysis and complex networks. In addition, to reduce the influence the phase lag between the time series has on the correlations, we propose dynamic time-warping (DTW) correlation coefficients to reflect the correlation degree between bivariate time series. Unlike previous studies that symbolized the time series only based on the correlation strength, the second-level symbol is set according to the correlation length during the coarse-graining process. This study presents a novel method to analyze bivariate time series and provides more information for investors and decision makers when investing in the stock market. We choose the closing prices of two stocks in China’s market as the sample and explore the evolutionary behavior of correlation modes from different resolutions. Furthermore, we perform experiments to discover the critical correlation modes between the bull market and the bear market on the high-resolution scale, the clustering effect during the financial crisis on the middle-resolution scale, and the potential pseudo period on the low-resolution scale. The experimental results exactly match reality, which provides powerful evidence to prove that our method is effective in financial time series analysis.     

融合时间隐语义填充和子群划分的推荐算法

随着个性化推荐技术的发展，推荐系统面临着越来越多的挑战。传统的推荐算法通常存在数据稀疏性和推荐精度低等问题。针对以上问题，提出了一种融合时间隐语义填充和子群划分的推荐算法[K]-TLFM（Time Based Latent Factor Model Integrated with [k]-means）。该算法利用融合时间因素的隐语义模型对原始用户物品评分矩阵缺失项进行填充，避免了用全局平均值或者用户/物品平均值补全矩阵带来的误差，有效缓解了数据稀疏性问题，同时融合时间因素有效地刻画了用户偏好随时间的变化；完成评分矩阵缺失项填充后，基于二分[k]-means聚类算法将偏好、兴趣特征相似的对象划分到同一个子群中，在目标用户所属的子群中基于选定的协同过滤算法为用户产生推荐列表，提高了推荐效率和准确性。在MovieLens和Netflix数据集上对该算法的推荐性能进行了对比实验，结果表明该算法具有更高的推荐精度。     

跨境电商保税仓库拣货策略研究

针对跨境电商客户订单的多品类且数量不稳定的特点，对跨境电商保税仓库拣货流程进行分析与优化。采用订单动态时间窗分拣策略，并在仓库拣货路径优化的基础上，建立了跨境电商保税仓库拣货模型。最后应用于某公司跨境电商保税仓库管理系统中，取得了较好的预期效果。