新桥硫铁矿二步骤回采工艺及充填方案的探讨

介绍了新桥硫铁矿二步骤回采工艺，并从技术经济角度对该矿所采用的充填料及充填系统进行了探讨。     

Data-mining based SQL injection attack detection using internal query trees

Detecting SQL injection attacks (SQLIAs) is becoming increasingly important in database-driven web sites. Until now, most of the studies on SQLIA detection have focused on the structured query language (SQL) structure at the application level. Unfortunately, this approach inevitably fails to detect those attacks that use already stored procedure and data within the database system. In this paper, we propose a framework to detect SQLIAs at database level by using SVM classification and various kernel functions. The key issue of SQLIA detection framework is how to represent the internal query tree collected from database log suitable for SVM classification algorithm in order to acquire good performance in detecting SQLIAs. To solve the issue, we first propose a novel method to convert the query tree into an n-dimensional feature vector by using a multi-dimensional sequence as an intermediate representation. The reason that it is difficult to directly convert the query tree into an n-dimensional feature vector is the complexity and variability of the query tree structure. Second, we propose a method to extract the syntactic features, as well as the semantic features when generating feature vector. Third, we propose a method to transform string feature values into numeric feature values, combining multiple statistical models. The combined model maps one string value to one numeric value by containing the multiple characteristic of each string value. In order to demonstrate the feasibility of our proposals in practical environments, we implement the SQLIA detection system based on PostgreSQL, a popular open source database system, and we perform experiments. The experimental results using the internal query trees of PostgreSQL validate that our proposal is effective in detecting SQLIAs, with at least 99.6% of the probability that the probability for malicious queries to be correctly predicted as SQLIA is greater than the probability for normal queries to be incorrectly predicted as SQLIA. Finally, we perform additional experiments to compare our proposal with syntax-focused feature extraction and single statistical model based on feature transformation. The experimental results show that our proposal significantly increases the probability of correctly detecting SQLIAs for various SQL statements, when compared to the previous methods.     

Incrementally building frequent closed itemset lattice

A concept lattice is an ordered structure between concepts. It is particularly effective in mining association rules. However, a concept lattice is not efficient for large databases because the lattice size increases with the number of transactions. Finding an efficient strategy for dynamically updating the lattice is an important issue for real-world applications, where new transactions are constantly inserted into databases. To build an efficient storage structure for mining association rules, this study proposes a method for building the initial frequent closed itemset lattice from the original database. The lattice is updated when new transactions are inserted. The number of database rescans over the entire database is reduced in the maintenance process. The proposed algorithm is compared with building a lattice in batch mode to demonstrate the effectiveness of the proposed algorithm.     

Semantic weak signal tracing

The weak signal concept according to Ansoff has the aim to advance strategic early warning. It enables to predict the appearance of events in advance that are relevant for an organization. An example is to predict the appearance of a new and relevant technology for a research organization. Existing approaches detect weak signals based on an environmental scanning procedure that considers textual information from the internet. This is because about 80% of all data in the internet are textual information. The texts are processed by a specific clustering approach where clusters that represent weak signals are identified. In contrast to these related approaches, we propose a new methodology that investigates a sequence of clusters measured at successive points in time. This enables to trace the development of weak signals over time and thus, it enables to identify relevant weak signal developments for organization’s decision making in strategic early warning environment.     

On the robust control of stable minimum phase plants with large uncertainty in a time constant. A fractional-order control approach

This paper addresses the problem of designing controllers that are robust to a great uncertainty in a time constant of the plant. Plants must be represented by minimum phase rational transfer functions of an arbitrary order. The design specifications are: (1) a phase margin for the nominal plant, (2) a gain crossover frequency for the nominal plant, (3) zero steady state error to step commands, and (4) a constant phase margin for all the possible values of the time constant (T$T$): 0<T<∞$0<T<\infty$. We propose a theorem that defines the structure of the set of controllers that fulfil these specifications and show that it is necessary for these robust controllers to include a fractional-order PI$PI$ term. Examples are developed for both stable and unstable plants, and the results are compared with a standard PI$PI$ controller and a robust controller designed using the QFT$QFT$ methodology.     

Credit rating with a monotonicity-constrained support vector machine model

Deciding whether borrowers can fulfill their obligations is a major issue for financial institutions, and while various credit rating models have been developed to help achieve this, they cannot reflect the domain knowledge of human experts. This paper proposes a new rating model based on a support vector machine with monotonicity constraints derived from the prior knowledge of financial experts. Experiments conducted on real-world data sets show that the proposed method, not only data driven but also domain knowledge oriented, can help correct the loss of monotonicity in data occurring during the collecting process, and performs better than the conventional counterpart.     

Exploring the Interactions of Storylines from Informative News Events

Today＇s news readers can be easily overwhelmed by the numerous news articles online. To cope with information overload, online news media publishes timelines for continuously developing news topics. However, the timeline summary does not show the relationship of storylines, and is not intuitive for readers to comprehend the development of a complex news topic. In this paper, we study a novel problem of exploring the interactions of storylines in a news topic. An interaction of two storylines is signified by informative news events that play a key role in both storylines. Storyline interactions can indicate key phases of a news topic, and reveal the latent connections among various aspects of the story. We address the coherence between news articles which is not considered in traditional similarity-based methods, and discover salient storyline interactions to form a clear, global picture of the news topic. User preference can be naturally integrated into our method to generate query-specific results. Comprehensive experiments on ten news topics show the effectiveness of our method over alternative approaches.     

极限训练机在矿山开采最大下沉值估算中的应用

矿山开采过程中采空区地表往往会发生形变,研究其最大下沉值对安全生产具有重要意义,现有的开采最大下沉估算方法还有提升的空间。基于岩移数据决策与极限训练机(Extreme learning machine,ELM)算法,提出了一种估计最大下沉的新方法。该方法将采厚、倾角、平均采深、走向长度、倾向长度和覆岩岩性确定为最大下沉值影响因素;应用以Sigmoid方程为核函数、隐含层神经元个数为114的ELM模型对最大下沉值进行了估算。通过案例分析,ELM模型得到了优于传统算法如CHAID、Boosted Tree、ANN、BPNN和SVM的RMSE、MAE、MAPE、最大残差及秩相关系数,故认为该模型是一种有效的矿山开采最大下沉估算方法。     

超长、超大基坑开挖对邻近地铁结构安全影响的数值模拟分析

为了研究超长、超大基坑开挖对邻近地铁结构安全影响,以西安地铁2号线周边某基坑为工程实例,通过运用数值分析软件,结合场地的水文地质和工程地质条件,建立数值模型,进行施工过程的动态模拟,同时进行现场监测,经对比数值模拟结果及监测数据,表明运用PLAXIS软件能有效的模拟基坑开挖过程中的土体变形。同时工程采用的分段、分块开挖,中心岛顺做,周边预留土台反压、使用旋喷锚索的支护方案,对复杂条件下基坑开挖对邻近地铁结构的变形影响能起到有效的控制作用,可对类似工程的设计优化提供有益的参考及借鉴。     

金沙江典型水库水温影响因素敏感性分析

在拱坝温度应力计算等领域,库水温是重要的边界条件,对其计算精度要求较高,尤其对于高拱坝上游库水温而言,需要进行详细研究。库水温的影响因素较多,如河道的类型、水库的来水流量、来水温度、气象条件等,如何合理考虑各项因素对库水温分布的影响,对于计算的精确度意义重大。利用横向平均的二维水动力模型来研究金沙江上向家坝、溪洛渡、白鹤滩、乌东德等4座水动力特征相似水库的水温时空动态分布,并以典型水库溪洛渡为例,对该水库的来水流量、来水温度和气象条件等因素进行敏感性分析。研究成果可为后续几座水库的水温计算分析提供参考。