Application of artificial neural network method to predict the breakage properties of PGE bearing chromite ore

In the present investigation, systematic grinding experiments were conducted in a laboratory ball mill to determine the breakage properties of low-grade PGE bearing chromite ore. The population balance modeling technique was used to study the breakage parameters such as primary breakage distribution (B_{i, j}) and the specific rates of breakage (S_i). The breakage and selection function values were determined for six feed sizes. The results stated that the breakage follows the first-order grinding kinetics for all the feed sizes. It was observed that the coarser feed sizes exhibit higher selection function values than the finer feed size. Further, an artificial neural network was used to predict breakage characteristics of low-grade PGE bearing chromite ore. The predicted results obtained from the neural network modeling were close to the experimental results with a correlation of determination R² = 0.99 for both product size and selection function.     

论携号转网行为的私法规制

以电信用户入网协议为切入点,电信用户的携号转网行为应受到《合同法》《消费者权益保护法》等私法规范的保护与限制。该行为引起的格式条款解释、合同终止、违约责任、损失赔偿等问题,不能仅靠《电信条例》等公法规范来调整。应当积极引导广大消费者用户以私法领域相关规定为依据,转变争议解决思维,拓宽纠纷处理途径,合理选择维权手段,以保护自身合法民事权益,与行业监管部门共同推动电信行业有序发展。     

An expectation operator for belief functions in the Dempster–Shafer theory*

ABSTRACT

The main contribution of this paper is a new definition of expected value of belief functions in the Dempster–Shafer (D–S) theory of evidence. Our definition shares many of the properties of the expectation operator in probability theory. Also, for Bayesian belief functions, our definition provides the same expected value as the probabilistic expectation operator. A traditional method of computing expected of real-valued functions is to first transform a D–S belief function to a corresponding probability mass function, and then use the expectation operator for probability mass functions. Transforming a belief function to a probability function involves loss of information. Our expectation operator works directly with D–S belief functions. Another definition is using Choquet integration, which assumes belief functions are credal sets, i.e. convex sets of probability mass functions. Credal sets semantics are incompatible with Dempster's combination rule, the center-piece of the D–S theory. In general, our definition provides different expected values than, e.g. if we use probabilistic expectation using the pignistic transform or the plausibility transform of a belief function. Using our definition of expectation, we provide new definitions of variance, covariance, correlation, and other higher moments and describe their properties.     

公路治超必须从动态称重抓起

公路治超事业与动态称重事业是一对相互依存的共同体,治超离不开动态称重,动态称重也离不开治超事业。在"四个一流"公路治超体系建设的同时,与公路治超同呼吸共命运的动态称重技术也取得了很大发展,已经完成了由轴载荷计重模式向整车称重模式的转化,新技术已经具备高准确度、高稳定性、高通行率等特征,能够适应公路现场应用,计量结果完全不受车辆不规则驾驶的影响,精准可靠。公路治超事业需要准确的称重技术,动态称重技术需要公路治超的舞台,"四个一流"的公路治超信息化体系建设更离不开一流的动态称重技术。     

抽水蓄能电站施工现场安全文明施工三维策划应用研究

基于电网安全对稳定经济运行的重要作用,国家大力推进抽水蓄能电站的建设,"十三五"期间,将进入抽水蓄能电站建设高峰期,抽水蓄能电站施工现场点多面广,基建安全形势严峻,施工现场的安全是工程管理的核心因素[1].为了提升现场安全文明施工管理水平,其核心在于施工现场安全文明施工策划,策划的水平和可操作性决定了建筑施工过程中的安全管理的成效,而行之有效的策划应用模式成为关键.本文结合山东文登抽水蓄能电站安全文明施工三维策划管理及三维策划系统应用,解决了现有安全文明施工策划、现场安全布置、安全设施标准化等环节的局限性及不足,实现了策划的高效快捷、效果直观真切,探索出了安全文明施工策划应用的新途径.     

Individual-based multi-objective optimal structured treatment interruption for HIV infection

In this paper, based on the measurable quantities from an individual patient that has infection to human immunodeficiency virus (HIV) and his/her condition is near to acquired immune deficiency syndrome (AIDS), individual-based multi-objective optimal treatments have been proposed. Firstly, the most effective parameters of the patient in computing Long-term non-progressor (LTNP) equilibrium are derived using global sensitivity analysis (GSA). To accomplish GSA effectively, Latin hypercube sampling (LHS) and partial rank correlation coefficients (PRCC) are utilized to rank each of the parameters based on each state of the 5-dimensional model. Then, these results are used by Dempster–Shafer (D–S) evidence theory (DSET) to rank the most effective parameters comprehensively. Now, these effective identified parameters are estimated using extended Kalman filter (EKF), which its covariance matrices are optimized based on particle swarm optimization (PSO) algorithm. Thus, the proposed methodology gives a calibrated model corresponding to the individual patient. Based on this calibrated model, the LTNP equilibrium related to the individual patient is derived. Using the derived individual-based LTNP equilibrium optimal structured treatment interruption (STI) strategies are extracted by defining suitable multi-objective optimization problem and solving it through using non-dominated sorting genetic algorithm-II (NSGA-II). The results demonstrate that the proposed optimal treatments are able to effectively reach LTNP equilibrium with using the minimum and maximum drug usage of 3.6% and 35.1% of full drug usage treatment. Meanwhile, the different optimal treatments give the decision-makers enough flexibility to choose the suitable treatment based on existing facilities and necessities.     

一种有效折扣证据源的冲突证据合成方法

为解决证据组合规则中一票否决和弱决策证据在低冲突情况下出现的反直观推理的现象，提出一种有效折扣证据源的冲突证据合成方法。首先根据证据的信任函数和似然函数求得证据间的相似度，然后求出证据之间的支持程度，并确定折扣因子，最后采用Dempster组合规则合成利用折扣因子修正后的证据源。数值算例分析结果表明，改进后的证据组合方法可以有效地处理证据冲突。     

A practical data-flow verification scheme for business processes

Data in business processes becomes more and more important. Current standard languages for process modeling like BPMN 2.0 which include the data flow reflect this. Ensuring the correctness of the data flow in processes is challenging. Model checking, i.e., verifying properties of process models, is a well-known technique to this end. An important part of model checking is the construction of the state space of the model. However, state-space explosion typically is in the way of an effective verification. We study how to overcome this problem in our context by means of reduction. More specifically, we propose a reduction on the level of the process model. To our knowledge, this is new for the data-flow analysis of processes. The core of our approach are so-called regions of the process model that are relevant for the verification of properties describing the data flow. Non-relevant regions are candidates for reduction of the process model, yielding a smaller state space. Our evaluation shows that our approach works well on industrial process models.