混合马尔科夫预测模型及其在反洗钱中的应用研究

反洗钱中的一个重要问题是预测可疑账户未来可能发生的交易。马尔科夫模型在股票、商品价格、市场占有率等经济领域的预测中具有广泛的应用,但单一的马尔科夫模型的预测准确性有待提高。提出一种结合数据挖掘中聚类、关联规则和低序马尔科夫模型的混合马尔科夫模型,并在模型的建立过程中基于置信度进行剪枝以降低时间复杂度,最后将该模型用于预测反洗钱领域中账户之间的交易。实验表明,该模型具有较高的预测准确性,并在预测准确性和时间复杂度两者之间取得了较好的平衡。     

Learning based brain emotional intelligence as a new aspect for development of an alarm system

The multi criteria and purposeful prediction approach has been introduced and is implemented by the fast and efficient behavioral based brain emotional learning method. On the other side, the emotional learning from brain model has shown good performance and is characterized by high generalization property. New approach is developed to deal with low computational and memory resources and can be used with the largest available data sets. The scope of paper is to reveal the advantages of emotional learning interpretations of brain as a purposeful forecasting system designed to warning; and to make a fair comparison between the successful neural (MLP) and neurofuzzy (ANFIS) approaches in their best structures and according to prediction accuracy, generalization, and computational complexity. The auroral electrojet (AE) index are used as practical examples of chaotic time series and introduced method used to make predictions and warning of geomagnetic disturbances and geomagnetic storms based on AE index.     

Misfit versus latency

In this paper we present a framework that combines some ideas of the behavioral modeling approach and the prediction error modeling approach. It is shown that the proposed model selection procedure can be rephrased as an optimization problem that only depends on the model parameters. Experiments illustrate the potential of the so-called misfit versus latency framework.     

松辽盆地茂8井三维地震区薄互层储层预测方法及效果

本文针对松辽盆地陆相薄互层的沉积特点,结合茂８井三维地震区块的实际情况,采用“滑动相关法”确定地震地质层位,准确标定了各反射同相轴的地质含义。依据砂岩或砂层组在三维剖面上的特征,充分发挥人机联作解释系统的优势,利用振幅、频率对主要含油层系砂岩的横向变化进行了深入研究,经钻井验证取得了较好的效果。     

Identification of large-scale goaf instability in underground mine using particle swarm optimization and support vector machine

An approach which combines particle swarm optimization and support vector machine (PSO–SVM) is proposed to forecast large-scale goaf instability (LSGI). Firstly, influencing factors of goaf safety are analyzed, and following parameters were selected as evaluation indexes in the LSGI: uniaxial compressive strength (UCS) of rock, elastic modulus (E) of rock, rock quality designation (RQD), area ration of pillar (S_p), the ratio of width to height of the pillar (w/h), depth of ore body (H), volume of goaf (V), dip of ore body (α) and area of goaf (S_g). Then LSGI forecasting model by PSO-SVM was established according to the influencing factors. The performance of hybrid model (PSO + SVM = PSO–SVM) has been compared with the grid search method of support vector machine (GSM–SVM) model. The actual data of 40 goafs are applied to research the forecasting ability of the proposed method, and two cases of underground mine are also validated by the proposed model. The results indicated that the heuristic algorithm of PSO can speed up the SVM parameter optimization search, and the predictive ability of the PSO–SVM model with the RBF kernel function is acceptable and robust, which might hold a high potential to become a useful tool in goaf risky prediction research.     

Unobtrusive vehicle motion prediction cues reduced simulator sickness during passive travel in a driving simulator

This study investigated cues that permit prediction of turns during passive movement through a virtual environment. Effects on simulator sickness (SS), presence and enjoyment were examined. Subjects were exposed to complex visual motion through a cartoon-like simulated environment in a driving simulator. Forward velocity remained constant and the motion path was the same across all experimental conditions. Using a within-subject design, we examined visual paths that provided different levels of cue salience – detailed, simplified and no cues – for the upcoming simulated vehicle motion. Following each trial, participants completed questionnaires on SS, presence and enjoyment. After all of the trials were completed, a debriefing determined participants' perceptions of vehicle motion attributes and their awareness of the prediction cues. The results showed that SS in the no-cue condition was significantly greater than that in the conditions that provided vehicle motion cues. Presence and enjoyment responses were not different across the conditions. No participants reported differences between prediction cue conditions or recognized that the vehicle motion followed the same path across trials. However, participants tended to report that the motion was smoother for the detailed-cue than the no-cue condition. Participants ranked turn predictability as higher in conditions with prediction cues. The results support the hypothesis that unobtrusive and unreported motion cues may alleviate SS in a virtual environment.     

On the combination of user-based and item-based collaborative filtering

In this paper, we propose two new filtering algorithms which are a combination of user-based and item-based collaborative filtering schemes. The first one, Hybrid-Ib, identifies a reasonably large neighbourhood of similar users and then uses this subset to derive the item-based recommendation model. The second algorithm, Hybrid-CF, starts by locating items similar to the one for which we want a prediction, and then, based on that neighbourhood, it generates its user-based predictions. We start by describing the execution steps of the algorithms and proceed with extended experiments. We conclude that our algorithms are directly comparable to existing filtering approaches, with Hybrid-CF producing favorable or, in the worst case, similar results in all selected evaluation metrics.     

Modeling And Performance Evaluation Of Branch And Value Prediction In Ilp Processors

Speculative execution is one of the key issues to boost the performance of future generation microprocessors. In this paper, we introduce a novel approach to evaluate the effects of branch and value prediction, which allow the processor to execute instructions beyond the limits of control and true data dependences. Until now, almost all the estimations of their performance potential under different scenarios have been obtained using trace-driven or execution-driven simulation. Occasionally, some simple deterministic models have been used. We employ an analytical model based on recently introduced Fluid Stochastic Petri Nets (FSPNs) in order to capture the dynamic behavior of an ILP processor with aggressive use of prediction techniques and speculative execution. Here we define the FSPN model, derive the state equations for the underlying stochastic process and present performance evaluation results to illustrate its usage in deriving measures of interest. Our implementation-independent stochastic modeling framework reveals considerable potential for further research in this area using numerical solution of systems of partial differential equations and/or discrete-event simulation of FSPN models.     

江西经济发展走势对运输需求的分析与预测

区域经济的发展是运输需求的源泉。新世纪以来,江西经济进入了持续稳定发展的快车道,极大地促进了运输需求的持续增长,运输线路长度不断延长,全社会运输量不断增加。随着江西经济社会快速发展,城镇化进程的加快,资源环境约束压力的加大,运输需求结构提升,对运输需求提出了更高的要求,运输需求多样化、个性化、机动化、快速化和网络化的趋势更加显著。     

横波预测技术在延川南地区储层预测中的应用

在延川南地区,通过开展测井横波模拟研究,获得测井的横波速度;采用叠前弹性参数反演技术计算纵、横波阻抗;利用砂泥岩在纵横波阻抗交汇图上的差异进行石盒子组砂岩储层预测,利用煤层与围岩在密度和阻抗方面的差异预测煤层的分布空间。通过横波资料可以有效地获得其它弹性参数,提高叠前反演的可靠性,降低叠后储层预测的多解性,提高储层预测精度。