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71.
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 (Sp), 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 (Sg). 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. 相似文献
72.
《Ergonomics》2012,55(6):608-624
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. 相似文献
73.
《国际计算机数学杂志》2012,89(9):1077-1096
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. 相似文献
74.
《国际计算机数学杂志》2012,89(1):19-46
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. 相似文献
75.
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。 相似文献
76.
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。 相似文献
77.
陈亚琳 《江汉石油职工大学学报》2012,25(3):7-9
随机模拟是一种基于地质统计学的储层预测方法,将井资料和波阻抗反演结果联合进行模拟,可以提高预测的精度,适用于滚动勘探开发初期及中后期。本次应用在对储层测井响应特征和物性参数分析的基础上,通过直方、变差分析,选择合理的反演参数,进行随机模拟,进而预测储层的分布规律,实际应用表明,随机模拟方法可提高储层反演的分辨率。 相似文献
78.
沉积相与物探技术相结合,是对盐湖盆地潜江凹陷特殊地质条件以及实际精细勘探的需要。可用成熟的复合相控砂体预测技术在潜江组储层预测中区分多种复杂岩性、识别不同厚度砂体。 相似文献
79.
Hongyi Zhang Jianling Zhao Yuying JiaXuewen Xu Cencun TangYangxian Li 《Expert systems with applications》2012,39(4):4094-4101
Artificial neural network (ANN) was developed to predict the morphology of TiO2 nanotube prepared by anodization. The collected experimental data was simplified in an innovative approach and used as training and validation data, and the morphology of TiO2 nanotube was considered as three parameters including the degree of order, diameter and length. Applying radial basis function neural network to predict TiO2 nanotube degree of order and back propagation artificial neural network to predict the nanotube diameter and length were emphasized in this paper. Some important problems such as the selection of training data, the structure and parameters of the networks were discussed in detail. It was proved in this paper that ANN technique was effective in the prediction work of TiO2nanotube fabrication process. 相似文献
80.
This paper is concerned with identification of linear parameter varying (LPV) systems in an input-output setting with Box-Jenkins (BJ) model structure. Classical linear time invariant prediction error method (PEM) is extended to the LPV PEM. Under the new LPV framework, identification of two types of input-output LPV models is considered: one is based on parameter interpolation and the other is based on model interpolation. The effectiveness of the proposed solution is validated by comparison with other existing LPV identification approaches through simulation examples and demonstrated by experiment studies. 相似文献