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1.
海南小型露天采石场矿山数量多且呈遍地开花式分布,且不规范的开采对环境造成较大的破坏,与绿色矿山之路背道而行,相对大型矿山规范化的治理,小型矿山治理比较滞后,本文以日富采石场地质环境治理为例,通过摸清当地的地质环境条件,结合矿山现状,因地制宜地制定出日富采石场地质环境治理的具体措施,通过治理可获得林地2.6hm2,矿山安全隐患及地貌景观等矿山地质环境问题得到有效地防治,有效解决农业灌溉及周边虾塘养殖用水缺乏问题,实现较好的社会经济效益,为今后治理类似矿山提供参考。 相似文献
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现场可编程门阵列(FPGA)内部资源众多,其中互连资源出现故障的概率远远高于片内其他资源,而在以往许多互连测试研究中,所生成的测试配置存在无法覆盖反馈桥接故障的难题,所以较难有测试配置实现故障列表的100%覆盖。因此通过约束桥接故障只发生在单个查找表(LUT)内的信号线上,并结合单项函数,对反馈桥接故障模型进行优化改进,从根本上解决难题;然后对优化后的反馈桥接故障设置相应的约束条件,再使用布尔可满足性理论(SAT)生成满足约束条件的测试配置。采用优化后的故障模型对ISCAS"89基准电路进行了测试配置生成实验,结果表明生成的测试向量解决了反馈桥接故障的覆盖难题,并且在实现故障列表的100%覆盖下,优化后的故障模型所需要的测试配置数最少。 相似文献
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Machine learning algorithms have been widely used in mine fault diagnosis. The correct selection of the suitable algorithms is the key factor that affects the fault diagnosis. However, the impact of machine learning algorithms on the prediction performance of mine fault diagnosis models has not been fully evaluated. In this study, the windage alteration faults (WAFs) diagnosis models, which are based on K-nearest neighbor algorithm (KNN), multi-layer perceptron (MLP), support vector machine (SVM), and decision tree (DT), are constructed. Furthermore, the applicability of these four algorithms in the WAFs diagnosis is explored by a T-type ventilation network simulation experiment and the field empirical application research of Jinchuan No. 2 mine. The accuracy of the fault location diagnosis for the four models in both networks was 100%. In the simulation experiment, the mean absolute percentage error (MAPE) between the predicted values and the real values of the fault volume of the four models was 0.59%, 97.26%, 123.61%, and 8.78%, respectively. The MAPE for the field empirical application was 3.94%, 52.40%, 25.25%, and 7.15%, respectively. The results of the comprehensive evaluation of the fault location and fault volume diagnosis tests showed that the KNN model is the most suitable algorithm for the WAFs diagnosis, whereas the prediction performance of the DT model was the second-best. This study realizes the intelligent diagnosis of WAFs, and provides technical support for the realization of intelligent ventilation. 相似文献
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One of the major challenges in wireless body area networks (WBANs) is sensor fault detection. This paper reports a method for the precise identification of faulty sensors, which should help users identify true medical conditions and reduce the rate of false alarms, thereby improving the quality of services offered by WBANs. The proposed sensor fault detection (SFD) algorithm is based on Pearson correlation coefficients and simple statistical methods. The proposed method identifies strongly correlated parameters using Pearson correlation coefficients, and the proposed SFD algorithm detects faulty sensors. We validated the proposed SFD algorithm using two datasets from the Multiparameter Intelligent Monitoring in Intensive Care database and compared the results to those of existing methods. The time complexity of the proposed algorithm was also compared to that of existing methods. The proposed algorithm achieved high detection rates and low false alarm rates with accuracies of 97.23% and 93.99% for Dataset 1 and Dataset 2, respectively. 相似文献
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基于叠后地震波形指示反演在薄储层预测方面的优势,以及叠前弹性参数较叠后弹性参数信息更丰富、对储层的敏感性更高的特点,根据岩石物理资料,应用地震驱动+储层构型约束的高精度叠前随机反演方法,探寻定量表征优质薄页岩的技术,以期为深层页岩气地质甜点预测提供技术支撑。首先,基于道集波形相似性、AVO特征和空间距离的三变量优选方法提取结构相似的井数据作为空间估值样本,然后建立待判别道集初始模型;其次,以统计的弹性阻抗作为先验信息,应用“基于叠前道集特征指示的马尔科夫链-蒙特卡洛随机模拟算法”进行叠前地震波形指示反演,最终得到高精度的叠前弹性参数反演成果。实际应用表明,应用所提方法有效预测了龙一段一亚段1-2小层优质页岩厚度,基于特征参数的井震高频模拟精确模拟了龙一段一亚段1-2小层地质甜点参数,为页岩气勘探提供了技术支撑。 相似文献
8.
Leo H. Chiang Birgit Braun Zhenyu Wang Ivan Castillo 《American Institute of Chemical Engineers》2022,68(6):e17644
In the Industry 4.0 era, the chemical industry is embracing broad adoption of artificial intelligence (AI) and machine learning (ML) methods. This article provides a holistic view of how the industry is transforming digitally towards AI at scale. First, a historical perspective on how the industry used AI to aid humans in better decision-making is shown. Then state-of-the-art AI research addressing industrial needs on reliability and safety, process optimization, supply chain, material discovery, and reaction engineering is highlighted. Finally, a vision of the plant of the future is illustrated with critical components of AI-ready culture, model life cycle management, and renewed role of humans in chemical manufacturing. 相似文献
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The rate of penetration (ROP) model is of great importance in achieving a high efficiency in the complex geological drilling process. In this paper, a novel two-level intelligent modeling method is proposed for the ROP considering the drilling characteristics of data incompleteness, couplings, and strong nonlinearities. Firstly, a piecewise cubic Hermite interpolation method is introduced to complete the lost drilling data. Then, a formation drillability (FD) fusion submodel is established by using Nadaboost extreme learning machine (Nadaboost-ELM) algorithm, and the mutual information method is used to obtain the parameters, strongly correlated with the ROP. Finally, a ROP submodel is established by a neural network with radial basis function optimized by the improved particle swarm optimization (RBFNN-IPSO). This two-level ROP model is applied to a real drilling process and the proposed method shows the best performance in ROP prediction as compared with conventional methods. The proposed ROP model provides the basis for intelligent optimization and control in the complex geological drilling process. 相似文献