基于机器学习的格点气温预报订正方法

使用2017年9月至2021年3月国家级业务化运行的智能网格实况分析产品和欧洲中期天气预报中心全球模式(EC)产品,根据湖北省的地理分布特征构建6个分区,采用基于LightGBM机器学习算法建立的气温预报方法,生成湖北省0.05°×0.05°格点气温预报产品。利用2021年4—9月的预报产品和格点实况资料进行检验,结果表明:基于机器学习的气温预报方法(MLT)取得了较好的预报效果,其在0～72 h时效内优于中央气象台下发的气温精细化指导预报(SCMOC)和EC产品;MLT在山区的误差较平原大,但山区的订正幅度大于平原,日最高气温的订正幅度大于日最低气温的订正幅度;4—9月MLT、SCMOC、EC产品的平均绝对误差(MAE)日变化都呈现了白天偏高、夜间偏低、午后凸起的单峰特征,MLT的MAE值较SCMOC和EC产品的更低,并且在转折性天气中仍具有优势;站点检验与格点检验结论一致,基于格点建模的气温预报产品对站点预报同样得到了订正。机器学习在格点气温的模式订正方面可以作为一个行之有效的手段。     

离心式泥浆机械净化设备的研制与应用

本文介绍了两种离心式泥浆净化设备——NL-227型离心除泥器和WL-230型连续除泥离心机的结构、特点和应用效果,讨论了几种净化设备的分离物的粒度分布.     

Comparing Human and Neural Network Learning of Climate Categories

This study compares how humans and neural networks classify climate types. Human subjects were asked to classify climates from monthly temperature and precipitation patterns. To model their learning process, the same data were used to produce input vectors that trained a pattern associator neural network. Both human subjects and the neural network classified climates accurately after 10 rounds of supervised learning. The neural network successfully modeled the rate of human learning and the ability to learn specific climate categories. Moreover, the neural network weights used to classify climates correspond to distinct visual characteristics in temperature and precipitation. These results suggest that neural networks can model the formation of visual categories.     

人工心肺机流量计的研制

本文介绍了如何应用单片微型计算机和光电传感器组成人工心肺机(或称体外循环机)流量计。该仪器对人工心肺机的泵的各种转速能够准确地采集,对不同的患者能够精确地计算出所需血液流量,从而定量地控制输送给病人的血液流量,以保证心脏手术顺利进行,改变了临床手术时手工计算的落后情况。流量计是一台新型的智能化仪表。     

Learning about learning: lessons from public engagement and deliberation on urban river restoration

This paper provides a new discussion of how people learn through deliberative processes, drawing upon empirical analysis of a novel public engagement process for urban river restoration. Such critical evaluation is rare and yet will be crucial to both theoretical development and learning about engagement practice, not least in a policy area subject to strong regulatory drivers for public participation. The analysis supports two important learning mechanisms – the use of 'gatekeepers' of knowledge, interests and values, and the privileging of narrative. It provides new evidence of instrumental and communicative learning about shared priorities and criteria for effective river restoration that evolved through the deliberative process and directly informed the restoration scheme. It is important to question whether and how such site or context-specific learning might inform other restoration schemes. Finally, the paper questions the often ignored issue of expert learning, not least the issue of the link between individual and organizational learning.     

Deriving rules from activity diary data: A learning algorithm and results of computer experiments

 Activity-based models consider travel as a derived demand from the activities households need to conduct in space and time. Over the last 15 years, computational or rule-based models of activity scheduling have gained increasing interest in time-geography and transportation research. This paper argues that a lack of techniques for deriving rules from empirical data hinders the further development of rule-based systems in this area. To overcome this problem, this paper develops and tests an algorithm for inductively deriving rules from activity-diary data. The decision table formalism is used to exhaustively represent the theoretically possible decision rules that individuals may use in sequencing a given set of activities. Actual activity patterns of individuals are supplied to the system as examples. In an incremental learning process, the system progressively improves on the selection of rules used for reproducing the examples. Computer experiments based on simulated data are performed to fine-tune rule selection and rule value update functions. The results suggest that the system is effective and fairly robust for parameter settings. It is concluded, therefore, that the proposed approach opens up possibilities to derive empirically tested rule-based models of activity scheduling. Follow-up research will be concerned with testing the system on empirical data. Received: 31 January 2001 / Accepted: 13 September 2001     

塔里木盆地库车油气系统中、新生界的流体压力结构和油气成藏机制

库车油气系统中、新生界流体压力纵向结构可分为台阶式、中凸式、均斜式 3种类型。平面分布可分两个带 3个区 :北带主要是台阶式压力结构分布区 ,南带的大部分是中凸式压力结构分布区 ,南带的南缘为均斜式压力结构分布区 ,构成不同类型的封存箱。这种压力结构分异主要是喜玛拉雅晚期 ( 5Ma)天山向南强烈推挤造成的 ;喜玛拉雅早期 ,整个油气系统压力结构大体是一致的 ,深浅层基本为正常压力 ,也即均为均斜式压力结构分布区。喜玛拉雅早、晚期的流体压力封存箱规模不等 ,油气运聚的环境不同 ,因而不同时期、不同地带具有各不相同的油气成藏机制。初步可概括为 3种机制和 3种成藏模式 :早期封存箱内成藏机制———牙哈模式 ;晚期封存箱内成藏机制———克拉苏模式 ;封存箱外成藏机制———大宛齐模式     

利用一种新的神经网络模型识别点状地图符号

着重讨论了用一种新的神经网络模型识别点状地图符号的过程，主要包括网络的结构特点和学习算法以及学习训练过程，并验证了用该网络进行点状地图符号识别的有效性。     

Cuttability assessment of hard coal seams

Summary A number of field and laboratory tests have been carried out on more than 15 coal seams of compressive strengths ranging from 19 MPa to 44 MPa to evolve methods which would help in the selection of suitable coaling machines for hard coal seams. The effect of physico-mechanical properties on cuttability were studied in the laboratory for all these coal seams to identify the relevant parameters affecting the specific energy of coal cuttability. These data were subjected to regression analysis to find the best fit for estimation of laboratory specific energy of coal samples on the basis of simple laboratory and field tests for the strength parameters. Field studies were also conducted over a large number of active mechanized coal faces to study in situ cuttability along with the geo-mining conditions of the site. The field and the laboratory data so generated were correlated and an attempt is made to establish a relationship for estimating the field specific energy for a particular capacity of coaling machines by considering the geo-mining domain of the field in totality.     

机房干扰源的分析

从理论上分析了机房中干扰产生的原因,提出了压低干扰的措施。