基于FXLMS算法的矿用有源耳罩的研究

基于矿井中低频噪声突出,存在时间长、危害大,尤其周期性噪声比重较大,而传统降噪技术对其降噪效果并不明显,提出了基于fxlms 算法的自适应有源降噪耳罩降低矿井中的低频周期性信号,并采用离线建模方法建立了次级通道模型,利用MATLAB8.0拟合了64阶的滤波器,对 矿井中具有代表性的1kHz低频周期噪声和伴有随机噪声的1kHz低频周期性噪声进行了降噪仿真研究。结果表明:1kHz低频周期性噪声的降噪量 达到了30dB。而且在本模型下,随机噪声对低频周期性噪声的降噪效果几乎没有影响。     

公共危机管理中的组织学习研究

目前国外对危机管理的研究已经趋于成熟,已经进一步提出了在危机中学习的概念并取得一些进展。而在经历了几次大的危机之后,国内学者开始关注公共危机管理。随着危机种类越来越多,发生频率越来越高,政府的公共部门必须要具备组织学习的能力。但是国内对于组织学习研究在公共管理领域的延伸还十分有限,我国仅少量学者探讨了学习型政府,对其它类型公共部门的组织学习研究则更为罕见。显然,这种研究的缺失不利于公共组织构建组织学习能力和提高服务水平。未来的国内学习型组织研究需要在公共组领域有所突破,特别是危机情境下的公共组织学习。因此本文以分析文献的方式,借鉴国内外学者对公共危机管理,以及组织学习的研究,希望能为公共部门组织学习的深层探索提供思路。     

基于微信平台微学习的安全宣教新模式研究

为促进安全宣传教育,打破传统安全宣教的局限性与不足,提出了基于微信平台微学习的安全宣教新模式。对安全宣教的内涵进行了分析,从现有宣教模式的不足出发,结合微信平台在移动教育上的优势,论证了基于微信平台开展安全宣教的可行性;分析了构建基于微信平台微学习的安全宣教新模式的核心理论:负荷理论、活动理论和安全教育理论;遵循PDCA循环构建了基于微信平台微学习的安全宣教新模式,包括前端分析、安全宣教实施过程、安全宣教绩效评估和安全宣教模式改进4个阶段;提出了应用安全宣教新模式的优化建议。研究结果表明:基于微信平台微学习的安全宣教新模式可以弥补现有安全宣教方法的不足,并能丰富安全宣传教育的理论基础。     

How does it feel and how does it look? The role of employee motivation in organizational learning type

Employees' work outcomes vary as a function of their focus on exploring new possibilities versus exploiting current opportunities. But what determines how employees divide their attention between these contrasting work behaviors? Drawing on studies on work motivation and employees' impression management concerns, we examine how intrinsic work motivation and self‐enhancement motivation relate to the exploration–exploitation balance and how environmental dynamism moderates these relationships. Based on the analyses of a sample of 638 employees in 34 organizations in Finland, we find that intrinsic work motivation is positively associated with employees' focus on exploration relative to exploitation. By contrast, self‐enhancement motivation negatively associates with exploration relative to exploitation, but this relationship is nonlinear, such that as self‐enhancement motivation increases, its positive association with exploitation diminishes. The findings also show that the hypothesized nonlinear relationship between self‐enhancement motivation and exploration is particularly pronounced in stable business environments. Our findings contribute to organizational learning research and provide a new theoretical perspective on pursuing exploration and exploitation in organizations.     

矿山井巷模型快速构建与突水避灾三维动态仿真

为准确规划人员最优避灾路径,以矿井现场实测数据为基础,联合Python与Blender快速生成矿山三维井巷模型,建立基于时空动态性的突水漫延仿真平台;综合考虑不同因素对巷道通行状况的影响,建立一种支持从源节点至多目标节点的前N条最优避灾路径搜索方法;调用Blender时间线功能,建立人员避灾逃生演示模型;以山东某矿为工...     

The detection of drowsiness using a driver monitoring system

Abstract

Objective: Drowsiness is a major cause of driver impairment leading to crashes and fatalities. Research has established the ability to detect drowsiness with various kinds of sensors. We studied drowsy driving in a high-fidelity driving simulator and evaluated the ability of an automotive production-ready driver monitoring system (DMS) to detect drowsy driving. Additionally, this feature was compared to and combined with signals from vehicle-based sensors.

Methods: The National Advanced Driving Simulator was used to expose drivers to long, monotonous drives. Twenty participants drove for about 4?h in the simulator between 10 p.m. and 2 a.m. They were allowed to use cruise control and traffic was sparse and semirandom, with both slower- and faster-moving vehicles. Observational ratings of drowsiness (ORDs) were used as the ground truth for drowsiness, and several dependent measures were calculated from vehicle and DMS signals. Drowsiness classification models were created that used only vehicle signals, only driver monitoring signals, and a combination of the 2 sources.

Results: The model that used DMS signals performed better than the one that used only vehicle signals; however, the combination of the two performed the best. The models were effective at discriminating low levels of drowsiness from moderate to severe drowsiness; however, they were not effective at telling the difference between moderate and severe levels. A binary model that lumped drowsiness into 2 classes had an area under the receiver operating characteristic (ROC) curve of 0.897.

Conclusions: Blinks and saccades have been shown to be predictive of microsleeps; however, it may be that detection of microsleeps and lane departures occurs too late. Therefore, it is encouraging that the model was able to distinguish mild from moderate drowsy driving. The use of automation may make vehicle-based signals useless for characterizing driver states, providing further motivation for a DMS. Future improvements in impairment detection systems may be expected through a combination of improved hardware, physiological measures from unobtrusive sensors and wearables, and the intelligent integration of environmental variables like time of day and time on task.     

考虑数据去噪分解的滑坡位移组合预测研究

为准确掌握滑坡位移变化规律,基于滑坡变形监测结果统计,对位移数据进行去噪分解处理,将滑坡位移数据分解为趋势项和误差项,并分别利用优化多核极限学习机和Arima模型构建预测模型,以实现滑坡位移的组合预测.结果表明:Morlet复小波较传统去噪模型分解效果更优,且通过优化处理,能更好地提高其分解能力;通过对多核极限学习机的...     

基于特征提取的输油管道泄漏系数预测*

为准确预测管道泄漏系数,估计管道泄漏量,以基于瞬变流方法的模拟数据为例,建立多个管道泄漏系数预测模型(多层感知机、长短期记忆网络、随机森林、支持向量机以及K近邻回归),综合考虑管道流量和压力数据特点,提出序列提取法和均值提取法2种管道时序数据预处理方法,模型评价指标为相关系数（R2）和平均绝对百分比误差（MAPE）。研究结果表明:随机森林和多层感知机的抗噪性较强,在5%的噪声影响下,模型准确度下降幅度较小;均值提取法去噪功能较好,可在一定程度上降低噪声影响;基于均值提取法的多层感知机模型效果相对较好,R2为0.997 5,MAPE为1.599%,研究结果可为准确预测管道泄漏系数、估计泄漏量提供指导。     

A role-playing game as a tool to facilitate social learning and collective action towards Climate Smart Agriculture: Lessons learned from Apuí, Brazil

Addressing the global challenges of climate change (CC), food security and poverty alleviation requires enhancing the adaptive capacity and mitigation potential of land use systems. To this end, Climate Smart Agriculture (CSA) aims to identify land use practices that sustainably increase productivity, enhance climate change (CC) adaptation and contribute to CC mitigation. A transition towards CSA requires technical, but also socio-institutional changes, for improved smallholder agricultural systems. Such changes may be triggered by stakeholder participation processes that stimulate social learning and collective action. This article evaluates whether a role-playing game (RPG) is an effective participatory tool to encourage social learning and collective action among local stakeholders towards adoption of CSA strategies. We designed and implemented an RPG with three groups of farmers in Apuí (Southern Amazonas), evaluating the game’s impact on social learning by interviewing each farmer before and after the RPG. Our findings show that the RPG induced not only technical learning, but also socio-institutional learning and engagement for collective action, though outcomes varied between different RPG sessions and among farmer participants.     

城市交通道路氮氧化物浓度的CART回归树预测研究

提出了基于CART回归树的氮氧化物(NO_x)浓度预测模型,利用杭州市延安路路边空气质量监测站2016年6—9月空气污染物监测数据和同期延安路路段车辆抓拍识别数据,通过数据处理、影响因素分析及CART回归树构造,搭建了NO_x浓度预测模型.实验分析结果表明,相对于支持向量机和BP神经网络预测模型,基于CART回归树的NO_x浓度预测模型的预测精度有大幅度提升,可决系数在0.92以上;同时,对环境条件差异较大的G20会议期间NO_x浓度进行预测分析,结果表明,CART回归树方法的预测精度比其它方法更高,能够适应不同条件下的预测需求.