基于粗糙集的支持向量机地下水质量评价模型

以辽宁绥中县第四系松散岩类孔隙水的10组水质监测数据为基础,选取pH值、Cl^-、SO₄^2-、NH₄⁺、NO₂^-、NO₃^-、F^-、总硬度、总溶解固体等14项水质评价指标,采用粗糙集对指标进行约简,将基于属性依赖度和信息熵的启发式算法结合,获得属性约简集,应用支持向量机分别评价约简前后的地下水质量.结果表明,属性约简将14项水质指标精简为8项,水质评价结果与约简前保持一致,区域地下水普遍在III类以上,部分地区铁、"三氮"等超标,不适宜饮用.粗糙集和支持向量机的联合应用,在保证分类能力的前提下有效地减少冗余指标,降低运算维度,保证水质评价的合理性.     

密闭受限空间可燃气体爆炸特性数值模拟研究

密闭受限空间中可燃气体的爆炸研究对于石油及天然气工业的安全生产具有重要意义.以RNG湍流模型及EBU-Arrehnius燃烧模型为基础,建立了可燃气体单步化学反应湍流爆炸模型,以有限体积法求解爆炸流动及反应控制方程,从而对二维受限空间中可燃气体爆炸的过程及规律进行了数值模拟,模拟结果与实验数据有着较好的吻合性.所做的工作为受限空间中可燃气体爆炸特性及规律的进一步研究及工业防爆抑爆技术的工艺实施、系统设计和关键参数计算提供了理论依据.     

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.     

Improvement of driver active interventions during automated driving by displaying trajectory pointers—A driving simulator study

Abstract

Objective: The handover of vehicle control from automated to manual operation is a critical aspect of interaction between drivers and automated driving systems (ADS). In some cases, it is possible that the ADS may fail to detect an object. In this event, the driver must be aware of the situation and resume control of the vehicle without assistance from the system. Consequently, the driver must fulfill the following 2 main roles while driving: (1) monitor the vehicle trajectory and surrounding traffic environment and (2) actively take over vehicle control if the driver identifies a potential issue along the trajectory. An effective human–machine interface (HMI) is required that enables the driver to fulfill these roles. This article proposes an HMI that constantly indicates the future position of the vehicle.

Methods: This research used the Toyota Dynamic Driving Simulator to evaluate the effect of the proposed HMI and compares the proposed HMI with an HMI that notifies the driver when the vehicle trajectory changes. A total of 48 test subjects were divided into 2 groups of 24: One group used the HMI that constantly indicated the future position of the vehicle and the other group used the HMI that provided information when the vehicle trajectory changed.

The following instructions were given to the test subjects: (1) to not hold the steering wheel and to allow the vehicle to drive itself, (2) to constantly monitor the surrounding traffic environment because the functions of the ADS are limited, and (3) to take over driving if necessary.

The driving simulator experiments were composed of an initial 10-min acclimatization period and a 10-min evaluation period. Approximately 10?min after the start of the evaluation period, a scenario occurred in which the ADS failed to detect an object on the vehicle trajectory, potentially resulting in a collision if the driver did not actively take over control and manually avoid the object.

Results: The collision avoidance rate of the HMI that constantly indicated the future position of the vehicle was higher than that of the HMI that notified the driver of trajectory changes, χ² = 6.38, P < .05. The steering wheel hands-on and steering override timings were also faster with the proposed HMI (t test; P < .05).

Conclusions: This research confirmed that constantly indicating the position of the vehicle several seconds in the future facilitates active driver intervention when an ADS is in operation.     

燃气-蒸汽联合循环供热项目主机选型方案刍议

发展燃气-蒸汽联合循环供热对于解决经济发展和环境保护具有重要作用，在我国经济发达地区已经开始大规模推广。阐述了燃气一蒸汽联合循环发电、供热技术、特点等，对主机选型方案提出建议。     

漫反射光谱手工与机器制样方法测试结果与对比分析

矿物含量及其变化有可能蕴藏着环境信息。赤铁矿和针铁矿是常见的两种化合物,在各种类型的沉积物中基本都有分布。在古气候研究中,赤铁矿和针铁矿的含量变化可以用来指示气候的干湿变化。漫反射光谱方法(DRS)可以进行赤铁矿和针铁矿含量测算,具有灵敏度高、速度快、准确度高等特点。这种方法虽然很实用,但是对于样品量比较大的研究来说,其手工磨样速度比较慢,而用机器研磨则会大幅提高样品制备效率,但是机器研磨是否会对分析结果产生影响尚未有研究报道。本文对30个河流相和湖泊相样品进行了漫反射光谱对比分析实验,发现手工和机器磨样在磁性矿物浓度上的变化趋势基本一致,但是两者之间存在一定的差异。因此,如果研究目的是希望从整体变化趋势来反演气候变化的过程,用机器研磨方法可以达到要求且效率明显提高;如果是进行浓度含量等其它方面的研究,则传统的手工方法是优先选择。     

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.     

基于PCA-AHPSO-SVR的煤层瓦斯含量预测研究

为了提高煤层瓦斯含量预测的准确性和科学性,通过主成分分析方法对影响煤层瓦斯含量的7个因素进行特征提取,消除影响因素之间的相关性,减少维度;用支持向量回归机对提取的因素进行训练,并用改进的自适应混合粒子群算法对SVR的参数进行优化,提出PCA-AHPSO-SVR模型;与PCA-PSO-SVR,PSO-SVR这2个模型在相同环境下进行30次运行比较。研究结果表明:研究提出的PCA-AHPSO-SVR模型较其他2种模型平均准确率分别提高5.51%和9.32%,稳定性更佳,可满足工程实际需求。     

某大型机加车间数控机床噪声暴露特点与分析研究

为研究大型机加车间噪声现状和特点,选取某大型机加车间数控机床开展现场实测,并对噪声测量结果进行分析。结果显示:数控机床产生的噪声是非稳态噪声;数控机床产生的噪声在车间传播,声场分布不均匀;加工某转向工件频率在500～5 300 Hz,峰值频率分别为500,800,2 000,5 000 Hz;对机加数控机床操作岗位工作人员的8 h噪声暴露量进行测量,测量值为89.2 dB(A)。研究结果表明:评价大型机加车间噪声,测量重点应选在机床操作岗位和人行通道;应重点关注被测机床周围20 m内的噪声值;机加工作场所测量时间段的选择取决于作业持续时间。