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城市轨道交通噪声及其控制研究 总被引:4,自引:0,他引:4
近年来,随着经济的发展,修建城市轨道交通已成为解决大城市交通问题的首要选择,但同时也给沿线的居民和建筑物带来了噪声的环境污染。在分析城市轨道交通的噪声产生机理和传播规律的基础上,提出相应的控制措施和途径,为解决城市轨道交通发展中的噪声环境问题提供参考。 相似文献
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交通噪声是造成高校环境噪声超标的主要因素,尤其对城市道路下穿校园的新校区影响更大。运用噪声预测方法对在建的安徽理工大学新校区进行噪声预测评估,为其环境规划提供相应的依据和技术支持。通过测量统计校园周围道路不同类型的机动车流量数据,结合测量已有道路的交通噪声,利用Cadna/A噪声预测软件,建立新校区交通噪声模型,计算并绘制噪声网格分布图,并提出相应噪声控制措施。结果表明,通过采取在校园四周增加围墙,并在南北下穿城市道路旁设置声屏障等措施后,可以改善校园声环境,使其达到国家I类地区噪声规定标准。 相似文献
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由于公路隧道有坚硬的装饰面,车辆辐射的噪声,沿着隧道纵向传播很少衰减。这样,行驶在隧道里的司机或许会感到好像在他的后面车子,正要向自己的车子靠近。比较重要的影响是隧道的出口所辐射的噪声而引起的。这种噪声对隧道口附近居民,有时会引起严重的噪声污染。 相似文献
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已往有关交通噪声研究的报导,均是晴天情况的。涉及气候因素(例如下雨)对车辆产生的总燥声级影响的研究,仅限于单车轮胎噪声方面,所观察到的增值高达10dB(A)。少数研究测量了在下雨情况下城市交通噪声的频谱或声级。在墨西哥市某些地区进行城市噪声研究的过程中,有机会作了雨天和晴天的记录,并将在这些情况下记录的频谱加以比较。对在这些情况下测得的噪声级也作了比较,但因交通流量变化,难以获得条件相似的足够记录,或者获得具有充分代表性的统计采样,以进行比较。因此,本文主要论及频谱方面,但也对所得噪声级数据作一些评论。 相似文献
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目的 随着洗衣机产品人机交互界面信息数量在有限区域内的不断增加,构建基于不同交互模式的洗衣机产品可用性评价指标体系,有助于在产品研发的早、中期阶段进行改进,并发现现有产品或原型的可用性问题。方法 采用文献调查法以及用户调研法,完成洗衣机产品的可用性评估模型的构建,通过情景模拟实验法,对按键、按键加旋钮、触屏三种交互模式下洗衣机产品的可用性进行评估与分析。结果 随着洗衣机界面有限操控区域内信息数量的增多,三种交互模式对用户体验质量的影响,得出了可用性评价等级。结论 在有限的操作区域内,当信息数量低于十四时,三种交互模式的用户体验基本相似,随着信息数量的增加,按键加旋钮的交互模式的任务成功率优于按键和触屏,而触屏的交互模式在效率、有用性和满意度方面,均优于按键和按键加旋钮。 相似文献
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Mohammed Gollapalli Atta-ur-Rahman Dhiaa Musleh Nehad Ibrahim Muhammad Adnan Khan Sagheer Abbas Ayesha Atta Muhammad Aftab Khan Mehwash Farooqui Tahir Iqbal Mohammed Salih Ahmed Mohammed Imran B. Ahmed Dakheel Almoqbil Majd Nabeel Abdullah Omer 《计算机、材料和连续体(英文)》2022,73(1):295-310
The fast-paced growth of artificial intelligence applications provides unparalleled opportunities to improve the efficiency of various systems. Such as the transportation sector faces many obstacles following the implementation and integration of different vehicular and environmental aspects worldwide. Traffic congestion is among the major issues in this regard which demands serious attention due to the rapid growth in the number of vehicles on the road. To address this overwhelming problem, in this article, a cloud-based intelligent road traffic congestion prediction model is proposed that is empowered with a hybrid Neuro-Fuzzy approach. The aim of the study is to reduce the delay in the queues, the vehicles experience at different road junctions across the city. The proposed model also intended to help the automated traffic control systems by minimizing the congestion particularly in a smart city environment where observational data is obtained from various implanted Internet of Things (IoT) sensors across the road. After due preprocessing over the cloud server, the proposed approach makes use of this data by incorporating the neuro-fuzzy engine. Consequently, it possesses a high level of accuracy by means of intelligent decision making with minimum error rate. Simulation results reveal the accuracy of the proposed model as 98.72% during the validation phase in contrast to the highest accuracies achieved by state-of-the-art techniques in the literature such as 90.6%, 95.84%, 97.56% and 98.03%, respectively. As far as the training phase analysis is concerned, the proposed scheme exhibits 99.214% accuracy. The proposed prediction model is a potential contribution towards smart cities environment. 相似文献