黄泛平原古城“环城湖”与城市防洪减灾

以黄泛平原为研究背景,以该地城内形态相似的“环城湖”为研究对象,通过对其构成要素、历史成因、演变过程、空间格局等方面的研究,探讨了“环城湖”在黄河洪水与泥沙作用下的生成机制与演变规律.认为:古城“环城湖”是特定自然条件下黄河泛滥引起生态环境改变与古代城市营建模式共同作用的结果,在古城营建过程中承担着泄洪、排涝等防洪减灾功能,并在当今城市建设中仍发挥着重要作用;只有充分了解古城“环城湖”的形成过程与演变机制,才能重新认识和定位“环城湖”的当代价值,从本质上把握古城“环城湖”的特色与精华.     

大底盘层间隔震结构抗震性能研究

随着我国城市化进程的加快,高层建筑已屡见不鲜,而大底盘塔式结构最为常见.由于大底盘和塔楼之间刚度和质量的突变,使其抗震性能明显降低,如何提高这类结构的抗震性能成为工程界需要解决的重要问题之一.目前,工程中普遍使用的方法是使用变形缝将塔楼与附属建筑分开,但这种做法不仅构造麻烦,而且也给结构的正常使用带来不便.本文研究通过...     

Fenton接触氧化法强化石英砂—锰砂滤料处理铁锰矿井废水

为了解决目前常用的接触氧化法除铁除锰工艺中铁离子存在时锰离子难去除的问题,本文重点研究了Fenton接触氧化法强化石英砂-锰砂滤料的除铁除锰效率和机理。结果表明:Fenton试剂+锰砂+石英砂工艺除铁锰的效果很好,影响因素的最佳值如下:加入H2O2形成Fenton试剂后可以强化石英砂-锰砂工艺除铁除锰的效果,当H2O2投加量为0.15 mg/L、滤速为8 m/h、pH为7时,铁离子的去除率可达到92%,当H2O2投加量为0.17mg/L、滤速为8 m/h、pH为7时,锰离子的去除率可以达到97%。     

基于ARM嵌入式系统的服装绘图仪的设计

服装绘图仪的系统核心采用LPC2132微处理器,内嵌实时操作系统uC/OS-II,采用多任务处理方式。主要设计了XY型服装绘图仪的系统机械结构与笔头机构,详细介绍控制系统的体系结构,实现了电机驱动及系统软件,并在此基础上做了测试。     

Herfindahl index applied to Fourier analysis

Since the harmonic components in the signal to be analyzed are uncertain and diverse, it is difficult to make the sampling frequency and the signal length be harmonically related to the component frequencies included in the signal, and the leakage is produced in the discrete Fourier transform. Based on the definition of the Herfindahl index in economics, the spectrum concentration can be defined and computed. The appropriate number of points can be decided according to the peak location of the spectrum concentration. The method and the examples are specified in detail in the paper.     

Round-trip driving effects on driving performances and mental workload under different traffic rules

This study investigates the effect of switching between different traffic rules (left-versus right-hand traffic) on driving performance and mental workload. A driving simulation environment was developed according to the real environment. Two urban roads with different traffic systems were simulated. Twenty participants executed intersection turns and continuous car-following behavior in four simulated driving stages, including driving with familiar, unfamiliar, second time unfamiliar, and back to familiar traffic rules. The mean and standard deviations for speed, distance headway, and the standard deviation of lateral position were recorded as driving performance. Mental workload was determined using the NASA-TLX and Rating Scale Mental Effort questionnaires. One-way analysis of variance was used to evaluate the differences between the four driving stages using subjective and objective measures. The results showed that significant differences were obtained in all measures when driving in the four driving situations, except for the speed standard deviation. The car-following behavior was the most unsafe (significantly larger standard deviations for distance headway and mental workload) when driving in unfamiliar road traffic compared with the other stages. When driving under unfamiliar traffic rules for the second time, the mental workload was significantly relieved and the lane-keeping ability significantly improved. The results indicated that providing an adaptive runway for drivers to familiarize themselves with different traffic rules is necessary to improve driving performance and safety. These findings provide useful information for designing bridges linking two places with different traffic rules to increase traffic safety.     

Hybrid robust convolutional autoencoder for unsupervised anomaly detection of machine tools under noises

Anomaly detection of machine tools plays a vital role in the machinery industry to sustain efficient operation and avoid catastrophic failures. Compared to traditional machine learning and signal processing methods, deep learning has greater adaptive capability and end-to-end convenience. However, challenges still exist in recent research in anomaly detection of machine tools based on deep learning despite the marvelous endeavors so far, such as the necessity of labeled data for model training and insufficient consideration of noise effects. During machine operation, labeled data is often difficult to obtain; the collected data contains varying degrees of noise disturbances. To address the above challenges, this paper develops a hybrid robust convolutional autoencoder (HRCAE) for unsupervised anomaly detection of machine tools under noises. A parallel convolutional distribution fitting (PCDF) module is constructed, which can effectively fuse multi-sensor information and enhance network robustness by training in parallel to better fit the data distribution with unsupervised learning. A fused directional distance (FDD) loss function is designed to comprehensively consider the distance and angle differences among the data, which can effectively suppress the influence of noises and further improve the model robustness. The proposed method is validated by real computer numerical control (CNC) machine tool data, obtaining better performance of unsupervised anomaly detection under different noises compared to other popular unsupervised improved autoencoder methods.     

Immersive and desktop virtual reality in virtual fashion stores: a comparison between shopping experiences

Virtual Reality - With the high growth and prosperity of e-commerce, the retail industry needs to explore new technologies that improve digital shopping experiences. In the current technological...     

Smart experience-oriented customer requirement analysis for smart product service system: A novel hesitant fuzzy linguistic cloud DEMATEL method

Smart product service system (PSS) has become an essential strategy to transform towards digital servitization for manufacturing companies. By leveraging smart capabilities, smart PSS aims to create superior user experience in a smart context. To develop a successful smart PSS, customer requirement management from smart experience perspective is necessary. However, it is a challenging task to identify and evaluate diverse, implicit and interrelated smart experience-oriented customer requirement (SEO-CR) in smart PSS context. Hence, this paper proposes an effective methodology to elicit and analyze SEO-CRs. At first, a generic, two-dimensional SEO-CR system is presented as a basis to derive the tailored SEO-CRs for various smart PSS applications. Second, a novel HFLC-DEMATEL (hesitant fuzzy linguistic cloud-based Decision-making and trial evaluation laboratory) method is proposed to accurately evaluate the priority and complicated interaction of SEO-CRs, considering the hesitancy, fuzziness and randomness under uncertain decision environment. Some new operations (e.g., cloud total-relation matrix and weight determination method) and a cloud influence relation map are developed to fully take advantage of cloud model in DEMATEL implementation. Finally, a real case of smart vehicle service system (SVSS) is presented. The 18 SEO-CRs of the SVSS are derived based on the generalized SEO-CRs. By using HFLC-DEMATEL, some important SEO-CRs in context of SVSS are identified, such as autonomous and convenience. The finding of results can help designers make proper decisions in design and development of SVSS with a superior smart experience. The effectiveness and reliability of the proposed method are validated by conducting some comparative analyses.