Differences between English and Chinese Sales Promotion Letters

This paper aims to figure out the differences in English and Chinese sales promotion letters by analyzing ten selected letters from business writing books and websites, which might provide more valuabl...     

Characterization of chaotic multiscale features on the time series of melt index in industrial propylene polymerization system

The chaos characteristics of melt index have been first explored, and the Hilbert–Huang transform method and time delay embedding method are applied to multiscale dynamic analysis on the time series of the melt index (MI) in the propylene polymerization industry. The research results show that the embedding delay is 2, the embedding dimension is 5, the correlation dimension D₂ is 1.57, and the maximum Lyapunov exponent is 0.143 for the melt index series, which provide clear evidence of chaotic multiscale features in the propylene polymerization process. Three intrinsic mode functions (IMFs) are decomposed from the melt index time series; the presence of non-integer fractal correlation dimension and positive finite maximum Lyapunov exponent are found in some IMF components. The PP melt index series are divided into two chaotic signals, a determined signal and a random signal respectively, and its complexity is therefore reduced. Furthermore, the coupling of subscale structures of the propylene polymerization is explored with the dimension of interaction dynamics and a robust algorithm for detecting interdependence. It is found that IMF(2) is the main driver in the coupling system of IMF(1)and IMF(2). All these provide a guideline for studying propylene polymerization process with chaotic multiscale theory and may offer more candidate tools to model and control propylene polymerization system in the future.     

Real time exploration and management of large medical volumetric datasets on small mobile devices—Evaluation of remote volume rendering approach

In this paper I present the architecture of system that can be used for real time exploration and management of large medical volumetric datasets. The new state of the art solution presented in this paper is an example of visual data management system. System prototype evaluation proved that it is possible to use low-powered (and cheap) up-to-date mobile devices with programmable GPUs as the remote interfaces for exploration of large volumetric medical data. The implementation was done with high-level programming language that enables portability between different hardware models. The lack of lossy compression enables to display high quality medical images visualizations without any simplifications and noises in frequency domain. The prototype of system is capable to remotely render and send to a client (for example cell phone or tablet) rendered data with frequency 30 fps with limited resolution during interaction. One second after the interaction is finished client machine receives full resolution image. The evaluation of the system was performed on volumetric computed tomography angiography image with approximate size 512³ voxels.     

基于智能视觉的假肢虚拟再生仿真技术的算法研究

研究假肢虚拟再生仿真方法。腿部结构较为复杂,残缺再生过程需要根据不同腿部残缺根源部位进行方向和角度的预测,但是由于腿部角度和生长方向推断过程信息过少,造成传统的虚拟腿部残缺再生仿真无法取得足够信息,只能根据简单的向上,造成模拟效果不好。为此,提出了一种基于智能视觉的假肢虚拟再生仿真方法。根据误差四元数状态变换相关理论,能够对假肢虚拟再生的方向和旋转角度进行有效的预测,从而为假肢虚拟再生仿真提供准确的依据。根据双目立测三维模拟方法,建立假肢虚拟再生仿真三维模型。实验结果表明,利用该算法进行假肢虚拟再生仿真,可以真实的反映残肢不同位置特征点的相关性,从而为假肢虚拟再生手术提供依据。     

基于RBF神经网络的成都市城镇建设用地需求预测

以成都市为研究区域,利用城镇建设用地和社会经济统计数据,从因素和时间二维角度出发,分别选用RBF神经网络模型和GM(1,1)模型,在模型比较优选的基础上对成都市城镇建设用地需求进行了预测。结果表明:RBF神经网络模型与GM(1,1)模型相比,前者的平均绝对误差和误差均方根较低,且线性拟合效果更佳,是一种精度较高的预测方法;据预测,近期2015年和远期2020年研究区城镇建设用地需求量将分别达到145 986.10hm2和182 321.26hm2,研究结果既能为具有类似数据"突变"特征的城市开展城镇建设用地需求预测提供方法借鉴,又能为研究区土地资源的可持续利用和土地利用总体规划的定期修编提供决策依据。     

卫星遥感技术在森林资源动态变化预测的应用研究

利用遥感技术提出一种面向卫星遥感技术的森林资源动态变化预测方法,该方法可以根据拍摄到的遥感图像信息进行分析和识别,准确的分析出当前森林资源的健康状态,从而预测出森林资源在未来一段时间内病虫害的变化趋势。     

基于专利分析概念模型的中国机器人技术发展预测

基于专利分析的概念模型,从技术发展方向、技术发展水平和技术发展潜力三个方面对中国机器人技术发展进行预测,以验证模型的应用性。预测结果表明：中国机器人整体技术处于成长期,但主要的子技术还存在明显差异：其中机械手子技术已位于成长期的中后期阶段,转向机构子技术则位于成长期的前期阶段,非电变量的控制或调节系统技术以及一般的控制或调节系统则刚进入成长期。因此,企业、高校以及科研院所应积极抓住机器人技术发展的大好时机,开展联合攻关,实现机器人关键技术的重大突破,促进产业转型与升级,提升先进制造业的产业技术竞争力。     

The Future Development of Energy-Environment-Economy Integrated Assessment Modelling

<正>One of the greatest challenges faced by the energy and economic system is the risk of environmental and climate change.The models that have been developed to study the principle and dynamics of the energy,environment,and economic system are called the     

青海省东乘公麻金矿床Ⅵ号矿体原生晕特征及深部找矿预测

东乘公麻金矿床是青海省巴颜喀拉造山带北缘Au-Sb成矿带上新发现的中型金矿床,由于受“生态保护第一,尊重群众意愿”政策影响,勘查工作主要以深部勘查为主,本文选择该矿床有代表性的VI号矿体,研究其构造原生晕轴向分带序列。研究结果表明：VI号矿体原生晕分带序列为：（Au、Sb、As）→（Bi、Pb）→（Ag、Cu、Zn）;分带序列呈现“反分带”趋势,地球化学参数“反转”,前、尾晕共存,预测VI号矿体深部延伸较好或有隐伏矿体存在的可能性,圈定了成矿靶区,以期为矿山下一步探矿工作提供科学依据。     

Prediction of stress levels in the workplace using surrounding stress

Occupational stress has a significant adverse effect on workers’ well-being, productivity, and performance and is becoming a major concern for both individual companies and the overall economy. To reduce negative consequences, early detection of stress is a key factor. In response several stress prediction methods have been proposed, whose primary aim is to analyse physiological and behavioural data. However, evidence suggests that solutions based on physiological and behavioural data alone might be challenging when implemented in real-world settings. These solutions are sensitive to data problems arising from losses in signal quality or alterations in body responses, which are common in everyday activities. The contagious nature of stress and its sensitivity to the surroundings can be used to improve these methods. In this study, we sought to investigate automatic stress prediction using both surrounding stress data, which we define as close colleagues’ stress levels and the stress level history of the individuals. We introduce a real-life, unconstrained study conducted with 30 workers monitored over 8 weeks. Furthermore, we propose a method to investigate the effect of stress levels of close colleagues on the prediction of an individual’s stress levels. Our method is also validated on an external, independent dataset. Our results show that surrounding stress can be used to improve stress prediction in the workplace, where we achieve 80% of F-score in predicting individuals’ stress levels from the surrounding stress data in a multiclass stress classification.