基于LCP径向偏振光输出的掺镱MOPA脉冲光纤激光器

为实现光纤激光器径向偏振光的高效输出,提出并搭建了基于液晶聚合物(LCP)的纳秒脉冲掺镱(Yb)主振荡器功率放大(MOPA)系统。该系统采用空间相位转换法,利用LCP涡旋半波片将全光纤MOPA激光器输出的高峰值功率、窄线宽、线偏振、高斯形分布的纳秒脉冲信号转换为横向强度呈空心环状分布的拉盖尔-高斯光。MOPA激光器系统由窄线宽连续种子源、电光强度调制器和后续的5级YDF放大器组成,通过实验获得了20.1 W的稳定LP01模输出。而后的LCP涡旋波片用作空间模式转换器,最终获得的平均输出功率为19.5 W,脉宽为10 ns,重复频率为10 kHz,横向剖面呈规则空心环形的径向偏振光输出,模式转换效率可达97%。另外,通过PBS测量法测得径向偏振光的模式纯度约为88.5%,兼具高功率与高纯度的优势。     

A peapod-like ZnO@C design with internal void space to relieve its large-volume-change as lithium-ion battery anode

Peapod-like ZnO@C with internal void space has been synthesized by calcination of ZnO/ZnOHF@polydopamine nanorods. By designing both the large void space between particles and external elastic carbon shell, the large volume change of ZnO during charge-discharge process could be effectively relieved. Moreover, the carbon shell functioned as an electronic conductor and elastic barrier, could accelerate the reaction kinetics and confine stable SEI films formation on the outer protective layer to further improve the structural integrity. Benefiting from these structure advantages, the peapod-like ZnO@C presents a prominent electrochemical performance with a retained discharge capacity of 565.1 mA h g^?1 at 0.2 A g^?1 and high rate capacity of 246.6 mA h g^?1 even at 4 A g^?1.     

Chromatic adaptation in a 2D photograph demonstrated by a D‐up viewer

It is said that we cannot have color constancy in a photograph. The concept of recognized visual space of illumination (RVSI) asserts that chromatic adaptation occurs when one perceives the illumination that is filling a space and not the objects in the space. It predicts then that if one perceives a 3D scene in a photograph, then color constancy will occur in the photograph. In this work, a dimension‐up (D‐up) viewer was developed to perceive a 3D scene on a 2D photograph, and the effect of chromatic adaptation was measured by the color appearance of a gray patch placed at the center of the photograph. Subjects saw the patch as a vivid color when they saw a photograph that had been taken under colored illumination, which is a normal experience in a real space observation. When the color appearance was measured by the elementary color naming method, the amount of chromaticness of the patch in percentage and the apparent hue were very similar to those observed in the 2‐room technique, thus confirming the prediction by the RVSI theory.     

A Novel Beam Search to Improve Neural Machine Translation for English-Chinese

Neural Machine Translation (NMT) is an end-to-end learning approach for automated translation, overcoming the weaknesses of conventional phrase-based translation systems. Although NMT based systems have gained their popularity in commercial translation applications, there is still plenty of room for improvement. Being the most popular search algorithm in NMT, beam search is vital to the translation result. However, traditional beam search can produce duplicate or missing translation due to its target sequence selection strategy. Aiming to alleviate this problem, this paper proposed neural machine translation improvements based on a novel beam search evaluation function. And we use reinforcement learning to train a translation evaluation system to select better candidate words for generating translations. In the experiments, we conducted extensive experiments to evaluate our methods. CASIA corpus and the 1,000,000 pairs of bilingual corpora of NiuTrans are used in our experiments. The experiment results prove that the proposed methods can effectively improve the English to Chinese translation quality.     

基于亲生物理论的医疗空间色彩设计研究

人类在漫长的进化过程中形成了对自然的本能需求,亲生物设计从生物进化的角度以更系统和更合理的方法将自然重新融入现代建筑环境,构建人与场所之间的积极交互。源于光、水、风、植物、动物等自然元素的亲生物色彩设计具有令人愉悦的艺术感染力,动态的视觉效果暗示着蓬勃的生命力,能够系统、清晰地传递复杂的环境信息,引发观者的情感共鸣和情感依恋。从环境疗愈的角度看,具有亲生物特征的色彩设计对改善患者在医疗空间中的体验,激发患者的自我康复能力具有重要意义。亲生物设计是可持续发展设计中不可或缺的一环,两者的结合有助于创造兼具低环境影响和高情感联系的设计,以实现真正和持久的可持续性发展。     

基于深度EPG的智能导航平台建设

随着三网融合的大力推进以及双向互动业务的大力开展,运营商使用原有简单的EPG不足以支撑开展更多的增值业务,不能主动为用户推荐节目,不能为运营商带来新的收入机会。作为重要的创新能力支撑系统,介绍了江苏有线智能导航平台的设计与应用,为相关广电运营商提供一定的设计参考。     

Efficient Flexible M-Tree Bulk Loading Using FastMap and Space-Filling Curves

Many database applications currently deal with objects in a metric space. Examples of such objects include unstructured multimedia objects and points of interest (POIs) in a road network. The M-tree is a dynamic index structure that facilitates an efficient search for objects in a metric space. Studies have been conducted on the bulk loading of large datasets in an M-tree. However, because previous algorithms involve excessive distance computations and disk accesses, they perform poorly in terms of their index construction and search capability. This study proposes two efficient M-tree bulk loading algorithms. Our algorithms minimize the number of distance computations and disk accesses using FastMap and a space-filling curve, thereby significantly improving the index construction and search performance. Our second algorithm is an extension of the first, and it incorporates a partitioning clustering technique and flexible node architecture to further improve the search performance. Through the use of various synthetic and real-world datasets, the experimental results demonstrated that our algorithms improved the index construction performance by up to three orders of magnitude and the search performance by up to 20.3 times over the previous algorithm.     

ED-SWE: Event detection based on scoring and word embedding in online social networks for the internet of people

Online social media networks are gaining attention worldwide, with an increasing number of people relying on them to connect, communicate and share their daily pertinent event-related information. Event detection is now increasingly leveraging online social networks for highlighting events happening around the world via the Internet of People. In this paper, a novel Event Detection model based on Scoring and Word Embedding (ED-SWE) is proposed for discovering key events from a large volume of data streams of tweets and for generating an event summary using keywords and top-k tweets. The proposed ED-SWE model can distill high-quality tweets, reduce the negative impact of the advent of spam, and identify latent events in the data streams automatically. Moreover, a word embedding algorithm is used to learn a real-valued vector representation for a predefined fixed-sized vocabulary from a corpus of Twitter data. In order to further improve the performance of the Expectation-Maximization (EM) iteration algorithm, a novel initialization method based on the authority values of the tweets is also proposed in this paper to detect live events efficiently and precisely. Finally, a novel automatic identification method based on the cosine measure is used to automatically evaluate whether a given topic can form a live event. Experiments conducted on a real-world dataset demonstrate that the ED-SWE model exhibits better efficiency and accuracy than several state-of-art event detection models.     

“流空间”视角下的城市空间网络结构研究——以成渝双城经济圈为例

推动区域协调发展已经成为构建高质量发展国土空间布局的客观需要和解决发展不平衡问题的内在需求，以城市群为核心的经济圈建设成为驱动高质量发展的重要空间层级。为实现成渝双城经济圈的区域一体化及区域多层级协调发展，将流动的生产资源与城市空间结构相结合，基于人流、信息流、技术流、经济流四大流动资源，采用社会网络分析方法，分别从点、线、面3个维度对成渝双城经济圈空间网络结构特征进行分析。研究表明：经济圈内部呈现以成都、重庆为双核心的结构，区域间相互作用，城市中心性整体表现出内部优于外部的空间特征，核心城市对外围城市未充分发挥带动作用；圈内城市链接多表现出高度不对称性，链接对称地区主要集中在成渝中轴地区，城市间的联系有待加强，经济圈外围城市与外界联系较少；区域内部小团体现象明显，团体间联系密度存在差异，整体来看城市集群格局尚未形成，跨省域、跨地区的联系不够，成渝双城经济圈主要是西侧地区围绕成都发展。最后，为构建成渝经济圈一体化及区域多层级协调发展的空间格局，建议突破行政区域限制，构建多领域的跨区域共享协作通道；加强中部地区发展动力，提高外围城市的资源吸收能力；注重经济圈内城市交通建设及信息融合发展，突出核心城市的引领作用和外溢属性，促进城市间技术交流与经济融合。     

Grid Search for Predicting Coronary Heart Disease by Tuning Hyper-Parameters

Diagnosing the cardiovascular disease is one of the biggest medical difficulties in recent years. Coronary cardiovascular (CHD) is a kind of heart and blood vascular disease. Predicting this sort of cardiac illness leads to more precise decisions for cardiac disorders. Implementing Grid Search Optimization (GSO) machine training models is therefore a useful way to forecast the sickness as soon as possible. The state-of-the-art work is the tuning of the hyperparameter together with the selection of the feature by utilizing the model search to minimize the false-negative rate. Three models with a cross-validation approach do the required task. Feature Selection based on the use of statistical and correlation matrices for multivariate analysis. For Random Search and Grid Search models, extensive comparison findings are produced utilizing retrieval, F1 score, and precision measurements. The models are evaluated using the metrics and kappa statistics that illustrate the three models’ comparability. The study effort focuses on optimizing function selection, tweaking hyperparameters to improve model accuracy and the prediction of heart disease by examining Framingham datasets using random forestry classification. Tuning the hyperparameter in the model of grid search thus decreases the erroneous rate achieves global optimization.