冬季CO2水玻璃砂促硬防蠕变技术

介绍了使用一种专用改性荆加入普通水玻璃中，使CO2水玻璃砂在低温环境下也能速硬，可防止铸型（芯）蠕变的技术。     

An ergonomics study of thumb movements on smartphone touch screen

This study investigated the relationships between thumb muscle activity and thumb operating tasks on a smartphone touch screen with one-hand posture. Six muscles in the right thumb and forearm were targeted in this study, namely adductor pollicis, flexor pollicis brevis, abductor pollicis brevis (APB), abductor pollicis longus, first dorsal interosseous (FDI) and extensor digitorum. The performance measures showed that the thumb developed fatigue rapidly when tapping on smaller buttons (diameter: 9 mm compared with 3 mm), and moved more slowly in flexion–extension than in adduction–abduction orientation. Meanwhile, the electromyography and perceived exertion values of FDI significantly increased in small button and flexion–extension tasks, while those of APB were greater in the adduction–abduction task. This study reveals that muscle effort among thumb muscles on a touch screen smartphone varies according to the task, and suggests that the use of small touch buttons should be minimised for better thumb performance.     

3D Shape Segmentation and Labeling via Extreme Learning Machine

We propose a fast method for 3D shape segmentation and labeling via Extreme Learning Machine (ELM). Given a set of example shapes with labeled segmentation, we train an ELM classifier and use it to produce initial segmentation for test shapes. Based on the initial segmentation, we compute the final smooth segmentation through a graph‐cut optimization constrained by the super‐face boundaries obtained by over‐segmentation and the active contours computed from ELM segmentation. Experimental results show that our method achieves comparable results against the state‐of‐the‐arts, but reduces the training time by approximately two orders of magnitude, both for face‐level and super‐face‐level, making it scale well for large datasets. Based on such notable improvement, we demonstrate the application of our method for fast online sequential learning for 3D shape segmentation at face level, as well as realtime sequential learning at super‐face level.     

MPaaS: Mobility prediction as a service in telecom cloud

Mobile applications and services relying on mobility prediction have recently spurred lots of interest. In this paper, we propose mobility prediction based on cellular traces as an infrastructural level service of telecom cloud. Mobility Prediction as a Service (MPaaS) embeds mobility mining and forecasting algorithms into a cloud-based user location tracking framework. By empowering MPaaS, the hosted 3rd-party and value-added services can benefit from online mobility prediction. Particularly we took Mobility-aware Personalization and Predictive Resource Allocation as key features to elaborate how MPaaS drives new fashion of mobile cloud applications. Due to the randomness of human mobility patterns, mobility predicting remains a very challenging task in MPaaS research. Our preliminary study observed collective behavioral patterns (CBP) in mobility of crowds, and proposed a CBP-based mobility predictor. MPaaS system equips a hybrid predictor fusing both CBP-based scheme and Markov-based predictor to provide telecom cloud with large-scale mobility prediction capacity.     

变电站典型光通信模式研究

目前,通信条件已发生了革命性的变化,由早期的载波、微波和电缆等通信介质发展成为以光缆为主的通信网络.电力系统以OPGW光缆、ADSS光缆为主,已覆盖了几乎所有的输变电场所.因此,应建立与之适应的通信模式.通过研究现代通信条件、变电站通信的需求,提出了统一化的、简单的变电站通信模式.     

基于多径路由负载均衡的动态源路由协议*

提出了一种DSR的改进多径路由协议（LBDSRM）。采用开销小的综合链路状态路由判据算法,协议具有链路状态实时监控与适时调整路由功能,在链路变化较大的情况下,主动通知有路由冗余的源节点改用或重点使用次选路由;而且协议提出了适时退避算法,解决了多径任务与单径任务争用链路时网络公平性问题。仿真结果表明新协议能有效避免节点拥塞,达到较好的动态负载均衡,实现网络资源的充分利用。     

单周控制光伏并网系统的最大功率控制研究

单周控制的单级全桥光伏并网系统具有容量大、效率高、成本低等优点.但现有基于单周控制的光伏电池最大功率跟踪存在着难以搜索最大功率点、难以稳定工作在最大功率点以及搜索精度差等缺点.针对以上不足,提出了基于增量电导的光伏电池最大功率跟踪算法,该算法将光伏电池输出功率引入到单周控制的输入量中,同时优化了光伏电池输出电压的给定量.该算法在硬件上实现简单,仿真和实验结果表明该算法能够准确跟踪光伏电池最大功率点,并具有较高的精度和稳定性.     

Correlations between characteristics of maximum influence and degree distributions in software networks

Software systems can be represented as complex networks and their artificial nature can be investigated with approaches developed in network analysis.Influence maximization has been successfully applied on software networks to identify the important nodes that have the maximum influence on the other parts.However,research is open to study the effects of network fabric on the influence behavior of the highly influential nodes.In this paper,we construct class dependence graph(CDG)networks based on eight practical Java software systems,and apply the procedure of influence maximization to study empirically the correlations between the characteristics of maximum influence and the degree distributions in the software networks.We demonstrate that the artificial nature of CDG networks is reflected partly from the scale free behavior:the in-degree distribution follows power law,and the out-degree distribution is lognormal.For the influence behavior,the expected influence spread of the maximum influence set identified by the greedy method correlates significantly with the degree distributions.In addition,the identified influence set contains influential classes that are complex in both the number of methods and the lines of code(LOC).For the applications in software engineering,the results provide possibilities of new approaches in designing optimization procedures of software systems.     

"计算机网络基础"课程"金课"建设初探

为了响应教育部关于建设"金课"的倡议,基于我院计算机科学与技术专业的"计算机网络基础"课程特点,结合该专业的毕业要求,修订了课程目标;根据课程目标,重新整合了教学内容,确定了线上线下混合式教学模式;构建课程目标达成度评价体系,并将其用于后续教学过程.实践结果表明,该教学改革符合混合式"金课"的建设要求,提升了教学效果.