中国作为最大的能源消费国家,正面临着能源短缺、环境污染等诸多挑战。 SCIENCE CHINA Chemistry (《中国科学:化学》英文版)2017年第11期邀请厦门大学 田中群 院士及 郑南峰 教授担任特邀编辑,组织出版了“ Strategies for Developing Energy-Related Physical Chemistry ”专题,对能源化学中的合成气转化、燃烧化学、电化学储能、高温电解以及能源化学系统工程等领域的最新进展做了综述与展望,以期对未来该领域的研究提供借鉴及指导。 以下是专题目录,欢迎关注。点击下方链接 可查阅全文↙ https://link.springer.com/journal/11426/60/11/page/1 Special Topic: Strategies for Developing Energy-Related Physical Chemistry EDITORIAL Special topic on strategies for developing energy-related physical chemistry Zhong-Qun Tian Nanfeng Zheng Sci China Chem , 2017, 60(11): 1377–1378 PERSPECTIVES A perspective on hydrogen production via high temperature steam electrolysis Xinbing Chen, Chengzhi Guan, Guoping Xiao, Cheng Peng Jian-Qiang Wang Sci China Chem , 2017, 60(11): 1379–1381 Reaction coupling as a promising methodology for selective conversion of syngas into hydrocarbons beyond Fischer-Tropsch Kang Cheng, Jincan Kang, Qinghong Zhang Ye Wang Sci China Chem , 2017, 60(11): 1382–1385 Perspective on the RDs in energy chemistry systems engineering Yingru Zhao, Xiangyan Zhan, Shiqi Zhang Meng Wang Sci China Chem , 2017, 60(11): 1386–1390 REVIEWS Challenges and perspectives of combustion chemistry research WenhaoYuan, Yuyang Li Fei Qi Sci China Chem , 2017, 60(11): 1391–1401 New electrochemical energy storage systems based on metallic lithium anode—the research status, problems and challenges of lithium-sulfur, lithium-oxygen and all solid state batteries Liangyu Li, Chunguang Chen Aishui Yu Sci China Chem , 2017, 60(11): 1402–1412
SS6: Sensor Data Mining For Tracking Description: The rapid development of advanced sensors and their joint application provide a foundation for new paradigms to combat the challenges that arise in target detection, tracking and forecasting in harsh environments with poor prior information. As a consequence, the sensor community has expressed interest in novel data mining methods coupling traditional statistical techniques for substantial performance enhancement. For example, the advent of multiple/massive sensor systems provides very rich observation at high frequency yet low financial cost, which facilitates novel perspectives based on data clustering and model learning to deal with false alarms and misdetection, given little statistical knowledge about the objects, sensors and the background. Numerical fitting and regression analysis provide another unlimited means to utilize the unstructured context information such as “the trajectory is smooth” for continuous-time target trajectory estimation. Incorporating additional, readily available information to constrain the adaptive response and to combat poor scenario knowledge, has shown promise as a means of restoring sensor capability over a range of challenging operating conditions as well as to deal with a variety of challenging problems that makes traditional approaches awkward. The purpose of this special section is to assemble and disseminate information on recent, novel advances in sensor signal and data mining techniques and approaches, and promote a forum for continued discussion on the future development. Both theoretical and practical approaches in the area are welcomed. Organizers: Tiancheng Li ( t.c.li@usal.es ) Haibin Ling ( hbling@temple.edu ) and Genshe Chen ( gchen@intfusiontech.com ) The topics of interest of this specialsection include but are not limited to: · Adaptive filtering · Learning for state space models · Manoeuvring target detectionand tracking · Object recognition/classificationusing sonar, radar, video, soft data sources, etc. · Clustering approaches fortracking · Regression analysis for trajectoryestimation · Multiple Intelligent dataassociation/fusion · Machine learning technology fortracking Submission链接: http://www.fusion2017.org/submissions.html 欢迎投稿! The 20th International Conference on Information Fusion (Fusion 2017) will be held in Xi'an, China during July 10–13, 2017. Conference Venue: Wyndham Grand Xian South Video of Xi'an : http://www.fusion2017.org/video/Fusion2017_2.ogv