紧框架的一个注记(英文)

假设Φ(x) ∈L2 (R)且具有紧支集。令V =span{Φ(x-k)｜k∈Z}。在这篇短文中 ,我们证明 :如果 {Φ(x -k) ｜k ∈Z}是V的界为 1的紧框架 ,那么{Φ(x-k) ｜k∈Z}

“双奶酪模式”下的STEM活动设计实践研究

当前的STEM活动设计往往缺乏严谨的科学论证过程,对学生的过程性评价不够全面和客观,教师在设计STEM活动内容时普遍没有关注各活动模块之间的内在联系。“双奶酪模式”下的STEM活动设计实践研究,融合了基于证据的学习理论、美国的5E教学模式以及设计思维方法,以发展学生的核心素养为目标,注重科学论证的严谨性,注重过程评价的全面性,注重活动设计的系统性。文章提供了“地球仪的创新设计”的活动设计脉络,并以其中模块“拨动时光的年轮”为例,阐述如何运用“双奶酪模式”完成此活动模块的设计。

An Investigation of Optimal Machine Learning Methods for the Prediction of ROTI

The rate of the total electron content(TEC)change index(ROTI)can be regarded as an effective indicator of the level of ionospheric scintillation,in particular in low and high latitude regions.An accurate prediction of the ROTI is essential to reduce the impact of the ionospheric scintillation on earth observation systems,such as the global navigation satellite systems.However,it is difficult to predict the ROTI with high accuracy because of the complexity of the ionosphere.In this study,advanced machine learning methods have been investigated for ROTI prediction over a station at high-latitude in Canada.These methods are used to predict the ROTI in the next 5 minutes using the data derived from the past 15 minutes at the same location.Experimental results show that the method of the bidirectional gated recurrent unit network(BGRU)outperforms the other six approaches tested in the research.It is also confirmed that the RMSEs of the predicted ROTI using the BGRU method in all four seasons of 2017 are less than 0.05 TECU/min.It is demonstrated that the BGRU method exhibits a high level of robustness in dealing with abrupt solar activities.

“战马”生物传感实验是这样炼成的

“战马”“袭”心传感实验研究组从观众中招募150名志愿者，通过高科技的生物传感技术全程观测并记录他们观看演出时的体感反应，从而更深入的了解人类的情感。这种剧场与科技的“跨界”实验在国内尚属首次，这其中的“剧情”是怎样展开的，让我们去探个究竟。

基于价值工程的排污深隧工程方案比选

近年来城市发展、人口膨胀,水体污染的现象越来越严重。武汉市中水污染的主要原因是仍保留合流制的排水系统,这促使排水系统溢流污染和区域内涝问题格外突出。而采用排污深隧系统取代传统的合流制可以基本消除溢流污染。同时将价值工程应用于排污深隧的决策阶段进行方案比选与优化,可有效降低成本提高收益,增强使用功能。

Coexistence Between Massive MIMO and Radar Communications:Performance Analysis

Frequency spectrum sharing between radar and communication systems has recently attracted substantial attention.We consider the coexistence between a massive multiple-input multiple-output(MIMO)downlink system and MIMO radar to enable the operation of these two systems with minimal mutual interference.Through an asymptotic analysis,we show that by using more antennas at the base station(BS),we can improve the performance of massive MIMO,while keeping the interference to the radar system unchanged.Additionally,if we use a large number of antennas at the BS and make the transmit power inversely proportional to the number of antennas,we can avoid the interference from the massive MIMO system to the radar system,with no compromise in the performance of the massive MIMO system.Closed-form expressions for the probability of detection of the radar system and the downlink spectral efficiency of the massive MIMO system,are derived.Furthermore,we propose a power allocation scheme which selects the transmit powers at the MIMO radar and BS to maximize the probability of detection for the MIMO radar.Interestingly,the optimal power allocation can be determined in closed-form.These results provide valuable insights into the practical coexistence between massive MIMO and radar systems.

A Model for Slicing JAVA Programs Hierarchically

Program slicing can be effectively used to debug, test, analyze, understand and maintain objectoriented software. In this paper, a new slicing model is proposed to slice Java programs based on their inherent hierarchical feature. The main idea of hierarchical slicing is to slice programs in a stepwise way, from package level, to class level, method level, and finally up to statement level. The stepwise slicing algorithm and the related graph reachability algorithms are presented, the architecture of the Java program Analyzing TOol (JATO) based on hierarchical slicing model is provided, the applications and a small case study are also discussed. Keywords software engineering - hierarchical model - program slicing - JAVA - stepwise algorithm - JATO This work is supported by the National Natural Science Foundation of China under Grant No.60473065 and the Outstanding Youth Teacher Support Foundation of Southeast University under Grant No.4009001011.Bi-Xin Li is a professor in Southeast University from Jan., 2004. He received the Ph.D. degree in computer software and theory from Nanjing University in 2001. From Apr. 2001 to Apr. 2002, he worked at TUCS (Turku Center for Computer Science) for one year as a post-doctoral researcher. From Apr. 2002 to Dec. 2003, he worked. at Department of Computer and Information Science, NTNU (Norwegian University of Science and Technology), and CWI (the Centrum voor Wiskunde en Informatica), both as an ERCIM Fellow. His current research interests include software construction, software testing, SQA techniques, software architecture and component techniques, safety-critical system and formal verification, etc.Xiao-Cong Fan is a senior researcher in the Inteligent Agent Lab of the Pennsylvania State University from 2002. He received the Ph.D. degree from Nanjing University in 1999. From 2000 to 2002, he worked at the turku Centre for Computer Science and the Computer Science Department of Abo Akademi University in Finland, where he participated in the projects SOCOS and SPROUT, which developed a methodology for software platform construction based on the Refinement Calculus. He currently works on formal agent theories in teamwork, and projects for applying these theories.Jun Pang is now a Ph.D. candidate in CWI, the Netherlands. He received the B.Sc. and M.Sc. degrees in computer science from Nanjing University, China, in 1997 and 2000. His research interests include protocol verification, process algebra, safety critical systems, security, testing, software architecture etc.Jian-Jun Zhao is an associate professor of computer science at Fukuoka Institute of Technology, Japan. He received the B.S. degree in computer science from Tsinghua University, China, in 1987, and the Ph.D. degree in computer science from Kyushu University, Japan, in 1997. His research interests include program analysis and compiler, software architecture analysis, aspect-oriented software development, and ubiquitous computing environment.

Algebraic manipulation detection codes

Algebraic manipulation detection codes are a cryptographic primitive that was introduced by Cramer et al. (Eurocrypt 2008). It encompasses several methods that were previously used in cheater detection in secret sharing. Since its introduction, a number of additional applications have been found. This paper contains a detailed exposition of the known results about algebraic manipulation detection codes as well as some new results.

一种切片JAVA程序的层次模型

程序切片的初始概念出现在1979年Mark Weiser的博士论文中。从此其他研究者们也提出一些程序切片，而这些程序切片的概念和计算方法与Weiser的比起来略有不同。一个重要的区别就是出现了动态和静态之分，即前者考虑程序的特定输入，而后者不需要。直到20世纪90年代，由于面向对象程序设计语言的兴起。

A review: Challenges and opportunities for artificial intelligence and robotics in the offshore wind sector

The UK has set plans to increase offshore wind capacity from 22GW to 154GW by 2030. With such tremendous growth, the sector is now looking to Robotics and Artificial Intelligence (RAI) in order to tackle lifecycle service barriers as to support sustainable and profitable offshore wind energy production. Today, RAI applications are predominately being used to support short term objectives in operation and maintenance. However, moving forward, RAI has the potential to play a critical role throughout the full lifecycle of offshore wind infrastructure, from surveying, planning, design, logistics, operational support, training and decommissioning. This paper presents one of the first systematic reviews of RAI for the offshore renewable energy sector. The state-of-the-art in RAI is analyzed with respect to offshore energy requirements, from both industry and academia, in terms of current and future requirements. Our review also includes a detailed evaluation of investment, regulation and skills development required to support the adoption of RAI. The key trends identified through a detailed analysis of patent and academic publication databases provide insights to barriers such as certification of autonomous platforms for safety compliance and reliability, the need for digital architectures for scalability in autonomous fleets, adaptive mission planning for resilient resident operations and optimization of human machine interaction for trusted partnerships between people and autonomous assistants. Our study concludes with identification of technological priorities and outlines their integration into a new ‘symbiotic digital architecture’ to deliver the future of offshore wind farm lifecycle management.

ON THE VALIDITY OF THE LOCAL FOURIER ANALYSIS

Local Fourier analysis(LFA)is a useful tool in predicting the convergence factors of geometric multigrid methods(GMG).As is well known,on rectangular domains with periodic boundary conditions this analysis gives the exact convergence factors of such met hods.When other boundary conditions are considered,however,this analysis was judged as been heuristic,with limited capabilities in predicting multigrid convergence rates.In this work,using the Fourier method,we extend these results by proving that such analysis yields the exact convergence factors for a wider class of problems,some of which can not be handled by the traditional rigorous Fourier analysis.

用“卡计算机”控制您的信息

不久之后,几乎在任何地方通过访问一个计算机化的网络,你可以为购买的东西付账,租一部电影在家里放映,改变你的生命保险策略,或者发送一个电子“信件”到朋友那里。虽然这种类型的单一综合系统还没有,但其部分成果已经实用。例如,商店电子付账已在许多国家使用,而更多的国家也在计划这样做。这类系统在减少机构开支,增加它们的安全和效率以及增强用户信任方面的确有着巨大的潜力。

Data-driven modelling of energy demand response behaviour based on a large-scale residential trial

Recent years have seen an increasing interest in Demand Response(DR),as a means to satisfy the growing flexibility needs of modern power grids.This increased flexibility is required due to the growing proportion of intermittent renewable energy generation into the energy mix,and increasing complexity in demand profiles from the electrification of transport networks.Currently,less than 2%of the global potential for demand-side flexibility is currently utilised,but a more widespread adoption of residential consumers as flexibility resources can lead to substantially higher utilisation of the demand-side flexibility potential.In order to achieve this target,acquiring a better understanding of how residential DR participants respond in DR events is essential–and recent advances in novel machine learning and statistical AI provide promising tools to address this challenge.This study provides an in-depth analysis of how residential customers have responded in incentive-based DR,utilising household-related data from a large-scale,real-world trial:the Smart Grid,Smart City(SGSC)project.Using a number of different machine learning approaches,we model the relationship between a household’s response and household-related features.Moreover,we examine the potential effects of households’features on the residential response behaviour,and highlight a number of key insights which raise questions about the reported level of consumers’engagement in DR schemes,and the motivation for different customers’response level.Finally,we explore the temporal structure of the response–and although we found no supporting evidence of DR responders learning over time for the available data from this trial,the proposed methodologies could be used for longer-term longitudinal DR studies.Our study concludes with a broader discussion of our findings and potential paths for future research in this emerging area.

Prediction of voltage distribution using deep learning and identified key smart meter locations

The energy landscape for the Low-Voltage(LV)networks is undergoing rapid changes.These changes are driven by the increased penetration of distributed Low Carbon Technologies,both on the generation side(i.e.adoption of micro-renewables)and demand side(i.e.electric vehicle charging).The previously passive‘fit-and-forget’approach to LV network management is becoming increasing inefficient to ensure its effective operation.A more agile approach to operation and planning is needed,that includes pro-active prediction and mitigation of risks to local sub-networks(such as risk of voltage deviations out of legal limits).The mass rollout of smart meters(SMs)and advances in metering infrastructure holds the promise for smarter network management.However,many of the proposed methods require full observability,yet the expectation of being able to collect complete,error free data from every smart meter is unrealistic in operational reality.Furthermore,the smart meter(SM)roll-out has encountered significant issues,with the current voluntary nature of installation in the UK and in many other countries resulting in low-likelihood of full SM coverage for all LV networks.Even with a comprehensive SM roll-out privacy restrictions,constrain data availability from meters.To address these issues,this paper proposes the use of a Deep Learning Neural Network architecture to predict the voltage distribution with partial SM coverage on actual network operator LV circuits.The results show that SM measurements from key locations are sufficient for effective prediction of the voltage distribution,even without the use of the high granularity personal power demand data from individual customers.