Address at the Launching Ceremony of the Silk Road Community Building Initiative in Romania by Mme.Xu Lyuping,Vice-President of China NGO Network for International Exchanges

Distinguished former Prime Minister Mr.Peter Roman,Distinguished Minister Mr.Niculae Badalau,Distinguished Mr.&Mrs.Bartha,Ladies and gentlemen,dear friends,It’s my great delight to attend the launching ceremony of the Silk Road Community Building Initiative in Romania,which is a major event to enhance the mutual understanding and trust,promote people-to-people connectivity as well as consolidate the foundation of the Belt and Road cooperation between China and Romania.

Seismic velocity inversion based on CNN-LSTM fusion deep neural network

Based on the CNN-LSTM fusion deep neural network,this paper proposes a seismic velocity model building method that can simultaneously estimate the root mean square(RMS)velocity and interval velocity from the common-midpoint(CMP)gather.In the proposed method,a convolutional neural network(CNN)Encoder and two long short-term memory networks(LSTMs)are used to extract spatial and temporal features from seismic signals,respectively,and a CNN Decoder is used to recover RMS velocity and interval velocity of underground media from various feature vectors.To address the problems of unstable gradients and easily fall into a local minimum in the deep neural network training process,we propose to use Kaiming normal initialization with zero negative slopes of rectifi ed units and to adjust the network learning process by optimizing the mean square error(MSE)loss function with the introduction of a freezing factor.The experiments on testing dataset show that CNN-LSTM fusion deep neural network can predict RMS velocity as well as interval velocity more accurately,and its inversion accuracy is superior to that of single neural network models.The predictions on the complex structures and Marmousi model are consistent with the true velocity variation trends,and the predictions on fi eld data can eff ectively correct the phase axis,improve the lateral continuity of phase axis and quality of stack section,indicating the eff ectiveness and decent generalization capability of the proposed method.

Adaptive fuze-warhead coordination method based on BP artificial neural network

The appropriate fuze-warhead coordination method is important to improve the damage efficiency of air defense missiles against aircraft targets. In this paper, an adaptive fuze-warhead coordination method based on the Back Propagation Artificial Neural Network(BP-ANN) is proposed, which uses the parameters of missile-target intersection to adaptively calculate the initiation delay. The damage probabilities at different radial locations along the same shot line of a given intersection situation are calculated, so as to determine the optimal detonation position. On this basis, the BP-ANN model is used to describe the complex and highly nonlinear relationship between different intersection parameters and the corresponding optimal detonating point position. In the actual terminal engagement process, the fuze initiation delay is quickly determined by the constructed BP-ANN model combined with the missiletarget intersection parameters. The method is validated in the case of the single-shot damage probability evaluation. Comparing with other fuze-warhead coordination methods, the proposed method can produce higher single-shot damage probability under various intersection conditions, while the fuzewarhead coordination effect is less influenced by the location of the aim point.

Does the Belt and Road Initiative Promote Value Chain Connection Between China and the Silk Road Countries?

The global value chains have become the core skeleton of the global economy.As a large-scale international cooperation initiative,the Belt and Road Initiative(BRI hereafter)may have a significant impact on the global economic landscape.In this context,the spatiotemporal pattern and evolution of the value chain connection of the Silk Road countries and whether the BRI will promote the value chain connections between China and these countries are important research questions for understanding the changing global economic landscape.This paper employs input-output analysis,network analysis and difference-in-differences based on Propensity Score Matching(PSM-DID)to conduct an in-depth quantitative study of these questions.The results show that,first,the overall value chain connection between China and the Silk Road countries has been rising since 2001.From the perspective of geographical distribution,Southeast Asia is the highest value chain connection region with China,and the growth in the central and eastern Europe is the most significant,whereas the central Asia is the lowest value connection region.From the perspective of complex network analysis,China’s position in the network of value flow among the Silk Road countries has been increasing continuously,and it has been in the lead position since 2008.Besides,the implementation of the BRI has had a significant positive influence on the overall value chain connection between China and the Silk Road countries,but this positive influence is limited to the central and eastern Europe region,whereas it is not significant in other regions.Finally,this paper suggests that to promote the development of value chain connection,the Silk Road countries need to develop more specific policies related to value chains.Policymakers need to be able to correctly identify the comparative advantages of the region and the types of value chains that are compatible with them and then find suitable partners and formulate targeted promotion policies.

Efficient Training of Multi-Layer Neural Networks to Achieve Faster Validation

Artificial neural networks(ANNs)are one of the hottest topics in computer science and artificial intelligence due to their potential and advantages in analyzing real-world problems in various disciplines,including but not limited to physics,biology,chemistry,and engineering.However,ANNs lack several key characteristics of biological neural networks,such as sparsity,scale-freeness,and small-worldness.The concept of sparse and scale-free neural networks has been introduced to fill this gap.Network sparsity is implemented by removing weak weights between neurons during the learning process and replacing them with random weights.When the network is initialized,the neural network is fully connected,which means the number of weights is four times the number of neurons.In this study,considering that a biological neural network has some degree of initial sparsity,we design an ANN with a prescribed level of initial sparsity.The neural network is tested on handwritten digits,Arabic characters,CIFAR-10,and Reuters newswire topics.Simulations show that it is possible to reduce the number of weights by up to 50%without losing prediction accuracy.Moreover,in both cases,the testing time is dramatically reduced compared with fully connected ANNs.

Directional neighbor discovery in mmWave wireless networks

The directional neighbor discovery problem,i.e,spatial rendezvous,is a fundamental problem in millimeter wave(mmWave)wireless networks,where directional transmissions are used to overcome the high attenuation.The challenge is how to let the transmitter and the receiver beams meet in space under deafness caused by directional transmission and reception,where no control channel,prior information,and coordination are available.In this paper,we present a Hunting based Directional Neighbor Discovery(HDND)scheme for ad hoc mmWave networks,where a node follows a unique sequence to determine its transmission or reception mode,and continuously r0-tates its directional beam to scan the neighborhood for other mmWave nodes.Through a rigorous analysis,we derive the conditions for ensured neighbor discovery,as well as a bound for the worst-case discovery time and the impact of sidelobes.We validate the analysis with extensive simulations and demonstrate the superior perfor-mance of the proposed scheme over several baseline schemes.

Spatial Interaction Network Analysis of Crude Oil Trade Relations between Countries along the Belt and Road

Based on the theories and methods of complex network,crude oil trade flows between countries along the Belt and Road(B&R,hereafter)are inserted into the Geo-space of B&R and form a spatial interaction network which takes the countries as nodes and takes the trade relations as edges.The networked mining and evolution analysis can provide important references for the research on trade relations among the B&R countries and the formulation of trade policy.This paper researches and discusses the construction,statistical analysis,top networks and stability of the crude oil trade network between the B&R countries from 2001 to 2020 from the perspectives of Geo-Computation for Social Sciences(GCSS)and spatial interaction.Firstly,evolutions of out-degree,in-degree,out-strength and in-strength of the top 10 countries in the crude oil trade network are computed and analyzed.Secondly,the top network method is used to explore the evolution characteristics of hierarchical structures.And finally,the sequential evolution characteristics of the crude oil trade network stability are analyzed utilizing the network stability measure method based on the trade relationship autocorrelation function.The analysis results show that Russia has the largest out-degree and out-strength,and China has the largest in-degree and in-strength.The crude oil trade volume of the top 10 import and export networks between 2001—2020 accounts for over 90%of the total trade volume of the crude oil trade network,and the proportion remains relatively stable.However,the stability of the network showed strong fluctuations in 2009,2012 and 2014,which may be closely related to major international events in these years,which could furtherly be used to build a correlation model between network volatility and major events.This paper explores how to construct and analyze the spatial interaction network of crude oil trade and can provide references for trade relations research and trade policy formulation of B&R countries.

PRI: An Periodically Receiver-Initiated Task Scheduling Algorithm

Task scheduling is a key problem for the distributed computation. This thesis analyzes receiver initiated(RI) task scheduling algorithm, finds its weakness and presents an improved algorithm PRI algorithm. This algorithm schedules the concurrent tasks onto network of workstation dynamically at runtime, and initiates task scheduling by the node of low load. The threshold on each node can be modified according to the system information which is periodically detected. Meanwhile, the detecting period can be adjusted in terms of the change of the system state. The result of the experiments shows that the PRI algorithm is superior to the RI algorithm.

TRANSIENT ANALYSIS OF NONUNIFORM TRANSMISSION LINES WITH NONLINEAR TERMINAL NETWORKS

A semi-analytical method in time domain is presented for analysis of the transient response of nonuniform transmission lines. In this method, the telegraph equations in time domain is differenced in space domain first, and is transformed into a set of first-order differential equations of voltage and current with respect to time. By integrating these differential equations with respect to time, and precise computation, the solution of these differential equations can be obtained. This method can solve the transient response of various kinds of transmission lines with arbitrary terminal networks. Particularly, it can analyze the nonuniform lines with initial conditions, for which there is no existing effective method to analyze the time response so far. The results obtained with this method are stable and accurate. Two examples are given to illustrate the application of this method.

A New Weight Initialization Method Using Cauchy’s Inequality Based on Sensitivity Analysis

In this paper, an efficient weight initialization method is proposed using Cauchy’s inequality based on sensitivity analy- sis to improve the convergence speed in single hidden layer feedforward neural networks. The proposed method ensures that the outputs of hidden neurons are in the active region which increases the rate of convergence. Also the weights are learned by minimizing the sum of squared errors and obtained by solving linear system of equations. The proposed method is simulated on various problems. In all the problems the number of epochs and time required for the proposed method is found to be minimum compared with other weight initialization methods.

Automatic Service Discovery of IP Cameras over Wide Area Networks with NAT Traversal

A novel framework for remote service discovery and access of IP cameras with Network address Translation (NAT) traversal is presented in this paper. The proposed protocol, termed STDP (Service Trader Discovery Protocol), is a hybrid combination of Zeroconf and SIP (Session Initial Protocol). The Zeroconf is adopted for the discovery and/or publication of local services;whereas, the SIP is used for the delivery of local services to the remote nodes. In addition, both the SIP-ALG (Application Layer Gateway) and UPnP (Universal Plug and Play)-IGD (Internet Gateway Device) protocols are used for NAT traversal. The proposed framework is well-suited for high mobility applications where the fast deployment and low administration efforts of IP cameras are desired.

环境产品贸易网络的结构特征及演进机制——基于共建“一带一路”国家的实证

加强环境产品贸易合作是推进区域绿色发展的重要途径。该研究基于社会网络分析视角,利用2000—2020年共建“一带一路”国家的54种环境产品贸易数据构建环境产品贸易网络,采用时间指数随机图模型(TERGM),探究区域环境产品贸易网络结构特征及其演化机制。研究发现:①样本区间内,环境产品贸易网络密度和平均聚类系数总体呈上升趋势,平均路径长度缩减,网络直径较短,但是受疫情影响,2020年网络特征指标数值明显下降。②意大利、中国、韩国和新加坡的点出度与点入度排名位于前列,在贸易网络中发挥“桥梁”与“枢纽”作用。中国后来居上,成为点出度与点入度最高的国家,引领区域环境产品贸易发展。③欧洲与亚洲是环境产品贸易网络中贸易合作最密集的两个地区,非洲、拉丁美洲地区的共建“一带一路”国家具有较少的环境产品贸易关系,且源自板块外进口关系数显著多于出口关系数。④TERGM分析表明,环境产品贸易网络存在显著的互惠性、连通性、稳定性和变异性。工业增加值、经济自由度和绿色创新存在发送效应和接收效应,人均GDP、环境规制存在发送效应。地理距离不利于形成环境产品贸易合作。⑤环境保护型产品、可再生能源产品、环境监测分析与评价设备贸易网络的形成和演化具有异质性。基于该结论,从引领区域绿色低碳发展、因地制宜深化环保产业合作、加快环保产业转型升级等方面,提出了深化和拓展“一带一路”环境产品贸易关系的对策建议。

“一带一路”共建国家间的技术扩散——基于技术内容与网络结构的分析

技术扩散是强化国际经济与创新联系的有效途径,是构建高质量开放创新体系的重要方式。国际技术扩散作为两个主体间的技术扩散活动具有方向性,不同国家的创新政策和技术发展战略侧重点不同,在技术扩散能力和技术吸收能力上均有差异。因而国家间需要构建高效、稳定的技术扩散网络,充分发挥自身的技术优势、补齐技术短板,进一步加大技术流动和增强创新能力。本研究基于技术扩散理论,运用专利引证数据构建“一带一路”共建国家间技术扩散网络,分时段纵向分析共建国家技术扩散网络的演化,分技术领域横向分析共建国家间技术扩散的特征,探索中国与共建国家间技术扩散的内容和路径。研究认为:中国在共建国家技术扩散网络中的核心地位逐步确立,且在“一带一路”倡议提出后中国的核心位置更加明显、技术扩散范围更加广泛;但整体技术扩散网络密度低,除中国外的其他国家间技术扩散关系构建较少;不同技术领域的技术扩散网络形态各有不同,其中化学技术领域的技术扩散最活跃、网络复杂度最高;俄罗斯在化学、机械工程、仪器、其他技术领域具有一定技术优势,并且对中国的技术扩散强度很大,而中国在电气工程技术领域更有技术扩散优势。

Design of a 3-tier smart middleware system for ubiquitous sensor networks

As ubiquitous sensor networks (USN) technologies and its middleware are still at its early stages, the system of the USN relies on the middleware and applications. The past sensor networks are assumed to be designed for specific applications, having data communication protocols tightly coupled to applications. To avoid these problems, several kinds of USN middleware have been researched and developed. However, most middleware of USN are still restricted by its own infrastructure so far. This paper proposes appropriate 3 tier Smart Middleware System (3SMS) for USN.

Research of Rural Power Network Reactive Power Optimization Based on Improved ACOA

In view of the serious reactive power loss in the rural network, improved ant colony optimization algorithm （ACOA） was used to optimize the reactive power compensation for the rural distribution system. In this study, the traditional ACOA was improved in two aspects： one was the local search strategy, and the other was pheromone mutation and re-initialization strategies. The reactive power optimization for a county＇s distribution network showed that the improved ACOA was practicable.

“一带一路”国家能源投资的风险识别与传导——基于复杂网络方法

基于复杂网络理论,构建了能源投资风险指标体系,建立了风险识别模型,并通过103个共建“一带一路”国家2010-2019年数据实证分析了中国能源行业对外直接投资风险演变特征、趋势及时空传导路径。研究结果表明:腐败控制、政府效率、监管质量、法律规则以及社会稳定等指标是中国能源产业海外投资的关键风险点;十年间中国能源对外直接投资风险传导性不断增强,从单一国家的投资风险看,印度尼西亚、土耳其、巴西内部的投资风险环境更为复杂;基于最小生成树(MST)的风险传导路径长度表明,投资风险在欧洲中亚、中东北非以及撒哈拉以南非洲地区的传导路径变短,联系日益紧密,风险加速积聚。

全状态约束下长行程混联机器人投影迭代鲁棒控制算法

针对全状态约束下的长行程混联机器人系统鲁棒性较差提出了基于自适应学习神经网络和等效误差函数的投影迭代鲁棒控制算法。基于自适应学习神经网络逼近未知的非线性项,提出投影迭代鲁棒控制算法,更新网络权值并估计逼近误差和随机外部扰动的未知上界;构造用于抵消初始时刻随机变化扩展误差的时变边界层,设计基于时变边界层和扩展误差的等效误差函数作为迭代控制器的主要控制变量以克服随机初始误差满足相同初始条件;在控制器设计中引入正切型障碍Lyapunov函数,确保系统状态在预定范围内。仿真实验结果证明了该方法的有效性,可在全状态约束下实现高精度强鲁棒性的轨迹跟踪。

历史主动精神与新时代网络意识形态建设研究

当下,网络已经成为现代社会的重要组成部分,网络传播已经成为意识形态传播的重要途径。网络意识形态安全事关社会主义主流意识形态在网络空间的主导性地位,直接关系到国家的政治安全。新时代背景下,如何通过对历史主动精神的深入剖析,为新时代网络意识形态建设提供强大的思想武器。由此,为深入推进新时代网络意识形态建设,应当充分运用历史主动精神。首先,通过对历史的学习和思考,为网络意识形态的建设提供借鉴和启示,并为人们在网络空间中找到归属感提供了深刻的内在支撑;其次,以历史革新力量推动网络主流意识形态的时代性和先进性;再次,以历史斗争精神夯实新时代意识形态安全建设。

面向岗位胜任力培养的网络防御综合实践课程建设

以网络防御岗位胜任力培养为目标,借鉴美国网络空间安全教育计划设计网络防御岗位的胜任力模型。首先,基于网络信息防御、网络信息防御综合演练课程设计实验实训内容;其次,基于攻防转换、迭代反馈教学模式支撑学生综合实践能力和岗位胜任力的培养。实践表明,教改后学生评教满意度提升7.2%,课程平均成绩提升15分,能在完成课程实验的基本要求上进行实战活动,参与处置多起实际网络攻击事件的调查取证,教学成效得到了相关单位一致好评。

多分支废弃油井层温衰减规律及注入参数对注采性能的影响

利用废弃油井开发地热资源是降低地热开发成本、促进商业推广的有效措施,其中层温衰减规律对地热系统取热效果具有重要影响。为此,以废弃油井储层为研究对象,结合泰森多边形理论对储层裂缝进行了划分,建立了三维油水两相热流耦合数值模型,分析了不同储层条件与布井结构下的层温衰减规律,探究了注入参数等因素对地热注采性能的影响。研究结果表明:(1)低渗透储层抽采30年生产温度最大差值可达22.08 K,高渗透储层(1.0×10^(-13) m^(2))温度过度衰减,长年限采热效率低;(2)储层含油率与生产温度和发电功率呈正相关关系,生产30年后差值最大可达11.57 K与0.12 MW;(3)分支井沿垂直方向会形成低温波谷,层温衰减高于储层其余部分,数量增加对注采影响小;(4)注入速率与发电功率、生产温度分别呈正、负相关关系,但注入速率超过89 kg/s时不利于长年限开采。结论认为:(1)储层取热性能对初始渗透率最为敏感,注入速率与储层含油率次之,优先开发低渗透储层有利于长年限采热;(2)应利用油田地热降低稠油黏度,形成地热辅助采油模式,以实现经济效应最大化;(3)选取6支分支井与89 kg/s的注入速率可得到可观取热效果和较好的经济效益。