IMPROVED MODEL OF MULTIPATH TRAFFIC ASSIGNENT AND NODE ASSIGNENT ALGORITHM

In the paper, multipeth traffic assignment model is improved, a quick aI8orithm,Nede Assignment Algorithm, of this medel is presented, and some problems such as dispolof network, arransement of assigned nodes and determination of parameters are discussed

Inspires effective alternatives to backpropagation:predictive coding helps understand and build learning

Artificial neural networks are capable of machine learning by simulating the hiera rchical structure of the human brain.To enable learning by brain and machine,it is essential to accurately identify and correct the prediction errors,referred to as credit assignment(Lillicrap et al.,2020).It is critical to develop artificial intelligence by understanding how the brain deals with credit assignment in neuroscience.

Glorious Missions Assigned by the Human History

Frontiers of Engineering Management,after two years’preparation,came,in spring2014,to offer its readers,with the mission and the editor inclination to promote the advancement of engineering management,so as to propel the advancement of engineering science and technology.It is a general consensus of the scientific communities that engineering science is

Channel assignment and power allocation for throughput improvement with PPO in B5G heterogeneous edge networks

In Beyond the Fifth Generation(B5G)heterogeneous edge networks,numerous users are multiplexed on a channel or served on the same frequency resource block,in which case the transmitter applies coding and the receiver uses interference cancellation.Unfortunately,uncoordinated radio resource allocation can reduce system throughput and lead to user inequity,for this reason,in this paper,channel allocation and power allocation problems are formulated to maximize the system sum rate and minimum user achievable rate.Since the construction model is non-convex and the response variables are high-dimensional,a distributed Deep Reinforcement Learning(DRL)framework called distributed Proximal Policy Optimization(PPO)is proposed to allocate or assign resources.Specifically,several simulated agents are trained in a heterogeneous environment to find robust behaviors that perform well in channel assignment and power allocation.Moreover,agents in the collection stage slow down,which hinders the learning of other agents.Therefore,a preemption strategy is further proposed in this paper to optimize the distributed PPO,form DP-PPO and successfully mitigate the straggler problem.The experimental results show that our mechanism named DP-PPO improves the performance over other DRL methods.

Development of SNP parentage assignment techniques in the yellowfin seabream Acanthopagrus latus

Acanthopagrus latus is an essential aquaculture species on the south coast of China.However,there is a lack of systematic breeding of A.latus,which considerably limits the sustainable development of A.latus.As a result,genetic improvements are urgently needed to breed new strains of A.latus with rapid growth and strong resistance to disease.During selective breeding,it is necessary to estimate the genetic parameters of the target trait,which in turn depends on an accurate disentangled pedigree for the selective population.Therefore,it is necessary to establish the parentage assignment technique for A.latus.In this study,95 individuals selected from their parents and their 14 families were used as experimental material.SNPs were developed by genome resequencing,and highly polymorphic SNPs were screened on the basis of optimized filtering parameters.A total of 14392738 SNPs were discovered and 205 SNPs were selected for parentage assignment using the CERVUS software.In the model where the gender of the parents is known,the assignment success rate is 98.61%for the male parent,97.22%for the female parent,and 95.83%for the parent pair.In the model where the gender of the parents is unknown,the assignment success rate is 100%for a single parent and 90.28%for the parent pair.The results of this study were expected to serve as a reference for the breeding of new varieties of A.latus.

A self-organization formation configuration based assignment probability and collision detection

The formation control of multiple unmanned aerial vehicles(multi-UAVs)has always been a research hotspot.Based on the straight line trajectory,a multi-UAVs target point assignment algorithm based on the assignment probability is proposed to achieve the shortest overall formation path of multi-UAVs with low complexity and reduce the energy consumption.In order to avoid the collision between UAVs in the formation process,the concept of safety ball is introduced,and the collision detection based on continuous motion of two time slots and the lane occupation detection after motion is proposed to avoid collision between UAVs.Based on the idea of game theory,a method of UAV motion form setting based on the maximization of interests is proposed,including the maximization of self-interest and the maximization of formation interest is proposed,so that multi-UAVs can complete the formation task quickly and reasonably with the linear trajectory assigned in advance.Finally,through simulation verification,the multi-UAVs target assignment algorithm based on the assignment probability proposed in this paper can effectively reduce the total path length,and the UAV motion selection method based on the maximization interests can effectively complete the task formation.

Radio resource management in energy harvesting cooperative cognitive UAV assisted IoT networks:A multi-objective approach

Cooperative communication through energy harvested relays in Cognitive Internet of Things(CIoT)has been envisioned as a promising solution to support massive connectivity of Cognitive Radio(CR)based IoT devices and to achieve maximal energy and spectral efficiency in upcoming wireless systems.In this work,a cooperative CIoT system is contemplated,in which a source acts as a satellite,communicating with multiple CIoT devices over numerous relays.Unmanned Aerial Vehicles(UAVs)are used as relays,which are equipped with onboard Energy Harvesting(EH)facility.We adopted a Power Splitting(PS)method for EH at relays,which are harvested from the Radio frequency(RF)signals.In conjunction with this,the Decode and Forward(DF)relaying strategy is used at UAV relays to transmit the messages from the satellite source to the CIoT devices.We developed a Multi-Objective Optimization(MOO)framework for joint optimization of source power allocation,CIoT device selection,UAV relay assignment,and PS ratio determination.We formulated three objectives:maximizing the sum rate and the number of admitted CIoT in the network and minimizing the carbon dioxide emission.The MOO formulation is a Mixed-Integer Non-Linear Programming(MINLP)problem,which is challenging to solve.To address the joint optimization problem for an epsilon optimal solution,an Outer Approximation Algorithm(OAA)is proposed with reduced complexity.The simulation results show that the proposed OAA is superior in terms of CIoT device selection and network utility maximization when compared to those obtained using the Nonlinear Optimization with Mesh Adaptive Direct-search(NOMAD)algorithm.

Reinforcement Learning Based Quantization Strategy Optimal Assignment Algorithm for Mixed Precision

The quantization algorithm compresses the original network by reducing the numerical bit width of the model,which improves the computation speed. Because different layers have different redundancy and sensitivity to databit width. Reducing the data bit width will result in a loss of accuracy. Therefore, it is difficult to determinethe optimal bit width for different parts of the network with guaranteed accuracy. Mixed precision quantizationcan effectively reduce the amount of computation while keeping the model accuracy basically unchanged. In thispaper, a hardware-aware mixed precision quantization strategy optimal assignment algorithm adapted to low bitwidth is proposed, and reinforcement learning is used to automatically predict the mixed precision that meets theconstraints of hardware resources. In the state-space design, the standard deviation of weights is used to measurethe distribution difference of data, the execution speed feedback of simulated neural network accelerator inferenceis used as the environment to limit the action space of the agent, and the accuracy of the quantization model afterretraining is used as the reward function to guide the agent to carry out deep reinforcement learning training. Theexperimental results show that the proposed method obtains a suitable model layer-by-layer quantization strategyunder the condition that the computational resources are satisfied, and themodel accuracy is effectively improved.The proposed method has strong intelligence and certain universality and has strong application potential in thefield of mixed precision quantization and embedded neural network model deployment.

Structural Characteristics and Influencing Factors of Carbon Emission Spatial Association Network:A Case Study of Yangtze River Delta City Cluster,China

City cluster is an effective platform for encouraging regionally coordinated development.Coordinated reduction of carbon emissions within city cluster via the spatial association network between cities can help coordinate the regional carbon emission management,realize sustainable development,and assist China in achieving the carbon peaking and carbon neutrality goals.This paper applies the improved gravity model and social network analysis(SNA)to the study of spatial correlation of carbon emissions in city clusters and analyzes the structural characteristics of the spatial correlation network of carbon emissions in the Yangtze River Delta(YRD)city cluster in China and its influencing factors.The results demonstrate that:1)the spatial association of carbon emissions in the YRD city cluster exhibits a typical and complex multi-threaded network structure.The network association number and density show an upward trend,indicating closer spatial association between cities,but their values remain generally low.Meanwhile,the network hierarchy and network efficiency show a downward trend but remain high.2)The spatial association network of carbon emissions in the YRD city cluster shows an obvious‘core-edge’distribution pattern.The network is centered around Shanghai,Suzhou and Wuxi,all of which play the role of‘bridges’,while cities such as Zhoushan,Ma'anshan,Tongling and other cities characterized by the remote location,single transportation mode or lower economic level are positioned at the edge of the network.3)Geographic proximity,varying levels of economic development,different industrial structures,degrees of urbanization,levels of technological innovation,energy intensities and environmental regulation are important influencing factors on the spatial association of within the YRD city cluster.Finally,policy implications are provided from four aspects:government macro-control and market mechanism guidance,structural characteristics of the‘core-edge’network,reconfiguration and optimization of the spatial layout of the YRD city cluster,and the application of advanced technologies.

不变性原则与Python亮点

针对目前Python教学中“以C语言知识解释Python语法”的倾向及其所导致的一些基本概念错误,回顾“软件危机”发生以来人们在“去变量赋值”副作用和函数式编程两个方面的努力,从不变性原则的高度阐述这些举措对于提高程序质量的意义,在此基础上提出Python程序设计语言的亮点并强调对于回归Python本质的重要性,目的是为进一步学习和应用打下坚实基础。

基于产业专利权人的新兴技术应用领域变化研究

把握新兴技术在不同应用领域的演化规律,对于充分挖掘新兴技术应用潜力,促进其产出最大的经济效应具有重要意义.目前这一领域研究十分薄弱.为此,本文首先界定了技术应用领域的概念及技术应用领域变化的含义,然后阐述了现有分析方法的局限性,并提出了沿着专利权人数量及所属产业领域的变化轨迹去发现新兴技术应用领域变化的思路,最后针对燃料电池和太阳能电池领域进行了实证分析.实证研究表明上述思路是可行的.

基于模糊多目标规划的防空反导火力分配

针对现有多目标火力分配(weapon target assignment,WTA)方法很难适用于不确定情况下防空反导作战的问题,提出了基于模糊多目标规划的防空反导WTA方法。首先,采用三角模糊数刻画不确定的目标威胁度,在考虑防空反导作战特点的基础上,基于模糊多目标规划建立了WTA模型;然后,根据必要性测度原理将含有模糊参数的目标函数进行了等价清晰化;接着,提出了具有单/双势阱的多目标量子行为粒子群算法用于求解WTA模型,该算法采用了单/双势阱位置更新方式、粒子混合随机变异方法、领导粒子两阶段选取方法;最后,通过实例仿真验证了模型的合理性和算法的有效性。

专利权人关联网络的社会网络分析方法研究

在方法体系层面上设计出适用于专利权人关联网络分析的社会网络分析方法体系框架,框架包括基于合作、基于引用和基于技术主题三个方面。其中,基于合作的专利权人合作网络分析方法包括全球规模、特定学科主题、特定专利权人和自我中心网络(ego网络)四种类型;基于引用的专利权人引用网络包括直接引用、专利共引和专利文献耦合网络三种类型;基于主题的专利权人主题关联网络包括专利分类号共现、分类号相似度计算和主题词共现三种类型。文章对整个方法体系的分析方法进行了系统的研究,包括基本原理、数据集的构建策略、分析方法的功能、不足、指标选取等方面。最后利用中国科学院2005～2008年的发明专利对基于合作和基于技术主题的专利权人关联网络进行了实证研究。

计算机系统基础课程的实验设计与分析

分析实验对计算机系统基础教育的重要性,结合课程需求和实际教学经验提出实验设计的几条基本原则,结合南京大学计算机科学与技术系计算机系统基础实验近3年的教学,分析实验设计的有效性和存在的问题,并提出解决思路。

基于海外专利布局的中国技术创新态势分析

为更好地了解中国高新技术发展态势、避免本土优势,搜集美专局(USPTO)授权的中国专利数据,运用专利计量方法和Vantage Point等分析工具从宏观到微观的角度分析国内相关领域的发展现状及影响力,同时也给中国跨国公司实施海外扩张战略提供参考。研究发现,2010年起基本专利国为美国的专利数量猛增,而从其主要专利权人及其性质来看,主要原因可能是在某些领域美国对中国的技术溢出增加。

国内可替代能源专利情报分析--基于技术主题和专利权人的双重视角

基于德温特数据库,对在我国申请的可替代能源领域专利进行计量及可视化分析,揭示国内该绿色技术领域的主题内容和专利权人特征。研究发现,在经历较长的萌芽期和低速发展期,2006年后专利申请增长迅猛,同时技术主题与国际趋势接近。然而国际企业在专利持有上占据明显优势,尤其是对高质量专利的占有,对我国可替代能源技术形成专利围剿态势。国内专利权人机构类型分布也不尽合理。对此,从技术研发方向选择以及企业管理与政府职能行为方面,提出相应的对策建议。

中国PCT专利申请的发展现状

对世界知识产权组织公布的中国申请的专利合作条约(PCT)专利进行定量分析,就其30余年来的发展趋势、国际地位、专利申请涉及领域以及申请机构等进行研究。研究结果发现:中国PCT专利申请数量增长迅速,在世界所占比例逐年增加;专利的领域分布不均衡,主要涉及电子信息产业;专利权人比较集中,以企业为主。研究结果可以为中国专利的国际保护及国际发展战略提供数据支持。

Curb parking pricing method based on parking choice behavior

In order to improve the use efficiency of curb parking, a reasonable curb parking pricing is evaluated by considering individual parking choice behavior. The parking choice behavior is analyzed from micro-aspects, and the choice behavior utility function is established combining trip time, search time, waiting time, access time and parking fee. By the utility function, a probit-based parking choice behavior model is constructed. On the basis of these, the curb parking pricing model is deduced by considering the constrained conditions, and an incremental assignment algorithm of the model is also designed. Finally, the model is applied to the parking planning of Tongling city. It is pointed out that the average parking time of curb parking decreases 34%, and the average turnover rate increases 67% under the computed parking price system. The results show that the model can optimize the utilization of static traffic facilities.

产业创新驱动发展中的基础研究主体——以物理学领域专利权人结构为例

基础研究是产业创新发展的重要驱动力。基于德温特专利数据,通过对美国物理学领域13个类别各前500个专利权人的数据分析表明,公司是最主要创新主体,这种现象有其合理性。相较于美国,中国公司虽然也是主要专利权人,但其占比相对偏低,而高校和科研院所占比明显偏高。推动产业创新发展,中国要更加重视基础研究在产业创新发展中的作用,充分凸显公司在基础研究领域的主体地位。

基于专利权人-分类号耦合分析的潜在合作关系网络研究

提出面向专利文献的专利权人-分类号耦合分析方法,用于考察专利权人之间基于分类号耦合而形成的潜在合作关系。以近五年太阳能汽车技术领域的德温特专利为例,构建耦合矩阵并进行可视化分析,将潜在合作关系网络与现实合作关系网络进行比较,对潜在合作关系网络开展因子分析,并将其结果进行可视化展示。结果表明,对于专利文献来说,潜在合作关系比现实合作关系更为普遍,且包含更为丰富的情报信息。专利权人-分类号耦合分析能够同时揭示合作与竞争两种潜在的关系。基于因子分析结果而进行的网络可视化分析能够呈现出更为明显的聚类效果,可以作为专利权人-分类号耦合分析的有效补充。在太阳能汽车领域日本的技术优势非常明显,在关键技术领域日本企业继续保持全球领先水平。中国拥有的专利总量居于世界前列,但中国企业个体的技术力量却相对单薄,尤其在核心技术领域仍处于劣势。