Joint Optimization for on-Demand Deployment of UAVs and Spectrum Allocation in UAVs-Assisted Communication

To improve the efficiency and fairness of the spectrum allocation for ground communication assisted by unmanned aerial vehicles(UAVs),a joint optimization method for on-demand deployment and spectrum allocation of UAVs is proposed,which is modeled as a mixed-integer non-convex optimization problem(MINCOP).An algorithm to estimate the minimum number of required UAVs is firstly proposed based on the pre-estimation and simulated annealing.The MINCOP is then decomposed into three sub-problems based on the block coordinate descent method,including the spectrum allocation of UAVs,the association between UAVs and ground users,and the deployment of UAVs.Specifically,the optimal spectrum allocation is derived based on the interference mitigation and channel reuse.The association between UAVs and ground users is optimized based on local iterated optimization.A particle-based optimization algorithm is proposed to resolve the subproblem of the UAVs deployment.Simulation results show that the proposed method could effectively improve the minimum transmission rate of UAVs as well as user fairness of spectrum allocation.

Computer System Reliability Allocation Method and Supporting Tool

This paper presents a computer system reliability allocation method that is based on the theory of statistic and Markovian chain,which can be used to allocate reliability to subsystem, to hybrid system and software modules. A relevant supporting tool built by us is introduced.

Resource Allocation Method of Device-to-Device Communication

In this paper, we study D2D (Device-to-Device) communication underlying LTE-Advanced uplink system. Since D2D communication reuses uplink resources with cellular communication in this scenario, it’s hard to avoid the inference between D2D users and cellular users. If there is no restriction for D2D communication on using the whole uplink frequency band, it will have a strong negative impact on cellular communication. In order to overcome this shortage, we propose a resource allocation method that D2D users and cellular users use orthogonal frequency resources. This method will effectively reduce the inference between both kinds of communication. However, an obvious disadvantage of this method is no effective use of uplink resources. Based on this, we propose an optimized resource allocation method that a specific cellular user will be chosen to reuse the RBs (Resource Block) of D2D users. These ideas will be taken into system-level simulation, and from the results of simulation we can see that the optimized method has the ability to improve overall system performance and limit inference for cell-edge users.

Incessant Allocation Method for Solving Transportation Problems

Industries require planning in transporting their products from production centres to the users end with minimal transporting cost to maximize profit. This process is known as Transportation Problem which is used to analyze and minimize transportation cost. This problem is well discussed in operation research for its wide application in various fields, such as scheduling, personnel assignment, product mix problems and many others, so that this problem is really not confined to transportation or distribution only. In the solution procedure of a transportation problem, finding an initial basic feasible solution is the prerequisite to obtain the optimal solution. Again, development is a continuous and endless process to find the best among the bests. The growing complexity of management calls for development of sound methods and techniques for solution of the problems. Considering these factors, this research aims to propose an algorithm “Incessant Allocation Method” to obtain an initial basic feasible solution for the transportation problems. Several numbers of numerical problems are also solved to justify the method. Obtained results show that the proposed algorithm is effective in solving transportation problems.

Adaptive subcarrier allocation for MIMO-OFDMA wireless systems using Hungarian method

MIMO-OFDMA is a promising technique for future broadband wireless communication systems. In this paper, the problem of allocating subcarriers among different users to maximize the total capacity is addressed. The optimal solution can be obtained by Hungarian method is proved, using two utility matrices, i.e. the Frobenius-norm matrix and the determinant matrix. Simulation results show that the proposed algorithm can achieve higher capacity than the existing algorithms, and it is globally optimal and easy to be implemented.

Reliability Allocation of Large Mining Excavator Electrical System Based on the Entropy Method with Failure and Maintenance Data

In the design of large mining excavator electrical system,a practical reliability allocation method was introduced to allocate system level reliability requirements into subsystem and component levels. During the reliability allocation process,factors from the fault and maintenance data were only considered in reliability allocation scheme. It could avoid the disturbance from expert experiences. The entropy method was also used to obtain weights of reliability allocation indexes of large mining excavators considering different factors. Then the failure rate allocation of subsystems and components could be completed.

Using the Simplex Method for a Type of Allocation Problems

In this study we discuss the use of the simplex method to solve allocation problems whose flow matrices are doubly stochastic. Although these problems can be solved via a 0 - 1 integer programming method, H. W. Kuhn [1] suggested the use of linear programming in addition to the Hungarian method. Specifically, we use the existence theorem of the solution along with partially total unimodularity and nonnegativeness of the incidence matrix to prove that the simplex method facilitates solving these problems. We also provide insights as to how a partition including a particular unit may be obtained.

Regional Differences and Optimal Allocation of Cultivated Land Utilization Benefits Based on Improved TOPSIS Method:A Case Study of Guangxi

Taking 14 prefecture-level cities in Guangxi as an object,this paper explored the difference of cultivated land utilization efficiency in Guangxi,comprehensively evaluated the economic,social,and ecological aspects,and analyzed the regional differences in cultivated land use efficiency in Guangxi from 2005 to 2018 based on the improved TOPSIS method.By studying the quantitative change trends and spatial differences of the utilization benefit of cultivated land resources in Guangxi,it came up with recommendations for optimizing the allocation of cultivated land from the quantity and quality of cultivated land.It is intended to provide a scientific and theoretical reference for improving the use efficiency of cultivated land in Guangxi.

A nested partitioning-based solution method for seru scheduling problem with resource allocation

This paper investigates the production scheduling problems of allocating resources and sequencing jobs in the seru production system(SPS).As a new-type manufacturing mode arising from Japanese production practices,seru production can achieve efficiency,flexibility,and responsiveness simultaneously.The production environment in which a set of jobs must be scheduled over a set of serus according to due date and different execution modes is considered,and a combination optimization model is provided.Motivated by the problem complexity and the characteristics of the proposed seru scheduling model,a nested partitioning method(NPM)is designed as the solution approach.Finally,computational studies are conducted,and the practicability of the proposed seru scheduling model is proven.Moreover,the efficiency of the nested partitioning solution method is demonstrated by the computational results obtained from different scenarios,and the good scalability of the proposed approach is proven via comparative analysis.

Optimal Power Allocation for Multiuser OFDM-Based Cognitive Heterogeneous Networks

Heterogeneous network(HetNet) as a promising technology to improve spectrum efficiency and system capacity has been concerned by many scholars, which brings huge challenges for power allocation and interference management in multicell network structures. Although some works have been done for power allocation in heterogeneous femtocell networks, most of them focus centralized schemes for single-cell network under interference constraint of macrocell user. In this paper, a sum-rate maximization based power allocation algorithm is proposed for a downlink cognitive Het Net with one macrocell network and multiple microcell networks. The original power allocation optimization problem with the consideration of cross-tier interference constraint, maximum transmit power constraint of microcell base station and inter-cell interference of microcell networks is converted into a geometric programming problem which can be solved by Lagrange dual method in a distributed way. Simulation results demonstrate the performance and effectiveness of the proposed algorithm by comparing with the equal power allocation scheme.

Carbon emission permit allocation and trading

Nowadays our earth is faced with grim challenge of global climate change. All countries should go into action jointly to mitigate climate change. Carbon emission permit allocation and trading are two important issues to realize global cooperation. In this paper two kinds of comprehensive carbon emission permit allocation methods not only considering equity but also efficiency were advanced. After the carbon permit price was determined, the trading situations in various regions or countries in the world and the global benefits gained from emission trading were expounded. Moreover, the impact of carbon emission permit trading on Chinas economy was analyzed.

Implementation of an open-source customizable minimization program for allocation of patients to parallel groups in clinical trials

Current minimization programs do not permit full control over different aspects of minimization algorithm such as distance or probability measures and may not allow for unequal allocation ratios. This article describes the implementation of “MinimPy” an open-source minimization program in Python programming language, which provides full customizetion of minimization features. MinimPy supports naive and biased coin minimization together with various new and classic distance measures. Data syncing is provided to facilitate minimization of multicenter trial over the network. MinimPy can easily be modified to fit special needs of clinical trials and in particular change it to a pure web application, though it currently supports network syncing of data in multi-center trials using network repositories.

引入重要度修正因子的复杂光电系统可靠性分配研究

以某新型复杂光电系统为研究对像,针对传统评分分配法客观性差、分配偏差大的缺点,构建重要度修正因子,提出了一种引入重要度修正因子的可靠性评分分配法。首先,运用模糊层次分析法,得到功能单元对上级功能单元/系统可靠性影响的相对重要度;然后,基于相对重要度,按照专家评分规则,得到功能单元对上级功能单元/系统可靠性影响的绝对重要度;最后,基于相对重要度和绝对重要度,构建功能单元重要度修正因子,以此修正各功能单元的评分结果。经对比验证,关重功能单元可靠性分配值较传统评分分配值至少提高6%,引入重要度修正因子的评分分配法能有效削弱传统评分分配法的主观影响,释放关重功能单元可靠性余量,提高功能单元可靠性分配的科学性、合理性,进而提高复杂系统可靠性设计水平。

基于改进阿林斯法的数控机床子系统可靠性分配

根据采集到的某国产数控机床的故障信息进行分析整理并进行子系统划分,结合数控机床相关性模型改进可靠性分配方法进行目标值分配.应用层次分析法建立影响因素隶属度集,采取DEMATEL决策实验室分析法确定故障相关数据的中心度影响因素,结合维修性、维修时间、故障停时、故障次数及复杂度确定隶属度函数集,对于各影响因素的权重的确定采用熵权法,进而计算出数控机床的可靠性分配因子.将影响度因素引入阿林斯分配法进行改进,使改进后的方法适用于各子系统串联且存在故障相关性的系统.数值算例表明,相较于传统阿林斯法,本文方法能够减少子系统对故障时间的要求.同时,为验证本文分配结果的合理性,采用蒙特卡洛仿真法以分配结果为基础对目标机床整机进行可靠性预计,计算结果证明了方法的可行性.

多因素下风力发电机组系统可靠性分配方法的研究

针对目前可靠性分配结果较为主观的问题,以串联系统的可靠性分配方法为基础,提出了一种基于改进模糊层次分析法的可靠性分配方法。采用三标度法对多个系统可靠性影响因素进行专家评判,通过模糊层次分析法初步确定可靠性分配权重,然后对分配权重初值进行熵权法处理,建立综合分配权重计算模型,最终确定各子系统的可靠性分配权重,进而实现对机械系统可靠度分配模型的建立。以风力发电机组系统为例,将该方法应用在风力发电机组可靠性分配中。通过改进模糊层次分析法与传统的模糊层次分析法以及其它方法对该风力发电机组系统的可靠性分配结果,进行对比分析。结果表明:该方法不仅有效;而且相比于文献中的分配结果有所降低,更符合机组的实际运行情况,验证了该方法的合理性。

金融资产配置与中老年人口生活满意度

人口老龄化背景下,我国中老年人口金融资产配置与生活满意度的关联值得探究。基于中国健康与养老追踪调查微观数据的实证研究发现,存款等非风险性金融资产的增加对中老年群体生活满意度具有显著正向影响,而风险性金融资产和负债的增加则会降低其生活满意度;影响程度随金融资产量、年龄组别、早期经历而具有异质性特征,且结论通过一系列稳健性检验。具体而言,中老年金融资产配置影响其生活满意度的直接效应占主导地位,间接效应传导途径包括健康状况、安全感和获得感,其中健康状况的贡献度最大。进一步研究表明,金融资产对中老年群体生活满意度的影响存在示范效应,而负债对生活满意度的影响既不存在示范效应、也不存在攀比效应;经济政策的不确定性对生活满意度有显著的负向影响。由此,增强中老年群体金融素养教育,加强对中老年群体健康的关注,提高中老年群体的安全感,加强中老年人口社会资本积累,以强化示范效应、保持宏观经济稳健运行。

基于多目标协调的混合储能功率自适应分配方法

混合储能具有良好的功率可控特性,在孤岛微网中常被用作功率缓冲器,补偿分布式电源和负荷多变的功率潮流.围绕储能的功率响应问题,本文设计了混合储能的功率控制方案,并提出了一种基于多目标协调的子单元功率自适应分配方法.首先基于对母线功率供求平衡关系的分析,设计了主从并联型储能功率控制方案.其次利用积分器的“时空调零”特性,实现了子单元的功率自主分配;基于此,考虑了不同荷电状态(SOC)下储能最大充放电功率的差异,设计了子单元的SOC分层管理和多目标功率协调策略.仿真结果表明,所提策略实现的功率分配效果满足子单元的功率响应特性;随着超级电容SOCscn偏移加深,所提策略对SOCscn的优化能力越强,初始SOCscn为97%时能提高10.10%的优化性能,这保障了超级电容作为电压源的功率输出能力;所提策略降低了超级电容常态下的蓄电池的输出深度,蓄电池的最大功率深度降低了46.70%,这提高了蓄电池的使用寿命.

基于算力-能量全分布式在线共享的5G网络负荷管理策略

5G与边缘计算等信息基础设施海量部署造成运营商用电成本上升,需推动边缘网络与电网的能量互动以节能降本。现有研究重点关注边缘网络参与日前经济调度,未考虑可再生能源和网络流量双重随机性造成的网络能量供需不平衡问题。针对强随机环境下的网络负荷管理问题,提出面向虚拟化边缘网络的能量实时管理策略。首先,以网络用能成本最小化为目标,构建联合网络资源管理、储能充放电与能量共享模型。其次,针对未来网络信息未知无法直接求解的问题,提出基于随机对偶次梯度法的在线管理策略。然后,针对资源共享涉及运营商隐私问题,提出全分布式的计算资源与能量协同共享算法。最后,仿真验证表明,所提在线算法在无需先验知识的前提下有效减少了5G边缘网络的用能成本。

新质生产力的分配向度:按劳分配与共同富裕之契合性论析

马克思按劳分配思想是内含对生产、分配、交换和消费环节规定的系统性思想,按劳分配是推动实现共同富裕的分配方式,但这一系统思想在现实中表现为生产端、分配端、交换端和消费端的“系统悖论”。按劳分配与共同富裕的理论兼容性与实践契合性应从描述性、规范性和系统性三重视野观照:现行两套分配体系边界的逐渐消融要求重新审视通往共富之路;按劳分配内蕴的革命性精神是当下推动共同富裕的根本精神;新质生产力发展下生产物化趋势与消费人化本性间的张力造成的“劳动”分配标准的弱化呼唤“新质”的分配标准。新质生产力的“颠覆性”影响要求对未来社会分配方式进行初步构建:在新质生产力的解放初期实行按“劳”分配+按“要素”分配;新质生产力深入发展阶段实行按“所有权”分配;在新质生产力的高度发达阶段实行按“需”分配。

联邦边缘智能网络碳排放评估及优化

近年来,通信技术的持续演进导致通信网络的能耗显著增加。随着人工智能(AI,artificial intelligence)技术与算法在通信网络中的广泛应用和深度部署,未来6G智能通信网络架构和技术演进将对通信网络的节能减排带来更为严峻的挑战。基于边缘计算和分布式联邦学习的联邦边缘智能(FEI,federated edge intelligence)网络已被普遍认为是实现6G网络内生智能的关键路径之一。然而,如何评估和优化联邦边缘智能网络的综合碳排放量仍然是一大难题。为解决该问题,首先,提出了一种联邦边缘智能网络碳排放评估框架和方法。其次,基于该评估框架和方法提出3种联邦边缘智能网络碳排放优化方案,包括动态能量交易(DET,dynamic energy trading)、动态任务分配(DTA,dynamic task allocation)和动态能量交易与任务分配(DETA,dynamic energy trading and task allocation)。最后,通过自行搭建的真实硬件平台,并利用真实世界的碳强度数据集进行联邦边缘智能网络生命周期碳排放仿真。实验结果表明,3种优化方案均能在不同场景和约束条件下显著减少联邦边缘智能网络的碳排放,为下一代智能通信网络的可持续发展和实现绿色低碳6G网络提供了依据。