增加GSM系统网络容量的研究

本文针对日益激化的移动终端用户需求与受限频率资源的GSM系统网络容量间的矛盾,提出了GSM理论网络容量与载干比C／I间的计算方法,对GSM系统网络容量与移动通话质量间的关系进行了定量分析,同时提出了AMR-HR、COMMONBCCH等新技术,在尽可能维护无线通话质量的情况下提升GSM网络容量。     

基于资源的需求描述方法

针对需求分析中的系统需求描述问题,本文提出了一种以资源描述需求的方法,分析了这种方法与传统需求描述方法的异同;实现了对资源及需求的形式化描述,并详细讲述了获取需求的过程;最后给出了需求的存储形式和需求同资源的映射算法。     

Optimal and Effective Resource Management in Edge Computing

Edge computing is a cloud computing extension where physical computers are installed closer to the device to minimize latency. The task of edge data centers is to include a growing abundance of applications with a small capability in comparison to conventional data centers. Under this framework, Federated Learning was suggested to offer distributed data training strategies by the coordination of many mobile devices for the training of a popular Artificial Intelligence (AI) model without actually revealing the underlying data, which is significantly enhanced in terms of privacy. Federated learning (FL) is a recently developed decentralized profound learning methodology, where customers train their localized neural network models independently using private data, and then combine a global model on the core server together. The models on the edge server use very little time since the edge server is highly calculated. But the amount of time it takes to download data from smartphone users on the edge server has a significant impact on the time it takes to complete a single cycle of FL operations. A machine learning strategic planning system that uses FL in conjunction to minimise model training time and total time utilisation, while recognising mobile appliance energy restrictions, is the focus of this study. To further speed up integration and reduce the amount of data, it implements an optimization agent for the establishment of optimal aggregation policy and asylum architecture with several employees’ shared learners. The main solutions and lessons learnt along with the prospects are discussed. Experiments show that our method is superior in terms of the effective and elastic use of resources.     

Network resource management mechanisms in SDN enabled WSNs: A comprehensive review

Wireless technologies usually have very limited computing, memory, and battery power that require the optimal management of network resources to increase network performance. The optimization of these network resources provides an efficient network topology, traffic control, routing, and data aggregation. This study presents a qualitative and quantitative investigation to evaluate the efficient network resource management mechanisms for software defined wireless sensor networks (SDN-enabled WSNs) from the beginning of network design to reliable data delivery. In this paper, a taxonomy of network resource management research studies is proposed. A detailed analysis of SDN-enabled WSNs architecture, SDN controllers, topology discovery, routing approaches, flow rules installation, and data aggregation is also discussed. Furthermore, the comparative analysis of resource provisioning methods along with various simulation tools is presented. Moreover, this review outlines open research challenges and prospective future directions for network resource management in SDN-enabled WSNs.     

一种水下足式机器人的设计方案研究

海洋蕴含着丰富的自然资源,对海洋资源的合理开发与运用可以极大的缓解能源短缺问题,但由于海洋的复杂环境所带来的资源开发难度也不容忽略;针对这系列问题,对一种新型水下航行器进行研究,开展了水下足式机器人的总体设计分析,并通过三维建模对机器人进行结构设计;利用ANSYS对机器人关键运动部件进行应力分析,对机器人的设计进行完善;利用FLUENT软件进行流体模拟仿真,计算得到机器人腿部在水下运动时的受力特性;使用Adams软件对机器人的运动进行动力学仿真,计算机器人腿部扭矩输出特性;结合仿真结果对机器人的硬件系统进行选型,完成水下足式机器人的总体设计。     

Cost Effective Optimal Task Scheduling Model in Hybrid Cloud Environment

In today’s world, Cloud Computing (CC) enables the users to access computing resources and services over cloud without any need to own the infrastructure. Cloud Computing is a concept in which a network of devices, located in remote locations, is integrated to perform operations like data collection, processing, data profiling and data storage. In this context, resource allocation and task scheduling are important processes which must be managed based on the requirements of a user. In order to allocate the resources effectively, hybrid cloud is employed since it is a capable solution to process large-scale consumer applications in a pay-by-use manner. Hence, the model is to be designed as a profit-driven framework to reduce cost and make span. With this motivation, the current research work develops a Cost-Effective Optimal Task Scheduling Model (CEOTS). A novel algorithm called Target-based Cost Derivation (TCD) model is used in the proposed work for hybrid clouds. Moreover, the algorithm works on the basis of multi-intentional task completion process with optimal resource allocation. The model was successfully simulated to validate its effectiveness based on factors such as processing time, make span and efficient utilization of virtual machines. The results infer that the proposed model outperformed the existing works and can be relied in future for real-time applications.     

基于HAGA的D2D-NOMA资源分配优化算法

针对无线系统带宽资源有限、基站负载压力大、传输时延长等问题,提出一种基于非正交多址接入技术的D2D系统吞吐量最大化资源分配算法。在不同用户的服务质量约束条件下,建立D2D系统吞吐量最大化资源分配模型。该模型的优化目标是一个混合整数非线性规划问题,将其解耦为信道匹配与功率分配2个子问题并分别进行处理,利用自适应惩罚函数法处理约束条件并提出一种基于爬山策略的自适应遗传算法以对问题进行求解。仿真结果表明,与GA、AGA算法相比,该算法能够有效提高D2D系统的吞吐量,且收敛性能更好。     

我国工业固体废物资源化战略研究

本文针对我国工业固体废物产生量大、综合利用不足的突出问题,研究了我国资源开发、工业活动与工业固体废物产生的客观规律,以及未来发展趋势和资源化利用潜力。针对我国在工业固体废物资源化管理模式、法制体系建设、经济调节措施、技术及产业市场等方面存在的突出问题,结合我国生态文明建设中关于环境保护和资源循环利用的总体要求,提出了应将工业固体废物资源化战略作为我国资源、环境战略重要补充的总体战略目标,并提出了未来的发展路线图、阶段目标及重点任务。     

Due-window assignment and scheduling with general position-dependent processing times involving a deteriorating and compressible maintenance activity

In this paper, we consider common due-window assignment and scheduling problems with general position-dependent processing times involving deteriorating and compressible maintenance activity on a single machine. Two models associated with maintenance activity are examined in this article, in which the maintenance length is assumed to be either time-dependent and compressible or position-dependent and compressible. The objective is to find jointly the location and size of due-window, position of maintenance as well as resource amount allocated to it, and job sequence to minimise a total cost function based on earliness, tardiness, window location, window size and resource cost. We show that the problem considered in each of the two models’ setting can be optimally solved with polynomial time algorithm by reducing to assignment problem. Finally, two examples are provided to illustrate the solution procedures.     

AutoProc: An automatic proactive replication scheme for P2P storage

Due to the prevalence of peer departures, P2P storage systems need to constantly generate replicas for high durability. However, few existing systems are able to select the right replication rate that balances replication cost and durability, e.g., minimizing replication cost without threatening the durability target. In this paper, we propose a method called AutoProc to automate the replication process in P2P storage systems. AutoProc employs two key techniques to enable cost-effective replication: correct...