Optimal Base Station Positioning and Channel Assignment for 3G Mobile Networks by Integer Programming

In this paper, discrete mathematical programming approaches are used to solve the frequency allocation and cell site selection problem in an integrated setup. Both CDMA (code division multiple access) and FD/TDMA (frequency/time division multiple access) technologies will be important for 3rd generation mobile systems. If all users share the same bandwidth, base transmitter stations should be placed such that a maximum of traffic can be carried at low interference rates. The expected traffic is represented by spatially scattered weighted nodes. The problem to select an optimal set of base station locations from a given pool of configurations is formulated as an integer linear program and solved by combinatorial optimization methods. For systems which employ FD/TDMA schemes, the cell site optimization process depends on the assignment of channels. We suggest an integrated linear programming approach to solve both objectives in a single planning step. Because of the problems' tremendous complexity, special branch-and-bound procedures are developed as exact and approximate solution methods. An examples is given for a typical urban scenario with base transmitters below roof tops.     

用于蛋白同时复性及纯化的制备型装置中流动相用量的优化研究

 在生物大分子的高效液相色谱分离中 ,由计量置换保留模型可得出生物大分子在色谱柱上的保留行为取决于流动相中置换剂的浓度的结论。据此提出了用于蛋白同时复性及纯化的制备型装置 (USRPP)中最小流动相用量的估算公式 ,并进一步得出在保持最小流动相用量不变的条件下 ,改变流动相流速和线性梯度时间几乎不影响制备型USRPP分离蛋白的分离度和复性效率的结论。该结论与实验结果一致。     

Design and evaluation of a low-power mobile shaker for vibration tests on heavy wheeled vehicles

In the field of mobile agricultural and off-road machinery, there is a growing interest to examine the dynamic behaviour of the vehicle under construction with a view to improve passenger comfort and machine performance. As many of these machine constructors are small sized, specialised companies are required to execute vibration tests and to evaluate vibration levels. Because most of these machines are heavy constructions with exceptional proportions, it is obvious to perform vibration tests on the factory floor by means of a low power mobile excitation device such as an electro-hydraulic shaker. This paper shows that an air spring, parallel to the actuator of an electro-hydraulic shaker, results in considerable power savings. The behaviour of the shaker is discussed at its resonance frequencies. For small band excitation signals, extra power savings can be achieved by tuning the spring stiffness and by using the air spring at constant pressure. Experiments on a one degree of freedom vertical shaker show good agreement with the theory and demonstrate that the different mode shapes of an agricultural tractor can be sufficiently excited with a nominal hydraulic power of 500 W.     

信息系统中的可移动Agent

介绍了可移动Agent的特性,可移动Agent可在异质计算机网络中移动,它能感知网络的状态,监控系统并与其它Agent进行交互,导航模型可以让Agent适应网络的变化并自主制定导航计划,从而方便、有效、智能地完成信息检索的任务．     

可动氧与载体对钴基催化剂的乙烷氧化脱氢性能的影响

 报道了未担载的氧化钴与担载在SiO2和Al2O3上的氧化钴的乙烷氧化脱氢行为,发现未担载的氧化钴在150℃的低温下就有活性．XRD研究表明,Co3O4为活性晶相组分,其含量与催化剂的焙烧温度密切相关,经500℃和1000℃焙烧的未担载的氧化钴中Co3O4晶相分别占100％和66％．对于担载型氧化钴催化剂,载体与氧化钴间的相互作用以及不同的氧化钴担载量导致催化性能发生明显改变．氧化钴与载体间的作用越强,催化活性越低．Co3O4也是担载型氧化钴催化剂的活性晶相．催化剂的O2－TPD－MS和H2－TPR研究结果表明,反应的活性氧物种为在低温下易脱除和易还原并具有较高流动性的可动氧．     

高效液相色谱手性流动相添加法拆分奥昔布宁对映体

采用C18固定相,以羟丙基-β-环糊精为手性流动相添加剂,建立了奥昔布宁对映体的高效液相色谱拆分方法。考察了手性添加剂、有机极性调节剂、缓冲盐的种类和浓度以及流动相的pH值和流速及柱温等因素对对映体分离的影响。在最佳分离条件下,奥昔布宁对映体的分离度为1.54,检测限为1.0 ng。该方法简便,重复性好,比手性固定相法更加经济。     

液相色谱手性固定相法直接拆分外消旋金属簇合物

在自制的涂敷型纤维素-三(3,5-二甲基苯基氨基甲酸酯)手性固定相上直接拆分了新近合成的外消旋四面体金属簇合物,得到了满意的色谱分离结果,为进一步实现色谱制备具有旋光活性的金属簇合物提供了基础条件。同时还考察了温度及流动相中的极性改性剂醇的种类及结构对手性拆分的影响。结果发现,醇的立体结构 极性对簇合物的手性拆分均有影响。在相同的色谱条件下,用正构醇作极性改性剂对该金属簇合物的拆分效果比用异丙醇好。     

基于无线PKI-PMI实现安全的移动电子政务

移动电子政务作为电子政务一个新的分支,为电子政务提供了强大的扩展能力和移动能力,针对移动电子政务的安全需求,探讨了将无线PKI-PMI应用于实现安全移动电子政务的方案,在分析移动电子政务和无线PKI体系结构的基础上,讨论了该方案的关键技术。     

Space-Air-Ground Integrated Mobile Crowdsensing for Partially Observable Data Collection by Multi-Scale Convolutional Graph Reinforcement Learning

Mobile crowdsensing (MCS) is attracting considerable attention in the past few years as a new paradigm for large-scale information sensing. Unmanned aerial vehicles (UAVs) have played a significant role in MCS tasks and served as crucial nodes in the newly-proposed space-air-ground integrated network (SAGIN). In this paper, we incorporate SAGIN into MCS task and present a Space-Air-Ground integrated Mobile CrowdSensing (SAG-MCS) problem. Based on multi-source observations from embedded sensors and satellites, an aerial UAV swarm is required to carry out energy-efficient data collection and recharging tasks. Up to date, few studies have explored such multi-task MCS problem with the cooperation of UAV swarm and satellites. To address this multi-agent problem, we propose a novel deep reinforcement learning (DRL) based method called Multi-Scale Soft Deep Recurrent Graph Network (ms-SDRGN). Our ms-SDRGN approach incorporates a multi-scale convolutional encoder to process multi-source raw observations for better feature exploitation. We also use a graph attention mechanism to model inter-UAV communications and aggregate extra neighboring information, and utilize a gated recurrent unit for long-term performance. In addition, a stochastic policy can be learned through a maximum-entropy method with an adjustable temperature parameter. Specifically, we design a heuristic reward function to encourage the agents to achieve global cooperation under partial observability. We train the model to convergence and conduct a series of case studies. Evaluation results show statistical significance and that ms-SDRGN outperforms three state-of-the-art DRL baselines in SAG-MCS. Compared with the best-performing baseline, ms-SDRGN improves 29.0% reward and 3.8% CFE score. We also investigate the scalability and robustness of ms-SDRGN towards DRL environments with diverse observation scales or demanding communication conditions.     

Gaussian-Based Adaptive Fish Migration Optimization Applied to Optimization Localization Error of Mobile Sensor Networks

Location information is the primary feature of wireless sensor networks, and it is more critical for Mobile Wireless Sensor Networks (MWSN) to monitor specific targets. How to improve the localization accuracy is a challenging problem for researchers. In this paper, the Gaussian probability distribution model is applied to randomize the individual during the migration of the Adaptive Fish Migration Optimization (AFMO) algorithm. The performance of the novel algorithm is verified by the CEC 2013 test suit, and the result is compared with other famous heuristic algorithms. Compared to other well-known heuristics, the new algorithm achieves the best results in almost 21 of all 28 test functions. In addition, the novel algorithm significantly reduces the localization error of MWSN, the simulation results show that the accuracy of the new algorithm is more than 5% higher than that of other heuristic algorithms in terms of mobile sensor node positioning, and more than 100% higher than that without the heuristic algorithm.