基于NS2仿真的Ad hoc网络AODV路由协议性能研究

Ad hoc移动网络是一种无任何有线基础设施的多跳、临时性自治系统。在进行Ad hoc各种仿真过程中,都需要设定一定的场景和节点数目,合理设置两者之间的关系对于仿真结果的好坏起着重要的作用。文章使用NS2仿真常见的Ad hoc网络设定场景,从数据正确接收率、端到端平均时延和路由开销三方面对比分析出最优的节点设置数目。最后提出了一种性能综合判定方法,对仿真结果表明：在场景1000 m×800 m的环境,设置节点在35左右为最佳,在此基础上,研究通信时发送节点为10左右网络性能最优。     

基于移动通信的嵌入式智能家居系统

该系统采用嵌入式技术、移动通信通讯技术和java语言相结合的方式,搭建了一个家庭智能管理平台。该平台可以将家中的电器、防盗系统、影视娱乐系统有机的结合起来,实现对家用电器和各个子系统的智能管理。同时能通过短信和Internet网络等方式,实现对家庭系统的远程监控。该系统具有非常灵活的可移植性和扩展性,开发成本低,该系统开创性的在手机客户端实现了C/S网络结构,具有独立的运行平台,具有更高的安全性和实时性,在市场上具有很好的发展前景。     

基于BP神经网络的导弹武器系统生存能力评估

导弹武器系统的生存能力是评估装备在使用阶段质量特性的一个关键指标。利用BP神经网络理论,在对某型导弹武器系统生存能力评估指标体系分析的基础上,建立了装备生存能力评估模型。最后结合实例利用MATLAB对该模型进行了仿真,结果表明该模型可较好地克服人为因素和不确定因素的影响,具有一定的借鉴价值。     

船舶柴油机NOx排放特性神经网络预测中的试验设计

研究船舶柴油机NOx排放特性神经网络预测中的学习样本选取试验设计方法。根据用于主机的船舶柴油机可能持续运行范围的工况变化特点,提出采用功率因素变边界的均匀设计法进行试验设计选取样本,并验证了其可行性。研究结果表明,变边界均匀设计法选取的样本用于神经网络训练,预测精度明显高于随机样本选取法。4位级变边界均匀设计法选取的样本训练得到的神经网络模型,NOx排放浓度预测误差小于3.8%,NOx比排放预测误差小4.5%。     

基于CNN和LSTM融合特征提取的车内声品质评价模型研究

基于深度学习方法建立的车内声品质评价模型不需要高度依赖声学理论和经验知识,可以有效提取深层次特征,客观高效地获得符合主观感受的评价结果。为获取噪声中符合人耳对声音感受的频率信息,便于在深度学习中进行特征提取,采用对数梅尔频谱和时频遮掩相结合的方法对采集到的噪声样本进行预处理。为有效提取车内噪声深层次特征,融合卷积神经网络（Convolution Neural Network,CNN）和长短时记忆网络（Long Short-Term Memory Network,LSTM）各自的优点,建立了融合特征提取层。使用全连接和Softmax输出单元组合构建了分类器模块。在合适的超参数下,模型通过充足的训练获得了96.88%的训练准确度。使用大量样本对模型进行验证,得到93.69%的验证准确度;采用混淆矩阵对模型进一步验证,总体的预测评价等级与真实评价等级偏差不大,证明模型的预测结果与主观评价结果具有很好的一致性。     

Spreading distinctive insignia benefits from mobile service: evidence from a mobile application of transportation

ABSTRACT

Mobile commerce (m-commerce) faces a growing array of client devices for which applications are designed. Applications developed for general consumer use must be compatible with a wide range of devices. This research investigates how perceived usefulness and enjoyment affect intention to use a mobile application service (T-Express). In this study, we formulate a commitment model from the perspective of the social identity and technology acceptance perspective. Empirical data were collected by conducting a field survey of users of T-Express from Taiwan High Speed Rail Corporation (THSRC) stations in Taiwan. The main findings of the study are that wireless technology provides great value to businesses when it allows consumers to access real-time data to make timely decisions that either decrease costs or increase revenues; and to achieve a higher intention to use mobile applications, there should be a clear communication process between mobile application and passenger. Our findings indicate that one of the more overt illustrations of the power of packaging symbolism involves package design strategies that serve to provide a radical new upscale positioning for the THSRC brand.     

A Bayesian network approach for population synthesis

Agent-based micro-simulation models require a complete list of agents with detailed demographic/socioeconomic information for the purpose of behavior modeling and simulation. This paper introduces a new alternative for population synthesis based on Bayesian networks. A Bayesian network is a graphical representation of a joint probability distribution, encoding probabilistic relationships among a set of variables in an efficient way. Similar to the previously developed probabilistic approach, in this paper, we consider the population synthesis problem to be the inference of a joint probability distribution. In this sense, the Bayesian network model becomes an efficient tool that allows us to compactly represent/reproduce the structure of the population system and preserve privacy and confidentiality in the meanwhile. We demonstrate and assess the performance of this approach in generating synthetic population for Singapore, by using the Household Interview Travel Survey (HITS) data as the known test population. Our results show that the introduced Bayesian network approach is powerful in characterizing the underlying joint distribution, and meanwhile the overfitting of data can be avoided as much as possible.     

机车车地数据传输通道整合方案研究

针对铁路机车车地传输通道建设存在冗余的现状,提出了一种基于中国机车远程监测与诊断系统(CMD系统)的多系统车地数据传输通道整合方案,该方案对数据传输安全和传输效率进行了加强和优化,并结合实际需求,通过半实物仿真的方式对数据传输过程进行验证。该方案避免了车载设备重复投资、车载天线信号互相干扰的问题,可以减少通信资费与降低管理难度,并为机车数据统一应用提供了技术基础。     

高速铁路基础设施综合检测监测技术探讨

从高速铁路基础设施检测监测及数据分析应用等方面探讨中国高速铁路基础设施综合检测监测技术发展方向。面向高铁工务、电务、供电综合维修生产一体化需求,按照"资源集中、统一管理、综合应用"原则,融合发展综合巡检和搭载式检测设备、统型固定监测装置和小型专业检测仪器,构建铁路基础设施综合一体化检测监测体系;与此同时,加强铁路基础设施运维管理信息系统建设,利用数据处理、融合分析和数据挖掘技术,提前发现设备存在的安全隐患,分析掌握基础设施状态演变规律和预测其变化趋势,并依据设备技术状态进行预防式维修养护。最后,针对我国现有检测监测体系,从综合检测技术、综合维修管理技术和检测数据分析应用方面提出创新意见。     

The station-free sharing bike demand forecasting with a deep learning approach and large-scale datasets

The station-free sharing bike is a new sharing traffic mode that has been deployed in a large scale in China in the early 2017. Without docking stations, this system allows the sharing bike to be parked in any proper places. This study aimed to develop a dynamic demand forecasting model for station-free bike sharing using the deep learning approach. The spatial and temporal analyses were first conducted to investigate the mobility pattern of the station-free bike sharing. The result indicates the imbalanced spatial and temporal demand of bike sharing trips. The long short-term memory neural networks (LSTM NNs) were then developed to predict the bike sharing trip production and attraction at TAZ for different time intervals, including the 10-min, 15-min, 20-min and 30-min intervals. The validation results suggested that the developed LSTM NNs have reasonable good prediction accuracy in trip productions and attractions for different time intervals. The statistical models and recently developed machine learning methods were also developed to benchmark the LSTM NN. The comparison results suggested that the LSTM NNs provide better prediction accuracy than both conventional statistical models and advanced machine learning methods for different time intervals. The developed LSTM NNs can be used to predict the gap between the inflow and outflow of the sharing bike trips at a TAZ, which provide useful information for rebalancing the sharing bike in the system.