成品油罐车智能管理系统

针对当前成品油配送运距及运行线路的管理缺陷,以及传统物理签封、电子签封的不足,给出了结合物联网技术和无线通信技术,将GPS定位、射频签封、车载液位仪、车辆行驶路线、车辆行驶运距、车载摄像等功能模块有机结合起来,共同组成成品油罐车智能管理系统的设计方案。该方案改变了以往电子签封仅仅具有路途防偷盗的功能弱点,实现了全过程、全时段、多功能的车辆监控,从而全面提升了油品数质量及物流管理水平。     

用于智能组卷的自适应小生境复合遗传算法

传统遗传算法的选择策略缺乏多样性保护机制,易出现早熟收敛。为解决智能组卷问题,采取小生境技术完成遗传操作中的种群进化机制。利用个体浓度的大小,设置自适应变异算子,保证种群多样性,防止种群陷入局部收敛;增加阈值以保证算法在接近最优解时回归到自适应遗传算法,简化算法计算量,加快算法的收敛速度。本文提出一种自适应与小生境技术复合遗传算法,来均衡算法的全局搜索和局部快速开发能力。最后,实例验证了所提算法的有效性。     

基于Java EE多层架构的进销存管理系统

针对传统的进销存管理系统的开发方式所带来的一些问题,提出使用基于S2SH框架的N层体系结构来开发系统,介绍Struts2、Hibernate和Spring三个框架,分析系统功能,阐述系统实现所使用的一些关键技术,并使用基于S2SH框架的N层体系结构对系统进行总体架构设计,有效提高了系统的可维护性、可扩展性和可复用性。     

火焰动画快速模拟的新方法

针对火焰动画难以快速建模和绘制的问题,提出了完全利用GPU过程化生成火焰动画的高效算法。先用建模工具在预处理阶段构建一个由平面正方形组成的三维模型,在绘制阶段通过GPU编程对这些方形进行Billboard方式的变换,并用一些简单公式对他们的运动方式、大小和颜色来用过程化方式进行控制,最后通过alpha混合得到最终结果。算法完全在GPU里实现,能满足实时的绘制要求。     

基于参数区间不确定性分析的无人机辅助物联网鲁棒优化

无人机辅助物联网技术被广泛应用于灾害应急通信中,但物联网设备的通信中存在多种不确定因素。针对位置参数区间存在不确定性的问题,提出了无人机辅助物联网通信的鲁棒优化方法。使用椭球不确定集对地面物联网设备位置参数区间不确定性进行描述,建立了包含不确定性的物联网设备通信鲁棒优化模型,并对模型进行了求解。为提高模型求解的准确性,提出一个有关位置参数区间不确定和椭球误差域之间的相关分析方法,分析了两类不确定因素对物联网设备通信模型的影响程度。以地面物联网设备最优部署和通信最小功率为目的,设计了仿真实验。结果表明,在不确定因素存在时,所提优化模型能有效实现地面物联网设备间的最优部署和通信功率最小化。     

Design of composite data acquisition gateway based on 5G network

The integrated data collection system hasn't changed much over the years. Their growth is dominated by digital filtering, and they get a new avionics bus. Time division multiplexing is still used to communicate with the system's data collector. These schemes make use of commands and data buses. While this method works, it has many drawbacks. These shortcomings are overwhelming the strict system architecture, system bandwidth limits, and expertise to only obtain an avionics bus, otherwise system bandwidth and one-way flow of data and control. This lead for high-end video recorders. To quietly monitor our actions and valuable information provides important warnings. However, it is usually completed by a device with this small processing power, which tracks the device, stores the data of the user's data, and is limited in processing. Therefore, it is important to store and process user data in the cloud to track 5 G network activity. This article proposes a simple 5G network gateway solution for custom user monitoring equipment. Collects charge data on Network 5G and Charging-aware multi-mode based routing protocol (CMRP). The CMRP routing mechanism is not yet stable, but the changes depend on the state of the energy charge sensor. This cannot be complemented by energy-efficient sensors and routing protocols that consume less energy.     

Data monitoring and sports injury prediction model based on embedded system and machine learning algorithm

Managing sports performance is very important in the sports industry. Performance, the executives, centers on boosting competitor execution and decreasing the danger of injury. Several factors contribute to these goals, including player health, emotional status, exercise load and physical intensity requirements. Generally speaking, injury prediction is an essential component of injury prevention, and successful identification of injury prediction is a primary indicator for effective prevention. The proposed Artificial Neural Network (ANN) objective is to develop and use early-doing ability and exercise load data to validate a hierarchical machine learning prediction system with accurate detection of player injuries. The physical and workload that requires detection of this early personalized damage can be avoided with specific help. The framework is used to test 21 soccer players’ sports information from various sources, including gathered and inside burden information, outside burden information, and review information. The entirety of this information is fused into the proposed framework to improve the exactness of harm expectation. This calculation distinguishes competitors in danger of injury, with their early intervention available.     

The application of the methodology for secure cyber–physical systems design to improve the semi-natural model of the railway infrastructure

The paper suggests a new methodology for secure cyber–physical systems design. The proposed methodology consists of two main cycles. The main goal of the first cycle is in design of the system model, while the second one is about development of the system prototype. The key idea of the methodology is in providing of the most rational solutions that are improving the security of cyber–physical systems. Such solutions are called alternatives and built according to functional requirements and non-functional limitations to the system. Each cycle of the methodology consists of the verification process and seven stages that are associated with the used cyber–physical system model. The objective of the verification process is in checking of constructed models and prototypes in terms of their correctness and compatibility. The model represents cyber–physical systems as sets of building blocks with network between them, takes elements internal structure into account and allows direct and reverse transformations. The novelty of the suggested methodology is in the combination of design, development and verification techniques within a single approach. To provide an example of the design methodology application, in this paper it is used to improve the semi-natural model of the railway infrastructure.     

Paradigm and performance analysis of distributed frequent itemset mining algorithms based on Mapreduce

FrequentItemsetMining (FIM) is one of the most important data mining tasks and is the foundation of many data mining tasks. In Big Data era, centralized FIM algorithms cannot meet the needs of FIM for big data in terms of time and space, so Distributed Frequent Itemset Mining (DFIM) algorithms have been designed to meet the above challenges. In this paper, LocalGlobal and RedistributionMining which are two main paradigms of DFIM algorithm are discussed; Two algorithms of these paradigms on MapReduce named LG and RM are proposed while MapReduce is a popular distributed computing model, and also the related work is discussed. The experimental results show that the RM algorithm has better performance in terms of computation and scalability of sites, and can be used as the basis for designing the DFIM algorithm based on MapReduce. This paper also discusses the main ideas of improving the DFIM algorithms based on MapReduce.     

RAFNet: RGB-D attention feature fusion network for indoor semantic segmentation

Semantic segmentation based on the complementary information from RGB and depth images has recently gained great popularity, but due to the difference between RGB and depth maps, how to effectively use RGB-D information is still a problem. In this paper, we propose a novel RGB-D semantic segmentation network named RAFNet, which can selectively gather features from the RGB and depth information. Specifically, we construct an architecture with three parallel branches and propose several complementary attention modules. This structure enables a fusion branch and we add the Bi-directional Multi-step Propagation (BMP) strategy to it, which can not only retain the feature streams of the original RGB and depth branches but also fully utilize the feature flow of the fusion branch. There are three kinds of complementary attention modules that we have constructed. The RGB-D fusion module can effectively extract important features from the RGB and depth branch streams. The refinement module can reduce the loss of semantic information and the context aggregation module can help propagate and integrate information better. We train and evaluate our model on NYUDv2 and SUN-RGBD datasets, and prove that our model achieves state-of-the-art performances.