面向车辆状态信息广播的功率控制算法

针对车载自组织网（VANET）中节点以固定功率发送信息导致的信道资源无法优化分配的问题,以车辆周期发送的状态信息的广播为研究对象,提出一种可自适应于车流密度变化的VANET功率控制算法。该算法通过定义功率控制周期构建并更新直接邻居列表,根据直接邻居车辆的位置来调整发射功率,进而控制节点的广播信息覆盖范围,实现信道资源的优化分配以及节点路由性能的优化。仿真结果验证了所提出算法的有效性,表明该算法能够有效根据车辆密度自适应调整发射功率、降低信道占用率并提高直接邻居数据包投递率,从而保证安全信息的有效传输。     

VANETs中的车辆移动规律性及轨迹预测研究

车辆的移动是受人意识控制的,有规律的移动,通过对车辆已知轨迹数据的分析,建立历史轨迹模型,可以实现对其未来轨迹的预测。采集了大量真实的车辆轨迹数据,通过计算信息熵分析车辆运动的规律性,为预测车辆轨迹提供依据。根据车载自组织网络的特殊应用场景以及车辆移动的规律性,利用车辆轨迹的历史数据构建状态转移矩阵,提出了基于马尔科夫链的车辆轨迹预测方法,仿真结果表明,该方法可以实现对车辆轨迹的有效预测。对影响预测精度的一些因素进行了对比分析。     

一种车联网分簇方法与簇内中继选择方法

针对车联网低时延、高可靠性的通信需求,提出了基于簇稳定的车辆分簇方法,有效增加通信时间,提高通信可靠性。在此基础上,研究功率受限的情况下车联网簇内数据分发的中继选择问题,提出了一种基于功率预分配的中继选择方法。该方法簇内车辆协作采用HDAF转发协议,在中继选择之前先对源节点和潜在中继节点进行功率分配,求取系统中断概率最小的功率分配因子,然后比较融入功率优化因子的各个节点的等效信道增益,选择出最优中继节点集合。数值结果表明:基于簇稳定的车辆分簇方法相比基于地理位置的分簇方法具有更高的稳定性。同时,提出的中继选择方法在相同条件下比传统的单中继选择方案和全中继选择方案具有更小的中断概率。     

A detection method for intronic snoRNA genes using extended-weight-updating SOM with appearance probability of bases

Small nucleolar RNAs (snoRNAs) are known that they will participate with RNA modification. However, detail functions of snoRNAs have not been clear still yet. In order to make clear functions of snoRNA, finding more snoRNAs and studying their works in cells are required. In this paper, we propose a method to detect snoRNA genes using extended-weight-updating self-organizing map (eSOM). An input vector to eSOM consists of a feature vector and a target vector. Different from a conventional SOM, a winner node for an input vector is decided by the feature vector only, and all the weights around the winner node are updated to be close to the input vector. We employ bases appearance probabilities and complementary base pair ratio for a feature vector. A target vector is a flag which is 1.0 or 0.0 for a positive or a negative sample, respectively. Experimental results showed our method achieved 91 and 93 % detection ratio for boxC/D and boxH/ACA type snoRNA genes, respectively.     

一种采用社团信息的链接预测方法

链接预测研究如何利用网络中已有的信息预测可能存在的关系链接,目前已成为数据挖掘领域的热点研究问题之一。社会网络中普遍存在社团（community）结构,社团对链接的形成有重要的影响,但在大多数链接预测方法中未得到深入研究。针对这一现象,本文提出一种新的链接预测方法,采用社团信息改进节点对样本的描述并在监督学习框架中学习和预测。在现实数据集FaceBook和ACF中的实验结果表明,加入社团信息的链接预测方法获得了更高的准确率。     

VANET中一种链路稳定度的计算方法

在车用自组织网络VANET中,由于移动节点运动速度快且移动受道路拓扑约束等影响,导致链路容易断裂。路由选择是实现VANET的关键技术之一,没有高效的路由选择算法,VANET就无法工作,链路稳定度是衡量路由算法性能的重要因素。基于概率思想提出一种链路稳定度的计算方法来衡量链路的健壮性及可靠性。实验结果表明,所提出的链路稳定度计算方法是一种适合VANET场景的计算方法。     

VANET中一种基于多目标优化的自适应广播方案

针对车载网V ANET(Vehicular Ad hoc Network)中的广播策略,提出新颖的自动分发方案(Autonomic Dissemination Method,ADM)。通过ADM传输消息,自适应网络的密度和消息的优先级变化。在实施A DM过程中,采用两个步骤:离线优化过程和在线适应网络特性。仿真结果表明,提出的A DM方案在消息传递率、传输时延和干扰方面得到提高。     

Towards integrated ADME prediction: past, present and future directions for modelling metabolism by UDP-glucuronosyltransferases

Undesirable absorption, distribution, metabolism, excretion (ADME) properties are the cause of many drug development failures and this has led to the need to identify such problems earlier in the development process. This review highlights computational (in silico) approaches that have been used to identify the characteristics of ligands influencing molecular recognition and/or metabolism by the drug-metabolising enzyme UDP-gucuronosyltransferase (UGT). Current studies applying pharmacophore elucidation, 2D-quantitative structure metabolism relationships (2D-QSMR), 3D-quantitative structure metabolism relationships (3D-QSMR), and non-linear pattern recognition techniques such as artificial neural networks and support vector machines for modelling metabolism by UGT are reported. An assessment of the utility of in silico approaches for the qualitative and quantitative prediction of drug glucuronidation parameters highlights the benefit of using multiple pharmacophores and also non-linear techniques for classification. Some of the challenges facing the development of generalisable models for predicting metabolism by UGT, including the need for screening of more diverse structures, are also outlined.     

A hybrid data-driven and mechanism-basedmethod for vehicle trajectory prediction

Ensuring the safe and efficient operation of self-driving vehicles relies heavily on accurately predicting their future trajectories.Existing approaches commonly employ an encoder–decoder neural network structure to enhance information extraction duringthe encoding phase. However, these methods often neglect the inclusion of road rule constraints during trajectory formulationin the decoding phase. This paper proposes a novel method that combines neural networks and rule-based constraints in thedecoder stage to improve trajectory prediction accuracy while ensuring compliance with vehicle kinematics and road rules.The approach separates vehicle trajectories into lateral and longitudinal routes and utilizes conditional variational autoencoder(CVAE) to capture trajectory uncertainty. The evaluation results demonstrate a reduction of 32.4% and 27.6% in the averagedisplacement error (ADE) for predicting the top five and top ten trajectories, respectively, compared to the baseline method.     

A feasible directions method for nonsmooth convex optimization

We propose a new technique for minimization of convex functions not necessarily smooth. Our approach employs an equivalent constrained optimization problem and approximated linear programs obtained with cutting planes. At each iteration a search direction and a step length are computed. If the step length is considered “non serious”, a cutting plane is added and a new search direction is computed. This procedure is repeated until a “serious” step is obtained. When this happens, the search direction is a feasible descent direction of the constrained equivalent problem. To compute the search directions we employ the same formulation as in FDIPA, the Feasible Directions Interior Point Algorithm for constrained optimization. We prove global convergence of the present method. A set of numerical tests is described. The present technique was also successfully applied to the topology optimization of robust trusses. Our results are comparable to those obtained with other well known established methods.     

Supervised vehicle trajectory prediction using orthogonal image map for urban automated driving

Artificial Life and Robotics - In urban automated driving, it is important to generate appropriate behaviors according to surrounding circumstances. The automated vehicle is generally equipped with...     

Topology control algorithm for underwater wireless sensor networks using GPS-free mobile sensor nodes

Underwater wireless sensor networks (UWSNs) have been developed for underwater applications, such as resource exploration, pollution monitoring, and tactical surveillance. The topology control techniques of UWSNs and terrestrial wireless sensor networks are significantly different because of the particularity of underwater environments and acoustic communication, such as mobility pattern, propagation delay and energy consumption. Due to multifarious factors of underwater environments, node mobility becomes a non-negligible issue. However, GPS may not be feasible because of the limitations of satellite coverage or obstructions in adverse underwater environments. In particular, anchored sensor nodes towed by wires are prone to offset around their static positions, causing each node to move within a spherical crown surface (spherical crown mobility pattern). Nevertheless, most previous studies have not focused on this specific mobility pattern. In the current paper, a mobility model for UWSNs nodes is constructed, and three representative topology control objectives are attained. A distributed radius determination algorithm is designed for the mobility-based topology control problem. Results of theoretic analysis prove that the proposed algorithm is convergent, and it has preferable approximate ratios and polynomial complexity. Performance of the algorithm is analyzed through simulation experiments, which indicate a well-constructed topology. Every objective can still be upgraded without the dynamic location information of mobile nodes.     

A launch vehicle stability analysis using the parameter plane method

An attitude control system, which uses the acceleration vector attitude as the control variable, has been postulated for control of the first stage of a large space booster. The goal of this system is to minimize lateral drift. A stability analysis of this system based upon the parameter plane method is described.     

A review of current methods to generate synthetic spatial microdata using reweighting and future directions

Synthetic spatial microdata enable analyses of artificial populations in the form of individual unit record files at a small area level. They allow analyses of estimates of variables that are otherwise not available at this small area level, while preserving the confidentiality of personal data. This type of data has mainly been used to provide more detailed census data and for spatial microsimulation modelling: for example to analyse social policy and population changes, transportation, marketing strategies or health outcomes. We argue that many potential applications for synthetic spatial microdata remain to be developed. One reason for this is the lack of information about and confidence in this type of data. Introductory literature about creating synthetic spatial microdata and discussions on the decisions that need to be taken during the data generation process are rare. In this paper, we therefore review currently existing methods to generate synthetic spatial microdata in a manner which will support most readers who are considering this approach, and we address the main issues of the data generation process with regards to analyses of neighbourhood level data. We discuss further possible applications of these data and the importance of synthetic spatial microdata.     

Stock market one-day ahead movement prediction using disparate data sources

There are several commercial financial expert systems that can be used for trading on the stock exchange. However, their predictions are somewhat limited since they primarily rely on time-series analysis of the market. With the rise of the Internet, new forms of collective intelligence (e.g. Google and Wikipedia) have emerged, representing a new generation of “crowd-sourced” knowledge bases. They collate information on publicly traded companies, while capturing web traffic statistics that reflect the public’s collective interest. Google and Wikipedia have become important “knowledge bases” for investors. In this research, we hypothesize that combining disparate online data sources with traditional time-series and technical indicators for a stock can provide a more effective and intelligent daily trading expert system. Three machine learning models, decision trees, neural networks and support vector machines, serve as the basis for our “inference engine”. To evaluate the performance of our expert system, we present a case study based on the AAPL (Apple NASDAQ) stock. Our expert system had an 85% accuracy in predicting the next-day AAPL stock movement, which outperforms the reported rates in the literature. Our results suggest that: (a) the knowledge base of financial expert systems can benefit from data captured from nontraditional “experts” like Google and Wikipedia; (b) diversifying the knowledge base by combining data from disparate sources can help improve the performance of financial expert systems; and (c) the use of simple machine learning models for inference and rule generation is appropriate with our rich knowledge database. Finally, an intelligent decision making tool is provided to assist investors in making trading decisions on any stock, commodity or index.     

基于SOM的无线通信话务量最优加权组合预测

针对单个预测模型难以准确刻画无线通信话务量的演变规律,并考虑数据自身的多样性,提出了基于自组织映射（Self-Organizing Maps,SOM）神经网络的无线通信话务量最优加权组合预测方法。该方法利用SOM神经网络对话务量数据进行自动聚类,并对聚类后的每类数据,分别确定相应最优加权组合预测的权重,进而获得相应的预测值。实验结果表明,所提出方法不仅能提高话务量预测的精度,还能增强预测系统的稳定性。     

Computer vision for behaviour-based safety in construction: A review and future directions

The process of identifying and bringing to the fore people’s unsafe behaviour is a core function of implementing a behaviour-based safety (BBS) program in construction. This can be a labour-intensive and challenging process but is needed to enable people to reflect and learn about how their unsafe actions can jeopardise not only their safety but that of their co-workers. With advances being made in computer vision, the capability exists to automatically capture and identify unsafe behaviour and hazards in real-time from two-dimensional (2D) digital images/videos. The corollary developments in computer vision have stimulated a wealth of research in construction to examine its potential application to practice. Hindering the application of computer vision in construction has been its inability to accurately, and generalise the detection of objects. To address this shortcoming, developments in deep learning have provided computer vision with the ability to improve the accuracy, reliability and ability to generalise object detection and therefore its usage in construction. In this paper we review the developments of computer vision studies that have been used to identify unsafe behaviour from 2D images that arises on construction sites. Then, in light of advances made with deep learning, we examine and discuss its integration with computer vision to support BBS. We also suggest that future computer-vision research should aim to support BBS by being able to: (1) observe and record unsafe behaviour; (2) understand why people act unsafe behaviour; (3) learn from unsafe behaviour; and (4) predict unsafe behaviour.     

Container-based cluster orchestration systems: A taxonomy and future directions

Containers, enabling lightweight environment and performance isolation, fast and flexible deployment, and fine-grained resource sharing, have gained popularity in better application management and deployment in addition to hardware virtualization. They are being widely used by organizations to deploy their increasingly diverse workloads derived from modern-day applications such as web services, big data, and internet of things in either proprietary clusters or private and public cloud data centers. This has led to the emergence of container orchestration platforms, which are designed to manage the deployment of containerized applications in large-scale clusters. These systems are capable of running hundreds of thousands of jobs across thousands of machines. To do so efficiently, they must address several important challenges including scalability, fault tolerance and availability, efficient resource utilization, and request throughput maximization among others. This paper studies these management systems and proposes a taxonomy that identifies different mechanisms that can be used to meet the aforementioned challenges. The proposed classification is then applied to various state-of-the-art systems leading to the identification of open research challenges and gaps in the literature intended as future directions for researchers.     

A novel method for vehicle detection in high-resolution aerial remote sensing images using YOLT approach

Multimedia Tools and Applications - Owing to the innovative challenge stood by an intergovernmental military alliance, we have proposed a model to find novel solutions in the areas of data...