Maritime piracy situation modelling with dynamic Bayesian networks

A generative model for modelling maritime vessel behaviour is proposed. The model is a novel variant of the dynamic Bayesian network (DBN). The proposed DBN is in the form of a switching linear dynamic system (SLDS) that has been extended into a larger DBN. The application of synthetic data fabrication of maritime vessel behaviour is considered. Behaviour of various vessels in a maritime piracy situation is simulated. A means to integrate information from context based external factors that influence behaviour is provided. Simulated observations of the vessels kinematic states are generated. The generated data may be used for the purpose of developing and evaluating counter-piracy methods and algorithms. A novel methodology for evaluating and optimising behavioural models such as the proposed model is presented. The log-likelihood, cross entropy, Bayes factor and the Bhattacharyya distance measures are applied for evaluation. The results demonstrate that the generative model is able to model both spatial and temporal datasets.     

Traffic light recognition exploiting map and localization at every stage

Traffic light recognition is being intensively researched for the purpose of reducing traffic accidents at intersections and realizing autonomous driving. However, conventional vision-based approaches have several limitations due to full image scanning, always-on operation, various different types of traffic lights, and complex driving environments. In particular, it might be impossible to recognize a relevant traffic light among multiple traffic lights at multiple intersections. To overcome such limitations, we propose an effective architecture that integrates a vision system with an accurate positioning system and an extended digital map. The recognition process is divided into four stages and we suggest an extended methodology for each stage. These stages are: ROI generation, detection, classification, and tracking. The 3D positions of traffic lights and slope information obtained from an extended digital map enable ROIs to be generated accurately, even on slanted roads, while independent design and implementation of individual recognition modules for detection and classification allow for selection according to the type of traffic light face. Such a modular architecture gives the system simplicity, flexibility, and maintainable algorithms. In addition, adaptive tracking that exploits the distance to traffic lights allows for seamless state estimation through smooth data association when measurements change from long to short ranges. Evaluation of the proposed system occurred at six test sites and utilized two different types of traffic lights, seven states, sloped roads, and various environmental complexities. The experimental results show that the proposed system can recognize traffic lights with 98.68% precision, 92.73% recall, and 95.52% accuracy in the 10.02–81.21 m range.     

Near-field microwave identification and quantitative evaluation of liquid ingress in honeycomb sandwich structures

Honeycomb sandwich structures are susceptible to liquid ingress, which causes a serious degradation of performance. Herein, a near-field microwave nondestructive detection technique was proposed to detect, identify, and quantitatively evaluate liquid ingress in honeycomb sandwich structures. Based on the microwave reflection spectrums, liquids of different polarity properties were identified. The amplitude of reflection microwaves was found nearly linear with respect to the height of the intruding liquids in the near field of the coaxial adapter probe. A simple characteristic peak method (CPM) based on line scans was presented and applied to quantify the size of liquid ingress region, and it turned out to be quite accurate with relative errors less than 0.5%. In summary, the near-field microwave testing technique proposed in this study is effective to detect, identify, and quantify liquid ingress in honeycomb sandwich structures.     

Bioconvection of gravitactic micro-organisms in rectangular enclosures

This paper investigates the gravitactic bioconvection in rectangular enclosures. The governing equations are the continuity, the Navier–Stokes equations with the Boussinesq approximation and the diffusion equation for the motile micro-organisms. The control volume method is used to solve numerically the complete set of governing equations. The effects of bioconvection Peclet number from 0.1 to 10 and the aspect ratio from 1 to 5 are investigated on the onset of bioconvection. It was found that the bifurcation was subcritical in all cases. The critical Rayleigh number is decreased with increasing bioconvection Peclet number and with increasing aspect ratio.     

Traffic network equilibrium problems with demands uncertainty and capacity constraints of arcs by scalarization approaches

This paper focuses on the vector traffic network equilibrium problem with demands uncertainty and capacity constraints of arcs, in which, the demands are not exactly known and assumed as a discrete set that contains finite scenarios. For different scenario, the demand may be changed, which seems much more reasonable in practical programming. By using the linear scalarization method, we introduce several definitions of parametric equilibrium flows and reveal their mutual relations. Meanwhile, the relationships between the scalar variational inequality as well as the (weak) vector equilibrium flows are explored, meanwhile, some necessary and sufficient conditions that ensure the (weak) vector equilibrium flows are also considered. Additionally, by means of nonlinear scalarization functionals, two kinds of equilibrium principles are derived. All of the derived conclusions contain the demands uncertainty and capacity constraints of arcs, thus the results proposed in this paper improved some existing works. Finally, some numerical examples are employed to show the merits of the improved conclusions.     

干湿循环下花岗岩残积土的崩解试验与微观机理研究

针对花岗岩残积土遇水易软化、崩解的特性,通过干湿循环下花岗岩残积土的崩解试验,研究了花岗岩残积土的压实度和干湿循环次数对其崩解特性和崩解参数指标的影响。得到以下结论：花岗岩残积土的压实度越小、经历的干湿循环次数越多,崩解速率越快,试样完全崩解所需的时间越短;拟合了平均崩解速率与干湿循环次数、压实度之间的函数关系式。利用电镜扫描试验,分析干湿循环作用下花岗岩残积土的微观结构变化,解释了崩解机理：随着干湿循环次数增加,片状颗粒间的层叠结构遭到破坏,颗粒的团聚性减弱,平面孔隙率增大,结构趋向疏松,加速了花岗岩残积土的崩解。     

基于车路协同的单点信号控制优化方法和模型

车路协同技术能全方位获取道路网络上车辆个体的运行状态信息,为交通信号控制提供了新的数据源。针对现有交通信号控制系统存在的不足,引入预测时间窗概念,提出了车路协同环境下单点信号控制优化的基本思路和流程;分析了交叉口区域单个车辆的运行状态,提出了单个车辆的延误和停车次数计算方法,在此基础上,以整个交叉口延误和停车次数最小为优化目标,建立了单点信号控制参数优化模型及求解算法。运用VISSIM软件进行了仿真实验,结果表明,文中方法优于常用的单点感应控制方法,在交通流量较低时平均停车次数减少约16.9%,在交通流量较大时平均延误和平均排队长度分别减少约29.4%和28.9%。     

Predictive maintenance decision-making for variable faults with non-equivalent costs of fault severities

Predictive Maintenance is crucial for production systems as it helps maintaining the reliability and availability of components/equipment as well as preventing unexpected shutdowns during production. Traditional maintenance strategies mostly focus on the predictive diagnosis of fault types and identical maintenance decisions would be delivered for the equipment with the same fault type. It often results in “over-maintenance” as the variable fault severities may require non-equivalent costs of maintenance resources. To tackle this problem, this paper aims at developing a fault prediction model firstly predicting fault severity and fault type simultaneously and subsequently providing distinguished maintenance strategy for variable faults accordingly, through which the abnormal faults of equipment can be effectively prevented, and machines can be efficiently and economically maintained based on the model suggested decisions. The main works in this study are 1) The fault features based on monitored vibration signals are extracted from multi-domains, and most significant features are selected by L1-Support Vector Machine (L1-SVM) together with variance filtering method; 2) A parallel fault prediction model based on Back propagation Neural Network and Long Short Term Memory Neural Network (BP-LSTM) is proposed, which is used to predict the fault type and fault degree simultaneously; 3) A Deep Q-Network (DQN)-based maintenance decision-making model is established for different fault types with various fault severities.     

Hierarchical classification method based on selective learning of slacked hierarchy for activity recognition systems

Physical activity recognition using wearable sensors has gained significant interest from researchers working in the field of ambient intelligence and human behavior analysis. The problem of multi-class classification is an important issue in the applications which naturally has more than two classes. A well-known strategy to convert a multi-class classification problem into binary sub-problems is the error-correcting output coding (ECOC) method. Since existing methods use a single classifier with ECOC without considering the dependency among multiple classifiers, it often fails to generalize the performance and parameters in a real-life application, where different numbers of devices, sensors and sampling rates are used. To address this problem, we propose a unique hierarchical classification model based on the combination of two base binary classifiers using selective learning of slacked hierarchy and integrating the training of binary classifiers into a unified objective function. Our method maps the multi-class classification problem to multi-level classification. A multi-tier voting scheme has been introduced to provide a final classification label at each level of the solicited model. The proposed method is evaluated on two publicly available datasets and compared with independent base classifiers. Furthermore, it has also been tested on real-life sensor readings for 3 different subjects to recognize four activities i.e. Walking, Standing, Jogging and Sitting. The presented method uses same hierarchical levels and parameters to achieve better performance on all three datasets having different number of devices, sensors and sampling rates. The average accuracies on publicly available dataset and real-life sensor readings were recorded to be 95% and 85%, respectively. The experimental results validate the effectiveness and generality of the proposed method in terms of performance and parameters.     

轨道交通与常规公交计划调度协调模型

根据福利经济学理论,分析了轨道交通与常规公交运营协调系统的消费者剩余和生产者剩余;并以社会福利最大化为目标,分别建立了非协调计划调度模型和计划调度协调模型,同时给出了模型求解步骤。结合算例,对比分析了轨道交通与常规公交计划调度协调实施前后换乘网络的运营效果。结果表明,实行运营协调控制能够增加社会福利,使出行乘客和运营企业都获益。