Representing financial data streams in digital simulations to support data flow design for a future Digital Twin

Discrete Event Simulations may be used to forecast detailed production system behaviour under future conditions and support better informed decision making. However, data beyond production metrics, e.g. financial information, is also necessary for most significant decisions. Herein a modelling approach is proposed and demonstrated based on simulating both production and financial transactions. The contribution of this paper is to represent the base production events as well as additional events which trigger financial transactions. The method is demonstrated for an idealised production business, employing Discrete Event Simulation and examining the impact of system labour arrangements on the business cash flow with a variable demand based on a normal probability distribution function (with distinct means and variances for each product). Based on the financial transactions a representation of a detail commercial scenario may be modelled alongside the production events. The presented studies identify the potential sensitivity of analysis to the volume and timing of transactions on accumulated accounting metrics such as profit. Additionally the paper illustrates how such a modelling approach can be used to characterise the system and assess specific control strategies when both production and finance data streams are available.     

Dynamic window flow control on a high-speed wide-area data network

This paper considers sliding-window flow control for data traffic on a high-speed, wide-area, virtual-circuit backbone network. The objectives are to allocate network bandwidth fairly, to utilize bandwidth efficiently, and to prevent buffer overflow within the network, but without requiring gross overengineering of memory in the switching nodes. The network uses round robin queueing disciplines at routers and switches. A signaling protocol is described for in-call adjustment of windows and per-virtual-circuit buffer allocations as a function of network congestion, while maintaining consistent allocations along each virtual circuit. Simulations indicate that the protocol works as expected between the edges of the wide-area network. The wide-area backbone may interconnect local-area networks on which the hosts use transport-level protocols such as TCP. The backbone network protocol works satisfactorily with the host-to-host transport provided that the hosts use a retransmission strategy such as Jacobson-Karels to deal with possible packet losses at the edge of the backbone network.     

不平衡数据的集成分类算法综述

集成学习是通过集成多个基分类器共同决策的机器学习技术,通过不同的样本集训练有差异的基分类器,得到的集成分类器可以有效地提高学习效果。在基分类器的训练过程中,可以通过代价敏感技术和数据采样实现不平衡数据的处理。由于集成学习在不平衡数据分类的优势,针对不平衡数据的集成分类算法得到广泛研究。详细分析了不平衡数据集成分类算法的研究现状,比较了现有算法的差异和各自存在的优点及问题,提出和分析了有待进一步研究的问题。     

Precognitive control: open- and closed-loop steering in a lane-change manoeuvre

Abstract

This paper presents two experiments in which steering a lane-change manoeuvre is analysed as a precognitive control task. The suggestion that the dependence on instantaneous visual feedback is low during such a task was tested by analysing steering performance under conditions both with and without visual occlusion. Steering force served as the main independent variable in experiment I, which was performed in a fixed-base driving simulator. A 1 s visual occlusion period during the initial steering-wheel movement to the left was given every other manoeuvre. The results indicate that steering force reduces steering-wheel amplitude variability, this effect also being reflected in the maximum heading angle variability.

Steering-wheel angle amplitude was the main independent variable in experiment II, which was carried out in the instrumented car. In this experiment the duration of the occlusion period was 3 s, i.e. covering the complete pull-out phase of the manoeuvre. The results show that variability in steering-wheel movement amplitude increases about linearly with the amplitude: standard deviations are about 9% of the amplitude. For both experiments I and II the results correspond quite well with those of an earlier reproduction experiment. Regarding the withdrawal of visual feedback it appears that occlusion affects the mutual tuning of steering amplitude and timing, rather than affecting these quantities separately. The vehicle-motion effects resulting from occlusion are relatively small and it can be concluded therefore that a temporary withdrawal of visual feedback has no dramatic effects in the pre-programmed task considered here.     

Visual strategies for the control of steering toward a goal

We have shown that people steer toward a target by aligning their heading with the target when target egocentric direction is not available for steering [24]. Here we examined what visual strategies people use to steer toward a target when target egocentric direction is available for steering. The display simulated a participant walking over a ground plane with a target placed off to one side. The participant’s simulated heading in the display was displaced 10° away from the participant’s straight ahead. A textured ground display that provided dense global optic flow and an empty ground display that provided nearly no flow were tested. Participants were instructed to use a joystick to control their simulated self-motion in the display to (a) steer toward the target, (b) center the target at their straight ahead, or (c) minimize the target drift on the screen. We found that participants produced similar heading error profiles when they were instructed to steer toward the target or to center the target straight ahead, but not when they were instructed to minimize the target movement on the screen. Furthermore, regardless of the instructions received, final heading errors were about 5° smaller with the textured than with the empty ground display, indicating the effect of optic flow on the control performance. We conclude that when target egocentric direction is available for steering, people do not steer toward the target by canceling its optical drift. Optic flow contributes to steering toward a target even when control could be based on egocentric direction alone.     

汽车转向防抱死制动控制系统研究

为了研究解决车辆转向过程中防抱死制动稳定性问题,设计了一种由执行级、协调级组成的分层控制系统,在执行级,设计了基于遗传算法的汽车ABS最优滑模控制器;设计了基于遗传算法的汽车转向滑模控制器。在协调级,针对制动和转向两个子系统提出协调控制方案,给出具体协调策略。用仿真结果验证所设计控制算法的稳定性和有效性。     

WeVoS-ViSOM: An ensemble summarization algorithm for enhanced data visualization

This study presents a novel version of the Visualization Induced Self-Organizing Map based on the application of a new fusion algorithm for summarizing the results of an ensemble of topology-preserving mapping models. The algorithm is referred to as Weighted Voting Superposition (WeVoS). Its main feature is the preservation of the topology of the map, in order to obtain the most accurate possible visualization of the data sets under study. To do so, a weighted voting process between the units of the maps in the ensemble takes place, in order to determine the characteristics of the units of the resulting map. Several different quality measures are applied to this novel neural architecture known as WeVoS-ViSOM and the results are analyzed, so as to present a thorough study of its capabilities. To complete the study, it has also been compared with the well-know SOM and its fusion version, with the WeVoS-SOM and with two other previously devised fusion Fusion by Euclidean Distance and Fusion by Voronoi Polygon Similarity—based on the analysis of the same quality measures in order to present a complete analysis of its capabilities. All three summarization methods were applied to three widely used data sets from the UCI Repository. A rigorous performance analysis clearly demonstrates that the novel fusion algorithm outperforms the other single and summarization methods in terms of data sets visualization.     

数据流控制流相叠加的可执行测试序列生成研究

提出了一种可执行的测试序列的自动生成策略。先从UML模型中提取出协议模型的EFSM信息,然后利用变迁可执行性分析自动生成可执行的测试序列。该生成策略同时结合了数据流测试和控制流测试的优点,既具有比较高的错误覆盖率,又对通信协议中的数据部分进行了充分的测试,从而提高了一致性测试的效率。     

Information flow control on encrypted data for service composition among multiple clouds

Homomorphic encryption allows the direct operations on encrypted data, which provides a promising way to protect outsourcing data in clouds. However, it can not guarantee the end-to-end data security if different cloud services are composed together. Especially for the operations on encrypted data, it may violate the standard noninterference, which can not be solved by traditional information flow control approaches. In order to analyze the information flow with encrypted data, we define a new type of flow called the encryption flow to describe the dependence relationship among different encrypted data objects across multiple services. Based on the new definition on encrypted flow, we propose the secure information flow verification theorem and specify the improved security constraints on each service component. Then a distributed information flow control framework and algorithm are designed for verification on regular and encrypted flow across multiple clouds. Through the experiments, we can obtain that our approach is more appropriate for the verification across multiple clouds and provides a more effective way compared with centralized verification approaches.     

Towards efficient operation of internet data center networks: Joint data placement and flow control for cost optimization

The problem of cost-efficient operation of data center networks used to deliver file sharing services is studied. The aggregate costs are split into server-load-related and link-load-related shares. Thus, the problem of interest is formulated as one of joint data placement and flow control, and mixed integer-linear programming is used to compute the optimal solution. The high complexity of the latter motivated us to design two additional sets of strategies, based on data coding and heuristics, respectively. With coding, a distributed algorithm for the problem is developed. In the simulation experiments, carried out based on actual data center information, network topology and link cost, as well as electricity prices, the advantages of data coding, in particular in the context of multicast, and the impact of different factors such as the network topology and service popularity, on the total cost incurred by all considered strategies, are examined. Network coding with multicast is shown to provide cost savings in the order of 30–80%, depending on the specific context under consideration, relative to the other optimization strategies and heuristic methods examined in this work.     

数据链路层的流量和差错控制机制的分析

分析了停止等待（Stop-and Wait)ARQ及连续请求（Go-back-N)ARQ两种数据链路层流量、差错控制机制，并用评价各种链路控制方案报主要参数之一对两种方案的性能做了定量的分析。     

基于数据流和控制流的分布式星载计算机

空间太阳望远镜是中国科学院国家天文台正在研制的我国第1颗太阳同步轨道天文卫星。以空间太阳望远镜高速科学数据处理系统为例,介绍了星载计算机系统分布式构型和基于数据流、控制流分开的设计思想和技术实现,分析了星载计算机系统的网络拓扑、通信并行度、响应时间和容错技术的应用。最后给出了该系统原理样机的调试结果和进一步工作的探讨。     

Optimal data placement on disks: a comprehensive solution fordifferent technologies

The problem of optimally placing data on disks (ODP) to maximize disk-access performance has long been recognized as important. Solutions to this problem have been reported for some widely available disk technologies, such as magnetic CAV and optical CLV disks. However, important new technologies such as multizoned magnetic disks, have been recently introduced. For such technologies no formal solution to the ODP problem has been reported. In this paper, we first identify the fundamental characteristics of disk-device technologies which influence the solution to the ODP problem. We develop a comprehensive solution to the problem that covers all currently available disk technologies. We show how our comprehensive solution can be reduced to the solutions for existing disk technologies, contributing thus a solution to the ODP problem for multizoned disks. Our analytical solution has been validated through simulations and through its reduction to the known solutions for particular disks. Finally, we study how the solution for multizoned disks is affected by the disk and data characteristics     

Nonlinear modelling and control of the flow over aerofoils using CFD simulations

A simulation based approach for nonlinear dynamical modelling and feedback control of the drag to lift ratio for aerofoils is investigated through case studies involving NACA 23012, ag13 and b737a aerofoils. The flow around the aerofoils is studied via numerical solutions of the 2D Navier–Stokes (NS) equations. A standard computational fluid dynamics (CFD) solver is extended to be able to measure desired feedback values and to apply a control input to the flow field. The proposed modelling and control approach is based on first determining the measurement points and injection points on the aerofoil for the control input. Then, to estimate the dynamical model, some input–output data are collected by injecting a chirp input flow to the field and saving the measurement data. Next a Hammerstein–Wiener (HW) type nonlinear dynamical model of the flow field is estimated using system identification. For control design, the nonlinear part of the model is eliminated by means of inverse functions, followed by the application of automated tuning methods to the linear part to obtain the closed-loop system. The results show that the designed feedback control system can reduce the drag to lift ratio considerably as compared to the unactuated case.     

A comprehensive study on mid-level representation and ensemble learning for emotional analysis of video material

Multimedia Tools and Applications - In today’s society where audio-visual content such as professionally edited and user-generated videos is ubiquitous, automatic analysis of this content is...     

ROSE: robust online self-adjusting ensemble for continual learning on imbalanced drifting data streams

Machine Learning - Data streams are potentially unbounded sequences of instances arriving over time to a classifier. Designing algorithms that are capable of dealing with massive, rapidly arriving...     

An ensemble of filters and classifiers for microarray data classification

In this paper a new framework for feature selection consisting of an ensemble of filters and classifiers is described. Five filters, based on different metrics, were employed. Each filter selects a different subset of features which is used to train and to test a specific classifier. The outputs of these five classifiers are combined by simple voting. In this study three well-known classifiers were employed for the classification task: C4.5, naive-Bayes and IB1. The rationale of the ensemble is to reduce the variability of the features selected by filters in different classification domains. Its adequacy was demonstrated by employing 10 microarray data sets.