adaptive location update mechanism for network-constrained moving objects in changeful traffic conditions

Natl Chiao Tung Univ, Dept Comp Sci

基于网络受限移动对象数据库的交通流统计分析模型

网络动态交通流的统计分析技术是目前移动计算及智能运输系统领域的一个重要研究方向．然而，现有的交通流统计分析方法(如基于固定传感器的方法、高空交通流监视方法、浮动车法等)存在着信息量少、数据处理复杂、精确度及效率低下、通信代价高昂等缺陷．为了有效地提高交通流统计分析的效率与精度，提出了一种基于网络受限移动对象数据库的交通流统计分析方法(network constrained moving objects database based traffic flow statistical analysis．NMOD—TFSA)．通过对移动对象所提交的位置更新信息进行联机统计，NMOD-TFSA能够实时地获取交通网络各部分的动态交通参数．由于在数据采集时考虑了道路网络的拓扑结构，NMOD．TFSA有效地降低了通信及计算的代价；此外，NMoD—TFSA所采集的数据能够反映移动对象完整的时空轨迹，因此为数据分析提供了更为丰富的信息，提高了数据处理的精度．实验结果表明，与目前通行的浮动车法相比，NMOD—TFSA有效地降低了 通信及计算代价，提高了交通流统计分析的精度与灵活性．

网络受限移动对象不确定性轨迹的索引

近年来，人们对于如何表示和处理移动对象的不确定性进行了研究，提出了一些较为有效的模型和算法。但是，在如何索引移动对象的不确定时空轨迹方面，相关的研究工作十分有限。为了解决上述问题，本文提出了一种网络受限移动对象不确定轨迹的索引结构(UTIi—Tree)，并给出了相关的索引更新及查询算法。在该索引结构的支持下，移动对象数据库不仅可以快速地处理对移动对象过去可能位王的查询，而且能够对其现在及将来的可能位置进行高效的查询处理。

网络受限移动对象过去、现在及将来位置的索引

提出了一种适合于网络受限移动对象数据库的动态轨迹R树索引结构(network-constrained moving objects dynamic trajectory R-Tree,简称NDTR-Tree).NDTR-Tree不仅能够索引移动对象的整个历史轨迹,而且能够动态地索引和维护移动对象的当前及将来位置.为了比较相关索引结构及算法的性能,进行了详细的实验.实验结果表明,与现有的基于道路网络的移动对象索引方法如MON-Tree和FNR-Tree等相比,NDTR-Tree有效地提高了对网络受限移动对象动态全轨迹的查询处理性能.

基于路网的不确定性轨迹预测

移动对象的轨迹预测研究已成为当前移动对象研究中关注的热点,移动对象的轨迹预测技术具有高度的研究价值及广阔的应用前景.目前移动对象的轨迹预测方法主要是针对历史轨迹确定的欧氏空间轨迹预测,但有相当一部分的应用要求预测历史轨迹存在不确定性的移动对象在受限路网中的轨迹.为了解决这一问题,首先提出了不确定性轨迹的生成方法及其表示形式,然后提出了一种基于路网的不确定性轨迹频繁模式挖掘算法,最后给出了利用索引快速查找轨迹模式并进行预测的方法.实验结果表明该方法具有较高的预测准确率、较好的查询效率以及较低的存储空间.

Modelo de leilão multiperíodo para sistemas hidrotérmicos com representação da transmissão

Pós-graduação em Engenharia Elétrica - FEB

Método primal-dual previsor-corretor de pontos interiores e exteriores com estratégias de correção de inércia e suavização hiperbólica aplicado ao problema de despacho econômico com ponto de carregamento de válvula e representação da transmissão

Pós-graduação em Engenharia Elétrica - FEB

The Sensor Network Workbench: Towards Functional Specification, Verification and Deployment of Constrained Distributed Systems

As the commoditization of sensing, actuation and communication hardware increases, so does the potential for dynamically tasked sense and respond networked systems (i.e., Sensor Networks or SNs) to replace existing disjoint and inflexible special-purpose deployments (closed-circuit security video, anti-theft sensors, etc.). While various solutions have emerged to many individual SN-centric challenges (e.g., power management, communication protocols, role assignment), perhaps the largest remaining obstacle to widespread SN deployment is that those who wish to deploy, utilize, and maintain a programmable Sensor Network lack the programming and systems expertise to do so. The contributions of this thesis centers on the design, development and deployment of the SN Workbench (snBench). snBench embodies an accessible, modular programming platform coupled with a flexible and extensible run-time system that, together, support the entire life-cycle of distributed sensory services. As it is impossible to find a one-size-fits-all programming interface, this work advocates the use of tiered layers of abstraction that enable a variety of high-level, domain specific languages to be compiled to a common (thin-waist) tasking language; this common tasking language is statically verified and can be subsequently re-translated, if needed, for execution on a wide variety of hardware platforms. snBench provides: (1) a common sensory tasking language (Instruction Set Architecture) powerful enough to express complex SN services, yet simple enough to be executed by highly constrained resources with soft, real-time constraints, (2) a prototype high-level language (and corresponding compiler) to illustrate the utility of the common tasking language and the tiered programming approach in this domain, (3) an execution environment and a run-time support infrastructure that abstract a collection of heterogeneous resources into a single virtual Sensor Network, tasked via this common tasking language, and (4) novel formal methods (i.e., static analysis techniques) that verify safety properties and infer implicit resource constraints to facilitate resource allocation for new services. This thesis presents these components in detail, as well as two specific case-studies: the use of snBench to integrate physical and wireless network security, and the use of snBench as the foundation for semester-long student projects in a graduate-level Software Engineering course.

Safe Compositional Specification of Network Systems With Polymorphic, Constrained Types

In the framework of iBench research project, our previous work created a domain specific language TRAFFIC [6] that facilitates specification, programming, and maintenance of distributed applications over a network. It allows safety property to be formalized in terms of types and subtyping relations. Extending upon our previous work, we add Hindley-Milner style polymorphism [8] with constraints [9] to the type system of TRAFFIC. This allows a programmer to use for-all quantifier to describe types of network components, escalating power and expressiveness of types to a new level that was not possible before with propositional subtyping relations. Furthermore, we design our type system with a pluggable constraint system, so it can adapt to different application needs while maintaining soundness. In this paper, we show the soundness of the type system, which is not syntax-directed but is easier to do typing derivation. We show that there is an equivalent syntax-directed type system, which is what a type checker program would implement to verify the safety of a network flow. This is followed by discussion on several constraint systems: polymorphism with subtyping constraints, Linear Programming, and Constraint Handling Rules (CHR) [3]. Finally, we provide some examples to illustrate workings of these constraint systems.

A forward constrained selection algorithm for probabilistic neural network

A new probabilistic neural network (PNN) learning algorithm based on forward constrained selection (PNN-FCS) is proposed. An incremental learning scheme is adopted such that at each step, new neurons, one for each class, are selected from the training samples arid the weights of the neurons are estimated so as to minimize the overall misclassification error rate. In this manner, only the most significant training samples are used as the neurons. It is shown by simulation that the resultant networks of PNN-FCS have good classification performance compared to other types of classifiers, but much smaller model sizes than conventional PNN.

Security constrained optimal active power flow via network model and interior point method

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

A barrier method for constrained nonlinear optimization using a modified Hopfield network

The ability of neural networks to realize some complex nonlinear function makes them attractive for system identification. This paper describes a novel barrier method using artificial neural networks to solve robust parameter estimation problems for nonlinear model with unknown-but-bounded errors and uncertainties. This problem can be represented by a typical constrained optimization problem. More specifically, a modified Hopfield network is developed and its internal parameters are computed using the valid-subspace technique. These parameters guarantee the network convergence to the equilibrium points. A solution for the robust estimation problem with unknown-but-bounded error corresponds to an equilibrium point of the network. Simulation results are presented as an illustration of the proposed approach.

Market-driven security-constrained transmission network expansion planning

This paper presents a Bi-level Programming (BP) approach to solve the Transmission Network Expansion Planning (TNEP) problem. The proposed model is envisaged under a market environment and considers security constraints. The upper-level of the BP problem corresponds to the transmission planner which procures the minimization of the total investment and load shedding cost. This upper-level problem is constrained by a single lower-level optimization problem which models a market clearing mechanism that includes security constraints. Results on the Garver's 6-bus and IEEE 24-bus RTS test systems are presented and discussed. Finally, some conclusions are drawn. © 2011 IEEE.

Power network in the loop : subsystem testing using a switching amplifier

“Hardware in the Loop” (HIL) testing is widely used in the automotive industry. The sophisticated electronic control units used for vehicle control are usually tested and evaluated using HIL-simulations. The HIL increases the degree of realistic testing of any system. Moreover, it helps in designing the structure and control of the system under test so that it works effectively in the situations that will be encountered in the system. Due to the size and the complexity of interaction within a power network, most research is based on pure simulation. To validate the performance of physical generator or protection system, most testing is constrained to very simple power network. This research, however, examines a method to test power system hardware within a complex virtual environment using the concept of the HIL. The HIL testing for electronic control units and power systems protection device can be easily performed at signal level. But performance of power systems equipments, such as distributed generation systems can not be evaluated at signal level using HIL testing. The HIL testing for power systems equipments is termed here as ‘Power Network in the Loop’ (PNIL). PNIL testing can only be performed at power level and requires a power amplifier that can amplify the simulation signal to the power level. A power network is divided in two parts. One part represents the Power Network Under Test (PNUT) and the other part represents the rest of the complex network. The complex network is simulated in real time simulator (RTS) while the PNUT is connected to the Voltage Source Converter (VSC) based power amplifier. Two way interaction between the simulator and amplifier is performed using analog to digital (A/D) and digital to analog (D/A) converters. The power amplifier amplifies the current or voltage signal of simulator to the power level and establishes the power level interaction between RTS and PNUT. In the first part of this thesis, design and control of a VSC based power amplifier that can amplify a broadband voltage signal is presented. A new Hybrid Discontinuous Control method is proposed for the amplifier. This amplifier can be used for several power systems applications. In the first part of the thesis, use of this amplifier in DSTATCOM and UPS applications are presented. In the later part of this thesis the solution of network in the loop testing with the help of this amplifier is reported. The experimental setup for PNIL testing is built in the laboratory of Queensland University of Technology and the feasibility of PNIL testing has been evaluated using the experimental studies. In the last section of this thesis a universal load with power regenerative capability is designed. This universal load is used to test the DG system using PNIL concepts. This thesis is composed of published/submitted papers that form the chapters in this dissertation. Each paper has been published or submitted during the period of candidature. Chapter 1 integrates all the papers to provide a coherent view of wide bandwidth switching amplifier and its used in different power systems applications specially for the solution of power systems testing using PNIL.