188 research outputs found
Where Should Traffic Sensors Be Placed on Highways?
This paper investigates the practical engineering problem of traffic sensors
placement on stretched highways with ramps. Since it is virtually impossible to
install bulky traffic sensors on each highway segment, it is crucial to find
placements that result in optimized network-wide, traffic observability.
Consequently, this results in accurate traffic density estimates on segments
where sensors are not installed. The substantial contribution of this paper is
the utilization of control-theoretic observability analysis -- jointly with
integer programming -- to determine traffic sensor locations based on the
nonlinear dynamics and parameters of traffic networks. In particular, the
celebrated asymmetric cell transmission model is used to guide the placement
strategy jointly with observability analysis of nonlinear dynamic systems
through Gramians. Thorough numerical case studies are presented to corroborate
the proposed theoretical methods and various computational research questions
are posed and addressed. The presented approach can also be extended to other
models of traffic dynamics
State-Robust Observability Measures for Sensor Selection in Nonlinear Dynamic Systems
This paper explores the problem of selecting sensor nodes for a general class
of nonlinear dynamical networks. In particular, we study the problem by
utilizing altered definitions of observability and open-loop lifted observers.
The approach is performed by discretizing the system's dynamics using the
implicit Runge-Kutta method and by introducing a state-averaged observability
measure. The observability measure is computed for a number of perturbed
initial states in the vicinity of the system's true initial state. The sensor
node selection problem is revealed to retain the submodular and modular
properties of the original problem. This allows the problem to be solved
efficiently using a greedy algorithm with a guaranteed performance bound while
showing an augmented robustness to unknown or uncertain initial conditions. The
validity of this approach is numerically demonstrated on a
combustion reaction network.Comment: To Appear in the 62 IEEE Conference on Decision and
Control (CDC'2023), Singapore, Decemeber 202
Revisiting the Optimal PMU Placement Problem in Multi-Machine Power Networks
To provide real-time visibility of physics-based states, phasor measurement
units (PMUs) are deployed throughout power networks. PMU data enable real-time
grid monitoring and control -- and is essential in transitioning to smarter
grids. Various considerations are taken into account when determining the
geographic, optimal PMU placements (OPP). This paper focuses on the
control-theoretic, observability aspect of OPP. A myriad of studies have
investigated observability-based formulations to determine the OPP within a
transmission network. However, they have mostly adopted a simplified
representation of system dynamics, ignored basic algebraic equations that model
power flows, disregarded including renewables such as solar and wind, and did
not model their uncertainty. Consequently, this paper revisits the
observability-based OPP problem by addressing the literature's limitations. A
nonlinear differential algebraic representation (NDAE) of the power system is
considered and implicitly discretized -- using various different discretization
approaches -- while explicitly accounting for uncertainty. A moving horizon
estimation approach is explored to reconstruct the joint differential and
algebraic initial states of the system, as a gateway to the OPP problem which
is then formulated as a computationally tractable integer program (IP).
Comprehensive numerical simulations on standard power networks are conducted to
validate various aspects of this approach and test its robustness to various
dynamical conditions
Gramian Angular Field Transformation-Based Intrusion Detection
Cyber threats are increasing progressively in their frequency, scale, sophistication, and cost. The advancement of such threats has raised the need to enhance intelligent intrusion-detection systems. In this study, a different perspective has been developed for intrusion detection. Gramian angular fields were adapted to encode network traffic data as images. Hereby, a way to reveal bilateral feature relationships and benefit from the visual interpretation capability of deep-learning methods has been opened. Then, image-encoded intrusions were classified as binary and multi-class using convolutional neural networks. The obtained results were compared to both conventional machine-learning methods and related studies. According to the results, the proposed approach surpassed the success of traditional methods and produced success rates that were close to the related studies. Despite the use of complex mechanisms such as feature extraction, feature selection, class balancing, virtual data generation, or ensemble classifiers in related studies, the proposed approach is fairly plain -- involving only data-image conversion and classification. This shows the power of simply changing the problem space
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