Short-Term Traffic Flow Prediction Based on the Efficient Hinging Hyperplanes Neural Network

Traffic flow (TF) prediction is an important and yet a challenging task in transportation systems, since the TF involves high nonlinearities and is affected by many elements. Recently, neural networks have attracted much attention for TF prediction, but they are commonly black boxes with complex architectures and difficult to be interpreted,...

Selecting Informative Data Samples for Model Learning Through Symbolic Regression

Continual model learning for nonlinear dynamic systems, such as autonomous robots, presents several challenges. First, it tends to be computationally expensive as the amount of data collected by the robot quickly grows in time. Second, the model accuracy is impaired when data from repetitive motions prevail in the training set and outweigh...

MOHA: A Multi-Mode Hybrid Automaton Model for Learning Car-Following Behaviors

This paper proposes a novel hybrid model for learning discrete and continuous dynamics of car-following behaviors. Multiple modes representing driving patterns are identified by partitioning the model into groups of states. The model is visualizable and interpretable for car-following behavior recognition, traffic simulation, and human-like...

Effects of Target Signal Shape and System Dynamics on Feedforward in Manual Control

The human controller (HC) in manual control of a dynamical system often follows a visible and predictable reference path (target). The HC can adopt a control strategy combining closed-loop feedback and an open-loop feedforward response. The effects of the target signal waveform shape and the system dynamics on the human feedforward dynamics...

Effects of Preview Time in Manual Tracking Tasks

In manual control tasks, preview of the target trajectory ahead is often limited by poor lighting, objects, or display edges. This paper investigates the effects of limited preview, or preview time, in manual tracking tasks with single- and double-integrator controlled element dynamics. A quasi-linear human controller model is used to predict...

Detection of sensor data injection attacks with multiplicative watermarking

In this paper, the problem of detecting stealthy false-data injection attacks on the measurements is considered. We propose a multiplicative watermarking scheme, where each sensor’s output is individually fed to a SISO watermark generator whose parameters are supposed to be unknown to the adversary. Under such a scenario, the detectability...

A generalized model for short-term forecasting of solar irradiance

In recent years, as the share of solar power in the electrical grid has been increasing, accurate methods for forecasting solar irradiance have become necessary to manage the electrical grid. More specifically, as solar generators are geographically dispersed, it is very important to have general models that can predict solar irradiance without...

Prognostics and health monitoring of electronic system: A review

Miniaturization of electronics, reduction of time to market and new functionalities in the current context of autonomous driving, electrification and connectivity, are bringing new reliability challenges. Prognostics and Health Management (PHM) can be used effectively to address some of the key challenges, in particular new challenges...

Exploring challenges and solutions for container transportation using rail: A modelling and simulation gaming study

Rail is a cost-effective and environment friendly freight transport modality when used efficiently. Stakeholders around Dutch ports are discouraged to choose rail due to uncertain train schedules and the dispersed nature of freight flows across terminals in the port. To understand the challenges and opportunities of freight transport using rail,...

A columnar architecture for modern risk management systems

3D digital city models form the basis for flow simulations (e.g. wind flow and water runoff), urban planning, underand over- ground formation analysis, and they are very important for automated anomaly detection on man made structures. They consist of large collections of semantically rich objects which have many properties such as material and...