Distributed Traffic Signal Control for Maximum Network Throughput

We propose a distributed algorithm for controlling traffic signals. Our algorithm is adapted from backpressure routing, which has been mainly applied to communication and power networks. We formally prove that our algorithm ensures global optimality as it leads to maximum network throughput even though the controller is constructed and implemented in a completely distributed manner. Simulation results show that our algorithm significantly outperforms SCATS, an adaptive traffic signal control system that is being used in many cities

Blue Carbon Science, Management and Policy Across a Tropical Urban Landscape

The ability of vegetated coastal ecosystems to sequester high rates of “blue” carbon over millennial time scales has attracted the interest of national and international policy makers as a tool for climate change mitigation. Whereas focus on blue carbon conservation has been mostly on threatened rural seascapes, there is scope to consider blue carbon dynamics along highly fragmented and developed urban coastlines. The tropical city state of Singapore is used as a case study of urban blue carbon knowledge generation, how blue carbon changes over time with urban development, and how such knowledge can be integrated into urban planning alongside municipal and national climate change obligations. A systematic review of blue carbon studies in Singapore was used to support a qualitative review of Singapore’s blue carbon ecosystems, carbon budget, changes through time and urban planning and policy. Habitat loss across all blue carbon ecosystems is coarsely estimated to have resulted in the release of ∼12.6 million tonnes of carbon dioxide since the beginning of the 20th century. However, Singapore’s remaining blue carbon ecosystems still store an estimated 568,971 – 577,227 tonnes of carbon (equivalent to 2.1 million tonnes of carbon dioxide) nationally, with a small proportion of initial loss offset by habitat restoration. Carbon is now a key topic on the urban development and planning agenda, as well as nationally through Singapore’s contributions to the Paris Agreement. The experiences of Singapore show that coastal ecosystems and their blue carbon stocks can be successfully managed along an urban coastline, and can help inform blue carbon science and management along other rapidly urbanizing coastlines throughout the tropics

Model-based health monitoring for a vehicle steering system with multiple faults of unknown types

This paper presents a model-based fault diagnosis and prognosis scheme for a vehicle steering system. The steering system is modeled as a hybrid system with continuous dynamics and discrete modes using the hybrid bond graph tool. Multiple faults of different types, i.e., abrupt fault, incipient fault, and intermittent fault, are considered using the concept of Augmented Global Analytical Redundancy Relations (AGARRs). A fault discriminator is constructed to distinguish the type of faults once they are detected. After that, a fault identification scheme is proposed to estimate the magnitude of abrupt faults, the characteristic of intermittent faults, and the degradation behavior of incipient faults. The fault identification is realized by using a new adaptive hybrid differential evolution (AHDE) algorithm with less control parameters. Based on the identified degradation behavior of incipient faults, prognosis is carried out to predict the remaining useful life of faulty components. The proposed algorithm is verified experimentally on the steering system of a CyCab electric vehicle.Accepted versio

Prediction of multiple failures for a mobile robot steering system

Fault diagnosis and failure prognosis are critical techniques to improve the safety and reliability of modern complex electromechanical systems. In this paper, a model-based prognosis method is developed to deal with multiple incipient faults in a mobile robot steering system. This method utilizes the concept of Augmented Global Analytical Redundancy Relations (AGARRs) to handle failures with both parametric and non-parametric nature. In order to realize multiple failures prediction, a multiple Hybrid Particle Swarm Optimization (HPSO) algorithm is proposed. Simulation results verify the effectiveness of the proposed method in a front steering system of a CyCab mobile robot

Sensor selection and placement using low complexity dynamic programming

In this paper, a novel approach is proposed for sensor selection and placement in systems for the purpose of fault detection and isolation (FDI). This new approach benefits from the basic fact that faults are embedded in the analytical redundancy relations (ARRs) and that the occurrence of a fault will cause the corresponding ARRs to change. For FDI purposes, each ARR is connected to a set of sensors that represent the measurable variables. New concepts of fault associated sets and fault distinguishable sets are introduced to develop a low complexity dynamic programming algorithm to minimize the number of sensors needed and simultaneously to guarantee all possible faults being detectable and isolable. A case study of a fuel-cell system shows that the proposed method performs well when the numbers of faults and sensors are moderate

Fault detection isolation and estimation in a vehicle steering system

Recently, a bond-graph-based fault detection and isolation (FDI) framework has been developed with a new concept of global analytical redundancy relations (GARRs) (Low, Wang, Arogeti, and Luo, 2009, 2010; Low, Wang, Arogeti, and Zhang, 2010). This new concept allows the fault diagnosis for hybrid systems which consist of both continuous dynamics and discrete modes. A failure of a safety critical system such as the steering system of an automated guided vehicle may cause severe damage. Such failure can be avoided by an early detection and estimation of faults. In this paper, the newly developed FDI method is studied in details using an electrohydraulic steering system of an electric vehicle. The steering system and faults are modeled as a hybrid dynamic system by the hybrid bond graph (HBG) modeling technique. GARRs are then derived systematically from the HBG model with a specific causality assignment. Fault detection, isolation, and estimation are applied, experimental setup is described, and results are discussed

Attention based graph Bi-LSTM networks for traffic forecasting

Traffic forecasting is of great importance to vehicle routing, traffic signal control and urban planning. However, traffic forecasting task is challenging due to several factors, such as complex spatial topological structure and dynamic changing of traffic status. Most existing methods have limited ability to capture both spatial and temporal dependence of traffic data. In this paper, we propose a novel end-to-end deep learning model, Attention based Graph Bi-LSTM networks (AGBN) to perform the traffic forecasting task. It uses graph convolutional network (GCN) to extract spatial features and bidirectional long short-term memory networks (Bi-LSTM) to capture the temporal dependence. The attention mechanism is used to select relevant features at all time steps. Experiments show that our model could extract both spatial and temporal dependence well and outperforms other baselines on real-world traffic datasets.National Research Foundation (NRF)Accepted versionThis research is supported by National Research Foundation (NRF) Singapore, ST Engineering-NTU Corporate Lab under its NRF Corporate Lab@ University Scheme