Machine Learning-based Classification of Combustion Events in an RCCI Engine Using Heat Release Rate Shapes

Reactivity controlled compression ignition (RCCI) mode offers high thermal efficiency and low nitrogen oxides (NOx) and soot emissions. However, high cyclic variability at low engine load and high pressure rise rates at high loads limit RCCI operation. Therefore, it is important to control the combustion event in an RCCI engines to prevent abnormal engine combustion. To this end, combustion in RCCI mode was studied by analyzing the heat release rates calculated from the in-cylinder pressure data at 798 different operating conditions. Five distinct heat release shapes are identified. These different heat release traces were characterized based on start of combustion, burn duration, combustion phasing, maximum pressure rise rate, maximum amount of heat release, maximum in-cylinder gas temperature and pressure. Both supervised and unsupervised machine learning approaches are used to classify different types of heat release rates. K-means clustering, an unsupervised algorithm, could not cluster the heat release traces distinctly. Convolution neural network (CNN) and decision trees, supervised classification algorithms, were designed to classify the heat release rates. The CNN algorithm showed 70% accuracy in predicting the shapes of heat release rates while decision tree resulted in 74.5% accuracy in predicting different heat release rate traces

LPV modeling of nonlinear systems: A multi‐path feedback linearization approach

This article introduces a systematic approach to synthesize linear parameter‐varying (LPV) representations of nonlinear (NL) systems which are described by input affine state‐space (SS) representations. The conversion approach results in LPV‐SS representations in the observable canonical form. Based on the relative degree concept, first the SS description of a given NL representation is transformed to a normal form. In the SISO case, all nonlinearities of the original system are embedded into one NL function, which is factorized, based on a proposed algorithm, to construct an LPV representation of the original NL system. The overall procedure yields an LPV model in which the scheduling variable depends on the inputs and outputs of the system and their derivatives, achieving a practically applicable transformation of the model in case of low order derivatives. In addition, if the states of the NL model can be measured or estimated, then a modified procedure is proposed to provide LPV models scheduled by these states. Examples are included to demonstrate both approaches

Ten questions concerning integrating smart buildings into the smart grid

Recent advances in information and communications technology (ICT) have initiated development of a smart electrical grid and smart buildings. Buildings consume a large portion of the total electricity production worldwide, and to fully develop a smart grid they must be integrated with that grid. Buildings can now be ‘prosumers’ on the grid (both producers and consumers), and the continued growth of distributed renewable energy generation is raising new challenges in terms of grid stability over various time scales. Buildings can contribute to grid stability by managing their overall electrical demand in response to current conditions. Facility managers must balance demand response requests by grid operators with energy needed to maintain smooth building operations. For example, maintaining thermal comfort within an occupied building requires energy and, thus an optimized solution balancing energy use with indoor environmental quality (adequate thermal comfort, lighting, etc.) is needed. Successful integration of buildings and their systems with the grid also requires interoperable data exchange. However, the adoption and integration of newer control and communication technologies into buildings can be problematic with older legacy HVAC and building control systems. Public policy and economic structures have not kept up with the technical developments that have given rise to the budding smart grid, and further developments are needed in both technical and non-technical areas

Plant Root Phenotyping Using Deep Conditional GANs and Binary Semantic Segmentation

This paper develops an approach to perform binary semantic segmentation on Arabidopsis thaliana root images for plant root phenotyping using a conditional generative adversarial network (cGAN) to address pixel-wise class imbalance. Specifically, we use Pix2PixHD, an image-to-image translation cGAN, to generate realistic and high resolution images of plant roots and annotations similar to the original dataset. Furthermore, we use our trained cGAN to triple the size of our original root dataset to reduce pixel-wise class imbalance. We then feed both the original and generated datasets into SegNet to semantically segment the root pixels from the background. Furthermore, we postprocess our segmentation results to close small, apparent gaps along the main and lateral roots. Lastly, we present a comparison of our binary semantic segmentation approach with the state-of-the-art in root segmentation. Our efforts demonstrate that cGAN can produce realistic and high resolution root images, reduce pixel-wise class imbalance, and our segmentation model yields high testing accuracy (of over 99%), low cross entropy error (of less than 2%), high Dice Score (of near 0.80), and low inference time for near real-time processing

Cooperative Output Regulation of Multiagent Linear Parameter-Varying Systems

The output regulation problem is examined in this paper for a class of heterogeneous multiagent systems whose dynamics are governed by polytopic linear parameter-varying (LPV) models. The dynamics of the agents are decoupled from each other but the agents’ controllers are assumed to communicate. To design the cooperative LPV controllers, analysis conditions for closed-loop system are first established to ensure stability and reference tracking. Then, the LPV control synthesis problem is addressed, where the offline solution to a time-varying Sylvester equation will be used to determine and update in real time the controller state-space matrices. Two numerical examples will be finally given to demonstrate the efficacy of the proposed cooperative design method

Heterogeneity-Aware Graph Partitioning for Distributed Deployment of Multiagent Systems

In this work, we examine the distributed coverage control problem for deploying a team of heterogeneous robots with nonlinear dynamics in a partially known environment modeled as a weighted mixed graph. By defining an optimal tracking control problem, using a discounted cost function and state-dependent Riccati equation (SDRE) approach, a new partitioning algorithm is proposed to capture the heterogeneity in robots dynamics. The considered partitioning cost, which is a state-dependent proximity metric, penalizes both the tracking error and the control input energy that occurs during the movement of a robot, on a straight line, to an arbitrary node of the graph in a predefined finite time. We show that the size of the subgraph associated with each robot depends on its resources and capabilities in comparison to its neighbors. Also, a distributed deployment strategy is proposed to optimally distribute robots aiming at persistently monitoring specified regions of interest. Finally, a series of simulations and experimental studies is carried out to demonstrate the viability and efficacy of the proposed methodology in deploying heterogeneous multiagent systems

Optimal path planning for a team of heterogeneous drones to monitor agricultural fields

In this work, we investigate the problem of finding the minimum coverage time of an agricultural field using a team of heterogeneous unmanned aerial vehicles (UAVs). The aerial robotic system is assumed to be heterogeneous in terms of the equipped cameras’ field of view, flight speed, and battery capacity. The coverage problem is formulated as a vehicle routing problem (VRP) [1] with two significant extensions. First, the field is converted into a graph, including nodes and edges generated based on sweep direction and the minimum length of UAVs’ footprints. Second, the underlying optimization problem accounts for aerial vehicles having different sensor footprints. A series of simulation experiments are carried out to demonstrate that the proposed strategy can yield a satisfactory monitoring performance and offer promise to be used in practice

Agricultural field coverage using cooperating unmanned ground vehicles

In this paper, a distributed algorithm with obstacle avoidancecapability is presented to deploy a group of ground robots forfield-based agriculture applications. To this end, the field (consisting of many plots) is first modeled as a directed graph, and therobots are deployed to collect data from some important areas ofthe field (e.g., areas with high water stress or biotic stress). Thekey idea is to formulate the underlying problem as a locationaloptimization problem and then find the optimal solution based onthe Voronoi partitioning of the associated graph. The proposedpartitioning method is validated through simulation studies, aswell as experiments using a group of mobile robots

A distributed approach for estimation of information matrix in smart grids and its application for anomaly detection

Statistical structure learning (SSL)-based approaches have been employed in the recent years to detect different types of anomalies in a variety of cyber-physical systems (CPS). Although these approaches outperform conventional methods in the literature, their computational complexity, need for large number of measurements and centralized computations have limited their applicability to large-scale networks. In this work, we propose a distributed, multi-agent maximum likelihood (ML) approach to detect anomalies in smart grid applications aiming at reducing computational complexity, as well as preserving data privacy among different players in the network. The proposed multi-agent detector breaks the original ML problem into several local (smaller) ML optimization problems coupled by the alternating direction method of multipliers (ADMM). Then, these local ML problems are solved by their corresponding agents, eventually resulting in the construction of the global solution (network\u27s information matrix). The numerical results obtained from two IEEE test (power transmission) systems confirm the accuracy and efficiency of the proposed approach for anomaly detection