Feature transformation enhances downstream task performance by generating informative features through mathematical feature crossing. Despite the advancements in deep learning, feature transformation remains essential, particularly for structured data, where deep models often struggle to capture complex feature interactions effectively. Prior literature on automated feature transformation has achieved notable success but often relies on heuristics or exhaustive searches, leading to inefficient and time-consuming processes. Recent works employ reinforcement learning (RL) to enhance traditional approaches through a more effective trial-and-error way. However, two key limitations remain: 1) Dynamic feature expansion during the transformation process, which introduces instability and increases the time complexity of the learning procedure for RL agents; 2) Insufficient cooperation and communication between agents, which results in suboptimal feature crossing operations and degraded model performance. To address them, we propose a novel heterogeneous multi-agent RL framework to enable cooperative and scalable feature transformation. The framework comprises three heterogeneous agents, grouped into two types, each designed to select essential features and operations for feature crossing. To enhance communication among these agents, we implement a shared critic mechanism that facilitates information exchange during the feature transformation process. This collaboration enables the agents to learn more intelligent and effective transformation policies. To handle the dynamically expanding feature space, we tailor multi-head attention-based feature agents to select suitable features for feature crossing. This design facilitates scalable decision-making and effective candidate selection based on comprehensive global feature space information. Additionally, we introduce a state encoding technique during the optimization process to stabilize and enhance the learning dynamics of the RL agents, resulting in more robust and reliable transformation policies. Finally, we conduct extensive experiments to validate the effectiveness, efficiency, robustness, and interpretability of our model. Our code and dataset are publicly available on GitHub.

The application of autonomous vehicles (AVs) requires a safe and efficient decision-making approach for diverse and complex traffic environments. Most existing methodologies focus on specific scenarios or tasks, and are not sufficiently effective for real-world driving situations. This paper proposes a Safety Interval Reserve (SIR) model to quantify safety time margin, which is inspired by human driving behavior. Concurrently, a SIR network is developed to parametrically delineate the changes of SIR under dynamic traffic conditions. Consequently, a SIR network based decision-making approach (DMA-SIR) is designed to unify the macro path planning and micro behavior decision-making. The macro layer optimizes the global path with considering driving efficiency, while the micro layer generates local driving behavior to ensure safety during decision-making. The DMA-SIR facilitates multi-task management through a unified model and is grounded in motion mechanism. Besides, dataset validation demonstrates the anthropomorphic characteristics of DMA-SIR. As a result, it offers an interpretable method that effectively avoid the black box problem. Finally, extensive simulations experiments are conducted using 48 complex scenarios to verify the performance of DMA-SIR. The results show that DMA-SIR can efficiently handle multi-vehicle scenarios and generate driving trajectory with significant greater efficiency, safety and comfort compared to other methods.

Modern robotic systems frequently engage in complex multi-agent interactions, many of which are inherently multi-modal, i.e., they can lead to multiple distinct outcomes. To interact effectively, robots must recognize the possible interaction modes and adapt to the one preferred by other agents. In this work, we propose MultiNash-PF, an efficient algorithm for capturing the multimodality in multi-agent interactions. We model interaction outcomes as equilibria of a game-theoretic planner, where each equilibrium corresponds to a distinct interaction mode. Our framework formulates interactive planning as Constrained Potential Trajectory Games (CPTGs), in which local Generalized Nash Equilibria (GNEs) represent plausible interaction outcomes. We propose to integrate the potential game approach with implicit particle filtering, a sample-efficient method for non-convex trajectory optimization. We utilize implicit particle filtering to identify the coarse estimates of multiple local minimizers of the game’s potential function. MultiNash-PF then refines these estimates with optimization solvers, obtaining different local GNEs. We show through numerical simulations that MultiNash-PF reduces computation time by up to 50% compared to a baseline. We further demonstrate the effectiveness of our algorithm in real-world human-robot interaction scenarios, where it successfully accounts for the multi-modal nature of interactions and resolves potential conflicts in real-time.

Model predictive control (MPC) has proven useful in enabling safe and optimal motion planning for autonomous vehicles. In this paper, we investigate how to achieve MPC-based motion planning when a neural state-space model represents the vehicle dynamics. As the neural state-space model will lead to highly complex, nonlinear and nonconvex optimization landscapes, mainstream gradient-based MPC methods will struggle to provide viable solutions due to heavy computational load. In a departure, we propose the idea of model predictive inferential control (MPIC), which seeks to infer the best control decisions from the control objectives and constraints. Following this idea, we convert the MPC problem for motion planning into a Bayesian state estimation problem. Then, we develop a new implicit particle filtering/smoothing approach to perform the estimation. This approach is implemented as banks of unscented Kalman filters/smoothers and offers high sampling efficiency, fast computation, and estimation accuracy. We evaluate the MPIC approach through a simulation study of autonomous driving in different scenarios, along with an exhaustive comparison with gradient-based MPC. The simulation results show that the MPIC approach has considerable computational efficiency despite complex neural network architectures and the capability to solve large-scale MPC problems for neural state-space models.

Aviation industry is moving towards more electric aircraft, where both non-propulsive and propulsive loads are electrified. While bringing in various benefits, electric propulsion system (EPS) introduces extra complexity and weight to aircraft, as well as raises the reliability concern. New design and analysis tools are required to size the EPS while meeting stringent reliability requirements. This paper investigates how to consolidate readability into the EPS sizing process and makes two-fold contributions. First, a probabilistic algorithm is proposed to assess the reliability of the EPS admitting a directed acyclic graph topology. The algorithm reduces the directed acyclic graph to a layered tree, which simplifies the calculation of joint probability of nodes in each layer. Second, we formulate the reliability-based EPS sizing as an integer nonlinear programming problem, where the reliability requirements are posed as constraints. Preliminary simulation validates the proposed method.

Cyber-physical systems (CPSs) have attracted increasing attention in recent years due to their promise for substantial and long-term benefits to society, economy, environment, and citizens. In addition, the rapid advances in computing, communication, and storage technologies have resulted in a revolution in the information communication technology domain and domination in the industry context. The utilization of CPSs in industrial settings has led to industrial cyber-physical systems (ICPSs), which, in conjunction with the information-driven interactions, enables large-scale cooperation in industrial facilities and among all the stakeholders of the value chain. Hence, the research on ICPSs is essential, especially with respect to the engineering of such systems for industrial applications. This article presents an overview of recent developments in ICPSs. We first introduce the architecture of ICPSs. Then, we review the developments of ICPSs in relevant research domains. Finally, this article concludes by presenting some potential future research directions on ICPSs.

In this paper, the dynamic modeling of a coupled-inductor-based DC-DC converter is investigated. Due to the time varying characteristics of converter parameters such as the output load and input voltage, the operating point of the converter changes within time. Therefore, traditional small signal modeling approaches are not accurate since the converter is linearized around a specific operating point. This paper seeks to address this problem by employing an online parameter identification technique to dynamically estimate the parameters. The identification is achieved through Kalman filtering. First, the small signal modeling of the converter is derived including the leakage inductance effect. Then, the Kalman filter is improved and applied to identify the control-to-output transfer function parameters. Extensive simulation results are provided to validate the robust and proper operation of presented modeling procedure.

In this paper, a new modular, reconfigurable battery energy storage system is presented. The presented structure integrates power electronic converters with a switch-based reconfigurable array to build a smart battery energy storage system (SBESS). The proposed design can dynamically reconfigure the connection between the battery modules to connect a module in series/parallel or bypass a faulty module. The reconfigurability along with the use of the converters will bring a few important advantages, including better safety, robust power supply despite faults, variable voltage or power output, flexible individual control of battery modules, and balanced use of batteries. Further, the modular design allows to scale up to construct large-size SBESS. This work also elaborates the operation principles for the proposed SBESS design and illustrates its effectiveness through simulation study.

Summary form only given, as follows. A complete record of the panel discussion was not made available for publication as part of the conference proceedings. Universal digital access to technology can be as seemingly straightforward as providing electricity access to a remote location or as overwhelmingly complicated as developing a healthcare system that provides immediate and secure access to medical experts, insurance companies, and all of their accompanying infrastructure. This session, featuring members of the SSIT Technical Committee on Universal Access to Technology, discusses how scholars, researchers, practitioners, and educators can actively reduce this digital divide which separates communities and individuals on the basis of ethnicity, religious conviction, sexuality, gender identity, income, age and in many other ways. Drawing on their professional experiences, panelists will discuss strategies for placing humanitarian concerns at the centre of all we do as we strive towards universal digital access; they will demonstrate how to carefully and ethically balance social, cultural and technological dimensions of society to the benefit of all people, particularly those living in rural and underserved areas; and they will elaborate on the role of education, encouragement, and empowerment in the pursuit of these goals.

The rapidly growing use of lithium-ion batteries across various industries highlights the pressing issue of optimal charging control, as charging plays a crucial role in the health, safety, and life of batteries. The literature increasingly adopts model predictive control (MPC) to address this issue, taking advantage of its capability of performing optimization under constraints. However, the computationally complex online constrained optimization intrinsic to MPC often hinders real-time implementation. This article is thus proposed to develop a framework for real-time charging control based on explicit MPC (eMPC), exploiting its advantage in characterizing an explicit solution to an MPC problem, to enable real-time charging control. This article begins with the formulation of MPC charging based on a nonlinear equivalent circuit model. Then, multisegment linearization is conducted to the original model, and applying the eMPC design to the obtained linear models leads to a charging control algorithm. The proposed algorithm shifts the constrained optimization to offline by precomputing explicit solutions to the charging problem and expressing the charging law as piecewise affine functions. This drastically reduces not only the online computational costs in the control run but also the difficulty of coding. Extensive numerical simulation and experimental results verify the effectiveness of the proposed eMPC charging control framework and algorithm. The research results can potentially meet the needs for real-time battery management running on embedded hardware.

This paper proposes a new equivalent circuit model for rechargeable batteries by modifying a double-capacitor model proposed in [1]. It is known that the original model, when compared to other equivalent circuit models, can better address the rate capacity effect and energy recovery effect inherent to batteries. However, it is a purely linear model and includes no representation of a battery's nonlinear phenomena. Hence, this work transforms the original model by introducing a nonlinear-mapping-based voltage source, with the modification justified by an analysis and comparison with the single-particle model. The new nonlinear double-capacitor model is evaluated extensively, with a parameter identification method proposed and validation performed on a number of experimental datasets. The evaluation shows that the proposed model offers excellent predictive capability. With high fidelity and low mathematical complexity, the proposed new model is advantageous for real-time battery management applications.

Successful operation of a battery pack necessitates an effective charging management. This study presents a systematic investigation that blends control design with control implementation for battery charging. First, it develops a multimodule charger for a serially connected battery pack, which allows each cell to be charged independently by a modified isolated buck converter. Then, it presents the development of a two-layer hierarchical charging control approach to be run on this charger. The top-layer control schedules the optimal charging currents through a multiobjective optimization that takes into account user demand, cell equalization, temperature, and operating constraints. The bottom-layer control is developed using the passivity theory to ensure that the charger can well track the scheduled charging current, and its stability is proven using the Lyapunov stability theory. Extensive simulation and experiments are provided to thoroughly validate the proposed charger and the hierarchical charging control approach.