With growing emphasis on sustainable manufacturing, we address the energy-aware Simple Assembly Line Balancing Problem with Power Peak Minimization (SALB3PM). We propose a metaheuristic to solve the SALB3PM. Our metaheuristic combines (1) a multi-start framework with three neighborhood operators (insertion, swap, delay incrementing) and (2) a dynamic penalty mechanism for handling cycle-time infeasibility. We designed and compared four algorithm variants to evaluate the impact of components. We conducted computational experiments on benchmark instances. The results show that our best variant achieves an average optimality gap of 2.38% when considering known optima, and optimal solutions were achieved in all runs for over 50% of instances. The approach contributes to sustainable manufacturing through energy-efficient production line design.
One intriguing but difficult route to scalable quantum information processing (QIP) is linear optical quantum computing (LOQC). The systematic growth of LOQC is examined in this research. It focuses on significant experimental developments, fault-tolerant methods, and the enduring difficulties influencing the field area. In addition, it highlights the incorporation of Gottesman-Kitaev-Preskill (GKP) encoding which is an essential step towards fault-tolerant quantum computation. We analyze the importance of the Knill-Laflamme-Milburn (KLM) protocol in addressing the probabilistic limits of two-qubit gates. Strong quantum structures are made possible by this synergy, which increases resistance to phase errors and photon loss. Notwithstanding these developments, problems with state preparation, measurement accuracy, and error correction still exist, requiring multidisciplinary efforts to improve methods and investigate fresh ideas.Beyond its scope, the knowledge gathered from LOQC may have an impact on secure quantum communication networks and hybrid quantum systems. Enhancing error correction codes, creating new quantum optical technologies, and encouraging cooperation within quantum computing paradigms should be the main goals of future research. The present work intends to stimulate more developments and push QIP towards scalable and useful quantum computation by reviewing the state of LOQC today and defining strategic directions.
This work develops PF-4DGS, an novel framework for 4D Gaussian splatting that addresses the challenges associated with the reliance on accurate prior knowledge of camera poses in dynamic scene modeling. Our approach employs a pose-free optimization strategy that simultaneously estimates camera parameters and reconstructs the scene within a unified framework. We introduce a stable initialization technique and an efficient joint optimization loop that simultaneously improves scene reconstruction and camera tracking. Comprehensive evaluations on real-world datasets demonstrate that PF-4DGS achieves accuracy comparable to leading methods, even without prior camera pose information. This advancement marks a huge breakthrough in Gaussian splatting and promotes the application of this technique in dynamic environments.
Artificial intelligence applications are used across various domains, including education, commerce, science, language, law, and healthcare. With the rapid advancement of technology, new artificical applications continue to emerge. Among these, chatbots are the most widely discussed, with ChatGPT being the most prominent one. Although several studies have evaluated ChatGPT, few offer specific recommendations regarding its usability, particularly in terms of its user interface. In this study, the design of ChatGPT’s web user interface is evaluated through expert review and system usability assessment. The findings indicate that the most significant issue is the “match between system and the real world,” based on Nielsen’s heuristics. In contrast, “aesthetic and minimalist design” received the most positive feedback from participants. While the system achieved a usability score of 80, the results also revealed a few inadequately integrated functions.
Explainable AI Planning (XAIP) is a pivotal research area focused on enhancing the transparency, interpretability, and trustworthiness of automated planning systems. This paper provides a comprehensive review of XAIP, emphasizing key techniques for plan explanation, such as contrastive explanations, hierarchical decomposition, and argumentative reasoning frameworks. We explore the critical role of argumentation in justifying planning decisions and address the challenges of replanning in dynamic and uncertain environments, particularly in high-stakes domains like healthcare, autonomous systems, and logistics. Additionally, we discuss the ethical and practical implications of deploying XAIP, highlighting the importance of human-AI collaboration, regulatory compliance, and uncertainty handling. By examining these aspects, this paper aims to provide a detailed understanding of how XAIP can improve the transparency, interpretability, and usability of AI planning systems across various domains.
Wireless Sensor Networks are becoming more and more crucial to the advancement of numerous technologies, particularly when combined with Big Data platforms. Although this connection has a lot of potential, there are challenging security challenges as well. Even though WSNs have been the subject of a lot of research, the security requirements for WSNs functioning in Big Data environments have not yet been thoroughly examined. It is also a crucial use of IoT, allowing sensors to exchange a variety of data. However, because of its inherent unreliability and natural surroundings, such a network is susceptible to numerous types of attacks, including insider attacks. Intrusion detection systems (IDSs) are commonly used in WSNs to protect against insider assaults by putting in place the right procedures and techniques. However, sensors may produce too much data in the big data era, which could reduce the efficiency of WSN computing. An overview of the security concerns and difficulties facing WSNs in the big data era is provided in this study. In order to improve the detection of insider threats and the overall security posture of WSNs, a literature review on cybersecurity IDS on WSN in the context of big data is finally suggested. It highlights advancements in IDS methodologies, including federated learning, machine learning, deep learning, and big data techniques.
This paper is dedicated to the control of hydrogen production with an experimental proton exchange membrane water electrolyzer in the context of renewable energy sources. Two control laws, iP and PI controllers have been evaluated under several scenarii including renewable energy sources variations. A discussion of the performances of the controllers allows to formulate several open issues for the control of PEMWE in a renewable energy context.
This study presents the Flexible Representation for Quantum Images (FRQI) Pairs method, a novel approach that leverages Quantum Recurrent Neural Networks (QRNN) for image classification. The proposed method achieves an accuracy of 74.60% on the full Modified National Institute of Standards and Technology (MNIST) handwritten digit data set, demonstrating its effectiveness in handling quantum encoded data for classification tasks.By reducing the size of the QRNN by the exponential factor, the FRQI Pairs method highlights the potential of integrating quantum computing principles with neural network architectures, offering a promising direction for advancing quantum machine learning.The research evaluates the FRQI Pairs method against existing quantum and classical models, demonstrating its competitive performance against other state-of-the-art approaches and showing potential for future advancements in the field. This research opens avenues for further exploration of quantum preprocessing and hybrid model architectures, marking a step forward in the application of quantum machine learning.
This paper presents the development and validation of a digital twin for a scaled-down electric vehicle (EV) emulator, designed to replicate longitudinal vehicle dynamics under diverse operating conditions. The emulator integrates a separately excited DC motor (SEDCM), a four-quadrant DC-DC converter, a battery emulator, and a mechanical load emulator. The system models tractive effort, aerodynamic drag, and gradient resistance using Newton's second law. In contrast to conventional graphical modeling tools (e.g., block diagrams and bond graphs), the adopted Energetic Macroscopic Representation (EMR) framework offers clear advantages by explicitly representing energy interactions and facilitating the systematic derivation of control structures. A control strategy developed within this framework governs energy flow across the powertrain, enabling accurate speed control via armature voltage regulation. Experimental tests conducted on a Lucas-Nulle test bench show strong correlation with simulation results. The study also introduces a methodology to compute the maximum admissible vehicle mass - determined to be 13.5 kg for a 180 W motor operating at 1900 rpm - based on acceleration and slope constraints. Furthermore, a switching algorithm for the bidirectional converter ensures reliable four quadrant operation. Overall, the proposed framework provides a scalable and effective approach for EV emulation, control design, and energy management validation.
Importance sampling is a Monte Carlo technique for efficiently estimating the likelihood of rare events by biasing the sampling distribution towards the rare event of interest. By drawing weighted samples from a learned proposal distribution, importance sampling allows for more sample-efficient estimation of rare events or tails of distributions. A common choice of proposal density is a Gaussian mixture model (GMM). However, estimating full-rank GMM covariance matrices in high dimensions is a challenging task due to numerical instabilities. In this work, we propose using mixtures of probabilistic principal component analyzers (MPPCA) as the parametric proposal density for importance sampling methods. MPPCA models are a type of low-rank mixture model that can be fit quickly using expectation-maximization, even in high-dimensional spaces. We validate our method on three simulated systems, demonstrating consistent gains in sample efficiency and quality of failure distribution characterization.
Multi-agent Path Finding (MAPF) is the problem of planning collision-free movements of agents so that they get from where they are to where they need to be. Commonly, agents are located on a graph and can traverse edges. This problem has many variations and has been studied for decades. Two such variations are the continuous-time and the lifelong MAPF problems. In the former, edges have non-unit lengths and volumetric agents can traverse them at any real-valued time. In the latter, agents must attend to a continuous stream of incoming tasks. Much work has been devoted to designing solution methods within these two areas. To our knowledge, however, the combined problem of continuous-time lifelong MAPF has yet to be addressed. This work addresses continuous-time lifelong MAPF with volumetric agents by presenting the fast and sub-optimal Continuous-time Prioritized Lifelong Planner (CPLP). CPLP continuously assigns agents to tasks and computes plans using a combination of two path planners; one based on CCBS and the other based on SIPP. Experimental results with up to 800 agents on graphs with up to 12 000 vertices demonstrate practical performance, where maximum planning times fall within the available time budget. Additionally, CPLP ensures collision-free movement even when failing to meet this budget. Therefore, the robustness of CPLP highlights its potential for real-world applications.
A stochastic Model Predictive Control strategy for control systems with communication networks between the sensor node and the controller and between the controller and the actuator node is proposed. Data packets are subject to random delays and packet loss is possible; acknowledgments for received packets are not provided. The expected value of a quadratic cost is minimized subject to linear constraints; the set of all initial states for which the resulting optimization problem is guaranteed to be feasible is provided. The state vector of the controlled plant is shown to converge to zero with probability one.
This article proposes a robust MPC law for systems with known constant disturbances. The steps are to find an upper bound for the robust performance objective and to solve an optimization problem with control input constraints. The components of the input are a feedback and a feedforward term which accounts for the constant known disturbance. The performance of the method is analyzed in a Matlab simulation by varying the tuning parameters.
This paper deals with the simultaneous estimation of parameters and state variables for a class of algebro-differential linear parameter-varying (S-LPV) systems by employing a generalized dynamic adaptive observer. Sufficient conditions for the existence of the observer, ensuring stability with respect to linear matrix inequalities (LMIs) constraints through the utilization of Lyapunov stability theory, are provided. The main contribution of this paper is a methodology that generalizes proportional and proportional-integral adaptive observers. Furthermore, it enables a degree of robustness against uncertainties and modeling inaccuracies, while also enhancing steady-state accuracy. To demonstrate the observer performance, an academic example is included.
This paper investigates the vulnerability of marine vessels to cyber-attacks on their rudder servo systems. We present a state-space representation of the rudder servo system under cyber-attacks and propose a detection and identification methodology by using an interacting multiple-model unscented Kalman filter (IMM-UKF). An active-resilient control scheme is then developed that utilizes the multiple-model structure and dynamic reconfiguration to counter certain cyber-attack modes. We develop and propose a multiple-model control recovery methodology by utilizing the eigenstructure assignment method. By utilizing the proposed resilient control scheme for the rudder servo system, the eigenvalues of the vessel under cyber-attacks remain the same as those of the attack-free system. Consequently, the vessel can maintain and track its course and trajectory in the presence of cyber-attacks on the rudder servo system. In the conducted numerical case study, the effectiveness of the proposed multiple model resilient control scheme against cyber-attacks on the rudder servo system is investigated and demonstrated.
This work presents a novel and practical real-time switching control policy for the dynamic hard shoulder running lane control on motorways. Using the hard shoulder as a running lane in peak traffic periods increases the motorway capacity at bottlenecks, increasing throughput and avoiding the onset of congestion. The proposed policy enables a smooth switching on of the hard shoulder lane at peak periods. It is based on properties of the fundamental diagram of motorway traffic with easy and unambiguous interpretation and implementation by traffic operators. Simulation analysis on a 5 km-long motorway stretch with three lanes and one hard shoulder lane using the AIMSUN microscopic simulator is demonstrated. The results have showed that the proposed switching control improves the motorway throughput compared to rival control schemes.
This paper introduces a novel framework for integrating solar-powered electric vehicles (SPEVs) into smart parking infrastructures, primarily focusing on optimizing energy utilization. The proposed framework relies on Model Predictive Control (MPC) to ensure efficient power flow management within smart parking infrastructures. Notably, the paper emphasizes the constraints necessary to ensure the safety and optimal performance of SPEVs and their charging requirements. Results show the effectiveness of the proposed approach, not only in preventing energy management issues but also in substantially reducing reliance on energy procurement from the grid. This integrated system contributes to a more sustainable and cost-effective energy ecosystem, representing a noteworthy advancement in electric mobility infrastructure.
This paper presents an MPC-based control scheme for unmanned aerial vehicles (UAVs) of quadrotor type that can operate in real-time. The proposed approach is based on a multi-layer model predictive control architecture that uses linear parameter varying (LPV) and feedback linearization techniques in the inner and outer layer, respectively. In the outer layer, the non-linear dynamics of the transitional control are linearized using feedback linearization, followed by the implementation of a combination of linear model predictive control (MPC) and moving horizon estimator (MHE) schemes. In the inner layer, the attitude control utilizes the LPV approach to formulate an LPV MPC controller and MHE estimator. Through simulation using a high-fidelity simulator of a real UAV, the effectiveness of the proposed control scheme is demonstrated in terms of tracking performance and computation time. Furthermore, a comparison with other similar approaches in the literature is included to showcase the advantages of the proposed scheme.
This paper introduces a solution designed to predict accident risk in the most accident-prone zones in France. Drawing on a comprehensive dataset of 210,007 accidents spanning four years, 22 high-risk zones are identified for focused models development. The predictive models leverage 35 features from four data sources, enabling a tailored approach to the identified zones. To enable real-time predictions, non-accident scenarios are generated based on temporal and spatial dimensions of each accident, resulting in a dataset comprising 770,880 observations. The dataset exhibits a significant imbalance, with only 8.2% representing accidents. To address this challenge, we employ the SMOTE technique. Then train and evaluate 11 predictive models. The initial version of the Weighted Classification Model (WCM) proposed in the literature is adapted to our context. Additionally, an enhanced version of the WCM is introduced, incorporating Exponential Ponderation to refine the weighted classification score. The Evaluation results demonstrate improved predictive performances, particularly in terms of precision, recall, and F1 score. The enhanced WCM exhibits a 1.3% improvement in the F1 score compared to the classical version. Real-time evaluations are conducted in France and showcase promising results, underscoring the practical applicability of the proposed solution.
In thermal engineering, maintaining the temperature precisely at a desired setpoint is an essential objective for many processes. When heating actuators act locally and a few point sensors provide observations at different locations, the problem is complex. This communication deals with the control of a thermal system whose evolution is described by a parabolic partial differential equation (the domain is a thin steel plate subjected to heat sources on its upper surface, and to natural convection on its boundaries). The mathematical model remains valid in a 2D domain under the condition of negligible plate thickness. Equilibrium is reached when the combined effects of heat supplied by the sources and cooling induced by the surrounding environment balance out. The aim of this study is to achieve this state of equilibrium in a finite time by appropriately controlling the heat sources. The challenge of identifying these flows is addressed in the form of an inverse heat conduction problem (known to be ill-posed), and the implementation of the conjugate gradient regularization method is discussed.
