Search Results (675)

Hybrid modeling techniques are increasingly important for improving predictive accuracy and control in biomanufacturing, particularly in data-limited conditions. This study develops and experimentally validates a hybrid deep learning model predictive control (MPC) framework for recombinant P. pastoris fed-batch fermentations. Bayesian optimization and grid search techniques were employed to identify the best-performing hybrid model architecture: an LSTM layer with 2 hidden units followed by a fully connected layer with 8 nodes and ReLU activation. This design balanced accuracy (NRMSE 4.93%) and computational efficiency (AICc 998). This architecture was adapted to a new, smaller dataset of bacteriophage Qβ coat protein production using transfer learning, yielding strong predictive performance with low validation (3.53%) and test (5.61%) losses. Finally, the hybrid model was integrated into a novel MPC system and experimentally validated, demonstrating robust real-time substrate feed control in a way that allows it to maintain specific target growth rates. The system achieved predictive accuracies of 6.51% for biomass and 14.65% for product estimation, with an average tracking error of 10.64%. In summary, this work establishes a robust, adaptable, and efficient hybrid modeling framework for MPC in P. pastoris bioprocesses. By integrating automated architecture searching, transfer learning, and MPC, the approach offers a practical and generalizable solution for real-time control and supports scalable digital twin deployment in industrial biotechnology. Full article

This paper proposes a hybrid methodology for one-step-ahead torsional torque estimation in an electric drive with an elastic connection. The approach integrates Nonlinear Autoregressive Neural Networks with Exogenous Inputs (NARX NNs) and model-based feedback. The NARX model uses real-time and historical motor speed and torque signals as inputs while leveraging physics-derived torsional torque as a feedback input to refine estimation accuracy and robustness. While model-based methods provide insight into system dynamics, they lack predictive capability—an essential feature for proactive control. Conversely, standalone NARX NNs often suffer from error accumulation and overfitting. The proposed hybrid architecture synergises the adaptive learning of NARX NNs with the fidelity of physics-based feedback, enabling proactive vibration damping. The method was implemented and evaluated on a two-mass drive system using an IP controller and additional torsional torque feedback. Results demonstrate high accuracy and reliability in one-step-ahead torsional torque estimation, enabling effective proactive vibration damping. MATLAB 2024a/Simulink and dSPACE 1103 were used for simulation and hardware-in-the-loop testing. Full article

Highly infectious and pathogenic viruses seriously threaten global public health, underscoring the need for rapid and accurate diagnostic methods to effectively manage and control outbreaks. In this study, we developed a comprehensive Surface-Enhanced Raman Scattering–Lateral Flow Immunoassay (SERS-LFIA) detection system that integrates SERS scanning imaging with artificial intelligence (AI)-based result discrimination. This system was based on an ultra-sensitive SERS-LFIA strip with SiO₂-Au NSs as the immunoprobe (with a theoretical limit of detection (LOD) of 1.8 pg/mL). On this basis, a negative–positive discrimination method combining SERS scanning imaging with a deep learning model (ResNet-18) was developed to analyze probe distribution patterns near the T line. The proposed machine learning method significantly reduced the interference of abnormal signals and achieved reliable detection at concentrations as low as 2.5 pg/mL, which was close to the theoretical Raman LOD. The accuracy of the proposed ResNet-18 image recognition model was 100% for the training set and 94.52% for the testing set, respectively. In summary, the proposed SERS-LFIA detection system that integrates detection, scanning, imaging, and AI automated result determination can achieve the simplification of detection process, elimination of the need for specialized personnel, reduction in test time, and improvement of diagnostic reliability, which exhibits great clinical potential and offers a robust technical foundation for detecting other highly pathogenic viruses, providing a versatile and highly sensitive detection method adaptable for future pandemic prevention. Full article

This study explores the design and implementation of a control system integrating the anti-lock braking system (ABS) with frequency-modulated continuous wave (FMCW) radar technology to enhance safety and performance in autonomous vehicles. The proposed system employs a hybrid fuzzy logic controller (FLC) and proportional-integral-derivative (PID) controller to improve braking efficiency and vehicle stability under diverse driving conditions. Simulation results showed significant enhancements in stopping performance across various road conditions. The integrated system exhibited a marked improvement in braking performance, achieving significantly shorter stopping distances across all evaluated surface conditions—including dry concrete, wet asphalt, snowy roads, and icy roads—compared with scenarios without ABS. These results highlight the system’s ability to dynamically adapt braking forces to different conditions, significantly improving safety and stability for autonomous vehicles. The limitations are acknowledged, and directions for real-world validation are outlined to ensure system robustness under diverse environmental conditions. Full article

Graph-based multi-view clustering is a fundamental analysis method that learns the similarity matrix of multi-view data. Despite its success, it has two main limitations: (1) complementary information is not fully utilized by directly combining graphs from different views; (2) existing multi-view clustering methods do not adequately address redundancy and noise in the data, significantly affecting performance. To address these issues, we propose the Enhanced Similarity Matrix Learning (ES-MVC) for multi-view clustering, which dynamically integrates global graphs from all views with local graphs from each view to create an improved similarity matrix. Specifically, the global graph captures cross-view consistency, while the local graph preserves view-specific geometric patterns. The balance between global and local graphs is controlled through an adaptive weighting strategy, where hyperparameters adjust the relative importance of each graph, effectively capturing complementary information. In this way, our method can learn the clustering structure that contains fully complementary information, leveraging both global and local graphs. Meanwhile, we utilize a robust similarity matrix initialization to reduce the negative effects caused by noisy data. For model optimization, we derive an effective optimization algorithm that converges quickly, typically requiring fewer than five iterations for most datasets. Extensive experimental results on diverse real-world datasets demonstrate the superiority of our method over state-of-the-art multi-view clustering methods. In our experiments on datasets such as MSRC-v1, Caltech101, and HW, our proposed method achieves superior clustering performance with average accuracy (ACC) values of 0.7643, 0.6097, and 0.9745, respectively, outperforming the most advanced multi-view clustering methods such as OMVFC-LICAG, which yield ACC values of 0.7284, 0.4512, and 0.8372 on the same datasets. Full article

This survey provides a comprehensive review of the integration of large language models (LLMs) into autonomous robotic systems, organized around four key pillars: locomotion, navigation, manipulation, and voice-based interaction. We examine how LLMs enhance robotic autonomy by translating high-level natural language commands into low-level control signals, supporting semantic planning and enabling adaptive execution. Systems like SayTap improve gait stability through LLM-generated contact patterns, while TrustNavGPT achieves a 5.7% word error rate (WER) under noisy voice-guided conditions by modeling user uncertainty. Frameworks such as MapGPT, LLM-Planner, and 3D-LOTUS++ integrate multi-modal data—including vision, speech, and proprioception—for robust planning and real-time recovery. We also highlight the use of physics-informed neural networks (PINNs) to model object deformation and support precision in contact-rich manipulation tasks. To bridge the gap between simulation and real-world deployment, we synthesize best practices from benchmark datasets (e.g., RH20T, Open X-Embodiment) and training pipelines designed for one-shot imitation learning and cross-embodiment generalization. Additionally, we analyze deployment trade-offs across cloud, edge, and hybrid architectures, emphasizing latency, scalability, and privacy. The survey concludes with a multi-dimensional taxonomy and cross-domain synthesis, offering design insights and future directions for building intelligent, human-aligned robotic systems powered by LLMs. Full article

Ultraviolet (UV) communication, characterized by non-line-of-sight (NLOS) scattering, holds substantial potential for enabling communication networking in unmanned aerial vehicle (UAV) formations within strong electromagnetic interference environments. This paper proposes a networking protocol for large-scale mobile ultraviolet communication networks (LSM-UVCN). In large-scale networks, the proposed protocol establishes multiple non-interfering transmission paths based on a connection matrix simultaneously, ensuring reliable space division multiplexing (SDM) and optimizing the utilization of network channel resources. To address frequent network topology changes in mobile scenarios, the protocol employs periodic maintenance of the connection matrix, significantly reducing the adverse impacts of node mobility on network performance. Simulation results demonstrate that the proposed protocol achieves superior performance in large-scale mobile UV communication networks. By dynamically adjusting the connection matrix update frequency, it adapts to varying node mobility intensities, effectively minimizing control overhead and data loss rates while enhancing network throughput. This work underscores the protocol’s adaptability to dynamic network environments, providing a robust solution for high-reliability communication requirements in complex electromagnetic scenarios, particularly for UAV swarm applications. The integration of SDM and adaptive matrix maintenance highlights its scalability and efficiency, positioning it as a viable technology for next-generation wireless communication systems in challenging operational conditions. Full article

This study investigates the application of the Dueling Double Deep Q-Network (3DQN) algorithm to optimize traffic signal control at a major urban intersection in Danang City, Vietnam. The objective is to enhance signal timing efficiency in response to mixed traffic flow and real-world traffic dynamics. A simulation environment was developed using the Simulation of Urban Mobility (SUMO) software version 1.11, incorporating both a fixed-time signal controller and two 3DQN models trained with 1 million (1M-Step) and 5 million (5M-Step) iterations. The models were evaluated using randomized traffic demand scenarios ranging from 50% to 150% of baseline traffic volumes. The results demonstrate that the 3DQN models outperform the fixed-time controller, significantly reducing vehicle delays, with the 5M-Step model achieving average waiting times of under five minutes. To further assess the model’s responsiveness to real-time conditions, traffic flow data were collected using YOLOv8 for object detection and SORT for vehicle tracking from live camera feeds, and integrated into the SUMO-3DQN simulation. The findings highlight the robustness and adaptability of the 3DQN approach, particularly under peak traffic conditions, underscoring its potential for deployment in intelligent urban traffic management systems. Full article

A methodology for detecting systematic errors in sets of equally accurate, uncorrelated, aggregate measurements is proposed and applied within the automatic real-time dispatch control system of a copper concentrator plant (CCP) to refine the technical and economic performance indicators (EPIs) computed by the system. This work addresses and solves the problem of selecting and obtaining reliable measurement data by exploiting the redundant measurements of process streams together with the balance equations linking those streams. This study formulates an approach for integrating cloud technologies, machine learning methods, and forecasting into information control systems (ICSs) via predictive analytics to optimize CCP production processes. A method for combining the hybrid cloud infrastructure with an LSTM-DNN neural network model has been developed, yielding a marked improvement in TEP for copper concentration operations. The forecasting accuracy for the key process parameters rose from 75% to 95%. Predictive control reduced energy consumption by 10% through more efficient resource use, while the copper losses to tailings fell by 15–20% thanks to optimized reagent dosing and the stabilization of the flotation process. Equipment failure prediction cut the amount of unplanned downtime by 30%. As a result, the control system became adaptive, automatically correcting the parameters in real time and lessening the reliance on operator decisions. The architectural model of an ICS for metallurgical production based on the hybrid cloud and the LSTM-DNN model was devised to enhance forecasting accuracy and optimize the EPIs of the CCP. The proposed model was experimentally evaluated against alternative neural network architectures (DNN, GRU, Transformer, and Hybrid_NN_TD_AIST). The results demonstrated the superiority of the LSTM-DNN in forecasting accuracy (92.4%), noise robustness (0.89), and a minimal root-mean-square error (RMSE = 0.079). The model shows a strong capability to handle multidimensional, non-stationary time series and to perform adaptive measurement correction in real time. Full article

As public concern over environmental pollution and the urgent need for sustainable development grow, the popularity of new-energy vehicles has increased. Hybrid electric vehicles (HEVs) represent a significant segment of this movement, undergoing robust development and playing an important role in the global transition towards sustainable mobility. Among the various factors affecting the fuel economy of HEVs, energy management strategies (EMSs) are particularly critical. With continuous advancements in vehicle communication technology, vehicles are now equipped to gather real-time traffic information. In response to this evolution, this paper proposes an optimization method for the adaptive equivalent consumption minimization strategy (A-ECMS) equivalent factor that incorporates traffic information and efficient optimization algorithms. Building on this foundation, the proposed method integrates the charge depleting–charge sustaining (CD-CS) strategy to create a combined EMS that leverages traffic information. This approach employs the CD-CS strategy to facilitate vehicle operation in the absence of comprehensive global traffic information. However, when adequate global information is available, it utilizes both the CD-CS strategy and the A-ECMS for vehicle control. Simulation results indicate that this combined strategy demonstrates effective performance, achieving fuel consumption reductions of 5.85% compared with the CD-CS strategy under the China heavy-duty truck cycle, 4.69% under the real vehicle data cycle, and 3.99% under the custom driving cycle. Full article

In this paper, we propose an innovative essential multi-secret image sharing (EMSIS) scheme that integrates sensor data to securely and efficiently share multiple secret images of varying importance. Secret images are categorized into hierarchical levels and encoded into essential shadows and fault-tolerant non-essential shares, with access to higher-level secrets requiring higher-level essential shadows. By incorporating sensor data, such as location, time, or biometric input, into the encoding and access process, the scheme enables the context-aware and adaptive reconstruction of secrets based on real-world conditions. Experimental results demonstrate that the proposed method not only strengthens hierarchical access control, but also enhances robustness, flexibility, and situational awareness in secure image distribution systems. Full article

Robotic-assisted therapy is an increasingly vital approach for upper-limb rehabilitation, offering consistent, high-intensity training critical to neuroplastic recovery. However, current control strategies often lack robustness against uncertainties and external disturbances, limiting their efficacy in dynamic, real-world settings. Addressing this gap, this study proposes a novel control framework for the iTbot—a 2-DoF end-effector rehabilitation robot—by integrating differential flatness theory with a derivative-free Kalman filter (DFK). The objective is to achieve accurate and adaptive trajectory tracking in the presence of unmeasured dynamics and human–robot interaction forces. The control design reformulates the nonlinear joint-space dynamics into a 0-flat canonical form, enabling real-time computation of feedforward control laws based solely on flat outputs and their derivatives. Simultaneously, the DFK-based observer estimates external perturbations and unmeasured states without requiring derivative calculations, allowing for online disturbance compensation. Extensive simulations across nominal and disturbed conditions demonstrate that the proposed controller significantly outperforms conventional flatness-based control in tracking accuracy and robustness, as measured by reduced mean absolute error and standard deviation. Experimental validation under both simple and repetitive physiotherapy tasks confirms the system’s ability to maintain sub-millimeter Cartesian accuracy and sub-degree joint errors even amid dynamic perturbations. These results underscore the controller’s effectiveness in enabling compliant, safe, and disturbance-resilient rehabilitation, paving the way for broader deployment of robotic therapy in clinical and home-based environments. Full article

Chronic rhinosinusitis (CRS) is a prevalent inflammatory condition of the nasal and paranasal mucosa that significantly impacts the quality of life. Postoperative inflammation and polyp recurrence remain common despite advances in endoscopic sinus surgery (ESS), prompting interest in localized corticosteroid delivery systems. This review analyzes bioresorbable steroid-releasing implants and corticosteroid-impregnated nasal dressings, focusing on their pharmacologic mechanisms, safety, and clinical outcomes. A synthesis of findings from randomized trials and observational studies was performed, with emphasis on devices such as Propel™, NasoPore, Merocel, SinuBand FP, and gel-based dressings. The Propel implant demonstrated robust evidence for reducing adhesions and inflammation with negligible systemic absorption. NasoPore and Merocel provided structural support and localized steroid delivery but lacked controlled-release kinetics. Gel-based dressings and SinuBand FP offered anatomic adaptability, with limited systemic effects. Some methods showed systemic steroid exposure in cortisol monitoring. Corticosteroid-releasing devices enhance ESS outcomes through localized therapy. While Propel is the most validated, other devices remain viable alternatives in specific clinical contexts. Comprehensive safety monitoring and standardized trials are essential to optimize their integration into postoperative care. Full article

This study introduces a novel control strategy tailored to nonlinear systems with non-minimum phase (NMP) characteristics. The framework leverages reinforcement learning within a cascade control architecture that integrates an Actor–Critic structure. Controlling NMP systems poses significant challenges due to the inherent instability of their internal dynamics, which hinders effective output tracking. To address this, the system is reformulated using the Byrnes–Isidori normal form, allowing the decoupling of the input–output pathway from the internal system behavior. The proposed control architecture consists of two nested loops: an inner loop that applies input–output feedback linearization to ensure accurate tracking performance, and an outer loop that constructs reference signals to stabilize the internal dynamics. A key innovation in this design lies in the incorporation of symmetry principles observed in both system behavior and control objectives. By identifying and utilizing these symmetrical structures, the learning algorithm can be guided toward more efficient and generalized policy solutions, enhancing robustness. Rather than relying on classical static optimization techniques, the method employs a learning-based strategy inspired by previous gradient-based approaches. In this setup, the Actor—modeled as a multilayer perceptron (MLP)—learns a time-varying control policy for generating intermediate reference signals, while the Critic evaluates the policy’s performance using Temporal Difference (TD) learning. The proposed methodology is validated through simulations on the well-known Inverted Pendulum system. The results demonstrate significant improvements in tracking accuracy, smoother control signals, and enhanced internal stability compared to conventional methods. These findings highlight the potential of Actor–Critic reinforcement learning, especially when symmetry is exploited, to enable intelligent and adaptive control of complex nonlinear systems. Full article

This article proposes a method to solve nonlinear optimal control problems with arbitrary performance indices and terminal constraints, which is based on the neighboring extremal method and Gauss pseudospectral collocation. Firstly, a quadratic performance index is formulated, which minimizes the second-order variation of the nonlinear performance index and fully considers the deviations in initial states and terminal constraints. Secondly, the first-order necessary conditions are applied to derive the perturbation differential equations involving deviations in state and costate variables. Therefore, a quadratic optimal control problem is formulated, which is subject to such perturbation differential equations. Thirdly, the Gauss pseudospectral collocation is used to transform the differential and integral operators into algebraic operations. Therefore, an analytical solution of the control correction can be successfully derived in the polynomial space, which comes close to the optimal solution. This method has a fast computation speed and low computational complexity due to the discretization at orthogonal points, making it suitable for online applications. Finally, some simulations and comparisons with the optimal solution and other typical methods have been carried out to evaluate the performance of the method. Results show that it not only performs well in computational efficiency and accuracy but also has great adaptability and optimality. Moreover, Monte Carlo simulations have been conducted. The results demonstrate that it has strong robustness and excellent performance even in highly dispersed environments. Full article