Search Results (2,842)

This study introduces an innovative self-regulating intelligent optimal balancing control framework for inverted pendulum-type mechatronic platforms, designed to enhance reference tracking accuracy and improve disturbance rejection capability. The control procedure is synthesized by synergistically integrating a baseline Linear Quadratic Regulator (LQR) with a fuzzy controller via a customized linear decomposition function (LDF). The LDF dissociates and transforms the LQR control law into compounded state tracking error and tracking error derivative variables that are eventually used to drive the fuzzy controller. The principal contribution of this study lies in the adaptive modulation of these compounded variables using reconfigurable tangent hyperbolic functions driven by the cubic power of the error signals. This nonlinear preprocessing of the input variables selectively amplifies large errors while attenuating small ones, thereby improving robustness and reducing oscillations. Moreover, a model-free online self-tuning law dynamically adjusts the variation rates of the hyperbolic functions through dissipative and anti-dissipative terms of the state errors, enabling autonomous reconfiguration of the nonlinear preprocessing layer. This dual-level adaptation enhances the flexibility and resilience of the controller under perturbations. The robustness of the designed controller is substantiated via tailored experimental trials conducted on the Quanser rotary pendulum platform. Comparative results show that the prescribed scheme reduces pendulum angle variance by 41.8%, arm position variance by 34.6%, and average control energy by 28.3% relative to the baseline LQR, while outperforming conventional fuzzy-LQR by similar margins. These results show that the prescribed controller significantly enhances disturbance rejection and tracking accuracy, thereby offering a numerically superior control of inverted pendulum systems. Full article

To meet the increasing demands of energy efficiency and real-time performance in autonomous driving systems, this paper presents a lightweight and robust LiDAR SLAM framework designed with power-aware considerations. The proposed system introduces three core innovations. First, it replaces traditional ordered point cloud indexing with a 2D range image projection, significantly reducing memory usage and enabling efficient feature extraction with curvature-based criteria. Second, a multi-stage outlier rejection mechanism is employed to enhance feature robustness by adaptively filtering occluded and noisy points. Third, we propose a dynamically filtered local mapping strategy that adjusts keyframe density in real time, ensuring geometric constraint sufficiency while minimizing redundant computation. These components collectively contribute to a SLAM system that achieves high localization accuracy with reduced computational load and energy consumption. Experimental results on representative autonomous driving datasets demonstrate that our method outperforms existing approaches in both efficiency and robustness, making it well-suited for deployment in low-power and real-time scenarios within intelligent transportation systems. Full article

This paper presents a comprehensive comparative analysis of high-voltage, high-frequency pulse generation techniques for Pockels cell drivers. These drivers are critical in electro-optic systems for laser modulation, where nanosecond-scale voltage pulses with amplitudes of several kilovolts are required. The study reviews key design challenges, with particular emphasis on thermal management strategies, including air, liquid, solid-state, and phase-change cooling methods. Different high-voltage, high-frequency pulse generation architectures including vacuum tubes, voltage multipliers, Marx generators, Blumlein structures, pulse-forming networks, Tesla transformers, switching-mode power supplies, solid-state switches, and high-voltage operational amplifiers are systematically evaluated with respect to cost, complexity, stability, and their suitability for driving capacitive loads. The analysis highlights hybrid approaches that integrate solid-state switching with modular multipliers or pulse-forming circuits as offering the best balance of efficiency, compactness, and reliability. The findings provide practical guidelines for developing next-generation high-performance Pockels cell drivers optimized for advanced optical and laser applications. Full article

This paper examines the evolving trajectory of the Social Determinants of Health (SDOH), tracing their development from early observational studies to contemporary, interdisciplinary frameworks that emphasize structural inequities and relational dynamics. It explores foundational milestones such as the Whitehall studies, the Heckler Report, and the World Health Organization’s conceptual models, which positioned SDOH as key drivers of population health. The paper highlights how upstream determinants—such as governance, policy, and socioeconomic systems—influence downstream health outcomes through mechanisms of social stratification and unequal access to resources. While SDOH are increasingly applied in clinical and educational settings, significant challenges persist, including underinvestment in community systems, fragmented care models, and political rollbacks of equity-centered policies. The paper critiques deterministic and deficit-focused framings of SDOH and underscores a shift toward more relational, context-sensitive, and agency-oriented approaches, reflected in the emerging concept of “social dynamics of health.” It highlights the importance of experiential education, competency-based curricula, and digital innovations in driving systemic transformation. Emphasis is placed on reimagining SDOH pedagogy and expanding interdisciplinary, data-driven research to bridge the gap between knowledge and practice. Amid shifting political landscapes, sustaining health equity efforts requires embracing adaptive, participatory models that acknowledge power, community agency, and structural change. Full article

A three-phase inverter generates non-sinusoidal voltages, contains high order harmonics, and concentrates on switching frequency multiples. Supplying an induction machine (IM) with a voltage source inverter (VSI) increases the acoustic noise content which becomes unbearable, particularly for systems needing a moderate level of electric traction. The discrete tonal bands produced by the IM stator current spectrum controlled by the fixed pulse width modulation (PWM) technique have damaging effects on the electronic noise source. Moreover, it has been factually proven that the noise content is strongly associated with the harmonics of the source feeding electric machine. Thus, the harmonic content is influenced by the control strategy VSI to produce pulse width modulation (PWM). Currently, the investigation of new advanced control techniques for variable speed drives has developed into a potential investigation file. Two fundamental topologies for a three-phase inverter have been suggested in the literature, namely two- and three-level topologies. Therefore, this paper investigated the effect of variable and fixed PWM strategies, such as random PWM (RPWM) and space vector PWM (SVPWM), on the noise generated by an IM, powered with a two- or three-level inverter. Simulation results showed the validity and efficiency of the proposed variable RPWM strategy in reducing sideband harmonics for both the two and three levels at different switching frequencies and modulation indexes. The proposed PWM strategies were further evaluated by the results of equivalent experiments on an IM fed by a two-level VSI. The experimental measurements of harmonic current and noise spectra demonstrate that the acoustic noise is reduced and dispersed totally for the RPWM strategy. Full article

Traditional energy harvesting systems, such as photovoltaics and wind power, often rely on external environmental conditions and are typically associated with contact-based vibration wear and bulky structures. This study introduces light-fueled self-vibration to propose a self-harvesting system, consisting of liquid crystal elastomer fibers, two resistors, and two piezoelectric cantilever beams arranged symmetrically. Based on the photothermal temperature evolution, we derive the governing equations of the liquid crystal elastomer fiber–piezoelectric beam system. Two distinct states, namely a self-harvesting state and a static state, are revealed through numerical simulations. The self-oscillation results from light-induced cyclic contraction of the liquid crystal elastomer fibers, driving beam bending, stress generation in the piezoelectric layer, and voltage output. Additionally, the effects of various system parameters on amplitude, frequency, voltage, and power are analyzed in detail. Unlike traditional vibration energy harvesters, this light-fueled self-harvesting system features a compact structure, flexible installation, and ensures continuous and stable energy output. Furthermore, by coupling the light-responsive LCE fibers with piezoelectric transduction, the system provides a non-contact actuation mechanism that enhances durability and broadens potential application scenarios. Full article

A large number of practical systems show pronounced fractional-order features. In comparison with integer-order calculus, fractional-order calculus has been demonstrated to possess enhanced precision in the description of the dynamic behavior of complex systems. The increase in control accuracy and flexibility results from this improvement. This study explores a direct-drive wind power generation system featuring permanent magnets, which incorporates fractional-order direct current bus (DC-bus) capacitor and fractional-order inductor–capacitor–inductor (FOLCL) grid-connected filter. For the machine-side rectifier, a fractional-order sliding mode (FOSM) speed outer-loop control and a fractional-order proportional–integral (FOPI) current inner-loop control were designed. A voltage outer-loop control using FOSM and a current inner-loop control using FOPI were developed for the grid-side inverter. Through simulation analyses under various wind speeds and grid fault conditions, it is demonstrated that compared to a control strategy using FOPI controllers in both inner and outer loops, the proposed control scheme which employs a FOSM outer-loop and reduces the overshoot of DC-bus voltage and grid-connected current by 21.51% and 32.49%, respectively, under sudden wind speed changes. Furthermore, during grid voltage sag faults, the maximum drop in DC-bus voltage and grid-connected active power are reduced by 65.38% and 33.38%, respectively. These results highlight the proposed method’s superior dynamic and static performance, as well as enhanced resistance to disturbances. Full article

In this study, the impact of the electric motor size and the hybridization ratio of a Fuel Cell Electric Bus on its vehicle performance (i.e., gradeability and acceleration) and fuel consumption was investigated using the ADVISOR software. The investigation first involved a parametric analysis with different electric motor and fuel cell sizes for the dynamic performance metrics, specifically the 0–60 km/h vehicle acceleration and the maximum gradeability (%) at a constant speed of 20 km/h. The results revealed that the acceleration is most sensitive to fuel cell power. Regarding gradeability, a more complex relationship was observed: when the electric motor power was below 215 kW, gradeability remained consistently low regardless of the fuel cell size. However, for motors exceeding 215 kW, fuel cell power then became a significant influencing factor on the vehicle’s climbing capability. Subsequently, the analysis focused on the effect of the hybridization ratio, which represents the power balance between the fuel cell and the energy storage system, varied between 0 and 0.8. Results showed that increasing the hybridization ratio decreases gradeability and acceleration performance and increases total energy consumption. This trade-off is quantitatively illustrated by the results over the Central Business District (CBD) driving cycle. For instance, the pure battery-electric configuration (a hybridization ratio of 0), featuring a 296 kW battery system, recorded a gradeability of 12.4% and an acceleration time of 16.3 s, while consuming 28,916 kJ. At an intermediate hybridization ratio of 0.4 (composed of a 118.4 kW fuel cell and a 177.6 kW battery), performance remained high with a gradeability of 12.2% and an acceleration of 17.3 s, but the energy consumption increased to 43,128 kJ. Finally, in the fuel-cell-dominant configuration with a hybridization ratio of approximately 0.8 (a 236.8 kW fuel cell and a 59.2 kW battery), gradeability dropped to 8.4%, acceleration time deteriorated to 38.9 s, and total energy consumption increased further to 52,678 kJ over the CBD driving cycle. Full article

Organoid technology has emerged as a revolutionary tool in cancer research, offering physiologically accurate, three-dimensional models that preserve the histoarchitecture, genetic stability, and phenotypic complexity of primary tumors. These self-organizing structures, derived from adult stem cells, induced pluripotent stem cells, or patient tumor biopsies, recapitulate critical aspects of tumor heterogeneity, clonal evolution, and microenvironmental interactions. Organoids serve as powerful systems for modeling tumor progression, assessing drug sensitivity and resistance, and guiding precision oncology strategies. Recent innovations have extended organoid capabilities beyond static culture systems. Integration with microfluidic organoid-on-chip platforms, high-throughput CRISPR-based functional genomics, and AI-driven phenotypic analytics has enhanced mechanistic insight and translational relevance. Co-culture systems incorporating immune, stromal, and endothelial components now permit dynamic modeling of tumor–host interactions, immunotherapeutic responses, and metastatic behavior. Comparative analyses with conventional platforms, 2D monolayers, spheroids, and patient-derived xenografts emphasize the superior fidelity and clinical potential of organoids. Despite these advances, several challenges remain, such as protocol variability, incomplete recapitulation of systemic physiology, and limitations in scalability, standardization, and regulatory alignment. Addressing these gaps with unified workflows, synthetic matrices, vascularized and innervated co-cultures, and GMP-compliant manufacturing will be crucial for clinical integration. Proactive engagement with regulatory frameworks and ethical guidelines will be pivotal to ensuring safe, responsible, and equitable clinical translation. With the convergence of bioengineering, multi-omics, and computational modeling, organoids are poised to become indispensable tools in next-generation oncology, driving mechanistic discovery, predictive diagnostics, and personalized therapy optimization. Full article

The electrification of automotive powertrains has accelerated research efforts in the modeling, control, and monitoring of electric drive systems, where reliability, safety, and efficiency are key enablers for mass adoption. Despite a large corpus of literature addressing individual aspects of electric drives, current surveys remain fragmented, typically focusing on either multiphysics modeling of machines and converters, or advanced control algorithms, or diagnostic and prognostic frameworks. This review provides a comprehensive perspective that systematically integrates these domains, establishing direct connections between high-fidelity models, control design, and monitoring architectures. Starting from the fundamental components of the automotive power drive system, the paper reviews state-of-the-art strategies for synchronous motor modeling, inverter and DC/DC converter design, and advanced control schemes, before presenting monitoring techniques that span model-based residual generation, AI-driven fault classification, and hybrid approaches. Particular emphasis is given to the interplay between functional safety (ISO 26262), computational feasibility on embedded platforms, and the need for explainable and certifiable monitoring frameworks. By aligning modeling, control, and monitoring perspectives within a unified narrative, this review identifies the methodological gaps that hinder cross-domain integration and outlines pathways toward digital-twin-enabled prognostics and health management of automotive electric drives. Full article

As global industry enters the digital era, automation is becoming increasingly pervasive. Due to their superior efficiency and reliability, Permanent Magnet Synchronous Motors (PMSMs) are playing an increasingly prominent role in industrial applications. Sliding Mode Control (SMC) has emerged as a modern control strategy that is widely employed not only in PMSM drive systems, but also across broader power and industrial control domains. This technique effectively mitigates key challenges associated with PMSMs, such as nonlinear behavior and susceptibility to external disturbances, thereby enhancing the precision of speed and torque regulation. This paper provides a thorough review and evaluation of recent advancements in SMC as applied to PMSM control. It outlines the fundamentals of SMC, explores various SMC-based strategies, and introduces integrated approaches that combine SMC with optimization algorithms. Furthermore, it compares these methods, identifying their respective strengths and limitations. This paper concludes by discussing current trends and potential future developments in the application of SMC for PMSM systems. Full article

With the rapid advancement of artificial intelligence technology, the widespread application of deep learning in computer vision is driving the transformation of agricultural pest detection and control toward greater intelligence and precision. This paper systematically reviews the evolution of agricultural pest detection and control technologies, with a special focus on the effectiveness of deep-learning-based image recognition methods for pest identification, as well as their integrated applications in drone-based remote sensing, spectral imaging, and Internet of Things sensor systems. Through multimodal data fusion and dynamic prediction, artificial intelligence has significantly improved the response times and accuracy of pest monitoring. On the control side, the development of intelligent prediction and early-warning systems, precision pesticide-application technologies, and smart equipment has advanced the goals of eco-friendly pest management and ecological regulation. However, challenges such as high data-annotation costs, limited model generalization, and constrained computing power on edge devices remain. Moving forward, further exploration of cutting-edge approaches such as self-supervised learning, federated learning, and digital twins will be essential to build more efficient and reliable intelligent control systems, providing robust technical support for sustainable agricultural development. Full article

The demand for higher data rates and energy efficiency in wireless communication systems drives power amplifiers (PAs) into nonlinear operation, causing signal distortions that hinder performance. Digital Pre-Distortion (DPD) addresses these distortions, but existing systems face challenges with complexity, adaptability, and resource limitations. This paper introduces DRL-DPD, a Deep Reinforcement Learning-based solution for DPD that aims to reduce computational burden, improve adaptation to dynamic environments, and minimize resource consumption. To ensure safety and regulatory compliance, we integrate an ad-hoc Safe Reinforcement Learning algorithm, CRE-DDPG (Cautious-Recoverable-Exploration Deep Deterministic Policy Gradient), which prevents ACLR measurements from falling below safety thresholds. Simulations and hardware experiments demonstrate the potential of DRL-DPD with CRE-DDPG to surpass current DPD limitations in both local and remote configurations, paving the way for more efficient communication systems, especially in the context of 5G and beyond. Full article

The optimal control of cooling water systems is of great significance for energy saving in chiller plants. Previously optimal control methods optimize the flow rate, temperature or temperature difference setpoints but cannot control pumps and cooling tower fans directly. This study proposes a direct optimal control method for pumps and fans based on derivative control strategy by decoupling water flow rate optimization and airflow rate optimization, which can make the total power of chillers, pumps and fans approach a minimum. Simulations for different conditions were performed for the validation and performance analysis of the optimal control strategy. The optimization algorithms and implementation methods of direct optimal control were developed and validated by experiment. The simulation results indicate that total power approaches a minimum when the derivative of total power with respect to water/air flow rate approaches zero. The power-saving rate of the studied chiller plant is 13.2% at a plant part-load ratio of 20% compared to the constant-speed pump/fan mode. The experimental results show that the direct control method, taking power frequency as a controlled variable, can make variable frequency drives regulate their output frequencies to be equal to the optimized power frequencies of pumps and fans in a timely manner. Full article

Lower limb functional degeneration has become prevalent, notably reducing the core strength that drives motor control. Squats are frequently used in lower limb training, improving overall muscle strength. However, performing continuously with improper techniques can lead to dynamic knee instability. It worsens with little to no motivation to perform these power training motions. Hence, it is crucial to have a gaming-based exercise tracking system to adaptively enhance the user experience without causing injury or falls. In this work, 28 healthy subjects performed exergame-based squat training, and dynamic kinematic features were recorded. The five features acquired from a depth camera-based inertial measurement unit (IMU) (1—Knee shakiness, 2—Knee distance, and 3—Squat depth) and an Anima forceplate sensor (4—Sway velocity and 5—Sway area) were assessed using a Spearman correlation coefficient-based feature selection method. An input vector that defines knee instability is used to train and test the Long Short-Term Memory (LSTM) and Support Vector Machine (SVM) models for binary classification. The results showed that knee instability events can be successfully classified and achieved a high accuracy of 96% in both models with sets 1, 2, 3, 4, and 5 and 1, 2, and 3. The feature selection results indicate that the LSTM network with the proposed combination of input features from multimodal sensors can successfully perform real-time tracking of knee instability. Furthermore, the findings demonstrate that this multimodal approach yields improved classifier performance with enhanced accuracy compared to using features from a single modality during lower limb therapy. Full article