工业工程最新文献

Biological fractal dynamics inspire a new route to analog fractional-order circuit design. Bridging biological systems and electronic architectures, an operator-based framework uses the force–electricity analogy to approximate fractional-order behavior with conventional integer-order components. This approach connects the memory and nonlocal effects described by fractional calculus with the physical construction of circuit systems. Supported by theoretical analysis and circuit simulations, it reveals the intrinsic relationship between fractal operators and fractional-order circuits, offering a systematic foundation for the design of high-performance, integrable fractal circuits.

Fractional calculus, with its unique advantage in describing memory and nonlocal effects, has become a key tool for modeling nonlinear dynamic systems. Its integration with circuit systems has opened up a new paradigm for modern circuit design. However, the development of analog fractional-order circuits (AFCs) is limited by the difficulty in physical implementation of ideal fractional-order elements (fractances) and the lack of systematic design theories. Based on biological dynamics in physical fractal space and operatorization thought, this article aims to provide a novel approach to address the above challenges. The core work of this paper is to systematically introduce operatorization thought into the field of circuits and construct a universal fractional-order system design framework applicable to multiple disciplines. Unlike traditional methods relying on backend optimization, this research focuses on the intrinsic construction of frontend fractional-order systems. By deeply understanding the physical connotation of fractional-order operators and leveraging the force-electricity analogy principle, the approximation of fractional-order circuits using classical integer-order components is realized. Theoretical analysis and circuit simulations demonstrate that this method not only simplifies the design process but also reveals the inherent connection between fractional-order circuits and fractal operators from a physical perspective, providing a solid theoretical basis and innovative path for the design of high-performance, integrable fractal circuits.

Digitalization and emerging technologies are increasing the demand for wireless sensing and the Internet of Things (IoT), which provide opportunities for autonomous sources of electricity in the form of energy harvesting systems. This paper focuses on the challenges in hybrid piezoelectric-electromagnetic kinetic energy harvesting systems that deliver output power at milliwatt levels, sufficient for current IoT electronics. The main task in the employment of energy harvesting technology for industrial applications is transitioning from laboratory test samples to industrial-scale prototype deployment, with emphasis on sensitivity and uncertainty analyses of energy harvesting parameters. This paper analyses this problem using a single-degree-of-freedom model for a hybrid piezoelectric-electromagnetic kinetic energy harvester, where the effect of uncertainty in design and material input parameters on harvested power outputs is examined. Industrial application uncertainties, including manufacturing and geometric tolerances, uncertainties in material parameters, and fluctuations in ambient input parameters, are assessed and analyzed using the Saltelli method. Key findings highlight the amplification of uncertainties, with mechanical damping identified as the most influential parameter of harvested power. By investigating piezoelectric and electromagnetic coupling factors, this study provides actionable insights for optimizing hybrid energy harvesters and adjusting coupling parameters for maximal output power generation.

This paper proposes a hybrid control strategy for flexible-link manipulators that combines offline trajectory planning with adaptive tracking to suppress residual elastic vibration (REV) under unknown bounded disturbances. A rigid–flexible coupling dynamics model is first established using the floating-frame method and Lagrange's equations, forming a differential–algebraic system as the foundation for controller design and optimization. A parameterized cosine-based joint trajectory is optimized via particle swarm optimization to reduce endpoint deformation. The resulting trajectory is tracked using an adaptive sliding-mode backstepping controller enhanced with a nonlinear disturbance observer. Simulations show that the optimized trajectory reduces REV amplitude and shortens motion time compared with the reference trajectories considered in this study. Under the considered numerical conditions, the proposed controller yields smaller residual deformation than the selected proportional–derivative reference controller in both the disturbance-free and disturbed cases. These results indicate that, within the adopted modeling assumptions, disturbance description, observer applicability range, and actuator-amplitude-constrained setting, the proposed strategy can improve tracking performance and vibration suppression for the flexible manipulator.

In this study, an improved mass-spring model that treats the lubricating air film as distributed springs is proposed to calculate the static and dynamic angular stiffness of aerostatic thrust bearings. The proposed model refines the traditional mass-spring model by introducing the spatially varying mechanical properties of the air film and the frequency-dependent behavior of dynamic coefficients. Axial and tilt characteristics are coupled through distributed equivalent air springs, where spring coefficients are determined by the static air pressure and the transient perturbation pressure. On the basis of the improved model, a fast calculation method for static and dynamic angular stiffness is presented by periodically extending the axial characteristics of a single symmetric sector instead of utilizing the full model. The results from the fast calculation method with a single symmetric model show good agreement with those obtained from the Reynolds equation and computational fluid dynamics method with a full model, while reducing computation time by 88.0% in static analysis and 98.6% in dynamic analysis. Moreover, the applicability and limitations of this model are investigated, indicating that it has a broader applicability in dynamic angular stiffness analysis compared with static analysis. Within the applicable range, the static and dynamic designs of the thrust bearing are performed. The effects of air film thickness, orifice diameter, and orifice location on axial and angular stiffness are analyzed. This research establishes an efficient approach for the analysis of tilt characteristics in aerostatic bearings. Furthermore, the limitations of the proposed model provide valuable guidance for the appropriate application of the mass-spring model in the dynamic study of spindles within mechanical systems.

The emergence of narrative medicine offers new possibilities for the research of bioethics.This paper proposes the concept of "life narrative ethics" and uses the three life attributes-natural life,spiritual life,and social life-as an analytical framework to reveal the ethical demands of life narratives in the finiteness of the physical body,the rupture of self-identity,and the transformation of social roles.On this basis,this paper distills four core principles:centering on life stories,dignity integration,interpretive humility,and reciprocal narrative relationships.These principles respectively establish the epistemological basis,value goals,attitude requirements,and practical approaches for ethical judgment.The applicability and explanatory power of these principles are analyzed through a case of a patient's death.This paper argues that life narrative ethics represents a progression from relational ethics to substantive value ethics,providing new theoretical tools and practical guidelines for medical practitioners to understand the patients' holistic lives and respond to their profound ethical demands.

Digital health has improved the efficiency of information processing while also changing how patients' illness narratives enter healthcare systems.Patient narrative flattening refers to the excessive compression of patient narratives during digital processing,whereby temporal trajectories,life contexts,value preferences,and other dimensions of illness experience are diminished.It is mainly manifested in the compression of narrative content during expression,the fragmentation of information in clinical documentation,and the fixation of information in subsequent use.Such flattening may constrain clinical understanding,affect clinical decision-making and patient-clinician interaction,and allow governance risks to accumulate when these data are further used in research,evaluation,and algorithmic applications.In the digital era,narrative medicine should attend to the loss of patient narratives in digital transformation and preserve their significance for clinical understanding and healthcare practice.

In this study, a robust position control strategy is presented for a two-stage electro-hydraulic actuator system using a fractional-order proportional–integral–derivative (FOPID) controller tuned by Kirchhoff's law algorithm (KLA). Electro-hydraulic systems exhibit strong nonlinearities, parameter uncertainties, and external disturbances, making accurate position control challenging. Classical integer-order controllers such as PI and PID often provide limited flexibility in shaping transient dynamics under such conditions, leading to performance degradation when system parameters vary. Although advanced nonlinear and adaptive control strategies can improve robustness, their implementation complexity and tuning burden may restrict their practical applicability. In addition, many existing metaheuristic-based tuning approaches rely on algorithm-specific parameters and may exhibit inconsistent convergence behavior across different operating conditions. To address these limitations, a control-oriented mathematical model of the electro-hydraulic positioning system is first derived from linearized valve flow relations, actuator continuity equations, and load dynamics, and a reduced-order transfer function preserving the dominant behavior is obtained for controller design. The FOPID controller is adopted to enlarge the tuning space through fractional integral and derivative orders, enabling finer dynamic shaping than classical integer-order structures. The controller parameters are determined automatically by formulating a multi-term time-domain objective function that penalizes overshoot, settling time, and accumulated tracking error. The KLA metaheuristic, inspired by electrical circuit laws, is employed to solve this constrained optimization problem without introducing algorithm-specific control parameters. Under identical optimization settings (population size 25, 40 iterations, 20 independent runs), the proposed KLA approach achieves the best statistical performance among the tested optimizers, with a minimum fitness value of 1.2511, mean value of 1.2881, and standard deviation of 0.0258, outperforming APO, PSO, and DE according to Wilcoxon signed-rank tests (p ≈ 8.86 × 10^–5). For a 20 cm step command, the KLA-tuned FOPID controller yields a rise time of 0.1664 s, settling time of 0.3034 s, overshoot of 0.0124%, and IAE of 1.5244 cm·s, improving all metrics relative to both competing optimizers and KLA-tuned PI, PIDF, and 2-DOF PID controllers. Additional simulations demonstrate stable and accurate tracking under multi-level and time-varying references, parametric variations from $$ to $$, and combined disturbance and measurement noise. The results indicate that the KLA-based FOPID design provides a fast, accurate, and robust control solution for electro-hydraulic positioning systems.

Linear-quadratic Gaussian (LQG) control is a well-established method for optimal control through state estimation, particularly in stabilizing an inverted pendulum on a cart. In standard laboratory setups, sensor redundancy enables direct measurement of configuration variables using displacement sensors and rotary encoders. However, in outdoor environments, dynamically stable mobile platforms—such as Segways, hoverboards, and bipedal robots—often have limited sensor availability, restricting state estimation primarily to attitude stabilization. Since the tilt angle cannot be directly measured, it is typically estimated through sensor fusion, increasing reliance on inertial sensors and necessitating a lightweight, self-contained perception module. Prior research has not incorporated accelerometer data into the LQG framework for stabilizing pendulum-like systems, as jerk states are not explicitly modeled in the Newton–Euler formalism. In this paper, we address this gap by leveraging local differential flatness to incorporate higher-order dynamics into the system model. This refinement enhances state estimation, enabling a more robust LQG controller that predicts accelerations for dynamically stable mobile platforms.

Ethical dilemmas in real-world medical settings expose the limitations of bioethics,represented by the "four-principle framework," in dynamic and personalized clinical decision-making.The development of narrative medicine provides more space for showcasing the unique context of life,alleviating the predicament of normative ethics.However,current ethical practices in narrative medicine have not responded to the theoretical problem of the lack of ontology,moral subject,and reflective process in principlism,calling for the further exploration of the moral issues inherent in the narrative process and outcomes.This paper,based on the ontology of life narrative,proposes a new understanding of narrative bioethics in healthcare.Through comparative analysis of an ethical case involving living organs,this paper demonstrates the necessity and challenges of shifting ethical decision-making from reflective equilibrium to narrative co-construction.