Fuzzy Random Traveling Salesman Problem

The travelling salesman problem is to find a shortest path from the travelling salesman’s hometown, make the round of all the towns in the set, and finally go back home. This paper investigates the travelling salesman problem with fuzzy random travelling time. Three concepts are proposed: expected shortest path, (α, β)-path and chance shortest path according to different optimal desire. Correspondingly, by using the concepts as decision criteria, three fuzzy random programming models for TSP are presented. Finally, a hybrid intelligent algorithm is designed to solve these models, and some numerical examples are provided to illustrate its effectiveness

FINITE ELEMENT ANALYZE OF THE FIRST METATARSAL VERTICAL ARCH OF THE FOOT IN THE HIGH-HEELED GAIT

A two-dimensional numerical model of the foot, incorporating, for the first time in the literature, realistic geometric and material properties of both skeletal and soft tissue components of the foot, was developed for biomechanical analysis of its structural behavior during gait. Using a Finite Element solver, the stress distribution within the first metatarsal vertical: arch of the foot (FMVA) structure was obtained and regions of elevated stresses for three subphases of the stance (heel-strike, push-off, and toe-off) were located. Validation of the pressure state was achieved by comparing model predictions of contact pressure distribution with Novel Pedar. The presently developed measurement and numerical analysis tools open new approaches for clinical applications, from simulation of the development mechanisms of common foot disorders to pre-and post-interventional evaluation of their treatment

PLANTAR MECHANICS INFLUENCE OF DIFFERENT AND EXTRINSIC BIOMECHANICS INSTRUMENTALlTIES IN STANDING VERTICAL JUMP

The research has studied and compared with the areal characters of plantar pressure distribution in standing vertical jump, MMP (mean maximum pressure), MVP (mean value pressure) and the plantar force changes. The research has studied the formation of the shockproof mechanism in different designed shockproof systems, the aim is to guide to design shockproof shoes and to strengthen people's understanding to sports shockproof shoes

Voltage Build-Up Analysis of Self-Excited Induction Generator With Multi-Timescale Reduced-Order Model

Self-excited induction generator (SEIG) has received a lot of attentions for its increasing application in distributed generation systems with the essential feature of low cost. To analysis, the dynamic and transient performance of SEIG, several modifications of the mathematical models have been developed for improving the regulation of voltage and frequency. But these models are still complicated to be used in practice. Based on the transient equivalent circuit, a reduced-order model of SEIG with complex transformation in the two-phase stationary reference frame is realized for the transient analysis of voltage build-up. In this simplified model, the coefficients of the characteristic polynomial with multi-timescale time constants are proposed. Moreover, the physical interpretation of system transient behavior with the reconstructed time constants is established and visualized. Particularly, the upper and lower limits of the capacitance and speed for the SEIG with different parameters variation are simulated and analyzed respectively. The validation and the accuracy of the SEIG model are verified for the transient analysis of the voltage build-up. It is proved that the reduced-order model can be effectively used to insight the dynamic stability of SEIG voltage build-up with the multi-timescale

Federated Conformal Predictors for Distributed Uncertainty Quantification

Conformal prediction is emerging as a popular paradigm for providing rigorous uncertainty quantification in machine learning since it can be easily applied as a post-processing step to already trained models. In this paper, we extend conformal prediction to the federated learning setting. The main challenge we face is data heterogeneity across the clients -- this violates the fundamental tenet of \emph{exchangeability} required for conformal prediction. We propose a weaker notion of \emph{partial exchangeability}, better suited to the FL setting, and use it to develop the Federated Conformal Prediction (FCP) framework. We show FCP enjoys rigorous theoretical guarantees and excellent empirical performance on several computer vision and medical imaging datasets. Our results demonstrate a practical approach to incorporating meaningful uncertainty quantification in distributed and heterogeneous environments. We provide code used in our experiments \url{https://github.com/clu5/federated-conformal}.Comment: 23 pages, 18 figures, accepted to International Conference on Machine Learning (ICML) 202

Energy Storage - Driving towards a clean energy future

No abstract available