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With the benefit of partially or entirely offloading computations to a nearby server, mobile edge computing gives user equipment (UE) more powerful capability to run computationally intensive applications. However, a critical challenge emerged: how to select the optimal set of components to offload considering the UE performance as well as its battery usage constraints. In this paper, we propose a novel energy and performance efficient deep learning based offloading algorithm. The optimal offloading schemes of components based on remaining energy and its performance can be determined by our proposed algorithm. All of these considerations are modeled as a cost function; then, a deep learning network is trained to compute the solution by which the optimal offloading scheme can be determined. Experimental results show that the proposed method is superior to existing methods in terms of energy and performance constraints. [ABSTRACT FROM AUTHOR]

To evaluate the lifetime and reliability of long-life, high-reliability products under limited resources, accelerated degradation testing (ADT) technology has been widely applied. Furthermore, the Bayesian evaluation method for ADT can comprehensively utilize historical information and overcome the limitations caused by small sample sizes, garnering significant attention from scholars. However, the traditional ADT Bayesian evaluation method has inherent shortcomings and limitations. Due to the constraints of small samples and an incomplete understanding of degradation mechanisms or accelerated mechanisms, the selected evaluation model may be inaccurate, leading to potentially inaccurate evaluation results. Therefore, describing and quantifying the impact of model uncertainty on evaluation results is a challenging issue that urgently needs resolution in the theoretical research of ADT Bayesian methods. This article addresses the issue of model uncertainty in the ADT Bayesian evaluation process. It analyzes the modeling process of ADT Bayesian and proposes a new model averaging evaluation method for ADT Bayesian based on relative entropy, which, to a certain extent, can resolve the issue of evaluation inaccuracy caused by model selection uncertainty. This study holds certain theoretical and engineering application value for conducting ADT Bayesian evaluation under model uncertainty. [ABSTRACT FROM AUTHOR]

The accelerated degradation testing (ADT) Bayesian evaluation method comprehensively utilizes product degradation data under accelerated stress levels collected over a short period of time and multiple sources of prior information, such as historical information, similar product information, simulation information, etc., to conduct life and reliability evaluation. Through the prior distribution, prior information affects the ADT Bayesian evaluation results ultimately. However, different evaluators may obtain different prior distributions based on the same prior information due to varying experiences or rules, which may lead to differences in the ADT Bayesian evaluation results. Therefore, it is necessary to analyze and study the impact of prior distribution uncertainty on the ADT Bayesian evaluation results while also finding criteria to judge the quality of prior distributions. This paper focuses on the ADT Bayesian evaluation method based on the Wiener process and the Arrhenius relation, studying the influence of different prior distributions on the robustness of ADT Bayesian evaluation results. Additionally, based on the deviance information criterion (DIC), a criterion for selecting prior distributions in the ADT Bayesian evaluation method is proposed. Through carrying out uncertainty analysis of prior distribution in the ADT Bayesian evaluation method, a theoretical system and framework for analyzing prior uncertainty in ADT Bayesian evaluation based on DIC are established, providing a better foundation for the practical application of the ADT Bayesian evaluation method in engineering. [ABSTRACT FROM AUTHOR]

This paper, based on the idea of redundancy angle discretisation, proposes an obstacle avoidance method for the fixed tip pose trajectory of a seven degrees-of-freedom (7-DOF) modular manipulator. First, for the case in which a specific redundancy angle is given, the analytical solutions of the redundant manipulator left 6-DOF subchain are found. Then, through the discretisation of the redundancy angle, the concept of the self-motion space of the tip pose is proposed and is extended to the concept of the self-motion space of the trajectory. Based on this discrete space, a path-planning algorithm is proposed to help select the appropriate redundancy angles to obtain the collision-free solution set of the fixed Cartesian trajectory. However, due to the large fluctuation of the obtained path, a path optimisation method based on the path cost is proposed to smooth the path, and the continuous and collision-free solution set of the manipulator tip's trajectory is obtained. The method proposed in this paper provides a new thought for the problem of collision-free solution set planning for the Cartesian trajectory of a 7-DOF manipulator and it has great application potential in working environments with high accuracy requirements for the trajectory. [ABSTRACT FROM AUTHOR]

In this paper, an improved algorithm of wavelet packet-chaos model for life prediction of space relays based on volterra series is proposed. In the proposed method, the high and low frequency time sequence components of performance parameters are obtained by employing the improved wavelet packet transform to decompose the performance parameters of the relay into multiple scales. Then the optimization algorithm of parameters in volterra series is improved, and is used to construct a chaotic forecasting model for the high and low frequency time sequence components gained by the wavelet packet transform. At last, the chaotic forecasting results of the high and low frequency components are combined by taking the wavelet packet reconstruction approach, so as to predict the lifetime of the studied space relay. The algorithm can predict the life curve of the relay accurately and reflect the characteristics of the relay performance with sufficient accuracy. The proposed method is validated via a case study of a space relay. [ABSTRACT FROM AUTHOR]