Enhancing TRU burning and Am transmutation in Advanced Recycling Reactor

This paper presents about conceptual designs of Advanced Recycling Reactor (ARR) focusing on enhancement in transuranics (TRU) burning and americium (Am) transmutation. The design has been conducted in the context of the Global Nuclear Energy Partnership (GNEP) seeking to close nuclear fuel cycle in ways that reduce proliferation risks, reduce the nuclear waste in the US and further improve global energy security. This study strives to enhance the TRU burning and the Am transmutation, assuming the development of related technologies in this study, while the ARR based on mature technologies was designed in the previous study. It has followed that the provided TRU burning core is designed to burn TRU at 28 kg/TW_thh, by adding moderator pins of B₄C (Enriched B-11) and the Am transmutation core will be able to transmute Am at 34 kg/TW_thh, by locating Am blanket of AmN around the TRU burning core. It indicates that these concepts improve TRU burning by 40-50% than the previous core and can transmute Am effectively, keeping the void reactivity acceptable.     

Auto term window method and its parameter selection

In this paper, the parameter selection in Auto term window (ATW) method is discussed and analyzed in the theoretical and experimental analysis. ATW method can suppress cross terms in Wigner–Ville distribution (WVD) effectively and applied in rotary machinery feature extraction and fault diagnosis. According to the characteristic that the auto terms and cross terms have no correlation, the ATW method introduces a window function which covers the auto terms in the time–frequency domain to windowing the WVD results. Aimed at the two difficulties in this new method which are window function selection and threshold value determination, this paper discusses the parameters optimization process in detail. The suitable window function with good time–frequency distribution and energy accumulation has been selected and compared to some other existent approaches. The optimal parameters are determined in the following analysis and the simulation examples are used to prove the validation of the results.     

A novel ball bearing fault diagnosis approach based on auto term window method

A new bearing fault diagnosis method based on auto term window (ATW) method is proposed in this paper. Ball bearing is the foremost important and also much easier to be damaged component in the rotation machinery. Vibration signature analysis of machine components is a commonly fault detect technique employed in ball bearing systems. The new fault diagnosis method proposed in this paper is applied to extract fault features in bearing vibration signals. On the base of the Wigner–Ville distribution (WVD) analysis of the bearing signal, the ATW method can not only suppress cross terms effectively, but also strengthen the energy of the auto terms and enhance the feature extraction effect, which is important in the following fault diagnosis research. The ball bearing fault experiment results proved the effectiveness of the ATW method in the last section of this paper.     

An effective communication and computation model based on a hybridgraph-deeplearning approach for SIoT

Social Edge Service (SES) is an emerging mechanism in the Social Internet of Things (SIoT) orchestration for effective user-centric reliable communication and computation. The services are affected by active and/or passive attacks such as replay attacks, message tampering because of sharing the same spectrum, as well as inadequate trust measurement methods among intelligent devices (roadside units, mobile edge devices, servers) during computing and content-sharing. These issues lead to computation and communication overhead of servers and computation nodes. To address this issue, we propose the HybridgrAph-Deep-learning (HAD) approach in two stages for secure communication and computation. First, the Adaptive Trust Weight (ATW) model with relation-based feedback fusion analysis to estimate the fitness-priority of every node based on directed graph theory to detect malicious nodes and reduce computation and communication overhead. Second, a Quotient User-centric Coeval-Learning (QUCL) mechanism to formulate secure channel selection, and Nash equilibrium method for optimizing the communication to share data over edge devices. The simulation results confirm that our proposed approach has achieved effective communication and computation performance, and enhanced Social Edge Services (SES) reliability than state-of-the-art approaches.