Leakage-free optimization of micro herringbone grooved journal bearings with a virtual node method

Purpose This study aims to develop a new method to treat the numerical singularity at the critical nodes of two skew coordinates, and optimize the leakage of micro herringbone grooved journal bearings (MHGJBs) with this method. Design/methodology/approach A side leakage numerical algorithm is proposed by using the skew meshes with a virtual node (SMVN) method to evaluate the effects of groove angle, bank/groove ratio, groove depth and groove number on load capacity, friction and side leakage of MHGJB. Findings The SMVN method is effective in treating the numerical singularity at the critical nodes of two skew coordinates. Besides, a group of optimized parameters of micro herringbone groove is obtained which can not only minimize the side leakage but also improve the load capacity and friction force. Originality/value A virtual node method was proposed, which can significantly improve the calculation accuracy in the side leakage model.

Multi-Modeshape Reservoir Computing Using a Continuous MEMS Microbeam

Delay-based Reservoir computing (RC) offers great potential in time-series problems, especially when applied in hardware due to its low computational power and its compact nature. However, this approach suffers from a large computational delay because of the serial probing of virtual nodes. To address this disadvantage, this paper presents the use of a continuous MEMS arch for Delay-based RC. This novel approach reduces the computational delay by using fewer virtual nodes through maintaining sufficient virtual node coupling and nonlinear complexity. As a demonstration, we show that a single MEMS arch is capable of performing a binary waveform classification task of a multi-frequency square-and-triangle waveform problem with a success rate > 96% using only 10 virtual nodes compared to 40 virtual nodes in a typical implementation. The reduction in the number of virtual neurons is achieved by biasing the MEMS device using an AC source around its second modeshape.

Colocalized Sensing and Intelligent Computing in Micro-Sensors

This work presents an approach to delay-based reservoir computing (RC) at the sensor level without input modulation. It employs a time-multiplexed bias to maintain transience while utilizing either an electrical signal or an environmental signal (such as acceleration) as an unmodulated input signal. The proposed approach enables RC carried out by sufficiently nonlinear sensory elements, as we demonstrate using a single electrostatically actuated microelectromechanical system (MEMS) device. The MEMS sensor can perform colocalized sensing and computing with fewer electronics than traditional RC elements at the RC input (such as analog-to-digital and digital-to-analog converters). The performance of the MEMS RC is evaluated experimentally using a simple classification task, in which the MEMS device differentiates between the profiles of two signal waveforms. The signal waveforms are chosen to be either electrical waveforms or acceleration waveforms. The classification accuracy of the presented MEMS RC scheme is found to be over 99%. Furthermore, the scheme is found to enable flexible virtual node probing rates, allowing for up to 4× slower probing rates, which relaxes the requirements on the system for reservoir signal sampling. Finally, our experiments show a noise-resistance capability for our MEMS RC scheme.

Enhancing Fault Tolerance of Cloud Nodes using Replication Techniques

The cloud technologies are gaining boom in the field of information technology. But on the same side cloud computing sometimes results in failures. These failures demand more reliable frameworks with high availability of computers acting as nodes. The request made by the user is replicated and sent to various VMs. If one of the VMs fail, the other can respond to increase the reliability. A lot of research has been done and being carried out to suggest various schemes for fault tolerance thus increasing the reliability. Earlier schemes focus on only one way of dealing with faults but the scheme proposed by the the author in this paper presents an adaptive scheme that deals with the issues related to fault tolerance in various cloud infrastructure. The projected scheme uses adaptive behavior during the selection of replication and fine-grained checkpointing methods for attaining a reliable cloud infrastructure that can handle different client requirements. In addition to it the algorithm also determines the best suited fault tolerance method for every designated virtual node. Zheng, Zhou,. Lyu and I. King (2012).

A Multi Platform for Utility using open FMBTM Reference Architecture: Chalenges and Lessons Learned

The exponential growth of smart micro grids is making centralized control unmanageable. Data generated by grid-edge devices are also inaccessible due to the installation of private micro grids with proprietary communication protocols. The OpenFMBTM reference architecture solves this interoperability issue and eases the manageability of huge data by creating a virtual node that would allow exchange information between field devices with the use of publish/subscribe paradigm. However, the OpenFMBTM framework is yet to be adopted by industries but researches related to the implementation of this framework is being conducted with the aim to find out the cost and reliability on performance issues such as accuracy, scalability and security. Smart Grid Interoperability Panel (SGIP) provided a live demonstration of OpenFMBTM framework at DistribuTECH conference. DistribuTECH demo provides a guideline to setup simulators deployed in a single Linux machine. This paper discusses about the simulation demo and lessons learned to further developing the project. The implemented demo focuses on the use of MQTT communication protocol for transport layer data transfer. The experiment uses the guidelines of the DistribuTECH demo and addresses the challenge of deploying the framework in real devices at industry level.

EPVNE: An Efficient Parallelizable Virtual Network Embedding Algorithm

Virtual network embedding (VNE) problem is a key issue in network virtualization technology, and much attention has been paid to the virtual network embedding. However, very little research work focuses on parallelized virtual network embedding problems which assumes that the substrate infrastructure supports parallel computing and allows one virtual node to be mapped to multiple substrate nodes. Based on the work of Liang and Zhang, we extend the well-known VNE to parallelizable virtual network embedding (PVNE) in this paper. Furthermore, to the best of our knowledge, we give the first formulation of the PVNE problem. A new heuristic algorithm named efficient parallelizable virtual network embedding (EPVNE) is proposed to reduce the cost of embedding the VN request and increase the VN request acceptance ratio. EPVNE is a two-stage mapping algorithm, which first performs node mapping and then performs link mapping. In the node mapping phase, we present a simple and efficient virtual node and physical node sorting formula and perform the virtual node mapping in order. When mapping virtual nodes, we map virtual nodes to physical nodes that just meet the CPU requirements. Substrate nodes with more CPU resources will be retained for subsequent virtual network mapping requests. In the link mapping phase, Dijkstra’s algorithm is used to find a substrate path for each virtual link. Finally, simulations are carried out and simulation results show that our algorithm performs better than the existing heuristic algorithms.