A modified teaching–learning-based optimisation algorithm for bi-objective re-entrant hybrid flowshop scheduling

In this paper, a modified teaching–learning-based optimisation (mTLBO) algorithm is proposed to solve the re-entrant hybrid flowshop scheduling problem (RHFSP) with the makespan and the total tardiness criteria. Based on the simple job-based representation, a novel decoding method named equivalent due date-based permutation schedule is proposed to transfer an individual to a feasible schedule. At each generation, a number of superior individuals are selected as the teachers by the Pareto-based ranking phase. To enhance the exploitation ability in the promising area, the insertion-based local search is embedded in the search framework as the training phase for the TLBO. Due to the characteristics of the permutation-based discrete optimisation, the linear order crossover operator and the swap operator are adopted to imitate the interactions among the individuals in both the teaching phase and the learning phase. To store the non-dominated solutions explored during the search process, an external archive is used and updated when necessary. The influence of the parameter setting on the mTLBO in solving the RHFSP is investigated, and numerical tests with some benchmarking instances are carried out. The comparative results show that the proposed mTLBO outperforms the existing algorithms significantly.     

Hierarchical porous onion-shaped LiMn<Subscript>2</Subscript>O<Subscript>4</Subscript> as ultrahigh-rate cathode material for lithium ion batteries

Spinel LiMn₂O₄ is a widely utilized cathode material for Li-ion batteries. However, its applications are limited by its poor energy density and power density. Herein, a novel hierarchical porous onion-like LiMn₂O₄(LMO) was prepared to shorten the Li⁺ diffusion pathway with the presence of uniform pores and nanosized primary particles. The growth mechanism of the porous onion-like LiMn₂O₄ was analyzed to control the morphology and the crystal structure so that it forms a polyhedral crystal structure with reduced Mn dissolution. In addition, graphene was added to the cathode (LiMn₂O₄/graphene) to enhance the electronic conductivity. The synthesized LiMn₂O₄/graphene exhibited an ultrahigh-rate performance of 110.4 mAh·g^–1 at 50 C and an outstanding energy density at a high power density, maintaining 379.4 Wh·kg^–1 at 25,293 W·kg^–1. Besides, it shows durable stability, with only 0.02% decrease in the capacity per cycle at 10 C. Furthermore, the (LiMn₂O₄/graphene)/graphite full-cell exhibited a high discharge capacity. This work provides a promising method for the preparation of outstanding, integrated cathodes for potential applications in lithium ion batteries.

Open image in new window

     

Dynamically modulated intensity interrogation scheme using waveguide coupled surface plasmon resonance sensors

An electro-optically modulated intensity interrogation method based on tunable waveguide coupled surface plasmon resonance sensors has been proposed. It has been theoretically and experimentally demonstrated that the proposed scheme can enable sensitive measurement of measurand variations. By modulating the refractive index in the waveguide layer, this interrogation method yields modulated signal whose amplitude is related to measurand's refractive index. This amplitude modulated signal offers a higher signal to noise ratio and eliminates additive noise in the sensor system. A preliminary investigation using saline buffers with different NaCl concentrations shows a resolution of 2.3 × 10^?6 refractive index unit by our approach. Resolution can be controlled by the amplitude of the applied modulation voltage and can be further enhanced by optimizing the device structure or improving the electro-optical (E-O) coefficient of the E-O material. This approach is simple, stable, and promising for low-cost or multi-channel SPR biosensor applications.     

Robustness of fuzzy operators in environments with random perturbations

In many applications, such as intelligent decision systems, there are usually random perturbations caused by the constant changing of real situations, thus the analysis of the robustness with respect to random perturbations is practically important. In the side of fuzzy methods, a corresponding problem arises: will a small random perturbation of fuzzy input cause a big variance of fuzzy output? This paper study the robustness of fuzzy schemes in environments with random perturbations. It focuses on fuzzy algebraic operators, and proposes two methods to analyze their robustness in environments with random perturbations. The effectiveness and features of the methods are shown by simulations.     

Analysis and application of Bio-Inspired Multi-Net Security Model

With the rapid development of network technologies and deteriorating of network environment, traditional single-net security system cannot satisfy the security requirement. The excellent security performance of biological systems impels the bio-inspired network security theory to be a hot research area currently. Based on Bio-inspired Multidimensional Network Security Model we have put forward, we have advanced Bio-inspired Multi-Net Security system by implementing the functional distribution of different subnets of different transient states in Multi-Net Paralleling structure. Firstly, parameter estimation and modified algorithms of Hidden Markov Model are introduced to construct the mathematical mode of B-MNS; secondly, the integrated performance of our modified B-MNS has been tested and its simulation has been carried out. So the feasibility, validity and high efficiency of our model have been demonstrated theoretically, and practically.