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.

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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.     

单磁悬浮系统的纵向解耦控制研究

磁悬浮系统是复杂的强耦合系统,研究中未充分考虑轨道的弹性形变,会造成磁浮列车和轨道之间产生耦合振动的现象,而考虑这一因素对系统的影响无疑增加了研究的难度;为了便于问题的求解,采用非线性系统理论解耦的方法,对单电磁铁悬浮系统进行纵向解耦控制,并对解耦后的子系统进行极点配置使其满足要求的性能指标;仿真结果表明该方法有效地使复杂问题简单化,并且能够实现系统的全局稳定和良好的动态性能.     

Analysis by data mining in the emergency medicine triage database at a Taiwanese regional hospital

“Emergency medicine” is the front line of medical service a hospital provides; also it is the department people seek medical care from immediately after an emergency happens. The statistics by the Department of Health, Executive Yuan, indicate that over years, the number of people at the emergency department has been increasing. The US has introduced and practiced the triage system in the emergency medicine in 1960, whereby to aid the emergency department in allocating the patients, to give them appropriate medical care by the fast decision of the nurses and doctors in case of the patients’ seriousness through their judgment.This study takes on the knowledge contained in the massive data of unknown characteristics in the triage database at a Taiwanese regional hospital, using the cluster analysis and the rough set theory as tools for data mining to extract, with the analysis software ROSE2 (Rough Sets Data Explorer) and through rule induction technique, the imprecise, uncertain and vague information of rules from the massive database, and builds the model that is capable of simplifying massive data while maintaining the accuracy in classifying rules. After analyzing and evaluating the knowledge obtained from relevant mining in the hospitals past medical data for the consumption of emergency medical resources, this thesis proposes suggestions as reference for the hospitals in subsequent elevation of medical quality and decrease in operative costs.