Modified transform-based gamma correction for MRI tumor image denoising and segmentation by optimized histon-based elephant herding algorithm

Medical image processing is typically performed to diagnose a patient's brain tumor prior to surgery. In this study, a technique in denoising and segmentation was developed to improve medical image processing. The proposed approach employs multiple modules. In the first module, the noisy brain tumor image is transformed into multiple low- and high-pass tetrolet coefficients. In the second module, multiple low-pass tetrolet coefficients are applied through a modified transform-based gamma correction method. Generalized cross-validation is used on multiple high-pass tetrolet coefficients to obtain the best threshold value. In the third module, all enhanced coefficients are applied to the partial differential equation method. In the final module, the denoised image is applied to Atanassov's intuitionistic fuzzy set histon-based fuzzy clustering method with centroid optimization using an elephant herding method. Accordingly, the tumor part is segmented from the nontumor part in the magnetic resonance imaging brain images. The method was assessed in terms of peak signal-to-noise ratio, mean square error, specificity, sensitivity, and accuracy. The experimental results showed that the suggested method is superior to traditional methods.     

A Global Maximum Error Controller Based Nonlinearity Aware TPWL Method for Reducing Nonlinear Systems

Abstract

Model order reduction is a common practice to reduce large order systems so that their simulation and control become easy. Nonlinearity aware trajectory piecewise linear is a variation of trajectory piecewise linearization technique of order reduction that is used to reduce nonlinear systems. With this scheme, the reduced approximation of the system is generated by weighted sum of the linearized and reduced sub-models obtained at certain linearization points on the system trajectory. This scheme uses dynamically inspired weight assignment that makes the approximation nonlinearity aware. Just as weight assignment, the process of linearization points selection is also important for generating faithful approximations. This article uses a global maximum error controller based linearization points selection scheme according to which a state is chosen as a linearization point if the error between a current reduced model and the full order nonlinear system reaches a maximum value. A combination that not only selects linearization points based on an error controller but also assigns dynamic inspired weights is shown in this article. The proposed scheme generates approximations with higher accuracies. This is demonstrated by applying the proposed method to some benchmark nonlinear circuits including RC ladder network and inverter chain circuit and comparing the results with the conventional schemes.     

小尺度交通信号灯的检测与状态识别

交通信号灯识别包括检测和状态识别，在智能交通系统中发挥重要作用。基于YOLOv3算法提出了一种交通信号灯检测与状态识别模型。针对交通信号灯相较于交通场景中其他目标具有尺度小的特性进行了算法的设计：降低骨干网络的下采样倍率以增加小尺度目标的特征描述能力；通过增大特征图的尺度来改进多尺度特征融合；引入广义交并比作为检测任务的损失函数来改进目标边界框的回归效果。同时，根据交通信号灯本身的特性，使用颜色和形状约束的方法对信号灯进行状态识别和类别验证。最后在公开的Bosch交通信号灯数据集上和实际的城区道路进行了实验验证。实验结果表明，所提出的算法能够提升交通信号灯识别的精度和召回率，识别准确率可以达到90%左右。     

Hybrid make-to-stock and make-to-order systems: a taxonomic review

Production planning and control (PPC) systems that employ aspects from both make-to-order (MTO) and make-to-stock (MTS) production control are known as hybrid MTS/MTO systems. While both MTO and MTS separately have been studied extensively, their combined use has received less attention. However, the literature on this topic is growing and this paper shows that the review performed in this paper is an important addition to the field. We categorise relevant literature according to a novel taxonomy and show that hybrid MTS/MTO production control can be used in different contexts. In addition, an overview of the modelling techniques and methods used in these papers is provided. Based on the reviewed literature, relevant research questions and directions for future research are identified. Finally, it is shown that hybrid MTS/MTO production control is prevalent in practice by discussing research with industrial applications. The paper contains an overview of research on hybrid MTS/MTO production control to be used as reference for researchers active in the field, and provides managerial insights and directions for future research on this topic.     

State-space ARIMA for supply-chain forecasting

ARIMA is seldom used in supply chains in practice. There are several reasons, not the least of which is the small sample size of available data, which restricts the usage of the model. Keeping in mind this restriction, we discuss in this paper a state-space ARIMA model with a single source of error and show how it can be efficiently used in the supply-chain context, especially in cases when only two seasonal cycles of data are available. We propose a new order selection algorithm for the model and compare its performance with the conventional ARIMA on real data. We show that the proposed model performs well in terms of both accuracy and computational time in comparison with other ARIMA implementations, which makes it efficient in the supply-chain context.     

Images of a First‐Order Spin‐Reorientation Phase Transition in a Metallic Kagome Ferromagnet

First‐order phase transitions, where one phase replaces another by virtue of a simple crossing of free energies, are best known between solids, liquids, and vapors, but they also occur in a wide range of other contexts, including even elemental magnets. The key challenges are to establish whether a phase transition is indeed first order, and then to determine how the new phase emerges because this will determine thermodynamic and electronic properties. Here it is shown that both challenges are met for the spin reorientation transition in the topological metallic ferromagnet Fe₃Sn₂. The magnetometry and variable temperature magnetic force microscopy experiments reveal that, analogous to the liquid–gas transition in the temperature–pressure plane, this transition is centered on a first‐order line terminating in a critical end point in the field‐temperature plane. The nucleation and growth associated with the transition is directly imaged, indicating that the new phase emerges at the most convoluted magnetic domain walls for the high temperature phase and then moves to self‐organize at the domain centers of the high temperature phase. The dense domain patterns and phase coexistence imply a complex inhomogenous electronic structure, which can yield anomalous contributions to the electrical conductivity.     

Efficient geometrical parametrization for finite-volume-based reduced order methods

In this work, we present an approach for the efficient treatment of parametrized geometries in the context of proper orthogonal decomposition (POD)-Galerkin reduced order methods based on finite-volume full order approximations. On the contrary to what is normally done in the framework of finite-element reduced order methods, different geometries are not mapped to a common reference domain: the method relies on basis functions defined on an average deformed configuration and makes use of the discrete empirical interpolation method to handle together nonaffinity of the parametrization and nonlinearities. In the first numerical example, different mesh motion strategies, based on a Laplacian smoothing technique and on a radial basis function approach, are analyzed and compared on a heat transfer problem. Particular attention is devoted to the role of the nonorthogonal correction. In the second numerical example, the methodology is tested on a geometrically parametrized incompressible Navier-Stokes problem. In this case, the reduced order model is constructed following the same segregated approach used at the full order level.     

一种广义不可分支持向量机算法

针对标准的C-SVM(C-support vector machine)算法在处理很多实际分类问题时，对识别错误代价损失差异很大的极端情况表现出的局限性，提出一种通用的 广义支持向量机算法。根据识别错误后所付出的代价，可以把最优分类面向代价损失低的一方进行推移，留给代价损失高的一方更大的空间，提高其识别率，从而减小识别错误后带来的代价损失。该方法进一步提高了标准C SVM的适用性以及样本的正确识别率，将新算法应用到高分辨雷达距离像的识别中，实验证明,广义C-SVM能取得比传统C-SVM更好的识别效果。     

某校化工院一级学生实验室急救知识调查干预

了解化学与化工院大学生实验室安全和急救知识掌握现状,探讨提高大学生实验室安全和急救知识水平的有效途径。整群随机抽样法抽取湖南某校263名化工学院一年级学生,随机分为实验组和对照组,向实验组大学生发放实验室安全和急救知识速查手册并进行讲解,对照组则不进行任何干预,每组均于干预前和干预后发放实验室安全和急救知识问卷进行测试。结果表明:第一次调查发现2个组大约有12个题目(占总题目的 48%)的回答正确率低于60%;干预后,实验组有21个题目的回答正确率高于干预前。第一次调查时2个组有4个问题的回答正确率在80%以上,且实验组的干预无法提高其正确率。大学生实验室安全和急救知识水平亟待提高,进行有效的干预可以提高他们这方面的知识水平。