Quantum Logic Networks for Probabilistic Teleportation of an Arbitrary Three-Particle State

The scheme for probabilistic teleportation of an arbitrary three-particle state is proposed. By using single qubit gate and three two-qubit gates, efficient quantum logic networks for probabilistic teleportation of an arbitrary three-particle state are constructed.

Quantum Logic Network for Probabilistic Cloning Quantum States

We construct efficient quantum logic network for probabilistic cloning the quantum states used in imple mented tasks for which cloning provides some enhancement in performance.

Robust Feedback Set Stabilization of Logic Networks with State-Dependent Uncertain Switching and Control Constraints

This study investigates the robust feedback set stabilization of switched logic control networks(SLCNs)with state-dependent uncertain switching and control constraints.First,based on the properties of the semi-tensor product of matrices and the vector representation of logic,an SLCN with state-dependent uncertain switching and control constraints is expressed in algebraic form.Second,an input transformation and a switching model are constructed to transfer the original SLCN into one with a free control input and arbitrary switching.The equivalence between the set stabilizability of the original SLCN and that of the resulting SLCN is established.Based on such equivalence,the authors propose a necessary and sufficient condition for robust feedback set stabilizability.Finally,an example is presented to demonstrate the application of the results obtained.

Artificial Neural Network and Fuzzy Logic Based Techniques for Numerical Modeling and Prediction of Aluminum-5%Magnesium Alloy Doped with REM Neodymium

In this study, the mechanical properties of aluminum-5%magnesium doped with rare earth metal neodymium were evaluated. Fuzzy logic (FL) and artificial neural network (ANN) were used to model the mechanical properties of aluminum-5%magnesium (0-0.9 wt%) neodymium. The single input (SI) to the fuzzy logic and artificial neural network models was the percentage weight of neodymium, while the multiple outputs (MO) were average grain size, ultimate tensile strength, yield strength elongation and hardness. The fuzzy logic-based model showed more accurate prediction than the artificial neutral network-based model in terms of the correlation coefficient values (R).

Generating Markov Logic Networks Rulebase Based on Probabilistic Latent Semantics Analysis

Human Activity Recognition(HAR)has become a subject of concern and plays an important role in daily life.HAR uses sensor devices to collect user behavior data,obtain human activity information and identify them.Markov Logic Networks(MLN)are widely used in HAR as an effective combination of knowledge and data.MLN can solve the problems of complexity and uncertainty,and has good knowledge expression ability.However,MLN structure learning is relatively weak and requires a lot of computing and storage resources.Essentially,the MLN structure is derived from sensor data in the current scene.Assuming that the sensor data can be effectively sliced and the sliced data can be converted into semantic rules,MLN structure can be obtained.To this end,we propose a rulebase building scheme based on probabilistic latent semantic analysis to provide a semantic rulebase for MLN learning.Such a rulebase can reduce the time required for MLN structure learning.We apply the rulebase building scheme to single-person indoor activity recognition and prove that the scheme can effectively reduce the MLN learning time.In addition,we evaluate the parameters of the rulebase building scheme to check its stability.

A Neuro T-Norm Fuzzy Logic Based System

In this study, we are first examining well-known approach to improve fuzzy reasoning model (FRM) by use of the genetic-based learning mechanism [1]. Later we propose our alternative way to build FRM, which has significant precision advantages and does not require any adjustment/learning. We put together neuro-fuzzy system (NFS) to connect the set of exemplar input feature vectors (FV) with associated output label (target), both represented by their membership functions (MF). Next unknown FV would be classified by getting upper value of current output MF. After that the fuzzy truths for all MF upper values are maximized and the label of the winner is considered as the class of the input FV. We use the knowledge in the exemplar-label pairs directly with no training. It sets up automatically and then classifies all input FV from the same population as the exemplar FVs. We show that our approach statistically is almost twice as accurate, as well-known genetic-based learning mechanism FRM.

ARTIFICIAL NEURAL NETWORK AND FUZZY LOGIC CONTROLLER FOR GTAW MODELING AND CONTROL

An artificial neural network(ANN) and a self-adjusting fuzzy logiccontroller(FLC) for modeling and control of gas tungsten arc welding(GTAW) process are presented.The discussion is mainly focused on the modeling and control of the weld pool depth with ANN and theintelligent control for weld seam tracking with FLC. The proposed neural network can produce highlycomplex nonlinear multi-variable model of the GTAW process that offers the accurate prediction ofwelding penetration depth. A self-adjusting fuzzy controller used for seam tracking adjusts thecontrol parameters on-line automatically according to the tracking errors so that the torch positioncan be controlled accurately.

Implementation of Intelligent Network Service Logic

According to the features of Intelligent Network(IN)service logic,a method based ondata table to implement IN Service Logic is proposed.The method supports dynamic additionof IN service logic.

Intelligent Control of SIRES Using Neural Networks and Fuzzy Logic

Development of energy-resources-poor remote rural areas of the world has been discussed by many in the past. Harnessing locally available renewable energy resources as an environmentally friendly option is gaining momentum. Smart Integrated Renewable Energy Systems (SIRES) offer a resilient and economic path to “energize” the area and reach this goal. This paper discusses its intelligent control using neural networks and fuzzy logic.

面向学科交叉融合的综合性实训教学装置设计与教学实践

针对工程训练实训教学装置功能单一、缺乏多学科交叉与整合能力等问题,以多种特征的物料识别与分拣为应用背景,在综合应用PLC控制技术、传感器技术、气动控制与机电传动控制技术的基础上,结合数字图像处理与卷积神经网络技术,基于树莓派与Jetson嵌入式计算平台,自主研制了一种物料特征识别与分拣综合实训教学装置。与现有同类实训装置相比,该自制实训装置具有体积小,功能多和成本低的优势。实践教学表明,将传统PLC控制与人工智能技术元素有机结合,以及将课内教学延伸至创客活动和学生第二课堂,能有效提升学生的工程认知水平和工程实践能力,体现多学科交叉、多技术融合的高等工程教育特征。

创业网络从何而来?——决策逻辑视角下创业者结网研究回顾与展望

创业网络对于新创企业的存活和发展而言意义重大。伴随网络能动观的提出,创业网络研究开始关注创业者结网(Entrepreneurial Networking),探讨“创业网络从何而来”的问题,即创业者如何建立和发展创业网络。然而,既有研究未能清晰界定创业者结网的概念内涵及其背后的决策逻辑,对于创业网络来源多样性及其原因的体系化研究框架亟待整合。基于文献回顾,首先本文结合目标与行动先后顺序的不同理论假设,明晰创业者结网的概念内涵与策略类型;其次,梳理创业者“决策逻辑-结网模式”的逻辑链条,系统归纳创业网络“搜集”“汇集”“搜集+汇集”三条来源路径,并引入不确定性这一关键条件,从区域、企业和个体三个层次解构不同路径的适用边界;最后,从细化创业者结网概念、探索数字组织场景新议题、拓展研究方法三个角度提出未来研究方向。本文通过刻画创业者结网研究的“认知-行为”路径框架,为拓展能动观视角下创业网络来源研究以及推动创业网络微观机制研究提供了理论借鉴。

基于模糊逻辑的物联网流量攻击检测技术综述

物联网越来越多地出现在日常活动中,将我们周围多样化的物理设备连接到互联网,奠定了智慧城市、电子健康、精准农业等应用的基础。随着物联网应用的迅速普及,针对这类设备和服务的网络攻击数量也有所增加,且这些攻击具有不精确性和不确定性,使得对其进行正确检测和识别更加困难。为了应对上述挑战,学者们引入了基于模糊逻辑的攻击检测框架,在各种操作步骤中结合不同的模糊技术,以便在数据不准确和不确定时更精确地检测网络攻击。文中首先对物联网的安全性进行了详细的探讨,如其应对的安全挑战、所需的安全要求、面临的攻击类型等;其次对入侵检测系统(Intrusion Detection Systems,IDS)进行了描述,进而简述了物联网中IDS的基础框架;然后阐述了模糊逻辑的技术原理,分析了将其应用在流量攻击检测中的合理性;接着比较了各种基于不同技术的流量攻击检测方案,以说明它们在该领域的性能和重要性;最后总结了本文的主要工作,指出了未来的研究方向,为该领域的研究者提供了新的视角,以更好地应对不断升级的网络攻击。

“转译”与“逃逸”:中国媒体融合实践聚簇中的行动者网络

运用行动者网络理论(ANT),分析中国媒体融合实践中隐含的知识生产逻辑及其“实验”特性,并从知识生产与新闻生产的角度对中国媒体融合这一混杂的现象进行阐释,引出ANT分析的必要性。通过对媒体融合过程中人类行动者的追随及其“转译”行为的分析,揭示了中国媒体融合行动者网络的复杂性和虚弱性。同时,以技术等因素为例,对媒体融合聚簇形成过程中的非人行动者及其如何被人类行动者“转译”和如何“转译”人类行动者进行分析,指出技术与社会在媒体融合实践中是互嵌的,部分行动者的存在有着不同意义。分析发现,在中国的媒体融合行动者网络中,由于主要行动者无法形成“强制通过点”,未来的媒体融合探索还将持续很长一段时间。

光温因子驱动的园艺作物叶龄模型模拟精度比较

为了提高光温因子驱动的园艺作物通用性叶龄模拟模型的模拟精度,以黄瓜、芹菜、菠菜、小香芹、郁金香和茶叶为供试材料,进行了7年(2016—2022年)的分期播种试验,依据作物生长发育与关键气象因子(辐射和温度)的关系,采用4类建模方法(温差法、积温法、生理发育时间法和辐热积法)构建了园艺作物叶龄模拟模型,并以6种方式(平均值、最值均值、中值、逐步回归、BP神经网络和Elman神经网络)和2种集成逻辑(直接和分步)集成模拟结果,最终优化模型模拟精度。结果表明:1)2种集成逻辑下模型模拟精度均较高,且分步集成逻辑优于直接集成逻辑,平均绝对误差(mean absolute error,MAE)差值为0.31 d,平均相对误差(mean relative error,MRE)差值为0.33%,均方根误差(root mean square error,RMSE)差值为0.40 d,归一化均方根误差(normalized root mean square error,NRMSE)差值为0.46%;2)2种集成逻辑下模型最优时间尺度为逐时尺度,最优作物类型为茶叶,最优建模方法为Elman神经网络集成模拟模型。研究结果可为园艺作物智慧生产管理和可视化提供理论依据和技术支撑。

逻辑回归模型下激光传感器网络安全态势感知

为了分析不同威胁传播对激光传感器网络系统的影响,准确且全面评估系统的安全性,提出一种逻辑回归模型下激光传感器网络安全态势感知方法。将邻域粗糙集和逻辑回归模型结合,提取激光传感器网络安全态势要素。通过分析获取其不同要素的非时间序列特点,采用径向基函数(Radial Basis Function,RBF)神经网络获取网络安全态势值的非线性映射关系,构建激光传感器网络安全态势感知模型,引入混合递阶遗传算法对模型求解处理,实现网络安全态势感知。实验结果表明,所提方法获取激光传感器网络安全态势精度为96.4%,其安全态势值与实际值的误差保持在-3~3,可以获取精准的激光传感器网络安全态势变化情况,表明该方法具有一定的应用价值。

一阶逻辑中基于treelet图神经网络的前提选择

前提选择是解决自动定理证明器面对大规模问题时性能降低的有效方法。当前面向一阶逻辑中前提选择的主流图神经网络忽略了逻辑公式图内部的节点顺序信息。针对此问题,将一种面向高阶逻辑公式的保序方法拓展到一阶逻辑中,并提出了一种基于treelet的图神经网络模型。该模型在信息聚合时一部分聚合中心节点的父、子节点信息,另一部分聚合节点顺序信息。实验分析表明:基于treelet的图神经网络模型在前提选择任务中比最优的主流图神经网络模型的分类准确率提高了约2%。

消费异化视域下“饭圈文化”失范的批判与应对

“饭圈文化”是近年来流行于青少年粉丝群体的网络亚文化现象,一方面,“饭圈文化”给社会带来了正能量,在明星榜样的带动下为社会做出了正面贡献;另一方面,“饭圈”失范也给社会造成了巨大的负面影响。这一负面影响主要表现为,在资本逻辑的推动下,“饭圈文化”迅速发展壮大,但随着“饭圈”乱象的频发,“饭圈”失范引发了人们的广泛关注并带来系列批判与反思。从消费主义的视角出发,“饭圈文化”失范有着多重的消费逻辑,“饭圈文化”中的流量为王现象体现了流量消费逻辑,对粉丝群体的情感操纵则反映了其情感消费逻辑,而与商品的深度捆绑则呈现了商品消费逻辑。究其根源,资本逻辑是“饭圈文化”失范的根本动因,消费价值观异化、文化消费生态异化、消费心理异化等现实状态既是饭圈文化的生成动因,也是其现实后果。“饭圈文化”给青少年群体造成了严重的危害,不仅影响着他们的消费价值观,导致其消费心理的异化,而且对整个社会的消费生态都带来了负面影响。为此,不仅要规制资本逐利逻辑,完善市场消费监管机制,促进文化消费的健康有序发展,而且要积极培育民众的正确消费价值观,推动网络亚文化的良性发展,同时还要促进大众积极健康消费心态的养成,促进理性平和社会心态的广泛生成。

基于虚拟网络的无线网络应用

在基于虚拟网络技术的基础上进行无线网络组网,可以将一个传统的独立无线物理网络逻辑划分成不同平面的网络,每个网络平面可以根据企业的具体业务需求实现不同的网络功能,具有良好的扩展性和灵活性,支持跨地域组网,同时降低了企业无线网络的建设成本。多平面的网络相对独立,实现了不同业务之间的安全隔离,大大提高了企业无线网络的安全性。