APPLICATION OF FUZZY LOGIC IN WEIGHTED INFORMATION FUSION OF HAND GEOMETRY AND PALM PRINTS

Based on the theory of fuzzy logic, the method of obfuscating coefficient and reliability to fuse the information of hand geometry and palm prints for identity discrimination is proposed. The experiment proves that the method is useful and effective. Its identification rate is up to 90%, which is 20%-30% higher than that of using hand geometry or palm prints singly,thus it can be widely used in highly demanded security field, such as finance, entrance guard, etc.

Importance Analysis of a Multi-state System Based on Direct Partial Logic Derivatives and Multi-valued Decision Diagrams

Importance analysis quantifies the critical degree of individual component. Compared with the traditional binary state system,importance analysis of the multi-state system is more aligned with the practice. Because the multi-valued decision diagram( MDD) can reflect the relationship between the components and the system state bilaterally, it was introduced into the reliability calculation of the multi-state system( MSS). The building method,simplified criteria,and path search and probability algorithm of MSS structure function MDD were given,and the reliability of the system was calculated. The computing methods of importance based on MDD and direct partial logic derivatives( DPLD) were presented. The diesel engine fuel supply system was taken as an example to illustrate the proposed method. The results show that not only the probability of the system in each state can be easily obtained,but also the influence degree of each component and its state on the system reliability can be obtained,which is conducive to the condition monitoring and structure optimization of the system.

Application of fuzzy logic in weighted information of fusion fingerprints and palm prints

It is a developing job to distinguish identifications with information fusion of fingerprints and palm prints. It is also a very effective way to resolve the problem of low identification rate and low stability of single biology characteristic identification. Based on the theory of fuzzy logic theory, we bring out the method of obfuscating weigh coefficient and reliability to fuse the information of fingerprints and palm prints to realize high identification rate. The experiment proves the feasibility and effectiveness of this method and the identification rate can be more than 90%, which contributes useful experience to the research of identification using biology characteristics.

A weighted averaging method for signal probability of logic circuit combined with reconvergent fan-out structures

By analyzing the structures of circuits,a novel approach for signal probability estimation of very large-scale integration(VLSI)based on the improved weighted averaging algorithm(IWAA)is proposed.Considering the failure probability of the gate,first,the first reconvergent fan-ins corresponding to the reconvergent fan-outs were identified to locate the important signal correlation nodes based on the principle of homologous signal convergence.Secondly,the reconvergent fan-in nodes of the multiple reconverging structure in the circuit were identified by the sensitization path to determine the interference sources to the signal probability calculation.Then,the weighted signal probability was calculated by combining the weighted average approach to correct the signal probability.Finally,the reconvergent fan-out was quantified by the mixed-calculation strategy of signal probability to reduce the impact of multiple reconvergent fan-outs on the accuracy.Simulation results on ISCAS85 benchmarks circuits show that the proposed method has approximate linear time-space consumption with the increase in the number of the gate,and its accuracy is 4.2%higher than that of the IWAA.

Dombi-Normalized Weighted Bonferroni Mean Operators with Novel Multiple-Valued Complex Neutrosophic Uncertain Linguistic Sets and Their Application in Decision Making

Although fuzzy set concepts have evolved,neutrosophic sets are attractingmore attention due to the greater power of the structure of neutrosophic sets.The ability to account for components that are true,false or neither true nor false is useful in the resolution of real-life problems.However,simultaneous variations render neutrosophic sets unsuitable in specific circumstances.To enable the management of these sorts of issues,we combine the principle of multi-valued neutrosophic uncertain linguistic sets and complex fuzzy sets to develop the principle of multivalued complex neutrosophic uncertain linguistic sets.Multi-valued complex neutrosophic uncertain linguistic sets can contain grades of truth,abstinence,and falsity,and uncertain linguistic terms,which are expressed as complex numbers whose real and imaginary parts are limited to the unit interval.Some important Dombi laws are elaborated along with Bonferroni mean operators,which offer a flexible general structure with modifiable factors.Bonferroni means aggregation operators perform a significant role in conveying the magnitude level of options and characteristics.To determine relationships among any number of attributes,we develop multi-valued complex neutrosophic uncertain linguistic Dombi-normalized weighted Bonferroni mean operators and discuss their important properties with some special cases.By using these laws,we can deploy themulti-attribute decisionmaking(MADM)technique using the novel principle of multi-valued complex neutrosophic uncertain linguistic sets.To determine the power and flexibility of the elaborated approach,we resolve some numerical examples based on the proposed operator.Finally,the work is validated with the help of comparative analysis,a discussion of its advantages,and geometric expressions of the elaborated theories.

Real-Time Update of Sequence Placement Logic for High Arch Dams Based on Evidence Weight Discount

Sequence placement logic plays a significant role in construction simulation of high arch dams and directly affects the simulation process and results. To establish a sequence logic for dam block placement, the construction scheme, real-time construction process, and random factors of the site all need to be considered in detail. There are few studies available currently that take all these factors into consideration. To address this problem, a real-time update of sequence placement logic for high arch dams based on evidence weight discount is proposed in this study. First, the subjective weight of the dam block sequence priority criteria is built using a consistent matrix method based on the construction scheme. Second, using evidence theory, dynamic objective weight of the priority criteria and basic probability assignment is built. Finally, using a weight self-adaptive adjustment method and comprehensive evidence discounting, the placing probabilities of different dam blocks are obtained. A case study indicates that this method can realize real-time update of sequence placement logic. © 2017, Tianjin University and Springer-Verlag Berlin Heidelberg.

A Novel Design of Octal-Valued Logic Full Adder Using Light Color State Model

Due to the demand of high computational speed for processing big data that requires complex data manipulations in a timely manner,the need for extending classical logic to construct new multi-valued optical models becomes a challenging and promising research area.This paper establishes a novel octal-valued logic design model with new optical gates construction based on the hypothesis of Light Color State Model to provide an efficient solution to the limitations of computational processing inherent in the electronics computing.We provide new mathematical definitions for both of the binary OR function and the PLUS operation in multi valued logic that is used as the basis of novel construction for the optical full adder model.Four case studies were used to assure the validity of the proposed adder.These cases proved that the proposed optical 8-valued logic models provide significantly more information to be packed within a single bit and therefore the abilities of data representation and processing is increased.

Distributive Quantum Logic： Controlled-Error Approach

The idea that approximate exactness is the most we can and should expect scientific theories to yield underlies the formation and application of the multi-valued logic of approximation discussed in this paper. In this logic, inexactness （measured by truth values） is controlled and minimized by means of uniquely designed deductions. We show how the notion of equality （including substitution of equals） is handled within this logic and we apply it to certain principles and interpretations of quantum theory.

Model Predictive Yaw Control Using Fuzzy-Deduced Weighting Factor for Large-Scale Wind Turbines

Yaw control system plays an important role in helping large-scale horizontal wind turbines capture the wind energy.To track the stochastic and fast-changing wind direction,the nacelle is rotated by the yaw control system.Therein,a difficulty consists in the variation speed of the wind direction much faster than the rotation speed of the nacelle.To deal with this difficulty,model predictive control has been recently proposed in the literature,in which the previewed wind direction is employed into the predictive model,and the estimated captured energy and yaw actuator usage are two contradictive objectives.Since the performance of the model predictive control strat-egy relies largely on the weighting factor that is designed to balance the two objectives,the weighting factor should be carefully selected.In this study,a fuzzy-deduced scheme is proposed to derive the weighting factor of the mod-el predictive yaw control.For the proposed fuzzy-deduced strategy,the variation degree and the increment of the wind direction during the predictive horizon are used as the inputs,and the weighting factor is the output,which is dynamically adjusted.The proposed model predictive yaw control is demonstrated by some simulations using real wind data and its performance is compared with the conventional model predictive control with thefixed weighting factor.Comparison results confirm the outweighing performance of the proposed control strategy over the conventional one.

自动驾驶仿真多逻辑场景综合评价方法

基于场景的仿真测试方法已成为解决自动驾驶汽车性能验证的核心思路,该方法将车辆连续驾驶过程进行分割获取非重复的独立场景片段,并在仿真环境中进行测试。与测试过程相贴合,本文提出了一种自动驾驶仿真多逻辑场景综合评价方法。首先建立自动驾驶汽车多逻辑场景综合评价方法,明确兼顾场景自身特征信息及仿真过程信息的场景权重分析流程;通过暴露度、失控度、危害度建立逻辑场景自身特征信息权重;建立包含仿真精度信息、要素种类信息、参数空间信息、离散步长信息的仿真过程信息权重;基于HighD数据集抽取前车制动、前车左侧切入、前车右侧切入场景信息,并基于本文提出的方法及基准算法测试结果计算场景权重,最终获取两种被测算法在多逻辑场景中的综合评价结果。

Design Technique of I^2L Circuits Based on Multi-Valued Logic

This paper proposes the use of the current signal to express logic values and establishes the theory of grounded current switches suitable for I2L circuits.Based on the advantage that current signals are easy to be added, the design technique of I2L circuits by means of the multi-valued current signal is proposed.It is shown that simpler structure of I2L circuits can be obtained with this technique.

基于模糊逻辑的空调系统广义预测控制参数整定

针对多输入多输出(multiple-input-multiple-output,MIMO)变风量空调系统(variable air volume,VAV),本文提出了一种基于模糊逻辑和事件触发的广义预测控制(generalized predictive control,GPC)参数整定方法。针对多变量VAV空调系统,基于输出斜率等新型模糊目标参数及高斯双边形隶属度函数建立模糊预测模型,提高了隶属函数拟合度以更贴切地反映系统当前时刻状态。利用麻雀智能算法(sparrow search algorithm,SSA)构建隶属度函数,对GPC控制器中的柔化因子和加权系数进行在线分段整定,有效提升了系统性能。此外,在参数整定过程中,引入事件触发机制(event-triggered mechanism,ETM),在保证控制性能的同时,避免了不必要的控制器采样与更新,降低了系统在线计算量并减少了能源消耗。最后通过VAV空调系统仿真实验验证,证明了本文提出方法的可行性和有效性。

制冷站双目标权重自适应非线性预测控制

针对传统制冷站控制系统易产生振荡,且无法实现系统性能整体优化的问题,本文提出一种制冷站非线性预测控制策略,优化目标函数设计为满足建筑冷量需求的同时,尽可能提高系统整体能效.为解决上述两个优化目标之间的矛盾关系,本文采用模糊逻辑设计了优化目标权重自适应模块,实时求取权重因子最优解;针对非线性系统在线优化求解困难问题,本文提出了基于神经网络的非线性滚动优化算法,采用神经网络作为反馈优化控制器,并将系统优化目标函数作为在线寻优性能指标,结合Euler-Lagrange方法和随机梯度下降法对控制器权值和阈值进行在线寻优,算法计算量小,占用存储空间适中,便于采用低成本的现场控制器实现制冷站预测控制.仿真实验结果表明,本文所提出的预测控制策略与PID控制相比,在未加入优化目标函数权重自适应模块情况下,系统平均能效比提高约32.5%;进行优化目标函数权重自适应寻优后,系统平均能效提高约39.43%.

CWA Formalizations in Multi-Valued Logics

To enhance the expressive power and the declarative ability of a deductive database, various CWA (Closed World Assumption) formalizations including the naive CWA, the generalized CWA and the careful CWA are extended to multi-valued logics. The basic idea is to embed logic formulas into some polynomial ring. The extensions can be applied in a uniform manner to any finitely multi-valued logics. Therefore they are also of computational significance.

Spectral representation of correspondence of multi-valued logical functions

In cryptology, it is an important topic to study the best affine approach of functions. The best affine approach of Boolean functions has been discussed in ref. [1] by using the Walsh spectrum, of which the key problem is how to represent the correspondence of Boolean functions by using Walsh spectrum. For the multi-valued logical functions so far, the spectral representation of their correspondence has not been presented yet. This let-

农林文旅康产业融合发展内在逻辑、发展水平及发展路径研究——以五台山为例

本研究旨在探讨山西省农林文旅康产业融合发展的逻辑,并以五台山风景名胜区为例,构建评价体系,采用熵权TOPSIS模型和耦合协调度模型对其2018—2022年的发展水平和耦合协调程度进行评价。结果表明:五台山以农林为切入、以山水为背景、以生态为根基、以文化为灵魂、以康养为载体、以旅游为纽带,新质生产力赋能产业融合;受疫情影响旅游接待人次和收入变化波动较大,2022年五台山农林文旅康产业融合C值为0.664,发展水平最高;从各个层面来看,经济层面>生态层面>社会层面>文化层面,还需重点关注社会和文化方面;2021年和2022年耦合协调程度均为良好协调。并提出着力产业提质、丰富产品业态、拓展营销模式、盘活存量资产、强化科技支撑和凝聚产业融合等发展优化路径。本研究为农林文旅康产业融合发展提供评价体系和科学依据。

基于多权重主观逻辑的联邦学习攻击防御机制

联邦学习作为一种分布式学习框架,可以在保证各客户端本地数据安全的前提下共同训练一个全局模型。但在联邦学习过程中,存在恶意的参与方会提交错误的更新来阻止模型收敛,或者通过投毒攻击使模型的拟合偏离正常方向。传统的主观逻辑防御机制考虑的是互动频率、互动时间及互相之间的影响,而忽略了多源数据对信誉评价结果的影响。针对此问题,提出一种基于多权重主观逻辑的联邦学习攻击防御机制。该机制通过Shapley值计算客户端的贡献度,并从可信度、贡献度和新鲜度3方面对联邦学习中客户端的信誉进行评价。同时,通过引入区块链技术存储参数进一步提高模型的安全性。实验结果表明,在多源数据下,该机制能够准确识别投毒攻击并进行防御,同时保留较好的模型性能。

加权模糊逻辑

文中提出了一种加权模糊逻辑推理模型，该模型描述了不确定性问题求解领域中比较普遍存在的“加权逻辑与”、“加权逻辑或”和“加权逻辑综合”等模糊逻辑关系，该推理模型在国家“八六三”高技术项目“吉林省玉米生产智能化信息技术示范工程”中进行了应用．文中同时指出对３种加权模糊逻辑之间的关系、与３种加权模糊逻辑运算相关的真值计算方法、以及３种真值计算方法之间的关系等应作进一步研究．

不确定性推理技术在医学诊断中的应用研究

介绍了不确定性推理技术中几种主要方法,根据不同医学诊断的特点,设计了基于动态模糊逻辑和加权模糊逻辑相结合的计算机辅助医学诊断系统,最后通过一个实例说明了该方法的有效性。

导航传感器故障检测策略

为了提高故障检测灵敏度 ,分析了通常的故障检测中难以兼顾虚警概率和漏检概率的原因和船舶组合导航系统故障检测过程中噪声与故障对检测量的影响 .根据船舶组合导航系统故障检测的特点 ,提出了以模糊逻辑和指数加权平均处理检测量和阈值的故障检测策略 ,并用实船航行数据仿真 .该策略对未知输入等干扰不敏感而对故障敏感 ,且可根据故障的大小自动调节检测时间的长短 .对不易检测的小故障 ,自动延长检测时间以利用更多的信息从而提高检测的正确率 ;对于较大的故障 ,自动缩短检测时间从而减少检测延时和累积误差 。