Identification and Removal of Non-meteorological Echoes in Dual-polarization Radar Data Based on a Fuzzy Logic Algorithm

A major issue in radar quantitative precipitation estimation is the contamination of radar echoes by non-meteorological targets such as ground clutter,chaff,clear air echoes etc.In this study,a fuzzy logic algorithm for the identification of non-meteorological echoes is developed using optimized membership functions and weights for the dual-polarization radar located at Mount Sobaek.For selected precipitation and non-meteorological events,the characteristics of the precipitation and non-meteorological echo are derived by the probability density functions of five fuzzy parameters as functions of reflectivity values.The membership functions and weights are then determined by these density functions.Finally,the nonmeteorological echoes are identified by combining the membership functions and weights.The performance is qualitatively evaluated by long-term rain accumulation.The detection accuracy of the fuzzy logic algorithm is calculated using the probability of detection（POD）,false alarm rate（FAR）,and clutter–signal ratio（CSR）.In addition,the issues in using filtered dual-polarization data are alleviated.

Application of Fuzzy Logic Algorithm to Single-Molecule Force Spectroscopy of the Streptavidin-Biotin System

The rupture force of the streptavidin-biotin complex was investigated using atomic force microscopy (AFM). The most frequently observed rupture force (MFOF), which is essential for the evaluation of the potential landscape, was evaluated by processing 22,500 force curves using two methods. One method is a conventional method, which is usually built in commercial AFM systems, i.e., difference between the baseline value and the minimum force value in the force curve. The other is a detection of rupture events based on a fuzzy logic algorithm to detect the rupture event from analyzing the shape of the force curves. Our statistical analysis revealed that the conventional method exhibited a significant artifact, which is the increase in the population of small forces comparable to thermal noise of cantilevers, resulting in a smaller MFOF. Based on this finding, we discuss the choice of a method and its effecton the illustrated potential landscapes of ligand-receptor complexes.

High-Order Two-Dimension Cluster Competitive Activation Mechanisms Used for Performing Symbolic Logic Algorithms of Problem Solving

This paper presents a neural network approach, based on high-order two-dimension temporal and dynamically clustering competitive activation mecha-nisms, to implement parallel searching algorithm and many other symbolic logicalgorithms. This approach is superior in many respects to both the commonsequential algorithms of symbolic logic and the common neura.l network usedfor optimization problems. Simulations of problem solving examples prove theeffectiveness of the approach.

A fuzzy logic algorithm derived mechatronic concept prototype for crop damage avoidance during eco-friendly eradication of intra-row weeds

Crop damage during the intra-row weed eradiation is one of the biggest challenges in intercultural agricultural operations.Several available mechanical systems provide effective weeding but result in excess crop damage.On the other hand,chemical based systems have been raising serious environmental and food concerns.This study presents development of a cost-effectivemechatronic prototype for intra-rowweeding operation.The primary focus was on incurring minimal crop damage.The system integrates time of flight and inductive sensing into fuzzy logic algorithm for electronic control of a four-bar linkage mechanism(FBLM).The crank of FBLM was connected to the vertical rotary weed control shaft with weeding blades.The crop sensing triggers the electronic control to laterally shift the control shaft away from crop,proportional to the forward speed and soil conditions.The developed algorithm incorporates varied conditions of soil,forward speed,and plant spacing to calculate dynamic lateral shift speed(SRPM).The prototype was evaluated to determine the relationships between the operating conditions and electronic control parameters.Moreover,the plant damage was assessed under varied conditions of plant spacing,forward speeds,soil cone index,operational depth and electronic control parameters.The derived SRPM was established as the ultimate governing factor for avoiding crop damage that varied significantlywith electronic response time and soil strength(P<0.05).Plant damage increased significantly under higher forward speeds and lower plant spacing(P<0.05).Preliminary field evaluation of the developed prototype showed a significant potential of this system for effective control on weeds(>65%)and crop damage(<25%).

Fuzzy logic algorithm of hovering control for the quadrotor unmanned aerial system

Purpose–The purpose of this paper is to present a control strategy which uses two independent PID controllers to realize the hovering control for unmanned aerial systems(UASs).In addition,the aim of using two PID controller is to achieve the position control and velocity control simultaneously.Design/methodology/approach–The dynamic of the UASs is mathematically modeled.One PID controller is used for position tracking control,while the other is selected for the vertical component of velocity tracking control.Meanwhile,fuzzy logic algorithm is presented to use the actual horizontal component of velocity to compute the desired position.Findings–Based on this fuzzy logic algorithm,the control error of the horizontal component of velocity tracking control is narrowed gradually to be zero.The results show that the fuzzy logic algorithm can make the UASs hover still in the air and vertical to the ground.Social implications–The acquired results are based on simulation not experiment.Originality/value–This is the first study to use two independent PID controllers to realize stable hovering control for UAS.It is also the first to use the velocity of the UAS to calculate the desired position.

Type-2 Fuzzy Logic Controllers Based Genetic Algorithm for the Position Control of DC Motor

Type-2 fuzzy logic systems have recently been utilized in many control processes due to their ability to model uncertainty. This research article proposes the position control of (DC) motor. The proposed algorithm of this article lies in the application of a genetic algorithm interval type-2 fuzzy logic controller (GAIT2FLC) in the design of fuzzy controller for the position control of DC Motor. The entire system has been modeled using MATLAB R11a. The performance of the proposed GAIT2FLC is compared with that of its corresponding conventional genetic algorithm type-1 FLC in terms of several performance measures such as rise time, peak overshoot, settling time, integral absolute error (IAE) and integral of time multiplied absolute error (ITAE) and in each case, the proposed scheme shows improved performance over its conventional counterpart. Extensive simulation studies are conducted to compare the response of the given system with the conventional genetic algorithm type-1 fuzzy controller to the response given with the proposed GAIT2FLC scheme.

Hybrid Genetic Algorithms with Fuzzy Logic Controller

In this paper, a new implementation of genetic algorithms (GAs) is developed for the machine scheduling problem, which is abundant among the modern manufacturing systems. The performance measure of early and tardy completion of jobs is very natural as one's aim, which is usually to minimize simultaneously both earliness and tardiness of all jobs. As the problem is NP-hard and no effective algorithms exist, we propose a hybrid genetic algorithms approach to deal with it. We adjust the crossover and mutation probabilities by fuzzy logic controller whereas the hybrid genetic algorithm does not require preliminary experiments to determine probabilities for genetic operators. The experimental results show the effectiveness of the GAs method proposed in the paper.

基于模糊逻辑的引信检测机械手视觉定位方法

为了提高引信出厂检测的质量和效率,该文设计了一种引信自动检测系统。针对机械手与检测装置的自主对准和对接的问题,提出了一种基于模糊逻辑的引信与检测装置自主对准控制算法。以引信和检测装置的相对位置和偏差角作为控制变量,设计了二维模糊控制器。通过对控制器的动态调整,可以输出最优运动参数。与传统的引信检测方法相比,该算法进一步提高了引信检测的效率和定位精度。开发了引信全流程自动检测系统的物理样机,并进行了机器人现场操作试验,验证了所提出的视觉定位算法的有效性和工程实用性。

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。

基于改进北方苍鹰优化随机配置网络的网络流量预测模型

网络流量预测作为一种关键技术,能帮助实现网络资源的合理分配、优化网络性能以及提供高效的网络服务。随着网络环境的演变和发展,网络流量的多样性和复杂性增加,为了提高网络流量的预测精度,提出了一种基于改进北方苍鹰优化随机配置网络(CNGO-SCN)的网络流量预测模型。随机配置网络作为一种具有监督机制的增量式模型,在解决大规模数据回归和预测问题方面具有良好的优势。但是,一些超参数的选择影响了随机配置网络的准确性。针对这一问题,利用北方苍鹰算法对影响随机配置网络性能的正则化参数和比例因子进行优化,得到最佳数值。而北方苍鹰算法由于初始种群的随机分布导致种群个体质量不佳,因此引入混沌逻辑映射提升初始解的质量。将优化后的模型应用于英国学术网、欧洲某城市核心网网络流量数据集和合作企业搭建的网络协同制造云平台交换机接口的真实流量数据集,并与多种神经网络模型进行对比,以验证所提模型的网络流量预测能力。实验结果表明,该模型对比其他神经网络模型具有更高的预测精度,在实际应用场景中处理复杂数据时具备更加优秀的预测能力,该模型的预测误差下降了0.9%~99.7%。

数据驱动的水泵水轮机综合特性曲线优化表达

基于一水泵水轮机的水轮机工况综合特性曲线,文章旨在提出一种数据驱动的三维综合特性曲线表达方法,并提出遗传模糊算法用于寻找最优运行点,基于文章所提出的方法,单位流量、单位转速、效率、导叶开度等关系得到了直观清晰的表达,最优运行范围也得到了有效确认,对电站高效运行、提高经济效益具有参考意义。

从网络社会到平台社会:算法风险的媒介化治理逻辑演进

网络社会和平台社会诠释了媒介化作为元过程嵌入社会结构并产生了构型力量。从传统媒介逻辑到网络媒介逻辑再到算法逻辑,彰显了网络化到平台化的媒介逻辑演进。在此基础上深度挖掘媒介化治理概念体系和实践范畴,考察平台社会算法风险的媒介化治理过程,阐释媒介化治理操作系统如何嵌入算法风险治理实践机制具有重要意义。本研究从行动、过程、资源和效果四个维度建构起媒介化治理的逻辑,分别是以主客体间性重构人机互动,构建媒介化治理的行动与交往秩序;以可编程嵌入风险自治实践,凸显媒介化治理的过程自主意识;以协商性激励多元主体共治,作为媒介化治理资源及权力分配依据;倡导以公共价值激发算法“向善”,以此作为媒介化治理的效果目标。

自动泊车系统规划与控制方法研究

为了对泊车过程中的车辆进行跟踪控制,基于车辆动力学模型,研究设计了一种线性二次型控制器,使用B-spline曲线规划了一条曲率连续的泊车路径,针对控制器设计过程中权重系数选择困难,引入模糊逻辑算法对控制器进行优化处理,节省了人工成本,然而模糊逻辑算法的计算是实时的,且不同速度下车辆状态不同,因此将基于Frobenius范数的更新策略加入程序计算中以降低计算量,利用MATLAB/Carsim联合仿真验证了改进后控制系统的运算时间降为0.2 ms,实时性提升92%,适应度提升30%.

数学方法在计算机算法中的应用实践

在现代社会不断发展的过程中,信息技术和计算机技术也在不断发展,计算机算法也被广泛应用在现实生活中。人们在使用计算机编程时,要求设计计算机算法,是将数学方法作为基础实现的。在社会不断发展的过程中,数学方法中也添加了逻辑推算思想和方法。所以,针对数学方法应用在计算机算法中的实践进行分析,从而为计算机算法的发展提供借鉴。

面向数字化未来的“计算思维”

在不断变化、迅猛发展的数字世界,越来越多的人认为“计算思维”应该成为21世纪每个人的基本技能,而不仅仅是从事计算机工作的专业人士。如何认识“计算思维”和培养“计算思维”能力已重新引起国际教育界的关注。“计算思维”本身是人类科学思维的一个组成部分,其反映计算机科学家如何把人类解决问题的思维过程数字化,从而让计算机来解决复杂问题的思维过程或思维框架。当反思人类在计算机上得以实现的有效解决问题的程序、策略和工具的时候,无论从事何种职业,都能够使学习、工作和社会生活更加有效,更加成功。因此,当前的学校教育中有需要重新审视“计算思维”在青少年学生成长中的意义。基于此,从“计算思维”概念的诞生说起,探讨“计算思维”含义及与教育的联系,比较“计算思维”与“数学思维”的区别,并介绍欧盟等国际组织的义务教育“计算思维”培养策略。据此提出,“计算思维”之所以重新回到人们的视野,是因为借助由数字化有效执行形式表示出来的问题解决思维过程,不仅可以有效提升人们问题解决的能力,还将批判性思维与计算能力结合起来,是创新解决现实问题的基础;在义务教育阶段,如何在更广的意义上引入“计算思维”,而不局限于“信息技术与工程”,是一个非常值得关注的方向,建议相关部门组织力量加强在义务教育阶段拓展“计算思维”培养的研究和实践探索。

马克思技术批判理论视域下的平台算法探析

马克思的技术批判理论揭示了资本主义社会中技术存在的二重性,即技术潜在蕴含着人类解放的维度与资本主义社会现实的技术异化。共产主义最终将扬弃资本主义技术异化,恢复无产阶级对技术的占有。算法作为数字社会的底层逻辑架构,其与数据在数字时代的重要性日趋凸显,二者统一于平台资本体系。平台体系中的算法与资本存在共谋,处于异化状态。马克思的技术批判理论可以透视数字劳动者“困在算法里”和算法推荐两种算法异化样态,揭示资本增殖和意识形态控制的实质,并逐步构建超越资本逻辑的共同富裕逻辑,贯穿平台算法治理,使算法从异化转化为善用,助力于共同富裕的实现。

算法预测性警务的风险辨析及应对

随着算法时代的到来,在总结吸收问题导向警务和热点警务模式特性的基础上,通过对“不安全因素”的边际判断以及算法决策机制,算法预测性警务实现了对警情的预报预测预警。但算法预测性警务也存在风险,包括计算机学习的样本缺陷、技术上的利维坦化以及算法黑箱在内的技术风险,还包括算法在嵌入警察权的过程中可能带来的警察权扩张、“全景敞视主义”的自由困境、破坏警民关系等制度风险。为此,除了扩大数据池以及加强计算机学习模型的优化等技术升级保障外,更要做好个人信息保护与国家监管之间、算法预测的工具理性与伦理道德之间、算法技术垄断与公民参与之间三方面的平衡,以更好地应对算法预测性警务在未来可能面对的风险。

基于关联规则的绿色特高压换流站设备选型逻辑自适应优化算法

特高压换流站中组成设备众多,在运行时会产生较大的耗损,有必要实施设备选型优化,对此,提出一种基于关联规则的绿色特高压换流站设备选型逻辑自适应优化算法。利用关联规则,选取影响绿色特高压换流站设备选型的主要因素,构建绿色特高压换流站设备选型逻辑描述函数,并设置约束条件。利用粒子群算法自适应优化选择特高压换流站设备型号,实现绿色特高压换流站设备选型逻辑优化。结果表明,3种自适应优化后的绿色特高压换流站设备选型方案应用后,产生的耗损要小于原始特高压换流站设备选型方案。

基于电网多源数据的电力知识图谱构建方法

电力知识图谱构建方法重复采集电网多源数据,针对构建得到的知识图谱置信度过小的问题,设计一种基于电网多源数据的电力知识图谱构建方法。通过设定实体电网多源数据采集架构,采用局部线性嵌入算法筛选重复采集的电网多源数据。将电力知识间的关系转变可识别特征数值,抽取多源数据中的电力知识划分图谱层级,构建电力知识图谱结构。结合电力知识采集环境完成实验,实验结果表明,文中设计的构建方法准确率为97%,召回率为0.78%,F值为93,满足电力知识图谱的构建需求。