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.

A disjoint algorithm for seismic reliability analysis of lifeline networks

The algorithm is based on constructing a disjoin kg t set of the minimal paths in a network system.In this paper, cubic notation was used to describe the logic function of a network in a well-balanced state,and then the sharp-product operation was used to construct the disjoint minimal path set of the network.A computer program has been developed,and when combined with decomposition technology,the reliability of a general lifeline network can be effectively and automatically calculated.

Task scheduling for multi-electro-magnetic detection satellite with a combined algorithm

Task scheduling for electro-magnetic detection satellite is a typical combinatorial optimization problem. The count of constraints that need to be taken into account is of large scale. An algorithm combined integer programming with constraint programming is presented. This algorithm is deployed in this problem through two steps. The first step is to decompose the original problem into master and sub-problem using the logic-based Benders decomposition; then a circus combines master and sub-problem solving process together, and the connection between them is general Benders cut. This hybrid algorithm is tested by a set of derived experiments. The result is compared with corresponding outcomes generated by the strength Pareto evolutionary algorithm and the pure constraint programming solver GECODE, which is an open source software. These tests and comparisons yield promising effect.

Nonlinear inverse modeling of sensor based on back-propagation fuzzy logical system

Objective To correct the nonlinear error of sensor output,a new approach to sensor inverse modeling based on Back-Propagation Fuzzy Logical System(BP FS) is presented.Methods The BP FS is a computationally efficient nonlinear universal approximator,which is capable of implementing complex nonlinear mapping from its input pattern space to the output with fast convergence speed.Results The neuro-fuzzy hybrid system,i.e.BP FS,is then applied to construct nonlinear inverse model of pressure sensor.The experimental results show that the proposed inverse modeling method automatically compensates the associated nonlinear error in pressure estimation,and thus the performance of pressure sensor is significantly improved.Conclusion The proposed method can be widely used in nonlinearity correction of various kinds of sensors to compensate the effects of nonlinearity and temperature on sensor output.

Fault current Characterization Based on Fuzzy Algorithm for DOCR Application

Penetration of distribution generation (DG) into power system might disturb the existing fault diagnosis system. The detection of fault, fault classification, and random changes of direction of fault current cannot always be monitored and determined via on-line by conventional fault diagnosis system due to DG penetration. In this paper, a fault current characterization which based on fuzzy logic algorithm (FLA) is proposed. Fault detection, fault classification, and fault current direction are extracted after processing the measurement result of three-phase line current. The ability of fault current characterization based on FLA is reflected in directional overcurrent relay (DOCR) model. The proposed DOCR model has been validated in microgrid test system simulation in Matlab environment. The simulation result showed accurate result for different fault location and type. The proposed DOCR model can operate as common protection device (PD) unit as well as unit to improve the effectiveness of existing fault diagnosis system when DG is present.

A New Fuzzy Adaptive Genetic Algorithm

Multiple genetic algorithms (GAs) need a large population size, which will take a long time for evolution. A new fuzzy adaptive GA is proposed in this paper. This algorithm is more effective in global search while keeping the overall population size constant. The simulation results of function optimization show that with the proposed algorithm, the phenomenon of premature convergence can be overcome effectively, and a satisfying optimization result is obtained.

A STUDY OF FUZZY LOGICAL PETRI NETS AND ITS APPLICATION

In this paper, a fuzzy Petri net approach to modelling fuzzy rule-based reasoning is proposed. Logical Petri net (LPN) and fuzzy logical Petri net (FLPN) are defined. The backward reasoning algorithm based on sub-fuzzy logical Petri net is given. It is simpler than the conventional algorithm of forward reasoning from initial propositions. An application to the partial fault model of a car engine in paper Portinale's(1993) is used as an illustrative example of FLPN.

Applying GA and Fuzzy Logic to Breakdown Diagnosis for Spinning Process

In this study, an effective search methodology based on fuzzy logic is applied to narrow down search range for the possible breakdown causes. Moreover a genetic algorithm (GA) is employed to directly find the intervals of solution to the inverse fuzzy inference problem during diagnosis procedure. Through the assistance of the developed intelligent diagnosis system, an inspector can be easier and more effective to find various possible occurred breakdown causes by judging from the observed symptoms during manufacturing process. An application of the developed intelligent diagnosis system to tracing the breakdown causes occurred during spinning process is reported in this study. The results show that the accuracy and efficiency of the diagnosis system are as promising as expected.

基于融合WM算法的Mamdani型模糊推理的板桥损伤定量分析

为对板桥局部位置的损伤程度进行定量分析,提出1种基于融合WM算法的Mamdani型模糊推理板桥损伤定量分析方法,探讨影响面指标和损伤程度之间的独特联系,并通过MIDAS及Python模拟计算并分析板的损伤程度。研究结果表明:模糊逻辑系统具有较为出色的记忆能力。当融合WM算法到模糊逻辑系统中时,不论是单点损伤还是多点损伤,都能对板桥的损伤程度进行准确识别;当未融合WM算法时,由于其模糊规则没有进行置信度计算,将错误的规则也计算在内,最终导致定量结果精度下降。研究结果可为板桥结构的损伤评估和维护策略提供较为科学准确的参考。

A New Neuro-Fuzzy Adaptive Genetic Algorithm

Novel neuro-fuzzy techniques are used to dynamically control parameter settings ofgenetic algorithms (GAs).The benchmark routine is an adaptive genetic algorithm (AGA) that uses afuzzy knowledge-based system to control GA parameters.The self-learning ability of the cerebellar modelariculation controller (CMAC) neural network makes it possible for on-line learning the knowledge onGAs throughout the run.Automatically designing and tuning the fuzzy knowledge-base system,neuro-fuzzy techniques based on CMAC can find the optimized fuzzy system for AGA by the renhanced learningmethod.The Results from initial experiments show a Dynamic Parametric AGA system designed by theproposed automatic method and indicate the general applicability of the neuro-fuzzy AGA to a widerange of combinatorial optimization.

Self-Sustained Boundedness of Logical and Quantal Error at Semantic Intelligence

It is demonstrated that the recently introduced semantic intelligence spontaneously maintains bounded logical and quantal error on each and every semantic trajectory, unlike its algorithmic counterpart which is not able to. This result verifies the conclusion about the assignment of equal evolutionary value to the motion on the set of all the semantic trajectories sharing the same homeostatic pattern. The evolutionary value of permanent and spontaneous maintenance of boundedness of logical and quantal error on each and every semantic trajectory is to make available spontaneous maintenance of the notion of a kind intact in the long run.

模糊逻辑控制在模块化车辆轨迹的应用研究

针对电动汽车和混合动力电动汽车的动力配置,提出了一种应用于模块化全轮驱动汽车的优化模糊逻辑控制(FLC)策略。在车辆的动力学模型中,应用魔术公式来定义与弯道或行驶/中断情况下的载荷传递的轮胎打滑。通过控制器来调整轮内电机扭矩,以实现车辆的轨迹纠偏。提出了遗传算法的多目标优化方法,研究了车辆的质量、重心位置、轮距和轴距等因素对车辆鲁棒性的控制影响,并与传统的PID控制方法进行了对比,表明所提的FLC能够有效控制车辆的轨迹。最后通过半实物实验,验证了所提FLC控制策略的有效性。

算法政治推送:一个命题的解构与梳理

“算法推送”是基于内容生成、个性推荐以及检索过滤等算法技术,为用户提供信息获取、检索、处理以及呈现等一系列活动的过程。人工智能时代,算法推送强大的信息支配能力深刻地影响网络空间的传播秩序,构建了基于算法的权力微观运行机制,并逐渐演化为政治行动者、大型咨询公司以及社会团体等实现自身利益的重要手段,衍生了一系列风险,如介入重大政治进程、扰动主流意识形态、强化网络认知域作战等。算法推送与政治生态交织互嵌的背后,反映了传统政治权力结构与运作方式的解构与重组,资本支配下技术工具对政治的大范围自动化渗透,数字威权主义下寡头治理的趋势,以及“后真相”时代下民主形态的更迭。

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。