Robot Localization Using Fuzzy Logic

2005 ◽  
Author(s):  
Avinash G. Dharne ◽  
Suhada Jayasuriya
Keyword(s):  
Fuzzy Logic ◽  
Grid Point ◽  
Computational Cost ◽  
Computation Time ◽  
Position Estimation ◽  
Fuzzy Rules ◽  
Estimation Methods ◽  
Robot Localization ◽  
Multiple Sensors ◽  
Grid Points

Robot Localization is an issue of vital importance for the functioning of autonomous mobile robots. Location information, allows a robot to navigate complex environments and perform local tasks successfully. In mobile sensor networks, this information facilitates important functions like topology control, collision avoidance and development and security of routing protocols. This issue can be divided into the problems of global position estimation, and once that is achieved, of local position tracking. To tackle these, two distinct methods have been used in the past. One is the use of specialized hardware and another is the use of probabilistic Bayesian estimation methods. This paper proposes the use of Fuzzy Logic to tackle this problem. Fuzzy Logic allows us to do away with strict probabilistic rules and to set up heuristic fuzzy rules. It also reduces computation time. A grid-based map is used to describe the environment of the robot and the robot’s confidence in it’s position at each grid-point is determined using sensor measurements. In case the robot is receiving information from multiple sensors, this paper demonstrates the robustness of the scheme to inaccurate sensor information or robot confidence within practical limits. This paper also applies the fuzzy rules to track the robot’s position as it moves. In order to reduce computational cost, this paper proposes limiting the computation of confidences to significant grid-points only.

Different inference approaches for the estimators of the sushila distribution

2021 ◽  
Vol 16 (4) ◽  
pp. 251-260
Author(s):  
Marcos Vinicius de Oliveira Peres ◽  
Ricardo Puziol de Oliveira ◽  
Edson Zangiacomi Martinez ◽  
Jorge Alberto Achcar
Keyword(s):  
Maximum Likelihood ◽  
Least Squares ◽  
Bayesian Method ◽  
Weighted Least Squares ◽  
Computational Cost ◽  
Estimation Method ◽  
Computation Time ◽  
Ordinary Least Squares ◽  
Likelihood Method ◽  
Estimation Methods

In this paper, we order to evaluate via Monte Carlo simulations the performance of sample properties of the estimates of the estimates for Sushila distribution, introduced by Shanker et al. (2013). We consider estimates obtained by six estimation methods, the known approaches of maximum likelihood, moments and Bayesian method, and other less traditional methods: L-moments, ordinary least-squares and weighted least-squares. As a comparison criterion, the biases and the roots of mean-squared errors were used through nine scenarios with samples ranging from 30 to 300 (every 30rd). In addition, we also considered a simulation and a real data application to illustrate the applicability of the proposed estimators as well as the computation time to get the estimates. In this case, the Bayesian method was also considered. The aim of the study was to find an estimation method to be considered as a better alternative or at least interchangeable with the traditional maximum likelihood method considering small or large sample sizes and with low computational cost.

Determination of Internal and External Surface Area of Atomistic Porous Carbons

2019 ◽  
Vol 798 ◽  
pp. 169-174
Author(s):  
Poomiwat Phadungbut ◽  
Chanon Bunsaksit ◽  
Kemmatat Dachdecho ◽  
Chosita Kedtip
Keyword(s):  
Specific Surface ◽  
Surface Area ◽  
Grid Point ◽  
Computational Cost ◽  
Porous Carbons ◽  
Fine Grid ◽  
Surface Areas ◽  
Carbon Structures ◽  
Atomistic Models ◽  
Grid Points

In this work, we propose the computational algorithm to compute the temperature-independent specific surface area of atomistic models of porous carbons. Concisely, the method is to divide the simulation space into fine grid points, place the selected probe molecule on each grid point and then check whether that insertion is accessible or inaccessible regions based on molecular force field. Three models of porous carbon structures are chosen as examples for estimating internal and external specific surface areas and trend of surface curvature. By comparing the computational cost, our proposed technique significantly requires less time-consuming than the physisorption. Therefore, for atomistic models of porous carbons, we recommend that our proposed method be more efficient and accurate than the experimentally and computationally traditional physisorption.

scholarly journals 3-D tomographic limb sounder retrieval techniques: irregular grids and Laplacian regularisation

2019 ◽  
Vol 12 (2) ◽  
pp. 853-872 ◽  
Author(s):  
Lukas Krasauskas ◽  
Jörn Ungermann ◽  
Stefan Ensmann ◽  
Isabell Krisch ◽  
Erik Kretschmer ◽  
...  
Keyword(s):  
Ad Hoc ◽  
Computational Cost ◽  
Synthetic Data ◽  
Computation Time ◽  
Physical Parameters ◽  
Modelling Problem ◽  
Tikhonov Regularisation ◽  
Grid Points

Abstract. Multiple limb sounder measurements of the same atmospheric region taken from different directions can be combined in a 3-D tomographic retrieval. Mathematically, this is a computationally expensive inverse modelling problem. It typically requires an introduction of some general knowledge of the atmosphere (regularisation) due to its underdetermined nature. This paper introduces a consistent, physically motivated (no ad-hoc parameters) variant of the Tikhonov regularisation scheme based on spatial derivatives of the first-order and Laplacian. As shown by a case study with synthetic data, this scheme, combined with irregular grid retrieval methods employing Delaunay triangulation, improves both upon the quality and the computational cost of 3-D tomography. It also eliminates grid dependence and the need to tune parameters for each use case. The few physical parameters required can be derived from in situ measurements and model data. Tests show that a 82 % reduction in the number of grid points and 50 % reduction in total computation time, compared to previous methods, could be achieved without compromising results. An efficient Monte Carlo technique was also adopted for accuracy estimation of the new retrievals.

Performance Comparison of MANET, VANET and FANET

2018 ◽  
Vol 8 (5) ◽  
pp. 132
Author(s):  
Aarti Sahu ◽  
Laxmi Shrivastava
Keyword(s):  
Fuzzy Logic ◽  
Wireless Network ◽  
Packet Loss ◽  
Ad Hoc Network ◽  
Fuzzy Logic Controller ◽  
Ad Hoc ◽  
Mobile Ad Hoc Network ◽  
Performance Comparison ◽  
Fuzzy Rules ◽  
Mobile Ad Hoc

A wireless ad hoc network is a decentralized kind of wireless network. It is a kind of temporary Computer-to-Computer connection. It is a spontaneous network which includes mobile ad-hoc network (MANET), vehicular ad-hoc network (VANET) and Flying ad-hoc network (FANET). Mobile Ad Hoc Network (MANET) is a temporary network that can be dynamically formed to exchange information by wireless nodes or routers which may be mobile. A VANET is a sub form of MANET. It is an technology that uses vehicles as nodes in a network to make a mobile network. FANET is an ad-hoc network of flying nodes. They can fly independently or can be operated distantly. In this research paper Fuzzy based control approaches in wireless network detects & avoids congestion by developing the ad-hoc fuzzy rules as well as membership functions.In this concept, two parameters have been used as: a) Channel load b) The size of queue within intermediate nodes. These parameters constitute the input to Fuzzy logic controller. The output of Fuzzy logic control (sending rate) derives from the conjunction with Fuzzy Rules Base. The parameter used input channel load, queue length which are produce the sending rate output in fuzzy logic. This fuzzy value has been used to compare the MANET, FANET and VANET in terms of the parameters Throughput, packet loss ratio, end to end delay. The simulation results reveal that usage of Qual Net 6.1 simulator has reduced packet-loss in MANET with comparing of VANET and FANET.

scholarly journals Image-based textile decoding

2020 ◽  
pp. 1-14
Author(s):  
Siqiang Chen ◽  
Masahiro Toyoura ◽  
Takamasa Terada ◽  
Xiaoyang Mao ◽  
Gang Xu
Keyword(s):  
Deep Learning ◽  
Grid Point ◽  
Intermediate Representation ◽  
Training Set ◽  
Binary Matrix ◽  
The Matrix ◽  
Correct Pattern ◽  
Grid Points ◽  
Textile Fabric ◽  
Direct Correspondence

A textile fabric consists of countless parallel vertical yarns (warps) and horizontal yarns (wefts). While common looms can weave repetitive patterns, Jacquard looms can weave the patterns without repetition restrictions. A pattern in which the warps and wefts cross on a grid is defined in a binary matrix. The binary matrix can define which warp and weft is on top at each grid point of the Jacquard fabric. The process can be regarded as encoding from pattern to textile. In this work, we propose a decoding method that generates a binary pattern from a textile fabric that has been already woven. We could not use a deep neural network to learn the process based solely on the training set of patterns and observed fabric images. The crossing points in the observed image were not completely located on the grid points, so it was difficult to take a direct correspondence between the fabric images and the pattern represented by the matrix in the framework of deep learning. Therefore, we propose a method that can apply the framework of deep learning viau the intermediate representation of patterns and images. We show how to convert a pattern into an intermediate representation and how to reconvert the output into a pattern and confirm its effectiveness. In this experiment, we confirmed that 93% of correct pattern was obtained by decoding the pattern from the actual fabric images and weaving them again.

Accelerated 3D blind separation of convolved mixtures based on the fast iterative shrinkage thresholding algorithm for adaptive multiple subtraction

Geophysics ◽  
2018 ◽  
Vol 83 (2) ◽  
pp. V99-V113 ◽  
Author(s):  
Zhong-Xiao Li ◽  
Zhen-Chun Li
Keyword(s):  
Field Data ◽  
Optimization Problem ◽  
Computational Cost ◽  
Computation Time ◽  
Blind Separation ◽  
L1 Norm ◽  
Norm Minimization ◽  
Iterative Shrinkage ◽  
Thresholding Algorithm ◽  
Iterative Shrinkage Thresholding

After multiple prediction, adaptive multiple subtraction is essential for the success of multiple removal. The 3D blind separation of convolved mixtures (3D BSCM) method, which is effective in conducting adaptive multiple subtraction, needs to solve an optimization problem containing L1-norm minimization constraints on primaries by the iterative reweighted least-squares (IRLS) algorithm. The 3D BSCM method can better separate primaries and multiples than the 1D/2D BSCM method and the method with energy minimization constraints on primaries. However, the 3D BSCM method has high computational cost because the IRLS algorithm achieves nonquadratic optimization with an LS optimization problem solved in each iteration. In general, it is good to have a faster 3D BSCM method. To improve the adaptability of field data processing, the fast iterative shrinkage thresholding algorithm (FISTA) is introduced into the 3D BSCM method. The proximity operator of FISTA can solve the L1-norm minimization problem efficiently. We demonstrate that our FISTA-based 3D BSCM method achieves similar accuracy of estimating primaries as that of the reference IRLS-based 3D BSCM method. Furthermore, our FISTA-based 3D BSCM method reduces computation time by approximately 60% compared with the reference IRLS-based 3D BSCM method in the synthetic and field data examples.

Analysis and Simulation of Low Speed Sensorless Vector Control with Two Current Signal Injection Schemes

2013 ◽  
Vol 722 ◽  
pp. 361-368
Author(s):  
Yu Dong Li ◽  
Bo Zhou ◽  
Ying Liu ◽  
Cheng Liang Zhao
Keyword(s):  
Vector Control ◽  
Permanent Magnet Synchronous Motor ◽  
Low Frequency ◽  
Simulation Models ◽  
Position Estimation ◽  
Estimation Methods ◽  
Current Signal ◽  
Rotor Position ◽  
Sensorless Vector Control ◽  
Signal Injection

Two current signal injection methods, the fluctuating high frequency (HF) current signal injection and low frequency (LF) current signal injection, were discussed. The basic principles of rotor position self-sensing using these current signal injection and the key techniques in implementation were presented and the simulation models for sensorless vector control systems of a permanent magnet synchronous motor (PMSM) were established using two proposed rotor position estimation methods. Comparative simulation study of the fluctuating HF current signal injection and LF current signal injection was investigated, and draw the conclusions that the saliency-tracking scheme using fluctuating HF current signal injection possesses simpler configuration and better speed-adjustable performance both in static and dynamic.

scholarly journals UNRAVELLING DIABETIC RETINOPATHY THROUGH IMAGE PROCESSING, NEURAL NETWORKS AND FUZZY LOGIC – A REVIEW

2017 ◽  
Vol 10 (4) ◽  
pp. 32
Author(s):  
Srinivasan A ◽  
Sudha S
Keyword(s):  
Image Processing ◽  
Fuzzy Logic ◽  
Diabetic Retinopathy ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Computation Time ◽  
Automated System ◽  
Morphological Operations ◽  
Advanced Stage ◽  
Sensitivity Specificity

One of the main causes of blindness is diabetic retinopathy (DR) and it may affect people of any ages. In these days, both young and old ages are affected by diabetes, and the di abetes is the main cause of DR. Hence, it is necessary to have an automated system with good accuracy and less computation time to diagnose and treat DR, and the automated system can simplify the work of ophthalmologists. The objective is to present an overview of various works recently in detecting and segmenting the various lesions of DR. Papers were categorized based on the diagnosing tools and the methods used for detecting early and advanced stage lesions. The early lesions of DR are microaneurysms, hemorrhages, exudates, and cotton wool spots and in the advanced stage, new and fragile blood vessels can be grown. Results have been evaluated in terms of sensitivity, specificity, accuracy and receiver operating characteristic curve. This paper analyzed the various steps and different algorithms used recently for the detection and classification of DR lesions. A comparison of performances has been made in terms of sensitivity, specificity, area under the curve, and accuracy. Suggestions, future workand the area to be improved were also discussed.Keywords: Diabetic retinopathy, Image processing, Morphological operations, Neural network, Fuzzy logic. 

Spatio-temporal variability of temperature and potential evapotranspiration over India

2016 ◽  
Vol 7 (4) ◽  
pp. 810-822 ◽  
Author(s):  
P. Sonali ◽  
D. Nagesh Kumar
Keyword(s):  
Hydrological Cycle ◽  
Potential Evapotranspiration ◽  
Grid Point ◽  
Rank Correlation ◽  
Trend Detection ◽  
Detection Analysis ◽  
Change Impact ◽  
Spatio Temporal ◽  
Grid Points ◽  
Significant Factors

Worldwide, major changes in the climate are expected due to global warming, which leads to temperature variations. To assess the climate change impact on the hydrological cycle, a spatio-temporal change detection study of potential evapotranspiration (PET) along with maximum and minimum temperatures (Tmax and Tmin) over India have been performed for the second half of the 20th century (1950–2005) both at monthly and seasonal scale. From the observed monthly climatology of PET over India, high values of PET are envisioned during the months of March, April, May and June. Temperature is one of the significant factors in explaining changes in PET. Hence seasonal correlations of PET with Tmax and Tmin were analyzed using Spearman rank correlation. Correlation of PET with Tmax was found to be higher compared to that with Tmin. Seasonal variability of trend at each grid point over India was studied for Tmax, Tmin and PET separately. Trend Free Pre-Whitening and Modified Mann Kendall approaches, which consider the effect of serial correlation, were employed for the trend detection analysis. A significant trend was observed in Tmin compared to Tmax and PET. Significant upward trends in Tmax, Tmin and PET were observed over most of the grid points in the interior peninsular region.

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