scholarly journals Determining traffic congestion utilizing a fuzzy logic model and Floating Car Data (FCD)

2021 ◽  
Vol 4 ◽  
pp. 1-8
Author(s):  
Maja Kalinic ◽  
Jukka M. Krisp
Keyword(s):  
Fuzzy Logic ◽  
Traffic Congestion ◽  
Fuzzy Inference ◽  
Fuzzy Model ◽  
Linear Functions ◽  
Inference System ◽  
Floating Car Data ◽  
Federal Highway ◽  
Almost All

Abstract. Traffic congestion is a dynamic spatial and temporal process and as such might not be possible to model with linear functions of various dependent variables. That leaves a lot of space for non-linear approximates, such as neutral networks and fuzzy logic. In this paper, the focus is on the fuzzy logic as a possible approach for dealing with the problems of measuring traffic congestion. We investigate the application of this framework on a selected case study, and use floating car data (FCD) collected in Augsburg, Germany. A fuzzy inference system is built to detect degrees of congestion on a federal highway B17. With FCD, it is possible to obtain local speed information on almost all parts of the network. This information, together with collected vehicle location, time and heading, can be further processed and transformed into valuable information in the form of trip routes, travel times, etc. Initial results are compared with traditional method of expressing levels of congestion on a road network e.g. Level of Service – LOS. The fuzzy model, with segmented mean speed and travel time parameters, performed well and showed to be promising approach to detect traffic congestions. This approach can be further improved by involving more input parameters, such as density or vehicle flow, which might reflect traffic congestion event even more realistically.

scholarly journals Development of a Fuzzy Model for Differentiating Peanut Plant from Broadleaf Weeds Using Image Features

2020 ◽  
Author(s):  
Adel Bakhshipour ◽  
Hemad Zareiforoush
Keyword(s):  
Fuzzy Logic ◽  
Feature Selection ◽  
Decision Trees ◽  
Fuzzy Inference ◽  
Fuzzy Model ◽  
Principal Component ◽  
Image Features ◽  
Fuzzy Classifier ◽  
Inference System ◽  
Peanut Plant

Abstract A combination of decision tree (DT) and fuzzy logic techniques was used to develop a fuzzy model for differentiating peanut plant from weeds. Color features and wavelet-based texture features were extracted from images of peanut plant and its three common weeds. Two feature selection techniques namely Principal Component Analysis (PCA) and Correlation-based Feature Selection (CFS) were applied on input dataset and three Decision Trees (DTs) including J48, Random Tree (RT), and Reduced Error Pruning (REP) were used to distinguish between different plants. In all cases, the best overall classification accuracies were achieved when CFS-selected features were used as input data. The obtained accuracies of J48-CFS, REP-CFS, and RT-CFS trees for classification of the four plant categories namely peanut plant, Velvetleaf, False daisy, and Nicandra, were 80.83%, 80.00% and 79.17% respectively. Along with these almost low accuracies, the structures of the decision trees were complex making them unsuitable for developing a fuzzy inference system. The classifiers were also used for differentiating peanut plant from the group of weeds. The overall accuracies on training and testing datasets were respectively 95.56% and 93.75% for J48-CFS; 92.78% and 91.67% for REP-CFS; and 93.33% and 92.59% for RT-CFS DTs. The results showed that the J48-CFS and REP-CFS were the most appropriate models to set the membership functions and rules of the fuzzy classifier system. Based on the results, it can be concluded that the developed DT-based fuzzy logic model can be used effectively to discriminate weeds from peanut plant in the form of machine vision-based cultivating systems.

scholarly journals Floating Car Data and Fuzzy Logic for classifying congestion indexes in the city of Shanghai

2019 ◽  
Vol 2 ◽  
pp. 1-7
Author(s):  
Maja Kalinic ◽  
Jukka M. Krisp
Keyword(s):  
Fuzzy Logic ◽  
Traffic Congestion ◽  
Fuzzy Inference ◽  
Inference Model ◽  
Floating Car Data ◽  
The Individual ◽  
The City

<p><strong>Abstract.</strong> In this paper, we use Floating Car Data from the city of Shanghai and Fuzzy Inference model to detect congestion indexes throughout the city. We aim to investigate to which extent traffic congestion is severe during afternoon rush hour. Additionally, we compare our results to the ones obtained by calculating congestion indexes on conventional way. Although we do not argue that our model is the best measure of congestion, it does allow the mechanism to combine different measures and to incorporate the uncertainty in the individual measures so that the compound picture of congestion can be reproduced.</p>

scholarly journals Development of a Fuzzy Model for Differentiating Peanut Plant from Broadleaf Weeds Using Image Features

2020 ◽  
Author(s):  
Adel Bakhshipour ◽  
Hemad Zareiforoush
Keyword(s):  
Fuzzy Logic ◽  
Feature Selection ◽  
Decision Trees ◽  
Fuzzy Inference ◽  
Fuzzy Model ◽  
Principal Component ◽  
Image Features ◽  
Fuzzy Classifier ◽  
Inference System ◽  
Peanut Plant

Abstract A combination of decision tree (DT) and fuzzy logic techniques was used to develop a fuzzy model for differentiating peanut plant from weeds. Color features and wavelet-based texture features were extracted from images of peanut plant and its three common weeds. Two feature selection techniques namely Principal Component Analysis (PCA) and Correlation-based Feature Selection (CFS) were applied on input dataset and three Decision Trees (DTs) including J48, Random Tree (RT), and Reduced Error Pruning (REP) were used to distinguish between different plants. In all cases, the best overall classification accuracies were achieved when CFS-selected features were used as input data. The obtained accuracies of J48-CFS, REP-CFS, and RT-CFS trees for classification of the four plant categories namely peanut plant, Velvetleaf, False daisy, and Nicandra, were 80.83%, 80.00% and 79.17% respectively. Along with these almost low accuracies, the structures of the decision trees were complex making them unsuitable for developing a fuzzy inference system. The classifiers were also used for differentiating peanut plant from the group of weeds. The overall accuracies were 95.56% for J48-CFS, 92.78% for REP-CFS, and 93.33% for RT-CFS DTs. The results showed that the J48-CFS and REP-CFS were the most appropriate models to set the membership functions and rules of the fuzzy classifier system. Based on the results, it can be concluded that the developed DT-based fuzzy logic model can be used effectively to discriminate weeds from peanut plant in the form of machine vision-based cultivating systems.

scholarly journals Development of a fuzzy model for differentiating peanut plant from broadleaf weeds using image features

Plant Methods ◽  
2020 ◽  
Vol 16 (1) ◽  
Author(s):  
Adel Bakhshipour ◽  
Hemad Zareiforoush
Keyword(s):  
Fuzzy Logic ◽  
Feature Selection ◽  
Decision Trees ◽  
Fuzzy Inference ◽  
Fuzzy Model ◽  
Principal Component ◽  
Image Features ◽  
Fuzzy Classifier ◽  
Inference System ◽  
Peanut Plant

AbstractA combination of decision tree (DT) and fuzzy logic techniques was used to develop a fuzzy model for differentiating peanut plant from weeds. Color features and wavelet-based texture features were extracted from images of peanut plant and its three common weeds. Two feature selection techniques namely Principal Component Analysis (PCA) and Correlation-based Feature Selection (CFS) were applied on input dataset and three Decision Trees (DTs) including J48, Random Tree (RT), and Reduced Error Pruning (REP) were used to distinguish between different plants. In all cases, the best overall classification accuracies were achieved when CFS-selected features were used as input data. The obtained accuracies of J48-CFS, REP-CFS, and RT-CFS trees for classification of the four plant categories namely peanut plant, Velvetleaf, False daisy, and Nicandra, were 80.83%, 80.00% and 79.17% respectively. Along with these almost low accuracies, the structures of the decision trees were complex making them unsuitable for developing a fuzzy inference system. The classifiers were also used for differentiating peanut plant from the group of weeds. The overall accuracies on training and testing datasets were respectively 95.56% and 93.75% for J48-CFS; 92.78% and 91.67% for REP-CFS; and 93.33% and 92.59% for RT-CFS DTs. The results showed that the J48-CFS and REP-CFS were the most appropriate models to set the membership functions and rules of the fuzzy classifier system. Based on the results, it can be concluded that the developed DT-based fuzzy logic model can be used effectively to discriminate weeds from peanut plant in the form of machine vision-based cultivating systems.

Investigating the potential of redundant manipulators in narrow channels

2020 ◽  
pp. 095440622096451
Author(s):  
Ashish Singla ◽  
Jyotindra Narayan ◽  
Himanshu Arora
Keyword(s):  
Case Studies ◽  
Fuzzy Inference ◽  
Redundant Manipulators ◽  
Redundancy Resolution ◽  
Inference System ◽  
Narrow Channels ◽  
First Case ◽  
Task Priority ◽  
Patient Side

In this paper, an attempt has been made to investigate the potential of redundant manipulators, while tracking trajectories in narrow channels. The behavior of redundant manipulators is important in many challenging applications like under-water welding in narrow tanks, checking the blockage in sewerage pipes, performing a laparoscopy operation etc. To demonstrate this snake-like behavior, redundancy resolution scheme is utilized using two different approaches. The first approach is based on the concept of task priority, where a given task is split and prioritize into several subtasks like singularity avoidance, obstacle avoidance, torque minimization, and position preference over orientation etc. The second approach is based on Adaptive Neuro Fuzzy Inference System (ANFIS), where the training is provided through given datasets and the results are back-propagated using augmentation of neural networks with fuzzy logics. Three case studies are considered in this work to demonstrate the redundancy resolution of serial manipulators. The first case study of 3-link manipulator is attempted with both the approaches, where the objective is to track the desired trajectory while avoiding multiple obstacles. The second case study of 7-link manipulator, tracking trajectory in a narrow channel, is investigated using the concept of task priority. The realistic application of minimum-invasive surgery (MIS) based trajectory tracking is considered as the third case study, which is attempted using ANFIS approach. The 5-link spatial redundant manipulator, also known as a patient-side manipulator being developed at CSIR-CSIO, Chandigarh is used to track the desired surgical cuts. Through the three case studies, it is well demonstrated that both the approaches are giving satisfactory results.

scholarly journals Vessel Multi-Parametric Collision Avoidance Decision Model: Fuzzy Approach

2021 ◽  
Vol 9 (1) ◽  
pp. 49
Author(s):  
Tanja Brcko ◽  
Andrej Androjna ◽  
Jure Srše ◽  
Renata Boć
Keyword(s):  
Fuzzy Logic ◽  
Collision Avoidance ◽  
Decision Model ◽  
Fuzzy Inference ◽  
Weather Conditions ◽  
Target Vessel ◽  
Planning Tool ◽  
Inference System ◽  
International Regulations ◽  
Input Variables

The application of fuzzy logic is an effective approach to a variety of circumstances, including solutions to maritime anti-collision problems. The article presents an upgrade of the radar navigation system, in particular, its collision avoidance planning tool, using a decision model that combines dynamic parameters into one decision—the collision avoidance course. In this paper, a multi-parametric decision model based on fuzzy logic is proposed. The model calculates course alteration in a collision avoidance situation. First, the model collects input data of the target vessel and assesses the collision risk. Using time delay, four parameters are calculated for further processing as input variables for a fuzzy inference system. Then, the fuzzy logic method is used to calculate the course alteration, which considers the vessel’s safety domain and International Regulations for Preventing Collisions at Sea (COLREGs). The special feature of the decision model is its tuning with the results of the database of correct solutions obtained with the manual radar plotting method. The validation was carried out with six selected cases simulating encounters with the target vessel in the open sea from different angles and at any visibility. The results of the case studies have shown that the decision model computes well in situations where the own vessel is in a give-way position. In addition, the model provides good results in situations when the target vessel violates COLREG rules. The collision avoidance planning tool can be automated and serve as a basis for further implementation of a model that considers the manoeuvrability of the vessels, weather conditions, and multi-vessel encounter situations.

Determining the safety culture in a gun factory in Turkey: A fuzzy approach

2020 ◽  
pp. 1-11
Author(s):  
Gökçen A. Çiftçioğlu ◽  
Mehmet A. N. Kadırgan ◽  
Ahmet Eşiyok
Keyword(s):  
Safety Culture ◽  
Fuzzy Inference ◽  
Fuzzy Model ◽  
Self Awareness ◽  
Inference System ◽  
Five Factors ◽  
Initial Questionnaire ◽  
Proposed Model ◽  
Assessment Questionnaire

Safety culture is a very complex phenomenon due to its intangible nature. It is tough to measure and express it with numerical values, as there is no simple indicator to measure it. This paper presents a fuzzy inference system that measures the safety culture. First of all, a safety culture assessment questionnaire is developed by utilizing related literature. The initial questionnaire had 29 items. The questionnaire is applied to 259 employees within the gun manufacturing factory. After making an exploratory factor analysis, the questionnaire is based on five factors with 25 items. The safety culture indicators are defined as; safety follow-up audit reporting, employees’ self-awareness, operational safety commitment, management’s safety commitment, safety orientedness. Normality, reliability, and correlation analysis are performed. Then a fuzzy model is constructed with five inputs and one output. The inputs are the five factors mentioned above, and the output generated is the safety culture result, which is between 0-1. The presented fuzzy model produces reliable results indicating the safety culture level from the employees’ eyes. Beyond exploring the employees’ safety culture, the proposed model can easily be understood by the practitioners from various sectors. Furthermore, the model is straightforward to customize for various fields of industry.

Sustainable management of water demand using fuzzy inference system: a case study of Kenyir Lake, Malaysia

2021 ◽  
Author(s):  
Nor Najwa Irina Mohd Azlan ◽  
Marlinda Abdul Malek ◽  
Maslina Zolkepli ◽  
Jamilah Mohd Salim ◽  
Ali Najah Ahmed
Keyword(s):  
Fuzzy Inference System ◽  
Water Demand ◽  
Sustainable Management ◽  
Fuzzy Inference ◽  
Inference System ◽  
System A

scholarly journals A Soft Computing Approach to Crack Detection and Impact Source Identification with Field-Programmable Gate Array Implementation

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Arati M. Dixit ◽  
Harpreet Singh
Keyword(s):  
Fuzzy Logic ◽  
Soft Computing ◽  
Fuzzy Inference System ◽  
Source Identification ◽  
Field Programmable Gate Array ◽  
Crack Detection ◽  
Fuzzy Inference ◽  
Inference System ◽  
Field Programmable ◽  
Computing Approach

The real-time nondestructive testing (NDT) for crack detection and impact source identification (CDISI) has attracted the researchers from diverse areas. This is apparent from the current work in the literature. CDISI has usually been performed by visual assessment of waveforms generated by a standard data acquisition system. In this paper we suggest an automation of CDISI for metal armor plates using a soft computing approach by developing a fuzzy inference system to effectively deal with this problem. It is also advantageous to develop a chip that can contribute towards real time CDISI. The objective of this paper is to report on efforts to develop an automated CDISI procedure and to formulate a technique such that the proposed method can be easily implemented on a chip. The CDISI fuzzy inference system is developed using MATLAB’s fuzzy logic toolbox. A VLSI circuit for CDISI is developed on basis of fuzzy logic model using Verilog, a hardware description language (HDL). The Xilinx ISE WebPACK9.1i is used for design, synthesis, implementation, and verification. The CDISI field-programmable gate array (FPGA) implementation is done using Xilinx’s Spartan 3 FPGA. SynaptiCAD’s Verilog Simulators—VeriLogger PRO and ModelSim—are used as the software simulation and debug environment.

Studying Fitting Effect by ANFIS in the Electrodialysis Process

2011 ◽  
Vol 268-270 ◽  
pp. 336-339
Author(s):  
Guo Lin Jing ◽  
Wen Ting Du ◽  
Quan Zhou ◽  
Song Tao Li
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Back Propagation ◽  
Fuzzy Model ◽  
Least Square Method ◽  
Least Square ◽  
Back Propagation Algorithm ◽  
Inference System ◽  
Error Back Propagation ◽  
Error Back Propagation Algorithm

Fuzzy system is known to predict model in the electrodialysis process. This paper aimed to study fitting effect by ANFIS in a laboratory scale ED cell. Separation percent of NaCl solution is mainly as a function of concentration, temperature, flow rate and voltage. Besides, ANFIS(Adaptive Neuro-Fuzzy Inference System) based on Sugeno fuzzy model, its structure was similar to neural network and could generate fuzzy rules automatically, using the error back propagation algorithm and least square method to adjust the parameters of fuzzy inference system. We obtained fitted values of separation percent by ANFIS. Separation percent from experiments compared with the fitted values of separation percent. The result is shown that the correlation coefficient is 0.988. Therefore, it is verified as a good performance in the electrodialysis process.

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