scholarly journals GPS Data Correction Based on Fuzzy Logic for Tracking Land Vehicles

Mathematics ◽  
2021 ◽  
Vol 9 (21) ◽  
pp. 2818
Author(s):  
Pedro J. Correa-Caicedo ◽  
Horacio Rostro-González ◽  
Martin A. Rodriguez-Licea ◽  
Óscar Octavio Gutiérrez-Frías ◽  
Carlos Alonso Herrera-Ramírez ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Embedded System ◽  
Fuzzy Systems ◽  
Intelligent System ◽  
Unscented Kalman Filter ◽  
The Other ◽  
Linear Functions ◽  
Sensor Technology ◽  
Raspberry Pi ◽  
Land Vehicles

GPS sensors are widely used to know a vehicle’s location and to track its route. Although GPS sensor technology is advancing, they present systematic failures depending on the environmental conditions to which they are subjected. To tackle this problem, we propose an intelligent system based on fuzzy logic, which takes the information from the sensors and correct the vehicle’s absolute position according to its latitude and longitude. This correction is performed by two fuzzy systems, one to correct the latitude and the other to correct the longitude, which are trained using the MATLAB ANFIS tool. The positioning correction system is trained and tested with two different datasets. One of them collected with a Pmod GPS sensor and the other a public dataset, which was taken from routes in Brazil. To compare our proposal, an unscented Kalman filter (UKF) was implemented. The main finding is that the proposed fuzzy systems achieve a performance of 69.2% higher than the UKF. Furthermore, fuzzy systems are suitable to implement in an embedded system such as the Raspberry Pi 4. Another finding is that the logical operations facilitate the creation of non-linear functions because of the ‘if else’ structure. Finally, the existence justification of each fuzzy system section is easy to understand.

Intelligent Hybrid System: A Reliability-Based Failure Management Application

2001 ◽  
Vol 5 (6) ◽  
pp. 307-314 ◽  
Author(s):  
Jose Aguilar ◽  
◽  
Mariela Cerrad ◽  
Katiuska Morillo ◽  
◽  
...  
Keyword(s):  
Neural Networks ◽  
Genetic Algorithms ◽  
Fuzzy Logic ◽  
Historical Data ◽  
Intelligent System ◽  
The Other ◽  
Hybrid Architecture ◽  
System A ◽  
Reliability Centered Maintenance ◽  
Management Application

The integration of different intelligent techniques (such as Artificial Neural Networks, Fuzzy Logic, Genetic Algorithms, etc.) into a hybrid architecture allows to overcome their individual limitations. In industrial environments, these intelligent techniques can be combined to reach more effective solutions to complex problems. On the other hand, failure management in processes, equipment or plants, acquires more importance in modern industry every day, in order to minimize unexpected faults and guaranties a greater reliability, safety, disposition and productivity in the industry. In this paper, an intelligent system is designed for failure management based on Reliability Centered Maintenance methodology, Fuzzy Logic and Neural Networks. The system proposes the maintenance tasks according to the historical data of the equipment.

scholarly journals Helicopter Control Using Fuzzy Logic and Narma-L2 Techniques

2020 ◽  
Vol 12 (5) ◽  
pp. 1-14
Author(s):  
Noor Salam Al-Fallooji ◽  
◽  
Maysam Abbod
Keyword(s):  
Fuzzy Logic ◽  
Intelligent System ◽  
Moving Average ◽  
Linear Quadratic Regulator ◽  
Optimization Method ◽  
The Other ◽  
Linear Quadratic ◽  
Lqr Controller ◽  
Artificial Neural Network Ann ◽  
Yaw Angle

Helicopter instability is one of the most limitations that should be addressed in a nonlinear application. Accordingly, researchers are invited to design a robust and reliable controller to obtain a stable system and enhance its overall performance. The present study focuses on the use of the intelligent system in controlling the pitch and yaw angles. This lead to controlling the elevation and the direction of the helicopter. Further to the application of the Linear Quadratic Regulator (LQR) controller, this research implemented the Proportional Integral Derivative (PID), Fuzzy Logic Control (FLC), and Artificial Neural Network (ANN). The results show that FLC achieved a good controllability for both angles, particularly for the pitch angle in comparison to the nonlinear auto regressive moving average (NARMA-L2). Moreover, NARMA-L2 requires further improvement by using, for example, the swarm optimization method to provide better controllability. The PID controller, on the other hand, had a greater capability in controlling the yaw angle in comparison to the other controllers implemented. Accordingly, it is suggested that the integration of PID and FLC may lead to more optimal outcomes.

scholarly journals Soil Fertility Review using Fuzzy Logic

2021 ◽  
Author(s):  
Gunasekaran Prabakaran ◽  
◽  
Dhandapani Vaithiyanathan ◽  
Madhavi Ganesan ◽  
◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Soil Fertility ◽  
Fuzzy Systems ◽  
Positive Impact ◽  
Economic Status ◽  
Environmental Issues ◽  
The Other ◽  
Future Generations ◽  
Cropping Pattern ◽  
Socio Economic Status

The goal of the study is to improve and maintain the soil fertility. Fundamentally the term soil fertility covers larger proposition and often encompass environmental issues. There had been many attempts addressing the hurdles encountered in ensuring soil fertility. Analyzing the data about the fertilizers consumption by the farmers, we demonstrate the effectiveness of fuzzy based system in achieving maximum productivity together with high cognizance to soil fertility. The proposed fuzzy systems address the solution of the soil luxuriance hurdles in terms of pesticides poisoning especially farmland. The usage of pesticides poses a serious threat to the health of the environment affecting adversely the future generations. On the other hand, it is important not only to preserve soil fertility but also to plant the crops as well. The proposed work have been constructed based on the usage of fertilizers with diverse cropping pattern randomly selected during a pair of cropping cycle and found that the repetition of the cycle failing miserably as the soil fertility gravely damaged. The suggested procedure will enhance the environmental ecosystem and improving the socio-economic status of farmers. Moreover, this increase in the farm products has a positive impact to the Gross Domestic Product (GDP) of a country.

The Use of Fuzzy Logic for Online Monitoring of Manufacturing Machine: An Intelligent System

2017 ◽  
Vol 2 (11) ◽  
pp. 31-39 ◽  
Author(s):  
Ashner Gerald P. Novilla ◽  
August Anthony N. Balute ◽  
Dennis B. Gonzales
Keyword(s):  
Fuzzy Logic ◽  
Embedded System ◽  
Monitoring System ◽  
Intelligent System ◽  
Human Labor ◽  
The Status ◽  
Fuzzy Logic Approach ◽  
Iot Devices ◽  
Mean Time ◽  
Manufacturing Machine

Internet of Things (IoT) has positioned itself at the front of rapid technological advancements in order to lessen human labor. The concept of making the world smart arises from the basis that “things” can be connected using the Internet. In this paper, the researchers will be describing an effective implementation of an intelligent monitoring system for manufacturing machine unit which is used in industry environment. The propose system design can be installed in manufacturing machines in order to solve management problems, maintenance, shortens the mean time to repair and predict mean time to fail. The researchers have designed a system based on internet of thing for monitoring using the fuzzy logic approach. It consists of monitoring the normal activities of the manufacturing machines in order to build a reference of its condition; then a real-time monitoring and analysis of gathered data from the sensors is accomplished. The status is carried out using a Fuzzy Logic based network. It will give the users the current activity of the manufacturing machine and will also provide information about health status. This system had been incorporated through internet using host, network, Ethernet module, embedded system gateway, sensors, microcontroller unit (MCU) and other components. This paper discusses how monitoring system can be implemented and how the use of cloud computing technology along with IoT devices can be used so that the data collected by these devices can be safely stored, monitored and analyzed.

A Gentle Introduction to Fuzzy Logic and Its Applications to Intelligent Agents Design

2011 ◽  
pp. 61-93
Author(s):  
Andre de Korvin
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Sets ◽  
Intelligent Agents ◽  
Fuzzy Systems ◽  
Intelligent Systems ◽  
Intelligent System ◽  
Membership Functions ◽  
Neuro Fuzzy ◽  
Sugeno Model ◽  
Using Data

The purpose of this chapter is to present the key properties of fuzzy logic and adaptive nets and demonstrate how to use these, separately and in combination, to design intelligent systems. The first section introduces the concept of fuzzy sets and their basic operations. The t and s norms are used to define a variety of possible intersections and unions. The next section shows two ways to estimate membership functions, polling experts, and using data to optimize parameters. Section three shows how any function can be extended to arguments that are fuzzy sets. Section four introduces fuzzy relations, fuzzy reasoning, and shows the first steps to be taken to design an intelligent system. The Mamdami model is defined in this section. Reinforcement-driven agents are discussed in section five. Sections six and seven establish the basic properties of adaptive nets and use these to define the Sugeno model. Finally, the last section discusses neuro-fuzzy systems in general. The solution to the inverted pendulum problem is given by use of fuzzy systems and also by the use of adaptive nets. The ANFIS and CANFIS architectures are defined.

scholarly journals Air Learning: a deep reinforcement learning gym for autonomous aerial robot visual navigation

2021 ◽  
Author(s):  
Srivatsan Krishnan ◽  
Behzad Boroujerdian ◽  
William Fu ◽  
Aleksandra Faust ◽  
Vijay Janapa Reddi
Keyword(s):  
Reinforcement Learning ◽  
Embedded System ◽  
Broad Class ◽  
Visual Navigation ◽  
Raspberry Pi ◽  
Latency Distribution ◽  
Hardware In The Loop ◽  
Resource Constrained ◽  
Aerial Robot ◽  
Policy Optimization

AbstractWe introduce Air Learning, an open-source simulator, and a gym environment for deep reinforcement learning research on resource-constrained aerial robots. Equipped with domain randomization, Air Learning exposes a UAV agent to a diverse set of challenging scenarios. We seed the toolset with point-to-point obstacle avoidance tasks in three different environments and Deep Q Networks (DQN) and Proximal Policy Optimization (PPO) trainers. Air Learning assesses the policies’ performance under various quality-of-flight (QoF) metrics, such as the energy consumed, endurance, and the average trajectory length, on resource-constrained embedded platforms like a Raspberry Pi. We find that the trajectories on an embedded Ras-Pi are vastly different from those predicted on a high-end desktop system, resulting in up to $$40\%$$ 40 % longer trajectories in one of the environments. To understand the source of such discrepancies, we use Air Learning to artificially degrade high-end desktop performance to mimic what happens on a low-end embedded system. We then propose a mitigation technique that uses the hardware-in-the-loop to determine the latency distribution of running the policy on the target platform (onboard compute on aerial robot). A randomly sampled latency from the latency distribution is then added as an artificial delay within the training loop. Training the policy with artificial delays allows us to minimize the hardware gap (discrepancy in the flight time metric reduced from 37.73% to 0.5%). Thus, Air Learning with hardware-in-the-loop characterizes those differences and exposes how the onboard compute’s choice affects the aerial robot’s performance. We also conduct reliability studies to assess the effect of sensor failures on the learned policies. All put together, Air Learning enables a broad class of deep RL research on UAVs. The source code is available at: https://github.com/harvard-edge/AirLearning.

scholarly journals Supervised committee machine with artificial intelligence for prediction of fluoride concentration

2013 ◽  
Vol 15 (4) ◽  
pp. 1474-1490 ◽  
Author(s):  
Ata Allah Nadiri ◽  
Elham Fijani ◽  
Frank T.-C. Tsai ◽  
Asghar Asghari Moghaddam
Keyword(s):  
Artificial Intelligence ◽  
Fuzzy Logic ◽  
Ground Water ◽  
Intelligent System ◽  
Fluoride Concentration ◽  
Mixing Zone ◽  
Ann Model ◽  
Committee Machine ◽  
Hydrogeological Characteristics ◽  
Model Predictions

The study introduces a supervised committee machine with artificial intelligence (SCMAI) method to predict fluoride in ground water of Maku, Iran. Ground water is the main source of drinking water for the area. Management of fluoride anomaly needs better prediction of fluoride concentration. However, the complex hydrogeological characteristics cause difficulties to accurately predict fluoride concentration in basaltic formation, non-basaltic formation, and mixing zone. SCMAI predicts fluoride by a nonlinear combination of individual AI models through an artificial intelligent system. Factor analysis is used to identify effective fluoride-correlated hydrochemical parameters as input to AI models. Four AI models, Sugeno fuzzy logic, Mamdani fuzzy logic, artificial neural network (ANN), and neuro-fuzzy are employed to predict fluoride concentration. The results show that all of these models have similar fitting to the fluoride data in the Maku area, and do not predict well for samples in the mixing zone. The SCMAI employs an ANN model to re-predict the fluoride concentration based on the four AI model predictions. The result shows improvement to the CMAI method, a committee machine with the linear combination of AI model predictions. The results also show significant fitting improvement to individual AI models, especially for fluoride prediction in the mixing zone.

The Use of Fuzzy Logic in Shipping and Shipbuilding Market Modeling, Analysis, and Forecasting

1996 ◽  
Vol 12 (02) ◽  
pp. 85-98
Author(s):  
Jun Li ◽  
Michael G. Parsons
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Systems ◽  
Freight Rate ◽  
New Order ◽  
Market Mechanisms ◽  
Promising Alternative ◽  
Market Participants ◽  
Model Free ◽  
Modeling Analysis ◽  
Oil Tanker

Fuzzy logic is a technique that attempts to systematically and mathematically emulate human reasoning. This paper investigates the feasibility of applying fuzzy logic to transportation and shipbuilding market modeling, analysis and forecasting. Fuzzy systems called fuzzy decision modelers (FDMs) are developed based on fuzzy logic techniques to model the crude oil tanker freight rate market, the tanker new order market and the tanker scrapping market. Our results show that the FDMs are able to model and forecast these economic systems very well. In addition, the FDMs also provide valuable insights into market mechanisms and market participants' decision-making patterns. The FDMs are mathematical model-free, nonlinear systems capable of capturing complicated relationships among economic variables. The FDMs are easy to develop and easy to interpret. These advantages of fuzzy systems suggest that fuzzy logic techniques are a promising alternative in shipping and shipbuilding market modeling, analysis and forecasting.

RANCANG BANGUN INTERNET OF THINGS SERVER DENGAN MENGGUNAKAN ACTIVEMQ ARTEMIS UNTUK MITIGASI BENCANA DI RASPBERRY PI 3

Transmisi ◽  
2020 ◽  
Vol 22 (4) ◽  
pp. 117-122
Author(s):  
Sadr Lufti Mufreni ◽  
Esi Putri Silmina
Keyword(s):  
Internet Of Things ◽  
Embedded System ◽  
Raspberry Pi

Indonesia merupakan negara kepulauan yang mempunyai lebih dari 13.000 pulau. Wilayahnya terletak di antara Samudera Hindia dan Samudera Pasifik dan dilewati oleh Pacific Ring of Fire sehingga banyak gunung berapi aktif. Berdasarkan letak geografis mempunyai potensi tsunami dan gempa bumi cukup tinggi. Diperlukan rencana penanggulangan bencana yang baik untuk menekan risiko yang bisa terjadi, salah satunya dengan mitigasi bencana. Mitigasi bencana adalah serangkaian upaya untuk mengurangi risiko bencana, baik melalui pembangunan fisik maupun penyadaran dan peningkatan kemampuan menghadapi ancaman bencana. Mitigasi bencana diperlukan untuk mengurangi dampak yang ditimbulkan terutama korban jiwa. Salah satunya dengan menggunakan sistem peringatan dini. Sistem peringatan dini terdiri dari 3 komponen utama yaitu sensor untuk mendapatkan nilai dari suatu lingkungan, controller untuk mengolah nilai yang diterima, dan aksi yang dilakukan berdasarkan hasil dari pengolahan. Untuk membuat sistem yang efektif diperlukan komunikasi yang memadai. Messaging queue digunakan oleh industri untuk komunikasi antar perangkat lunak, perangkat keras, dan embedded system. Penelitian berfokus pada penggunaan ActiveMQ Artemis sebagai messaging queue sebagai server untuk komunikasi dengan internet of things (IoT). Keunggulan ActiveMQ Artemis dapat dijalankan di Raspberry Pi 3 dengan sedikit modifikasi. Hasil penelitian membuktikan bahwa ActiveMQ Artemis dapat digunakan untuk komunikasi IoT pada simulasi sistem mitigasi bencana.

Sign in / Sign up

Export Citation Format

Share Document