Mostrando ítems 1-6 de 6

Analysis of the emotions in a multi-robot system in emergent contexts

Gil, Angel; Puerto Cuadros, Eduard Gilberto; Aguilar, Jose; Dapena, Eladio (Cybernetics and SystemsReino Unido, 2021-01-12)

In this paper, we propose an emotional model for robots in a multi-robot system, in order to allow emerging behaviors. The emotional model uses four universal emotions: anger, disgust, sadness, and joy, assigned to each ...
An Ar2p Deep Learning Architecture for the Discovery and the Selection of Features

Aguilar, Jose; Puerto, E.; Vargas, R.; Reyes, J. (Neural Processing LettersPaíses Bajos, 2019-07-13)

In the context of pattern recognition processes with machine learning algorithms, either through supervised, semi-supervised or unsupervised methods, one of the most important elements to consider are the features that are ...
Autonomous Cycles of Data Analysis Tasks for the Automation of the Production Chain of MSMEs for the Agroindustrial Sector

Fuentes, Jairo; Aguilar, Jose; Montoya, Edwin; Pinto, Ángel (Information (Switzerland), 2024-02-05)

In this paper, we propose autonomous cycles of data analysis tasks for the automation of the production chains aimed to improve the productivity of Micro, Small and Medium Enterprises (MSMEs) in the context of agroindustry. ...
Learning algorithm for the recursive pattern recognition model

Puerto Cuadros, Eduard Gilberto; Aguilar, Jose (Applied Artificial IntelligenceInglaterra, 2016-10-28)

In this work, we incorporate a learning algorithm to the recursive pattern recognition model, based on the systematic functioning of the human neocortex presented in previous works. This algorithm has two mechanisms: the ...
Recognition of the driving style in vehicle drivers

Cordero, Jorge; Aguilar, Kristell; Chávez, Danilo; Puerto, Eduard; Aguilar, Jose (SensorsSuiza, 2020-05-02)

This paper presents three different approaches to recognize driving style based on a hierarchical-model. Specifically, it proposes a hierarchical model for the recognition of the driving style for advanced driver-assistance ...
A recursive patterns matching model for the dynamic pattern recognition problem

Puerto Cuadros, Eduard Gilberto; Aguilar, Jose; Chavez Garcia, Danilo (Applied Artificial IntelligenceReino Unido, 2018-06-04)

This paper defines a new recursive pattern matching model based on the theory of the systemic functioning of the human brain, called pattern recognition theory of mind, in the context of the dynamic pattern recognition ...

Envíos recientes

Analysis of the emotions in a multi-robot system in emergent contexts
...
Gil, Angel | 2021-01-12

In this paper, we propose an emotional model for robots in a multi-robot system, in order to allow emerging behaviors. The emotional model uses four universal emotions: anger, disgust, sadness, and joy, assigned to each robot based on the level of satisfaction of its basic needs. These four universal emotions lie on a spectrum where depending where the emotion of the robot lies, can affect its behavior and of its neighboring robots. The more negative the emotion is, the more individualistic it becomes in its decisions (anger, sadness or disgust). The more positive the robot is in its emotion, the more it will consider the group and global goals (joy). Each robot is able to recognize another robot′s emotion in the system based on their current state, using the AR2P (AR2P for its acronym in Spanish: Algoritmo Recursivo de Reconocimiento de Patrones) recognition algorithm. In this way, it can use this information of the emotions to decide with whom collaborate. Specifically, the paper addresses emotions’ influence on the behavior of the system, at the individual and collective levels, and the emotions’ effects on the emergent behaviors of the multi-robot system. The paper explores the emerging behavior in two multi-robot scenarios; nectar harvesting and object transportation. The results show that the emotions are important to the emergent behavior in a multi-robot system.

LEER
An Ar2p Deep Learning Architecture for the Discovery and the Selection of Features
...
Aguilar, Jose | 2019-07-13

In the context of pattern recognition processes with machine learning algorithms, either through supervised, semi-supervised or unsupervised methods, one of the most important elements to consider are the features that are used to represent the phenomenon to be studied. In this sense, this paper proposes a deep learning architecture for Ar2p, which is based on supervised and unsupervised mechanisms for the discovery and the selection of features for classification problems (called Ar2p-DL). Ar2p is an algorithm of pattern recognition based on the systematic functioning of the human brain. Ar2p-DL is composed of three phases: the first phase, called feature analysis, is supported by two feature-engineering approaches to discover or select atomic features/descriptors. The feature engineering approach used for the discovery, is based on a classical clustering technique, K-means; and the approach used for the selection, is based on a classification technique, Random Forest. The second phase, called aggregation, creates a feature hierarchy (merge of descriptors) from the atomic features/descriptors (it uses as aggregation strategy the DBSCAN algorithm). Finally, the classification phase carries out the classification of the inputs based on the feature hierarchy, using the classical Ar2p algorithm. The last phase of Ar2p-DL uses a supervised learning approach, while the first phases combine supervised and unsupervised learning approaches. To analyze the performance of Ar2p-DL, several tests have been made using different benchmarks (datasets) from the UCI Machine Learning Repository, in order to compare Ar2p-DL with other classification methods.

LEER
Autonomous Cycles of Data Analysis Tasks for the Automation of the Production Chain of MSMEs for the Agroindustrial Sector
...
Fuentes, Jairo | 2024-02-05

In this paper, we propose autonomous cycles of data analysis tasks for the automation of the production chains aimed to improve the productivity of Micro, Small and Medium Enterprises (MSMEs) in the context of agroindustry. In the autonomous cycles of data analysis tasks, each task interacts with the others and has different functions, in order to reach the goal of the cycle. In this article, we identify three industrial-automation processes within the production chain, in which autonomous cycles can be applied. The first cycle is responsible to identify the type of input to be transformed—such as quantity, quality, time, and cost—based on information from the organization and its context. The second cycle selects the technological level used in the raw-material transformation, characterizing the platform of plant processing. The last cycle identifies the level of specialization of the generated product, such as the quality and value of the product. Finally, we apply the first autonomous cycle to define the type of input to be transformed in a coffee factory.

LEER
Learning algorithm for the recursive pattern recognition model
...
Puerto Cuadros, Eduard Gilberto | 2016-10-28

In this work, we incorporate a learning algorithm to the recursive pattern recognition model, based on the systematic functioning of the human neocortex presented in previous works. This algorithm has two mechanisms: the first, called Aprendizaje_nuevo, is used to learn new patterns and creates a new pattern recognition module in the model. The other, called Aprendizaje_por_refuerzo, is used to reinforce a pattern and adapts the module that represents the pattern to the changes in it. The algorithm is tested in various contexts (text and images) to analyze its capacities of learning and of recognition of the model.

LEER
Recognition of the driving style in vehicle drivers
...
Cordero, Jorge | 2020-05-02

This paper presents three different approaches to recognize driving style based on a hierarchical-model. Specifically, it proposes a hierarchical model for the recognition of the driving style for advanced driver-assistance systems (ADAS) for vehicles. This hierarchical model for the recognition of the style of the car driving considers three aspects: the driver emotions, the driver state, and finally, the driving style itself. In this way, the proposed hierarchical pattern is composed of three levels of descriptors/features, one to recognize the emotional states, another to recognize the driver state, and the last one to recognize the driving style. Each level has a set of descriptors, which can be sensed in a real context. Finally, the paper presents three driving style recognition algorithms based on different paradigms. One is based on fuzzy logic, another is based on chronicles (a temporal logic paradigm), and the last is based on an algorithm that uses the idea of the recognition process of the neocortex, called Ar2p (Algoritmo Recursivo de Reconocimiento de Patrones, for its acronym in Spanish). In the paper, these approaches are compared using real datasets, using different metrics of interest in the context of the Internet of the Things, in order to determine their capabilities of reasoning, adaptation, and the communication of information. In general, the initial results are encouraging, specifically in the cases of chronicles and Ar2p, which give the best results.

LEER
A recursive patterns matching model for the dynamic pattern recognition problem
...
Puerto Cuadros, Eduard Gilberto | 2018-06-04

This paper defines a new recursive pattern matching model based on the theory of the systemic functioning of the human brain, called pattern recognition theory of mind, in the context of the dynamic pattern recognition problem. Dynamic patterns are characterized by having properties that change in intervals of time, such as a pedestrian walking or a car running (the negation of a dynamic pattern is a static pattern). Novel contributions of this paper include: (1) Formally develop the concepts of dynamic and static pattern, (2) design a recursive pattern matching model, which exploits the idea of recursivity and time series in the recognition process, and the unbundling/integration of pattern to recognize, and (3) develop strategies of pattern matching from two major orientations: recognition of dynamic patterns oriented by characteristic, or oriented by perception. The model is instantiated in several cases, to analyze its performance.

LEER

‹›

Repositorio Digital

Listar BA. Grupos de Investigación por autor "50ad5b371a57d0bc8149a95c0f62781c"

Analysis of the emotions in a multi-robot system in emergent contexts

An Ar2p Deep Learning Architecture for the Discovery and the Selection of Features

Autonomous Cycles of Data Analysis Tasks for the Automation of the Production Chain of MSMEs for the Agroindustrial Sector

Learning algorithm for the recursive pattern recognition model

Recognition of the driving style in vehicle drivers

A recursive patterns matching model for the dynamic pattern recognition problem

Envíos recientes

Analysis of the emotions in a multi-robot system in emergent contexts

An Ar2p Deep Learning Architecture for the Discovery and the Selection of Features

Autonomous Cycles of Data Analysis Tasks for the Automation of the Production Chain of MSMEs for the Agroindustrial Sector

Learning algorithm for the recursive pattern recognition model

Recognition of the driving style in vehicle drivers

A recursive patterns matching model for the dynamic pattern recognition problem

Portales Institucionales

Enlaces de Interés

Contactos

Listar BA. Grupos de Investigación por autor "50ad5b371a57d0bc8149a95c0f62781c"

Analysis of the emotions in a multi-robot system in emergent contexts ﻿

An Ar2p Deep Learning Architecture for the Discovery and the Selection of Features ﻿

Autonomous Cycles of Data Analysis Tasks for the Automation of the Production Chain of MSMEs for the Agroindustrial Sector ﻿

Learning algorithm for the recursive pattern recognition model ﻿

Recognition of the driving style in vehicle drivers ﻿

A recursive patterns matching model for the dynamic pattern recognition problem ﻿

Envíos recientes

Analysis of the emotions in a multi-robot system in emergent contexts

An Ar2p Deep Learning Architecture for the Discovery and the Selection of Features

Autonomous Cycles of Data Analysis Tasks for the Automation of the Production Chain of MSMEs for the Agroindustrial Sector

Learning algorithm for the recursive pattern recognition model

Recognition of the driving style in vehicle drivers

A recursive patterns matching model for the dynamic pattern recognition problem

Portales Institucionales

Analysis of the emotions in a multi-robot system in emergent contexts

An Ar2p Deep Learning Architecture for the Discovery and the Selection of Features

Autonomous Cycles of Data Analysis Tasks for the Automation of the Production Chain of MSMEs for the Agroindustrial Sector

Learning algorithm for the recursive pattern recognition model

Recognition of the driving style in vehicle drivers

A recursive patterns matching model for the dynamic pattern recognition problem