367 research outputs found
A Real-Time Unsupervised Neural Network for the Low-Level Control of a Mobile Robot in a Nonstationary Environment
This article introduces a real-time, unsupervised neural network that learns to control a two-degree-of-freedom mobile robot in a nonstationary environment. The neural controller, which is termed neural NETwork MObile Robot Controller (NETMORC), combines associative learning and Vector Associative Map (YAM) learning to generate transformations between spatial and velocity coordinates. As a result, the controller learns the wheel velocities required to reach a target at an arbitrary distance and angle. The transformations are learned during an unsupervised training phase, during which the robot moves as a result of randomly selected wheel velocities. The robot learns the relationship between these velocities and the resulting incremental movements. Aside form being able to reach stationary or moving targets, the NETMORC structure also enables the robot to perform successfully in spite of disturbances in the enviroment, such as wheel slippage, or changes in the robot's plant, including changes in wheel radius, changes in inter-wheel distance, or changes in the internal time step of the system. Finally, the controller is extended to include a module that learns an internal odometric transformation, allowing the robot to reach targets when visual input is sporadic or unreliable.Sloan Fellowship (BR-3122), Air Force Office of Scientific Research (F49620-92-J-0499
A Model of Operant Conditioning for Adaptive Obstacle Avoidance
We have recently introduced a self-organizing adaptive neural controller that learns to control movements of a wheeled mobile robot toward stationary or moving targets, even when the robot's kinematics arc unknown, or when they change unexpectedly during operation. The model has been shown to outperform other traditional controllers, especially in noisy environments. This article describes a neural network module for obstacle avoidance that complements our previous work. The obstacle avoidance module is based on a model of classical and operant conditioning first proposed by Grossberg ( 1971). This module learns the patterns of ultrasonic sensor activation that predict collisions as the robot navigates in an unknown cluttered environment. Along with our original low-level controller, this work illustrates the potential of applying biologically inspired neural networks to the areas of adaptive robotics and control.Office of Naval Research (N00014-95-1-0409, Young Investigator Award
An Unsupervised Neural Network for Real-Time Low-Level Control of a Mobile Robot: Noise Resistance, Stability, and Hardware Implementation
We have recently introduced a neural network mobile robot controller (NETMORC). The controller is based on earlier neural network models of biological sensory-motor control. We have shown that NETMORC is able to guide a differential drive mobile robot to an arbitrary stationary or moving target while compensating for noise and other forms of disturbance, such as wheel slippage or changes in the robot's plant. Furthermore, NETMORC is able to adapt in response to long-term changes in the robot's plant, such as a change in the radius of the wheels. In this article we first review the NETMORC architecture, and then we prove that NETMORC is asymptotically stable. After presenting a series of simulations results showing robustness to disturbances, we compare NETMORC performance on a trajectory-following task with the performance of an alternative controller. Finally, we describe preliminary results on the hardware implementation of NETMORC with the mobile robot ROBUTER.Sloan Fellowship (BR-3122), Air Force Office of Scientific Research (F49620-92-J-0499
Cyclical differences emerge in border city economies
Business cycles ; Maquiladora ; North American Free Trade Agreement
Cyclical differences emerge in border city economies
Maquiladora ; North American Free Trade Agreement ; Business cycles
A design process for the adoption of composite materials and supply chain reconfiguration supported by a software tool
Modelo tipológico-discursivo argumentativo para el aprendizaje-enseñanza de la escritura académica en una universidad privada de la región Lambayeque
El objetivo de esta investigación fue proponer un modelo tipológico-discursivo argumentativo para el aprendizaje-enseñanza de la escritura académica en una universidad privada de la región Lambayeque. El tipo de investigación es descriptivopropositivo, diseño no experimental. Se analizó un corpus de 107 test de escritura
académica aplicado durante el ciclo 2022-I. Los resultados mostraron que la mayoría de los estudiantes universitarios se encuentran en el nivel inicio de aprendizaje
de escritura académica. Se consideró seguir una estrategia didáctica, no lineal, sino
recursiva: revisión de textos, textualización y planificación. Se concluyó que el diagnóstico del nivel de aprendizaje de escritura académica indica el predominio del
aprendizaje en nivel de inicio, con un respaldo significativo del nivel en proceso; en
los fundamentos teóricos que sustentan el modelo tipológico-discursivo argumentativo, se sistematizó un conjunto de ideas y proposiciones que sitúan la escritura
académica argumentativa en el campo cohesionado de las disciplinas epistemoló-
gicas, pedagógicas y sociales; en el diseño del modelo, la construcción de la propuesta emergió desde la aplicación de instrumentos, análisis de datos e identificación de hallazgos; en relación con la validación del modelo, la propuesta fue valorada en su funcionalidad, constitución y coherenci
Gait Activity Classification on Unbalanced Data from Inertial Sensors Using Shallow and Deep Learning
Radical-cation salt with novel BEDT-TTF packing motif containing tris(oxalato)germanate(IV)
The synthesis, crystal structure and resistivity of a new semiconducting BEDT-TTF radical-cation salt containing the tris(oxalato)germanate(IV) anion is reported. BEDT-TTF4[Ge(C2O4)3].0.5dichloromethane crystallizes in the space group P21/c, a = 18.322(7), b = 11.919(4), c = 32.746(11) Å, β = 105.797(5)°, V = 6881(4) Å3, T = 295(1) K, Z = 4. Electrical resistivity measurements show that BEDT-TTF4[Ge(C2O4)3].0.5dichloromethane is a semiconductor with an activation energy of 0.224 eV and room temperature resistivity of 212 Ω cm
Gestión de reclamaciones digitales y competencias transversales en los servidores civiles de la Municipalidad Provincial de Picota, San Martín - 2022
La presente investigación tuvo como objetivo general determinar la relación que existe
entre la gestión de reclamaciones digitales y competencias transversales en los
servidores civiles de la Municipalidad Provincial de Picota, San Martín - 2022. El tipo
de investigación fue básico de diseño no experimental. La muestra fue de 75
servidores civiles de una población que estuvo constituida por 104 servidores de la
entidad. La técnica utilizada fue la encuesta y el instrumento el cuestionario, los cuales
muestran una confiabilidad que supera el 0.9 con lo que se evidencia que estos poseen
un excelente coeficiente de alfa de Cronbach. Asimismo, respecto a lo resultados, estos
arrojaron que la relación existente entre la gestión de reclamaciones digitales y
competencias transversales refleja un coeficiente de correlación de 0,618 y
significancia bilateral de 0.000, de lo que se concluye que la relación existente es alta
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