Reinforcing the Driving Quality of Soccer Playing Robots by Anticipation (Verbesserung der Fahreigenschaften von fußballspielenden Robotern durch Antizipation)

2005 ◽  
Vol 47 (5) ◽  
Author(s):  
Alexander Gloye ◽  
Fabian Wiesel ◽  
Oliver Tenchio ◽  
Mark Simon
Keyword(s):  
Neural Network ◽  
Response Function ◽  
Black Box ◽  
System Response ◽  
Control Software ◽  
Ideal Behavior ◽  
Straight Path ◽  
Control Computer ◽  
The Ideal

SummaryThis paper shows how an omnidirectional robot can learn to correct inaccuracies when driving, or even learn to use corrective motor commands when a motor fails, whether partially or completely. Driving inaccuracies are unavoidable, since not all wheels have the same grip on the surface, or not all motors can provide exactly the same power. When a robot starts driving, the real system response differs from the ideal behavior assumed by the control software. Also, malfunctioning motors are a fact of life that we have to take into account. Our approach is to let the control software learn how the robot reacts to instructions sent from the control computer. We use a neural network, or a linear model for learning the robot's response to the commands. The model can be used to predict deviations from the desired path, and take corrective action in advance, thus increasing the driving accuracy of the robot. The model can also be used to monitor the robot and assess if it is performing according to its learned response function. If it is not, the new response function of the malfunctioning robot can be learned and updated. We show, that even if a robot loses power from a motor, the system can re-learn to drive the robot in a straight path, even if the robot is a black-box and we are not aware of how the commands are applied internally.

The Application Process of Wavelet Neural Network in Intelligent Evaluation of the Quality of Engineering Anchor Poles

2011 ◽  
Vol 243-249 ◽  
pp. 2969-2972
Author(s):  
Rui Jun Li ◽  
Ya Qing Shi ◽  
Jian Suo Ma ◽  
Xi Yan Jiang
Keyword(s):  
Neural Network ◽  
Wavelet Neural Network ◽  
Application Process ◽  
Destructive Testing ◽  
Network Training ◽  
Quality Detection ◽  
Non Destructive ◽  
The Ideal

Most detection means on the anchorage integrity today still remain on the destructive testing level, which can hardly meet the actual needs of quality detection on large volumes of anchor poles in the anchorage engineering. This paper presents the application process of wavelet neural network in the non-destructive intelligent testing on the quality of engineering anchor poles. Taking the project of "Management Buildings and Museum of China Marine Sports School" in Qingdao as an example, this paper uses neural toolbox of MATLAB to do the network training by selecting training and simulation samples. The ideal training results indicate that with the help of neural toolbox of MATLAB, the application process of wavelet neural network can not only make intelligent evaluation of the quality of engineering anchor poles, but also make up traditional means, which can not detect large volumes of anchor poles.

Convolutional Neural Network for Customer’s Opinion on Amazon Products

2019 ◽  
Vol 8 (3) ◽  
pp. 6634-6643 ◽  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Sentiment Analysis ◽  
Latent Dirichlet Allocation ◽  
Opinion Mining ◽  
Text Documents ◽  
Customer Churn ◽  
Learning Classifier ◽  
Review Spam

Opinion mining and sentiment analysis are valuable to extract the useful subjective information out of text documents. Predicting the customer’s opinion on amazon products has several benefits like reducing customer churn, agent monitoring, handling multiple customers, tracking overall customer satisfaction, quick escalations, and upselling opportunities. However, performing sentiment analysis is a challenging task for the researchers in order to find the users sentiments from the large datasets, because of its unstructured nature, slangs, misspells and abbreviations. To address this problem, a new proposed system is developed in this research study. Here, the proposed system comprises of four major phases; data collection, pre-processing, key word extraction, and classification. Initially, the input data were collected from the dataset: amazon customer review. After collecting the data, preprocessing was carried-out for enhancing the quality of collected data. The pre-processing phase comprises of three systems; lemmatization, review spam detection, and removal of stop-words and URLs. Then, an effective topic modelling approach Latent Dirichlet Allocation (LDA) along with modified Possibilistic Fuzzy C-Means (PFCM) was applied to extract the keywords and also helps in identifying the concerned topics. The extracted keywords were classified into three forms (positive, negative and neutral) by applying an effective machine learning classifier: Convolutional Neural Network (CNN). The experimental outcome showed that the proposed system enhanced the accuracy in sentiment analysis up to 6-20% related to the existing systems.

La Formación Continua y el Desarrollo de Competencias en los Docentes en Ejercicio del Nivel Secundario

2017 ◽  
Vol 1 (2) ◽  
Author(s):  
Leonor Alexandra Rodríguez Álava
Keyword(s):  
Teaching Practice ◽  
Training And Development ◽  
Middle Level ◽  
Teaching Skills ◽  
Practicing Teachers ◽  
Active Subject ◽  
Continuous Preparation ◽  
Analysis And Synthesis ◽  
The Ideal

Este artículo está encaminado a caracterizar el proceso de formación continua del docente del nivel medio en ejercicio asociado a la formación y desarrollo de sus competencias docentes, para lo que fueron utilizados métodos como   el análisis y síntesis, inducción y deducción, abstracción y concreción, la entrevista, la encuesta y  el cuestionario, donde a partir de sus resultados se  llega a la consideración de que la formación continua es la vía idónea para la formación y desarrollo de competencias docentes en los profesores en ejercicio, donde se debe asumir un modelo que propicie la reflexión sobre la propia práctica del docente, un clima de colaboración   y el profesor como sujeto activo de ese proceso.   Palabras claves: calidad educativa,   competencias docentes,   educador, estudio, preparación continua,  ABSTRACT   This article aims to characterize the process of education for teachers of middle level associated with exercise training and development of their teaching skills, for which methods were used as analysis and synthesis, induction and deduction, abstraction and concreteness, interview and questionnaire survey, where from their results leads to the consideration that the training is the ideal way for the formation and development of teaching skills in practicing teachers, where they must assume a model that encourages reflection on own teaching practice, a climate of collaboration and the teacher as an active subject of that process Keywords: quality of education, teaching skills, teacher, study, continuous preparation

ANALISIS DE CALIDAD DE SERVICIO EN REDES IPv6

2016 ◽  
Vol 26 (1) ◽  
pp. 7
Author(s):  
Jose Carlos Tavara Carbajal
Keyword(s):  
Quality Of Service ◽  
Packet Loss ◽  
Traffic Control ◽  
Video Traffic ◽  
Control Software ◽  
Delay Jitter ◽  
End To End Delay ◽  
Analysis Of Results ◽  
Ipv6 Protocol

RESUMENEste documento tiene como objetivo analizar el comportamiento de la calidad del servicio del protocolo IPv6 sobre el tráfico de video, para esto se realizó sobre un entorno real y se llevó acabo el análisis de resultados a través de un software estadístico de control del tráfico.Palabras Clave.-  Calidad de Servicio, Ancho de Banda, Retardo, Fluctuación de Retardo, Pérdidas de Paquetes.ABSTRACTThis paper has aimed to analyze of the service quality of the IPv6 protocol on video traffic, this was about a real environment and was conducted analysis of results through statistical traffic control software. Key words- Quality of Service, Bandwidth, End to end delay, Jitter, Packet loss.

ANALISIS DE CALIDAD DE SERVICIO EN REDES IPv6

2016 ◽  
Vol 26 (1) ◽  
pp. 1
Author(s):  
Jose Carlos Tavara Carbajal
Keyword(s):  
Quality Of Service ◽  
Packet Loss ◽  
Traffic Control ◽  
Video Traffic ◽  
Control Software ◽  
Delay Jitter ◽  
End To End Delay ◽  
Analysis Of Results ◽  
Ipv6 Protocol

Este documento tiene como objetivo analizar el comportamiento de la calidad del servicio del protocolo IPv6 sobre el tráfico de video, para esto se realizó sobre un entorno real y se llevó acabo el análisis de resultados a través de un software estadístico de control del tráfico.Palabras Clave.-  Calidad de Servicio, Ancho de Banda, Retardo, Fluctuación de Retardo, Pérdidas de Paquetes.ABSTRACT  This paper has aimed to analyze of the service quality of the IPv6 protocol on video traffic, this was about a real environment and was conducted analysis of results through statistical traffic control software.  Key words.- Quality of Service, Bandwidth, End to end delay, Jitter, Packet loss.

ANALISIS DE CALIDAD DE SERVICIO EN REDES IPv6

2016 ◽  
Vol 26 (1) ◽  
pp. 7
Author(s):  
Jose Carlos Tavara Carbajal
Keyword(s):  
Quality Of Service ◽  
Packet Loss ◽  
Traffic Control ◽  
Video Traffic ◽  
Control Software ◽  
Delay Jitter ◽  
End To End Delay ◽  
Analysis Of Results ◽  
Ipv6 Protocol

RESUMENEste documento tiene como objetivo analizar el comportamiento de la calidad del servicio del protocolo IPv6 sobre el tráfico de video, para esto se realizó sobre un entorno real y se llevó acabo el análisis de resultados a través de un software estadístico de control del tráfico.Palabras Clave.-  Calidad de Servicio, Ancho de Banda, Retardo, Fluctuación de Retardo, Pérdidas de Paquetes.ABSTRACTThis paper has aimed to analyze of the service quality of the IPv6 protocol on video traffic, this was about a real environment and was conducted analysis of results through statistical traffic control software. Keywords- Quality of Service, Bandwidth, End to end delay, Jitter, Packet loss.

Humanism and the Human

2020 ◽  
Author(s):  
Joseph Winters
Keyword(s):  
Critical Theory ◽  
Michel Foucault ◽  
Karl Marx ◽  
Black Studies ◽  
Black Feminism ◽  
Working Through ◽  
A Site ◽  
The Ideal ◽  
Sylvia Wynter

This chapter engages humanism and its fundamental assumptions by working through critical theory, black feminism, and black studies. It contends that there is a tension at the heart of humanism—while the ideal human appears to be the most widespread and available category, it has been constructed over and against certain qualities, beings, and threats. To elaborate on this tension, this chapter revisits the work of authors like Karl Marx and Michel Foucault. Marx acknowledges that the human is a site of conflict and antagonism even as his thought betrays a lingering commitment to progress and humanism. Foucault goes further than Marx by underscoring the fabricated quality of man and the ways in which racism functions to draw lines between those who must live and those who must die. In response to Marx and Foucault’s tendency to privilege Europe, this chapter engages black feminism and Afro-pessimism—Sylvia Wynter, Hortense Spillers, and Frank Wilderson—who show how the figure of the human within humanism is defined in opposition to blackness.

scholarly journals Nodule Detection with Convolutional Neural Network Using Apache Spark and GPU Frameworks

2021 ◽  
Vol 11 (6) ◽  
pp. 2838
Author(s):  
Nikitha Johnsirani Venkatesan ◽  
Dong Ryeol Shin ◽  
Choon Sung Nam
Keyword(s):  
Neural Network ◽  
Radiation Dose ◽  
Convolutional Neural Network ◽  
Model Performance ◽  
Performance Comparison ◽  
Apache Spark ◽  
Training Time ◽  
Learning Framework ◽  
Proposed Model

In the pharmaceutical field, early detection of lung nodules is indispensable for increasing patient survival. We can enhance the quality of the medical images by intensifying the radiation dose. High radiation dose provokes cancer, which forces experts to use limited radiation. Using abrupt radiation generates noise in CT scans. We propose an optimal Convolutional Neural Network model in which Gaussian noise is removed for better classification and increased training accuracy. Experimental demonstration on the LUNA16 dataset of size 160 GB shows that our proposed method exhibit superior results. Classification accuracy, specificity, sensitivity, Precision, Recall, F1 measurement, and area under the ROC curve (AUC) of the model performance are taken as evaluation metrics. We conducted a performance comparison of our proposed model on numerous platforms, like Apache Spark, GPU, and CPU, to depreciate the training time without compromising the accuracy percentage. Our results show that Apache Spark, integrated with a deep learning framework, is suitable for parallel training computation with high accuracy.

scholarly journals Hybrid Reference Current Generation Theory for Solar Fed UPFC System

Energies ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 1527
Author(s):  
R. Senthil Kumar ◽  
K. Mohana Sundaram ◽  
K. S. Tamilselvan
Keyword(s):  
Neural Network ◽  
Power Factor ◽  
Reactive Power ◽  
Voltage Source ◽  
Unity Power Factor ◽  
Current Generation ◽  
Load Voltage ◽  
Reference Current Generation ◽  
Source Current

The extensive usage of power electronic components creates harmonics in the voltage and current, because of which, the quality of delivered power gets affected. Therefore, it is essential to improve the quality of power, as we reveal in this paper. The problems of load voltage, source current, and power factors are mitigated by utilizing the unified power flow controller (UPFC), in which a combination of series and shunt converters are combined through a DC-link capacitor. To retain the link voltage and to maximize the delivered power, a PV module is introduced with a high gain converter, named the switched clamped diode boost (SCDB) converter, in which the grey wolf optimization (GWO) algorithm is instigated for tracking the maximum power. To retain the link-voltage of the capacitor, the artificial neural network (ANN) is implemented. A proper control of UPFC is highly essential, which is achieved by the reference current generation with the aid of a hybrid algorithm. A genetic algorithm, hybridized with the radial basis function neural network (RBFNN), is utilized for the generation of a switching sequence, and the generated pulse has been given to both the series and shunt converters through the PWM generator. Thus, the source current and load voltage harmonics are mitigated with reactive power compensation, which results in attaining a unity power factor. The projected methodology is simulated by MATLAB and it is perceived that the total harmonic distortion (THD) of 0.84% is attained, with almost a unity power factor, and this is validated with FPGA Spartan 6E hardware.

Sign in / Sign up

Export Citation Format

Share Document