Synergistic control of forearm based on accelerometer data and artificial neural networks

In the present study, we modeled a reaching task as a two-link mechanism. The upper arm and forearm motion trajectories during vertical arm movements were estimated from the measured angular accelerations with dual-axis accelerometers. A data set of reaching synergies from able-bodied individuals was used to train a radial basis function artificial neural network with upper arm/forearm tangential angular accelerations. The trained radial basis function artificial neural network for the specific movements predicted forearm motion from new upper arm trajectories with high correlation (mean, 0.9149-0.941). For all other movements, prediction was low (range, 0.0316-0.8302). Results suggest that the proposed algorithm is successful in generalization over similar motions and subjects. Such networks may be used as a high-level controller that could predict forearm kinematics from voluntary movements of the upper arm. This methodology is suitable for restoring the upper limb functions of individuals with motor disabilities of the forearm, but not of the upper arm. The developed control paradigm is applicable to upper-limb orthotic systems employing functional electrical stimulation. The proposed approach is of great significance particularly for humans with spinal cord injuries in a free-living environment. The implication of a measurement system with dual-axis accelerometers, developed for this study, is further seen in the evaluation of movement during the course of rehabilitation. For this purpose, training-related changes in synergies apparent from movement kinematics during rehabilitation would characterize the extent and the course of recovery. As such, a simple system using this methodology is of particular importance for stroke patients. The results underlie the important issue of upper-limb coordination.

Artificial neural network models for predicting 1-year mortality in elderly patients with intertrochanteric fractures in China

The mortality rate of older patients with intertrochanteric fractures has been increasing with the aging of populations in China. The purpose of this study was: 1) to develop an artificial neural network (ANN) using clinical information to predict the 1-year mortality of elderly patients with intertrochanteric fractures, and 2) to compare the ANN's predictive ability with that of logistic regression models. The ANN model was tested against actual outcomes of an intertrochanteric femoral fracture database in China. The ANN model was generated with eight clinical inputs and a single output. ANN's performance was compared with a logistic regression model created with the same inputs in terms of accuracy, sensitivity, specificity, and discriminability. The study population was composed of 2150 patients (679 males and 1471 females): 1432 in the training group and 718 new patients in the testing group. The ANN model that had eight neurons in the hidden layer had the highest accuracies among the four ANN models: 92.46 and 85.79% in both training and testing datasets, respectively. The areas under the receiver operating characteristic curves of the automatically selected ANN model for both datasets were 0.901 (95%CI=0.814-0.988) and 0.869 (95%CI=0.748-0.990), higher than the 0.745 (95%CI=0.612-0.879) and 0.728 (95%CI=0.595-0.862) of the logistic regression model. The ANN model can be used for predicting 1-year mortality in elderly patients with intertrochanteric fractures. It outperformed a logistic regression on multiple performance measures when given the same variables.

HYBRID ARTIFICIAL NEURAL NETWORK APPLIEDTO MODELING SCFE OF BASIL AND ROSEMARY OILS

This work presents the results of a Hybrid Neural Network (HNN) technique as applied to modeling SCFE curves obtained from two Brazilian vegetable matrices. A series Hybrid Neural Network was employed to estimate the parameters of the phenomenological model. A small set of SCFE data of each vegetable was used to generate an extended data set, sufficient to train the network. Afterwards, other sets of experimental data, not used in the network training, were used to validate the present approach. The series HNN correlates well the experimental data and it is shown that the predictions accomplished with this technique may be promising for SCFE purposes.

Previsão de estabilidade de óleos vegetais através da rede de inteligência artificial

Redes Neurais Artificiais são técnicas computacionais que se utilizam de um modelo matemático capaz de adquirir conhecimentos pela experiência; esse comportamento inteligente da rede provém das interações entre unidades de processamento, denominadas de neurônios artificiais. O objetivo deste trabalho foi criar uma rede neural capaz de prever a estabilidade de óleos vegetais, a partir de dados de suas composições químicas, visando um modelo para a previsão da shelf-life de óleos vegetais, tendo como parâmetros apenas dados de suas composições químicas. Os primeiros passos do processo de desenvolvimento da rede consistiram na coleta de dados relativos ao problema e sua separação em um conjunto de treinamento e outro de testes. Estes conjuntos apresentaram como variáveis dados de composição química, que incluíram os valores totais em ácidos graxos, fenóis, tocoferóis e a composição individual em ácidos graxos. O passo seguinte foi a execução do treinamento, onde o padrão de entrada apresentado à rede como parâmetro de estabilidade foi o índice de peróxido, determinado experimentalmente por um período de 16 dias de armazenagem na ausência de luz, a 65ºC. Após o treinamento foi testada a capacidade de previsão adquirida pela rede, em função do parâmetro de estabilidade adotado, mas com um novo grupo de óleos. Seguindo o teste, foi determinada a correlação linear entre os valores de estabilidade previstos pela rede e aqueles determinados experimentalmente. Com os resultados obtidos, pode-se confirmar a viabilidade de previsão da estabilidade de óleos vegetais pela rede neural, a partir de dados de sua composição química, utilizando como parâmetro de estabilidade o índice de peróxido.

An artificial neural network model for prediction of quality characteristics of apples during convective dehydration

In this study, the effects of hot-air drying conditions on color, water holding capacity, and total phenolic content of dried apple were investigated using artificial neural network as an intelligent modeling system. After that, a genetic algorithm was used to optimize the drying conditions. Apples were dried at different temperatures (40, 60, and 80 °C) and at three air flow-rates (0.5, 1, and 1.5 m/s). Applying the leave-one-out cross validation methodology, simulated and experimental data were in good agreement presenting an error < 2.4 %. Quality index optimal values were found at 62.9 °C and 1.0 m/s using genetic algorithm.

Artificial neural networks (ANN): prediction of sensory measurements from instrumental data

The objective of this study was to predict by means of Artificial Neural Network (ANN), multilayer perceptrons, the texture attributes of light cheesecurds perceived by trained judges based on instrumental texture measurements. Inputs to the network were the instrumental texture measurements of light cheesecurd (imitative and fundamental parameters). Output variables were the sensory attributes consistency and spreadability. Nine light cheesecurd formulations composed of different combinations of fat and water were evaluated. The measurements obtained by the instrumental and sensory analyses of these formulations constituted the data set used for training and validation of the network. Network training was performed using a back-propagation algorithm. The network architecture selected was composed of 8-3-9-2 neurons in its layers, which quickly and accurately predicted the sensory texture attributes studied, showing a high correlation between the predicted and experimental values for the validation data set and excellent generalization ability, with a validation RMSE of 0.0506.

Tuberculosis in renal transplant patients: The experience of a single center in Medellín-Colombia, 2005-2013

Introduction: Tuberculosis is a common opportunistic infection in renal transplant patients. Objective: To obtain a clinical and laboratory description of transplant patients diagnosed with tuberculosis and their response to treatment during a period ranging from 2005 to 2013 at the Pablo Tobón Uribe Hospital. Methods: Retrospective and descriptive study. Results: In 641 renal transplants, tuberculosis was confirmed in 12 cases. Of these, 25% had a history of acute rejection, and 50% had creatinine levels greater than 1.5 mg/dl prior to infection. The disease typically presented as pulmonary (50%) and disseminated (33.3%). The first phase of treatment consisted of 3 months of HZRE (isoniazid, pyrazinamide, rifampicin and ethambutol) in 75% of the cases and HZME (isoniazid, pyrazinamide, moxifloxacin and ethambutol) in 25% of the cases. During the second phase of the treatment, 75% of the cases received isoniazid and rifampicin, and 25% of the cases received isoniazid and ethambutol. The length of treatment varied between 6 and 18 months. In 41.7% of patients, hepatotoxicity was associated with the beginning of anti-tuberculosis therapy. During a year-long follow-up, renal function remained stable, and the mortality rate was 16.7%. Conclusion: Tuberculosis in the renal transplant population studied caused diverse nonspecific symptoms. Pulmonary and disseminated tuberculosis were the most frequent forms and required prolonged treatment. Antituberculosis medications had a high toxicity and mortality. This infection must be considered when patients present with a febrile syndrome of unknown origin, especially during the first year after renal transplant.

Inteligência artificial e teoria de resolução de problemas

Este artigo é uma tentativa de delinear as principais características da pesquisa numa nova área de estudos a chamada Inteligência Artificial (AI). Os itens 1 e 2 constituem um rápido histórico da AI e seus pressupostos básicos. O item 3 trata da teoria de resolução de problemas, desenvolvida por A. Newell e H. Simon. O item 4 procura mostrar a relevância da AI para a Filosofia, em especial para a filosofia da Mente e para a Teoria do Conhecimento.

Robots, intencionalidade e inteligência artificial

O artigo aborda problemas filosóficos relativos à natureza da intencionalidade e da representação mental. A primeira parte apresenta um breve histórico dos problemas, percorrendo rapidamente alguns episódios da filosofia clássica e da filosofia contemporânea. A segunda parte examina o Chinese Room Argument (Argumento do Quarto do Chinês) formulado por J. Searle. A terceira parte desenvolve alguns argumentos visando mostrar a inadequação do modelo funcionalista de mente na construção de robots. A conclusão (quarta parte) aponta algumas alternativas ao modelo funcionalista tradicional, como, por exemplo, o conexionismo.

Modelling the colour difference by artificial neural network

This thesis work studies the modelling of the colour difference using artificial neural network. Multilayer percepton (MLP) network is proposed to model CIEDE2000 colour difference formula. MLP is applied to classify colour points in CIE xy chromaticity diagram. In this context, the evaluation was performed using Munsell colour data and MacAdam colour discrimination ellipses. Moreover, in CIE xy chromaticity diagram just noticeable differences (JND) of MacAdam ellipses centres are computed by CIEDE2000, to compare JND of CIEDE2000 and MacAdam ellipses. CIEDE2000 changes the orientation of blue areas in CIE xy chromaticity diagram toward neutral areas, but on the whole it does not totally agree with the MacAdam ellipses. The proposed MLP for both modelling CIEDE2000 and classifying colour points showed good accuracy and achieved acceptable results.

Alianzas estrategicas y encadenamiento productivo Colombia - España - India

Bogotá Emprende

Cómo clasificar correctamente su negocio y no fracasar en el intento : Revisión 3.1 adaptada y revisada para Colombia por el DANE

Bogotá Emprende

Demanda de productos Bogotanos en las 10 principales ciudades de Colombia

Bogotá Emprende