scholarly journals A Neural Network Model for Learning 3D Object Representations Through Haptic Exploration

2021 ◽  
Vol 15 ◽  
Author(s):  
Xiaogang Yan ◽  
Steven Mills ◽  
Alistair Knott

Humans initially learn about objects through the sense of touch, in a process called “haptic exploration.” In this paper, we present a neural network model of this learning process. The model implements two key assumptions. The first is that haptic exploration can be thought of as a type of navigation, where the exploring hand plays the role of an autonomous agent, and the explored object is this agent's “local environment.” In this scheme, the agent's movements are registered in the coordinate system of the hand, through slip sensors on the palm and fingers. Our second assumption is that the learning process rests heavily on a simple model of sequence learning, where frequently-encountered sequences of hand movements are encoded declaratively, as “chunks.” The geometry of the object being explored places constraints on possible movement sequences: our proposal is that representations of possible, or frequently-attested sequences implicitly encode the shape of the explored object, along with its haptic affordances. We evaluate our model in two ways. We assess how much information about the hand's actual location is conveyed by its internal representations of movement sequences. We also assess how effective the model's representations are in a reinforcement learning task, where the agent must learn how to reach a given location on an explored object. Both metrics validate the basic claims of the model. We also show that the model learns better if objects are asymmetrical, or contain tactile landmarks, or if the navigating hand is articulated, which further constrains the movement sequences supported by the explored object.

2020 ◽  
Vol 11 (3) ◽  
pp. 178
Author(s):  
Syamsul Bahri

Sunlight is a source of energy for living things in general. In reality, the intensity of solar radiation is an environmental parameter that has positive and negative impacts on human life in particular. Furthermore, the knowledge on the characteristics of solar radiation, including its distribution pattern, is considered by many circles, both policy-makers and researchers in the environmental field. This study aims to create a solar radiation model in response to meteorological factors such as wind speed, air pressure and temperature, humidity, and rainfall using the Wavelet Neural Network (WNN). The modeling of solar radiation in this study is carried out by simultaneously utilizing its advantages as a hybrid model that combines the neural network model and the wavelet method. These advantages through the learning process (supervised learning) are multiplied through the use of the wavelet transform as a pre-processing data method and two type wavelets function, B-spline and Morlet wavelets, as an activation function in the neural network learning process. The WNN model was analyzed in two cases of meteorological variables, which are with and without rainfall. The results based on the root of the mean square error (RMSE) indicator show that the WNN model in these two cases is quite accurate. Meanwhile, the other indicator shows that the interval of the data distribution from the model is within the actual range. This implies that the predicted intensity of the solar radiation will be in a safe position in its adverse effect when the model is used as a reference.


Author(s):  
R.A. Klestov ◽  
◽  
A.V. Klyuev ◽  
V.Yu. Stolbov ◽  
◽  
...  

The division of data for training a neural network into training and test data in various proportions to each other is investigated. The question is raised about how the quality of data distribution and their correct annotation can affect the final result of constructing a neural network model. The paper investigates the algorithmic stability of training a deep neural network in problems of recognition of the microstructure of materials. The study of the stability of the learning process makes it possible to estimate the performance of a neural network model on incomplete data distorted by up to 10%. Purpose. Research of the stability of the learning process of a neural network in the classification of microstructures of functional materials. Materials and methods. Artificial neural network is the main instrument on the basis of which produced the study. Different subtypes of deep convolutional networks are used such as VGG and ResNet. Neural networks are trained using an improved backpropagation method. The studied model is the frozen state of the neural network after a certain number of learning epochs. The amount of data excluded from the study was randomly distributed for each class in five different distributions. Results. Investigated neural network learning process. Results of experiments conducted computing training with gradual decrease in the number of input data. Distortions of calculation results when changing data with a step of 2 percent are investigated. The percentage of deviation was revealed, equal to 10, at which the trained neural network model loses its stability. Conclusion. The results obtained mean that with an established quantitative or qualitative deviation in the training or test set, the results obtained by training the network can hardly be trusted. Although the results of this study are applicable to a particular case, i.e., microstructure recognition problems using ResNet-152, the authors propose a simpler technique for studying the stability of deep learning neural networks based on the analysis of a test, not a training set.


1989 ◽  
Vol 25 (Supplement) ◽  
pp. 26-27
Author(s):  
R. Suzuki ◽  
Y. Uno ◽  
M. Kawato

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