scholarly journals An Ensemble Learning Method for Robot Electronic Nose with Active Perception

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3941
Author(s):  
Shengming Li ◽  
Lin Feng ◽  
Yunfei Ge ◽  
Li Zhu ◽  
Liang Zhao
Keyword(s):  
Ensemble Learning ◽  
Electronic Nose ◽  
Low Cost ◽  
Training Data ◽  
The Novel ◽  
Learning Method ◽  
Active Perception ◽  
Odor Perception ◽  
Semiconductor Gas Sensors ◽  
Perception Function

The electronic nose is the olfactory organ of the robot, which is composed of a large number of sensors to perceive the smell of objects through free diffusion. Traditionally, it is difficult to realize the active perception function, and it is difficult to meet the requirements of small size, low cost, and quick response that robots require. In order to address these issues, a novel electronic nose with active perception was designed and an ensemble learning method was proposed to distinguish the smell of different objects. An array of three MQ303 semiconductor gas sensors and an electrochemical sensor DART-2-Fe5 were used to construct the novel electronic nose, and the proposed ensemble learning method with four algorithms realized the active odor perception function. The experiment results verified that the accuracy of the active odor perception can reach more than 90%, even though it used 30% training data. The novel electronic nose with active perception based on the ensemble learning method can improve the efficiency and accuracy of odor data collection and olfactory perception.

A NEW ENSEMBLE LEARNING ALGORITHM USING REGIONAL CLASSIFIERS

2013 ◽  
Vol 22 (04) ◽  
pp. 1350025 ◽  
Author(s):  
BYUNGWOO LEE ◽  
SUNGHA CHOI ◽  
BYONGHWA OH ◽  
JIHOON YANG ◽  
SUNGYONG PARK
Keyword(s):  
Ensemble Learning ◽  
Learning Algorithm ◽  
Ensemble Methods ◽  
Feature Space ◽  
Training Data ◽  
Learning Method ◽  
Weighted Voting ◽  
Ensemble Learning Algorithm ◽  
Voting Scheme ◽  
Base Learner

We present a new ensemble learning method that employs a set of regional classifiers, each of which learns to handle a subset of the training data. We split the training data and generate classifiers for different regions in the feature space. When classifying an instance, we apply a weighted voting scheme among the classifiers that include the instance in their region. We used 11 datasets to compare the performance of our new ensemble method with that of single classifiers as well as other ensemble methods such as RBE, bagging and Adaboost. As a result, we found that the performance of our method is comparable to that of Adaboost and bagging when the base learner is C4.5. In the remaining cases, our method outperformed other approaches.

scholarly journals A LEARNING METHOD TO DETERMINE HOW TO APPROACH AN UNKNOWN OBJECT TO BE GRASPED

2011 ◽  
Vol 08 (03) ◽  
pp. 579-606 ◽  
Author(s):  
BENJAMIN D. BALAGUER ◽  
STEFANO CARPIN
Keyword(s):  
Learning Algorithm ◽  
Training Session ◽  
Training Data ◽  
Support Vector ◽  
The Novel ◽  
Learning Method ◽  
End Effector ◽  
Novel Object ◽  
Time Performance ◽  
Feature Based

We present a learning algorithm to determine the appropriate approaching pose to grasp a novel object. Our method focuses on the computation of valid end-effector orientations in order to make contact with the object at a given point. The system achieves this goal by generalizing from positive examples provided by a human operator during an offline training session. The technique is feature-based since it extracts salient attributes of the object to be grasped rather than relying on the availability of models or trying to build one. To compute the desired orientation, the robot performs three steps at run time. Using a multi-class Support Vector Machine (SVM), it first classifies the novel object into one of the object classes defined during training. Next, it determines its orientation, and, finally, based on the classification and orientation, it extracts the most similar example from the training data and uses it to grasp the object. The method has been implemented on a full-scale humanoid robotic torso equipped with multi-fingered hands and extensive results corroborate both its effectiveness and real-time performance.

scholarly journals An Adaptive Deep Ensemble Learning Method for Dynamic Evolving Diagnostic Task Scenarios

Diagnostics ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 2288
Author(s):  
Kaixiang Su ◽  
Jiao Wu ◽  
Dongxiao Gu ◽  
Shanlin Yang ◽  
Shuyuan Deng ◽  
...  
Keyword(s):  
Machine Learning ◽  
Ensemble Learning ◽  
Model Performance ◽  
Optimal Number ◽  
Training Data ◽  
Learning Method ◽  
Learning Models ◽  
Proposed Model ◽  
Public Datasets ◽  
Machine Learning Models

Increasingly, machine learning methods have been applied to aid in diagnosis with good results. However, some complex models can confuse physicians because they are difficult to understand, while data differences across diagnostic tasks and institutions can cause model performance fluctuations. To address this challenge, we combined the Deep Ensemble Model (DEM) and tree-structured Parzen Estimator (TPE) and proposed an adaptive deep ensemble learning method (TPE-DEM) for dynamic evolving diagnostic task scenarios. Different from previous research that focuses on achieving better performance with a fixed structure model, our proposed model uses TPE to efficiently aggregate simple models more easily understood by physicians and require less training data. In addition, our proposed model can choose the optimal number of layers for the model and the type and number of basic learners to achieve the best performance in different diagnostic task scenarios based on the data distribution and characteristics of the current diagnostic task. We tested our model on one dataset constructed with a partner hospital and five UCI public datasets with different characteristics and volumes based on various diagnostic tasks. Our performance evaluation results show that our proposed model outperforms other baseline models on different datasets. Our study provides a novel approach for simple and understandable machine learning models in tasks with variable datasets and feature sets, and the findings have important implications for the application of machine learning models in computer-aided diagnosis.

scholarly journals A SELECTIVE LEARNING METHOD TO IMPROVE THE GENERALIZATION OF MULTILAYER FEEDFORWARD NEURAL NETWORKS

2001 ◽  
Vol 11 (02) ◽  
pp. 167-177 ◽  
Author(s):  
I. M. GALVÁN ◽  
P. ISASI ◽  
R. ALER ◽  
J. M. VALLS
Keyword(s):  
Neural Networks ◽  
Learning Strategy ◽  
Time Series Prediction ◽  
Feedforward Neural Networks ◽  
Training Data ◽  
The Novel ◽  
Learning Method ◽  
Data Set ◽  
Dynamic Selection

Multilayer feedforward neural networks with backpropagation algorithm have been used successfully in many applications. However, the level of generalization is heavily dependent on the quality of the training data. That is, some of the training patterns can be redundant or irrelevant. It has been shown that with careful dynamic selection of training patterns, better generalization performance may be obtained. Nevertheless, generalization is carried out independently of the novel patterns to be approximated. In this paper, we present a learning method that automatically selects the training patterns more appropriate to the new sample to be predicted. This training method follows a lazy learning strategy, in the sense that it builds approximations centered around the novel sample. The proposed method has been applied to three different domains: two artificial approximation problems and a real time series prediction problem. Results have been compared to standard backpropagation using the complete training data set and the new method shows better generalization abilities.

scholarly journals Siamese Reconstruction Network: Accurate Image Reconstruction from Human Brain Activity by Learning to Compare

2019 ◽  
Vol 9 (22) ◽  
pp. 4749
Author(s):  
Lingyun Jiang ◽  
Kai Qiao ◽  
Linyuan Wang ◽  
Chi Zhang ◽  
Jian Chen ◽  
...  
Keyword(s):  
Deep Learning ◽  
Human Brain ◽  
Brain Activity ◽  
Feature Space ◽  
Training Data ◽  
Reconstruction Method ◽  
Learning Method ◽  
Training Samples ◽  
Visual Reconstruction ◽  
Relationship Of

Decoding human brain activities, especially reconstructing human visual stimuli via functional magnetic resonance imaging (fMRI), has gained increasing attention in recent years. However, the high dimensionality and small quantity of fMRI data impose restrictions on satisfactory reconstruction, especially for the reconstruction method with deep learning requiring huge amounts of labelled samples. When compared with the deep learning method, humans can recognize a new image because our human visual system is naturally capable of extracting features from any object and comparing them. Inspired by this visual mechanism, we introduced the mechanism of comparison into deep learning method to realize better visual reconstruction by making full use of each sample and the relationship of the sample pair by learning to compare. In this way, we proposed a Siamese reconstruction network (SRN) method. By using the SRN, we improved upon the satisfying results on two fMRI recording datasets, providing 72.5% accuracy on the digit dataset and 44.6% accuracy on the character dataset. Essentially, this manner can increase the training data about from n samples to 2n sample pairs, which takes full advantage of the limited quantity of training samples. The SRN learns to converge sample pairs of the same class or disperse sample pairs of different class in feature space.

scholarly journals Experiments with Neural Networks in the Identification and Control of a Magnetic Levitation System Using a Low-Cost Platform

2021 ◽  
Vol 11 (6) ◽  
pp. 2535
Author(s):  
Bruno E. Silva ◽  
Ramiro S. Barbosa
Keyword(s):  
Neural Networks ◽  
Neural Control ◽  
Magnetic Levitation ◽  
Low Cost ◽  
Training Data ◽  
Neural Controllers ◽  
Levitation System ◽  
Internal Model Controller ◽  
Magnetic Levitation System ◽  
And Performance

In this article, we designed and implemented neural controllers to control a nonlinear and unstable magnetic levitation system composed of an electromagnet and a magnetic disk. The objective was to evaluate the implementation and performance of neural control algorithms in a low-cost hardware. In a first phase, we designed two classical controllers with the objective to provide the training data for the neural controllers. After, we identified several neural models of the levitation system using Nonlinear AutoRegressive eXogenous (NARX)-type neural networks that were used to emulate the forward dynamics of the system. Finally, we designed and implemented three neural control structures: the inverse controller, the internal model controller, and the model reference controller for the control of the levitation system. The neural controllers were tested on a low-cost Arduino control platform through MATLAB/Simulink. The experimental results proved the good performance of the neural controllers.

scholarly journals Pocket MUSE: an affordable, versatile and high-performance fluorescence microscope using a smartphone

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Yehe Liu ◽  
Andrew M. Rollins ◽  
Richard M. Levenson ◽  
Farzad Fereidouni ◽  
Michael W. Jenkins
Keyword(s):  
High Performance ◽  
Point Of Care ◽  
Low Cost ◽  
Consumer Electronics ◽  
Practical Implementation ◽  
The Novel ◽  
Point Of Care Diagnostics ◽  
Optical Module ◽  
Optical Configuration ◽  
User Friendly

AbstractSmartphone microscopes can be useful tools for a broad range of imaging applications. This manuscript demonstrates the first practical implementation of Microscopy with Ultraviolet Surface Excitation (MUSE) in a compact smartphone microscope called Pocket MUSE, resulting in a remarkably effective design. Fabricated with parts from consumer electronics that are readily available at low cost, the small optical module attaches directly over the rear lens in a smartphone. It enables high-quality multichannel fluorescence microscopy with submicron resolution over a 10× equivalent field of view. In addition to the novel optical configuration, Pocket MUSE is compatible with a series of simple, portable, and user-friendly sample preparation strategies that can be directly implemented for various microscopy applications for point-of-care diagnostics, at-home health monitoring, plant biology, STEM education, environmental studies, etc.

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