scholarly journals Robustness of Neural Network Calibration Model for Accurate Spatial Positioning

2021 ◽  
Author(s):  
JIanyu Dou ◽  
Chong pan ◽  
Jianhua Liu
Keyword(s):  
Neural Network ◽  
Calibration Model ◽  
Spatial Positioning

scholarly journals Estimating maturity by measuring pH, sugar, dry matter, water and vitamin C content of cashew apple (Anacardium occidentale) from remote spectral reflectance data using neural network

2021 ◽  
pp. 1029-1034
Author(s):  
Eliane K. Assoi ◽  
Olivier K. Bagui ◽  
Benoit K. Kouakou ◽  
Adolphe Y. Gbogbo ◽  
Doudjo Soro ◽  
...  
Keyword(s):  
Neural Network ◽  
Vitamin C ◽  
Water Content ◽  
Dry Matter ◽  
Bootstrap Method ◽  
Sugar Content ◽  
Raw Material ◽  
Calibration Model ◽  
Dry Matter Content ◽  
Cashew Apple

In agricultural sector, maturity is the main decision criterion for starting the harvest. This criterion is usually revealed by a number of parameters such as pH, sugar, dry matter, water and vitamin C, which are informative but technically tedious to measure. The cashew apple is the hypertrophied peduncle which is attached to the cashew nut. It is a nutritious (very juicy fruit (85 to 90% water), sweet (7 to 13% carbohydrates), acidic and vitamin C content) fruit with high therapeutic and medicinal properties. The cashew apple is used as a raw material for many industrial applications (juice and alcohol). This research was conducted as a preliminary step towards the development of a real-time remote sensing technique for assessing the quality of tropical fruits. Spectral acquisitions were carried out from intact cashew apple using optical system composed reflector coupled with spectrometer USB 4000 FL from Ocean Optics (350-1100 nm). Immediately after spectral acquisition, the samples were analyzed by using chemical methods (sugar content, dry matter content, water content, vitamin C and pH). Preprocessing treatment method, bootstrap method was required to create statistical new samples and to increase the number of samples required. This method was used to improve the predictive performance of calibration model. Statistical models of prediction were developed using an artificial neural network (ANN) method. The results obtained from the models built by ANN showed strong relationships between predicted and experimental values: (Rsquare = 0.9870, RMSE= 0.0262) for pH, (Rsquare=0.9869, RMSE=0.1392) for Sugar, (Rsquare=0.9726, RMSE=0.3333) for water content, (Rsquare=0.9703, RMSE=0.3464) for vitamin C and (Rsquare=0.9922, RMSE= 5.0304, RMSE=5.0304) for dry matter. These results confirm the potential of visible spectroscopy to predict quality parameters of cashew apples remotely and make decisions about best harvest time

Study on Calibrating Method of FOB Position Tracker Based on Neural Network

2014 ◽  
Vol 556-562 ◽  
pp. 5945-5950
Author(s):  
Shan Shan Li ◽  
Zhong Xiang Zhu ◽  
Bo Liu ◽  
Zheng He Song ◽  
En Rong Mao ◽  
...  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Human Computer Interaction ◽  
High Accuracy ◽  
Bp Algorithm ◽  
Calibration Model ◽  
Operational Principle ◽  
Sampling Points ◽  
Computer Interaction

Due to the instability and low precision of electromagnetic position trackers and the inefficiency of existing calibrating methods, a method with high accuracy and effectiveness for FOB (Flock of Birds) calibration was studied. The components, operational principle, merits and drawbacks of FOB were briefly introduced. The positions of 343 sampling points set in the effective working area were measured and the data was processed for trainings and tests of the calibration model established using genetic algorithm and BP algorithm. Experiments were conducted to verify the effectiveness of the method and the results showed the calibrated tracker’s average errors in the X, Y, and Z direction were 0.86cm, 0.70cm and 0.83cm respectively, meeting the requirements of human-computer interaction.

scholarly journals Hand–Eye Calibration Algorithm Based on an Optimized Neural Network

Actuators ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 85
Author(s):  
Jiang Hua ◽  
Liangcai Zeng
Keyword(s):  
Neural Network ◽  
Coordinate System ◽  
Calibration Method ◽  
Calibration Model ◽  
The Neural Network ◽  
Calibration Algorithm ◽  
Robot Grasping ◽  
Relationship Of ◽  
The Relationship ◽  
Camera Coordinate System

A robot can identify the position of a target and complete a grasping based on the hand–eye calibration algorithm, through which the relationship between the robot coordinate system and the camera coordinate system can be established. The accuracy of the hand–eye calibration algorithm affects the real-time performance of the visual servo system and the robot manipulation. The traditional calibration technique is based on a perfect mathematical model AX = XB, in which the X represents the relationship of (A) the camera coordinate system and (B) the robot coordinate system. The traditional solution to the transformation matrix has a certain extent of limitation and instability. To solve this problem, an optimized neural-network-based hand–eye calibration method was developed to establish a non-linear relationship between robotic coordinates and pixel coordinates that can compensate for the nonlinear distortion of the camera lens. The learning process of the hand–eye calibration model can be interpreted as B=fA, which is the coordinate transformation relationship trained by the neural network. An accurate hand–eye calibration model can finally be obtained by continuously optimizing the network structure and parameters via training. Finally, the accuracy and stability of the method were verified by experiments on a robot grasping system.

scholarly journals Evaluating and Improving the Reliability of Gas-Phase Sensor System Calibrations Across New Locations for Ambient Measurements and Personal Exposure Monitoring

2019 ◽  
Author(s):  
Sharad Vikram ◽  
Ashley Collier-Oxandale ◽  
Michael Ostertag ◽  
Massimiliano Menarini ◽  
Camron Chermak ◽  
...  
Keyword(s):  
Neural Network ◽  
Random Forest ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Environmental Conditions ◽  
Low Cost ◽  
Calibration Model ◽  
Architecture Model ◽  
Pollutant Concentrations ◽  
Sensor Package

Abstract. Advances in ambient environmental monitoring technologies are enabling concerned communities and citizens to collect data to better understand their local environment and potential exposures. These mobile, low-cost tools make it possible to collect data with increased temporal and spatial resolution providing data on a large scale with unprecedented levels of detail. This type of data has the potential to empower people to make personal decisions about their exposure and support the development of local strategies for reducing pollution and improving health outcomes. However, calibration of these low-cost instruments has been a challenge. Often, a sensor package is calibrated via field calibration. This involves colocating the sensor package with a high-quality reference instrument for an extended period and then applying machine learning or other model fitting technique such as multiple-linear regression to develop a calibration model for converting raw sensor signals to pollutant concentrations. Although this method helps to correct for the effects of ambient conditions (e.g., temperature) and cross-sensitivities with non-target pollutants, there is a growing body of evidence that calibration models can overfit to a given location or set of environmental conditions on account of the incidental correlation between pollutant levels and environmental conditions, including diurnal cycles. As a result, a sensor package trained at a field site may provide less reliable data when moved, or transferred, to a different location. This is a potential concern for applications seeking to perform monitoring away from regulatory monitoring sites, such as personal mobile monitoring or high-resolution monitoring of a neighborhood. We performed experiments confirming that transferability is indeed a problem and show that it can be improved by collecting data from multiple regulatory sites and building a calibration model that leverages data from a more diverse dataset. We deployed three sensor packages to each of three sites with reference monitors (nine packages total) and then rotated the sensor packages through the sites over time. Two sites were in San Diego, CA, with a third outside of Bakersfield, CA, offering varying environmental conditions, general air quality composition, and pollutant concentrations. When compared to prior single-site calibration, the multi-site approach exhibits better model transferability for a range of modeling approaches. Our experiments also reveal that random forest is especially prone to overfitting, and confirms prior results that transfer is a significant source of both bias and standard error. Bias dominated in our experiments, suggesting that transferability might be easily increased by detecting and correcting for bias. Also, given that many monitoring applications involve the deployment of many sensor packages based on the same sensing technology, there is an opportunity to leverage the availability of multiple sensors at multiple sites during calibration. We contribute a new neural network architecture model termed split-NN that splits the model into two-stages, in which the first stage corrects for sensor-to-sensor variation and the second stage uses the combined data of all the sensors to build a model for a single sensor package. The split-NN modeling approach outperforms multiple linear regression, traditional 2- and 4-layer neural network, and random forest models.

QSPR Studies on n-Octanol/Water Partition Coefficient of Polychlorinated Biphenyls by Using Artificial Neural Network

2012 ◽  
Vol 455-456 ◽  
pp. 925-929
Author(s):  
Long Jiao
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Polychlorinated Biphenyls ◽  
Partition Coefficient ◽  
Quantitative Relationship ◽  
Calibration Model ◽  
Structure Property ◽  
Qspr Model ◽  
Water Partition Coefficient ◽  
Artificial Neural

Quantitative structure property relationship (QSPR) model for predicting the n-octanol/water partition coefficient, Kow, of 21 polychlorinated biphenyls (PCBs) was investigated. The structure of the investigated PCBs is mathematically characterized by using molecular distance-edge vector (MDEV) index, a topological index which is developed based on the topological method. The calibration model of Kow was developed by using radial basis function artificial neural network (RBF ANN). Leave one out cross validation was carried out to assess the predictive ability of the developed QSPR model. The R2 between the predicted and experimental logKow is 0.9793. The prediction RMS%RE for the 21 PCBs is 1.92. It is demonstrated that there is a quantitative relationship between the MDEV index and the Kow of the 21 PCBs. RBF ANN is shown to practicable for developing the QSPR model for Kow of PCBs.

Estimating Deterioration Level of Aircraft Engines

2007 ◽  
Author(s):  
Hai Qiu ◽  
Neil Eklund ◽  
Weizhong Yan ◽  
Piero Bonissone ◽  
Feng Xue ◽  
...  
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Real World ◽  
Transfer Functions ◽  
Calibration Model ◽  
Aircraft Engines ◽  
Deterioration Model ◽  
Engine Model ◽  
Flow Capacity ◽  
Module Efficiency

This paper describes an approach to estimate the deterioration level of aircraft engines using engine monitoring data and a physics-based engine model. The estimation process is carried out by a neural network, which is trained by data generated using a physical-based engine model complemented with an empirically derived engine deterioration model. The deterioration model allows manipulation of several engine health parameters, such as module efficiency and flow capacity, to simulate engine deterioration. Simulated sensor outputs are used to build independent transfer functions relating the sensor values to a deterioration level. A calibration model corrects the sensor readings to a reference condition so that the effect of variation of operating condition is minimized. The proposed approach can be used to assess engine deterioration level in real time. The proposed deterioration estimation approach is validated using real-world engine data.

Sign in / Sign up

Export Citation Format

Share Document