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.

Sales Forecast for Garment Companies with Cluster Analysis and Modified Neural Networks

2011 ◽  
Vol 127 ◽  
pp. 490-495
Author(s):  
Li Yu ◽  
Yun Chen
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Cluster Analysis ◽  
Sales Forecast ◽  
The Neural Network ◽  
Linkage Cluster ◽  
Relationship Of ◽  
Marketing Resource Allocation ◽  
The Relationship

For the companies of the garment industry, managers often dedicate their efforts to forecast the sales accurately while making decisions for marketing resource allocation and scheduling. Based on the historical database, this paper constructs a method to investigate the relationship of the relating factors and sales values. The proposed method combines the cluster analysis and modified neural networks to fulfill the sales forecast task. Firstly, the average linkage cluster algorithm is applied to cluster similar sales values. Secondly, a modified neural network is used to investigate the mapping relationship between those influencing factors and the sales clusters. The method employs a self-adjust mechanism to determine the structure of the neural network. The effectiveness of the proposed method is illustrated with a case study of a garment company in Shanghai.

scholarly journals Study on process of chemical fiber filament automatic doffing system based on simulation platform and machine learning

2021 ◽  
Vol 16 ◽  
pp. 155892502110548
Author(s):  
Hongxin Zhu ◽  
Kun Zou ◽  
Wenlan Bao
Keyword(s):  
Neural Network ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Production Line ◽  
Simulation Platform ◽  
Chemical Fiber ◽  
Production Lines ◽  
The Neural Network ◽  
Spss Software ◽  
The Relationship

In recent years, a large number of automatic equipment has been introduced into the chemical fiber filament doffing production line, but the related research on the fully automatic production line technology is not yet mature. At present, it is difficult to collect data due to test costs and confidentiality. This paper proposes to develop a simulation platform for a chemical fiber filament doffing production line, which enables us to effectively obtain data and quantitatively study the relationship between the number of manual interventions and other process parameters of the production line. Considering that the parameter research is a multi-factor problem, an orthogonal test was designed by using SPSS software and was carried out by using a simulation platform. The multiple linear regression (MLR) and the neural network optimized by genetic algorithm were adopted to fit the relationship between the number of manual interventions and other parameters of the production line. The SPSS software was applied to obtain the standardized coefficients of the multiple linear regression fitting and the neural network mean impact value (MIV) algorithm was applied to obtain the magnitude and direction of the impact of different parameters on the number of manual interventions. The above results provide important reference for the design of similar new production lines and for the improvement of old production lines.

An improvement to the ANN-enhanced flow rating method

2021 ◽  
Author(s):  
Bernhard Schmid
Keyword(s):  
Neural Network ◽  
Electrical Conductivity ◽  
Water Resources ◽  
Flow Rate ◽  
Major Ions ◽  
Rate Of Change ◽  
Data Set ◽  
Input Variables ◽  
Relationship Of ◽  
The Relationship

<p>The work reported here builds upon a previous pilot study by the author on ANN-enhanced flow rating (Schmid, 2020), which explored the use of electrical conductivity (EC) in addition to stage to obtain ‘better’, i.e. more accurate and robust, estimates of streamflow. The inclusion of EC has an advantage, when the relationship of EC versus flow rate is not chemostatic in character. In the majority of cases, EC is, indeed, not chemostatic, but tends to decrease with increasing discharge (so-called dilution behaviour), as reported by e.g. Moatar et al. (2017), Weijs et al. (2013) and Tunqui Neira et al.(2020). This is also in line with this author’s experience.</p><p>The research presented here takes the neural network based approach one major step further and incorporates the temporal rate of change in stage and the direction of change in EC among the input variables (which, thus, comprise stage, EC, change in stage and direction of change in EC). Consequently, there are now 4 input variables in total employed as predictors of flow rate. Information on the temporal changes in both flow rate and EC helps the Artificial Neural Network (ANN) characterize hysteretic behaviour, with EC assuming different values for falling and rising flow rate, respectively, as described, for instance, by Singley et al. (2017).</p><p>The ANN employed is of the Multilayer Perceptron (MLP) type, with stage, EC, change in stage and direction of change in EC of the Mödling data set (Schmid, 2020) as input variables. Summarising the stream characteristics, the Mödling brook can be described as a small Austrian stream with a catchment of fairly mixed composition (forests, agricultural and urbanized areas). The relationship of EC versus flow reflects dilution behaviour. Neural network configuration 4-5-1 (the 4 input variables mentioned above, 5 hidden nodes and discharge as the single output) with learning rate 0.05 and momentum 0.15 was found to perform best, with testing average RMSE (root mean square error) of the scaled output after 100,000 epochs amounting to 0.0138 as compared to 0.0216 for the (best performing) 2-5-1 MLP with stage and EC as inputs only.    </p><p> </p><p>References</p><p>Moatar, F., Abbott, B.W., Minaudo, C., Curie, F. and Pinay, G.: Elemental properties, hydrology, and biology interact to shape concentration-discharge curves for carbon, nutrients, sediment and major ions. Water Resources Res., 53, 1270-1287, 2017.</p><p>Schmid, B.H.: Enhanced flow rating using neural networks with water stage and electrical conductivity as predictors. EGU2020-1804, EGU General Assembly 2020.</p><p>Singley, J.G., Wlostowski, A.N., Bergstrom, A.J., Sokol, E.R., Torrens, C.L., Jaros, C., Wilson, C.,E., Hendrickson, P.J. and Gooseff, M.N.: Characterizing hyporheic exchange processes using high-frequency electrical conductivity-discharge relationships on subhourly to interannual timescales. Water Resources Res. 53, 4124-4141, 2017.</p><p>Tunqui Neira, J.M., Andréassian, V., Tallec, G. and Mouchel, J.-M.: A two-sided affine power scaling relationship to represent the concentration-discharge relationship. Hydrol. Earth Syst. Sci. 24, 1823-1830, 2020.</p><p>Weijs, S.V., Mutzner, R. and Parlange, M.B.: Could electrical conductivity replace water level in rating curves for alpine streams? Water Resources Research 49, 343-351, 2013.</p>

scholarly journals Predicting Wins, Losses and Attributes’ Sensitivities in the Soccer World Cup 2018 Using Neural Network Analysis

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3213 ◽  
Author(s):  
Amr Hassan ◽  
Abdel-Rahman Akl ◽  
Ibrahim Hassan ◽  
Caroline Sunderland
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Average Distance ◽  
Deep Understanding ◽  
The Neural Network ◽  
Testing Error ◽  
Training Error ◽  
Using Data ◽  
The Relationship

Predicting the results of soccer competitions and the contributions of match attributes, in particular, has gained popularity in recent years. Big data processing obtained from different sensors, cameras and analysis systems needs modern tools that can provide a deep understanding of the relationship between this huge amount of data produced by sensors and cameras, both linear and non-linear data. Using data mining tools does not appear sufficient to provide a deep understanding of the relationship between the match attributes and results and how to predict or optimize the results based upon performance variables. This study aimed to suggest a different approach to predict wins, losses and attributes’ sensitivities which enables the prediction of match results based on the most sensitive attributes that affect it as a second step. A radial basis function neural network model has successfully weighted the effectiveness of all match attributes and classified the team results into the target groups as a win or loss. The neural network model’s output demonstrated a correct percentage of win and loss of 83.3% and 72.7% respectively, with a low Root Mean Square training error of 2.9% and testing error of 0.37%. Out of 75 match attributes, 19 were identified as powerful predictors of success. The most powerful respectively were: the Total Team Medium Pass Attempted (MBA) 100%; the Distance Covered Team Average in zone 3 (15–20 km/h; Zone3_TA) 99%; the Team Average ball delivery into the attacking third of the field (TA_DAT) 80.9%; the Total Team Covered Distance without Ball Possession (Not in_Poss_TT) 76.8%; and the Average Distance Covered by Team (Game TA) 75.1%. Therefore, the novel radial based function neural network model can be employed by sports scientists to adapt training, tactics and opposition analysis to improve performance.

scholarly journals Modeling the Relationship of Precipitation and Water Level Using Grid Precipitation Products with a Neural Network Model

2020 ◽  
Vol 12 (7) ◽  
pp. 1096
Author(s):  
Zeqiang Chen ◽  
Xin Lin ◽  
Chang Xiong ◽  
Nengcheng Chen
Keyword(s):  
Neural Network ◽  
Water Level ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Machine Learning Algorithms ◽  
Jinsha River ◽  
Precipitation Water ◽  
Relationship Of ◽  
The Relationship

Modeling the relationship between precipitation and water level is of great significance in the prevention of flood disaster. In recent years, the use of machine learning algorithms for precipitation–water level prediction has attracted wide attention in flood forecasting and other fields; however, a clear method to model the relationship of precipitation and water level using grid precipitation products with a neural network model is lacking. The issues of the method include how to select a neural network model, as well as how to influence the modeling results with different types and resolutions of remote sensing data. The purpose of this paper is to provide some findings for the issues. We used the back-propagation (BP) neural network and a nonlinear autoregressive exogenous model (NARX) time series network to model the relationship between precipitation and water level, respectively. The water level of Pingshan hydrographic station at a catchment area in the Jinsha River Basin was simulated by the two network models using three different grid precipitation products. The results showed that when the ground station data are missing, the grid precipitation product is a good alternative to construct the precipitation–water level relationship. In addition, using the NARX network as a model fitting network using extra inputs was better than using the BP neural network; the Nash efficiency coefficients of the former were all higher than 97%, while the latter were all lower than 94%. Furthermore, the input of grid products with different spatial resolutions has little significant effect on the modeling results of the model.

scholarly journals Investigation of a Multitasking System for Automatic Ship Berthing in Marine Practice Based on an Integrated Neural Controller

Mathematics ◽  
2020 ◽  
Vol 8 (7) ◽  
pp. 1167
Author(s):  
Van Suong Nguyen
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Coordinate System ◽  
Numerical Simulations ◽  
Neural Controller ◽  
The Neural Network ◽  
Artificial Neural ◽  
Ship Berthing

In this article, a multitasking system is investigated for automatic ship berthing in marine practices, based on artificial neural networks (ANNs). First, a neural network with separate structures in hidden layers is developed, based on a head-up coordinate system. This network is trained once with the berthing data of a ship in an original port to conduct berthing tasks in different ports. Then, on the basis of the developed network, an integrated mechanism including three negative signs is linked to achieve an integrated neural controller. This controller can bring the ship to a berth on each side of the ship in different ports. The whole system has the ability to berth for different tasks without retraining the neural network. Finally, to validate the effectiveness of the proposed system for automatic ship berthing, numerical simulations were performed for berthing tasks, such as different ports, and berthing each side of the ship. The results indicate that the proposed system shows a good performance in automatic ship berthing.

scholarly journals Relationships between the results of various methods of urea analysis in native and enriched milk

2008 ◽  
Vol 53 (No. 2) ◽  
pp. 64-76 ◽  
Author(s):  
P. Hering ◽  
O. Hanuš ◽  
J. Frelich ◽  
J. Pytloun ◽  
A. Macek ◽  
...  
Keyword(s):  
Calibration Method ◽  
Poor Correlation ◽  
Standard Samples ◽  
Health And Nutrition ◽  
Milk Urea ◽  
Sources Of Variation ◽  
Enriched Milk ◽  
Relationship Of ◽  
The Relationship

Milk urea concentration (MUC) is a suitable indicator of the health and nutrition state of dairy cows. MUC is in relation to their reproduction performance, longevity and technological milk indicators. The interpretation correctness of results depends on their reliability. There are a lot of principles of MUC analyses. Their results can be affected by a number of interferential factors. Disproportions were noticed in practice. Therefore the sources of variation in results are studied. The goal of this study was to investigate relationships between different methods of MUC determination with the use of standard samples of native milk with an artificial urea addition. After evaluation I (<i>n</i> = 7) the results of methods BI-1 and BI-2 (photometrical ones with diacetylmonoxime) were disqualified because of poor recovery (R), poor correlation (C) with other methods, higher random error (RER) and highest systematic error (SE). Evaluation II is more effective with stricter discrimination limits. Cs of all methods mutually (0.977 up to 0.998; <i>P</i> < 0.001) confirmed the methods as effective with the exception of BI-2 with poor Cs (0.713 up to 0.774), poor <i>R</i> (16.0 up to 69.0%) and high RER ±5.292 mg/100 ml. R of better methods was 44.0 up to 96.7%. The BI-1 method had good Cs (0.986 up to 0.994; <i>P</i> < 0.001), higher SE –7.546 mg/100 ml and poorer R (48.5 up to 75.3%). BI-1 method was a case of mistaken performance. BI method could be improved by the use of more samples in calibration. FT-MIR method (infra-analysis) has good addition R 69.5 up to 95.0% and Cs 0.981 up to 0.994 (<i>P</i> < 0.001). EH method (photometrical one with Ehrlich’s agent) has good R 59.0 up to 96.7%, higher SE 4.755 (I) and 2.556 (II) mg/100 ml and close Cs 0.977 up to 0.994 (<i>P</i> < 0.001). UR method (ureolytical difference-conductometric) showed the best combination of results about R, C, SE and RER. MUC measurement was almost independent of fat in milk (<i>r</i> = 0.16 for UR and 0.01 for FT-MIR; <i>P</i> > 0.05) and MUC of both the methods did not increase significantly with lactose increase ((<i>r</i>= 0.16 and 0.27; <i>P</i> > 0.05), which increased logically ((<i>r</i> = –0.88; <i>P</i> < 0.001) during the fat concentration increase. The relationship of MUC results between UR and FT-MIR was significant (validation (<i>r</i> = 0.96; <i>P</i> < 0.001) at average difference –0.93 ± 1.663 mg/100 ml. It is possible to see the result reliability as good after calibration performance of FT-MIR according to results of UR. It is not necessary to see the effects of fat, protein and lactose on MUC methods as substantial. FT-MIR method for MUC has good result reliability at the use of native milk samples, incidentally with urea additions. It is suitable to calibrate the FT-MIR method according to specific determination of MUC (UR). However, the most important for elimination of disproportions is the calibration method with concrete audited R, though nonspecific.

The Projector Calibration Based on ZHANG’s Self-Calibration Method

2014 ◽  
Vol 981 ◽  
pp. 364-367
Author(s):  
Guang Yu ◽  
Bo Yang Yu ◽  
Shu Cai Yang ◽  
Li Wen ◽  
Wen Fei Dong ◽  
...  
Keyword(s):  
Camera Calibration ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Calibration Method ◽  
Linear Calibration ◽  
Self Calibration ◽  
Operation Process ◽  
Projector Calibration ◽  
Calibration Algorithm ◽  
Relationship Of

Projector calibration can be seen as a special case of the camera calibration. It can establish the relationship of the three dimensional space coordinates for points and projector image coordinates for points DMD by using a projector to project coding pattern. In camera calibration, ZHANG’s self-calibration was conducted in the maximum likelihood linear refinement. Operation process takes the lens distortion factors into account finding out the camera internal and external parameters finally. Using this algorithm to the projector calibration can solve the traditional linear calibration algorithm which is complex and poor robustness. Otherwise, it can improve the practicability of calibration method. This method can both calibrate the internal and external parameters of projector, which can solve the problem of independently inside or outside calibration.

Nonlinear Calibration of Thermocouple Sensor Using Least Squares Support Vector Regression

2012 ◽  
Vol 443-444 ◽  
pp. 302-308
Author(s):  
Yao Jun Yu
Keyword(s):  
Least Squares ◽  
Support Vector Regression ◽  
Calibration Method ◽  
Support Vector ◽  
Calibration Model ◽  
Measured Temperature ◽  
Measuring Instrument ◽  
The Relationship ◽  
Nonlinear Calibration ◽  
Temperature Output

Calibration is an important operation in the instrumentation industry for determining the relationship between the output (s) (or response) of a measuring instrument and the value of the input (s). This paper proposes a nonlinear calibration method based on least squares support vector regression (LS-SVR) with the output voltage of thermocouple sensor as input and the measured temperature output to eliminate the nonlinear errors in detection process. Firstly, the nonlinear calibrator, expressed by power series, was established based on the principle of inverse model. And then the parameters of the calibrator were identified by LS-SVR. Through this calibrator, the desired linear characteristics of thermocouple sensor could be obtained. Finally, platinum-rhodium 30– platinum-rhodium 6-thermocouple (B-type) was taken as an example, and experimental results show that the proposed calibration method is efficient in the temperature range from 400°C to 1800°C. And the method has an advantage of analytical expression of the calibration model.

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