scholarly journals An Intravascular Catheter Bending Recognition Method for Interventional Surgical Robots

Machines ◽  
2022 ◽  
Vol 10 (1) ◽  
pp. 42
Author(s):  
Wei Wei ◽  
Dong Yang ◽  
Li Li ◽  
Yuxuan Xia
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Force Feedback ◽  
Detection Methods ◽  
Mechanical Transmission ◽  
Intravascular Catheter ◽  
Surgical Robots ◽  
Force Detection ◽  
Robot System ◽  
Robotic Surgeries

Robot-assisted interventional surgery can greatly reduce the radiation received by surgeons during the operation, but the lack of force detection and force feedback is still a risk in the operation which may harm the patient. In those robotic surgeries, the traditional force detection methods may have measurement losses and errors caused by mechanical transmission and cannot identify the direction of the force. In this paper, an interventional surgery robot system with a force detection device is designed and a new force detection method based on strain gauges is proposed to detect the force and infer the bending direction of the catheter in the vessel by using BP neural network. In addition, genetic algorithm is used to optimize the BP neural network, and the error between the calculated results and the actual results is reduced by 37%, which improves the accuracy of catheter bending recognition. Combining this new method with traditional force sensors not only reduces the error caused by the traditional mechanical transmission, but also can detect the bending direction of the catheter in the blood vessel, which greatly improves the safety of the operation.

scholarly journals An Electronic Nose Technology to Quantify Pyrethroid Pesticide Contamination in Tea

Chemosensors ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 30
Author(s):  
Xiaoyan Tang ◽  
Wenmin Xiao ◽  
Tao Shang ◽  
Shanyan Zhang ◽  
Xiaoyang Han ◽  
...  
Keyword(s):  
Neural Network ◽  
Electronic Nose ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Detection Methods ◽  
Layer Model ◽  
Pyrethroid Pesticide ◽  
Pyrethroid Pesticides ◽  
Hidden Layer ◽  
Network Pattern

The contamination of tea with toxic pesticides is a major concern. Additionally, because of improved detection methods, importers are increasingly rejecting contaminated teas. Here, we describe an electronic nose technique for the rapid detection of pyrethroid pesticides (cyhalothrin, bifenthrin, and fenpropathrin) in tea. Using a PEN 3 electronic nose, the text screened a group of metal oxide sensors and determined that four of them (W5S, W1S, W1W, and W2W) are suitable for the detection of the same pyrethroid pesticide in different concentrations and five of them (W5S, W1S, W1W, W2W, and W2S) are suitable for the detection of pyrethroid pesticide. The models for the determination of cyhalothrin, bifenthrin, and fenpropathrin are established by PLS method. Next, using back propagation (BP) neural network technology, we developed a three-hidden-layer model and a two-hidden-layer model to differentiate among the three pesticides. The accuracy of the three models is 96%, 92%, and 88%, respectively. The recognition accuracies of the three-hidden-layer BP neural network pattern and two-hidden-layer BP neural network pattern are 98.75% and 97.08%, respectively. Our electronic nose system accurately detected and quantified pyrethroid pesticides in tea leaves. We propose that this tool is now ready for practical application in the tea industry.

Study on the Shift Schedule Based on Driver Type of Automatic Mechanical Transmission

2013 ◽  
Vol 734-737 ◽  
pp. 2721-2724
Author(s):  
Peng Han ◽  
Xiu Sheng Cheng ◽  
Yin Shu Wang ◽  
Xi Liu
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Recognition System ◽  
Mechanical Transmission ◽  
Neural Network Classifier ◽  
Driving Style ◽  
Different Types ◽  
Shift Schedules ◽  
Adaptive Ability ◽  
Intelligent Recognition

An intelligent recognition system of driver type suitable for different drivers was studied in this paper,and the driving style recognition based on BP neural network classifier structure was designed to make different types of shift schedules to adapt to different drivers.The intelligent recognition of driver type was verified by simulation.The rusults showed that the intelligent recognition based on BP neural network classifier structure had good adaptive ability,which could meet the requirements of different types of drivers.

Research on Hydraulic Control System of Straightening Machine with Nine Rolls for Plates Based on BP Neural Network PID Controller

2013 ◽  
Vol 820 ◽  
pp. 117-121 ◽  
Author(s):  
Song Li ◽  
Jin Chun Song ◽  
Guan Gan Ren ◽  
Yan Cai
Keyword(s):  
Neural Network ◽  
Control System ◽  
Bp Neural Network ◽  
Pid Controller ◽  
Mechanical Transmission ◽  
Hydraulic Control ◽  
Control Precision ◽  
Transmission Equipment ◽  
High Control ◽  
Neural Network Pid

A mechanical transmission equipment of traditional straightening machine for plates are driven by worm gear and worm, which causes small straightening force, slow pressing speed and low control precision. However, screwdown control system of straightening machine can be driven by hydraulic system, which will lead to large straightening force, rapid pressing speed and high control precision. This system was designed for straightening machine with nine rolls for plates, its transfer function was deduced, and the analysis on its stability and time response was conducted. A BP neural network PID controller was utilized in the system for improving dynamic characteristics. It can be concluded that the system responds rapidly, and stability and control precision are high if BP neural network PID controller is used in the system.

scholarly journals Gearshift Sensorless Control for Direct-Drive-Type AMT Based on Improved GA-BP Neural Network Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Bo Li ◽  
Wenqing Ge ◽  
Qiang Li ◽  
Yujiao Li ◽  
Cao Tan
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Sensorless Control ◽  
Prediction Method ◽  
Absolute Error ◽  
Linear Displacement ◽  
Displacement Sensor ◽  
Mechanical Transmission ◽  
Direct Drive ◽  
Displacement Prediction

The automated mechanical transmission (AMT) based on the electromagnetic linear driving device (EMLDD) has good potential for shift performance. However, the direct-drive shifting mechanism based on the displacement sensor is difficult to meet the compactness of the structure and control robustness in complex environment. Through analyzing the working principle of the electromagnetic linear driving device and features of sensorless control strategy, a new displacement prediction method based on the improved GA-BP neural network is proposed to replace the displacement sensor. With current, voltage, and input shaft speed of the electromagnetic linear driving device as input, displacement prediction is obtained by the GA-BP neural network with improved selection factor. Finally, the experiment validated the effectiveness of displacement prediction based on the improved GA-BP neural network of shift control. The results showed that prediction accuracy of the improved GA-BP neural network was greater than 96% under all shift working conditions. The average RMSE was reduced by 21.8%, absolute error of displacement prediction was controlled within ±0.5 mm, and average shift time was less than 0.18 s. In this paper, the BP neural network is applied to complex linear displacement prediction, which has important application and popularization value.

scholarly journals Single-Tree Detection in High-Resolution Remote-Sensing Images Based on a Cascade Neural Network

2018 ◽  
Vol 7 (9) ◽  
pp. 367 ◽  
Author(s):  
Dong Tianyang ◽  
Zhang Jian ◽  
Gao Sibin ◽  
Shen Ying ◽  
Fan Jing
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
High Resolution ◽  
Bp Neural Network ◽  
Detection Method ◽  
Detection Methods ◽  
Remote Sensing Images ◽  
Tree Detection ◽  
Single Tree ◽  
Cascade Neural Network

Traditional single-tree detection methods usually need to set different thresholds and parameters manually according to different forest conditions. As a solution to the complicated detection process for non-professionals, this paper presents a single-tree detection method for high-resolution remote-sensing images based on a cascade neural network. In this method, we firstly calibrated the tree and non-tree samples in high-resolution remote-sensing images to train a classifier with the backpropagation (BP) neural network. Then, we analyzed the differences in the first-order statistic features, such as energy, entropy, mean, skewness, and kurtosis of the tree and non-tree samples. Finally, we used these features to correct the BP neural network model and build a cascade neural network classifier to detect a single tree. To verify the validity and practicability of the proposed method, six forestlands including two areas of oil palm in Thailand, and four areas of small seedlings, red maples, or longan trees in China were selected as test areas. The results from different methods, such as the region-growing method, template-matching method, BP neural network, and proposed cascade-neural-network method were compared considering these test areas. The experimental results show that the single-tree detection method based on the cascade neural network exhibited the highest root mean square of the matching rate (RMS_Rmat = 90%) and matching score (RMS_M = 68) in all the considered test areas.

A Hybrid NIDS Model Using Artificial Neural Network and D-S Evidence

2020 ◽  
pp. 477-488
Author(s):  
Chunlin Lu ◽  
Yue Li ◽  
Mingjie Ma ◽  
Na Li
Keyword(s):  
Neural Network ◽  
Intrusion Detection ◽  
Bp Neural Network ◽  
False Positive Rate ◽  
Detection Methods ◽  
Training Time ◽  
Dempster Shafer Theory ◽  
Positive Rate ◽  
Artificial Neural ◽  
Shafer Theory

Artificial Neural Networks (ANNs), especially back-propagation (BP) neural network, can improve the performance of intrusion detection systems. However, for the current network intrusion detection methods, the detection precision, especially for low-frequent attacks, detection stability and training time are still needed to be enhanced. In this paper, a new model which based on optimized BP neural network and Dempster-Shafer theory to solve the above problems and help NIDS to achieve higher detection rate, less false positive rate and stronger stability. The general process of the authors' model is as follows: firstly dividing the main extracted feature into several different feature subsets. Then, based on different feature subsets, different ANN models are trained to build the detection engine. Finally, the D-S evidence theory is employed to integration these results, and obtain the final result. The effectiveness of this method is verified by experimental simulation utilizing KDD Cup1999 dataset.

A Hybrid NIDS Model Using Artificial Neural Network and D-S Evidence

2016 ◽  
Vol 8 (1) ◽  
pp. 37-50 ◽  
Author(s):  
Chunlin Lu ◽  
Yue Li ◽  
Mingjie Ma ◽  
Na Li
Keyword(s):  
Neural Network ◽  
Intrusion Detection ◽  
Bp Neural Network ◽  
False Positive Rate ◽  
Experimental Simulation ◽  
Detection Methods ◽  
General Process ◽  
Training Time ◽  
Network Intrusion ◽  
Artificial Neural

Artificial Neural Networks (ANNs), especially back-propagation (BP) neural network, can improve the performance of intrusion detection systems. However, for the current network intrusion detection methods, the detection precision, especially for low-frequent attacks, detection stability and training time are still needed to be enhanced. In this paper, a new model which based on optimized BP neural network and Dempster-Shafer theory to solve the above problems and help NIDS to achieve higher detection rate, less false positive rate and stronger stability. The general process of the authors' model is as follows: firstly dividing the main extracted feature into several different feature subsets. Then, based on different feature subsets, different ANN models are trained to build the detection engine. Finally, the D-S evidence theory is employed to integration these results, and obtain the final result. The effectiveness of this method is verified by experimental simulation utilizing KDD Cup1999 dataset.

Research on Knowledge Representation in Choosing Mechanical Transmission Type

2012 ◽  
Vol 215-216 ◽  
pp. 1098-1101
Author(s):  
Rui Feng Bo ◽  
Rui Qin Li ◽  
Xing Quan Shen
Keyword(s):  
Neural Network ◽  
Knowledge Representation ◽  
Conceptual Design ◽  
Bp Neural Network ◽  
Numerical Data ◽  
Design System ◽  
Mechanical Transmission ◽  
Bp Network ◽  
System A ◽  
Trained Neural Network

To tackle the knowledge representation problem in developing intelligent conceptual design system, a BP network based approach is proposed, in which the related knowledge regarded various mechanical transmissions can be acquired and represented with the trained weight and threshold of neural network if they are turned into numerical data, encoded with binary strings, employed as learning samples, and fed into the constructed BP neural network. In a sense, the trained neural network can be used as a knowledge base of expert system to facilitate the choosing process of mechanical transmission. This paper provides a promising approach to deal with the automation of knowledge representation in conceptual design of mechanical transmission.

Sign in / Sign up

Export Citation Format

Share Document