Bolt Tightening Using Impact Wrench Based on Neural Networks

2000 ◽  
Vol 12 (6) ◽  
pp. 706-711
Author(s):  
Toru Fujinaka ◽  
◽  
Hirofumi Nakano ◽  
Michifumi Yoshioka ◽  
Sigeru Omatu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Clamping Force ◽  
The Neural Network ◽  
The Impact

A method for controlling the tightening operation of bolts using an impact wrench is proposed, where the neural network is employed for achieving proper clamping force. The characteristics of the clamping force depend on the kind of work to which bolts are tightened, thus a neural network is used for classification of the work. The clamping force, which can only be measured during the test run, is estimated online, using another neural network. Then appropriate input to the actuator of the impact wrench is determined, based on the estimated value of the clamping force.

Classification of Alloys with an Artificial Neural Network and Multivariate Calibration of Glow-Discharge Emission Spectra

1991 ◽  
Vol 45 (10) ◽  
pp. 1706-1716 ◽  
Author(s):  
Mark Glick ◽  
Gary M. Hieftje
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Glow Discharge ◽  
Multivariate Calibration ◽  
Emission Spectra ◽  
K Nearest Neighbor ◽  
The Neural Network ◽  
Calibration Sets ◽  
Artificial Neural

Artificial neural networks were constructed for the classification of metal alloys based on their elemental constituents. Glow discharge-atomic emission spectra obtained with a photodiode array spectrometer were used in multivariate calibrations for 7 elements in 37 Ni-based alloys (different types) and 15 Fe-based alloys. Subsets of the two major classes formed calibration sets for stepwise multiple linear regression. The remaining samples were used to validate the calibration models. Reference data from the calibration sets were then pooled into a single set to train neural networks with different architectures and different training parameters. After the neural networks learned to discriminate correctly among alloy classes in the training set, their ability to classify samples in the testing set was measured. In general, the neural network approach performed slightly better than the K-nearest neighbor method, but it suffered from a hidden classification mechanism and nonunique solutions. The neural network methodology is discussed and compared with conventional sample-classification techniques, and multivariate calibration of glow discharge spectra is compared with conventional univariate calibration.

Neural Networks for Content-Based Image Retrieval

2011 ◽  
pp. 252-272 ◽  
Author(s):  
Brijesh Verma ◽  
Siddhivinayak Kulkarni
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Fuzzy Logic ◽  
Image Retrieval ◽  
Content Based Image Retrieval ◽  
The Neural Network ◽  
Benchmark Database ◽  
Overall Performance ◽  
Critical Literature

This chapter introduces neural networks for Content-Based Image Retrieval (CBIR) systems. It presents a critical literature review of both the traditional and neural network based techniques that are used in retrieving the images based on their content. It shows how neural networks and fuzzy logic can be used in interpretation of queries, feature extraction and classification of features by describing a detailed research methodology. It investigates a neural network based technique in conjunction with fuzzy logic to improve the overall performance of the CBIR systems. The results of the investigation on a benchmark database with a comparative analysis are presented in this chapter. The methodologies and results presented in this chapter will allow researchers to improve and compare their methods and it will also allow system developers to understand and implement the neural network and fuzzy logic based techniques for content based image retrieval.

XCSR Learning from Compressed Data Acquired by Deep Neural Network

2017 ◽  
Vol 21 (5) ◽  
pp. 856-867 ◽  
Author(s):  
Kazuma Matsumoto ◽  
Takato Tatsumi ◽  
Hiroyuki Sato ◽  
Tim Kovacs ◽  
Keiki Takadama ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Learning Classifier System ◽  
Rule Based ◽  
Classifier System ◽  
Learning Classifier ◽  
The Neural Network ◽  
Compressed Data

The correctness rate of classification of neural networks is improved by deep learning, which is machine learning of neural networks, and its accuracy is higher than the human brain in some fields. This paper proposes the hybrid system of the neural network and the Learning Classifier System (LCS). LCS is evolutionary rule-based machine learning using reinforcement learning. To increase the correctness rate of classification, we combine the neural network and the LCS. This paper conducted benchmark experiments to verify the proposed system. The experiment revealed that: 1) the correctness rate of classification of the proposed system is higher than the conventional LCS (XCSR) and normal neural network; and 2) the covering mechanism of XCSR raises the correctness rate of proposed system.

Artificial neural network ensembles for fatigue damage detection in aircraft

2016 ◽  
Vol 28 (7) ◽  
pp. 851-861 ◽  
Author(s):  
Ziemowit Dworakowski ◽  
Krzysztof Dragan ◽  
Tadeusz Stepinski
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Fatigue Cracks ◽  
Guided Wave ◽  
Multidimensional Data ◽  
Neural Network Ensemble ◽  
Practical Applications ◽  
The Neural Network ◽  
Neural Network Ensembles

Neural networks are commonly recognized tools for the classification of multidimensional data obtained in structural health monitoring (SHM) systems. Their configuration for a given scenario is, however, a challenging task, which limits the possibilities of their practical applications. In this article the authors propose using the neural network ensemble approach for the classification of SHM data generated by guided wave sensor networks. The overproduce and choose strategy is used for designing ensembles containing different types and sizes of neural networks. The proposed method allows for a significant increase of the state assessment reliability, which is illustrated by the results obtained from the practical industrial case of a full-scale aircraft test. The method is verified in the process of detecting fatigue cracks propagating in the aircraft load-carrying structure. The long-term experiments are performed in variable environmental conditions with a net of structure-embedded piezoelectric sensors.

scholarly journals Identification of Baikal phytoplankton inferred from computer vision methods and machine learning

2021 ◽  
pp. 1143-1146
Author(s):  
A.V. Lysenko ◽  
◽  
◽  
M.S. Oznobikhin ◽  
E.A. Kireev ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Computer Vision ◽  
Object Recognition ◽  
Light Microscope ◽  
Optimal Size ◽  
Optimal Parameters ◽  
The Neural Network

Abstract. This study discusses the problem of phytoplankton classification using computer vision methods and convolutional neural networks. We created a system for automatic object recognition consisting of two parts: analysis and primary processing of phytoplankton images and development of the neural network based on the obtained information about the images. We developed software that can detect particular objects in images from a light microscope. We trained a convolutional neural network in transfer learning and determined optimal parameters of this neural network and the optimal size of using dataset. To increase accuracy for these groups of classes, we created three neural networks with the same structure. The obtained accuracy in the classification of Baikal phytoplankton by these neural networks was up to 80%.

scholarly journals Multi-expert annotation of Crohn’s disease images of the small bowel for automatic detection using a convolutional recurrent attention neural network

2021 ◽  
Vol 09 (07) ◽  
pp. E1136-E1144
Author(s):  
Astrid de Maissin ◽  
Remi Vallée ◽  
Mathurin Flamant ◽  
Marie Fondain-Bossiere ◽  
Catherine Le Berre ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Crohn’S Disease ◽  
Crohn's Disease ◽  
Diagnostic Tools ◽  
The Neural Network ◽  
Before And After ◽  
Expert Annotation ◽  
The Impact

Abstract Background and study aims Computer-aided diagnostic tools using deep neural networks are efficient for detection of lesions in endoscopy but require a huge number of images. The impact of the quality of annotation has not been tested yet. Here we describe a multi-expert annotated dataset of images extracted from capsules from Crohn’s disease patients and the impact of the quality of annotations on the accuracy of a recurrent attention neural network. Methods Images of capsule were annotated by a reader first and then reviewed by three experts in inflammatory bowel disease. Concordance analysis between experts was evaluated by Fleiss’ kappa and all the discordant images were, again, read by all the endoscopists to obtain a consensus annotation. A recurrent attention neural network developed for the study was tested before and after the consensus annotation. Available neural networks (ResNet and VGGNet) were also tested under the same conditions. Results The final dataset included 3498 images with 2124 non-pathological (60.7 %), 1360 pathological (38.9 %), and 14 (0.4 %) inconclusive. Agreement of the experts was good for distinguishing pathological and non-pathological images with a kappa of 0.79 (P < 0.0001). The accuracy of our classifier and the available neural networks increased after the consensus annotation with a precision of 93.7 %, sensitivity of 93 %, and specificity of 95 %. Conclusions The accuracy of the neural network increased with improved annotations, suggesting that the number of images needed for the development of these systems could be diminished using a well-designed dataset.

scholarly journals Faint Object Classification Using Artificial Neural Networks

1994 ◽  
Vol 161 ◽  
pp. 249-252
Author(s):  
M. Serra-Ricart
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Back Propagation ◽  
Neural Network Classifier ◽  
The Neural Network ◽  
Faint Object ◽  
Artificial Neural ◽  
Astronomical Images

Artificial Neural Network techniques are applied to the classification of faint objects, detected in digital astronomical images, and a Bayesian classifier (the neural network classifier, NNC hereafter) is proposed. This classifier can be implemented using a feedforward multilayered neural network trained by the back-propagation procedure (Werbos 1974).

scholarly journals Classifying Descriptions of Goods with Artificial Neural Networks

2019 ◽  
Vol 1 (1) ◽  
pp. 53-57
Author(s):  
Vinicius Di Oliveira ◽  
Marcelo Ladeira
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
The Neural Network ◽  
Legal Document ◽  
Artificial Neural

The present study aims to evaluate the performance of an artificial neural network in the classification of merchandise descriptions indicated in electronic bills, legal document used to record all commercial transactions in Brazil. For this, a significant sample of the actual descriptions will be used as well as a overlook about the performance of the neural network with a KNN and a GBM algorithms forecasting the category of the merchandise each description refers. This paper brings a method for classifying descriptions of goods with Artificial Neural Networks. The descriptions are small non structured texts, maximum of 120 characters, relating to goods traded in commercial transactions.

scholarly journals Evaluation of Normalization Techniques on Neural Networks for the Prediction of 305-Day Milk Yield

2020 ◽  
pp. 354-367
Author(s):  
Asli AKILLI ◽  
Hulya ATIL
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Milk Yield ◽  
Conjugate Gradient ◽  
Network Architecture ◽  
Back Propagation ◽  
The Neural Network ◽  
Validation Set ◽  
The Impact ◽  
Quasi Newton

In this study, the impact of data preprocessing on the prediction of 305-day milk yield using neural networks were investigated with regard to the effect of different normalization techniques. Eight normalization techniques “Z-Score, Min-Max, D-Min-Max, Median, Sigmoid, Decimal Scaling, Median and MAD, TanhEstimators" and five different back propagation algorithms “Levenberg-Marquardt (LM), Bayesian Regularization (BR), Scaled Conjugate Gradient (SCG), Conjugate Gradient Back propagation with Powell-Beale Restarts (CGB) and Brayde Fletcher Gold Farlo Shanno Quasi Newton Back propagation (BFG)” were examined and tested comparatively for the analysis. Neural network architecture was optimized and tested with several experiments. Results of the analysis show that applying different normalization techniques affect the performance and the distribution of outputs influences the learning process of the neural network. The magnitude of the effects varied with the type of back propagation algorithms, activation functions, and network's architectural structure. According to the results of the analysis, the most successful performance value in the 305-day milk yield estimation was obtained by using the neural network structured by using the Decimal Scaling normalization technique with the Bayesian Regulation algorithm (R2Adj = 0.8181, RMSE= 0.0068, MAPE= 160.42 for test set; R2Adj =0.8141, RMSE= 0.0067, MAPE= 114.12 for validation set).

scholarly journals Evaluating the Impact of Optical Interconnects on a Multi-Chip Machine-Learning Architecture

Electronics ◽  
2018 ◽  
Vol 7 (8) ◽  
pp. 130 ◽  
Author(s):  
Yuhwan Ro ◽  
Eojin Lee ◽  
Jung Ahn
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Optical Interconnects ◽  
Performance Model ◽  
Training Time ◽  
Performance Improvements ◽  
Cluster Architecture ◽  
The Neural Network ◽  
The Impact

Following trends that emphasize neural networks for machine learning, many studies regarding computing systems have focused on accelerating deep neural networks. These studies often propose utilizing the accelerator specialized in a neural network and the cluster architecture composed of interconnected accelerator chips. We observed that inter-accelerator communication within a cluster has a significant impact on the training time of the neural network. In this paper, we show the advantages of optical interconnects for multi-chip machine-learning architecture by demonstrating performance improvements through replacing electrical interconnects with optical ones in an existing multi-chip system. We propose to use highly practical optical interconnect implementation and devise an arithmetic performance model to fairly assess the impact of optical interconnects on a machine-learning accelerator platform. In our evaluation of nine Convolutional Neural Networks with various input sizes, 100 and 400 Gbps optical interconnects reduce the training time by an average of 20.6% and 35.6%, respectively, compared to the baseline system with 25.6 Gbps electrical ones.

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