scholarly journals Exploration and Research of Human Identification Scheme Based on Inertial Data

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3444
Author(s):  
Zhenyi Gao ◽  
Jiayang Sun ◽  
Haotian Yang ◽  
Jiarui Tan ◽  
Bin Zhou ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Human Identification ◽  
Statistical Features ◽  
Human Identity ◽  
Test Set ◽  
The Neural Network ◽  
Network Feature ◽  
Feature Fitting ◽  
High Flexibility

The identification work based on inertial data is not limited by space, and has high flexibility and concealment. Previous research has shown that inertial data contains information related to behavior categories. This article discusses whether inertial data contains information related to human identity. The classification experiment, based on the neural network feature fitting function, achieves 98.17% accuracy on the test set, confirming that the inertial data can be used for human identification. The accuracy of the classification method without feature extraction on the test set is only 63.84%, which further indicates the need for extracting features related to human identity from the changes in inertial data. In addition, the research on classification accuracy based on statistical features discusses the effect of different feature extraction functions on the results. The article also discusses the dimensionality reduction processing and visualization results of the collected data and the extracted features, which helps to intuitively assess the existence of features and the quality of different feature extraction effects.

scholarly journals Recognition of Lubrication Defects in Cold Forging Process with a Neural Network

2006 ◽  
pp. 262-275 ◽  
Author(s):  
Bernard F. Rolfe ◽  
Yakov Frayman ◽  
Georgina L. Kelly ◽  
Saeid Nahavandi
Keyword(s):  
Neural Network ◽  
High Accuracy ◽  
Cold Forging ◽  
Statistical Features ◽  
Tool Failure ◽  
Forging Process ◽  
The Neural Network ◽  
Normal Production ◽  
Production Conditions

This chapter describes the application of neural networks to recognition of lubrication defects typical to industrial cold forging process. The accurate recognition of lubrication errors is very important to the quality of the final product in fastener manufacture. Lubrication errors lead to increased forging loads and premature tool failure. Lubrication coating provides a barrier between the work material and the die during the drawing operation. Several types of lubrication errors, typical to production of fasteners, were introduced to sample rods, which were subsequently drawn under both laboratory and normal production conditions. The drawing force was measured, from which a limited set of statistical features was extracted. The neural-network-based model learned from these features is able to recognize all types of lubrication errors to a high accuracy. The overall accuracy of the neural-network model is around 95% with almost uniform distribution of errors between all lubrication errors and the normal condition.

Bioethics In Neurospinal Patient Care

2018 ◽  
Vol 1 (2) ◽  
pp. 1
Author(s):  
Andi Asadul Islam
Keyword(s):  
Time Frame ◽  
Hippocratic Oath ◽  
Moral Vision ◽  
Human Identity ◽  
The Past ◽  
Harvey Cushing ◽  
Sharp Focus ◽  
Realistic Approach ◽  
Cord Injury

Neurosurgery is among the newest of surgical disciplines, appearing in its modern incarnation at the dawn of twentieth century with the work of Harvey Cushing and contemporaries. Neurosurgical ethics involves challenges of manipulating anatomical locus of human identity and concerns of surgeons and patients who find themselves bound together in that venture.In recent years, neurosurgery ethics has taken on greater relevance as changes in society and technology have brought novel questions into sharp focus. Change of expanded armamentarium of techniques for interfacing with the human brain and spine— demand that we use philosophical reasoning to assess merits of technical innovations.Bioethics can be defined as systematic study of moral challenges in medicine, including moral vision, decisions, conduct, and policies related to medicine. Every surgeon should still take the Hippocratic Oath seriously and consider it a basic guide to follow good medical ethics in medical practice. It is simple and embodies three of the four modern bioethics principles – Respecting autonomy, beneficence, nonmaleficence, and justice.Spinal cord injury (SCI) is a devastating condition often affecting young and healthy individuals around the world. Currently, scientists are pressured on many fronts to develop an all-encompassing “cure” for paralysis. While scientific understanding of central nervous system (CNS) regeneration has advanced greatly in the past years, there are still many unknowns with regard to inducing successful regeneration. A more realistic approach is required if we are interested in improving the quality of life of a large proportion of the paralyzed population in a more expedient time frame.

scholarly journals Instrumental Odour Monitoring System Classification Performance Optimization by Analysis of Different Pattern-Recognition and Feature Extraction Techniques

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 114
Author(s):  
Tiziano Zarra ◽  
Mark Gino K. Galang ◽  
Florencio C. Ballesteros ◽  
Vincenzo Belgiorno ◽  
Vincenzo Naddeo
Keyword(s):  
Pattern Recognition ◽  
Feature Extraction ◽  
Performance Optimization ◽  
Industrial Plant ◽  
Mathematical Treatment ◽  
Extraction Techniques ◽  
Peak Period ◽  
Linear Discriminant ◽  
Artificial Neural

Instrumental odour monitoring systems (IOMS) are intelligent electronic sensing tools for which the primary application is the generation of odour metrics that are indicators of odour as perceived by human observers. The quality of the odour sensor signal, the mathematical treatment of the acquired data, and the validation of the correlation of the odour metric are key topics to control in order to ensure a robust and reliable measurement. The research presents and discusses the use of different pattern recognition and feature extraction techniques in the elaboration and effectiveness of the odour classification monitoring model (OCMM). The effect of the rise, intermediate, and peak period from the original response curve, in collaboration with Linear Discriminant Analysis (LDA) and Artificial Neural Networks (ANN) as a pattern recognition algorithm, were investigated. Laboratory analyses were performed with real odour samples collected in a complex industrial plant, using an advanced smart IOMS. The results demonstrate the influence of the choice of method on the quality of the OCMM produced. The peak period in combination with the Artificial Neural Network (ANN) highlighted the best combination on the basis of high classification rates. The paper provides information to develop a solution to optimize the performance of IOMS.

scholarly journals Feature Extraction and Classification of Citrus Juice by Using an Enhanced L-KSVD on Data Obtained from Electronic Nose

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 916 ◽  
Author(s):  
Wen Cao ◽  
Chunmei Liu ◽  
Pengfei Jia
Keyword(s):  
Feature Extraction ◽  
Kernel Function ◽  
Electronic Nose ◽  
Classification Accuracy ◽  
Extraction Methods ◽  
Object Function ◽  
Optimal Value ◽  
Processed Products

Aroma plays a significant role in the quality of citrus fruits and processed products. The detection and analysis of citrus volatiles can be measured by an electronic nose (E-nose); in this paper, an E-nose is employed to classify the juice which is stored for different days. Feature extraction and classification are two important requirements for an E-nose. During the training process, a classifier can optimize its own parameters to achieve a better classification accuracy but cannot decide its input data which is treated by feature extraction methods, so the classification result is not always ideal. Label consistent KSVD (L-KSVD) is a novel technique which can extract the feature and classify the data at the same time, and such an operation can improve the classification accuracy. We propose an enhanced L-KSVD called E-LCKSVD for E-nose in this paper. During E-LCKSVD, we introduce a kernel function to the traditional L-KSVD and present a new initialization technique of its dictionary; finally, the weighted coefficients of different parts of its object function is studied, and enhanced quantum-behaved particle swarm optimization (EQPSO) is employed to optimize these coefficients. During the experimental section, we firstly find the classification accuracy of KSVD, and L-KSVD is improved with the help of the kernel function; this can prove that their ability of dealing nonlinear data is improved. Then, we compare the results of different dictionary initialization techniques and prove our proposed method is better. Finally, we find the optimal value of the weighted coefficients of the object function of E-LCKSVD that can make E-nose reach a better performance.

Pattern-Based Deep Learning Method to Extract Information from the Log Dataset

2021 ◽  
pp. 2150296
Author(s):  
Xi Li ◽  
Ting Wang ◽  
Shexiong Wang
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Data Sets ◽  
Learning Method ◽  
Statistical Features ◽  
Log Data ◽  
The Neural Network ◽  
Testing Data ◽  
Extract Information

It draws researchers’ attentions how to make use of the log data effectively without paying much for storing them. In this paper, we propose pattern-based deep learning method to extract the features from log datasets and to facilitate its further use at the reasonable expense of the storage performances. By taking the advantages of the neural network and thoughts to combine statistical features with experts’ knowledge, there are satisfactory results in the experiments on some specified datasets and on the routine systems that our group maintains. Processed on testing data sets, the model is 5%, at least, more likely to outperform its competitors in accuracy perspective. More importantly, its schema unveils a new way to mingle experts’ experiences with statistical log parser.

An Efficient Adaptive Algorithm for Electron Microscopic Image Enhancement and Feature Extraction

2019 ◽  
Vol 9 (1) ◽  
pp. 1-16
Author(s):  
Vivek Arya ◽  
Vipul Sharma ◽  
Garima Arya
Keyword(s):  
Feature Extraction ◽  
Contrast Enhancement ◽  
Block Size ◽  
Sigmoid Function ◽  
Microscopic Image ◽  
Electron Microscopic ◽  
Microscopic Images ◽  
Electron Microscopic Image ◽  
Block Based

In this article, a block-based adaptive contrast enhancement algorithm has been proposed, which uses a modified sigmoid function for the enhancement and features extraction of electron microscopic images. The algorithm is based on a modified sigmoid function that adapts according to the input microscopic image statistics. For feature extraction, the contrast of the image is very important and authentic property by which this article enhances the visual quality of the image. In this work, for better contrast enhancement of image, a block based on input value, combined with a modified sigmoid function that is used as contrast enhancer provides better EMF values for a smaller block size. It provides localized contrast enhancement effects adaptively which is not possible using other existing techniques. Simulation and experimental results demonstrate that the proposed technique gives better results compared to other existing techniques when applied to electron microscopic images. After the enhancement of microscopic images of actinomycetes, various important features are shown, like coil or spiral, long filament, spore and rod shape structures. The proposed algorithm works efficiently for different dark and bright microscopic images.

scholarly journals Phonetics and Ambiguity Comprehension Gated Attention Network for Humor Recognition

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-9 ◽  
Author(s):  
Xiaochao Fan ◽  
Hongfei Lin ◽  
Liang Yang ◽  
Yufeng Diao ◽  
Chen Shen ◽  
...  
Keyword(s):  
Neural Network ◽  
Semantic Information ◽  
Semantic Representation ◽  
Research Attention ◽  
Attention Network ◽  
Phonetic Information ◽  
The Neural Network ◽  
Ambiguous Words ◽  
Public Datasets

Humor refers to the quality of being amusing. With the development of artificial intelligence, humor recognition is attracting a lot of research attention. Although phonetics and ambiguity have been introduced by previous studies, existing recognition methods still lack suitable feature design for neural networks. In this paper, we illustrate that phonetics structure and ambiguity associated with confusing words need to be learned for their own representations via the neural network. Then, we propose the Phonetics and Ambiguity Comprehension Gated Attention network (PACGA) to learn phonetic structures and semantic representation for humor recognition. The PACGA model can well represent phonetic information and semantic information with ambiguous words, which is of great benefit to humor recognition. Experimental results on two public datasets demonstrate the effectiveness of our model.

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