scholarly journals Self-Powered Intelligent Human-Machine Interaction for Handwriting Recognition

Research ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Hang Guo ◽  
Ji Wan ◽  
Haobin Wang ◽  
Hanxiang Wu ◽  
Chen Xu ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Human Computer Interaction ◽  
Supervised Machine Learning ◽  
Triboelectric Nanogenerator ◽  
Human Machine Interaction ◽  
The Neural Network ◽  
Interaction Interface ◽  
Machine Interaction ◽  
Computer Interaction

Handwritten signatures widely exist in our daily lives. The main challenge of signal recognition on handwriting is in the development of approaches to obtain information effectively. External mechanical signals can be easily detected by triboelectric nanogenerators which can provide immediate opportunities for building new types of active sensors capable of recording handwritten signals. In this work, we report an intelligent human-machine interaction interface based on a triboelectric nanogenerator. Using the horizontal-vertical symmetrical electrode array, the handwritten triboelectric signal can be recorded without external energy supply. Combined with supervised machine learning methods, it can successfully recognize handwritten English letters, Chinese characters, and Arabic numerals. The principal component analysis algorithm preprocesses the triboelectric signal data to reduce the complexity of the neural network in the machine learning process. Further, it can realize the anticounterfeiting recognition of writing habits by controlling the samples input to the neural network. The results show that the intelligent human-computer interaction interface has broad application prospects in signature security and human-computer interaction.

scholarly journals Application of Machine Learning in the Control of Metal Melting Production Process

2020 ◽  
Vol 10 (17) ◽  
pp. 6048 ◽  
Author(s):  
Nedeljko Dučić ◽  
Aleksandar Jovičić ◽  
Srećko Manasijević ◽  
Radomir Radiša ◽  
Žarko Ćojbašić ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Cast Iron ◽  
Production Process ◽  
Support Vector Regression ◽  
White Cast Iron ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
The Neural Network ◽  
Metal Melting

This paper presents the application of machine learning in the control of the metal melting process. Metal melting is a dynamic production process characterized by nonlinear relations between process parameters. In this particular case, the subject of research is the production of white cast iron. Two supervised machine learning algorithms have been applied: the neural network and the support vector regression. The goal of their application is the prediction of the amount of alloying additives in order to obtain the desired chemical composition of white cast iron. The neural network model provided better results than the support vector regression model in the training and testing phases, which qualifies it to be used in the control of the white cast iron production.

scholarly journals Expressando Emoções e Sentimentos no Facebook

2020 ◽  
Author(s):  
Francielli Freitas Moro ◽  
Luciana Bolan Frigo
Keyword(s):  
Social Network ◽  
Human Computer Interaction ◽  
Computer Systems ◽  
Gender Diversity ◽  
User Needs ◽  
Human Machine Interaction ◽  
Design Interfaces ◽  
Sociological Approaches ◽  
Machine Interaction ◽  
Computer Interaction

Computer systems are increasingly adapting to user needs. Human-machine interaction or human-computer interaction (HCI), as it is known, has discussed sociological approaches in order to design interfaces taking into account user's differences. This article presents an analysis of the Facebook social network based on the evolution of traditional HCI and some of its concepts for feminist HCI, thus exploring its functionality and evaluating it in this context. Surveys based on the concepts of feminist HCI were applied to evaluate this methodology and the impacts on gender diversity in these systems. The results indicate that most users seek more freedom to express themselves at the system and its content.

Component-based machine learning paradigm for discovering rate-dependent and pressure-sensitive level-set plasticity models

2021 ◽  
pp. 1-13
Author(s):  
Nikolaos Napoleon Vlassis ◽  
Waiching Sun
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Path Dependence ◽  
Time History ◽  
Supervised Machine Learning ◽  
Generation Process ◽  
Data Generation ◽  
Modeling Framework ◽  
The Neural Network ◽  
Path Dependent

Abstract Conventionally, neural network constitutive laws for path-dependent elasto-plastic solids are trained via supervised learning performed on recurrent neural network, with the time history of strain as input and the stress as input. However, training neural network to replicate path-dependent constitutive responses require significant more amount of data due to the path dependence. This demand on diverse and abundance of accurate data, as well as the lack of interpretability to guide the data generation process, could become major roadblocks for engineering applications. In this work, we attempt to simplify these training processes and improve the interpretability of the trained models by breaking down the training of material models into multiple supervised machine learning programs for elasticity, initial yielding and hardening laws that can be conducted sequentially. To predict pressure-sensitivity and rate dependence of the plastic responses, we reformulate the Hamliton-Jacobi equation such that the yield function is parametrized in the product space spanned by the principle stress, the accumulated plastic strain and time. To test the versatility of the neural network meta-modeling framework, we conduct multiple numerical experiments where neural networks are trained and validated against (1) data generated from known benchmark models, (2) data obtained from physical experiments and (3) data inferred from homogenizing sub-scale direct numerical simulations of microstructures. The neural network model is also incorporated into an offline FFT-FEM model to improve the efficiency of the multiscale calculations.

Touch-sensing fabric encapsulated with hydrogel for human-computer interaction

Soft Matter ◽  
2021 ◽  
Author(s):  
ruidong xu ◽  
Lijun Qu ◽  
Mingwei Tian
Keyword(s):  
Human Computer Interaction ◽  
Wearable Electronics ◽  
Human Machine Interaction ◽  
Machine Interaction ◽  
Computer Interaction

Flexible touch-sensing devices have raised extensive attention to wearable electronics and human-machine interaction. The ionic touch-sensing hydrogels are ideal candidates for these scenarios, but the absorbed water evaporates easily from...

scholarly journals Proceedings of The First Danish Human-Computer Interaction Research Symposium, Aarhus, Denmark, 27. November 2001

2001 ◽  
Vol 30 (555) ◽  
Author(s):  
Olav Bertelsen
Keyword(s):  
Human Computer Interaction ◽  
Joint Effort ◽  
Human Machine Interaction ◽  
Primary Motivation ◽  
Work Characteristic ◽  
Machine Interaction ◽  
Interaction Research ◽  
Computer Interaction

"The First Danish Human-Computer Interaction Research Symposium has been realised as a joint effort between sigchi.dk and Centre for Human-Machine Interaction. The primary motivation for this effort has been to stimulate networking and to create an overview of recent Danish HCI research. The present proceedings consist of the 25 extended abstracts accepted for the symposium, presenting a very broad range of work, characteristic for Danish HCI research. In addition, 3 thesis (in progress) summaries from the doctoral colloquium are included."

A Misfire Fault Diagnosis System Based on Improved Neural Network

2011 ◽  
Vol 383-390 ◽  
pp. 1549-1554
Author(s):  
Kai Yu Zhang ◽  
Ji Wen Deng ◽  
Di Lu
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Fault Diagnosis ◽  
New Man ◽  
The Neural Network ◽  
Automobile Engines ◽  
Interaction Interface ◽  
Fault Diagnosis System ◽  
The Common ◽  
Machine Interaction

Misfiring fault is one of the common faults of automobile engines. This paper presents an algorithm based improved neural network which is used for misfiring fault. It calculates the memberships of inputs and initializes the weights and thresholds of the neural network by genetic algorithm firstly, and then trains the improved neural network and uses it for diagnosis. By applying GUI function of MATLAB, a new man-machine interaction interface was designed. The results of experiment indicate that this algorithm can effectively carry out misfiring fault diagnosis.

Semi-automated Conversion of Clinical Trial Legacy Data into CDISC SDTM Standards Format Using Supervised Machine Learning

2021 ◽  
Author(s):  
Takuma Oda ◽  
Shih-Wei Chiu ◽  
Takuhiro Yamaguchi
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Study Data ◽  
Supervised Machine Learning ◽  
Gradient Boosting ◽  
Distributed Representation ◽  
The Neural Network ◽  
Automated Process ◽  
Legacy Data ◽  
The One

Abstract Objective This study aimed to develop a semi-automated process to convert legacy data into clinical data interchange standards consortium (CDISC) study data tabulation model (SDTM) format by combining human verification and three methods: data normalization; feature extraction by distributed representation of dataset names, variable names, and variable labels; and supervised machine learning. Materials and Methods Variable labels, dataset names, variable names, and values of legacy data were used as machine learning features. Because most of these data are string data, they had been converted to a distributed representation to make them usable as machine learning features. For this purpose, we utilized the following methods for distributed representation: Gestalt pattern matching, cosine similarity after vectorization by Doc2vec, and vectorization by Doc2vec. In this study, we examined five algorithms—namely decision tree, random forest, gradient boosting, neural network, and an ensemble that combines the four algorithms—to identify the one that could generate the best prediction model. Results The accuracy rate was highest for the neural network, and the distribution of prediction probabilities also showed a split between the correct and incorrect distributions. By combining human verification and the three methods, we were able to semi-automatically convert legacy data into the CDISC SDTM format. Conclusion By combining human verification and the three methods, we have successfully developed a semi-automated process to convert legacy data into the CDISC SDTM format; this process is more efficient than the conventional fully manual process.

scholarly journals EXPLORATORY RESEARCH ON CULTURAL COMPUTING FOR HUMAN-COMPUTER INTERACTION. THE CASE OF SUSTAINABILITY.

2021 ◽  
Vol 1 ◽  
pp. 1471-1480
Author(s):  
Lou Grimal ◽  
Inès di Loreto ◽  
Nadège Troussier
Keyword(s):  
Human Computer Interaction ◽  
New Technologies ◽  
Daily Basis ◽  
Engineering Practice ◽  
Exploratory Research ◽  
Human Machine Interaction ◽  
Design For Sustainability ◽  
Cultural Computing ◽  
Machine Interaction ◽  
Computer Interaction

AbstractThe digital transition refers to the fact that information technology (IT) tools are used in all our activities on a daily basis. In this article, we will study the use of IT tools in engineering activities. It is possible to say that today IT tools accompany engineers in their professional practices. This presence of computing has also enabled the development and considerable changes in human-technologies interactions. Moreover, the socio-economic context has evolved considerably, and environmental issues have taken on an important role in engineering. We ask whether and to what extent these two contexts (digital and ecological) have changed the expectations of design professionals with regard to IT tools. Should the way of addressing the type of human-machine interaction in engineering tools be modified in depth? The objective of this paper is to understand what types of human-computer interaction would allow a more satisfying user experience for those future engineers who are using new technologies and marked by the ecological urgency. To do so, we will focus on a particular engineering context (design for sustainability) and a particular engineering practice (LCA practice).

scholarly journals Comparison of Neural Network and Machine Learning Approaches in Prediction of Chronic Kidney Disease

2021 ◽  
Vol 10 (3) ◽  
Author(s):  
Shreya Nag ◽  
Nimitha Jammula
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Healthcare Providers ◽  
Accurate Diagnosis ◽  
Supervised Machine Learning ◽  
Learning Approaches ◽  
Data Set ◽  
The Neural Network

The diagnosis of a disease to determine a specific condition is crucial in caring for patients and furthering medical research. The timely and accurate diagnosis can have important implications for both patients and healthcare providers. An earlier diagnosis allows doctors to consider more methods of treatment, allowing them to have a greater flexibility of tailoring their decisions, and ultimately improving the patient’s health. Additionally, a timely detection allows patients to have a greater control over their health and their decisions, allowing them to plan ahead. As advancements in computer science and technology continue to improve, these two factors can play a major role in aiding healthcare providers with medical issues. The emergence of artificial intelligence and machine learning can aid in addressing the challenge of completing timely and accurate diagnosis. The goal of this research work is to design a system that utilizes machine learning and neural network techniques to diagnose chronic kidney disease with more than 90% accuracy based on a clinical data set, and to do a comparative study of the performance of the neural network versus supervised machine learning approaches. Based on the results, all the algorithms performed well in prediction of chronic kidney disease (CKD) with more that 90% accuracy. The neural network system provided the best performance (accuracy = 100%) in prediction of chronic kidney disease in comparison with the supervised Random Forest algorithm (accuracy = 99%) and the supervised Decision Tree algorithm (accuracy = 97%).

scholarly journals Discretization and machine learning approximation of BSDEs with a constraint on the Gains-process

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Idris Kharroubi ◽  
Thomas Lim ◽  
Xavier Warin
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Differential Equations ◽  
Numerical Experiments ◽  
Optimization Problem ◽  
Learning Approach ◽  
The Neural Network ◽  
Machine Learning Approach ◽  
Mesh Grid

AbstractWe study the approximation of backward stochastic differential equations (BSDEs for short) with a constraint on the gains process. We first discretize the constraint by applying a so-called facelift operator at times of a grid. We show that this discretely constrained BSDE converges to the continuously constrained one as the mesh grid converges to zero. We then focus on the approximation of the discretely constrained BSDE. For that we adopt a machine learning approach. We show that the facelift can be approximated by an optimization problem over a class of neural networks under constraints on the neural network and its derivative. We then derive an algorithm converging to the discretely constrained BSDE as the number of neurons goes to infinity. We end by numerical experiments.

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