scholarly journals In-process detection of grinding burn using machine learning

2021 ◽  
Author(s):  
Emil Sauter ◽  
Erkut Sarikaya ◽  
Marius Winter ◽  
Konrad Wegener
Keyword(s):  
Machine Learning ◽  
Machine Learning Algorithms ◽  
Limiting Factors ◽  
Process Time ◽  
New Approach ◽  
Time Frequency ◽  
Grinding Burn ◽  
Process Measurement ◽  
Feature Selector ◽  
Process Detection

AbstractThe improvement of industrial grinding processes is driven by the objective to reduce process time and costs while maintaining required workpiece quality characteristics. One of several limiting factors is grinding burn. Usually applied techniques for workpiece burn are conducted often only for selected parts and can be time consuming. This study presents a new approach for grinding burn detection realized for each ground part under near-production conditions. Based on the in-process measurement of acoustic emission, spindle electric current, and power signals, time-frequency transforms are conducted to derive almost 900 statistical features as an input for machine learning algorithms. Using genetic programming, an optimized combination between feature selector and classifier is determined to detect grinding burn. The application of the approach results in a high classification accuracy of about 99% for the binary problem and more than 98% for the multi-classdetection case, respectively.

scholarly journals Hierarchical Tactile Sensation Integration from Prosthetic Fingertips Enables Multi-Texture Surface Recognition

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4324
Author(s):  
Moaed A. Abd ◽  
Rudy Paul ◽  
Aparna Aravelli ◽  
Ou Bai ◽  
Leonel Lagos ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Sliding Contact ◽  
Tactile Sensation ◽  
Support Vector ◽  
Textured Surfaces ◽  
K Nearest Neighbor ◽  
Tactile Sensors ◽  
Time Frequency

Multifunctional flexible tactile sensors could be useful to improve the control of prosthetic hands. To that end, highly stretchable liquid metal tactile sensors (LMS) were designed, manufactured via photolithography, and incorporated into the fingertips of a prosthetic hand. Three novel contributions were made with the LMS. First, individual fingertips were used to distinguish between different speeds of sliding contact with different surfaces. Second, differences in surface textures were reliably detected during sliding contact. Third, the capacity for hierarchical tactile sensor integration was demonstrated by using four LMS signals simultaneously to distinguish between ten complex multi-textured surfaces. Four different machine learning algorithms were compared for their successful classification capabilities: K-nearest neighbor (KNN), support vector machine (SVM), random forest (RF), and neural network (NN). The time-frequency features of the LMSs were extracted to train and test the machine learning algorithms. The NN generally performed the best at the speed and texture detection with a single finger and had a 99.2 ± 0.8% accuracy to distinguish between ten different multi-textured surfaces using four LMSs from four fingers simultaneously. The capability for hierarchical multi-finger tactile sensation integration could be useful to provide a higher level of intelligence for artificial hands.

Developing a new approach for design support of subsurface constructed wetland using machine learning algorithms

2022 ◽  
Vol 301 ◽  
pp. 113868
Author(s):  
Xuan Cuong Nguyen ◽  
Thi Thanh Huyen Nguyen ◽  
Quyet V. Le ◽  
Phuoc Cuong Le ◽  
Arun Lal Srivastav ◽  
...  
Keyword(s):  
Machine Learning ◽  
Constructed Wetland ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Design Support ◽  
New Approach

scholarly journals Evaluation on NTM based Predictive Analytics on Rainfall and Flood Disaster Management

2020 ◽  
Vol 6 (10) ◽  
pp. 89-95
Author(s):  
Satwik P M and Dr. Meenatchi Sundram
Keyword(s):  
Machine Learning ◽  
Predictive Analytics ◽  
Learning Algorithm ◽  
Research Work ◽  
Evaluation Process ◽  
Machine Learning Algorithms ◽  
New Approach ◽  
Research Article ◽  
The Neural Networks ◽  
Evaluation Parameters

In this Research article, we presented a new approach for predicting the flood through the advanced Machine learning Algorithm which is one among the Neural networks class that outperforms itself in best data operations and predictive analytics. This Research article discusses in detail about the prediction of flood occurrences evaluation process. We interpreted the Research with many algorithms that is existing, and the Research work have been dealing with different research works inculcated and compared with different Research approaches. On Comparing to the Previous Researches its observed that the Neural Turing networks have been performing the prediction of the rainfall and flood-based disasters for the consecutive year counts of 10,15 and 20 with 93.8% accuracy. Here the Research is analyzed with various parameters and Comparing it with the other researches which is implemented with other machine learning algorithms. Comparing with the previous researches the Idea of the research have been described and evaluated with the different evaluation parameters including the number of iterations or Epochs.

scholarly journals Machine learning as the new approach in understanding biomarkers of suicidal behavior

2020 ◽  
Author(s):  
Alja Videtič Paska ◽  
Katarina Kouter
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Data Analysis ◽  
High Throughput ◽  
Suicidal Behavior ◽  
Technological Development ◽  
Machine Learning Algorithms ◽  
Classical Approach ◽  
New Approach

In psychiatry, compared to other medical fields, the identification of biological markers that would complement current clinical interview, and enable more objective and faster clinical diagnosis, implement accurate monitoring of treatment response and remission, is grave. Current technological development enables analyses of various biological marks in high throughput scale at reasonable costs, and therefore ‘omic’ studies are entering the psychiatry research. However, big data demands a whole new plethora of skills in data processing, before clinically useful information can be extracted. So far the classical approach to data analysis did not really contribute to identification of biomarkers in psychiatry, but the extensive amounts of data might get to a higher level, if artificial intelligence in the shape of machine learning algorithms would be applied. Not many studies on machine learning in psychiatry have been published, but we can already see from that handful of studies that the potential to build a screening portfolio of biomarkers for different psychopathologies, including suicide, exists.

scholarly journals The Student Study Experience – Analysing Student Study Choices

2020 ◽  
pp. 373-379
Author(s):  
Chris Edwards ◽  
Mark Gaved
Keyword(s):  
Machine Learning ◽  
Student Experience ◽  
Machine Learning Algorithms ◽  
Great Flexibility ◽  
New Approach ◽  
Database Structure ◽  
Study Experience ◽  
New Challenges ◽  
Model Database ◽  
Study Choice

As higher education institutions increasingly teach online and offer greater levels of choice to students (over which modules to study, in which order to study, and how long to extend study before qualification) new challenges are introduced. One of these challenges is how to maintain an understanding of the student experience. This understanding is necessary to provide feedback to both students and faculty, and institutionally for the continued enhancement of quality. This paper is the first attempt at providing a narrative describing one approach to this challenge and the experience within a large distance learning University. It demonstrates a new approach to data is key to enabling the analysis of student study pathways. For many years, this University has offered great flexibility of study and as wide a study choice as it is possible to offer with conventional modules. By design, the Institution holds high levels of data for all student study. However, whilst it is possible to create bespoke queries, we found that this has been insufficient to readily enable analysis of the student experience. By moving from a traditional relational database structure to a multi-model database, many of the difficulties are resolved. In this paper, we report on this approach and describe next steps, including the potential to apply machine learning algorithms and test other data theories like that of Markov Chains.

scholarly journals Parallel processing using big data and machine learning techniques for intrusion detection

2020 ◽  
Vol 9 (3) ◽  
pp. 553
Author(s):  
Alaeddine Boukhalfa ◽  
Nabil Hmina ◽  
Habiba Chaoni
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Processing Time ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Detection Accuracy ◽  
New Approach ◽  
Learning Techniques ◽  
Proposed Model ◽  
Multiple Devices

Currently, information technology is used in all the life domains, multiple devices produce data and transfer them across the network, these transfers are not always secured, they can contain new menaces invisible by the current security devices. Moreover, the large amount and variety of the exchanged data cause difficulties related to the detection time. To solve these issues, we suggest in this paper, a new approach based on storing the large amount and variety of network traffic data employing Big Data techniques, and analyzing these data with Machine Learning algorithms, in a distributed and parallel way, in order to detect new hidden intrusions with less processing time. According to the results of the experiments, the detection accuracy of the Machine Learning methods reaches 99.9 %, and their processing time has been reduced considerably by applying them in a parallel and distributed way, which proves that our proposed model is effective for the detection of new intrusions.

scholarly journals Cluster-Based Mutual Fund Classification and Price Prediction Using Machine Learning for Robo-Advisors

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Xiaofei Chen ◽  
Shujun Ye ◽  
Chao Huang
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Mutual Funds ◽  
Mutual Fund ◽  
Clustering Algorithm ◽  
Machine Learning Algorithms ◽  
Limited Data ◽  
Financial Industry ◽  
Price Movement ◽  
New Approach

The rise of FinTech has been meteoric in China. Investing in mutual funds through robo-advisor has become a new innovation in the wealth management industry. In recent years, machine learning, especially deep learning, has been widely used in the financial industry to solve financial problems. This paper aims to improve the accuracy and timeliness of fund classification through the use of machine learning algorithms, that is, Gaussian hybrid clustering algorithm. At the same time, a deep learning-based prediction model is implemented to predict the price movement of fund classes based on the classification results. Fund classification carried out using 3,625 Chinese mutual funds shows both accurate and efficient results. The cluster-based spatiotemporal ensemble deep learning module shows better prediction accuracy than baseline models with only access to limited data samples. The main contribution of this paper is to provide a new approach to fund classification and price movement prediction to support the decision-making of the next generation robo-advisor assisted by artificial intelligence.

scholarly journals ECG-based machine-learning algorithms for heartbeat classification

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Saira Aziz ◽  
Sajid Ahmed ◽  
Mohamed-Slim Alouini
Keyword(s):  
Machine Learning ◽  
Fractional Fourier Transform ◽  
Heart Diseases ◽  
Learning Model ◽  
Machine Learning Algorithms ◽  
Heartbeat Classification ◽  
Time Frequency ◽  
Ecg Signals ◽  
Signal Features ◽  
Machine Learning Model

AbstractElectrocardiogram (ECG) signals represent the electrical activity of the human hearts and consist of several waveforms (P, QRS, and T). The duration and shape of each waveform and the distances between different peaks are used to diagnose heart diseases. In this work, to better analyze ECG signals, a new algorithm that exploits two-event related moving-averages (TERMA) and fractional-Fourier-transform (FrFT) algorithms is proposed. The TERMA algorithm specifies certain areas of interest to locate desired peak, while the FrFT rotates ECG signals in the time-frequency plane to manifest the locations of various peaks. The proposed algorithm’s performance outperforms state-of-the-art algorithms. Moreover, to automatically classify heart disease, estimated peaks, durations between different peaks, and other ECG signal features were used to train a machine-learning model. Most of the available studies uses the MIT-BIH database (only 48 patients). However, in this work, the recently reported Shaoxing People’s Hospital (SPH) database, which consists of more than 10,000 patients, was used to train the proposed machine-learning model, which is more realistic for classification. The cross-database training and testing with promising results is the uniqueness of our proposed machine-learning model.

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