scholarly journals Optimization Techniques for Mining Power Quality Data and Processing Unbalanced Datasets in Machine Learning Applications

Energies ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 463
Author(s):  
Alvaro Furlani Bastos ◽  
Surya Santoso
Keyword(s):  
Machine Learning ◽  
Power Systems ◽  
Power Quality ◽  
Optimization Techniques ◽  
Machine Learning Algorithms ◽  
Quality Data ◽  
Successful Performance ◽  
Data Mining Approach ◽  
Machine Learning Applications ◽  
Learning Frameworks

In recent years, machine learning applications have received increasing interest from power system researchers. The successful performance of these applications is dependent on the availability of extensive and diverse datasets for the training and validation of machine learning frameworks. However, power systems operate at quasi-steady-state conditions for most of the time, and the measurements corresponding to these states provide limited novel knowledge for the development of machine learning applications. In this paper, a data mining approach based on optimization techniques is proposed for filtering root-mean-square (RMS) voltage profiles and identifying unusual measurements within triggerless power quality datasets. Then, datasets with equal representation between event and non-event observations are created so that machine learning algorithms can extract useful insights from the rare but important event observations. The proposed framework is demonstrated and validated with both synthetic signals and field data measurements.

scholarly journals An Efficient FPGA-Based Hardware Accelerator for Convex Optimization-Based SVM Classifier for Machine Learning on Embedded Platforms

Electronics ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1323
Author(s):  
Srikanth Ramadurgam ◽  
Darshika G. Perera
Keyword(s):  
Machine Learning ◽  
Convex Optimization ◽  
Learning Algorithms ◽  
Optimization Techniques ◽  
Machine Learning Algorithms ◽  
System Level ◽  
Svm Classifier ◽  
Hardware Accelerator ◽  
Machine Learning Applications ◽  
Embedded Platforms

Machine learning is becoming the cornerstones of smart and autonomous systems. Machine learning algorithms can be categorized into supervised learning (classification) and unsupervised learning (clustering). Among many classification algorithms, the Support Vector Machine (SVM) classifier is one of the most commonly used machine learning algorithms. By incorporating convex optimization techniques into the SVM classifier, we can further enhance the accuracy and classification process of the SVM by finding the optimal solution. Many machine learning algorithms, including SVM classification, are compute-intensive and data-intensive, requiring significant processing power. Furthermore, many machine learning algorithms have found their way into portable and embedded devices, which have stringent requirements. In this research work, we introduce a novel, unique, and efficient Field Programmable Gate Array (FPGA)-based hardware accelerator for a convex optimization-based SVM classifier for embedded platforms, considering the constraints associated with these platforms and the requirements of the applications running on these devices. We incorporate suitable mathematical kernels and decomposition methods to systematically solve the convex optimization for machine learning applications with a large volume of data. Our proposed architectures are generic, parameterized, and scalable; hence, without changing internal architectures, our designs can be used to process different datasets with varying sizes, can be executed on different platforms, and can be utilized for various machine learning applications. We also introduce system-level architectures and techniques to facilitate real-time processing. Experiments are performed using two different benchmark datasets to evaluate the feasibility and efficiency of our hardware architecture, in terms of timing, speedup, area, and accuracy. Our embedded hardware design achieves up to 79 times speedup compared to its embedded software counterpart, and can also achieve up to 100% classification accuracy.

scholarly journals Smooth input preparation for quantum and quantum-inspired machine learning

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Zhikuan Zhao ◽  
Jack K. Fitzsimons ◽  
Patrick Rebentrost ◽  
Vedran Dunjko ◽  
Joseph F. Fitzsimons
Keyword(s):  
Machine Learning ◽  
Quantum Algorithms ◽  
Machine Learning Algorithms ◽  
Low Rank ◽  
Smoothed Analysis ◽  
Machine Learning Applications ◽  
Data Points ◽  
Input Model ◽  
The Cost ◽  
Input Perturbation

AbstractMachine learning has recently emerged as a fruitful area for finding potential quantum computational advantage. Many of the quantum-enhanced machine learning algorithms critically hinge upon the ability to efficiently produce states proportional to high-dimensional data points stored in a quantum accessible memory. Even given query access to exponentially many entries stored in a database, the construction of which is considered a one-off overhead, it has been argued that the cost of preparing such amplitude-encoded states may offset any exponential quantum advantage. Here we prove using smoothed analysis that if the data analysis algorithm is robust against small entry-wise input perturbation, state preparation can always be achieved with constant queries. This criterion is typically satisfied in realistic machine learning applications, where input data is subjective to moderate noise. Our results are equally applicable to the recent seminal progress in quantum-inspired algorithms, where specially constructed databases suffice for polylogarithmic classical algorithm in low-rank cases. The consequence of our finding is that for the purpose of practical machine learning, polylogarithmic processing time is possible under a general and flexible input model with quantum algorithms or quantum-inspired classical algorithms in the low-rank cases.

scholarly journals Clinician checklist for assessing suitability of machine learning applications in healthcare

2021 ◽  
Vol 28 (1) ◽  
pp. e100251
Author(s):  
Ian Scott ◽  
Stacey Carter ◽  
Enrico Coiera
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Clinical Decision Making ◽  
Improve Patient Care ◽  
Clinical Decision ◽  
Routine Care ◽  
Machine Learning Algorithms ◽  
Clinical Settings ◽  
Machine Learning Applications ◽  
Key Issues

Machine learning algorithms are being used to screen and diagnose disease, prognosticate and predict therapeutic responses. Hundreds of new algorithms are being developed, but whether they improve clinical decision making and patient outcomes remains uncertain. If clinicians are to use algorithms, they need to be reassured that key issues relating to their validity, utility, feasibility, safety and ethical use have been addressed. We propose a checklist of 10 questions that clinicians can ask of those advocating for the use of a particular algorithm, but which do not expect clinicians, as non-experts, to demonstrate mastery over what can be highly complex statistical and computational concepts. The questions are: (1) What is the purpose and context of the algorithm? (2) How good were the data used to train the algorithm? (3) Were there sufficient data to train the algorithm? (4) How well does the algorithm perform? (5) Is the algorithm transferable to new clinical settings? (6) Are the outputs of the algorithm clinically intelligible? (7) How will this algorithm fit into and complement current workflows? (8) Has use of the algorithm been shown to improve patient care and outcomes? (9) Could the algorithm cause patient harm? and (10) Does use of the algorithm raise ethical, legal or social concerns? We provide examples where an algorithm may raise concerns and apply the checklist to a recent review of diagnostic imaging applications. This checklist aims to assist clinicians in assessing algorithm readiness for routine care and identify situations where further refinement and evaluation is required prior to large-scale use.

scholarly journals Distributed Learning Applications in Power Systems: A Review of Methods, Gaps, and Challenges

Energies ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 3654
Author(s):  
Nastaran Gholizadeh ◽  
Petr Musilek
Keyword(s):  
Machine Learning ◽  
Power Systems ◽  
Learning Algorithm ◽  
Single Point ◽  
Distributed Learning ◽  
Large Data ◽  
Multi Agent Systems ◽  
Power Quality Monitoring ◽  
Multi Agent ◽  
Learning Frameworks

In recent years, machine learning methods have found numerous applications in power systems for load forecasting, voltage control, power quality monitoring, anomaly detection, etc. Distributed learning is a subfield of machine learning and a descendant of the multi-agent systems field. Distributed learning is a collaboratively decentralized machine learning algorithm designed to handle large data sizes, solve complex learning problems, and increase privacy. Moreover, it can reduce the risk of a single point of failure compared to fully centralized approaches and lower the bandwidth and central storage requirements. This paper introduces three existing distributed learning frameworks and reviews the applications that have been proposed for them in power systems so far. It summarizes the methods, benefits, and challenges of distributed learning frameworks in power systems and identifies the gaps in the literature for future studies.

A new generic method to improve machine learning applications in official statistics

2021 ◽  
pp. 1-16
Author(s):  
Kevin Kloos
Keyword(s):  
Machine Learning ◽  
Mean Squared Error ◽  
Statistical Properties ◽  
Machine Learning Algorithms ◽  
Official Statistics ◽  
Academic Literature ◽  
Misclassification Bias ◽  
Squared Error ◽  
Machine Learning Applications ◽  
Applications Of Machine Learning

The use of machine learning algorithms at national statistical institutes has increased significantly over the past few years. Applications range from new imputation schemes to new statistical output based entirely on machine learning. The results are promising, but recent studies have shown that the use of machine learning in official statistics always introduces a bias, known as misclassification bias. Misclassification bias does not occur in traditional applications of machine learning and therefore it has received little attention in the academic literature. In earlier work, we have collected existing methods that are able to correct misclassification bias. We have compared their statistical properties, including bias, variance and mean squared error. In this paper, we present a new generic method to correct misclassification bias for time series and we derive its statistical properties. Moreover, we show numerically that it has a lower mean squared error than the existing alternatives in a wide variety of settings. We believe that our new method may improve machine learning applications in official statistics and we aspire that our work will stimulate further methodological research in this area.

scholarly journals Power Profile and Thresholding Assisted Multi-Label NILM Classification

Energies ◽  
2021 ◽  
Vol 14 (22) ◽  
pp. 7609
Author(s):  
Muhammad Asif Ali Rehmani ◽  
Saad Aslam ◽  
Shafiqur Rahman Tito ◽  
Snjezana Soltic ◽  
Pieter Nieuwoudt ◽  
...  
Keyword(s):  
Machine Learning ◽  
Energy Consumption ◽  
Power Systems ◽  
Management Strategies ◽  
Cost Effective ◽  
Machine Learning Algorithms ◽  
Optimal Load ◽  
Power Profile ◽  
Load Monitoring ◽  
Number Of Classes

Next-generation power systems aim at optimizing the energy consumption of household appliances by utilising computationally intelligent techniques, referred to as load monitoring. Non-intrusive load monitoring (NILM) is considered to be one of the most cost-effective methods for load classification. The objective is to segregate the energy consumption of individual appliances from their aggregated energy consumption. The extracted energy consumption of individual devices can then be used to achieve demand-side management and energy saving through optimal load management strategies. Machine learning (ML) has been popularly used to solve many complex problems including NILM. With the availability of the energy consumption datasets, various ML algorithms have been effectively trained and tested. However, most of the current methodologies for NILM employ neural networks only for a limited operational output level of appliances and their combinations (i.e., only for a small number of classes). On the contrary, this work depicts a more practical scenario where over a hundred different combinations were considered and labelled for the training and testing of various machine learning algorithms. Moreover, two novel concepts—i.e., thresholding/occurrence per million (OPM) along with power windowing—were utilised, which significantly improved the performance of the trained algorithms. All the trained algorithms were thoroughly evaluated using various performance parameters. The results shown demonstrate the effectiveness of thresholding and OPM concepts in classifying concurrently operating appliances using ML.

SeisBench: A toolbox for benchmarking and applying machine learning in seismology.

2021 ◽  
Author(s):  
Jack Woollam ◽  
Jannes Münchmeyer ◽  
Carlo Giunchi ◽  
Dario Jozinovic ◽  
Tobias Diehl ◽  
...  
Keyword(s):  
Machine Learning ◽  
Model Comparison ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Quality Data ◽  
Data Sets ◽  
Waveform Data ◽  
Detection Techniques ◽  
Benchmark Data

<p>Machine learning methods have seen widespread adoption within the seismological community in recent years due to their ability to effectively process large amounts of data, while equalling or surpassing the performance of human analysts or classic algorithms. In the wider machine learning world, for example in imaging applications, the open availability of extensive high-quality datasets for training, validation, and the benchmarking of competing algorithms is seen as a vital ingredient to the rapid progress observed throughout the last decade. Within seismology, vast catalogues of labelled data are readily available, but collecting the waveform data for millions of records and assessing the quality of training examples is a time-consuming, tedious process. The natural variability in source processes and seismic wave propagation also presents a critical problem during training. The performance of models trained on different regions, distance and magnitude ranges are not easily comparable. The inability to easily compare and contrast state-of-the-art machine learning-based detection techniques on varying seismic data sets is currently a barrier to further progress within this emerging field. We present SeisBench, an extensible open-source framework for training, benchmarking, and applying machine learning algorithms. SeisBench provides access to various benchmark data sets and models from literature, along with pre-trained model weights, through a unified API. Built to be extensible, and modular, SeisBench allows for the simple addition of new models and data sets, which can be easily interchanged with existing pre-trained models and benchmark data. Standardising the access of varying quality data, and metadata simplifies comparison workflows, enabling the development of more robust machine learning algorithms. We initially focus on phase detection, identification and picking, but the framework is designed to be extended for other purposes, for example direct estimation of event parameters. Users will be able to contribute their own benchmarks and (trained) models. In the future, it will thus be much easier to compare both the performance of new algorithms against published machine learning models/architectures and to check the performance of established algorithms against new data sets. We hope that the ease of validation and inter-model comparison enabled by SeisBench will serve as a catalyst for the development of the next generation of machine learning techniques within the seismological community. The SeisBench source code will be published with an open license and explicitly encourages community involvement.</p>

Machine Learning Applications in Computer Vision

2013 ◽  
pp. 896-926
Author(s):  
Mehrtash Harandi ◽  
Javid Taheri ◽  
Brian C. Lovell
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Object Recognition ◽  
Visual Recognition ◽  
Sequential Decision ◽  
Biologically Inspired ◽  
Transductive Learning ◽  
Machine Learning Applications ◽  
Appearance Models ◽  
Learning Frameworks

Recognizing objects based on their appearance (visual recognition) is one of the most significant abilities of many living creatures. In this study, recent advances in the area of automated object recognition are reviewed; the authors specifically look into several learning frameworks to discuss how they can be utilized in solving object recognition paradigms. This includes reinforcement learning, a biologically-inspired machine learning technique to solve sequential decision problems and transductive learning, and a framework where the learner observes query data and potentially exploits its structure for classification. The authors also discuss local and global appearance models for object recognition, as well as how similarities between objects can be learnt and evaluated.

Machine Learning Applications in Breast Cancer Diagnosis

2017 ◽  
pp. 465-490
Author(s):  
Syed Jamal Safdar Gardezi ◽  
Mohamed Meselhy Eltoukhy ◽  
Ibrahima Faye
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Feature Extraction ◽  
Machine Learning Algorithms ◽  
Learning Tools ◽  
Extraction Techniques ◽  
Cad Systems ◽  
Cad System ◽  
Machine Learning Applications ◽  
Detection And Diagnosis

Breast cancer is one of the leading causes of death in women worldwide. Early detection is the key to reduce the mortality rates. Mammography screening has proven to be one of the effective tools for diagnosis of breast cancer. Computer aided diagnosis (CAD) system is a fast, reliable, and cost-effective tool in assisting the radiologists/physicians for diagnosis of breast cancer. CAD systems play an increasingly important role in the clinics by providing a second opinion. Clinical trials have shown that CAD systems have improved the accuracy of breast cancer detection. A typical CAD system involves three major steps i.e. segmentation of suspected lesions, feature extraction and classification of these regions into normal or abnormal class and further into benign or malignant stages. The diagnostics ability of any CAD system is dependent on accurate segmentation, feature extraction techniques and most importantly classification tools that have ability to discriminate the normal tissues from the abnormal tissues. In this chapter we discuss the application of machine learning algorithms e.g. ANN, binary tree, SVM, etc. together with segmentation and feature extraction techniques in a CAD system development. Various methods used in the detection and diagnosis of breast lesions in mammography are reviewed. A brief introduction of machine learning tools, used in diagnosis and their classification performance on various segmentation and feature extraction techniques is presented.

Introduction to Machine Learning and Its Application

2020 ◽  
pp. 262-290
Author(s):  
Ladly Patel ◽  
Kumar Abhishek Gaurav
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Human Interaction ◽  
Machine Learning Algorithm ◽  
Past Data ◽  
Applications Of Machine Learning ◽  
Learning Frameworks ◽  
Learning Data

In today's world, a huge amount of data is available. So, all the available data are analyzed to get information, and later this data is used to train the machine learning algorithm. Machine learning is a subpart of artificial intelligence where machines are given training with data and the machine predicts the results. Machine learning is being used in healthcare, image processing, marketing, etc. The aim of machine learning is to reduce the work of the programmer by doing complex coding and decreasing human interaction with systems. The machine learns itself from past data and then predict the desired output. This chapter describes machine learning in brief with different machine learning algorithms with examples and about machine learning frameworks such as tensor flow and Keras. The limitations of machine learning and various applications of machine learning are discussed. This chapter also describes how to identify features in machine learning data.

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