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2020 Science and Artificial Intelligence Conference, S.A.I.ence 2020

作者： Tsvaki, Jetina J. Tailakov, Dmitry O. Pavlovskiy, Evgeniy N. Novosibirsk State University Dept. of Mathematics and Mechanics Novosibirsk Russia Kutateladze Institute of Thermophysics Sb Ras Laboratory for Fundamentals of Energy Technologies Novosibirsk Russia Novosibirsk State University Stream Data Analytics and Machine Learning Laboratory Novosibirsk Russia

ISBN: (纸本)9780738131115

Main goal of any industry is to increase productivity which in oil and gas field is to increase reservoir oil asset by producing oil in an effective and economically efficient manner. The objective of the study is to develop a water flood model for oil production enhancement using artificial neural networks and provide a model that maximizes oil production for a given water injection that in turn will extend mature fields life and decrease operational costs. Using the data comprising of daily water injection rates, oil production rates, water production, and gas production from the year 2004 to 2016 for 577 injection wells, 1344 production wells, and 36 events which had occurred during the course. Comparative analysis on the deep neural models such as Multi-Layer Perception, Convolutional Neural Networks, Long Short-Term Memory, and Gated Recurrent Neural Networks are used, and Gated Recurrent Neural Networks outperformed them. To minimize the loss and improve the performance of the water flood model tabular data mix-up was adopted on all the models above. The results showed that the data mixed up Gated Recurrent Neural Network outperformed all the other models. To maximize the oil production Nelder-Mead optimization method was adopted to find appropriate water injection rates. A simple two-layered multi-layer perceptron was used in modeling the nonlinear relationship between water injection and oil production to avoid function complexity. © 2020 IEEE.

关键词： Floods

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Problems and Prospectives of Big data Storage and Processing Standartization

Problems and Prospectives of Big Data Storage and Processing...

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IEEE Region 8 International Conference on Computational Technologies in Electrical and Electronics Engineering, SIBIRCON

作者： Evgeniy N. Pavlovskiy Stream Data Analytics and Machine Learning lab Novosibirsk State University Novosibirsk Russia

In the paper, we analyze the problem of standardization in the domain of storage and processing of big data in the application to the Internet of things. We highlight the underlying problems of big data; analyze the scientific, technological and economic barriers to the development of big data. We offer perspective research directions in the domain of development of standards of data representation.

关键词： Big data Internet of Things Standards Task analysis Neural networks data structures

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Could machine learning be used to approximate optimal building retrofit solutions with the use of easily accessible building data? 32

Could machine learning be used to approximate optimal buildi...

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32nd International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems, ECOS 2019

作者： Thrampoulidis, Emmanouil Mavromatidis, Georgios Lucchi, Aurelien Orehounig, Kristina Laboratory for Urban Energy Systems Empa Duebendorf Switzerland ETH Zurich Switzerland Institute for Machine Learning Data analytics lab ETH Zurich Switzerland

ISBN: (纸本)9788361506515

Building retrofit is of greatest importance to reduce the environmental footprint of the building stock. It typically refers to two types of interventions: the first pertaining to interventions on the building envelope, for instance by enhancing the thermal insulation of a building’s walls, and the second to energy system replacements. The latter includes not only the installation of a more efficient heating system, with the necessary capacity to meet the building's heat demands, but also the integration of renewable energy technologies and energy storage. Thus, there is a huge array of retrofit measure combinations to select from with different economic and environmental impacts. Even though there are tools and methodologies to handle the complex problem of deriving optimal retrofit solutions, they typically require increased level of expertise and highly heterogeneous building data, which are usually not available or challenging to obtain. As a result, building owners can find themselves in a quandary about how to further proceed with retrofitting their buildings. In this paper, we answer the question whether a machine-learning algorithm could be used as a surrogate model that can: (a) mimic the functionality of the retrofit process, and (b) derive near-optimal solutions with the use of building features that can be easily extracted, such as the building's ground floor area or age. To answer this question an artificial neural network-based model is trained with data collected from the case study of the city of Zurich. Results show that the proposed approach can approximate very well some dimensions of the building retrofit, such as the retrofit costs (R2 ≅ 0.971) and the energy system selection (f1 score ≅ 0.894), while for others, such as the electricity storage capacity (R2 ≅ 0.505), there is still room for improvement. Such surrogate retrofit models can be more easily shared with building owners, and can contribute towards accelerating the adoption of retrofit

关键词： Multiobjective optimization

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Reducing over-smoothness in speech synthesis using Generative Adversarial Networks

Reducing over-smoothness in speech synthesis using Generativ...

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IEEE Region 8 International Conference on Computational Technologies in Electrical and Electronics Engineering, SIBIRCON

作者： Leyuan Sheng Evgeniy N. Pavlovskiy Novosibirsk State University Novosibirsk Russia Stream Data Analytics and Machine Learning laboratory Novosibirsk State University Novosibirsk Russia

Speech synthesis is widely used in many practical applications. In recent years, speech synthesis technology has developed rapidly. However, one of the reasons why synthetic speech is unnatural is that it often has over-smoothness. In order to improve the naturalness of synthetic speech, we first extract the mel-spectrogram of speech and convert it into a real image, then take the over-smooth mel-spectrogram image as input, and use image-to-image translation Generative Adversarial Networks(GANs) framework to generate a more realistic mel-spectrogram. Finally, the results show that this method greatly reduces the over-smoothness of synthesized speech and is more close to the mel-spectrogram of real speech.

关键词： Generative adversarial networks Speech synthesis Spectrogram Generators Hidden Markov models Training

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Development of an Application for Audio-Visual-Tactile Brainwave Entrainment in Patients with Affective and Psychosomatic Disorders

Development of an Application for Audio-Visual-Tactile Brain...

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Annual Siberian Russian Workshop on Electron Devices and Materials (EDM)

作者： Vadim A. Korshunov Grigory R. Khazankin Dmitry S. Ivanishkin Novosibirsk State University Novosibirsk Russia Stream Data Analytics and Machine Learning laboratory Novosibirsk State University Novosibirsk Russia

Brainwave entrainment is used in different types of treatment, one of which is treating affective and psychosomatic disorders. The better result is achieved if individual alpha-peak frequency is taken into account during entrainment and when it includes audio visual and tactile stimulation. The program which allows running stimulation scenarios is required to be available on different platforms because patients need to continue the procedures even after finishing them in hospital. So they need to be able to download and install this control program on their personal computers. The required application was developed as a multiplatform one using. NET Core framework in order to reduce costs of further maintenance.

关键词： Headphones Visualization Neuroscience Hospitals Operating systems Linux Microcomputers

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Radar-Based Tremor Quantification Using Deep learning for Improved Parkinson's and Palliative Care Assessment

IEEE Transactions on Radar Systems

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IEEE Transactions on Radar Systems 2024年 2卷 1174-1185页

作者： Mejdani, Desar Braunig, Johanna Griebhammer, Stefan G. Krauss, Daniel Steigleder, Tobias Engel, Lukas Jukic, Jelena Rozhdestvenskaya, Anna Winkler, Jurgen Eskofier, Bjoern Ostgathe, Christoph Vossiek, Martin Institute of Microwaves and Photonics Erlangen91058 Germany Machine Learning and Data Analytics Laboratory Erlangen91058 Germany University Hospital Erlangen Department of Palliative Medicine Erlangen91054 Germany University Hospital Erlangen Department of Molecular Neurology Erlangen91054 Germany

Tremor is one of the most prevalent movement disorders, which is especially observed in patients with Parkinson's disease (PD) and other conditions common in palliative care (PC). Effective treatment and monitoring of disease progression are crucial in the context of PC for patients suffering from movement disorders. To this aim, accurate and continuous detection and assessment of tremor characteristics, such as the tremor frequency, is required. Current evaluations by clinicians conducted during sporadic consultations are subjective and intermittent. Radar sensors provide continuous, objective evaluations of tremor motion in patient monitoring, offering a contactless, light-independent, and privacy-preserving method that directly measures tremor's radial motion through the Doppler effect. As previous radar-based research lacks continuous tremor monitoring in realistic scenarios, this study uses a frequency-modulated continuous-wave (FMCW) radar to detect subtle tremor motions and estimates their frequencies amid challenges such as large body motion interference in a clinical setting. Seventeen healthy participants were instructed to mimic tremors in their right hand while performing three diagnostics movements frequently used in tremor assessment, and two activities that were inspired by common daily tasks encountered in PC settings. Tremor detection and frequency estimation was enabled using suitable radar signal preprocessing followed by a neural network comprising convolutional and recurrent layers. Reference frequencies were obtained from an inertial measurement unit (IMU) attached to the participants' right hands. Cross-validation revealed a mean absolute error (MAE) of 1.47 Hz in radar-based frequency estimation compared with the reference and a 90% accuracy in distinguishing the presence or absence of tremor, highlighting the proposed approach's high potential for future tremor assessment. © 2023 IEEE.

关键词： Frequency estimation

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Simple unsupervised keyphrase extraction using sentence embeddings

arXiv

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arXiv 2018年

作者： Bennani-Smires, Kamil Musat, Claudiu Hossmann, Andreaa Baeriswyl, Michael Jaggi, Martin Data Analytics & AI Swisscom AG Machine Learning and Optimization Laboratory EPFL

Keyphrase extraction is the task of automatically selecting a small set of phrases that best describe a given free text document. Supervised keyphrase extraction requires large amounts of labeled training data and generalizes very poorly outside the domain of the training data. At the same time, unsupervised systems have poor accuracy, and often do not generalize well, as they require the input document to belong to a larger corpus also given as input. Addressing these drawbacks, in this paper, we tackle keyphrase extraction from single documents with EmbedRank: a novel unsupervised method, that leverages sentence embeddings. EmbedRank achieves higher F-scores than graph-based state of the art systems on standard datasets and is suitable for real-time processing of large amounts of Web data. With EmbedRank, we also explicitly increase coverage and diversity among the selected keyphrases by introducing an embedding-based maximal marginal relevance (MMR) for new phrases. A user study including over 200 votes showed that, although reducing the phrases’ semantic overlap leads to no gains in F-score, our high diversity selection is preferred by humans. Copyright © 2018, The Authors. All rights reserved.

关键词： Embeddings

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Reducing over-smoothness in speech synthesis using Generative Adversarial Networks

arXiv

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arXiv 2018年

作者： Sheng, Leyuan Pavlovskiy, Evgeniy N. Department of Mathematics and Mechanics Novosibirsk State University Novosibirsk Russia Stream Data Analytics and Machine Learning laboratory Novosibirsk State University Novosibirsk Russia

关键词： Generative adversarial networks

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Harnessing Ensemble machine learning Models for Timely Diagnosis of Breast Cancer Metastasis: A Case Study on CatBoost, XGBoost, and LGBM

Harnessing Ensemble Machine Learning Models for Timely Diagn...

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Annual Siberian Russian Workshop on Electron Devices and Materials (EDM)

作者： Minh Sao Khue Luu Santanu Banerjee Evgeniy N. Pavlovskiy Bair N. Tuchinov Stream Data Analytics and Machine Learning Laboratory Novosibirsk State University Novosibirsk Russia Indian Institute of Technology (IIT) Kharagpur Kharagpur West Bengal India

ISBN: (数字)9798350389234

ISBN: (纸本)9798350353051

This study employs three advanced gradient boosting machine learning algorithms to assess potential disparities in healthcare delivery. We specifically investigate which factors contribute to a patient’s timely diagnosis of metastatic breast cancer using a public healthcare dataset. Our approach involves training and testing three separate models, as well as an ensemble model with automatically optimized weights. The models try to predict whether patients received a diagnosis of metastatic breast cancer within 90 days. Each model has different preprocessing and feature selection steps. The hyperparameter optimization is performed using the Optuna library in Python. Models are evaluated on the Kaggle platform, with our metrics indicating strong predictive performance; Categorical Boosting achieved an Area Under the Receiver Operating Characteristic Curve score of 0.813, Extreme Gradient Boosting reached 0.808, Light Gradient Boosting machine scored 0.805, and the ensemble model culminated at 0.808. Additionally, we analyze the set of features being used by all the best models and examine the impact of hyperparameters on the models’ overall performance.

关键词： Measurement Training Analytical models Codes Hospitals Predictive models Boosting

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System Architecture for Reading and Interpreting Physical Printouts of Medical Forms

System Architecture for Reading and Interpreting Physical Pr...

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Annual Siberian Russian Workshop on Electron Devices and Materials (EDM)

作者： Ekaterina Snegireva Grigory R. Khazankin Igor Mikheenko Stream Data Analytics and Machine Learning laboratory Novosibirsk State University Novosibirsk Russia Novosibirsk State University Novosibirsk Russia Meshalkin National Medical Research Center Novosibirsk Russia

This article describes the developed architecture of the system module for processing and interpreting analog medical data. Patients often undergo examinations in various medical institutions, and since their results are often handed out to the patient in printed form, the receiving institution transfers them to its database manually. There is also a tendency to completely refuse analog media and use only digital ones. But in this case, another problem appears - either loss or conversion of the accumulated analog base into digital format. These days, automatic document management systems for medical institutions - Health information systems (HIS) - are actively developing. The software module developed in accordance with the architecture described in the article can be used by developers of various HIS to automate the work with analog data. If it is necessary, it can also be freely expanded by adding new modules for working with various analog data. In this article, we take ECG scans and medical test results as examples of such data. As a result of the work undertaken the prototype of the designed system was developed and tested.

关键词： Waste materials databases Prototypes Systems architecture Computer architecture Electrocardiography Medical tests

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