A Classification Method for Judging the Depth of Chest Compression Based on CNN

2020 ◽  
Author(s):  
Liang Zhao ◽  
Yu Bao ◽  
Ruidong Ye ◽  
Aijuan Zhang ◽  
Yu Zhang
Keyword(s):  
Chest Compression ◽  
Classification Method ◽  
Accuracy Rate ◽  
One Dimensional ◽  
Acceleration Signal ◽  
Signal Waveform ◽  
Wave Interference ◽  
Starting Point ◽  
Sample Data ◽  
Two Stages

Abstract The displacement can be calculated based on the integrated value of the acceleration signal waveform obtained by the acceleration sensor or gyroscope. However, this method is not effective in accurate measurement. Although some studies have improved the method of calculating accurate distance values by overcoming the effects of sensor noise or integration delay, the evaluation is still affected by sensor accuracy and environment. However, there are some special displacements, such as the chest compression. The displacement is a reciprocating motion and will return to the starting point again. Therefore, the acceleration waveform changes have obvious characteristics in the two stages from moving to the equilibrium position and returning to the starting point. Therefore, we propose an embedded classification method based on one-dimensional Convolutional Neural Network (CNN), which directly learns from the data of chest compressions and performs the signal formed by the Classification, distinguish the signal waveform under the standard pressing distance, so as to replace the calculation of distance measurement, and is not affected by factors such as pressure occlusion and electromagnetic wave interference, and has certain practical value on site. We tagged compressions and collected data from the simulator. The experiment evaluates the proposed CNN structure, and compares the classification results of the sample data with several CNN networks and SVMs with different structures in the literature. The results show that with sufficient training, the proposed 1D-CNN method can achieve an accuracy rate of more than 95%, and balances the accuracy rate and the hardware requirements.

scholarly journals Recognition of Abnormal Chest Compression Depth Using One-Dimensional Convolutional Neural Networks

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 846
Author(s):  
Liang Zhao ◽  
Yu Bao ◽  
Yu Zhang ◽  
Ruidong Ye ◽  
Aijuan Zhang
Keyword(s):  
Chest Compression ◽  
Sensor Data ◽  
Evaluation Procedure ◽  
Support Vector ◽  
Portable Devices ◽  
Sensor Signal ◽  
Distance Measurements ◽  
Compression Depth ◽  
One Dimensional ◽  
Starting Point

When the displacement of an object is evaluated using sensor data, its movement back to the starting point can be used to correct the measurement error of the sensor. In medicine, the movements of chest compressions also involve a reciprocating movement back to the starting point. The traditional method of evaluating the effects of chest compression depth (CCD) is to use an acceleration sensor or gyroscope to obtain chest compression movement data; from these data, the displacement value can be calculated and the CCD effect evaluated. However, this evaluation procedure suffers from sensor errors and environmental interference, limiting its applicability. Our objective is to reduce the auxiliary computing devices employed for CCD effectiveness evaluation and improve the accuracy of the evaluation results. To this end, we propose a one-dimensional convolutional neural network (1D-CNN) classification method. First, we use the chest compression evaluation criterion to classify the pre-collected sensor signal data, from which the proposed 1D-CNN model learns classification features. After training, the model is used to classify and evaluate sensor signal data instead of distance measurements; this effectively avoids the influence of pressure occlusion and electromagnetic waves. We collect and label 937 valid CCD results from an emergency care simulator. In addition, the proposed 1D-CNN structure is experimentally evaluated and compared against other CNN models and support vector machines. The results show that after sufficient training, the proposed 1D-CNN model can recognize the CCD results with an accuracy rate of more than 95%. The execution time suggests that the model balances accuracy and hardware requirements and can be embedded in portable devices.

Perancangan Aplikasi Prediksi Kelulusan Tepat Waktu Bagi Mahasiswa Baru Dengan Teknik Data Mining (Studi Kasus: Data Akademik Mahasiswa STMIK Dipanegara Makassar)

2015 ◽  
Vol 1 (4) ◽  
pp. 270
Author(s):  
Muhammad Syukri Mustafa ◽  
I. Wayan Simpen
Keyword(s):  
Data Mining ◽  
Nearest Neighbor ◽  
Test Results ◽  
K Nearest Neighbor ◽  
Accuracy Rate ◽  
Sample Data ◽  
New Students ◽  
K Nearest Neighbor Algorithm ◽  
Using Data ◽  
Existing Data

Penelitian ini dimaksudkan untuk melakukan prediksi terhadap kemungkian mahasiswa baru dapat menyelesaikan studi tepat waktu dengan menggunakan analisis data mining untuk menggali tumpukan histori data dengan menggunakan algoritma K-Nearest Neighbor (KNN). Aplikasi yang dihasilkan pada penelitian ini akan menggunakan berbagai atribut yang klasifikasikan dalam suatu data mining antara lain nilai ujian nasional (UN), asal sekolah/ daerah, jenis kelamin, pekerjaan dan penghasilan orang tua, jumlah bersaudara, dan lain-lain sehingga dengan menerapkan analysis KNN dapat dilakukan suatu prediksi berdasarkan kedekatan histori data yang ada dengan data yang baru, apakah mahasiswa tersebut berpeluang untuk menyelesaikan studi tepat waktu atau tidak. Dari hasil pengujian dengan menerapkan algoritma KNN dan menggunakan data sampel alumni tahun wisuda 2004 s.d. 2010 untuk kasus lama dan data alumni tahun wisuda 2011 untuk kasus baru diperoleh tingkat akurasi sebesar 83,36%.This research is intended to predict the possibility of new students time to complete studies using data mining analysis to explore the history stack data using K-Nearest Neighbor algorithm (KNN). Applications generated in this study will use a variety of attributes in a data mining classified among other Ujian Nasional scores (UN), the origin of the school / area, gender, occupation and income of parents, number of siblings, and others that by applying the analysis KNN can do a prediction based on historical proximity of existing data with new data, whether the student is likely to complete the study on time or not. From the test results by applying the KNN algorithm and uses sample data alumnus graduation year 2004 s.d 2010 for the case of a long and alumni data graduation year 2011 for new cases obtained accuracy rate of 83.36%.

Extreme long waves over a varying bathymetry

2019 ◽  
Vol 878 ◽  
pp. 481-501 ◽  
Author(s):  
James G. Herterich ◽  
Frédéric Dias
Keyword(s):  
Conformal Mapping ◽  
Wave Breaking ◽  
Breaking Strength ◽  
Vertical Wall ◽  
Breaking Waves ◽  
Long Waves ◽  
Amplitude Wave ◽  
One Dimensional ◽  
Wave Interference ◽  
Initial Wave

Recent modelling work has shown that abrupt bathymetric transitions can produce dramatic amplifications of long waves, under the influence of both nonlinearity and dispersion. Here, the evolution of wave packets towards a vertical wall over a varying bathymetry is investigated with a one-dimensional conformal-mapping spectral code. In this system, wave breaking, runup and reflection, wave interference and bathymetric effects are highlighted. Wave breaking is examined with respect to geometric, kinematic and energetic conditions, with consistent results. The breaking strength is characterized for spilling and plunging based on initial wave period and amplitude. Non-breaking waves are amplified by reflection, interference and the bathymetry leading to large runups. In a typical example inspired by a real-world bathymetry, the maximum runup amplification approaches a factor of 12 – large enough for a 3 m amplitude wave to overtop a 30 m cliff.

scholarly journals Knowledge Embedded Semi-Supervised Deep Learning for Detecting Non-Technical Losses in the Smart Grid

Energies ◽  
2019 ◽  
Vol 12 (18) ◽  
pp. 3452 ◽  
Author(s):  
Xiaoquan Lu ◽  
Yu Zhou ◽  
Zhongdong Wang ◽  
Yongxian Yi ◽  
Longji Feng ◽  
...  
Keyword(s):  
Deep Learning ◽  
Electricity Consumption ◽  
Small Samples ◽  
Consumption Pattern ◽  
Feature Models ◽  
One Dimensional ◽  
Electricity Theft ◽  
Sample Data ◽  
Related Feature ◽  
Deep Learning Model

Non-technical losses (NTL) caused by fault or electricity theft is greatly harmful to the power grid. Industrial customers consume most of the power energy, and it is important to reduce this part of NTL. Currently, most work concentrates on analyzing characteristic of electricity consumption to detect NTL among residential customers. However, the related feature models cannot be adapted to industrial customers because they do not have a fixed electricity consumption pattern. Therefore, this paper starts from the principle of electricity measurement, and proposes a deep learning-based method to extract advanced features from massive smart meter data rather than artificial features. Firstly, we organize electricity magnitudes as one-dimensional sample data and embed the knowledge of electricity measurement in channels. Then, this paper proposes a semi-supervised deep learning model which uses a large number of unlabeled data and adversarial module to avoid overfitting. The experiment results show that our approach can achieve satisfactory performance even when trained by very small samples. Compared with the state-of-the-art methods, our method has achieved obvious improvement in all metrics.

scholarly journals One-dimensional and two-dimensional dynamics of cubic maps

2006 ◽  
Vol 2006 ◽  
pp. 1-13 ◽  
Author(s):  
Djellit Ilhem ◽  
Kara Amel
Keyword(s):  
Large Class ◽  
System Parameter ◽  
Two Dimensional ◽  
One Dimensional ◽  
Henon Maps ◽  
Hénon Maps ◽  
Starting Point ◽  
Good Starting Point ◽  
Polynomial Diffeomorphisms

We concentrate on the dynamics of one-dimensional and two-dimensional cubic maps, it describes how complex behaviors can possibly arise as a system parameter changes. This is a large class of diffeomorphisms which provide a good starting point for understanding polynomial diffeomorphisms with constant Jacobian and equivalent to a composition of generalized Hénon maps. Due to the theoretical and practical difficulties involved in the study, computers will presumably play a role in such efforts.

scholarly journals MARKOVIAN KMS-STATES FOR ONE-DIMENSIONAL SPIN CHAINS

1999 ◽  
Vol 02 (04) ◽  
pp. 645-661 ◽  
Author(s):  
LUIGI ACCARDI ◽  
VOLKMAR LIEBSCHER
Keyword(s):  
Quantum Spin ◽  
Spin Chains ◽  
Quantum Spin Chains ◽  
Kms States ◽  
One Dimensional ◽  
Locality Property ◽  
Conditional Expectations ◽  
Starting Point ◽  
Modular Automorphism ◽  
Quantum Spin Lattices

We characterize a class of quantum Markov states in terms of a locality property of their modular automorphism group or, equivalently, of their φ-conditional expectations and we give an explicit description of the structure of these states. This study is meant as a starting point for the investigation of the structure of Markovian KMS-states of quantum spin chains as well as of multidimensional quantum spin lattices.

Some analyses of 2-D median f-k filters

Geophysics ◽  
1995 ◽  
Vol 60 (4) ◽  
pp. 1157-1168 ◽  
Author(s):  
Guy Duncan ◽  
Greg Beresford
Keyword(s):  
Seismic Data ◽  
Apparent Velocity ◽  
One Dimensional ◽  
Median Filters ◽  
Median Process ◽  
Starting Point ◽  
Weighted Median ◽  
Weighted Median Filters ◽  
The Mean ◽  
Weight Coefficients

Two‐dimensional median filters can be designed so that they have properties similar to f-k fan filters. This is done by using the coefficients of a truncated impulse response of an f-k filter as the weight coefficients for the weighted median process. The filter is called a median f-k filter and can be used to discriminate between events on the basis of apparent velocity. The filter appears suitable as a poststack coherency filter because it produces less distortion at wavefield terminations than conventional f-k fan filters. One‐dimensional weighted median filters that include negative coefficients are a logical starting point for the analysis of median f-k filters since simple numerical techniques may be used to analyze the behavior of these filters. We show that median filters with negative coefficients do not provide an unbiased estimate of the mean and can misplace the position of steps. Faults on a stacked section may be modeled by steps, and therefore applying a median f-k filter to stacked seismic data could change the position of faults. However, the distortion of steps introduced by median f-k filters is shown to be less than the distortion produced by the corresponding linear f-k filter, and the error in step placement is small. We present simple model examples and a seismic field data example to illustrate differences between linear f-k filters and median f-k filters.

scholarly journals The sources of competitive advantage from the perspective of project management – results of empirical studies

Management ◽  
2019 ◽  
Vol 23 (1) ◽  
pp. 75-89
Author(s):  
Magdalena Dolata
Keyword(s):  
Project Management ◽  
Local Government ◽  
Competitive Advantage ◽  
Empirical Studies ◽  
Second Stage ◽  
Starting Point ◽  
Two Stages

Summary The aim of the paper is entering the discussion concerning the sources of competitive advantage in project management in basic local government units in Poland. The focus of the paper is placed on the selected elements of the sources of competitive advantage in basic local government units in Poland, namely tasks implemented in projects. The paper consists of two main parts. In the first part of the paper, a starting point was the presentation of the essence of competitiveness and competitive advantage in the process of project management from the perspective of local government units in Poland. In the second one, the results of the research proceedings concerning the importance of tasks implemented in projects in projects in basic local government units in Poland were discussed. The considerations presented in the paper refer to the results of the research proceedings, which took place in two stages. The first stage of the studies was conducted in 2013 and it concerned the years 2010-2012, the second stage – in 2018, and it concerned the years 2016-2018.

scholarly journals Revision of the neo-marxist concept of the “political spectacle"

Moldoscopie ◽  
2021 ◽  
pp. 70-79
Author(s):  
Vadzim Mikhailouski ◽  
Keyword(s):  
Political Process ◽  
European Parliament ◽  
Theoretical Development ◽  
Western Society ◽  
The Political ◽  
Political Spectacle ◽  
Verification Method ◽  
Starting Point ◽  
Marxist Concept ◽  
Two Stages

The article presents an analytical revision of the neo-Marxist concept of the “political spectacle”, in which the main position of political neo-Marxism is formed.There is a possibility of political choice within the framework of the real absence of a political alternative (the political alternative is illusory) in the Western political process. The revision is carried out in two stages: a theoretical revision of the concept (a postpositivist check for falsifiability and a proposal for ways of theoretical development) and an empirical revision of the concept (a positivist check for verifiability). Verification of the neo-Marxist concept of “political spectacle” is carried out on the material of political forces in European Parliament. The verification method is the content analysis of the program documents of the “European Parties”.The article proves that the neo-Marxist concept of “political spectacle” is not theoretically correct enough and does not correspond to the current empirical material. First, the concept proceeds from the normativist view of the manipulative domination of capitalism and thus does not take into account the coordinated functioning of the modern bourgeoisie and the proletariat. Secondly, the example of the 2019 European Parliament elections shows that anti-capitalist forces are present in the Western electoral process and politics. The author concludes that it is necessary to update the neo-Marxist concept of the “political spectacle” on new theoretical grounds. The starting point of the updated concept is the following: the “political spectacle” of capitalism begins after the anti-capitalist forces become the structural elements of the reproduction of capitalist hegemony. On new theoretical grounds, the potential of the concept of “political spectacle” can be directed not to fix the political alienation of Western society, but to explain the capitalist political space as a system that can adaptively accumulate its own systemic deviations (fluctuations).

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