scholarly journals Determination of coordinates of the earthquake hypocenter by the method of circles

2021 ◽  
Vol 48 (3) ◽  
pp. 52-58
Author(s):  
G. K. Aslanov ◽  
T. G. Aslanov
Keyword(s):  
Fourth Order ◽  
Intersection Point ◽  
Earthquake Source ◽  
Seismic Sensors ◽  
Earthquake Hypocenter ◽  
Source Data ◽  
Different Shapes ◽  
Intersection Line ◽  
Using Data

Objective. The aim of the study is to develop a method for determining the coordinates of the earthquake hypocenter using various combinations of second and fourth order figures as a geo-locus of the hypocenter position points.Method. It is known that the line of intersection of figures of the second and fourth orders, in the case of coincidence of focuses, is a circle. To determine the coordinates of the earthquake source, data from seismographs are used, which are used to construct figures of the second and fourth order, the intersection point of which is the hypocenter. When using data from two seismic sensors, there are two figures, the intersection line of which is a circle. A sphere with a radius equal to the radius of the circle is constructed through the center of this circle. For the other two pairs of seismic sensors, two more spheres are also formed, The intersection point of the three spheres obtained is the sought-for hypocenter of the earthquake.Result. A method has been developed for determining the coordinates of an earthquake source using different shapes of the second and fourth orders for different pairs of seismic sensors.Conclusion. The method allows one to select one of the second or fourth order figures for different pairs of seismic sensors, which makes it possible to reduce the error in determining the source coordinates.

scholarly journals ANOMALOUS ERRORS IN DETERMINING FOCUS COORDINATES EARTHQUAKES AND SUGGESTIONS FOR THEIR ELIMINATION

2019 ◽  
Vol 45 (4) ◽  
pp. 78-88
Author(s):  
G. K. Aslanov ◽  
T. G. Aslanov ◽  
U. A. Musaeva
Keyword(s):  
Seismic Wave ◽  
Seismic Waves ◽  
Earthquake Source ◽  
Wave Velocities ◽  
Seismic Sensors ◽  
Earthquake Hypocenter ◽  
Seismic Wave Velocities ◽  
The Times ◽  
Anomalous Errors

Objectives The aim of the study is to develop a method for estimating the speed of seismic waves in different directions of propagation and by taking into account the dimensions of the focus, reducing the error in determining the coordinates of the hypocenter. Method To find the hypocenter of the earthquake, the data of the seismic wave velocities, the differences in the times of arrival of seismic waves on seismic sensors and the error in determining the time difference are used. The data with an error determine the coordinates of the hypocenter using information from various combinations of seismic sensors. Processing the resulting array of coordinates, estimates the seismic wave velocities / or determines the spatial shape of the earthquake source and the coordinates of the hypocenter. According to the coordinates of the cinema center, the differences in the travel time of seismic waves are corrected and the distances to the seismic sensors are refined. Results After preliminary determination of the coordinates and shape of the earthquake source, if there are a large number of seismic sensors, it is possible to clarify the coordinates of the earthquake hypocenter taking into account the recommendations given in the works. Conclusion Using the proposed method implies the presence of a large number of sensors to determine the complex shape, the earthquake source. 

scholarly journals DETERMINATION OF EARTHQUAKE FOCUS COORDINATES USING THE CASSINI OVAL METHOD WITH SECOND- AND FOURTH-ORDER HYPERBOLA FIGURES

2020 ◽  
Vol 46 (4) ◽  
pp. 134-142
Author(s):  
B. I. Shakhtarin ◽  
T. G. Aslanov ◽  
U. R. Tetakaev
Keyword(s):  
Comparative Analysis ◽  
Fourth Order ◽  
Relative Position ◽  
Seismic Waves ◽  
Arrival Times ◽  
Earthquake Focus ◽  
Seismic Sensors ◽  
Calculation Results ◽  
The Difference

Objectives. To study the dependencies obtained when determining the coordinates of an earthquake hypocentre using the figures of fourth and second orders.Method. A comparative analysis of determining the coordinates of the earthquake focus using the Cassini oval method, both taking errors in the readings of seismic sensors into account the and ignoring them, is presented.Result. A new method is proposed for determining the coordinates of the earthquake hypocentre, which uses the fourth-order figure, the Cassini oval, in the calculations. A graph is obtained for the distribution of errors in determining the coordinates of the earthquake focus (using the Cassini oval) depending on the relative position of two seismic sensors with different values of their errors in determining the difference in travel times of seismic waves.Conclusion. Since the calculation results are independent of the error sign in determining the difference in the arrival times of seismic waves, the method is suitable for the initial determination of the coordinates of the earthquake hypocentre as well as for comparison with the results of other methods for identifying the error sign. 

Excess entropy of the systems of molecules differing in size and shape

1983 ◽  
Vol 48 (1) ◽  
pp. 192-198 ◽  
Author(s):  
Tomáš Boublík
Keyword(s):  
Equation Of State ◽  
Hard Spheres ◽  
Excess Entropy ◽  
Convex Bodies ◽  
Pure Substance ◽  
Liquid Equilibrium ◽  
Simple Rules ◽  
Different Shapes ◽  
The Mean

The excess entropy of mixing of mixtures of hard spheres and spherocylinders is determined from an equation of state of hard convex bodies. The obtained dependence of excess entropy on composition was used to find the accuracy of determining ΔSE from relations employed for the correlation and prediction of vapour-liquid equilibrium. Simple rules were proposed for establishing the mean parameter of nonsphericity for mixtures of hard bodies of different shapes allowing to describe the P-V-T behaviour of solutions in terms of the equation of state fo pure substance. The determination of ΔSE by means of these rules is discussed.

Research on a Generating Method of Spiral Flutes of Hourglass Worm Gear Hob

2017 ◽  
Author(s):  
Yang Jie ◽  
Li Haitao ◽  
Rui Chengjie ◽  
Wei Wenjun ◽  
Dong Xuezhu
Keyword(s):  
Mathematical Model ◽  
Manufacturing System ◽  
Worm Gear ◽  
Virtual Manufacturing ◽  
Transmission Ratio ◽  
Method Of Determination ◽  
Generating Method ◽  
Multiple Threads ◽  
Different Shapes

All of the cutting edges on an hourglass worm gear hob have different shapes and spiral angles. If the spiral angles are small, straight flutes are usually adopted. But for the hob with multiple threads, the absolute values of the negative rake angles at one side of the cutting teeth will greatly affect the cutting performance of the hob if straight flutes are still used. Therefore, spiral flutes are usually adopted to solve the problem. However, no method of determination of the spiral flute of the hourglass worm gear hob has been put forward till now. Based on the curved surface generating theory and the hourglass worm forming principle, a generating method for the spiral flute of the planar double enveloping worm gear hob is put forward in this paper. A mathematical model is built to generate the spiral flute. The rake angles of all cutting teeth of the hob are calculated. The laws of the rake angles of the cutting teeth of four hobs with different threads from one to four threads are analyzed when straight flutes and spiral flutes are adopted respectively. The laws between the value of the negative rake angles of the hob with four threads and the milling transmission ratio are studied. The most appropriate milling transmission ratio for generating the spiral flute is obtained. The machining of the spiral flutes is simulated by a virtual manufacturing system and the results verify the correctness of the method.

Generalized plane stress in a semi-infinite strip under arbitrary end-load

1964 ◽  
Vol 281 (1385) ◽  
pp. 184-206 ◽  
Keyword(s):  
Differential Equation ◽  
Ordinary Differential Equation ◽  
Boundary Conditions ◽  
Fourth Order ◽  
Plane Stress ◽  
Stress Function ◽  
Infinite Strip ◽  
Order Ordinary Differential Equation ◽  
Orthogonal Functions

The problem involves the determination of a biharmonic generalized plane-stress function satisfying certain boundary conditions. We expand the stress function in a series of non-orthogonal eigenfunctions. Each of these is expanded in a series of orthogonal functions which satisfy a certain fourth-order ordinary differential equation and the boundary conditions implied by the fact that the sides are stress-free. By this method the coefficients involved in the biharmonic stress function corresponding to any arbitrary combination of stress on the end can be obtained directly from two numerical matrices published here The method is illustrated by four examples which cast light on the application of St Venant’s principle to the strip. In a further paper by one of the authors, the method will be applied to the problem of the finite rectangle.

Fourth Order Derivative Spectrophotometric Determination of Lead with 1,10-Phenanthroline and Rose Bengal

1993 ◽  
Vol 26 (3) ◽  
pp. 523-539 ◽  
Author(s):  
D. Sreevalsan Nair ◽  
T. Prasada Rao ◽  
C. S. P. Iyer ◽  
A. D. Damodaran
Keyword(s):  
Spectrophotometric Determination ◽  
Fourth Order ◽  
Rose Bengal ◽  
Order Derivative

scholarly journals A NON-DESTRUCTIVE METHOD FOR ESTIMATING LEAF AREA OF Ceiba glaziovii (Kuntze) K. Schum.

FLORESTA ◽  
2019 ◽  
Vol 50 (1) ◽  
pp. 1063
Author(s):  
João Everthon da Silva Ribeiro ◽  
Francisco Romário Andrade Figueiredo ◽  
Ester Dos Santos Coêlho ◽  
Walter Esfrain Pereira ◽  
Manoel Bandeira de Albuquerque
Keyword(s):  
Leaf Area ◽  
Regression Models ◽  
Information Criterion ◽  
Coefficient Of Determination ◽  
Linear Measurements ◽  
Maximum Width ◽  
Bias Index ◽  
Different Shapes ◽  
Non Destructive

The determination of leaf area is of fundamental importance in studies involving ecological and ecophysiological aspects of forest species. The objective of this research was to adjust an equation to determine the leaf area of Ceiba glaziovii as a function of linear measurements of leaves. Six hundred healthy leaf limbs were collected in different matrices, with different shapes and sizes, in the Mata do Pau-Ferro State Park, Areia, Paraíba state, Northeast Brazil. The maximum length (L), maximum width (W), product between length and width (L.W), and leaf area of the leaf limbs were calculated. The regression models used to construct equations were: linear, linear without intercept, quadratic, cubic, power and exponential. The criteria for choosing the best equation were based on the coefficient of determination (R²), Akaike information criterion (AIC), root mean square error (RMSE), Willmott concordance index (d) and BIAS index. All the proposed equations satisfactorily estimate the leaf area of C. glaziovii, due to their high determination coefficients (R² ≥ 0.851). The linear model without intercept, using the product between length and width (L.W), presented the best criteria to estimate the leaf area of the species, using the equation 0.4549*LW.

scholarly journals Hydrogen Safety Prediction and Analysis of Hydrogen Refueling Station Leakage Accidents and Process Using Multi-Relevance Machine Learning

2021 ◽  
Vol 12 (4) ◽  
pp. 185
Author(s):  
Wujian Yang ◽  
Jianghao Dong ◽  
Yuke Ren
Keyword(s):  
Machine Learning ◽  
Gradient Descent ◽  
Prediction Algorithm ◽  
Hydrogen Energy ◽  
Neural Network Optimization ◽  
Gradient Descent Algorithm ◽  
Source Data ◽  
Using Data ◽  
Internal Relationships ◽  
Hydrogen Safety

Hydrogen energy vehicles are being increasingly widely used. To ensure the safety of hydrogenation stations, research into the detection of hydrogen leaks is required. Offline analysis using data machine learning is achieved using Spark SQL and Spark MLlib technology. In this study, to determine the safety status of a hydrogen refueling station, we used multiple algorithm models to perform calculation and analysis: a multi-source data association prediction algorithm, a random gradient descent algorithm, a deep neural network optimization algorithm, and other algorithm models. We successfully analyzed the data, including the potential relationships, internal relationships, and operation laws between the data, to detect the safety statuses of hydrogen refueling stations.

scholarly journals Penerapan K-Means Clustering Untuk Seleksi Frame Dominan Berbasis NTSC Pada Obyek Bergerak

2020 ◽  
Vol 7 (4) ◽  
pp. 745
Author(s):  
Rizka Indah Armianti ◽  
Achmad Fanany Onnilita Gaffar ◽  
Arief Bramanto Wicaksono Putra
Keyword(s):  
Feature Extraction ◽  
Video Data ◽  
Clustering Method ◽  
Data Frame ◽  
First Order ◽  
Feature Extraction And Selection ◽  
Different Shapes ◽  
Frame Set ◽  
Selection Of

<p class="Abstrak">Obyek dinyatakan bergerak jika terjadi perubahan posisi dimensi disetiap <em>frame</em>. Pergerakan obyek menyebabkan obyek memiliki perbedaan bentuk pola disetiap <em>frame-</em>nya. <em>Frame</em> yang memiliki pola terbaik diantara <em>frame</em> lainnya disebut <em>frame</em> dominan. Penelitian ini bertujuan untuk menyeleksi <em>frame</em> dominan dari rangkaian <em>frame</em> dengan menerapkan metode K-means <em>clustering</em> untuk memperoleh <em>centroid</em> dominan (<em>centroid</em> dengan nilai tertinggi) yang digunakan sebagai dasar seleksi <em>frame</em> dominan. Dalam menyeleksi <em>frame</em> dominan terdapat 4 tahapan utama yaitu akuisisi data, penetapan pola obyek, ekstrasi ciri dan seleksi. Data yang digunakan berupa data video yang kemudian dilakukan proses penetapan pola obyek menggunakan operasi pengolahan citra digital, dengan hasil proses berupa pola obyek RGB yang kemudian dilakukan ekstraksi ciri berbasis NTSC dengan menggunakan metode statistik orde pertama yaitu <em>Mean</em>. Data hasil ekstraksi ciri berjumlah 93 data <em>frame</em> yang selanjutnya dikelompokkan menjadi 3 <em>cluster</em> menggunakan metode K-Means. Dari hasil <em>clustering</em>, <em>centroid</em> dominan terletak pada <em>cluster</em> 3 dengan nilai <em>centroid</em> 0.0177 dan terdiri dari 41 data <em>frame</em>. Selanjutnya diukur jarak kedekatan seluruh data <em>cluster</em> 3 terhadap <em>centroid</em>, data yang memiliki jarak terdekat dengan <em>centroid</em> itulah <em>frame</em> dominan. Hasil seleksi <em>frame</em> dominan ditunjukkan pada jarak antar <em>centroid</em> dengan anggota <em>cluster</em>, dimana dari seluruh 41 data frame tiga jarak terbaik diperoleh adalah 0.0008 dan dua jarak bernilai  0.0010 yang dimiliki oleh <em>frame</em> ke-59, ke-36 dan ke-35.</p><p class="Abstrak"> </p><p class="Abstrak"><em><strong>Abstract</strong></em></p><p class="Abstract"><em>The object is declared moving if there is a change in the position of the dimensions in each frame. The movement of an object causes the object to have different shapes in each frame. The frame that has the best pattern among other frames is called the dominant frame. This study aims to select the dominant frame from the frame set by applying the K-means clustering method to obtain the dominant centroid (the highest value centroid) which is used as the basis for the selection of dominant frames. In selecting dominant frames, there are 4 main stages, namely data acquisition, determination of object patterns, feature extraction and selection. The data used in the form of video data which is then carried out the process of determining the pattern of objects using digital image processing operations, with the results of the process in the form of an RGB object pattern which is then performed NTSC-based feature extraction using the first-order statistical method, Mean. The data from feature extraction are 93 data frames which are then grouped into 3 clusters using the K-Means method. From the results of clustering, the dominant centroid is located in cluster 3 with a centroid value of 0.0177 and consists of 41 data frames. Furthermore, the proximity of all data cluster 3 to the centroid is measured, the data having the closest distance to the centroid is the dominant frame. The results of dominant frame selection are shown in the distance between centroids and cluster members, where from all 41 data frames the three best distances obtained are 0.0008, 0.0010, and 0.0010 owned by 59th, 36th and 35th frames.</em></p><p class="Abstrak"><em><strong><br /></strong></em></p><p> </p>

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