WO2016175342A2 - Procédé d'échantillonnage de données météorologiques océaniques mis en œuvre par ordinateur - Google Patents

Procédé d'échantillonnage de données météorologiques océaniques mis en œuvre par ordinateur Download PDF

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Publication number
WO2016175342A2
WO2016175342A2 PCT/KR2015/004194 KR2015004194W WO2016175342A2 WO 2016175342 A2 WO2016175342 A2 WO 2016175342A2 KR 2015004194 W KR2015004194 W KR 2015004194W WO 2016175342 A2 WO2016175342 A2 WO 2016175342A2
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data
meteorological
sampling
zone
weather
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PCT/KR2015/004194
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English (en)
Korean (ko)
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WO2016175342A3 (fr
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김윤식
김광수
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한국해양과학기술원
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Priority to PCT/KR2015/004194 priority Critical patent/WO2016175342A2/fr
Publication of WO2016175342A2 publication Critical patent/WO2016175342A2/fr
Publication of WO2016175342A3 publication Critical patent/WO2016175342A3/fr

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01W—METEOROLOGY
    • G01W1/00—Meteorology
    • G01W1/02—Instruments for indicating weather conditions by measuring two or more variables, e.g. humidity, pressure, temperature, cloud cover or wind speed
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01W—METEOROLOGY
    • G01W1/00—Meteorology
    • G01W1/10—Devices for predicting weather conditions
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/16—Matrix or vector computation, e.g. matrix-matrix or matrix-vector multiplication, matrix factorization
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16Z—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS, NOT OTHERWISE PROVIDED FOR
    • G16Z99/00—Subject matter not provided for in other main groups of this subclass

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  • the present invention relates to a marine meteorological sampling method performed by a computer, and more specifically, a sample weather condition extracted for analysis of a physical phenomenon having a characteristic sensitive to the meteorological environment, including an infrared signal analysis of a ship.
  • the present invention relates to a new concept of marine meteorological sampling, which is to get as close as possible to the statistical distribution characteristics of (Marine Meteorological Observation Data) and to reflect the correlations between marine meteorological variables.
  • the ship's InfraRed stealth performance is greatly influenced by ocean weather conditions.
  • Application of sea weather data (temperature, water temperature, relative humidity, wind speed, wind direction) on which the ship can operate for the analysis of ship IR signatures and detection ranges (for anti-ship missiles). Only by analyzing them can we reasonably predict the signal characteristics of the target box.
  • estimating the maximum value (or range of change) of the signal value the target ship will have is a very important reference in determining the signal requirements for a built ship. Therefore, accurate signal prediction is required, and how to set up the weather condition that has the greatest influence on the signal prediction result is the most important part of the signal prediction.
  • the weather condition setting method for the conventional infrared signal analysis is as follows. First, the monthly average and standard deviation are calculated for each weather variable (temperature, water temperature, etc.), and the signals for each of the 12 months are analyzed, and then the month representing the largest signal (or range) is checked (eg, January). And we consider the change characteristics of the signal according to the change of each weather element (sensitivity analysis). For example, the temperature condition for analyzing the case where the signal decreases as the temperature rises (inversely related) is "January average temperature-standard temperature deviation", and when the signal increases as the water temperature rises (proportional relationship). The water temperature conditions applied to "January average temperature + standard temperature deviation" apply.
  • the weather variables five of temperature, water temperature, relative humidity, wind speed, wind direction, etc.
  • the weather variables five of temperature, water temperature, relative humidity, wind speed, wind direction, etc.
  • it is assumed to represent the largest signal value among the signal analysis results of the target signal analysis results using the 'reference environmental conditions' (no comparison with the test results, because the actual ocean weather cannot be changed for the test). Because).
  • the conventional method of setting marine weather conditions includes the following serious problems.
  • the signal analysis is performed only on a few weather conditions extracted, and thus the analysis result is not close to the statistical distribution characteristic of the population (ocean meteorological observation data).
  • the present invention has been proposed to solve the above problems, the sample weather conditions extracted for the analysis of physical phenomena having characteristics sensitive to the weather environment, including the analysis of the infrared signal of the ship is a population (ocean meteorological observation data) It is an object of the present invention to provide a new concept of marine meteorological sampling, which is to be as close as possible to the statistical distribution characteristics of the system and to reflect the correlations between the meteorological variables.
  • the present invention provides a computer-based marine meteorological sampling method, to obtain a cumulative distribution function for each meteorological variable of the temperature, water temperature, relative humidity, wind speed, wind direction forming a population and to calculate the cumulative distribution function Dividing the number of slots into the number of slots to be extracted (4-1); Obtaining a probability of occurrence of the combined meteorological variables correlated with each other using principal component analysis, and setting a high priority of sampling to the data with low probability of occurrence; And dividing the zones between the weather variables and sampling the zones according to the priorities for each zone (4-3).
  • the cumulative distribution function is characterized in that evenly divided.
  • the priority is characterized by having a relation that is proportional to the number of slots and inversely proportional to the probability of occurrence.
  • the number of zones is characterized in that greater than the number of samples to be extracted.
  • the number of zones is characterized in that the product of the number of zones divided by the weather variable.
  • the division criteria of the zone is characterized in that based on the cumulative distribution function value for each weather variable.
  • the sampling procedure comprises the steps of: (1) checking the number of data included for each zone and obtaining the data ratio for each zone; The data ratio is sorted in ascending order and the data ratio is divided into the upper 50% region (large region) and the lower 50% region (small region), and the sampling is alternately made between the large region and the small region. Selecting a region to be done (2); Sorting by using different priority values for each data included in each of the selected zones, and sampling the data having the highest priority as a sample (3); Updating (4) the priority value between all remaining data each time a sample is extracted; And repeating the processes of 3 to 4 until a desired number of samples are extracted (5).
  • the data ratio is characterized by having a relation of n / S when the number of data for each zone is n and the number of data of the population is S.
  • the present invention enables the analysis by extracting the appropriate number of samples from the actual observed weather data for the analysis of physical phenomena having characteristics sensitive to the weather environment, including the infrared signal analysis of the ship. The various characteristics that can occur can be interpreted.
  • the analysis can be performed using a small number of samples and the results can be used to predict phenomena that may occur under actual marine weather conditions. .
  • the principal component analysis method was applied as a method to calculate the probability of occurrence of observation data when each weather variable has a correlation with each other, and the marine weather sampling method that can cover a wide range of weather change phenomenon is used. Implemented. In addition, by establishing zones and sampling in each zone according to the change range between each weather variable, a procedure was established to extract a sample corresponding to a wide population without being concentrated in a specific zone.
  • Ta air temperature
  • Ts water temperature
  • RH relative humidity
  • Ws wind speed
  • Wd wind direction
  • Ta temperature distribution
  • Ts water temperature
  • Ta temperature
  • RH relative humidity
  • Ta temperature distribution
  • Wd wind direction
  • FIG. 11 is a priority adjustment interval after the first sample is extracted in FIG. 10.
  • 13 is a temperature CDF distribution of a population and a sample in the present invention.
  • 15 is a water temperature CDF distribution of a population and a sample in the present invention.
  • 16 is a relative humidity PDF distribution of a population and a sample in the present invention.
  • 17 is a distribution of relative humidity CDF of a population and a sample in the present invention.
  • 19 is a wind speed CDF distribution of a population and a sample in the present invention.
  • 20 is a wind direction PDF distribution of a population and a sample population in the present invention.
  • 21 is a wind direction CDF distribution of a population and a sample in the present invention.
  • FIG. 23 is a comparison of temperature-relative humidity distribution characteristics of a population and a sample in the present invention.
  • 25 is a comparison of the wind speed-relative humidity distribution characteristics of the population and the sample population in the present invention.
  • FIG. 26 shows correlation coefficients between weather variables.
  • the marine meteorological sampling method according to the present invention needs to be preceded by the following steps (1) to (3) before it is implemented.
  • the data acquired through the process (1) may include data contaminated due to observation equipment error, etc., the data contaminated through the process is filtered out.
  • Meteorological Administration data observed bureau technical note 2006-2, "How to use the real-time quality management system for weather observation data", 2006) for quality inspection methods.
  • the amount of monthly data is analyzed and the number of monthly data is as similar as possible.
  • the meteorological data show little change of weather variables according to time changes (day / night), but the change of weather variables according to monthly changes is very large. Because of this, if data is concentrated or lacking in a given month, it may not be appropriate to serve as an appropriate population to represent the year-round weather characteristics of the sea.
  • the monthly data number based on the month with the smallest data number, the remaining month data is selected and removed using a random number.
  • the marine meteorological sampling method (corresponding to process (4)) according to the present invention may be implemented.
  • the marine meteorological sampling method according to the present invention will be described in detail by dividing step by step.
  • all processes of the marine meteorological sampling method according to the present invention can be performed through a computer.
  • the weather conditions to be applied for analysis in the present invention is not a weather condition (eg, mean value + standard deviation) generated by the assumption, but using a method of sampling from actual observed data.
  • the following three requirements apply to sampling.
  • the weather variables temperature, water temperature, relative humidity, wind speed, wind direction
  • the weather variables are as close as possible to the distribution characteristics of the population weather variables.
  • high priority for sampling is applied to data with low probability of occurrence in order to analyze various weather conditions as much as possible.
  • the extracted sample data can be distributed evenly without being concentrated in a specific area.
  • the principle is to divide the zones and to take one sample from each zone, taking into account the correlation between the variables.
  • sampling can be repeated in the same zone.
  • N the number of samples to be extracted
  • it should be appropriately limited by increasing the number of infrared signal analysis cases (and calculation time) to be performed later, and if it is too small, it may be difficult to reflect the statistical characteristics of the population. It is based on N 100 and can be changed to about 40 to 200).
  • PDF Probability Density Function
  • CDF Cumulative Distribution Function
  • Number of samples (N) the number of sample data extracted from the population, set to 100 in an embodiment of the present invention
  • a CDF is calculated for each variable of the population: the embodiment of FIG. 1 shows a CDF distribution for temperature Ta, water temperature Ts, and relative humidity RH, and the embodiment of FIG. (Ws), CDF distribution for wind direction (Wd).
  • CDF F (Xi)
  • Xi temperature Ta, water temperature Ts, relative humidity (RH), wind speed (Ws), wind direction (Wd)
  • X 1 temperature Ta
  • X 2 water temperature Ts
  • the CDF is an integral value of the PDF
  • the slope of the CDF means PDF
  • the uniformly divided CDF has the same meaning as the interval having the same probability. That is, the X value is divided into N sections so as to have a narrow X interval in a section having a high probability of occurrence and a wide X interval in a section having a low probability of occurrence (see FIGS. 3 and 5). Each of the divided sections is called a 'slot'.
  • the number of slots for each weather variable such as temperature (Ta), water temperature (Ts), relative humidity (RH), wind speed (Ws), and wind direction (Wd) becomes equal to the number of samples to be extracted. That is, if only one sample is extracted without overlapping or missing in each slot section for each variable (temperature, water temperature, etc.), the probability distribution of the extracted samples becomes equal to the probability distribution of the population. For example, in FIG. 3, if one sample corresponding to -9.3 ⁇ Ta ⁇ 3.6 slots is extracted, a sample corresponding to this slot can no longer be extracted, and a sample corresponding to 21.1 ⁇ Ta ⁇ 23.1 intervals is extracted. Once extracted, the sample corresponding to this slot can no longer be extracted. That is, the probability of occurrence of the two sections is equal to 10%, respectively.
  • each weather variable (Xk) can be converted into independent variables (Yk) by using Principal Component Analysis (PCA).
  • PCA Principal Component Analysis
  • the joint probability of the i-th data can be obtained as the following equation (Y variables are independent, so the joint probability can be obtained by multiplying each probability).
  • This combined probability means the probability of occurrence of each observation data. That is, in the present invention, using the principal component analysis method, it is possible to obtain the probability of occurrence of the combined variables having correlation with each other.
  • all points basically have 5 available slots (temperature, water temperature, relative humidity, wind speed, wind direction).
  • the points belonging to the same slot as A will reduce the number of available slots.
  • the point C (see FIG. 11) belonging to the same temperature slot as A has a sample (point A) corresponding to the temperature slot, and thus the available slot of the temperature is reduced from 1 to 0. Therefore, if the weather variables are two (temperature, water temperature) as in the above example, the N slots are reduced from 2 to 1 (generally, the weather variables are five, such as temperature, water temperature, relative humidity, wind speed, and wind direction, N slots will be reduced from 5 to 4 if the available slots of the other weather variables do not overlap A).
  • point D which belongs to the same water temperature slot as A, also reduces the available slot of water temperature from 1 to 0. That is, as shown in FIG. 11, the samples (dots) corresponding to the corresponding slots are first reduced by decreasing the number of available slots.
  • priority is determined by the number of available slots and the probability of occurrence in each observation data consisting of five variables such as temperature, water temperature, relative humidity, wind speed, and wind direction, and a sample is extracted. Each time you update the priority of all the remaining points.
  • priority (Rank, Ri) may be expressed as the following equation. However, the priority does not have to be in the form of the following equation, and may be a relation that is proportional to N slot (number of slots) and inversely proportional to P Ji (probability).
  • a block is set in consideration of correlations between various variables: As shown in FIGS. 6 to 8, there is a correlation between all weather variables.
  • each variable should be divided into three zones.
  • the total number of zones is 9 (3 2 ), and each zone can be marked as shown in FIG. At this time, the number of observation data located in all zones is not the same. In FIG.
  • Zone 10 the same number of data exists in all three horizontally divided zones (water temperature reference division), and the same number of data exists in all three vertically divided zones (temperature reference division), but horizontal and vertical
  • the present invention can be performed by extracting an appropriate number of samples from the actual observed weather data for the analysis of physical phenomena having characteristics sensitive to the weather environment, including the analysis of the infrared signal of the ship. By doing so, it is possible to analyze various characteristics that may occur in a real environment. In addition, by making it possible to extract a sample that retains the characteristics similar to those of the population, the analysis can be performed using a small number of samples and the results can be used to predict phenomena that may occur under actual marine weather conditions. .
  • the principal component analysis method was applied as a method to calculate the probability of occurrence of observation data when each weather variable has a correlation with each other, and the marine weather sampling method that can cover a wide range of weather change phenomenon is used. Implemented. In addition, by establishing zones and sampling in each zone according to the change range between each weather variable, a procedure was established to extract a sample corresponding to a wide population without being concentrated in a specific zone.
  • the sample weather conditions for the analysis of the physical phenomena having characteristics sensitive to the weather environment, including the infrared signal analysis of the ship, to be as close as possible to the statistical distribution characteristics of the population It provides a new marine weather sampling method reflecting the correlation, the present invention is a technology that can be widely used in the shipbuilding and marine industry to realize its practical and economic value.

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Abstract

L'invention concerne un procédé d'échantillonnage de données météorologiques océaniques mis en œuvre par ordinateur. L'objectif de l'invention est de fournir un procédé d'échantillonnage de données météorologiques océaniques d'après un nouveau concept. Les données de conditions météorologiques échantillonnées pour l'analyse d'un phénomène physique ayant des caractéristiques sensibles à un environnement météorologique, y compris l'analyse d'une signature infrarouge d'un navire, peuvent être aussi proches que possible des caractéristiques de distribution statistique détenues par une population (données d'observation météorologiques océaniques), et les corrélations entre les variables météorologiques océaniques peuvent être reflétées dans les données des conditions météorologiques. L'invention concerne un procédé d'échantillonnage de données météorologiques océaniques mis en œuvre par ordinateur, ledit procédé consistant à : obtenir une fonction de distribution cumulative pour chacune des variables météorologiques constituant une population, y compris la température atmosphérique, la température de l'eau, l'humidité relative, la vitesse du vent et la direction du vent ; diviser la fonction de distribution cumulative en autant d'intervalles que le nombre de données à échantillonner ; obtenir les probabilités d'occurrence des variables météorologiques associées ayant des corrélations entre elles au moyen d'un méthode d'analyse de composant principal, et attribuer une priorité d'échantillonnage supérieure aux données ayant une probabilité d'occurrence plus faible ; et diviser les blocs entre les variables météorologiques et les données d'échantillonnage pour chaque bloc en fonction de la priorité.
PCT/KR2015/004194 2015-04-27 2015-04-27 Procédé d'échantillonnage de données météorologiques océaniques mis en œuvre par ordinateur WO2016175342A2 (fr)

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