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Tytuł:
Modeling and optimization of activated carbon carbonization process based on support vector machine
Autorzy:
Liu, Gangyang
Zhang, Chunlong
Dou, Dongyang
Wei, Yinghua
Powiązania:
https://bibliotekanauki.pl/articles/1448262.pdf
Data publikacji:
2021
Wydawca:
Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
Tematy:
carbonization process
optimization
modeling
support vector machine
Opis:
Product prediction and process parameter optimization in the production process of activated carbon are very important for production. It can stabilize product quality and improve the economic efficiency of enterprises. In this paper, three process parameters of a carbonization furnace, namely feeding rate, rotation speed, and carbonization temperature, were adopted to build a quality optimization model for carbonized materials. First, an orthogonal test was designed to obtain the preliminary relationship between the process parameters and the quality indicators of a carbonized material and prepare data for modeling. Then, an improved SVR model was developed to establish the relationship between product quality indicators and process parameters. Finally, through the singlefactor experiments and the Monte Carlo method, the process parameters affecting the quality of a carbonized material were determined and optimized. This showed that a high-quality carbonized material could be obtained with a smaller feeding rate, larger rotation speed, and higher carbonization furnace temperature. The quality of activated carbon reached its maximum when the feeding rate was 1.0 t/h, the rotation speed was 90 r/h, and the temperature was 836°C. It can effectively improve the quality of carbonized materials.
Źródło:
Physicochemical Problems of Mineral Processing; 2021, 57, 2; 131-143
1643-1049
2084-4735
Pojawia się w:
Physicochemical Problems of Mineral Processing
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Issledovanie ispolʹzovaniâ fuzii peremennyh v processe primeneniâ metoda opornyh vektorov v diagnostic
Autorzy:
Jegorowa, Albina
Górski, Jarosław
Kurek, Jarosław
Powiązania:
https://bibliotekanauki.pl/articles/2200172.pdf
Data publikacji:
2020
Wydawca:
Szkoła Główna Gospodarstwa Wiejskiego w Warszawie. Wydawnictwo Szkoły Głównej Gospodarstwa Wiejskiego w Warszawie
Tematy:
diagnostics
tool wear
support vector machine
chipboard
drill
Opis:
Исследование использования фузии переменных в процессе применения метода опорных векторов в диагностике сверл во время обработки древесностружечной плиты. Целью работы было определение возможности слияния т.е. фузии переменных, определенных для диагностики режущего инструмента используемого во время сверления древесностружечной ламинированной плиты, в основе которого лежит метод опорных векторов. В результате применения данного метода удалось редуцировать набор переменных на 92,75 % к первоначальному, что позволило улучшить показатель точности классификации во время мониторинга за состоянием режущего инструмента, сократить время на тренировку системы и улучшить показатели генерализации. Проведенные исследования показали, что данный метод работает и значительно улучшает качество классификации неинвазивного метода диагностики сверл. Точность классификации составила 85,10%. Система не допускает ошибок между крайними классами. Количество ошибок между соседними классами незначительно.
Badanie wykorzystania fuzji cech diagnostycznych stosowanych podczas diagnostyki stopnia zużycia wierteł w trakcie obróbki płyt wiórowych laminowanych, z wykorzystaniem algorytmu maszyny wektorów wspierających. Celem pracy było określenie możliwości zastosowania fuzji zmiennych zdefiniowanych do diagnostyki narzędzia skrawającego stosowanego w trakcie wiercenia płyt wiórowych laminowanych, w oparciu o algorytm maszyny wektorów wspierających (SVM). W wyniku zastosowania tej metody możliwe było zmniejszenie zbioru zmiennych o 92,75%, do zbioru pierwotnego, co pozwoliło na poprawę dokładności klasyfikacji podczas monitorowania stanu narzędzi skrawających, skrócenie czasu uczenia oraz poprawę generalizacji. Badania wykazały, że metoda ta jest skuteczna, znacząco poprawiająca jakość klasyfikacji nieinwazyjnej diagnostyki wierteł. Dokładność klasyfikacji wyniosła 85,10%, a ponadto system nie dopuszcza do błędów pomiędzy klasami ekstremalnymi. Liczba błędów pomiędzy sąsiednimi klasami jest nieistotna.
Źródło:
Annals of Warsaw University of Life Sciences - SGGW. Forestry and Wood Technology; 2020, 110; 97--102
1898-5912
Pojawia się w:
Annals of Warsaw University of Life Sciences - SGGW. Forestry and Wood Technology
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Performance analysis of rough set–based hybrid classification systems in the case of missing values
Autorzy:
Nowicki, Robert K.
Seliga, Robert
Żelasko, Dariusz
Hayashi, Yoichi
Powiązania:
https://bibliotekanauki.pl/articles/2031102.pdf
Data publikacji:
2021
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
rough sets
support vector machine
fuzzy system
neural networks
Opis:
The paper presents a performance analysis of a selected few rough set–based classification systems. They are hybrid solutions designed to process information with missing values. Rough set-–based classification systems combine various classification methods, such as support vector machines, k–nearest neighbour, fuzzy systems, and neural networks with the rough set theory. When all input values take the form of real numbers, and they are available, the structure of the classifier returns to a non–rough set version. The performance of the four systems has been analysed based on the classification results obtained for benchmark databases downloaded from the machine learning repository of the University of California at Irvine.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2021, 11, 4; 307-318
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A Classification Method Related to Respiratory Disorder Events Based on Acoustical Analysis of Snoring
Autorzy:
Wang, Can
Peng, Jianxin
Zhang, Xiaowen
Powiązania:
https://bibliotekanauki.pl/articles/176601.pdf
Data publikacji:
2020
Wydawca:
Polska Akademia Nauk. Czasopisma i Monografie PAN
Tematy:
acoustical analysis
feature extraction
support vector machine
snoring sound
Opis:
Acoustical analysis of snoring provides a new approach for the diagnosis of obstructive sleep apnea hypopnea syndrome (OSAHS). A classification method is presented based on respiratory disorder events to predict the apnea-hypopnea index (AHI) of OSAHS patients. The acoustical features of snoring were extracted from a full night’s recording of 6 OSAHS patients, and regular snoring sounds and snoring sounds related to respiratory disorder events were classified using a support vector machine (SVM) method. The mean recognition rate for simple snoring sounds and snoring sounds related to respiratory disorder events is more than 91.14% by using the grid search, a genetic algorithm and particle swarm optimization methods. The predicted AHI from the present study has a high correlation with the AHI from polysomnography and the correlation coefficient is 0.976. These results demonstrate that the proposed method can classify the snoring sounds of OSAHS patients and can be used to provide guidance for diagnosis of OSAHS.
Źródło:
Archives of Acoustics; 2020, 45, 1; 141-151
0137-5075
Pojawia się w:
Archives of Acoustics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Ensemble machine learning methods to predict the balancing of ayurvedic constituents in the human body
Autorzy:
Rajasekar, Vani
Krishnamoorthi, Sathya
Saracevic, Muzafer
Pepic, Dzenis
Zajmovic, Mahir
Zogic, Haris
Powiązania:
https://bibliotekanauki.pl/articles/27312840.pdf
Data publikacji:
2022
Wydawca:
Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Tematy:
machine learning
artificial neural networks
diagnose
Ayurveda constituent
support vector machine
Opis:
In this paper, we demonstrate the result of certain machine-learning methods like support vector machine (SVM), naive Bayes (NB), decision tree (DT), k-nearest neighbor (KNN), artificial neural network (ANN), and AdaBoost algorithms for various performance characteristics to predict human body constituencies. Ayurveda-dosha studies have been used for a long time, but the quantitative reliability measurement of these diagnostic methods still lags. The careful and appropriate analysis leads to an effective treatment to predict human body constituencies. From an observation of the results, it is shown that the AdaBoost algorithm with hyperparameter tuning provides enhanced accuracy and recall (0.97), precision and F-score (0.96), and lower RSME values (0.64). The experimental results reveal that the improved model (which is based on ensemble-learning methods) significantly outperforms traditional methods. According to the findings, advancements in the proposed algorithms could give machine learning a promising future.
Źródło:
Computer Science; 2022, 23 (1); 117--132
1508-2806
2300-7036
Pojawia się w:
Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Machine learning methods for diagnosing the causes of die-casting defects
Autorzy:
Okuniewska, Alicja
Perzyk, Marcin
Kozłowski, Jacek
Powiązania:
https://bibliotekanauki.pl/articles/29519775.pdf
Data publikacji:
2023
Wydawca:
Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Tematy:
fault diagnosis
machine learning tools
neural network
classification trees
support vector machine
Opis:
The research was focused on analyzing the causes of high-pressure die-casting defects, more specifically on casting leakage, which is considered perhaps the most important and common defect. The real data used for modelling was obtained from a high-pressure die-casting foundry that manufactures aluminum cylinder blocks for the world’s leading automotive brands. This paper compares and summarizes the results of applying advanced modelling using artificial neural networks, regression trees, and support vector machines methods to select artificial neural networks as the most effective method to perform a multidimensional optimization of process parameters to diagnose the causes of die-casting defects and to indicate the future research scope in this area. The developed system enables the prediction of the level of defects in castings with satisfactory accuracy and is therefore a highly relevant reference for process engineers of high-pressure foundries. This article indicates exactly which process parameters significantly influence the formation of a defect in a casting.
Źródło:
Computer Methods in Materials Science; 2023, 23, 2; 45-56
2720-4081
2720-3948
Pojawia się w:
Computer Methods in Materials Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Daily Suspended Sediment Prediction Using Seasonal Time Series and Artificial Intelligence Techniques
Autorzy:
Üneş, Fatih
Taşar, Bestami
Demirci, Mustafa
Zelenakova, Martina
Kaya, Yunus Ziya
Varçin, Hakan
Powiązania:
https://bibliotekanauki.pl/articles/2069941.pdf
Data publikacji:
2021
Wydawca:
Politechnika Koszalińska. Wydawnictwo Uczelniane
Tematy:
prediction
neuro-fuzzy
sediment rating curve
support vector machine
suspended sediment
Opis:
Estimating the amount of suspended sediment in rivers correctly is important due to the adverse impacts encountered during the design and maintenance of hydraulic structures such as dams, regulators, water channels and bridges. The sediment concentration and discharge currents have usually complex relationship, especially on long term scales, which can lead to high uncertainties in load estimates for certain components. In this paper, with several data-driven methods, including two types of perceptron support vector machines with radial basis function kernel (SVM-RBF), and poly kernel learning algorithms (SVM-PK), Library SVM (LibSVM), adaptive neuro-fuzzy (NF) and statistical approaches such as sediment rating curves (SRC), multi linear regression (MLR) are used for forecasting daily suspended sediment concentration from daily temperature of water and streamflow in the river. Daily data are measured at Augusta station by the US Geological Survey. 15 different input combinations (1 to 15) were used for SVM-PK, SVM-RBF, LibSVM, NF and MLR model studies. All approaches are compared to each other according to three statistical criteria; mean absolute errors (MAE), root mean square errors (RMSE) and correlation coefficient (R). Of the applied linear and nonlinear methods, LibSVM and NF have good results, but LibSVM generates a slightly better fit under whole daily sediment values.
Źródło:
Rocznik Ochrona Środowiska; 2021, 23; 117--137
1506-218X
Pojawia się w:
Rocznik Ochrona Środowiska
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Application of evaluation algorithm for port logistics park based on PCA-SVM model
Autorzy:
Hu, B.
Powiązania:
https://bibliotekanauki.pl/articles/260528.pdf
Data publikacji:
2018
Wydawca:
Politechnika Gdańska. Wydział Inżynierii Mechanicznej i Okrętownictwa
Tematy:
port logistics demand
support vector machine
principal component analysis
economic hinterland
Opis:
To predict the logistics needs of the port, an evaluation algorithm for the port logistics park based on the PCASVM model was proposed. First, a quantitative indicator set for port logistics demand analysis was established. Then, based on the grey correlation analysis method, the specific indicator set of port logistics demand analysis was selected. The advantages of both principal component analysis and support vector machine algorithms were combined. The PCA-SVM model was constructed as a predictive model of the port logistics demand scale. The empirical analysis was conducted. Finally, from the perspective of the structure, demand, flow pattern and scale of port logistics demand, the future logistics demand of Shenzhen port was analysed. Through sensitivity analysis, the main influencing factors were found out, and the future development proposals of Shenzhen port were put forward. The results showed that the port throughput of Shenzhen City in 2016 was 21,328,200 tons. Compared with the previous year, it decreased by about 1.74 %. In summary, the PCA-SVM model accurately predicts the logistics needs of the port.
Źródło:
Polish Maritime Research; 2018, S 3; 29-35
1233-2585
Pojawia się w:
Polish Maritime Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Detection of Obstructive Sleep Apnea from ECG Signal Using SVM Based Grid Search
Autorzy:
Valavan, K. K.
Manoj, S.
Abishek, S.
Gokull Vijay, T. G.
Vojaswwin, P.
Rolant Gini, J.
Ramachandran, K. I.
Powiązania:
https://bibliotekanauki.pl/articles/1844601.pdf
Data publikacji:
2021
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
ECG signal
grid search
RR interval
sleep apnea
support vector machine
Opis:
Obstructive Sleep Apnea is one common form of sleep apnea and is now tested by means of a process called Polysomnography which is time-consuming, expensive and also requires a human observer throughout the study of the subject which makes it inconvenient and new detection techniques are now being developed to overcome these difficulties. Heart rate variability has proven to be related to sleep apnea episodes and thus the features from the ECG signal can be used in the detection of sleep apnea. The proposed detection technique uses Support Vector Machines using Grid search algorithm and the classifier is trained using features based on heart rate variability derived from the ECG signal. The developed system is tested using the dataset and the results show that this classification system can recognize the disorder with an accuracy rate of 89%. Further, the use of the grid search algorithm has made this system a reliable and an accurate means for the classification of sleep apnea and can serve as a basis for the future development of its screening.
Źródło:
International Journal of Electronics and Telecommunications; 2021, 67, 1; 5-12
2300-1933
Pojawia się w:
International Journal of Electronics and Telecommunications
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Infrasound Signal Classification Based on ICA and SVM
Autorzy:
Lu, Quanbo
Wang, Meng
Li, Mei
Powiązania:
https://bibliotekanauki.pl/articles/31339863.pdf
Data publikacji:
2023
Wydawca:
Polska Akademia Nauk. Czasopisma i Monografie PAN
Tematy:
independent component analysis
fast Fourier transform
support vector machine
infrasound signal
Opis:
A diagnostic technique based on independent component analysis (ICA), fast Fourier transform (FFT), and support vector machine (SVM) is suggested for effectively extracting signal features in infrasound signal monitoring. Firstly, ICA is proposed to separate the source signals of mixed infrasound sources. Secondly, FFT is used to obtain the feature vectors of infrasound signals. Finally, SVM is used to classify the extracted feature vectors. The approach integrates the advantages of ICA in signal separation and FFT to extract the feature vectors. An experiment is conducted to verify the benefits of the proposed approach. The experiment results demonstrate that the classification accuracy is above 98.52% and the run time is only 2.1 seconds. Therefore, the proposed strategy is beneficial in enhancing geophysical monitoring performance.
Źródło:
Archives of Acoustics; 2023, 48, 3; 191-199
0137-5075
Pojawia się w:
Archives of Acoustics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Water Quality Classification by Integration of Attribute-Realization and Support Vector Machine for the Chao Phraya River
Autorzy:
Sillberg, Chalisa Veesommai
Kullavanijaya, Pratin
Chavalparit, Orathai
Powiązania:
https://bibliotekanauki.pl/articles/1955579.pdf
Data publikacji:
2021
Wydawca:
Polskie Towarzystwo Inżynierii Ekologicznej
Tematy:
environmental data analysis
machine learning
SVM
support vector machine
water quality index
WQI
Opis:
The water quality index (WQI) is an essential indicator to manage water usage properly. This study aimed at applying a machine learning-based approach integrating attribute-realization (AR) and support vector machine (SVM) algorithm to classify the Chao Phraya River’s water quality. The historical monitoring dataset during 2008-2019 including biological oxygen demand (BOD), conductivity (Cond), dissolved oxygen (DO), faecal coliform bacteria (FCB), total coliform bacteria (TCB), ammonia (NH3-N), nitrate (NO3-N), salinity (Sal), suspended solids (SS), total nitrogen (TN), total dissolved solids (TDS), and turbidity (Turb), were processed via four studied steps: data pre-processing by means substituting method, contributing parameter evaluation by recognition pattern study, examination of the mathematic functions for quality classification, and validation of obtained approach. The results showed that NH3-N, TCB, FCB, BOD, DO, and Sal were the main attributes contributing orderly to water quality classification with confidence values of 0.80, 0.79, 0.78, 0.76, 0.69, and 0.64, respectively. Linear regression was the most suitable function to river water data classification than Sigmoid, Radial basis and Polynomial. The different number of attributes and mathematic functions promoted the different classification performance and accuracy. The validation confirmed that AR-SVM was a potent approach application to classify river water’s quality with 0.86-0.95 accuracy when applied three to six attributes.
Źródło:
Journal of Ecological Engineering; 2021, 22, 9; 70-86
2299-8993
Pojawia się w:
Journal of Ecological Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Selection of optimal coal blends in terms of ash fusion temperatures using Support Vector Machine (SVM) classifier - a case study for Polish coals
Autorzy:
Żogała, Alina
Rzychoń, Maciej
Łączny, Jacek M.
Róg, Leokadia
Powiązania:
https://bibliotekanauki.pl/articles/110177.pdf
Data publikacji:
2019
Wydawca:
Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
Tematy:
coal blends
ash fusion temperature
support vector machine
principal component analysis
machine learning
Opis:
One of the most important criteria for selecting coal for a given technology are the ash Fusion temperatures (AFTs). An effective way to regulate the AFTs so that they meet the criteria for a given industrial application is to form blends of different coals. The values of the AFTs in the blends are nonadditive, therefore they can't be calculated using the weighted average of the blend components. On the other hand, direct determination of ATFs values requires many additional time-consuming and expensive laboratory tests. Therefore, it is important to develop a solution that, in addition to the effective prediction of the values of AFTs, will also enable optimal selection of components of the blend in terms of its key parameters. The aim of the work was to develop an algorithm for the selection of the optimal coal blends in terms of AFTs for given industrial applications. This algorithm uses nonlinear classifying model which was built using machine learning method, support vector machine (SVM). To carry out the training samples of Polish hard coals from different mines of the Upper Silesian Coal Basin were used. The accuracy of the developed model is 92.3%. The results indicate the effectiveness of the proposed solution, which can find practical application in the form of an expert system used in the coal industry. The paper presents the concept of developed IT tool which has been tested for a selected case.
Źródło:
Physicochemical Problems of Mineral Processing; 2019, 55, 5; 1311-1322
1643-1049
2084-4735
Pojawia się w:
Physicochemical Problems of Mineral Processing
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Hybrid deep learning model-based prediction of images related to cyberbullying
Autorzy:
Elmezain, Mahmoud
Malki, Amer
Gad, Ibrahim
Atlam, El-Sayed
Powiązania:
https://bibliotekanauki.pl/articles/2142490.pdf
Data publikacji:
2022
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
cyberbullying
ResNet50
MobileNetV2
support vector machine
cyberprzemoc
maszyna wektorów wsparcia
Opis:
Cyberbullying has become more widespread as a result of the common use of social media, particularly among teenagers and young people. A lack of studies on the types of advice and support available to victims of bullying has a negative impact on individuals and society. This work proposes a hybrid model based on transformer models in conjunction with a support vector machine (SVM) to classify our own data set images. First, seven different convolutional neural network architectures are employed to decide which is best in terms of results. Second, feature extraction is performed using four top models, namely, ResNet50, EfficientNetB0, MobileNet and Xception architectures. In addition, each architecture extracts the same number of features as the number of images in the data set, and these features are concatenated. Finally, the features are optimized and then provided as input to the SVM classifier. The accuracy rate of the proposed merged models with the SVM classifier achieved 96.05%. Furthermore, the classification precision of the proposed merged model is 99% in the bullying class and 93% in the non-bullying class. According to these results, bullying has a negative impact on students’ academic performance. The results help stakeholders to take necessary measures against bullies and increase the community’s awareness of this phenomenon.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2022, 32, 2; 323--334
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Pomegranate fruit quality assessment using machine intelligence and wavelet features
Autorzy:
Kumar, A.
Rajpurohit, V.S.
Jirage, B.J.
Powiązania:
https://bibliotekanauki.pl/articles/2137.pdf
Data publikacji:
2018
Wydawca:
Instytut Ogrodnictwa
Tematy:
pomegranate
Punica granatum
fruit
quality assessment
artificial neural network
support vector machine
Źródło:
Journal of Horticultural Research; 2018, 26, 1
2300-5009
Pojawia się w:
Journal of Horticultural Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
System automatycznego wsparcia triażu wykorzystujący algorytm drzewa decyzyjnego i funkcję szans przeżycia
Automated triage supporting system with a decision tree algorithm and survival function
Autorzy:
Dobrowolski, Andrzej P.
Oskwarek, Paweł
Rokicki, Szymon
Wiktorzak, Paweł
Łubkowski, Piotr
Murawski, Piotr
Powiązania:
https://bibliotekanauki.pl/articles/24065020.pdf
Data publikacji:
2022
Wydawca:
Wojskowa Akademia Techniczna im. Jarosława Dąbrowskiego
Tematy:
parametry życiowe
triaż
sieć wektorów wspierających
vital signs
triage
support vector machine
Opis:
Zdarzenia z dużą liczbą poszkodowanych są elementem nieodłącznie związanym z działaniami na polu walki. różnica między triażem stosowanym na polu walki i tym dotyczącym cywilnych wypadków o charakterze masowym wynika bezpośrednio ze specyfiki zdarzenia i założonych celów. Podczas konfliktów zbrojnych priorytetem jest zrealizowanie postawionych zadań i celów. z punktu widzenia dowodzenia misja ratowania poszkodowanych odbywa się w dużej mierze po to, by mogli oni jak najszybciej wrócić do dalszych działań - priorytetem na polu walki jest wykonanie misji, a nie ratowanie wszystkich rannych. w trakcie konfliktów zbrojnych siły i środki zawsze będą ograniczone, a ewakuacja poszkodowanych będzie musiała się odbywać wieloetapowo lub będzie wydłużona w czasie. ratownicy często mają do czynienia z przedłużającą się opieką na polu walki i są zmuszeni zajmować się rannymi dużo dłużej niż podczas cywilnych zdarzeń o charakterze masowym. implementacja nowych rozwiązań technologicznych minimalizujących potencjalny błąd ludzki, gromadzących i automatycznie analizujących dane medyczne w czasie rzeczywistym, umożliwi - szczególnie w teatrze działań wojennych - szybszą identyfikację stanu poszkodowanych i wyznaczenie priorytetów. obecnie, gdy wojna przybiera zupełnie inną formę, należy szukać rozwiązań, które dadzą szansę przeżycia rannym. Priorytetem w przypadku zdarzeń o charakterze masowym staje się jak najszybsza ocena parametrów życiowych. Pozwala to na celowane udzielenie pomocy i ma zmniejszyć śmiertelność poszkodowanych oraz dać szansę dotarcia specjalistycznej pomocy. wykorzystanie sztucznej inteligencji umożliwi zoptymalizowanie działań ratowników już na etapie docierania na miejsce zdarzenia. w artykule przedstawiono nowatorski algorytm segregacji uwzględniający wartość tzw. funkcji szans przeżycia, który jest elementem systemu wspomagania decyzji ewakuacji medycznej opartego na integracji monitoringu i analizy parametrów życiowych żołnierza z systemem zabezpieczenia medycznego.
Events with a large number of casualties are an element inherent in activities on the battlefield. The difference in the triage used on the battlefield in relation to the triage used in the case of mass civil accidents results directly from the specificity of the event and the assumed goals. during armed conflicts, the priority is to achieve the tasks and goals set. From the point of view of the command, the mission to rescue the casualties takes place largely, so that they can be restored to further operations as soon as possible - the priority on the battlefield is to complete the mission, not to rescue all the wounded. during armed conflicts, forces and resources will always be limited, and the evacuation of the victims will have to be carried out in several stages or will be extended in time. rescuers often have to deal with prolonged care on the battlefield and they are forced to deal with the wounded much longer than during mass civilian incidents. The implementation of new technological solutions that minimise potential human error, collect and automatically analyse medical data in real time, will enable us - especially in the theater of war - faster identification of the condition of the injured and setting priorities. nowadays, when war takes a completely different form, solutions should be sought that will give the wounded a chance to survive. The priority in the case of MASCAL events is the quickest possible assessment of vital parameters that allow for targeted assistance, which are intended to reduce the mortality rate of the victims, give a chance to get specialist help. The use of artificial intelligence will make it possible to optimise the activities of rescuers already at the stage of reaching the scene of the event. The article presents an innovative segregation algorithm that takes into account the value of the so-called function of survival chances, which is an element of the medical evacuation decision support system, based on the integration of monitoring and analysis of soldier’s vital signs with the medical security system.
Źródło:
Biuletyn Wojskowej Akademii Technicznej; 2022, 71, 3; 31--67
1234-5865
Pojawia się w:
Biuletyn Wojskowej Akademii Technicznej
Dostawca treści:
Biblioteka Nauki
Artykuł
Wyświetlanie 1-15 z 46

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