Detector neural network vs connectionist ANNs

Parzhin, Yuri; Кosenko, Viktor; Podorozhniak, Andrii; Malyeyeva, Olga; Timofeyev, Volodymyr

doi:10.1016/j.neucom.2020.07.025

Cited by 8 publications

(7 citation statements)

References 17 publications

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“…Bong et al compared the performance of three kinetic models using the predicted value of the ANN model [24]. Parzhin et al designed an ANN algorithm for image processing [25]. Ali Sohani et al conducted a comprehensive parameter study using the ANN model [26].…”

Section: Discussionmentioning

confidence: 99%

Analysis of Environmental Controls on the Quasi-Ocean and Ocean CO2 Concentration by Two Intelligent Algorithms

Zhou

Wang

et al. 2021

Mathematical Problems in Engineering

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Previous studies have demonstrated CO2 absorption by soils in arid regions, where the absorbed CO2 is conjectured to be finally sequestrated in the “subterranean ocean”—groundwater. This study compares environmental controls of ocean CO2 concentration (surface ocean pCO2) and quasi-ocean CO2 concentration (deep-soil pCO2). We aim to explore the latent relationship, both linear and nonlinear between the environmental variables, and CO2 concentration, utilizing two intelligent algorithms—the partial least linear regression (PLSR) algorithm and the artificial neural network (ANN) algorithm. For quasi-ocean CO2 concentration, RPD <1.4 and R2 <40%. While for ocean CO2 concentration, RPD >1.4 and R2 is 99.7%. Linear relationships between the considered environmental controls and ocean CO2 concentration are proved; however, there is no evident relationship between most of the considered environmental controls and quasi-ocean CO2 concentration. Groundwater level is proved to be a relatively important environmental control on the quasi-ocean CO2 concentration, suggesting groundwater discharge/recharge as a significant modulator of soil CO2 absorption in arid regions.

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Section: Discussionmentioning

confidence: 99%

Analysis of Environmental Controls on the Quasi-Ocean and Ocean CO2 Concentration by Two Intelligent Algorithms

Zhou

Wang

et al. 2021

Mathematical Problems in Engineering

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“…Метод штучних нейронних мереж [15] у свою чергу має вагомий мінус у швидкодії і скоріше за все від розпізнавання у реальному часі доведеться відмовитися, якщо використовувати лише потужності SoC плати. Для реалізації було обрано методи глибокого навчання та SoC на Raspberry PI.…”

Section: рис 3 нахили відносно камериunclassified

Дослідження Системи Розпізнавання Автомобільних Номерів На Raspberry Pi

Podorozhniak¹,

Liubchenko²,

Petrukovich³

et al. 2023

CNCS

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Об’єктом дослідження даної роботи є засоби машинного навчання для автоматичного розпізнавання автомобільних номерів. Мета даної роботи полягає у дослідженні реалізації системи для розпізнавання автомобільних номерів засобами Raspberry PI 4. Основним завданням даного дослідження є аналіз аналогів існуючих систем автоматичного розпізнавання автомобільних номерів у середовищі апаратних продуктів Raspberry PI, а також реалізація власної системи та її дослідження. У результаті дослідження існуючих аналогів було виявлено певні проблеми та знайдено шляхи їх вирішення. Система використовує сучасні технології штучних згорткових нейронних мереж для вирішення поставленої задачі. Також слід зазначити, що розроблена система працює краще конкурентних на великих кутах зйомки. У результаті виконання роботи було реалізовано систему автоматичного розпізнавання автомобільних номерів засобами плати Raspberry PI для інтелектуальних вбудованих систем. Було проведено дослідження та виявлено великий потенціал запропонованої розробки у середовищах, де швидкість роботи системи не буде критичною

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“…It is also proposed to use promising developments in the theory of artificial intelligence for the control system of vehicles [8,9]. Such solutions are either quite expensive, or not accurate enough, or have only theoretical estimates of their operation, so it makes sense to look for other sources to improve the assessment of the location of agricultural machinery.…”

Section: Analysis Of Existing Sources and Setting The Task Of Researchmentioning

confidence: 99%

Model and Algorithms for Determining the Location and Position of Agricultural Machinery During the Movement

Podorozhniak

Balenko

Sobol

2021

ITSSI

Self Cite

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The subject of research is the navigation subsystem of autonomous control system to determine the location and position of agricultural machinery during the movement. The purpose of the work is to develop and research model and algorithms to determine the location and position of mobile agricultural machinery using a physical model. The following tasks are solved in the article: development of agricultural machinery physical model to collect information from sensors during movement, further development and research of applicability of algorithms for location and position determination. The following methods are used: methods of mathematical statistics, methods of information systems theory and data processing, methods of random signals filtration. The following results were obtained: during research, the agricultural machinery physical model to collect information from sensors during movement was created. The model includes a GPS receiver, an accelerometer, gyroscope and infrared encoders, to count the rotation of the wheels, as well as its own four wheelbase of agricultural machinery. The modernized GPS coordinate filtration algorithm using a geochex algorithm is proposed, which according to several successively obtained GPS coordinates calculates the hash received coordinates; if the coordinates have the same hash, it can be argued that the vehicle is in the segment of the area that corresponds to this hash. To determine the physical model position during the movement data from the accelerometer and the gyroscope was processed using Savitzky-Golay and Madgwick filters. With the use of wheels’ rotation data, the odometric algorithms for movement and location determining of the agricultural machinery physical model in motion were implemented. Conclusions: to improve the accuracy of estimating the location and position agricultural machinery, algorithms complexation of indicators from different navigation systems should be used to reduce the total error. Research results can be applied in the development of new and modifications of existing navigation subsystems of agricultural machinery autonomous control systems.

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Detector neural network vs connectionist ANNs

Cited by 8 publications

References 17 publications

Analysis of Environmental Controls on the Quasi-Ocean and Ocean CO2 Concentration by Two Intelligent Algorithms

Analysis of Environmental Controls on the Quasi-Ocean and Ocean CO2 Concentration by Two Intelligent Algorithms

Дослідження Системи Розпізнавання Автомобільних Номерів На Raspberry Pi

Model and Algorithms for Determining the Location and Position of Agricultural Machinery During the Movement

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