Modern object recognition algorithms have very high precision. At the same time, they require high computational power. Thus, widely used low-power IoT devices, which gather a substantial amount of data, cannot directly apply the corresponding machine learning algorithms to process it due to the lack of local computational resources. A method for fast detection and classification of moving objects for low-power single-board computers is shown in this paper. The developed algorithm uses geometric parameters of an object as well as scene-related parameters as features for classification. The extraction and classification of these features is a relatively simple process which can be executed by low-power IoT devices. The algorithm aims to recognize the most common objects in the street environment, e.g., pedestrians, cyclists, and cars. The algorithm can be applied in the dark environment by processing images from a near-infrared camera. The method has been tested on both synthetic virtual scenes and real-world data. The research showed that a low-performance computing system, such as a Raspberry Pi 3, is able to classify objects with acceptable frame rate and accuracy.
Abstract. This work considers motion sensors as parts of the smart lighting system on basis of Beaglebone microcomputer. Detection system is designed for the smart lighting system. Experimental investigations of the detection system were made with different motion sensors. Based on the results comparative analysis was performed and optimal conditions for the detection system operation were found.
IntroductionModern city lightning systems have raised requirements to energy conservation and resource efficiency due to growing power consumption for city lightening. Smart lightening systems meet such requirements.Advantages of the smart lightening system over ordinary one: -standard lightning systems have preset lightning time which does not depend on actual daylight hours and weather conditions that is solved by the smart lightning system that uses this dependence and assumes necessary measures; -switching universally used lightning systems on and off do not depend on the presence of pedestrians and moving vehicles resulting in pointless energy consumption during long period of time [1][2][3][4][5];-smart lightning control systems in contrast with ordinary ones monitor current status of each LED lamp and transfer information to the server that provides more efficient operation and timely failure removal; -hardware could be easily updated in accordance with installation location of the smart lightning system -busy highways, quiet suburban streets or city part alleys; -as smart lightning systems presuppose continuous monitoring and prompt troubleshooting that provides constant streets lighting and consequently influences on road safety [6]. To control smart lightning systems there are integrated systems that control lightning level, process sensor data signals and control communication with each other. Type of used sensors determines composition and functioning of the whole system as a sensor data signal ultimately indicates about the need to adjust lightning that is performed by the feedback of the majority of system units.
Reservoir development decisions strongly depend on our understanding on reservoir heterogeneity, which is often subject to sparse and conflicting data, interpretational bias and constraints imposed by the modelling assumptions. The work tackles a challenging task of accurately and quickly identifying and describing uncertainty in the spatial distribution of reservoir heterogeneity derived from geological well data and with respect to a geological concept. We propose a metric based machine-learning approach to identify and describe spatial trends in reservoir heterogeneity/facies property distribution using wireline and production data.
We demonstrate how the proposed method can help to partition reservoir heterogeneity and discover and verify spatial trends for a real mature producing field in the Western Siberia. The obtained clustering of reservoir facies based on the wireline logs (alpha-SP) demonstrated a good agreement with the reservoir zonation based on manual log interpretation and the geological concept. Clustering based on individual well production profiles has confirmed the reservoir partitioning and matched some of the reservoir features aligned with the prevailing geological concept. The outcome of the proposed method helps to improve the facies distribution model by integrating the discovered spatial trends into a geostatistical model and account for uncertainty in the depositional scenario that is difficult to quantify based on manual interpretation.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.