Analysis of the vehicle's flow based on the neural network and the SIFT method

The article presents a vehicle counting system based on TensorFlow neural network models and the SIFT machine vision method. An experimental comparison was made of five detectors consisting of metaarchitecture (Faster R-CNN, SSD) and neural networks extracting features (Resnet V1 100, Inception V2, Inception Resnet V2 and Mobilenet V1). The main aspects of these detectors are analyzed, such as accuracy, speed, memory consumption, the number of floating point operations per second and the number of trainable parameters of convolutional neural networks. The calculation of vehicles is carried out by an algorithm based on the SIFT method. This algorithm compares the descriptors of all vehicles in the frame at the current time with the descriptors at the previous time. Based on the maximum match of the descriptors, the algorithm assigns the vehicle identification number from the previous frame, and in the absence of matches creates a new identification number. This approach will make it possible to calculate vehicles more accurately and assess their trajectory and speed

Improving the energy efficiency of sorting centers by identifying objects and digit-letter information with neural networks

The article is devoted to the development and analysis of methods of identifying dynamic objects. A neural network with the architecture of SSD MobileNetV2 has been developed to solve the problem of detecting baggage tags and barcodes. Several approaches are considered to solve the problem of identifying digital-letter information: Tesseract, SSD InceptionV2, OpenCV and a convolutional neural network. The efficiency of the methods on real images was checked. It was concluded that electricity consumption can be reduced by 49.43%

Mathematical model of the pneumatic actuator follower system

The article considers a method of controlling the motion of the output links of the tracking system of pneumatic actuators of technological equipment actuators. Dynamic and qualitative characteristics are improved by means of proportional-integral-differential (PID) controller. The mathematical model of actuator system, which includes power and control parts, has been developed. By calculation experiment the dynamic characteristics of the actuator have been obtained, from which it has been found possible to reduce the energy consumed by the actuator system to about 30%

Mathematical model for controlling CO2 concentration in greenhouses late in MATLAB

The article is devoted to the development of an automated control system for irradiation facilities. The system will optimize the electricity cost for supplementary lighting plants in greenhouses without economic losses. A number of scientific researches studying chlorophyll, photosynthesis and processes in the green plant leaf were analyzed as the theoretical base for the present article. On their basis, a schematic structure of the photosynthetic apparatus of plants was developed and kinetic differential equations describing the structure and functioning of the photosynthetic chlorophyll apparatus of green plants were derived. The obtained mathematical expressions describe the changes in the concentrations of the base materials that participate in the formation of the photosynthetic apparatus structure and the accumulation of photosynthesis products and allow effective controlling of the irradiation process of plants. It has been achieved by adjusting the parameters of the illumination interval and the illumination area with the aid changing the number of switched on and off sources of optical radiation and changing the spectral component in a narrow limit

MiRNAs and lncRNAs in the regulation of innate immune signaling

The detection and defense against foreign agents and pathogens by the innate immune system is a crucial mechanism in the body. A comprehensive understanding of the signaling mechanisms involved in innate immunity is essential for developing effective diagnostic tools and therapies for infectious diseases. Innate immune response is a complex process involving recognition of pathogens through receptors, activation of signaling pathways, and cytokine production, which are all crucial for deploying appropriate countermeasures. Non-coding RNAs (ncRNAs) are vital regulators of the immune response during infections, mediating the body's defense mechanisms. However, an overactive immune response can lead to tissue damage, and maintaining immune homeostasis is a complex process in which ncRNAs play a significant role. Recent studies have identified microRNAs (miRNAs) and long non-coding RNAs (lncRNAs) as key players in controlling gene expression in innate immune pathways, thereby participating in antiviral defenses, tumor immunity, and autoimmune diseases. MiRNAs act by regulating host defense mechanisms against viruses, bacteria, and fungi by targeting mRNA at the post-transcriptional level, while lncRNAs function as competing RNAs, blocking the binding of miRNAs to mRNA. This review provides an overview of the regulatory role of miRNAs and lncRNAs in innate immunity and its mechanisms, as well as highlights potential future research directions, including the expression and maturation of new ncRNAs and the conservation of ncRNAs in evolution