INTELLIGENT CONTROL OF PNEUMATIC PRECISION SEEDING SYSTEMS: ALGORITHMS AND FIELD TESTING

Based on heat balance analysis, the dynamic heating model of asphalt tank of mixture equipment was established, the heating time and using time nodes were determined, and the trapezoidal dynamic heating process of asphalt tank was also formed to realize the intelligent control and reduce energy consumption. After analyzing different schemes, the control scheme of the combination of feed-forward and feedback to realize intelligent control system for asphalt tank was adopted, which could ensure that the asphalt in the tank is in the operating temperature and the mixing equipment works continuously when the asphalt was used completely. In addition, a simulated analysis of calculation model, which was adopted by intelligent control system, was made in combination of entitative engineering. Results showed that compared with traditional process of heating asphalt, this intelligent heating control system could reduce the heat consumption by 15.8% under the condition of continuous construction.

Ensemble of long short-term memory prediction and variable universe fuzzy control for seismic response reduction using electromagnetic dynamic vibration absorber

Lighting system control techniques in commercial buildings: Current trends and future directions

A description of a new class of organizational and technical systems is given – intelligent road-building compaction control systems, the principles of operation of which provide for ensuring a given quality of compaction of asphalt mixtures in the process of road construction. The development of systems of that class is due, in the first place, to the transition to digital and intelligent control systems in construction. A scientific problem related to the relevance of creating methods for intelligent control of compaction of asphalt mixtures that affect the quality of road surfaces was formulated. To succeed, the usage of neural network structures was proposed. The usage will allow real-time monitoring of changes in the compaction quality index and efficient controlling of road machines using both the driver's manual means and the automatic compaction control system. The properties of the system under study – which determine the principles of its functioning: intelligence, informativity, speed, interconnectedness of subsystems, controllability and modularity – were presented. The above principles of the functioning of the proposed system, which are part of the methodology for managing the quality of asphalt compaction of pavement roads, were implemented in a set of methods and algorithms. Among them should be noted a new method of intelligent continuous control of compaction in the process of laying asphalt mixtures, an advanced method of intelligent prediction of compaction quality by vibrating rollers.

Customising with 3D printing: The role of intelligent control

A convolutional neural network (CNN) was designed and built on an embedded building lighting control system to determine whether the application of CNN could increase the accuracy of image recognition and reduce energy consumption. Currently, lighting control systems rely mainly on information technology, with sensors to detect people’s existence or absence in an environment. However, due to the deviation of this perception, the accuracy of image detection is not high. In order to validate the effectiveness of the new system based on CNN, an experiment was designed and operated. The importance of the research lies in the fact that high image detection would bring in less energy consumption. The result of the experiment indicated that, when comparing the actual position with the positioning position, the difference was between 0.01 to 0.20 m, indicating that the image recognition accuracy of the CNN-based embedded control system was very high. Moreover, comparing the luminous flux of the designed system with natural light and the designed system without natural light with the system without intelligent control, the energy savings is about 40%.

Обоснован перечень задач самообучения интеллектуальных систем управления автономными роботами и мультиагентными робототехническими группировками. Обсуждаются основные проблемы создания интеллектуальных самообучающихся систем управления. Показано, что один из наиболее перспективных подходов к реализации процессов самообучения в интеллектуальных системах управления автономными роботами и многоагентными робототехническими группировками связан с использованием метода автоматического построения деревьев классификации. Приводятся результаты комплексного моделирования.

For energy supply in the Arctic regions, hybrid systems should be designed and equipped to ensure a high level of renewable energy penetration. Energy systems located in remote Arctic areas may experience many peculiar challenges, for example, due to the limited transport options throughout the year and the lack of qualified on-site maintenance specialists. Reliable operation of such systems in harsh climatic conditions requires not only a standard control system but also an advanced system based on predictions concerning weather, wind, and ice accretion on the blades. To satisfy these requirements, the current work presents an advanced intelligent automatic control system. In the developed control system, the transformation, control, and distribution of energy are based on dynamic power redistribution, dynamic control of dump loads, and a bi-directional current transducer. The article shows the architecture of the advanced control system, presents the results of field studies under the standard control approach, and models the performance of the system under different operating modes. Additionally, the effect of using turbine control to reduce the effects of icing is examined. It is shown that the advanced control approach can reduce fuel consumption in field tests by 22%. Moreover, the proposed turbine control scheme has the potential to reduce icing effects by 2% to 5%.

With the progress of computer technology and intelligent control system, it is necessary to introduce the PLC together with the current systems. In this paper, we conduct research on the new intelligent industrial control and communication system based on PLC technique. As Industry 4.0 at the core of intelligent factory, the products made in its basic idea is integrated with dynamic digital memory, perception and communication ability, carrying the whole supply chain and all kinds of information required for life cycle. PLC biggest characteristic is to constitute the traditional relay model computer software technology, formed a unique style in relay ladder graph based image programming language, the PLC control system in the form of program to achieve its control function. In the simulation section, we implement our proposed system in detail and conduct numerical analtsis and simulation. The result is acceptable and accurate.

The main problem faced in supplying power to remote consumers is that there are high logistical costs of delivering fuel and equipment for diesel power plants, poor density of transport infrastructure and, as a consequence, a high cost of fuel. There are also high operating costs at diesel power plants and specific fuel consumption; in addition, there is no monitoring and control automation. In view of a high wind potential of the Arctic territories, energy complexes and systems can be effectively modernized and constructed on the basis of modular wind-diesel power plants with an intelligent control system. A concept of and hardware solutions for an intelligent automatic control system are proposed, the use of which makes it possible to maximize the amount of electricity generated by renewable sources owing to dynamically redistributing the power between the hybrid energy complex components and, as a result, to minimize fuel consumption. An analysis of a controlled wind-diesel power plant has shown that by using an intelligent automatic control system, forecasting the electricity generation by a wind power plant, and arranging storage battery operation in a cyclic mode, it becomes possible to effectively cover the load schedule of an autonomous consumer and increase the extent of diesel fuel substitution by up to 60 % or more.

In the new stage for intelligent manufacturing of traditional Chinese medicine(TCM) from pilot demonstration to in-depth application and comprehensive promotion, how to raise the degree of intelligence for the process quality control system has become the bottleneck of the development of TCM production process control technology. This article has sorted out 226 TCM intelligent manufacturing projects that have been approved by the national and provincial governments since the implementation of the "Made in China 2025" plan and 145 related pharmaceutical enterprises. Then, the patents applied by these pharmaceutical enterprises were thoroughly retrieved, and 135 patents in terms of intelligent quality control technology in the production process were found. The technical details about intelligent quality control at both the unit levels such as cultivation, processing of crude herbs, preparation pretreatment, pharmaceutical preparations, and the production workshop level were reviewed from three aspects, i.e., intelligent quality sensing, intelligent process cognition, and intelligent process control. The results showed that intelligent quality control technologies have been preliminarily applied to the whole process of TCM production. The intelligence control of the extraction and concentration processes and the intelligent sensing of critical quality attributes are currently the focus of pharmaceutical enterprises. However, there is a lack of process cognitive patent technology for the TCM manufacturing process, which fails to meet the requirements of closed-loop integration of intelligent sensing and intelligent control technologies. It is suggested that in the future, with the help of artificial intelligence and machine learning methods, the process cognitive bottleneck of TCM production can be overcome, and the holistic quality formation mechanisms of TCM products can be elucidated. Moreover, key technologies for system integration and intelligent equipment are expected to be innovated and accelerated to enhance the quality uniformity and manufacturing reliability of TCM.

The thermal comfort and energy performance of a Personalized Radiant Cooling System (PRCS) designed for office environments is investigated. It integrates mobile cold storage, using phase change materials, with localised radiant cooling to enhance occupant comfort while reducing energy consumption. A two-stage numerical framework is employed, combining Computational Fluid Dynamics (CFD) simulations and the JOS-3 thermoregulation model to evaluate thermal comfort, followed by building performance simulations using EnergyPlus to assess energy demand. Results demonstrate that PRCS reduces perceived temperature by approximately 2 K and can maintain thermal comfort in office environments up to 28°C, reducing reliance on traditional HVAC systems. PRCS-based cooling solutions achieve significant energy savings between 50% and 83%, particularly in low-to-moderate occupancy scenarios.

Developing national standards for fuzzy logic-based control systems in energy-efficient HVAC operations is crucial for enhancing building performance and reducing energy consumption. Fuzzy logic, as an advanced computational technique, enables the development of intelligent control systems that can handle the inherent uncertainties and nonlinearities associated with HVAC operations. By integrating fuzzy logic into HVAC control systems, energy efficiency can be significantly improved through more precise regulation of heating, ventilation, and air conditioning processes. This paper explores the development of national standards for the implementation of fuzzy logic-based control systems in HVAC operations, addressing key aspects such as system design, performance metrics, and compliance requirements. The proposed standards aim to establish a unified framework that ensures the reliability, safety, and interoperability of fuzzy logic-based HVAC systems across various building types and climatic conditions. The standards will also provide guidelines for the calibration, testing, and validation of these systems, ensuring that they meet the desired energy efficiency targets while maintaining occupant comfort. Additionally, the paper discusses the potential challenges and considerations in developing these standards, such as the need for industry-wide collaboration, the integration of existing standards, and the adaptation to rapidly evolving technological advancements. Through the establishment of national standards, this research contributes to the broader goal of promoting energy-efficient building operations and supporting the transition to sustainable energy practices. The findings underscore the importance of developing a standardized approach to fuzzy logic-based control systems, which will facilitate their widespread adoption and ensure consistent performance across different applications. The paper concludes with recommendations for future research and development initiatives to further refine and expand the scope of these standards, ultimately contributing to more sustainable and energy-efficient HVAC operations. Keywords: Fuzzy Logic, Hvac Control Systems, Energy Efficiency, National Standards, Building Performance, Sustainable Energy, Intelligent Control Systems.

With the development of network communication, computer technology and artificial intelligence, it has become an inevitable development trend to promote "intelligent buildings" characterized by intelligence, comfort and safety. In order to solve the shortcomings of the existing research on intelligent building control system, this paper discusses the functional equation of CAN algorithm, artificial intelligence fuzzy controller and intelligent building, and aims at the intelligent building control system based on artificial intelligence and CAN algorithm. Hardware settings and parameter settings are briefly introduced. And the work flow design of the intelligent building control system structure based on artificial intelligence and CAN algorithm is discussed, and finally the application of the intelligent building control system based on artificial intelligence and CAN algorithm is experimentally tested. The correct number of switch control by artificial intelligence and CAN algorithm in the intelligent building control system is relatively high. The correct number of switch control in the interference environment is less than that in the humid environment, and the number of switch operations for the control system is the least in the humid environment. The error rate of 500 and up to 2500 is less than 5%, thus verifying the superiority of the application of intelligent building control system based on artificial intelligence and CAN algorithm.

Relevance. The growing need for energy-efficient lighting solutions and the development of smart home technologies create the need to develop intelligent lighting control systems. Modern systems must adapt to changing environmental conditions, ensure user comfort and significantly reduce energy consumption. Research object: intelligent lighting control systems based on microcontrollers with functions of adaptation to environmental conditions. Purpose of the article: conducting an analytical review of existing intelligent lighting control solutions, determining optimal technical solutions and identifying promising development directions. Research results: A comprehensive analysis of modern intelligent lighting control systems was conducted, key technologies of sensors, microcontrollers and adaptation algorithms were considered. The advantages and disadvantages of the main commercial solutions were identified, and optimal hardware and software platforms were identified. Conclusions. Intelligent lighting control systems demonstrate significant potential for energy savings (up to 60%) and increased user comfort. The most promising solutions are based on 32-bit ARM Cortex-M microcontrollers with integration of multiple sensors and machine learning algorithms.