Intelligent Control System Research Articles

MODELS OF REMOTE IDENTIFICATION OF PARAMETERS OF DYNAMIC OBJECTS USING DETECTION TRANSFORMERS AND OPTICAL FLOW

The tasks of remote identification of parameters of dynamic objects are important for various fields, including computer vision, robotics, autonomous vehicles, video surveillance systems, and many others. Traditional methods of solving these problems face the problems of insufficient accuracy and efficiency of determining dynamic parameters in conditions of rapidly changing environments and complex dynamic scenarios. Modern methods of identifying parameters of dynamic objects using technologies of detection transformers and optical flow are considered. Transformer detection is one of the newest approaches in computer vision that uses transformer architecture for object detection tasks. This transformer integrates the object detection and boundary detection processes into a single end-to-end model, which greatly improves the accuracy and speed of processing. The use of transformers allows the model to effectively process information from the entire image at the same time, which contributes to better recognition of objects even in difficult conditions. Optical flow is a motion analysis method that determines the speed and direction of pixel movement between successive video frames. This method allows obtaining detailed information about the dynamics of the scene, which is critical for accurate tracking and identification of parameters of moving objects. The integration of detection transformers and optical flow is proposed to increase the accuracy of identification of parameters of dynamic objects. The combination of these two methods allows you to use the advantages of both approaches: high accuracy of object detection and detailed information about their movement. The conducted experiments show that the proposed model significantly outperforms traditional methods both in the accuracy of determining the parameters of objects and in the speed of data processing. The key results of the study indicate that the integration of detection transformers and optical flow provides reliable and fast determination of parameters of moving objects in real time, which can be applied in various practical scenarios. The conducted research also showed the potential for further improvement of data processing methods and their application in complex dynamic environments. The obtained results open new perspectives for the development of intelligent monitoring and control systems capable of adapting to rapidly changing environmental conditions, increasing the efficiency and safety of their work.

Optimisation of light environment in architectural space based on multi strategy collaboration – A case study of a manual drawing classroom

From the perspective of dynamic lighting throughout the year, reflector and louvre are proposed to add to the south-facing window. The optimal parameter is 2.0 m height and 1.0 m width reflector and a louvre angle of 75°. However, when further analysing the indoor illumination and uniformity, the classroom still had high or low illumination during certain periods solely relying on natural lighting, indicating that a single strategy alone cannot meet the overall lighting environment comfort needs of the space. The study suggests that different lighting environments should be used in different seasons, periods and areas of the classroom. The proportion of natural light, combined light and artificial light environments should be 30%, 45% and 25%, respectively. At the same time, considering the transformation and optimisation of the lamps, the improvement measures for windows facing other directions in the classroom is recommended, the lamps be symmetrically distributed in the horizontal direction, with a hanging height of 1.9 m in the vertical direction, which is 0.4 m higher than the original. The lighting power is 34 W and 26 W, and corresponding optimisation strategies for the intelligent control system are proposed.

Knowledge mapping and evolutionary analysis of energy storage resource management under renewable energy uncertainty: a bibliometric analysis

The research on energy storage resource management is an important measure to cope with the present problem of uncertainty in the use of renewable energy, in order to explore the evolution of the research focus and future trend of energy storage resource management under the uncertainty of renewable energy, this paper adopts the method of bibliometric analysis, and conducts a visual analysis of the relevant 800 documents in the core collection of the Web of Science by using the CiteSpace to draw co-occurrence maps and analyze the collaborative networks of different countries/regions, institutions and authors in this field, and further conducted journal co-citation analysis, literature co-citation analysis and author co-citation analysis. The keyword co-occurrence, emergent analysis, and cluster co-occurrence analysis reveal the current research focus and trend in this field, and summarize and propose four future key focus directions: energy storage technology improvement, energy storage system integration, expansion of business models for energy storage resource management, and intelligent control of energy storage system, which provide new research paths for solving the problem of renewable energy uncertainty in the future.

Model Sharing and Scalability in the Real-Time Simulation and Intelligent Hierarchical Control of Discrete-Event Systems

Model Sharing and Scalability in the Real-Time Simulation and Intelligent Hierarchical Control of Discrete-Event Systems

Enhancing microgrid performance with AI‐based predictive control: Establishing an intelligent distributed control system

Enhancing microgrid performance with AI‐based predictive control: Establishing an intelligent distributed control system

Novel Intelligent control of photovoltaic system using ANFIS Gravitational Search for MPPT controller

Novel Intelligent control of photovoltaic system using ANFIS Gravitational Search for MPPT controller

Asymmetric flexible graphene oxide papers for moisture-driven actuators and water level indicators

Smart actuators have shown great potential for a range of industries, including artificial muscles, sensors, and smart devices. However, traditional actuators often suffer from small deformation and complex materials and manufacturing processes. Graphene oxide (GO) has emerged as an ideal candidate for moisture-responsive actuators due to its excellent moisture sensitivity. In this study, we present the development of a moisture-responsive actuator using a simple and straightforward approach based on a monolayer graphene oxide (GO) paper. By utilizing the different affinity of its two sides for water, the graphene oxide actuator demonstrates excellent performance in response to humidity gradients, achieving a large bending amplitude with a curvature of 3 cm−1. Furthermore, the graphene oxide actuator was used as a humidity-sensitive switch to perform on and off switch movements, demonstrating its capability for autonomous detection for real-time humidity monitoring. This work provides a new avenue for the development of moisture-responsive materials and their potential applications in the design of intelligent control systems, actuators, soft robots, artificial muscles, and switches.

Algorithms for Route Planning and Navigation of Unmanned Aerial Vehicles

A brief overview of the results of recent research published in open sources in the field of route planning and navigation algorithms for unmanned aerial vehicles (UAV) is presented. Works devoted to global and local planning of trajectories taking into account known and detected obstacles in flight, as well as issues of navigation of groups of drones, are considered. Various approaches are analyzed, including graph algorithms (A*, Dijkstra, Rapidly-exploring Random Trees), methods of data mining in real time, potential fields. Special attention is paid to work on the use of neural networks and machine learning, SLAM and multi-agent technologies for planning UAV routes. The advantages and disadvantages of the main groups of algorithms are considered. A conclusion is drawn about the prospects for using hybrid methods, as well as machine learning technologies, to build intelligent UAV traffic control systems.

IOT Based Air Conditioner Monitoring System

In the context of rising urbanization, the surge in air conditioner usage has become a significant contributor to energy consumption. To address this, our research introduces an innovative Air Conditioner Monitoring System (ACMS) designed to optimize energy usage, enhance system efficiency, and ensure optimal indoor conditions. The proposed system employs cutting-edge sensors to monitor crucial parameters, including current, voltage, power consumption, and room temperature in real-time. By integrating these metrics into a unified framework, the ACMS facilitates proactive management and enables users to make informed decisions for energy conservation. The collected data is processed through advanced algorithms to predict potential faults or inefficiencies, allowing for preemptive maintenance and reducing downtime. Furthermore, the system supports remote monitoring and control, providing users with the flexibility to manage air conditioning settings from any location. Practical implementation and performance evaluations demonstrate the system's effectiveness in improving energy efficiency, reducing operational costs, and enhancing overall sustainability. This research contributes to the development of intelligent solutions for air conditioning systems, addressing the growing need for energy conservation in the face of expanding urbanization. The modularity of the proposed system allows for seamless customization and integration of additional features, paving the way for future advancements in intelligent climate control systems.

CFD modeling of a modern wood stove - Soot formation

Wood stoves are important domestic heating appliances using renewable bioenergy. However, the emission of Particulate Matter (PM) is the major concern for the application of wood stoves. Thus, improvements in the design of wood stoves are required for the reduction of PM emissions (mainly soot) to fulfill the increasingly stringent pollutant emission limits. To achieve low PM emission of the wood stove, a 3D steady-state CFD model is used to simulate the wood stove with a focus on soot formation in this study. The model is evaluated with the experimental data, the results of which show that the CFD model provides a reasonable prediction of wood stove experiments, indicating that the CFD model can be used as an efficient tool to optimize the design of the wood stove. The model is further used to study the effects of injection direction and mass flow rate of the tertiary air on the soot particle emission. It is found that the horizontal- and down-direction injectors can effectively reduce soot formation and an optimized tertiary air mass flow rate exists in the modern wood stove system. These conclusions are very useful in designing and developing the intelligent control system of the modern wood stove.

A survey on reinforcement learning-based control for signalized intersections with connected automated vehicles

ABSTRACT Recent advancements in connected automated vehicles (CAVs) and reinforcement learning (RL) hold significant promise for enhancing intelligent traffic control systems. This paper conducts a systematic review of studies on RL-based urban traffic control at signalised intersections, highlighting the significant impact of CAVs on traffic control performance improvement. We first review the fundamental concepts of RL algorithms, establishing a foundational understanding for subsequent RL-based traffic control methods. We then review recent progress in RL-based traffic signal control using CV/CAV trajectory data, RL-based CAV trajectory planning, and the cooperative control of both traffic signals and CAVs at signalised intersections. Our aim is to provide researchers with a comprehensive roadmap for future research in RL-based traffic control at signalised intersections.

Research on the technical principles and applications of massage robots

Massage robots, as innovative therapeutic devices, have garnered significant attention and application due to technological advancements and society's increasing focus on wellness. However, amidst this progress, gaps persist in fully understanding their capabilities and optimizing their functionalities, highlighting the need for further research in this domain. This paper focuses on exploring the intricate technical principles, mechanical structure, sensing technology, and intelligent control systems of massage robots, with the aim of providing both theoretical foundations and practical insights for researchers and practitioners in this field. Specific inquiries encompass the elucidation of working principles, emulation of human massage techniques, mechanical design considerations, applications of sensor technology, and implementations of intelligent control systems within massage robots. Employing a blend of literature review, comparative analysis, and experimental validation, this paper aims to not only deepen the understanding of massage robot functionality but also contribute to the advancement and refinement of this technology, fostering innovation and growth within the industry.

Digital Twin System of Pest Management Driven by Data and Model Fusion

Protecting crops from pests is a major issue in the current agricultural production system. The agricultural digital twin system, as an emerging product of modern agricultural development, can effectively achieve intelligent control of pest management systems. In response to the current problems of heavy use of pesticides in pest management and over-reliance on managers’ personal experience with pepper plants, this paper proposes a digital twin system that monitors changes in aphid populations, enabling timely and effective pest control interventions. The digital twin system is developed for pest management driven by data and model fusion. First, a digital twin framework is presented to manage insect pests in the whole process of crop growth. Then, a digital twin model is established to predict the number of pests based on the random forest algorithm optimized by the genetic algorithm; a pest control intervention based on a twin data search strategy is designed and the decision optimization of pest management is conducted. Finally, a case study is carried out to verify the feasibility of the system for the growth state of pepper and pepper pests. The experimental results show that the virtual and real interactive feedback of the pepper aphid management system is achieved. It can obtain prediction accuracy of 88.01% with the training set and prediction accuracy of 85.73% with the test set. The application of the prediction model to the decision-making objective function can improve economic efficiency by more than 20%. In addition, the proposed approach is superior to the manual regulatory method in pest management. This system prioritizes detecting population trends over precise species identification, providing a practical tool for integrated pest management (IPM).

An integrated intelligent intersection management and control system design

While a fair amount of study has been done recently on integrated management and control models for large-scale traffic systems, there is comparatively little information available regarding the design of management and control systems for small-scale intersections. This study aims to create the structural diagram of a management system for intersections and control technologies. It does this by designing three distinct systems: congestion prediction, traffic signal timing, and congestion flow dredging. This study searches the current state of common technology and chooses the intersection integrated management system by means of comparison. Fuzzy TOPSIS algorithm and ADP adaptive dynamic programming are chosen as the calculation techniques of signal lamp timing, while BP neural network and ATT-GCN network are chosen as the key algorithms of congestion prediction after thorough investigation. In the end, conventional traffic management techniques like forbidding left turns are used to direct cars that haven't arrived at the busy crossroads beforehand. This paper aims to select the best model for an intersection management system currently available from the common technologies. The idealized management system and its structure diagram are designed in a straightforward manner, and they will serve as a guide for future road and vehicular intersection design.

Design and implementation of embedded hardware in furniture control based on DSP

The continuous progress of embedded technology and Internet of Things technology has accelerated the arrival of the era of home intelligence. By designing a smart home control system based on embedded DSP technology, it can detect temperature, humidity, flammable gas leakage, smoke, fire, and other indicators in the home environment, and carry out intelligent control of the detected security risks. The core of the system is the smart home management terminal, which is responsible for the network interconnection inside and outside the home. Terminal hardware includes a high-speed DSP chip, various interface modules, and so on. The software adopts the C/OS-II operating system to realize multi-task processing. This paper briefly summarizes DSP technology and embedded technology, and proposes the composition of intelligent home control system based on embedded DSP technology, as well as the hardware and software design and implementation of the system. Through the smart home system, users can control home conditions and obtain public service information anytime and anywhere, which is highly practical.

Advancing Sustainability: Paper for Enhancing Eco-Friendly Methodology in the Shanghai Tower

This proposal aims to enhance the eco-friendly methods of the Shanghai Tower, a prominent building in Shanghai, by implementing additional sustainable practices. The building already incorporates various environmental friendly technologies, such as vertical green vegetation, intelligent building skin, natural ventilation, passive solar heating, and more. This proposal suggests further enhancements, including optimization natural lighting, rainwater collection, ice-need cold technology, on-site renewable energy utilization, and intelligent control systems. By implementing these measures, the Shanghai Tower can emerge as a pioneering model for other buildings, making significant contributions to environmental preservation, energy efficiency, and sustainable development in the region.

Design of wireless transmission system for intelligent agricultural greenhouse data acquisition based on modbus communication protocol

Abstract Traditional agricultural greenhouses rely too much on manual labor and have a poor ability to adjust the key factors that affect crop growth, such as temperature, humidity, and illumination, resulting in low production efficiency and unstable product quality. Based on the MODBUS communication protocol, a set of intelligent agricultural greenhouse data acquisition wireless transmission systems is designed in this paper. Intelligent instruments, sensors, wireless transmission equipment, and smart control equipment (PLC) have been introduced in agricultural greenhouses. These sensors and instruments automatically collect data and wirelessly transmit it to the intelligent control system in the control room, which is located a few kilometers away. The intelligent control system processes the data and generates various control instructions which are then transmitted to the executive mechanism in agricultural greenhouses for implementation. We complete automatic adjustment of temperature, humidity, illuminance, and other parameters and operations. The intelligent, large-scale, and unmanned agricultural greenhouses are realized. It greatly improves the production efficiency and product quality of crops.

Mechanistic indoor air quality model and data-driven calibration for intelligent HVAC AI control system through real-time data in underground buildings

Abstract Modeling the dynamics of indoor air quality (IAQ) in subway environments is challenging due to the complex interplay of variables like subway schedules, ventilation, and passenger numbers. This study developed a high-precision mechanistic model for IAQ management and intelligent HVAC control in underground buildings, focusing on Y-station. Global Sensitivity Analysis (GSA) highlighted the significance of the train piston factor during operational hours and the increased sensitivity of penetration and deposition factors when trains are not operational. The model, calibrated in real-time using a Genetic Algorithm (GA), exhibited remarkable accuracy in simulating PM2.5 levels, affirming its effectiveness in forecasting future air quality. The model adeptly captures the complexities of air quality dynamics, providing a comprehensive understanding of temporal IAQ variations. The result demonstrates the model’s efficacy as a tool offering a foundation for strategies to forecast IAQ and control the HVAC system in underground buildings.

Anti-pollution intelligent window control system based on air detection

Anti-pollution intelligent window control system based on air detection

Intelligent LED lighting control system design and realization

Intelligent LED lighting control system design and realization