Intelligent Control System Research Articles

Airport Express Passenger Flow Prediction Based on “Time-Feature” Cooperative Attention

Accurate prediction of airport express passenger flow is the basis for realizing intelligent, refined and efficient management and control of the airport rail transit system, and is of great significance to improving airport service levels and operational efficiency. Due to the numerous influencing factors that overlap with each other, and the complex mechanism of factors affecting passenger flow timing, accurate prediction of airport express passenger flow is extremely challenging. This paper proposes an airport express rail passenger flow prediction model based on the "time-feature" collaborative attention mechanism, which achieves accurate capture of the impact of multi-dimensional factors on airport express rail passenger flow in different time series. Experiments were conducted based on actual passenger flow data of the Beijing Capital International Airport Express Rail Link, and the results showed the effectiveness of the proposed method.

Research and Analysis of a Smart System for Intelligent Control of PCM Water Storage Tank for Stock Farm Needs

Energy efficiency is a very important component in modern systems. Modern research examines innovative methods and techniques for increasing the efficiency of heat generation systems. A popular approach is to apply combined methods of using energy from different heat sources. This article describes а basic algorithm for developing an intelligent control for a system of hot-water storage tanks with PCM adapted for a stock farm's needs. The main components of the system are described along with the PCM advantages. The prerequisites for the system’s operation are analyzed and adapted for its management. The developed algorithm and a control system are tested in a simulated environment and are presented conclusions of the system’s energy efficiency improvement.

A hierarchical model of the information environment for railway power supply system critical objects control

It is proposed to consider the hierarchical model of the information environment as a structure according to its functions (implemented by the software part) and architecture (responsible for effective implementation of functional capabilities). It is proposed to improve the information management system of Ukrzaliznytsia’s critical power supply facilities, based on the developed hierarchical model of the information environment, which takes into account the presence of Ukrzaliznytsia’s own generation sources by integrating the appropriate control subsystem into the information system structure. The model emphasizes the integration of software and hardware components for data collection, pre-processing, and decision-making, contributing to a highly automated and intelligent control system. It proposes a multi-level structure incorporating artificial intelligence and advanced technologies to optimize electricity consumption and improve operational efficiency within the railway power supply system.

Design and Development of Intelligent Access Control System Integrating Hi3861 and K210

Design and Development of Intelligent Access Control System Integrating Hi3861 and K210

Dyna-PINN: Physics-informed deep dyna-q reinforcement learning for intelligent control of building heating system in low-diversity training data regimes

This paper introduces Dyna-PINN, a novel physics-informed Deep Dyna-Q (DDQ) reinforcement learning (RL) approach, designed to address the data-intensive training requirements and model-agnostic nature of the conventional model-free RL methods. The DDQ approach blends model-based and model-free elements to enhance both learning and decision-making processes. By utilizing a physics-informed neural network (PINN) based model, our method enriches the learning process with physical information, enhancing the agent's planning capabilities and leading to faster learning compared to conventional model-free RL methods like Deep Q-Network (DQN) in scenarios with low-diversity training data availability. Our results demonstrate that Dyna-PINN has 50% greater sample efficiency than DQN and outperforms rule-based control in terms of thermal discomfort. Due to physics incorporation, the Dyna-PINN implements a more logical and interpretable control policy. It shows consistently good performance compared to all control variants across low-diversity data scenarios, i.e., 6 weeks of building data, and in higher-diversity data regimes, i.e., 6 months of building energy data, demonstrating the value of physics incorporation into the RL training. Additionally, we present two other DDQ-based techniques, RC-DDQ and NN-DDQ, exploring the synergy between neural networks and physical data in intelligent control designs for building energy systems. Rigorous controller testing is performed using the Building Optimization and Testing Framework (BOPTEST), a high-fidelity simulator that closely represents a real building's operation. Through comprehensive comparisons and realistic simulations, our study underscores the effectiveness of incorporating physics-informed approaches into RL-based control strategies, paving the way for more efficient and robust building energy management systems.

Residual integral inverse reinforcement learning for intelligent self-healing control of unknown systems with actuator faults

Residual integral inverse reinforcement learning for intelligent self-healing control of unknown systems with actuator faults

Advancements in Optical Resonator Stability: Principles, Technologies, and Applications

This paper provides an overview of the study of optical resonant cavity stability, focusing on the relevant principles, key technological advances, and applications of optical resonant cavities in a variety of high-precision measurement techniques and modern science and technology. Firstly, the vibration characteristics, thermal noise, and temperature characteristics of the reference cavity are presented. Subsequently, the report extensively discusses the advances in key technologies such as mechanical vibration isolation, thermal noise control, and resistance to temperature fluctuations. These advances not only contribute to the development of theory but also provide innovative solutions for practical applications. Typical applications of optical cavities in areas such as laser gyroscopes, high-precision measurements, and gravitational wave detection are also discussed. Future research directions are envisioned, emphasising the importance of novel material applications, advanced vibration isolation technologies, intelligent temperature control systems, multifunctional integrated optical resonator design, and deepening theoretical models and numerical simulations.

Solid–liquid separation state control system for shale shaker based on computer vision

Shale shakers are crucial in drilling operations, separating solid–liquid mixtures, protecting equipment, improving efficiency, and supporting environmental protection and resource recovery. Typically, the solid–liquid separation status on shaker screens is assessed through manual inspection and experiential judgment, with the screen tilt angle adjusted manually. This method has inherent delays, failing to promptly detect and address screen surface abnormalities. To overcome this, a computer vision-driven intelligent control system has been proposed, enhancing drilling sieving operations’ efficiency. The system uses computer vision and servo motors to monitor and adjust the sieving state. Twin industrial cameras capture images, which undergo preprocessing like perspective correction, grayscale conversion, noise reduction, and illumination equalization. Local threshold segmentation and morphological analysis distinguish between liquid and solid states. The segmented images’ contours and area analysis precisely determine the separation state. Results are transmitted to motors via an industrial computer to adjust the sieve net angle. Servo motors on both sides manipulate the sliding block lifting mechanism for precise tilt adjustment. Experiments demonstrate that the method achieves an average accuracy of 93.635% in identifying mud edges. The system’s high efficiency and reliability ensure accurate identification and adjustment of the solid–liquid separation state, thereby optimizing drilling sieving workflows.

Research on “Playing Football” Type Roof Control in Fully-Mechanized Mining Face with a Super-Large Mining Height under the Background of 5G+ Big Data

With the increase of mining height at the working face, the influence range of roof fractures in the goaf increases, the advanced supporting pressure on the coal wall increases, ground pressure becomes more intense, and roof support becomes more difficult. Based on the analysis of ground pressure behavior in the first mining and caving stage, the normal mining stage, and the final mining breakthrough stage of the fully-mechanized mining face near 12,404, the relationship between conveyor current and coal speed is studied and compared. Based on the intelligent control system of the fully-mechanized mining face with a super-high mining height of 12,404 and the structure of the football team, the “playing football” roof control mode of the fully-mechanized mining face with super-high mining height under the background of 5G+ big data is put forward. The conclusions are as follows: In 12,404, the ground pressure was first mined. During normal mining, when the roof with a buried depth of more than 200 m is broken, the speed of the coal machine is kept within 12 m/min, and the full guard defends and controls the roof, pulling the lead frame through the area with severe ground pressure. When the roof is good, it is necessary to speed up the coal cutting and get rid of the pressure. When it is less than 200 m, it will overcome the local weighting, and show an offensive trend to speed up and increase production. In the final mining breakthrough stage, the speed of the coal machine should be controlled within 8 m/min, with attention to defense, guarding against roof leakage, and reducing waste rock.

Intelligent Rapid Asexual Propagation Technology—A Novel Aeroponics Propagation Approach

Various rapid propagation strategies have been discovered, which has facilitated large-scale plant reproduction and cultivar development. These methods, in many plant species, are used to rapidly generate large quantities (900 mini-tubers/m2) of high-quality propagule (free from contamination) at a relatively low cost in a small space. They are also used for plant preservation. This review article aims to provide potential applications for regeneration and clonal propagation. Plant propagation using advanced agrotechnology, such as aeroponics, is becoming increasingly popular among academics and industrialists. The advancement of asexual aeroponic propagation has been achieved through advancements in monitoring and control systems using IoT and smart sensor technology. New sensor technology systems have gained substantial interest in agriculture in recent years. It is used in agriculture to precisely arrange various operations and objectives while harnessing limited resources with minimal human intervention. Modern intelligent technologies and control systems simplify sensor data collection, making it more efficient than manual data collection, which can be slow and prone to errors. Specific ambient variables like temperature, humidity, light intensity, stock solution concentrations (nutrient water), EC (electrical conductivity), pH values, CO2 content, and atomization parameters (frequency and interval) are collected more effectively through these systems. The use of intelligent technologies provides complete control over the system. When combined with IoT, it aids in boosting crop quality and yield while also lowering production costs and providing data directly to tablets and smartphones in aeroponic propagation systems. It can potentially increase the system’s productivity and usefulness compared to the older manual monitoring and operating methods.

Fish feeding behavior quantification and automatic acoustic event detection: algorithmic optimization with deep learning

In recent years, the integration of sensor processing technologies with intelligent feeding control systems has become crucial for enhancing recirculating aquaculture systems (RAS). Intelligent feeding control significantly improves profitability and animal welfare in aquaculture by accurately monitoring feed intake and reducing waste. Acoustic methods have proven effective in automating feed monitoring, particularly in assessing feeding intensity. Automatic Audio Event Detection (AED) holds promise for identifying sound events associated with feeding activities, enabling precise monitoring of feeding behavior and estimation of feed consumption through frequency-based feeding signatures. Advancements in sensor technologies and computational capabilities enable real-time collection, processing, and analysis of acoustic data. However, handling high volumes of data with minimal latency and high accuracy poses challenges. To address these challenges, we propose a systematic approach: (1) Data preprocessing using wavelet transforms enhances efficiency and reduces computational overhead. (2) Data pruning focuses on informative features and reduces input data dimensionality, improving classification accuracy. (3) We adopt a weakly-labeled semi-supervised learning model with Convolutional Neural Networks (CNNs) to detect feeding events, improving classification performance. (4) Ensemble learning combines multiple classifiers to mitigate overfitting and enhance model generalization. Our methodology shows promise in accurately quantifying fish feeding behavior using acoustic signals.

Research on the Performance and Control Strategy of Electro-Hydraulic Servo System for Selective Hole Digging Tree Planter

Compared to agricultural environments, afforestation sites are more complex, often presenting issues such as undulating and uneven terrain. These conditions lead to instability in hole digging depth and plant spacing during continuous movement, and the hole shape may not meet expectations. Additionally, the hydraulic system exhibits slow response speed and long steady-state time, affecting the quality of sapling planting. To address these issues, this paper designs an intelligent planting control system for intermittent hole digging under continuous dynamic movement, based on a large tree planter. The focus is on studying the dynamic accuracy of the hole digging cylinder to resolve the instability of plant spacing and planting depth in actual planting processes. Firstly, a motion trajectory model of the intermittent hole digging mechanism is established to obtain the relationship between the displacement trajectory of the rotating cutter and the displacements of the floating cylinder and the hole digging cylinder. Secondly, a mathematical model of the electro-hydraulic servo system is established to control the dynamic accuracy of the hole digging operation. Finally, a Simulink simulation model of the system is established to analyze the performance indicators of the hydraulic system during operation using step and sinusoidal excitation signals. The test results show that the displacement of the hydraulic piston rod can ensure a linear extension trend within the range of 0 to 0.4 m, and the extension distance of the hole digging cylinder in the planting system is 0 to 0.35 m, ensuring linear change within this stroke. When the system’s extension command is 1 V, the actual output is 0.6 m, with a relative error of less than 10% compared to the simulation value, indicating that the control strategy can effectively improve the dynamic performance of the system. When the hydraulic system is in a steady-state extension state at 50 to 58.6 s, the relative error with the simulation value is 7.3%, meeting the “double ten indicators” requirement. The research results clearly verify the superior performance of the proposed intelligent control system, and the proposed control strategy has great potential in practical applications, promising to improve afforestation quality by stabilizing planting spacing and planting depth.

Application of low energy building thermal energy optimization based on image recognition and light sensors in urban green environment planning

The paper analyzes the application of thermal energy optimization scheme based on image recognition and light sensor technology in low-energy buildings, in order to improve building energy efficiency, reduce energy consumption and provide scientific basis for urban green environment planning. In this paper, image recognition technology is used to monitor the light change in the external environment of the building, and the indoor light intensity data is obtained by the light sensor in real time. Combined with big data analysis, building heat optimization model is established, intelligent control system is designed, and the heating and cooling system of the building is dynamically adjusted, so as to achieve efficient use of heat energy. The experimental results show that the thermal energy optimization method based on image recognition and light sensor has a good application prospect in low-energy buildings, which not only effectively reduces energy consumption, but also contributes to the sustainable development of urban green environment.

The design and implementation of a smart greenhouse control system

Abstract Against the dual backdrop of continuously improving agricultural production levels and the global scarcity of water resources, countries worldwide are actively exploring the topic of water conservation. The development of an intelligent irrigation control system could make a significant contribution to water conservation efforts, while also helping to increase crop yields. This project adopts a modular design approach, utilizing sensors to collect real-time environmental data surrounding the crops. If the data falls outside the predefined optimal range for crop growth, the system automatically initiates irrigation, cooling, and supplemental lighting. The hardware design primarily consists of a data perception module and a control terminal module. After testing, the system runs stably and can accurately obtain information such as soil moisture, light intensity and ambient temperature, and basically realize the digital intelligent control of crop growing environment.

Fuzzy control system design for landscape lamp of environment art based on multi-function

With the material needs that people constantly meet, the people begin to pursue the spiritual abundance, and the environmental art landscape lamp is thus born. At present, most of the landscape lamp controllers are realized through the full hardware circuit, and there are some defects such as troublesome installation, high maintenance cost and poor control effect. In view of this situation, this study uses the fuzzy control algorithm to establish the fuzzy control system of environmental art landscape lamps, aiming to optimize the accuracy and effect of light control. Firstly, the composition structure and lighting scene mode of the environmental art landscape lamp are researched, and then the intelligent light control system is designed by the adaptive fuzzy control algorithm. The results showed that the control accuracy of adaptive fuzzy control algorithm was above 95, and the running time of the system was within 1 minute. In addition, both experts and users scored above 9.5 in terms of satisfaction with the control system. To sum up, the light fuzzy control system designed in this study has good control performance and certain practical application value.

Research on intelligent control and emergency response system for power cable tunnels based on AI image recognition and ultra-wideband technology

Research on intelligent control and emergency response system for power cable tunnels based on AI image recognition and ultra-wideband technology

OPTIMIZATION OF HEAT CONSUMPTION IN CENTRAL HEATING SYSTEMS AT COMMERCIAL FACILITIES

Rising energy prices and stricter environmental regulations are exacerbating the problem of inefficient heat consumption in the commercial sector. Central heating systems of office buildings, shopping malls, and industrial complexes are often characterized by excessive thermal energy consumption, which leads to significant economic losses and a negative impact on the natural environment. The purpose of the study is to study the directions and trends of optimization in the field under consideration (taking into account modern technological capabilities, and economic factors). There are disagreements in the professional community about the priority of measures to improve energy efficiency: some experts prefer improving the thermal insulation of buildings, others — the introduction of intelligent control systems, and others — the integration of renewable energy sources. The present study demonstrates that the greatest effect is achieved with a systematic approach combining various initiatives. The conclusion is formulated that in the current conditions and the future, a comprehensive application of modern thermal insulation materials, highly efficient heating equipment, intelligent management mechanisms, and alternative energy sources is required. The article is of interest to engineers-designers of heating systems, energy managers of commercial facilities, specialists in energy efficiency of buildings, and heads of companies interested in optimizing operating costs and improving the environmental friendliness of their real estate.

The Application of Artificial Intelligence Technology in Mechanical Manufacturing and Automation

In order to achieve intelligent production and quality control, improve production efficiency and accuracy, the author proposes an application method of artificial intelligence technology in mechanical manufacturing and automation. The author aims to explore the specific application of artificial intelligence technology in the mechanical manufacturing industry and summarize the main advantages of automation technology. By analyzing these advantages, the aim is to provide targeted recommendations for future technological development. Meanwhile, the author aims to provide theoretical support for promoting the application of modern artificial intelligence technology and automation technology in the field of mechanical manufacturing. In the manufacturing process, intelligent production and quality control can be achieved, improving production efficiency and accuracy; In the field of automation, artificial intelligence technology can achieve intelligent control systems and autonomous decision-making, improving the flexibility and adaptability of production lines. Overall, the application of artificial intelligence technology has brought revolutionary changes to mechanical manufacturing, promoting the upgrading and development of the industry.

Theoretical basics of the self-learning system of intelligent locomotive decision support systems

Analysis of works in the field of artificial intelligence allows to make an assumption that today there is a sufficiently developed theoretical basis for the development of intelligent control systems for locomotive control. This will minimize the risks associated with the human factor on the railways. The paper presents the theoretical rationale for the development of a knowledge base for intelligent locomotive control systems. The approach and structure of the self-learning system of intelligent DSS is proposed, the advantage of which is the presence of a fuzzy classifier that works according to the set criteria and determines a fuzzy image of the current train situation. Learning a fuzzy classifier consists in finding a vector K that minimizes the distance between the results of logical inference and experimental data from the sample. The knowledge base is implemented using linguistic variables formalized by methods of fuzzy logic. The use of linguistic values makes it possible to design the base using the usual language of communication, which greatly simplifies both the design process itself and the analysis of the system's performance. Also, the knowledge base has the possibility of constant self-improvement. This happens in two ways. The first is by adding new rules to the knowledge base in case the current situation does not match the existing ones in the base, in which case an additional rule is created and checked for adequacy. The second way is a mechanism for ranking rules in the knowledge base. If the control action of the locomotive driver coincided with the recommendation of DSS in the current situation, then the rating of this recommendation (rule) increases, and in the future the rule selection algorithm will choose one or another control action for the current situation that has the highest rating (that is, it has already been verified several times person). The experiment has shown that the use of intelligent DSS has positive results. On average, the DSS made the correct train control decisions faster than the locomotive driver.

Innovative technologies of rail transport used by the Armed Forces of the Republic of Poland

Innovation in rail transport has a wide range of applications, which includes customer contact, train moni-toring with modern internet-connected sensors and data analysis, as well as the use of artificial intelli-gence for route optimization, customer service management and vehicle maintenance. The presented is-sues constitute a research niche in the field of the use of innovations in military rail transport, which en-courages research in this area. The article describes innovative technologies of rail transport used by the Armed Forces of the Republic of Poland. The subject of the research included in the article are the latest innovations in the field of rail transport technology and the analysis of their benefits and potential use by the Polish Armed Forces. These include: intelligent control systems, infrastructure maintenance, innova-tive designs of trains and wagons. The aim of the article is to identify the advantages of innovative tech-nologies and their potential application in the Polish Armed Forces. In order to achieve the objectives of the article outlined in this way, it is necessary to answer the following research questions: What innovative technologies of rail transport are used by the Polish Armed Forces? What are the prospects for the devel-opment of innovative rail transport technologies for defence purposes in Poland? What innovative solu-tions concerning the railway infrastructure in Poland could improve the safety of movements of troops by rail? The research hypothesis was adopted in the article: Innovative solutions in rail transport used by the Polish Armed Forces will effectively support the implementation of transports in the movement and transport subsystem (M&T). In order to verify the hypothesis, the following research methods were used: theoretical methods (analysis, synthesis, abstraction, inference, generalization) and the method of expert interview with representatives of the movement and transport subsystem. The presented approach allowed to analyse the problem from different perspectives, which makes it possible to conduct detailed research on this important and current issue.