Intelligent Lighting Control Research Articles

Research on the Application of Smart Home in Interior Design

Abstract: With the rapid development of science and technology, the smart home in the field of interior design came to prominence. This paper discusses the application of smart home in interior design in the current environment. Smart home integration of advanced technology, covering intelligent lighting, home appliances, security, and environmental control systems. It provides designers with new creative ideas, challenges the traditional design mode, and promotes the perfect combination of technology, and art to meet people's pursuit of a high quality of life. For the residents, a smart home brings a new life experience, providing a comfortable, convenient, safe and personalized living environment. Therefore, this paper combines the new concepts, methods, and elements brought by smart home to interior design, analyses how smart home injects new vitality and innovative elements into interior design, and finds that the smart home opens up new dimensions, enriches the functional connotation, provides aesthetic inspiration, and changes the design process and method for interior design, that smart home is of great significance in promoting interior design innovation and change.

INFORMATION SYSTEM OF STREET LIGHTING CONTROL IN A SMART CITY

Context. In the context of the rapid development of technologies and the implementation of the concept of smart cities, smart lighting becomes a key element of a sustainable and efficient urban environment. The research covers the analysis of aspects of the use of sensors, intelligent lighting control systems with the help of modern information technologies, in particular such as the Internet of Things. The use of such technologies makes it possible to automate the regulation of lighting intensity depending on external conditions, the movement of people or the time of a day. This contributes to the efficient use of electricity and the reduction of emissions into the atmosphere. Objective. The purpose of the paper is to analyze the procedures for creating an information system as a tool for monitoring and evaluating the level of illumination in a smart city with the aim of improving energy efficiency, safety, comfort and effective lighting management. The implementation of a smart lighting system for Lviv will help improve energy efficiency and community safety. Method. A content analysis of scientific publications was carried out, in which the results of research on the creation of street lighting monitoring systems in real urban environments were presented. The collection and analysis of data on street lighting in the city, such as energy consumption, illumination level, lamp operation schedules, and others, was carried out. Machine learning methods were used to analyze data and predict lighting needs. Using the UML methodology, the conceptual model of the street lighting monitoring information system was developed based on the identified needs and requirements. Results. The role of data processing technologies in creating effective lighting management strategies for optimal use of resources and meeting the needs of citizens is highlighted. The study draws attention to the challenges and opportunities of implementing smart lighting in cities, maximizing the positive impact of smart lighting on modern urban environments. The peculiarities of the development and use of an information system for controlling street lighting in a smart city are analyzed. The potential advantages and limitations of using the developed system are determined. Conclusions. The project on the creation of an information system designed to provide an energy-efficient lighting system in a smart city will contribute to increasing security, particularly, ensuring the safety of the community through integration with security systems, reducing energy consumption, through minimizing the electricity usage in periods when the need for lighting is not necessary. It has been determined that to implement an information system for remote monitoring and lighting control in a smart city, it is advisable to consider the possibility of using a complex lighting control system. Calculations were made on the example of Lviv for the city’s lighting needs. The use of motion sensors to determine the need to turn on lighting was analyzed. A conceptual model of the information system was developed using the object-oriented methodology of the UML notation. The main functionality of the information system is defined.

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.

Adaptive Broad Deep Reinforcement Learning for Intelligent Traffic Light Control

Adaptive Broad Deep Reinforcement Learning for Intelligent Traffic Light Control

An IoT-Based Smart Street Lighting System to Lower Energy Consumption

Smart Lighting System is an automated and intelligent lighting control system that can be deployed across multiple locations or in one location, utilizing various IoT connection protocols, devices, and sensors. The system is able to intelligently adjust to changes in ambient light conditions, traffic patterns, and pedestrian activity through the integration of Internet of Things (IoT) technologies. IoT-enabled sensors are installed in every street light, creating a network that connects to a central control unit. Using real-time data, this system dynamically modifies street light brightness to maximize energy efficiency and improve safety. It provides a simulation and optimization tool for street light placement, considering factors such as street length, light distance, and the number of lights ahead. This tool aids in visualizing the power consumption implications of different configurations, promoting efficient urban planning. Demonstrates the energy-saving potential of the proposed system. As the street light intervals and the number of lights ahead decreases, the optimal street light power significantly reduces, leading to substantial energy savings. The system's ability to dim or turn off lights during low-traffic or well-lit periods contributes to reduced operational costs and enhanced sustainability.

Intelligent LED lighting control system design and realization

Intelligent LED lighting control system design and realization

НЕЙРОСЕТЕВОЕ ПРОГНОЗИРОВАНИЕ ПАРАМЕТРОВ ТРАНСПОРТНЫХ ПОТОКОВ В ИНТЕЛЛЕКТУАЛЬНЫХ СИСТЕМАХ СВЕТОФОРНОГО РЕГУЛИРОВАНИЯ

A method is proposed for neural network prediction of congestion on road sections outside the control zones of transport detectors in the systems of “flexible” transport flow control at intersections with the traffic light regulation. The paper describes the operating principle of the original control system based on fuzzy logic. To develop a universal neural network solution that can be used to predict traffic on most road sections with common characteristics without the need for training for each case separately, it is proposed to identify 9 main types of sections and, accordingly, 9 neural networks. Each typical neural network differs in the amount of the input data and parameters. All neural networks are trained on samples obtained from analyzing traffic flow on road sections of each type. Typical characteristics of road sections, architecture, and parameters of neural networks are determined. Neural networks are trained and tested on the model data. Using computer simulation methods, the author assesses the control quality of the original intelligent traffic light control system, which has complete information about the transport flow (however, it is difficult to implement in practice), as well as a control system that uses a set of data obtained using transport detectors and supplemented with a neural network forecast. According to the experiment results, a system with a neural network forecast of congestion on road sections is inferior in quality of control by no more than 1.92% compared to an idealized system, which gives grounds to consider this method applicable in intelligent transport flow control systems at intersections with traffic light regulation.

Optimizing Ambiance: Intelligent RGB Lighting Control in Structures Using Fuzzy Logic

Managing red–green–blue (RGB) lighting conditions within structures may evoke emotions and positively influence behavior. Intelligent RGB lighting systems based on environmental data measurements can substantially enhance the perception of comfort. This study presents a challenge that requires a holistic and integrated approach to implement an automatic RGB artificial lighting control system that can be utilized in various structures and indoor environments. Initially, the challenge spans the identification of environmental variables directly impacting comfort up to the careful selection of suitable sensors. The result is the development of a sophisticated and autonomous system that can adjust RGB lighting in real time, creating environments that are both comfortable and energy-efficient. This automated system fosters the creation of appropriate atmospheres across different contexts. The identification and monitoring of environmental variables are achieved through a neuro-fuzzy control mechanism, where fuzzy rules and membership functions are defined based on late positive potential timings and the influence of artificial lighting on human emotions. The outcomes from this study are an interconnected system capable of performing both online and offline operations to enable the monitoring of environmental variables and the efficient management of artificial lighting based on these metrics. A pilot study, with reference to an EEG wave registry system, yielded significant results. These tests had a statistically relevant result with an average frequency of approximately 9.8 Hz, indicative of a state of comfort among people. Despite a 10% deviation margin, 87% of measurements during the test remained consistent. This research study contributes to human behavior by fostering a relaxing environment and enabling a reduction in energy consumption through the use of efficient lighting. Moreover, the environment intention enables the creation of stimuli in three emotional states: activation, relaxation, and neutral, allowing behavioral adaptation to an intention to occur automatically in fluctuating environmental conditions.

Intelligent Traffic Light Control System

Abstract: The "Intelligent traffic light control system " project introduces an innovative approach to traffic management, aiming to expedite emergency vehicle passage. Utilizing Zigbee and Radio-Frequency Identification (RFID) technology, this system assigns a unique RFID tag to each vehicle, enabling precise tracking and control at traffic junctions. The scenarios involving emergency vehicle clearance, the system's responsiveness shines. When an emergency vehicle clearance approaches, the system rapidly receives the signal from zigbee transmitter associated with it and promptly triggers a green signal at the traffic junction. This prioritization ensures that emergency vehicles can navigate the traffic with minimal delays, potentially saving lives. Another remarkable feature of the system is stolen vehicle detection capability. In the event that a vehicle with a specific RFID tag is reported as stolen, the system is programmed to take action. It sends immediate SMS notifications to relevant authorities, such as law enforcement or the vehicle owner. This proactive response enhances the chances of recovering stolen vehicles and contributes to overall security. The hardware components of this system include Arduino Uno. Arduino Uno serves as the central control unit, processing RFID data and executing signal adjustments, while the GSM module facilitates real-time communication, enabling SMS alerts and notifications. The " Intelligent traffic light control system " project exemplifies how RFID technology and zigbee technology can revolutionize traffic management. By incorporating data analysis and automation, this system offers a multifaceted solution to common traffic-related challenges.

Intelligent Lighting Control Systems for Energy Savings in Hospital Buildings Using Artificial Neural Networks

Lighting control systems are essential in modern building automation and smart homes, efficiently managing illumination to enhance energy conservation and user comfort. This project tackles energy consumption challenges in hospital buildings by introducing Intelligent Lighting Control Systems (ILCS) that take natural light and occupancy into account, driven by Artificial Neural Networks (ANN) and diverse machine learning algorithms. In our study, we collected sensor data, processed it, and designed a lighting control system employing a feedforward neural network and various machine learning algorithms. Surprisingly, our research found that a linear regression algorithm surpassed the ANN-based system in this context. We implemented a prototype, tested it on hardware, and obtained the expected results. This research marks progress towards optimizing energy use in hospital buildings and contributing to sustainability endeavors. By combining ILCS and machine learning, it offers a promising approach for more efficient and eco-friendly lighting systems

Research on Optimization of University Classroom Lighting Environment Based on Computer Simulation

In order to improve the comfort of classroom users and improve the lighting environment of university classrooms, this paper puts forward different optimization schemes based on computer simulation. Dialux, a lighting environment design software, is used to numerically simulate the lighting environment of university classrooms. The classroom lighting scheme is optimized and different solutions are proposed. Through computer simulation, the effect of lighting is compared and analyzed, in order to find the most beneficial scheme for students’ mental health, which provides the basis for intelligent control of classroom lighting. The results show that the average illuminance decreases with the increase of suspension height, and the illuminance uniformity increases first and then decreases with the increase of suspension height. The solution of adjusting the projection direction and surface reflectivity of lamps is not suitable, and upgrading the type of lamps can solve the lighting environment problems and control glare. Increasing the number of lamps and adjusting the arrangement of lamps can improve the lighting environment, increase the average value but reduce the uniformity, but lead to a significant increase in energy consumption. Intelligent control based on computer simulation can solve the coordination problem of energy consumption and lighting effect. Through comparative analysis of the effects of different schemes on the classroom lighting environment in colleges and universities based on computer simulation, this paper provides data support for the selection of improved schemes, which is conducive to the selection of more suitable intelligent lighting control schemes, to ensure students’ mental health and work efficiency, and has certain reference significance for creating a healthy and comfortable classroom lighting environment.

A Fuzzy Based Intelligent Traffic Light Control (ITLC) Method: An Implementation in Ankara City

The escalating global population and increased vehicle usage have worsened traffic congestion in metropolitan areas, a significant urban challenge. Addressing this, adaptive traffic light control methods, especially at intersections, are being developed to improve traffic flow and reduce waiting times. This study significantly contributes to this field by implementing Fuzzy Logic in intelligent traffic light systems, focusing on Ankara's Polatlı Refik Cesur intersection. Using the SUMO simulation platform and Python programming, it analyzed waiting times and queue lengths. The initial phase used queue length for each intersection arm as an input. Fuzzy logic rules then determined the output, prioritizing street or phase order for optimal flow. The study further proposed an Adaptive Neuro-Fuzzy Inference System (ANFIS)-based control plan. ANFIS merges neural network capabilities with fuzzy logic, using waiting time and queue length as inputs to regulate the green light duration. Compared to existing traffic systems, this model showed a substantial improvement. It achieved a 36.5% reduction in waiting times, underlining the efficiency of the Fuzzy Logic-based method. This approach not only enhances traffic management but also contributes significantly to the literature on intelligent traffic light control systems. By addressing key urban traffic issues, the study paves the way for future advancements in traffic management technologies. The findings highlight the potential of combining advanced computational methods, like ANFIS, with traditional traffic control techniques to optimize urban traffic flow, offering a blueprint for similar challenges in other metropolitan areas.

Development and Assessment of Internet of Things-Driven Smart Home Security and Automation with Voice Commands

With the rapid rise of digitalization in the global economy, home security systems have become increasingly important for personal comfort and property protection. The collaboration between humans, the Internet of Things (IoT), and smart homes can be highly efficient. Interaction considers convenience, efficiency, security, responsiveness, and automation. This study aims to develop and assess IoT-based home security systems utilizing passive infrared (PIR) sensors to improve user interface, security, and automation controls using voice commands and buttons across different communication protocols. The proposed system incorporates controls for lighting and intrusion monitoring, as well as assessing both the functionality of voice commands and the precision of intruder detection via the PIR sensors. Intelligent light control and PIR intruder detection with a variable delay time for response detection are unified into the research methodology. The test outcomes examine the average effective response time in-depth, revealing performance distinctions among wireless fidelity (Wi-Fi) and fourth- and fifth-generation mobile connections. The outcomes illustrate the reliability of voice-activated light control via Google Assistant, with response accuracy rates of 83 percent for Thai voice commands and 91.50 percent for English voice commands. Moreover, the Blynk mobile application provided exceptional precision regarding operating light-button commands. The PIR motion detectors have a one hundred percent detection accuracy, and a 2.5 s delay is advised for PIR detection. Extended PIR detection delays result in prolonged system response times. This study examines the intricacies of response times across various environmental conditions, considering different degrees of mobile communication quality. This study ultimately advances the field by developing an IoT system prepared for efficient integration into everyday life, holding the potential to provide improved convenience, time-saving effectiveness, cost-efficiency, and enhanced home security protocols.

Multi-Agent Attention Double Actor-Critic Framework for Intelligent Traffic Light Control in Urban Scenarios With Hybrid Traffic

In real-world urban environments, hybrid and disorder traffic brings new challenges for the intelligent traffic light control system (ITLCS). Apart from coordinating traffic flows around intersections, the ITLCS is responsive to ensuring high priority vehicles pass through intersections quickly. To this end, we formulate the multiple intersections' decision-making problem as a Semi-Markov game and propose a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">multi-agent attention double actor-critic (MAADAC)</i> framework to solve this game, integrating the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">options framework</i> with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">graph attention networks (GATs)</i> . Specifically, the options framework empowers agents to learn to make a long sequence of satisfactory decisions, such as keeping a reasonable phase for a short period to ensure high priority vehicles pass through intersections quickly. Besides, we adopt GATs to capture graph-structure mutual influences among agents. We set up a simulator based on real-world city road networks and conduct extensive experiments to evaluate the performance of MAADAC. The experimental results show that MAADAC can reduce high priority vehicles' waiting time in the interval of 18.16%-38.14% versus the density of vehicles in real-world urban scenarios over several state-of-the-art approaches. Also, our framework can guarantee the passing efficiency of high priority vehicles under various traffic conditions with the change in the proportion of high priority vehicles.

DERLight: A Deep Reinforcement Learning Traffic Light Control Algorithm with Dual Experience Replay

&lt;p&gt;In recent years, with the increasingly severe traffic environment, most cities are facing various traffic congestion problems, and the demand for intelligent regulation of traffic signals is also increasing. In this study, we propose a new intelligent traffic light control algorithm, dual experience replay light (DERLight), which innovatively and efficiently designs a dual experience replay training mechanism based on the classic deep Q network (DQN) framework and considers the dynamic epoch function. As results show that compared with some state-of-the-art algorithms, DERLight can shorten the average travel time of vehicles, increase the throughput at intersections, and also speed up the convergence of the network. In addition, the design of this algorithm framework is not only limited to the field of intelligent transportation, but also has transferability for some other fields.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;

An Electrothermally Actuated Microshutter Array With Enhanced Power Efficiency for Intelligent Lighting Control

An Electrothermally Actuated Microshutter Array With Enhanced Power Efficiency for Intelligent Lighting Control

Research on Intelligent Light Control System Based on Speech Recognition

Research on Intelligent Light Control System Based on Speech Recognition

ПРИМЕНЕНИЕ ИНТЕЛЛЕКТУАЛЬНЫХ СИСТЕМ УПРАВЛЕНИЯ ОСВЕЩЕНИЕМ ДЛЯ ПОВЫШЕНИЯ ЭНЕРГОЭФФЕКТИВНОСТИ

The article discusses the use of intelligent lighting control systems to improve energy efficiency. It describes the main components of such systems, modern technologies and methods of lighting control, as well as examples of their application in various industries. It analyzes methods for assessing energy efficiency and economic benefits from the implementation of intelligent systems. Particular attention is paid to technical and technological challenges, ethical and social aspects of using such systems. A forecast is made for the development of intelligent lighting control systems and possible innovations in this area are considered.

Smart Three-Phase Electrical Panel Based on IoT Integration and Intelligent Lighting Control

In the quest for efficient power distribution, this article explores the design and implementation of a smart three-phase electrical panel that seamlessly integrates Internet of Things (IoT) technology. The core of this innovation lies in the utilization of NodeMCU, coupled with Blynk platform connectivity, providing users with unprecedented control and monitoring capabilities. Alongside IoT functionality, the panel incorporates a manual push button for localized control and a photocell sensor, linked to a contactor for intelligent lighting management based on ambient conditions. The article delves into the intricacies of the design, outlining the components, implementation steps, and the dual modes of operation – online mode for remote control and offline mode for local functionality. The presented smart panel not only marks a significant advancement in power distribution but also serves as an example for the future of intelligent electrical systems.

Appropriate Nitrogen form Ratio and UV-A Supplementation Increased Quality and Production in Purple Lettuce (Lactuca sativa L.)

Purple lettuce (Lactuca sativa L. cv. Zhongshu Purple Lettuce) was chosen as the trial material, and LED intelligent light control consoles were used as the light sources. The purpose was to increase the yield and quality of purple lettuce while lowering its nitrate level. By adding various ratios of NO3−-N and NH4+-N to the nutrient solution and 20 µmol m−2 s−1 UV-A based on white, red, and blue light (130, 120, 30 µmol m−2 s−1), the effects of different NO3−-N/NH4+-N ratios (NO3−-N, NO3−-N/NH4+-N = 3/1, NH4+-N) and UV-A interaction on yield, quality, photosynthetic characteristics, anthocyanins, and nitrogen assimilation of purple lettuce were studied. In order to produce purple lettuce hydroponically under controlled environmental conditions, a theoretical foundation and technological specifications were developed, taking into account an appropriate UV-A dose and NO3−-N/NH4+-N ratio. Results demonstrate that adding a 20 µmol m−2 s−1 UV-A, and a NO3−-N/NH4+-N treatment of 3/1, significantly reduced the nitrate level while increasing the growth, photosynthetic rate, chlorophyll, carotenoid, and anthocyanin content of purple lettuce. The purple leaf lettuce leaves have an enhanced capacity to absorb nitrogen. Furthermore, plants have an acceleration of nitrogen metabolism, which raises the concentration of free amino acids and soluble proteins and promotes biomass synthesis. Thus, based on the NO3−-N/NH4+-N (3/1) treatment, adding 20 µmol m−2 s−1 UV-A will be helpful in boosting purple lettuce production and decreasing its nitrate content.