Explore the vast range of titles available.

High-sensitivity early fire detection is an essential prerequisite to intelligent building safety. However, due to the small changes and erratic fluctuations in environmental parameters in the initial combustion phase, it is always a challenging task. To address this challenge, this paper proposes a hybrid feature fusion-based high-sensitivity early fire detection and warning method for in-building environments. More specifically, the temperature, smoke concentration, and carbon monoxide concentration were first selected as the main distinguishing attributes to indicate an in-building fire. Secondly, the propagation neural network (BPNN) and the least squares support vector machine (LSSVM) were employed to achieve the hybrid feature fusion. In addition, the genetic algorithm (GA) and particle swarm optimization (PSO) were also introduced to optimize the BPNN and the LSSVM, respectively. After that, the outputs of the GA-BPNN and the PSO-LSSVM were fused to make a final decision by means of the D-S evidence theory, achieving a highly sensitive and reliable early fire detection and warning system. Finally, an early fire warning system was developed, and the experimental results show that the proposed method can effectively detect an early fire with an accuracy of more than 96% for different types and regions of fire, including polyurethane foam fire, alcohol fire, beech wood smolder, and cotton woven fabric smolder.

With the continuous development, computer technology promotes the rapid development of science, and the Internet of things (hereinafter referred to as IOT) technology is the product of the rapid development of science and technology. With the increasing popularity of IOT technology, modern technology has been gradually applied to various fields. Building intelligent system has also begun to apply IOT technology, which has become an important area of building intelligent system. A complete intelligent building system is composed of many different networks, which requires IOT technology to play an important role. Building intelligent system construction needs to rely on a variety of technologies, such as network system, information system, lighting system and security defense system, which will achieve various functional systems. Therefore, IOT technology can not only achieve the goal of unattended, but also reduce the cost of building management. Through IOT technology, we promote the communication between things and people. Firstly, this paper analyzes IOT technology. Finally, this paper lists the application of IOT technology in intelligent building system

Fully distributed intelligent building systems can be used to effectively reduce the complexity of building automation systems and improve the efficiency of the operation and maintenance management because of its self-organization, flexibility, and robustness. However, the parallel computing mode, dynamic network topology, and complex node interaction logic make application development complex, time-consuming, and challenging. To address the development difficulties of fully distributed intelligent building system applications, this paper proposes a user-friendly programming language called SwarmL. Concretely, SwarmL (1) establishes a language model, an overall framework, and an abstract syntax that intuitively describes the static physical objects and dynamic execution mechanisms of a fully distributed intelligent building system, (2) proposes a physical field-oriented variable that adapts the programming model to the distributed architectures by employing a serial programming style in accordance with human thinking to program parallel applications of fully distributed intelligent building systems for reducing programming difficulty, (3) designs a computational scope-based communication mechanism that separates the computational logic from the node interaction logic, thus adapting to dynamically changing network topologies and supporting the generalized development of the fully distributed intelligent building system applications, and (4) implements an integrated development tool that supports program editing and object code generation. To validate SwarmL, an example application of a real scenario and a subject-based experiment are explored. The results demonstrate that SwarmL can effectively reduce the programming difficulty and improve the development efficiency of fully distributed intelligent building system applications. SwarmL enables building users to quickly understand and master the development methods of application tasks in fully distributed intelligent building systems, and supports the intuitive description and generalized, efficient development of application tasks. The created SwarmL support tool supports the downloading and deployment of applications for fully distributed intelligent building systems, which can improve the efficiency of building control management and promote the application and popularization of new intelligent building systems.

The feasibility of achieving sustainable building development, an eco-friendly environment, and building investment conservation by integrating technological intelligence in buildings is highly viable. Intelligence features are, therefore, increasingly being incorporated in new designs and existing buildings to enhance the useful life, productivity and satisfaction of occupants, and a greener environment. This article evaluates the use of intelligent building systems in Nigeria. Primary data were obtained with the use of structured questionnaires that were self-administered to construction professionals in the private and public sectors in Lagos State. Data collected were analysed using descriptive and inferential statistics. Findings established that 90.24% of the respondents were aware of intelligent building systems, while practitioners who have worked or were working on buildings with intelligent features were limited to 51.2%. Approximately 64.60% of the respondents have used intelligent buildings previously, but only thirteen (13) notable buildings were identified to have employed intelligent building systems to a reasonable extent in the study area. The features of intelligent buildings with top level of awareness were CCTV system; access control and locks (mean = 3.96); alarms and alerts (mean = 3.92); HVAC system (mean = 3.90), and fire alarm system (mean = 3.89). Features with high level of utilisation include lighting system, which was the most utilised feature (mean = 3.57); fire alarm system (mean = 3.48); access control and lock; CCTV system (mean = 3.45), and HVAC system (mean = 3.43). The results showed that most of the features with a high level of awareness were also those with a high level of utilisation. The study established that the level of awareness and utilisation of intelligent building systems in the study area is high, but full adoption of the system is still low.  

We propose a novel dynamic gradient descent (DGD) framework integrated with reinforcement learning (RL) for AI-enhanced indoor environmental simulation, addressing the limitations of static optimization in dynamic settings. The proposed method combines a hybrid optimizer—stochastic gradient descent with momentum and adaptive learning rates—with an RL-driven meta-controller to dynamically adjust hyperparameters in response to real-time environmental fluctuations. The core innovation lies in the time-varying optimization landscape, where a Transformer-based policy network modulates the learning process based on a reward signal that balances prediction accuracy and parameter stability. Furthermore, the system employs a multilayer perceptron predictor trained on computational fluid dynamics-augmented data to model nonlinear thermal–airflow interactions, replacing conventional lumped-parameter models. The integration of these components enables autonomous adaptation to short-term disturbances (e.g., occupancy changes) and long-term drifts (e.g., seasonal variations) without manual recalibration. Experiments demonstrate that the framework significantly improves simulation accuracy and control efficiency compared to existing methods. The contributions include a unified adaptive optimization-RL architecture, a closed-loop hyperparameter control mechanism, and scalable implementation on GPU-accelerated hardware. This work advances the state-of-the-art in intelligent building systems by enabling self-tuning simulations for real-world dynamic environments.

The communication technology ZigBee has been widely adopted in wireless sensor networks (WSNs) for a wide range of industrial applications. However, although ZigBee provides low-power, low-cost mesh networking, it cannot guarantee steady and predictable network performance as channels are time-variant and highly attenuated by man-made obstacles. The networks also suffer from interference, especially in the important 2.4 GHz industrial, scientific, and medical (ISM) band. These degraded channel characteristics increase the number of hops, thus increasing both the packet error rate and transmission delays. In this paper, we report the deployment of a ZigBee-based WSN inside an existing building duct system utilized for intelligent waste collection in an industrial environment. The Received Signal Strength (RSS) and path losses were measured, revealing that the duct communication channel acts as a very effective waveguide, providing a more reliable and consistent network performance than conventional free space channels.

Introducing integrated, automatic control to buildings operating with the environmental quality management (EQM) system, we found that existing energy models are not suitable for use in integrated control systems as they poorly represent the real time, interacting, and transient effects that occur under field conditions. We needed another high-precision estimator for energy efficiency and indoor environment and to this end we examined artificial neural networks (ANNs). This paper presents a road map for design and evaluation of ANN-based estimators of the given performance aspect in a complex interacting environment. It demonstrates that in creating a precise representation of a mathematical relationship one must evaluate the stability and fitness under randomly changing initial conditions. It also shows that ANN systems designed in this manner may have a high precision in characterizing the response of the building exposed to the variable outdoor climatic conditions. The absolute value of the relative errors ( M a x A R E ) being less than 1.4% for each stage of the ANN development proves that our objective of monitoring and EQM characterization can be reached.

Since 1985, various intelligent technologies have introduced and policy-driven in Taiwan, the intelligent building system has gradually formed catering for the characteristics of Taiwan. Therefore, this study proceeds to explore current status and survey strategies of policy implementation for intelligent buildings development in Taiwan, and further obtained countermeasures for building developers and public sectors in implementation intelligent buildings. The study firstly proceeds to relevant literature review and analysis of intelligent buildings both domestic and foreign, and through statistic and analysis in case studies and content analysis method, acquires Taiwan intelligent building evaluation indicator items for normal and non-normal application of intelligent building system by building developers and public sectors. Through study found four evaluation indicator items of normal application, and one evaluation indicator items of non-normal application, as well as building developers selected quantity and items of evaluation indicator fewer than public sectors. It is expected that study result will providing assessment and references to building developers and public sectors for the direction and implement strategy of intelligent building development in future, and further promote the realization goal of sustainable intelligent green building in Taiwan.

This study presents a review of smart light-emitting diode (LED) lighting systems applied to smart buildings. The study is focused on drivers, protocols, technologies, communication networks and applications. An extended overview of the methodologies used for LED lighting control in smart buildings is addressed. The study also presents an integrated architecture able to achieve the necessary services and control methodologies for intelligent building energy management system for LED lightings systems in smart buildings.

The internet of things (IOT) is the key direction of a new generation of information technology innovation breakthrough in China, and it contains a huge space for the application of innovation. In the past few years, our internet of things technology (IOTT) research and application promotion has achieved initial success in many areas, especially in the intelligent construction fields. In this paper, we set the breakthrough point of the basic construction projects in the important cultural industry in Shandong Province --- Mass Media Building to analyze the application features of IOTT in the new modern intelligent building, and we explain the implementation patterns of each module of Weak Current System in detail.