Application of Intelligent Technology in Building Electrical Lighting Project

The rapid development of intelligent technology has led to its introduction into the field of electrical lighting in buildings.It provides more technical support for system design,use,and management,and creates a comfortable and safe living environment.The adoption of intelligent technology enables the creation of an intelligent management system,where controllers and sensors are used to adjust the light source within the building,monitor and manage the lighting system in real time,monitor the energy consumption and safety of the system,and achieve the goal of energy saving and emission reduction.This paper briefly discusses the application and significance of intelligent technology in electrical lighting and puts forward design ideas to optimize electrical lighting and measures for lighting system management.

Conceptual Issues in the Qualification of Intelligent Buildings

The reality of global warming must have been settled by now while the incidence of same has in very recent times adopted unprecedented dimensions. The global community continues to look for ways to combat the impact of climate change and technology is looked upon to deliver the innovations that would ensure a better tomorrow today. Rapid advancement of Information Technology (IT), is now transforming the way we create and interact with the built environment with the notion of Intelligent Buildings (IBs) underscoring its main features. However, these IBs utilize systems that require energy, and fossil fuels are currently the world’s primary energy sources;they can also irreparably harm the environment, exacerbating climate change. What then is the true essence of IBs? This paper, through review of existing literature, attempts to explore some issues associated with the conceptualization of IBs, highlighting how they are similar with other notional options that deliver the same benefits but without the needed IT systems or the energy required in running them. It also discusses the need to focus on less energy demanding and management approaches at design or occupancy of buildings as a way to reduce the demand and thus consumption of fossil fuels across the world.

Development of intelligent buildings in Bahrain

There is a growing demand for high building performance,providing better energy efficiency as well as high standard living and workplace. Intelligent Buildings (IBs) will meet the needs of sustainable development in built environment. The Kingdom of Bahrain's construction industry has grown significantly in past years to support economic growth,other business sectors,and the aim to be one of the world's major financial centres. This paper aims at investigating current issues regarding intelligent buildings in the Kingdom of Bahrain. Case studies of three buildings,which are Bahrain Financial Harbour (BFH),Bahrain World Trade Centre (BWTC) Project and the Bahrain City Centre (BCC) Shopping Mall,have been carried out via structured observation,interview and questionnaire survey in Bahrain in 2007. Through this study,we conclude that the pace of construction in Bahrain and the Middle East is such that there are lots of scopes for IBs to develop further,both from the perspective of the number of IBs being built and also the sophistication of their specifications.

Design of Multi-Sensors Cooperation Control System for Intelligent Building

This paper presents a design of intelligent building control system based on multi-sensors. In order to achieve comfort, low carbon and energy conservation for buildings. The design uses STC89C52 chip, combined with monitoring data of Pyroelectric infrared microwave dual sensor, temperature sensors and light sensors to control the optical system, the temperature control system and the security system. It helps to achieve low carbon and energy conservation. In addition, the energy-saving effect of a medium-sized multimedia classroom was analyzed. We analyze the feasibility of the system. It is proved that the system is low cost, high yield and easy to operate, with strong engineering application value.

Intelligent Building Load Scheduling Based on Multi-Objective Multi-Verse Algorithm

In the multi-objective of intelligent building load scheduling, aiming at the problem of how to select Pareto frontier scheme for multi-objective optimization algorithm, the current optimal scheme mechanism combined with multi-objective multi-verse algorithm is used to optimize the intelligent building load scheduling. The update mechanism is changed in updating the position of the universe, and the process of correction coding is omitted in the iterative process of the algorithm, which reduces the com-putational complexity. The feasibility and effectiveness of the proposed method are verified by the optimal scheduling experiments of residential loads.

Discussion on the Intelligent Design of Ultra-Low Energy Consumption Passive Buildings

With the continuous development of science and technology and the gradual improvement of modem building technology, people pay more and more attention to the introduction of advanced technology in architectural design, such as the application of intelligent technology. With the increasingly severe environmental situation, people are increasingly demanding the environmental performance and green performance of buildings. The establishment of ultra-low energy consumption passive buildings has become one of the key construction contents of construction projects. This paper mainly analyzes the design points and architectural forms of related buildings from the perspective of intelligent control.

Fault Diagnosis of an Intelligent Building Facility Using Bayesian Networks

There is great significance to diagnose the fault of an intelligent building facility for fault controlling, repairing, eliminating and preventing. As an example, this paper established a Bayesian networks model f or fault diagnosis of the refrigeration system of an intelligent building facility, gave the networks parameters, and analyzed the reasoning mechanism. Based on the model, some data was analyzed and diagnosed by adopting Bayesian networks reasoning platform GeNIe. The result shows that the diagnosis effect is more comprehensive and reasonable than the other method.

Deep Learning for Multivariate Prediction of Building Energy Performance of Residential Buildings

In the quest to minimize energy waste,the energy performance of buildings(EPB)has been a focus because building appliances,such as heating,ventilation,and air conditioning,consume the highest energy.Therefore,effective design and planning for estimating heating load(HL)and cooling load(CL)for energy saving have become paramount.In this vein,efforts have been made to predict the HL and CL using a univariate approach.However,this approach necessitates two models for learning HL and CL,requiring more computational time.Moreover,the one-dimensional(1D)convolutional neural network(CNN)has gained popularity due to its nominal computa-tional complexity,high performance,and low-cost hardware requirement.In this paper,we formulate the prediction as a multivariate regression problem in which the HL and CL are simultaneously predicted using the 1D CNN.Considering the building shape characteristics,one kernel size is adopted to create the receptive fields of the 1D CNN to extract the feature maps,a dense layer to interpret the maps,and an output layer with two neurons to predict the two real-valued responses,HL and CL.As the 1D data are not affected by excessive parameters,the pooling layer is not applied in this implementation.Besides,the use of pooling has been questioned by recent studies.The performance of the proposed model displays a comparative advantage over existing models in terms of the mean squared error(MSE).Thus,the proposed model is effective for EPB prediction because it reduces computational time and significantly lowers the MSE.

智算中心IB及RoCE网络技术探究

增强智算中心的网络可靠性、降低网络传输中的时延,是提升GPU集群算力水平的关键。因此,RDMA成为了智算中心的关键网络技术。本文详细论述了RDMA中智算中心参数网络普遍使用的IB和RoCE网络技术,并对其组网方式进行了探讨研究。对于智算中心需要极致性能的大模型训练场景时,建议采用IB组建参数网络集群。只需普通性能的大模型训练或中小模型训练场景时,考虑成本问题,可优先采用RoCE组建参数网络。

Employing Computational Intelligence to Generate More Intelligent and Energy Efficient Living Spaces

Our living environments are being gradually occupied with an abundant number of digital objects that have networking and computing capabilities. After these devices are plugged into a network, they initially advertise their presence and capabilities in the form of services so that they can be discovered and, if desired, exploited by the user or other networked devices. With the increasing number of these devices attached to the network, the complexity to configure and control them increases, which may lead to major processing and communication overhead. Hence, the devices are no longer expected to just act as primitive stand-alone appliances that only provide the facilities and services to the user they are designed for, but also offer complex services that emerge from unique combinations of devices. This creates the necessity for these devices to be equipped with some sort of intelligence and self-awareness to enable them to be self-configuring and self-programming. However, with this ＂smart evolution＂, the cognitive load to configure and control such spaces becomes immense. One way to relieve this load is by employing artificial intelligence （AI） techniques to create an intelligent ＂presence＂ where the system will be able to recognize the users and autonomously program the environment to be energy efficient and responsive to the user＇s needs and behaviours. These AI mechanisms should be embedded in the user＇s environments and should operate in a non-intrusive manner. This paper will show how computational intelligence （CI）, which is an emerging domain of AI, could be employed and embedded in our living spaces to help such environments to be more energy efficient, intelligent, adaptive and convenient to the users.

Integrated Building Envelope Design Process Combining Parametric Modelling and Multi-Objective Optimization

As an important element in sustainable building design, the building envelope has been witnessing a constant shift in the design approach. Integrating multi-objective optimization(MOO) into the building envelope design process is very promising, but not easy to realize in an actual project due to several factors, including the complexity of optimization model construction, lack of a dynamic-visualization capacity in the simulation tools and consideration of how to match the optimization with the actual design process. To overcome these difficulties, this study constructed an integrated building envelope design process(IBEDP) based on parametric modelling, which was implemented using Grasshopper platform and interfaces to control the simulation software and optimization algorithm. A railway station was selected as a case study for applying the proposed IBEDP, which also utilized a grid-based variable design approach to achieve flexible optimum fenestrations. To facilitate the stepwise design process, a novel strategy was proposed with a two-step optimization, which optimized various categories of variables separately. Compared with a one-step optimization,though the proposed strategy performed poorly in the diversity of solutions, the quantitative assessment of the qualities of Pareto-optimum solution sets illustrates that it is superior.

Vision-based Recognition Algorithm for Up-To-Date Indoor Digital Map Generations at Damaged Buildings

When firefighters are engaged in search and rescue missions inside a building at a risk of collapse,they have difficulty in field command and rescue because they can only simplymonitor the situation inside the building utilizing old building drawings or robots.To propose an efficient solution for fast search and rescue work of firefighters,this study investigates the generation of up-to-date digital maps for disaster sites by tracking the collapse situation,and identifying the information of obstacles which are risk factors,using an artificial intelligence algorithm based on low-cost robots.Our research separates the floor by using the mask regional convolutional neural network(R-CNN)algorithm,and determines whether the passage is collapsed or not.Then,in the case of a passage that can be searched,the floor pattern of the obstacles that exist on the floor that has not collapsed is analyzed,and obstacles are searched utilizing an image processing algorithm.Here,we can detect various unknown as well as known obstacles.Furthermore,the locations of obstacles can be estimated using the pixel values up to the bounding box of an existing detected obstacle.We conduct experiments using the public datasets collected by Carnegie Mellon university(CMU)and data collected by manipulating a low-cost robot equipped with a smartphone while roaming five buildings in a campus.The collected data have various floor patterns for objectivity and obstacles that are different from one another.Based on these data,the algorithm for detecting unknown obstacles of a verified study and estimating their sizes had an accuracy of 93%,and the algorithm for estimating the distance to obstacles had an error rate of 0.133.Through this process,we tracked collapsed passages and composed up-to-date digital maps for disaster sites that include the information of obstacles that interfere with the search and rescue work.

The Development of a Smart Mobile App for Building Façade Defects Inspections

The manual visual inspections of façade building defects are posing a high and increasing cost for building asset managers,particularly when inspections delay projects or require asset outages,visits to decommissioned sites or work within hazardous environments.This paper reports on the development,testing and delivery of a working mobile app prototype to facilitate the inspections and documentation of building facade condition monitoring.The work presented builds upon the development of an online platform for remote building inspection based on the integration of methodologies and tools,including VR(virtual reality),and digital photogrammetry to collect real-time data that support automated decision making.The mobile app:(i)allows the user to import 3D models and 2D building plans;(ii)provides the means of first-person exploration of models via a VR headset;and(iii)captures,records and catalogues images of façade defect types,and the date and time.An inspection case study was used to demonstrate and evaluate the mobile app prototype.The Building Inspector app allows building professionals to manage inspections and to track past and ongoing monitoring of the condition of building façades.

Analysis on Reformation of Digital Management Education of Prefabricated Construction Project Under the Background of Intelligent Construction

Prefabricated building is an important building development mode at present.It can improve the development speed and production efficiency of the construction industry,reduce the labor intensity of construction,play an important role in the development as well as the modernization of the construction industry.Due to the late development of prefabricated buildings,the talent training of prefabricated buildings lags behind,causing the development of prefabricated buildings to not achieve its required target.Therefore,the characteristics of intelligent construction need to be incorporated,the education of prefabricated buildings needs to be reformed,the education regarding digital management of prefabricated buildings should be improved,and more professional talents should be nurtured.

基于BIM技术与模拟退火算法的村镇轻钢框架结构智能设计方法

传统村镇住宅结构设计需要进行大量的人工试算与重复建模,而受制于建设成本,村镇住宅无法像城镇住宅一样通过设计师进行专业的结构设计与验算,其安全性与经济性均难以满足要求。为此,提出一种村镇轻钢框架结构智能设计方法,包括智能建模与智能优化两个环节。基于图层自动识别算法、光学字符识别技术、自适应分块算法提出村镇轻钢框架结构BIM智能建模方法,包括图层识别、轴文本数据提取、墙体轮廓提取等,智能建模结果基本满足实际工程要求。基于提出的两阶段模拟退火算法给出村镇轻钢框架结构的智能优化方法,优化速度较快,优化效果良好。通过实际工程案例对提出的智能设计方法进行验证,结果表明,提出的村镇轻钢框架结构智能设计方法具有可行性,与传统的人工设计方法相比,设计周期可缩短70%以上,材料用量、结构设计指标接近人工设计结果。

AI视域下图书馆视听资源智慧化加工的探索

新技术环境下网络视听服务蓬勃兴起,深刻影响和推动公共文化服务行业的变革与发展。视听资源是图书馆立体馆藏资源体系的重要组成部分,与图书馆传统的平面化文献资源相比,其立体化的资源呈现形式更具感染力、亲和力和传播力。论文探讨人工智能技术在图书馆视听资源的应用场景、实现功能,特别对人工智能技术在视听资源内容自动化识别、知识内容发现、知识体系构建、知识关联服务等方面的探索与应用进行了探讨,以期为其他机构在拓展智慧图书馆多维服务领域上开辟新的应用范围提供思路与参考。

建筑智能化系统顶层设计方法探讨

通过自上而下地展开,吸收质量功能配置的思想,对建设过程中影响智能化系统的因素进行统筹考量。通过逐层迭代的方式,直观展示不同技术方案对业主满意度的影响,为建筑设计提供可比较的方案效益评价,也为设计明确后续技术路径。

铁路三维电子沙盘搭建及其应用研究

为解决目前铁路行业电子沙盘存在的表达方式不一、应用内容单一、数据泄露风险大、数据加载效率低等问题,研究海量地理地质及BIM模型的数据处理、管理和应用方法,建立以BIM+GIS为技术核心的标准化电子沙盘,从而更好地服务于铁路设计和建造。根据鲁南高铁、拉林铁路、成渝中线高铁等项目实践总结,在中国铁路BIM联盟和国铁集团相关标准基础上提出了面向电子沙盘应用的地形、影像、BIM模型、专题图的数据制作标准,从坐标转换、数据转换、数据切片、知识图谱建立、模型发布、前端数据汇集的方法完成了电子沙盘的搭建。最后,在电子沙盘中实现了百公里级、多专业数据的在线高效组织和管理,提出的方法和标准适用于铁路设计、建设过程的管理和分析应用。