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Research on intelligent Automatic Building Model Based on computer BIM System

Research on Intelligent Automatic Building Model Based on Co...

引用

computers, Information Processing and Advanced Education (CIPAE), 2020 international conference on

作者： Chong Fan Wei Zhang Art Design College Hubei University of Technology Hubei China

This topic based on BIM model, Vue framework, B/S architecture will be distributed underground water source heat pump, lighting control, fresh air, envelope structure, video monitoring and other intelligent subsystems integrated into a unified platform. In this way, integrated management is realized. The system has achieved the goals of centralized management, systematic operation and maintenance, digitized information and three-dimensional visualization. This paper introduces a way to transform the basic category mode of construction industry into the expanded mode of Chengdu topographic map. In the whole building level from the information integration, visualization, positioning automation three aspects of comprehensive improvement. By conducting field visits to the operation manager and testing the prototype system, the reasons for the failure of the air conditioning system can be found more deeply.

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Internet of Things Enabled ML for Air Quality Assessment: Systematic Review

Internet of Things Enabled ML for Air Quality Assessment: Sy...

引用

international conference on ent Computing and control Systems (ICICCS)

作者： S Veera Manikandan Y Abilash S Hari Prasanth J Alfred Daniel R Santhosh Department of Computer Science and Engineering Karpagam Academy of Higher Education Coimbatore

A severe and pervasive environmental problem that affects the entire planet is Air Pollution (AP). Numerous researchers have focused on these issues while keeping human health in mind. One of the best methods to educate people about major health issues and safeguard human health from air pollution is through information about air quality predictions. Air pollution is one of the most difficult environmental issues, and it affects many major cities. Real-time monitoring of pollution data can help local officials assess the present state of the city’s traffic and reach well-informed decisions. In order to correctly estimate the pollutant concentrations, it is necessary to have an early system for monitoring and measuring the amount of AP using the Air Quality Index. Incorporating Internet of Things (IoT)-based devices may significantly change the AQ forecast dynamically, hence enhancing the AQ forecast. The accuracy and cost of the Both the AP prediction that is mentioned and the evaluation of it using various known methodologies are quite low. While AP prediction is still permitted in some sectors, machine learning (ML) algorithm development is expanding swiftly and looking into almost all fields and applications. This paper highlights numerous ML research projects related to AP monitoring and prediction using IoT sensor data in the context of diverse cities. This paper also summarizes historical and current data based on AQ prediction models, methods, and methodologies, examines current research methodology, the advantages and disadvantages of AQ prediction, as well as the challenges related to real-time AQ monitoring and prediction.

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Real-Time Defect Detection monitoring System For Extrusion-based 3D Food Printing Using Internet of Things

Real-Time Defect Detection Monitoring System For Extrusion-b...

引用

international conference on Smart Cities, Automation & intelligent Computing Systems (ICON-SONICS)

作者： Cholid Mawardi Agus Buono Karlisa Priandana Herianto Department of Computer Science Institut Pertanian Bogor Bogor Indonesia Department of Mechanical and Industrial Engineering Universitas Gadjah Mada Yogyakarta Indonesia

Quality control remains a significant challenge in 3D food printing, in which defects during the printing process will lead to resource and time wastage. This work proposes a novel approach integrating cameras, image processing, and supervised machine learning to autonomously assess the quality of 3D food printed items. At crucial stages of the printing process, images of partially manufactured parts are captured and analyzed based on its geometry. Utilizing smartphones and the Internet of Things for efficient monitoring, the designed system aims to minimize material wastage, a prevalent concern in the creative food industry. The monitoring tool employs a 3D printer embedded with Raspberry Pi and a primary camera, with image processing carried out by the Obico plugin on Raspberry Pi to issue commands for real-time performance monitoring. The implementation of this technology not only addresses early defect detection but also enhancing food design and minimizing environmental impact, presenting promising prospects for the future of the food industry.

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Research on monitoring, Early Warning and Prevention System of land Subsidence in Beijing-Tianjin-Hebei Region 10

Research on Monitoring, Early Warning and Prevention System ...

引用

10th Annual international conference on Material Science and environmental Engineering, MSEE 2022

作者： Ning, Di Zhu, Juyan Guo, Haipeng Zang, Xisheng Wang, Haigang Hebei Cangzhou Groundwater and Land Subsidence National Observation and Research Station No. 1 Caiyu Street Xinhua District Hebei Cangzhou China China Institute of Geo-Environment Monitoring No. 20 Dahui Temple Haidian District Beijing China

The Beijing-Tianjin-Hebei region, one of the most serious areas of ground subsidence in China, has entered the stage of high-speed development of synergistic integration, and the establishment of a unified and efficient, automatic and intelligent, mature and advanced monitoring and warning prevention system is inevitable. After reviewing a large amount of literature, we analyze and summarize that the ground subsidence in this region was formed earlier, and although the monitoring, early warning and prevention work is gradually promoted, there are still problems such as imperfect monitoring network, in depth early warning research and unsound joint prevention and control mechanism. Only with the integration of multi-source monitoring technology, increasingly refined early warning technology and multi-pronged prevention and control technology can the ground subsidence monitoring and early warning prevention and control system in Beijing, Tianjin and Hebei make great progress and gradually advance towards intelligence, refinement and wisdom. © Published under licence by IOP Publishing Ltd.

关键词： Subsidence

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Exploring Advanced Smart Sensing Technology for Enhanced Drip Irrigation Management in Greenhouses

Exploring Advanced Smart Sensing Technology for Enhanced Dri...

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Advances in Computing,Communication, Electrical, and Smart Systems (iCACCESS), international conference on

作者： Edralin R. Raro Thelma D. Palaoag College of Agriculture and Natural Resources Camarines Norte State College Daet Camarines Norte Philippines College of Information Technology and Computer Science University of the Cordilleras Baguio City Philippines

Drip irrigation systems have become an integral part of modern greenhouse farming practices as they help irrigate plants effectively and also supply plants with water and nutrients in precise amounts. Nevertheless, these technologies have to be perfectly adjusted to efficiently utilize water and boost crop yield. This research addresses two issues: firstly, the capacity of the sensing technology innovations for greenhouse drip irrigation to be more efficient, and secondly, sustainability. The study looked for several types of sensors and materials that can be used in the construction of the system section that take into consideration the information from the intelligent sensors and the monitoring of environmental parameters. The methodology used in the study was a methodological review. This paper will discuss the literature dealing with different mechanisms, tools, and materials to control environmental factors that could affect the drip irrigation system in greenhouses. The literature review presented several types of sensors, including temperature sensors, soil moisture sensors, and humidity sensors, which are the key components for intelligent greenhouse drip irrigation systems operating correctly. Embedding these sensors and materials in a comprehensive system has exhibited a positive effect on water consumption reduction and nutrient uptake by plants. The results of the discussion stress the high sensitivity to the choice of materials and sensors which consequence can be the deployment of the system for a long time and its high performance. Looking at the results, it is accordingly determined after the fact that sensor type and sensor materials constitute an essential part that would aid in achieving the best results with smart greenhouses through drip irrigation systems. These technologies ensure precise environmental monitoring and resource management, offering a pathway to sustainable and high-yield agriculture. The findings collected from this literature r

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Research Progress of Aquaculture environmental Factor Prediction Model Based on Machine Learning

Research Progress of Aquaculture Environmental Factor Predic...

引用

IEEE international conference on control Science and Systems Engineering (CCSSE)

作者： Haijing Qin YunChen Tian Hongpo Wang Qingfei Li Xueqi Cong Jinzhu Sui College of Computer and Information Engineering Tianjin Agricultural University Tianjin China Tianjin Key Laboratory of Aquatic Ecology and Aquaculture Tianjin China

The accurate aquaculture environmental factors prediction model is established using big data technology and machine learning algorithm. It has become a research hotspot because it can accurately predict water quality, strengthen water quality management and reduce aquaculture risks. This paper introduces the characteristics and innovations of aquaculture water quality prediction models based on shallow machine learning and deep learning. It expounds on the primary process and standard methods of aquaculture environment factor prediction models based on machine learning. Analyzing the model performance index, the integrity of data collection, scientific processing, and the rationality of the model-building method is the basis for establishing a high-quality model. At the same time, it also points out the existing problems of empirical determination model structure and the need to accelerate the collaborative development of water environment monitoring technology and water environment prediction technology in the future to establish an intelligent water environment regulation system and promote the development of intelligent aquaculture.

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Design of intelligent Light control System Based on NB-IoT

Design of Intelligent Light Control System Based on NB-IoT

引用

Machine Learning, computer Systems and Security (MLCSS), international conference on

作者： Zeng Xiaoling Zhang Jianping Chongqing Electronic Information College Chongqing China

At this stage, the level of urbanization in China is constantly improving, and the construction of “smart city” is gradually included in the ranks of national urban planning. As an important part of smart cities, intelligent street light are welcoming unprecedented development opportunities. The traditional street light control system generally adopts the methods of centralized line control,human inspection, and computer-side web monitoring. Aim at the lag of traditional street lamp management, and lack of real-time monitoring and remote control, this thesis designs a set of intelligent street light monitoring system based on NB-IoT (Narrow Band-Internet of Things).The system realizes the real-time control of street lighting system, environmental monitoring, spray cooling and dust suppression, etc. Cloud technology is used to calibrate the geographic location information and working status information of each street lamp. The test results show that the system is stable in operation and reliable in data transmission, reducing the cost and energy consumption of the traditional street lamp control nodes, and realizing the remote monitoring and intelligent management of the status of urban street lamps by the terminal equipment.

关键词： Cloud computing Smart cities Urban planning control systems Real-time systems Software Reliability

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Research and Application of Agricultural Internet of Things Technology in intelligent Agriculture 5

Research and Application of Agricultural Internet of Things ...

引用

5th international conference on computer Science and Information Engineering, ICCSIE 2020

作者： Yue, Shuyu Du, Yirong Zhang, Xiao Graduate School Hebei North University Zhangjiakou Hebei China School of Information Science and Engineering Hebei North University Zhangjiakou Hebei China

China's modernization of agriculture has been accelerating, the Internet of things and other high-tech level of rapid improvement, and intelligent agriculture is the main direction of modern agriculture. Firstly, this paper summarizes the technology of Internet of things and intelligent agriculture, and summarizes the intelligent technology and internet technology applied in the control process of agricultural production in the process of intelligent agriculture Through listing: intelligent Agricultural monitoring System based on Onenet Open Platform, intelligent agricultural irrigation system based on Internet of things technology, apple tree pest control expert system based on Internet of things, practical cases of agricultural product marketing platform based on Internet of things, the paper studies the application of Internet of things technology in environmental monitoring, irrigation, pest control, and marketing of agricultural products. In order to provide theoretical basis for farmers. © 2021 Published under licence by IOP Publishing Ltd.

关键词： Internet of things

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A Review of current practices and challenges in Green Data Centers: renewable energy sources, waste heat recovery, and intelligent management systems

A Review of current practices and challenges in Green Data C...

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Euromicro conference on Parallel, distributed and Network-Based Processing

作者： Marta Boscariol Elisa Cacciaguerra Paola Gasbarri Daniele Accardo Silvia Meschini Lavinia Chiara Tagliabue Department of Management University of Turin Turin Italy Department of Computer Science University of Turin Turin Italy

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

As digital transformation advances, Data Centers are crucial in supporting global digital infrastructure but contribute substantially to energy consumption and carbon emissions. Green Data Center technologies emerged to address such sustainability challenges, integrating energy-efficient designs, renewable energy, and waste heat recovery solutions. The review explores advancements in sustainable Data Centers, focusing on Building Information Modelling (BIM), Digital Twins (DTs), and intelligent management systems, that help mitigating environmental impacts. BIM and DTs support realtime monitoring, predictive analysis, and optimized energy management, helping operators minimize cooling needs, reduce inefficiencies, and maximize renewable energy *** interest in renewable energy sources such as solar power is increasing, highlighting hydrogen fuel cells potential in providing a scalable, low-emission alternative to conventional backup power. Waste heat recovery also plays a crucial role in Green Data Centers, with initiatives repurposing heat for district heating, industrial processes, and building heating. Current applications include Data Centers where heat recovery systems supply residential heating, support algae farms, and enhance university campus *** energy efficiency, Green Data Centers contribute to smart grid stability. Load-shifting capabilities enable power usage to be adjusted during peak and off-peak times, enhancing grid resilience and promoting sustainability. Future trends include AI-driven resource management, further carbon footprint reduction via green energy procurement, and advanced load distribution *** review synthesizes current research and practices, showcasing sustainable technologies in Green Data Centers, presenting case studies of successful implementations. It also highlights promising applications and existing challenges in achieving carbon-neutral infrastructures.

关键词： Data centers Reviews Green buildings Focusing Energy efficiency Digital twins Waste heat Sustainable development Heat recovery monitoring

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Deep Learning Object Detection for Vehicle-to-Everything Systems with ROS 2.0 distributed Communication Architecture

Deep Learning Object Detection for Vehicle-to-Everything Sys...

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IEEE international conference on Consumer Electronics-Taiwan (ICCE-TW)

作者： Chia-Hsin Cheng Kuo-Ting Huang Yung-Fa Huang Yi-Xuan Xu Kai-Siang Liao Department of Electrical Engineering National Formosa University Yunlin Taiwan Department of Information and Communication Engineering Chaoyang University of Technology Taiwan

ISBN: (数字)9798350386844

ISBN: (纸本)9798350386851

In this paper, we focus on object detection for automated mobile devices within a Vehicle-to-Everything (V2X) system. The V2X system is constructed using the Robot Operating System 2.0 (ROS 2.0), leveraging its distributed communication architecture to deploy roadside units and onboard units. This setup enables us to manage automated mobile devices for both movement and object detection. The object detection component employs YOLO for product recognition. The control aspect utilizes Simultaneous Localization and Mapping (SLAM) and Navigation2. It integrates environmental data, including 2D LiDAR SLAM and Inertial Measurement Unit (IMU) data, to enable obstacle avoidance and real-time monitoring of device location.

关键词： YOLO Simultaneous localization and mapping Accuracy Operating systems Object detection computer architecture Real-time systems

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