A deep-learning-assisted versatile electret sensor for moving object detection

For people improving the energy-saving performances of buildings, the applications of information communication, computer network, automation control and etc. are the current building energy-saving technologies. They form with a serial of technique measures for buildings with energy management systems running implementation and intelligent monitoring. This paper introduces and analyzes the information technology in building energy-saving. For building energy-saving on the demand for intelligent building energy monitoring, this paper proposed a system framework of Building Energy Monitoring and Analysis System based on the Internet of things, which has some enlightening in Building energy consumption further to achieve real-time monitoring and control, and improve the energy-saving of intelligent building.

Cation functionalized nylon composite nanofibrous mat as a highly positive friction layer for robust, high output triboelectric nanogenerators and self-powered sensors

Combined with IoT(Internet of Things) network communication technology, sensor technology and cloud computing technology, the existing intelligent environment monitoring system is improved, and a design of intelligent environment monitoring visualization platform based on Internet of things is given. Through this system, the data information of meteorological environment, water quality and main roads can be obtained timely and accurately. Through the development and implementation of system engineering, the feasibility of intelligent environment monitoring visualization platform based on Internet of things is verified.

Condition monitoring and management system (CMMS) for power transmission and distribution equipments is an important part of smart grid. This paper addresses issues relating to the techniques of CMMS and its application in Yunnan Power Grid. After introducing the basic concepts and essential characters of Intelligent CMMS, many technological challenging issues of it are discussed. And engineering research and application cases in Yunnan Power Grid for develop a fully perceptive smart are also introduced in detail, including the Data Supervise System (DSS), some original methods for faults diagnosis or disaster warning, and the research project about intelligent monitoring and full lifecycle management for power transmission and distribution equipments based on internet of things. Those are helpful to improve the reliability of power grid devices and ensure the safety of Yunnan Power Grid.

Against the backdrop of global energy structure decarbonization, distributed transformation, and the rapid development of low-power electronic devices and sensor networks, micro-energy supply and intelligent sensing have emerged as critical bottlenecks limiting their large-scale application. Triboelectric nanogenerators (TENGs), leveraging advantages such as compatibility with diverse materials and adaptability to flexible and miniaturized fabrication, can efficiently harvest widely available low-frequency, low-amplitude distributed mechanical energy in the environment. Additionally, they exhibit self-powered sensing characteristics, where output signals are directly correlated with external physical quantities, demonstrating unique strengths in the fields of micro-/nano-energy and intelligent monitoring. This article systematically reviews the research progress in TENGs; elucidates their working modes and power generation principles; summarizes material design, structural optimization, and performance enhancement strategies for efficient energy harvesting; and outlines the current state of self-powered sensing technologies. It highlights their engineering applications in intelligent monitoring scenarios such as drones, marine environments, infrastructure, and wearable devices. Addressing the existing technical bottlenecks and theoretical challenges in integrated energy harvesting–sensing–monitoring systems, the paper envisions future trends toward high performance, integration, and intelligence, providing valuable insights for fundamental research on and engineering applications of TENGs in micro-energy supply and intelligent monitoring.

In the context of urban space constraints, subway and underground projects have become crucial strategies to alleviate urban congestion and enhance residents’ quality of life. However, pit engineering, a frequent accident area in geotechnical engineering, urgently requires innovative safety monitoring technologies. Traditional monitoring methods face challenges such as high labor costs, lengthy monitoring cycles, high-risk working environments, and over-reliance on human judgment. To address these issues, this paper introduces an innovative monitoring system integrating Fiber Bragg Grating (FBG) sensing technology based on a subway pit project in Guangzhou. This system not only achieves fully automated data acquisition but also includes an intelligent monitoring cloud platform, providing unprecedented automated and intelligent monitoring solutions for support structures and the surrounding environment during mechanical shaft construction. The key findings of this paper include the following: (1) The breakthrough application of distributed optical fiber monitoring technology, including successfully deploying this advanced technology in complex pit engineering environments, enabling the precise and continuous monitoring of support structures and surrounding changes, and demonstrating its high effectiveness and intelligence in practical engineering. (2) The innovative design of an intelligent safety monitoring system. By integrating sensors and wireless communication technology, an efficient data networking architecture is constructed, supporting remote configuration and flexible adjustment of monitoring equipment, significantly enhancing data collection‘s real-time performance and continuity while greatly reducing safety risks for field staff, achieving an intelligent upgrade of monitoring work. (3) Comprehensive and accurate empirical analysis. During shaft excavation, the monitoring data collected by the system were stable and reliable, with all indicators maintained within reasonable ranges and closely matching expected changes caused by construction activities, validating the system’s practical application effectiveness in complex construction environments and providing a scientific basis for pit engineering safety management.

Since the beginning of the new century, our country has made more efforts and attached increasing importance to colleges and universities. The number and scale of various large-scale equipment in the teaching process of colleges and universities are also growing rapidly and steadily. However, the management of these large scale machinery and equipment is still dominated by traditional manual management mode, and there are practical problems existing in the use and management system such as low use efficiency, unreasonable purchase and obvious management loopholes. Based on these conditions, this paper takes the management system of largescale machinery and equipment as the research direction, and proposes financial index, internal process index, scientific research development index and interest-related index to construct an index system structure, then it uses a combined algorithm which integrated the improved Analytic Hierarchy Process (AHP) with the entropy weight method to calculate the impact factors of each index, at last, this paper puts forward specific countermeasures and suggestions according to the size of the impact factors, in the hopes of providing support for the use and management of equipment in the teaching process of colleges and universities.

In today's rapidly advancing information technology landscape, the Internet of Things (IoT) has profoundly impacted traditional agriculture. IoT technology, as an intelligent solution, enables real-time data collection, processing, and analysis, significantly enhancing the efficiency and intelligence of agricultural production. Among these advancements, the neural network-based intelligent agricultural monitoring system stands out. This system not only monitors the agricultural production environment in real-time but also employs deep learning algorithms to predict future trends, guiding agricultural practices to achieve precision farming. Therefore, exploring the intelligent agricultural monitoring and control system based on neural networks in the IoT environment holds significant importance.

With the rapid development of power system, there are a lot of state monitoring types and many advanced monitoring approaches for power equipment, which can greatly help us to assess power equipment and detect faults. Therefore, in this paper, we study on how to monitor states of power equipment based on Internet of things. Internet of things (IoT) is designed based on the wireless sensor network technology, radio frequency identification RFID technology, and it can be used in many areas, such as object recognition, intelligent monitoring. Furthermore, IoT contains three layers: 1) Application layer, 2) Network layer, and 3) Perceptive layer. Next, we analyze requirements of state monitoring for power equipment and then provide an effective solution. Finally, simulation results demonstrate the effectiveness of the proposed solution.

Chi, H.T.; Du, Y., and Brett, P.M., 2020. Design of a marine environment monitoring system based on the Internet of Things. In: Al-Tarawneh, O. and Megahed, A. (eds.), Recent Developments of Port, Marine, and Ocean Engineering. Journal of Coastal Research, Special Issue No. 110, pp. 256–260. Coconut Creek (Florida), ISSN 0749-0208.A good marine environment is the basis for the development and utilization of marine resources. Marine environment monitoring is an important part of marine environment protection. Therefore, the research on the marine environment monitoring technologies is of great significance. In order to realize intelligent monitoring of the marine environment, combining with the sensor technology, 4G communication technology and Zigbee communication technology, this study designed a marine environment monitoring system based on the Internet of Things (IoT). The system is composed of three parts: the information management subsystem, the data collection subsystem and the monitoring terminal subsystem; the structural composition and main implementation technologies of each subsystem are elaborated and the functions of each module in the system are tested by experiments. The test results show that, the IoT-based marine environment monitoring system can realize the functions of collecting, receiving, sending, analyzing, processing, querying and releasing marine environment data. This study provides a technical support for China's marine environment monitoring, and has important theoretical reference value for the research of intelligent marine environment monitoring in China.

The purpose of the project is to develop an Intelligent Neonatal Monitoring System based on temperature and pulse rate data. In the Neonatal Intensive Care Unit (NICU), there are premature babies and other ill babies who need extra care from the doctors, nurses as well as medical supplies. Therefore, an intelligent neonatal monitoring system should be a good solution in order to help them to observe neonates frequently and consistently. This system transmits the vital signs of the neonate such as body temperature and pulse rate to the Internet of Things (IoT) called ThingSpeak. The body temperature and the pulse rate will be detected by LM35 temperature sensor and pulse sensor respectively. These information will be sent to the IoT via ESP8266 Wi-Fi Shield. IoT helps the doctors and nurses to be connected with the neonate's vital signs and it is helpful in monitoring the neonates at anytime and anywhere through the internet. The percentage difference between LM35 temperature sensor and digital thermometer is less than 3% while the pulse rate can be varied according to the physical activity. This develops system will providing efficiency and reliability which will play a vital role for better care.

A self-powered triboelectric multi-information motion monitoring sensor and its application in wireless real-time control

The Internet of Things (IoT) is an ecosystem comprised of multiple devices and connections, a large number of users, and a massive amount of data. Deep learning is especially suited for these scenarios due to its appropriateness for "big data" difficulties and future concerns. Nonetheless, guaranteeing security and privacy has emerged as a critical challenge for IoT administration. In many recent cases, deep learning algorithms have proven to be increasingly efficient in performing security assessments for IoT devices without resorting to handcrafted rules. This research work integrates principal component analysis (PCA) for feature extraction with superior performance. Besides, the primary objective of this research work is to gather a comprehensive survey data on the types of IoT deployments, along with security and privacy challenges with good recognition rate. The deep learning method is performed through PCA feature extraction for improving the accuracy of the process. Our other primary goal in this study paper is to achieve a high recognition rate for IoT based image recognition. The CNN approach was trained and evaluated on the IoT image dataset for performance evaluation using multiple methodologies. The initial step would be to investigate the application of deep learning for IoT image acquisition. Additionally, when it comes to IoT image registering, the usefulness of the deep learning method has been evaluated for increasing the appropriateness of image recognition with good testing accuracy. The research discoveries on the application of deep learning in the Internet of Things (IoT) system are summarized in an image-based identification method that introduces a variety of appropriate criteria.

Internet of Things (IoT) and its applications are becoming commonplace with more devices, but always at risk of network security. It is therefore crucial for an IoT network design to identify attackers accurately, quickly and promptly. Many solutions have been proposed, mainly concerning secure IoT architectures and classification algorithms, but none of them have paid enough attention to reducing the complexity. Our proposal in this paper is an edge-cloud architecture that fulfills the detection task right at the edge layer, near the source of the attacks for quick response, versatility, as well as reducing the Cloud’s workload. We also propose a multi-attack detection mechanism called LocKedge (Low-Complexity Cyberattack Detection in IoT Edge Computing), which has low complexity for deployment at the edge zone while still maintaining high accuracy. LocKedge is implemented in two manners: centralized manner and federated learning manner in order to verify the performance of the architecture from different perspectives. The performance of our proposed mechanism is compared with that of other machine learning and deep learning methods using the most updated BoT-IoT data set. The results show that LocKedge outperforms other algorithms such as NN, CNN, RNN, KNN, SVM, KNN, RF and Decision Tree in terms of accuracy and NN in terms of complexity.

In the manufacturing process of machinery, measurement technology is the basis to ensure that the manufactured products meet the design requirements, and it is also an important technology for inspection and control. For its large-scale machinery products and equipment, measurement technology is the basis to ensure the correct installation of largescale machinery, and it is also the key to ensuring the safe and normal operation. Therefore, in the process of installation, sufficient attention must be paid to its measurement technology to improve the effective accuracy of mechanical products.