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This paper proposes an Intelligent Monitoring System (IMS) for Photovoltaic (PV) systems using affordable and cost-efficient hardware and also lightweight software that is capable of being easily implemented in different locations and having the capability to be installed in different types of PV power plants. IMS uses the Internet of Things (IoT) platform for handling data as well as Interoperability and Communication among the devices and components in the IMS. Moreover, IMS includes a personal cloud server for computing and storing the acquired data of PV systems. The IMS also consists of a web monitor system via some open-source and lightweight software that displays the information to multiple users. The IMS uses deep ensemble models for fault detection and power prediction in PV systems. A remarkable ability of the IMS is the prediction of the output power of the PV system to increase energy yield and identify malfunctions in PV plants. To this end, a long short-term memory (LSTM) ensemble neural network is developed to predict the output power of PV systems under different environmental conditions. On the other hand, the IMS uses machine learning-based models to detect numerous faults in PV systems. The fault diagnostic of IMS is based on the following stages. Firstly, major features are elicited through an analysis of Current–Voltage (I–V) characteristic curve under different faulty and normal events. Second, an ensemble learning model including Naive Bayes (NB), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM) is used for detecting and classifying fault events. To enhance the performance in the process of fault detection, a feature selection algorithm is also applied. A PV system has been designed and implemented for testing and validating the IMS under real conditions. IMS is an interoperable, scalable, and replicable solution for holistic monitoring of PV plant from data acquisition, storing, pre-and post-processing to malfunction and failure diagnosis, performance and energy yield assessment, and output power prediction.

Rapid developments in artificial intelligence and image processing have presented many new opportunities for defect detection in manufacturing processes. In this work, an intelligent image processing system has been developed to monitor inter-layer deposition quality during a wire arc additive manufacturing (WAAM) process. This system reveals the feasibility and future potential of using computer vision knowledge in WAAM. Information produced from this system is to be used in conjunction with other quality monitoring systems to verify the quality of fabricated components. It is tailored to identify the presence of defects relating to lack of fusion and voids immediately after the deposition of a given layer. The image processing system is built upon the YOLOv3 architecture and through moderate changes on anchor settings and achieves 53% precision on surface anomaly detection and 100% accuracy in identifying the fabricated components’ location, providing a prerequisite for high-precision assessment of welding quality. The work presented in this paper presents an inter-layer vision-based defect monitoring system in WAAM and serves to highlight the feasibility of developing such intelligent computer vision systems for monitoring the WAAM process for defects.

Accurate and timely surveys of rice diseases and pests are important to control them and prevent the reduction of rice yields. The current manual survey method of rice diseases and pests is time-consuming, laborious, highly subjective and difficult to trace historical data. To address these issues, we developed an intelligent monitoring system for detecting and identifying the disease and pest lesions on the rice canopy. The system mainly includes a network camera, an intelligent detection model of diseases and pests on rice canopy, a web client and a server. Each camera of the system can collect rice images in about 310 m 2 of paddy fields. An improved model YOLO-Diseases and Pests Detection (YOLO-DPD) was proposed to detect three lesions of Cnaphalocrocis medinalis, Chilo suppressalis, and Ustilaginoidea virens on rice canopy. The residual feature augmentation method was used to narrow the semantic gap between different scale features of rice disease and pest images. The convolution block attention module was added into the backbone network to enhance the regional disease and pest features for suppressing the background noises. Our experiments demonstrated that the improved model YOLO-DPD could detect three species of disease and pest lesions on rice canopy at different image scales with an average precision of 92.24, 87.35 and 90.74%, respectively, and a mean average precision of 90.11%. Compared to RetinaNet, Faster R-CNN and Yolov4 models, the mean average precision of YOLO-DPD increased by 18.20, 6.98, 6.10%, respectively. The average detection time of each image is 47 ms. Our system has the advantages of unattended operation, high detection precision, objective results, and data traceability.

The conventional quality control method of earth-rock dam is insufficient to meet the requirements of intelligent and fine management of dam filling construction in the new stage. With the expansion of construction scale and the overall improvement in information technology level, the traditional compaction quality control methods no longer meet the demands of modern mechanized rapid construction or provide real-time compaction information for engineering contractors. In response to the current conventional design and construction schemes for earth-rock dams, an intelligent real-time monitoring system established in this paper for compaction construction of earth-rock dams. This system utilizes advanced Beidou high-precision positioning and navigation technology, vibration-sensing IoT technology, real-time processing of massive construction information, and BIM technology. It achieves real-time intelligent monitoring of key compaction processes in dam construction and refined management of unit engineering. The intelligent system established provides crucial technical support for the safe and reliable operation of water conservancy and hydropower projects. Simultaneously, it enables real-time, remote, and intelligent monitoring of dam construction processes, significantly enhancing the technological content and informatization level of water conservancy engineering construction.

Sensor nodes in a wireless sensor network are influenced by the surrounding environment while monitoring data, which can lead to faults and data biases, resulting in erroneous decisions and losses. Identifying and classifying fault types is a challenge that still need to be addressed. The inertia weight and learning factor c were optimized to enhance the optimization ability of the particle swarm. Additionally, parameters such as in the hybrid kernel function, and the penalty coefficient C , were also optimized to improve classification accuracy. A diagnosis model of a hybrid extreme learning machine based on an updated particle swarm optimization for sensor node faults was developed. The dataset of water parameters was obtained based on constructing a monitoring system for intensive aquaculture. Four different proportions of fault data, respectively 5 ,10 ,15 , and 20 , were added to the dataset to create new datasets for training the model.Test results of the new diagnostic model show an average classification accuracy of 99.30 ,indicating that the proposed fault diagnosis model in this study enhances fault classification accuracy compared to other diagnostic algorithms.

In order to solve the current power dispatch monitoring difficulties, combined with the actual work requirements, research, and design of power dispatch monitoring system. This paper focuses on system design principles, demand analysis for a power dispatch monitoring system, and the design of the system’s functional application modules and database. In view of the inability of the system to monitor the cost of generator emissions and environmental pollution, the particle swarm algorithm is used to optimize this problem, and the system is tested and optimized for simulation analysis. The results show that the response time of the system is maintained within 5s under the condition of 600~1000 users logging into the system at the same time, and the other application tests of the system are passed. Under the same generator cost, the pollution emission of the system based on the PSO optimization algorithm is lower than that of the system based on the GA optimization algorithm, and the scheduling and monitoring utility is better. This paper’s research provides a valuable resource for the development of management systems that are comparable for power supply companies and the power industry as a whole.

To realize the comprehensive intelligent upgrade of the Three Gorges Dam safety intelligent monitoring system (IMS), we focus on three core pillars real‐time information processing, professional analytical evaluation, and digital management control systematically overcoming critical technical bottlenecks. By deeply integrating artificial intelligence (AI), Internet of Things (IOT), big data analysis, and geographic information system + building information modeling (GIS + BIM) ecosystems, we conducted a holistic diagnosis of existing monitoring systems to precisely identify operational pain points. Leveraging our proprietary innovations, including a GIS + BIM digital base, smart algorithm matrix, and BIM‐based finite element computing system, we successfully developed the Three Gorges Dam intelligent monitoring platform, delivering five core value propositions: (1) Achieve real‐time and historical aggregation of comprehensive data with dam safety management as the core, fully encompassing various types of environmental monitoring data. (2) Utilizing “GIS + BIM” as the technical foundation, construct a digital twin geometric model of the hub monitoring physical world, enabling intuitive and precise representation of engineering status. (3) Implement online rapid structural calculation, analysis, and early warning based on “BIM + Finite Element” technology, providing timely and reliable support for safety decision‐making. (4) Establish a monitoring data analysis model through machine learning intelligent algorithms, deeply mining data value to enable intelligent prediction of potential safety hazards. (5) Promote digital transformation of manual inspection workflows using “IOT + Micro‐INS” technology, enhancing inspection efficiency and accuracy. Additionally, our workflow engine ensures full‐process digital collaboration across safety monitoring operations, guaranteeing seamless interdepartmental coordination. These innovations have not only enhanced safety management efficiency but also cemented the Three Gorges Dam’s global leadership in hydraulic engineering. As a landmark achievement in national strategic infrastructure, it exemplifies the digital transformation of mega‐scale engineering projects in the modern era.

Background This study investigates the effectiveness of a self-developed intelligent monitoring system for home-based knee rehabilitation following total knee arthroplasty (TKA). Methods In this randomized controlled trial, 120 patients undergoing TKA were divided using random digit allocation. Preoperative and one-month postoperative assessments of knee function, quality of life, and isometric knee extension strength were conducted with the Intelligent Monitoring System. Patients received group-specific rehabilitation instructions pre-discharge and performed exercises for one month. Results Changes in isometric knee extensor strength on the affected side within one month post-surgery for the brace-monitored rehabilitation group showed a significant decrease three days after surgery compared to one day before surgery. Subsequent measurements taken at postoperative days 5, 7, 14, and 21 indicated a gradual increase in strength, although these increases did not reach statistical significance when compared with previous measurements. One month post-surgery, all groups demonstrated significant improvements in knee joint function and mobility compared to pre-surgery levels. Notably, the brace-monitored group showed statistically significant improvements in 36-Item Short-Form Health Survey (SF-36) scores over the conventional rehabilitation group. Conclusions The Intelligent Monitoring System provides effective real-time monitoring and guidance for home-based knee rehabilitation post-TKA. It significantly enhances knee joint function, isometric knee extension strength, and quality of life shortly after surgery compared to traditional rehabilitation methods. This system offers a promising approach for improving postoperative recovery in TKA patients. Trial registration This study was approved by the Medical Ethics Committee of Xiangya Hospital, Central South University (Ethics Approval Number 202209008-2). It was registered with the China Clinical Trial Registry, a primary registry of the World Health Organization’s International Clinical Trials Registry Platform (Registration Number ChiCTR2300068852).

The traffic in urban areas is primarily controlled by traffic lights, contributing to the excessive, if not properly installed, long waiting times for vehicles. The condition is compounded by the increasing number of road accidents involving pedestrians in cities across the world. Thus, this work presents an integrated tripartite module for an intelligent traffic light system. This system has enough ingredients for success that can solve the above challenges. The proposed system has three modules: the intelligent visual monitoring module, intelligent traffic light control module, and the intelligent recommendation module for emergency vehicles. The monitor module is a visual module capable of identifying the conditions of traffic in the streets. The intelligent traffic light control module configures many intersections in a city to improve the flow of vehicles. Finally, the intelligent recommendation module for emergency vehicles offers an optimal path for emergency vehicles. The evaluation of the proposed system has been carried out in Al-Sader city/Bagdad/Iraq. The intelligent recommendation module for the emergency vehicles module shows that the optimization rate average for the optimal path was in range 67.13% to 92%, where the intelligent traffic light control module shows that the optimization ratio was in range 86% to 91.8%.

This study analyses how the orientation of the measurement head in a magnetic diagnostic system affects the parameters of magnetic signals recorded during steel-cord conveyor belt inspection. The experiments were conducted on a laboratory test stand using a reference belt with artificial defects at two belt speeds and several sensitivity thresholds. Three types of head rotation were analyzed: longitudinal (OX), transverse (OY), and planar (OZ). For each configuration, a set of geometric signal parameters was calculated, including length, width, orientation, eccentricity, and solidity. The results showed that rotation about the OX axis caused the greatest geometric distortions (increased orientation_deg and eccentricity). Rotation about the OY axis produced amplitude asymmetry and changes in solidity (circularity), while rotation about the OZ axis resulted in twisting and displacement of the signal centroid. The total area (area_mm2) remained stable, confirming the geometric nature of the observed changes. Even small head deviations (5-10°) may introduce significant interpretation errors. Therefore, the application of geometric calibration and orientation compensation algorithms is recommended to improve the online diagnostic accuracy of the measurement system.