Intelligent Monitoring Method Research Articles

A novel intelligent monitoring method for the closing time of the taphole of blast furnace based on two-stage classification

A novel intelligent monitoring method for the closing time of the taphole of blast furnace based on two-stage classification

Detection Method of River Floating Objects based on Edge Computing

For the detection of objects floating in the river, most of the traditional intelligent video monitoring methods are used to monitor through manual viewing. During the traditional video data transmission and processing, the massive amount of data will put pressure on the background such as network and computing power. The new effective method is to crop and optimize YOLOV5s, add a specific image pre-processing module, and deploy it by edge computing, embed a SOC (System on Chip) chip in the web camera for real-time processing of video data. Simulation experiments prove that the above method can improve the accuracy and speed of the target detection network.

Remote intelligent monitoring method of automobile driving safety based on Internet of Things technology

Remote intelligent monitoring method of automobile driving safety based on Internet of Things technology

Road traffic anomaly monitoring and warning based on DeepWalk algorithm

Abstract In the complex urban road traffic network, a sudden accident leads to rapid congestion in the nearby traffic region, which even makes the local traffic network capacity quickly reduce. Therefore, an efficient monitoring system for abnormal conditions of the urban road network plays a crucial role in the tolerance of the urban road network. The traditional traffic monitoring system not only costs a lot in construction and maintenance, but also may not cover the road network comprehensively, which could not meet the basic needs of traffic management. Only a more comprehensive and intelligent monitoring method is able to identify traffic anomalies more effectively and quickly, so that it can provide more effective support for traffic management decisions. The extensive use of positioning equipment made us able to obtain accurate trajectory data. This paper presents a traffic anomaly monitoring and prediction method based on vehicle trajectory data. This model uses deep learning to detect abnormal trajectory on the traffic road network. The method effectively analyses the abnormal source and potential anomaly to judge the abnormal region, which provides an important reference for the traffic department to take effective traffic control measures. Finally, the paper uses Internet vehicle trajectory data from Chengdu (China) to test and obtains an accurate result.

Broodstock breeding behaviour recognition based on Resnet50-LSTM with CBAM attention mechanism

Broodstock has the characteristics of a long breeding time, small breeding quantity, and a high economic value. At the same time, the broodstock is weak during the spawning period, which needs to be observed in time. Therefore, studying the behaviours of broodstock (chasing, spawning, aggregation, feeding, and swimming normally) could help fish farmers to understand the health status of broodstock and adjust breeding strategies in time. However, it is challenging to recognise these behaviours due to the high similarity between these behaviours. In this study, a camera’s infrared mode was used to obtain the behaviour recognition data in weak light conditions, and the visible light mode of the camera was used to obtain the data in sufficient lighting conditions. A Resnet50-LSTM based approach was proposed to recognise five standard behaviours. Due to the advantages of deep feature extraction, Resnet50 extracted the features of the input data in different dimensions through multiple convolution. These features were input into the Long-Short-Term-Memory (LSTM) classification model, enabling the extraction of time-series behavioural information Convolutional Block Attention Module (CBAM) was added after each stage in the Resnet50 network, which was able to focus on important features and suppress unnecessary noise interference. Meanwhile, mish activation function and ranger optimiser were used to improve the accuracy of behaviour recognition. 3925 videos containing 981,250 frames were collected and tested to compare the performance of the proposed algorithm. The test results showed that our method achieved a recognition accuracy of 98.52%. Additionally, we compared the test accuracy between the proposed method with eight competitive models, and the test results showed that the average recognition accuracy of the eight models was 95.04%, 97.32%, 97.18%, 97.52%, 98.32%, 77.80%, 85.10%, 90.8%, respectively. Our method shows higher average recognition accuracy. Overall, this is an effective method for intelligent aquaculture monitoring. The algorithm meets the needs of behaviour recognition, which has good robustness in an aquaculture environment and provides a new strategy for smart aquaculture.

Intelligent Operation Monitoring of an Ultra-Precision CNC Machine Tool Using Energy Data

Ultra-precision CNC machine tools play a significant role in the machining of precision dies and molds, optics, consumer electronics, etc., Due to the nature of ultra-precision machining, a subtle change in process condition, machine anomalies, etc. may significantly influence the machining outcome. Hence, continuous monitoring of the equipment’s operation is required to better understand the variations associated with the process and the machine. The conventional monitoring platform requires comprehensive data analysis using multiple sensors, and controller data to detect, diagnose, and prognose machine and process conditions. This increases the cost and complexity of installing a monitoring platform. The energy consumption data contains valuable information that could be potentially used to identify machine and process variations. The information can also be used to develop potential energy-saving strategies in an effort towards Green Manufacturing. This paper proposes an intelligent energy monitoring method using a 1-dimensional convolutional neural network (1D-CNN) to effortlessly and accurately obtain the working status information of the machine with minimal retrofitting. The 1D-CNN uses the energy consumption data to determine the equipment’s operation status by identifying the working components and the feed rate of the moving axis. The hyper-parameters of the developed model were optimized to improve the prediction accuracy. The paper also compares different Deep Learning and Machine Learning algorithms to gauge their effective performance in this application. Finally, the model with the highest accuracy was validated on a 5-axis ultra-precision CNC machine tool. Results show that 1D-CNN performs better than multi-layer neural networks and machine learning algorithms in processing time-series datasets. The classification accuracy of 1D-CNN on the detection of operation status and feed rate of each axis can reach 95.7 and 91.4%, respectively. Further studies are currently in progress to improve prediction accuracy of the model, and to detect subtle changes in energy consumption which would enable identification of the machine and process anomalies.

Intelligent monitoring method of tridimensional storage system based on deep learning.

Growing international trade requires more flexible warehouse management to match it. In order to achieve more effective warehouse management efficiency, a shelf status–detection method based on deep learning is proposed. Firstly, the image acquisition of a multi-level shelf containing multiple bays is performed under different time and lighting conditions. Due to the difference in image characteristics between the bottom shelf on the ground and the upper shelf on the non-ground level, the collected images were divided into two groups: floor images and shelf images; and the warehouse status recognition was performed on the two groups separately. The two sets of images are cropped and center projection transformed separately to obtain the region of interest. On this basis, the improved residual network model is used to construct different depot detection models for the two sets of images, respectively, and the above algorithm is verified by actual measurements. In this paper, 102,614 images of 3246 depots with different states of non-ground layer, and 27,903 images of ground layer are collected. They are divided into training set and test set according to the ratio of 4:1, and the accuracy of training set is 99.6%, and the accuracy of test set is 99.3%. The experimental outcomes provide a theoretical method and technical support for the intelligent warehouse system management.

Cloud of Things in Crowd Engineering: A Tile-Map-Based Method for Intelligent Monitoring of Outdoor Crowd Density.

Managing citizen and community safety is one of the most essential services that future cities will require. Crowd analysis and monitoring are also a high priority in the current COVID-19 pandemic scenario, especially because large-scale gatherings can significantly increase the risk of infection transmission. However, crowd tracking presents several complex technical challenges, including accurate people counting and privacy preservation. In this study, using a tile-map-based method, a new intelligent method is proposed which is integrated with the cloud of things and data analytics to provide intelligent monitoring of outdoor crowd density. The proposed system can detect and intelligently analyze the pattern of crowd activity to implement contingency plans, reducing accidents, ensuring public safety, and establishing a smart city. The experimental results demonstrate the feasibility of the proposed model in detecting crowd density status in real-time. It can effectively assist with crowd management tasks such as monitoring, guiding, and managing crowds to ensure safety. In addition, the proposed algorithm provides acceptable performance.

Object Detection Related to Irregular Behaviors of Substation Personnel Based on Improved YOLOv4

The accurate and timely detection of irregular behavior of substation personnel plays an important role in maintaining personal safety and preventing power outage accidents. This paper proposes a method for irregular behaviors detection (IBD) of substation personnel based on an improved YOLOv4 which uses MobileNetV3 to replace the CSPDarkNet53 feature extraction network, depthwise separable convolution and efficient channel attention (ECA) to optimize the SPP and PANet networks, and four scales of feature maps to fuse to improve the detection accuracy. First, an image dataset was constructed using video data and still photographs preprocessed by the gamma correction method. Then, the improved YOLOv4 model was trained by combining Mosaic data enhancement, cosine annealing, and label smoothing skills. Several detection cases were carried out, and the experimental results showed that the proposed improved YOLOv4 model has high accuracy, with a mean average precision (mAP) of 83.51%, as well as a fast detection speed, with a frames per second (FPS) of 38.06 pictures/s. This represents better performance than other object detection methods, including Faster RCNN, SSD, YOLOv3, and YOLOv4. This study offers a reference for the IBD of substation personnel and provides an automated intelligent monitoring method.

Anti-interfering method for environmental foreign bodies for the microstrip antenna sensor

As a result of the rapid development of intelligent equipment, the importance of intelligent sensing technology for use in structural health monitoring applications is increasing. Among the emerging intelligent structure monitoring methods, microstrip antenna sensing technology is attracting considerable attention. The working environments for mechanical equipment are often harsh and highly differentiated. Multiple environmental jamming factors, including water, dust, ice, and other foreign bodies, will reduce the parameter monitoring reliability of microstrip antenna sensors. In this paper, an anti-interfering method for these environmental foreign bodies based on antenna cladding technology is presented. First, a multi-physical numerical model is established to study the mechanism by which the environmental foreign bodies affect the resonant frequency of the antenna sensor. The effects of the foreign bodies on the sensor’s resonant frequency are analyzed via simulations. The anti-interfering cladding design method for environmental bodies is then studied. The structure,a shape, and size of the environmental anti-interfering cladding are designed using an FR4 epoxy plate with good heat and moisture resistance, largely causing the fluctuation level of the sensor’s resonant frequency with changes in the environment to be reduced by more than 95%. The simulation and experimental results show that the proposed anti-interfering method for environmental foreign bodies using antenna cladding technology greatly improves the reliability of the microstrip antenna sensor and has considerable engineering application value.

Identification and Analysis of Emergency Behavior of Cage-Reared Laying Ducks Based on YoloV5

The behavior of cage-reared ducks is an important index to judge the health status of laying ducks. For the automatic recognition task of cage-reared duck behavior based on machine vision, by comparing the detection performance of YoloV4 (you only look once), YoloV5, and Faster-RCNN, this work selected the YoloV5 target detection network with the best performance to identify the three behaviors related to avoidance after a cage-reared duck emergency. The recognition average precision was 98.2% (neck extension), 98.5% (trample), and 98.6% (spreading wings), respectively, and the detection speed was 20.7 FPS. Based on this model, in this work, 10 duck cages were randomly selected, and each duck cage recorded video for 3 min when there were breeders walking in the duck house and no one was walking for more than 20 min. By identifying the generation time and frequency of neck extension out of the cage, trample, and wing spread, it was concluded that the neck extension, trampling, and wing spread behaviors of laying ducks increase significantly when they feel panic and fear. The research provides an efficient, intelligent monitoring method for the behavior analysis of cage-rearing of ducks and provides a basis for the health status judgment and behavior analysis of unmonitored laying ducks in the future.

Intelligent monitoring and evaluation for the prefabricated construction schedule

AbstractThe prefabricated building construction (PBC) project is sensitive to uncertainties due to the highly required coordination and interdependency among the installation activities, which may cause progress delay. Hence, it is necessary to monitor the installation progress and evaluate the schedule in terms of the project duration to take proactive control actions to avoid actual project delay. This study focuses on intelligent monitoring and evaluation for the PBC schedule by combining the computer vision‐based (CVB) technology, a weighted kernel density estimation (WKDE) method, and the earned duration management (EDM) method. Intelligent and real‐time far‐field detection of the prefabricated components (PCs) and workers is achieved through the CVB technology, which is, respectively, used to measure the progress status of the PC installation works and other manual works by means of the WKDE method. The PBC project duration is then predicted based on the monitored progress status to evaluate the schedule through the EDM method. The proposed intelligent schedule monitoring and evaluation method have been illustrated and justified through a field application. This study contributes to achieving intelligent schedule monitoring and evaluation for the PBC project.

A Graph Neural Network (GNN) Algorithm for Constructing the Evolution Process of Rural Settlement Morphology

Traditional statistical methods were mainly used to study the evolution process of rural settlement form and scale from a qualitative perspective, but it was difficult to quantitatively analyze the evolution process of the rural settlement form. Therefore, this paper proposed an intelligent monitoring method of rural settlement morphology evolution process based on the graph neural network (GNN) algorithm. Firstly, the specific working process of image feature extraction, analysis, and processing based on the graph neural network (GNN) algorithm was described. Secondly, combined with the change characteristics of rural settlement morphology evolution and scale development, the graphical neural network algorithm was used to effectively extract the morphological characteristics of rural settlements, and the monitoring information characterizing the dynamic changes of rural settlement morphology and scale was obtained through feature clustering. Finally, through experiments and using the graph neural network algorithm, the evolution process of rural settlement morphology was monitored in real time. The experimental results showed that the monitoring data obtained by this method were basically consistent with the actual statistical results, which showed that the intelligent monitoring method of the rural settlement form evolution process based on graph neural network algorithm can better reflect the dynamic change process of the rural settlement form and scale development. This study will provide some theoretical reference and guiding significance for the quantitative analysis of the evolution process of the rural settlement morphology and its influencing factors.

Intelligent monitoring method for tamping times during dynamic compaction construction using machine vision and pattern recognition

Dynamic compaction method (DCM) is currently one of the most commonly used foundation reinforcement techniques. However, manual monitoring is still the mainstream way of DCM tamping counting with low efficiency and high cost. This paper focuses on the tamping times of DCM, and proposes a non-contact Intelligent Monitoring Method for Tamping Times (IM2T2) based on machine vision and pattern recognition technology. The hammer detection methods based on cooperative targets, YOLOv4 and YOLOv4-tiny are compared, and then the motion model based on the hammer position of construction image series is proposed and the vision-based full automatic measurement of tamping times is realized. Moreover, a field test was carried out to verify the applicability of above method. The results of the tamping times measurement indicate that the proposed method can measure the count of tamping under general working conditions with quite high accuracy.

Tracking and Analysis of the Movement Behavior of European Seabass (Dicentrarchus labrax) in Aquaculture Systems

Monitoring and understanding fish behavior is crucial for achieving precision in everyday husbandry practices (i.e. for optimizing farm performance), and for improving fish welfare in aquaculture. Various intelligent monitoring and control methods, using mathematical models, acoustic methods and computer vision, have been recently developed for this reason. Here, a tracking algorithm based on computer vision that extracts short trajectories of individual European seabass in both recirculating aquaculture systems and sea cages was developed using videos from network cameras. Using this methodology, parameters such as instantaneous normalized speed, travel direction and preference for the tank surface by European seabass could be quantified. When testing the sensitivity of this algorithm for detecting fish swimming variations under different husbandry scenarios, we found that the algorithm could detect variations in all of the abovementioned parameters and could potentially be a useful tool for monitoring the behavioral state of European seabass.

Smart Boat Detection Based on Feature Pyramid Network and Deformable Convolution

The submarine cable guarantees the electricity and communication of the island residents. The operation of fishing boats poses a major threat to the submarine cable. Due to the complex environment, manual monitoring has defects such as strong subjective factors and easy fatigue. The paper adopts the intelligent monitoring method, using the object detection algorithm based on deep learning and the camera to monitor the boats on the sea. Use feature pyramid network to enhance the detection of smaller and farther boats. Use deformable convolution to solve the problem of few samples. Experimental results show that model can detect boats. The detection ability of the feature pyramid network is stronger, especially for the distant and smaller boat targets. Using deformable convolution can improve the accuracy of models trained on small dataset.

Discussion on Construction Analysis of Concrete Computer Intelligent Temperature Monitoring

In order to effectively monitor the temperature changes of the concrete during the construction phase, to avoid cracks caused by temperature. An intelligent computerized temperature monitoring method for concrete is proposed. Through the computer intelligent temperature monitoring system, the temperature changes during the construction phase can be obtained to better map the temperature distribution. The experimental results show that the computer-based intelligent temperature monitoring method for concrete proposed in this paper can effectively monitor the change of concrete temperature.

Cyber-Attack Detection and Cyber-Security Enhancement in Smart DC-Microgrid Based on Blockchain Technology and Hilbert Huang Transform

Due to the simultaneous development of DC-microgrids (DC-MGs) and the use of intelligent control, monitoring and operation methods, as well as their structure, these networks can be threatened by various cyber-attacks. Overall, a typical smart DC-MG includes battery, supercapacitors and power electronic devices, fuel cell, solar Photovoltaic (PV) systems, and loads such as smart homes, plug-in hybrid electrical vehicle (PHEV), smart sensors and network communication like fiber cable or wireless to send and receive data. Given these issues, cyber-attack detection and securing data exchanged in smart DC-MGs like CPS has been considered by experts as a significant subject in recent years. In this study, in order to detect false data injection attacks (FDIAs) in a MG system, Hilbert-Huang transform methodology along with blockchain-based ledger technology is used for enhancing the security in the smart DC-MGs with analyzing the voltage and current signals in smart sensors and controllers by extracting the signal details. Results of simulation on the different cases are considered with the objective of verifying the efficacy of the proposed model. The results offer that the suggested model can provide a more precise and robust detection mechanism against FDIA and improve the security of data exchanging in a smart DC-MG.

Research on Intelligent Evaluation and Monitoring Method for Cost of Transmission and Transformation Project

With the advancement of fine-grained management of transmission and transformation projects, the construction of an intelligent learning model for the assessment of cost levels is a prerequisite for the implementation of asset management of transmission and transformation projects. To this end, an intelligent assessment and monitoring method for the cost level of transmission and transformation projects is studied. In view of the high-dimensional small sample characteristics of transmission and transformation project cost data, this method constructs cost level evaluation indicators and monitoring error indicators, and proposes a random bit random forest-based transmission and transformation project cost data feature selection method. Based on this, an umbrella-type evaluation monitor was used to build a transmission and transformation project cost level evaluation and online monitoring model. The validity of the algorithm is verified with the actual substation project cost data of a province. The results show that the method proposed in this paper has good feature stability and evaluation accuracy, and can realize online dynamic monitoring of the transmission and substation project cost level.

Fault Detection and Classification for Photovoltaic Systems Based on Hierarchical Classification and Machine Learning Technique

Line-line (LL) and line-ground (LG) faults may not be detected by common protection devices in Photovoltaic (PV) arrays as these faults are not detectable under high fault impedance and low mismatch level. In recent years, many efforts have been devoted to overcome these challenges using intelligent methods. However, these methods could not classify the type of faults and diagnose their severity. In this article, we propose a novel and intelligent fault monitoring method to detect and classify LL and LG faults at the dc side of PV systems. For this purpose, the main features of current-voltage curves under different fault events and normal conditions are extracted. The faults are categorized using the hierarchical classification platform. Later, the LL and LG faults are detected and classified by machine learning methods. The proposed method aims to reduce the amount of dataset, which is required for the learning process, and also obtains a higher accuracy in detecting and classifying the fault events at low mismatch levels and high fault impedance compared with other fault diagnostic methods. The experimental results verify that the proposed method precisely detects and classifies LL and LG faults on PV systems under different conditions and severity with the accuracy of 96.66% and 91.66%, respectively.