Intelligent Monitoring Technology Research Articles

Application and comparative analysis of Intelligent Monitoring Technology for Grouted Pile Construction based on abaqus

In order to realize the intelligent monitoring of the high-precision positioning of the hole position and the real-time control of the verticality of the pile, an intelligent monitoring system was developed based on the combination positioning technology of BDS and UWB and the biaxial tilt sensor, and the numerical simulation and comparative analysis of the verticality of the pile were carried out by abaqus. The deviation of pile foundation in different directions and the deviation of pile body are controlled by the monitoring system, and abnormal warning is made when the deviation exceeds the permissible range.Through the application of intelligent monitoring system in the pile foundation engineering area of Changshui Airport, it is found that the plane offset and perpendicularity of all piles meet the standard requirements and the construction error is controlled at a small value. The results show that the application of intelligent inspection system can not only ensure the construction quality of pile foundation, but also meet and improve the level of digitization and information technology of smart construction site.

Advanced intelligent monitoring technologies for animals: A survey

Effective animal intelligent monitoring is of great value in terms of ecological protection and endangered specie conservation. At present, computer vision technologies have shed light on animal intelligent monitoring. Especially, numerous deep learning-based models and methods have been developed to address various challenges, and have made substantial strides in this field. However, there are still several problems to be solved and related areas to be mined, such as exploring new strategies to enhance the robustness and generalization ability of models, designing novel models for complex environments, and establishing large-scale publicly available animal datasets for performance verification of models. Therefore, we comprehensively elaborated and analyzed existing works on animal intelligent monitoring based on advanced information science technologies, so as to provide useful information assistance for relevant researchers. In this paper, we focus on three primary task fields: precise animal localization, tracking and individual identification. Specifically, we elucidate the definition and significance of each monitoring task, and summarize the baseline models for addressing different problems. We provide a specific analysis of strategies and prototypes of the models and methods employed in each tasks following by the technical progression from traditional machine learning to deep learning. In addition, we make a comparison and analysis of the relevant methods, summarize their similarities and differences between them, and point out the advantages and disadvantages of these methods. Finally, we present several unresolved challenges and problems in animal intelligent monitoring and provide potential research directions in the future. We expect that our review can serve as reference and guidance for related research fields.

A low-light image enhancement method for personnel safety monitoring in underground coal mines

Intelligent monitoring technology plays an important role in promoting the development of coal mine safety management. Low illumination in the coal mine underground leads to difficult recognition of monitoring images and poor personnel detection accuracy. To alleviate this problem, a low illuminance image enhancement method proposed for personnel safety monitoring in underground coal mines. Specifically, the local enhancement module maps low illumination to normal illumination at pixel level preserving image details as much as possible. The transformer-based global adjustment module is applied to the locally enhanced images to avoid over-enhancement of bright areas and under-illumination of dark areas, and to prevent possible color deviations in the enhancement process. In addition, a feature similarity loss is proposed to constrain the similarity of target features to avoid the possible detrimental effect of enhancement on detection. Experimental results show that the proposed method improves the detection accuracy by 7.1% on the coal mine underground personal dataset, obtaining the highest accuracy compared to several other methods. The proposed method effectively improves the visualization and detection performance of low-light images, which contributes to the personnel safety monitoring in underground coal mines.

Future of precision manufacturing: Integrating advanced metrology and intelligent monitoring for process optimization

Precision manufacturing is undergoing a transformative evolution fueled by the integration of advanced metrology techniques and intelligent monitoring systems. This abstract explores the future trajectory of precision manufacturing through the convergence of these technologies, focusing on their synergistic role in process optimization. Advanced metrology techniques, including high-resolution imaging, laser scanning, and non-contact surface measurement, provide unprecedented levels of accuracy and detail in capturing dimensional data. These techniques enable manufacturers to precisely analyze component geometry, surface finish, and tolerances, facilitating the production of parts with exceptional precision and quality. Moreover, the integration of metrology within the manufacturing process allows for real-time feedback, enabling rapid adjustments and corrections to ensure adherence to design specifications. Intelligent monitoring systems complement advanced metrology by continuously collecting data from various sensors embedded within manufacturing equipment. These systems utilize artificial intelligence (AI) and machine learning algorithms to analyze vast amounts of data in real-time, detecting anomalies, predicting equipment failures, and optimizing process parameters. By leveraging data-driven insights, manufacturers can enhance production efficiency, minimize downtime, and reduce scrap rates. The synergy between advanced metrology and intelligent monitoring extends beyond quality control to encompass holistic process optimization. Through the seamless integration of these technologies, manufacturers can achieve unparalleled levels of precision, efficiency, and flexibility in their operations. For instance, real-time metrology feedback combined with AI-driven monitoring enables adaptive manufacturing processes that dynamically adjust parameters based on changing environmental conditions or material properties. Furthermore, the future of precision manufacturing lies in the adoption of a digital twin approach, where virtual replicas of physical manufacturing systems are created and synchronized with real-time data. This enables predictive maintenance, virtual prototyping, and simulation-based optimization, leading to significant cost savings and accelerated innovation cycles. The future of precision manufacturing hinges on the integration of advanced metrology and intelligent monitoring technologies. By harnessing the synergies between these innovations, manufacturers can achieve unprecedented levels of precision, efficiency, and agility, driving forward the evolution of manufacturing in the digital era.

Exploration of intelligent monitoring technology for the operation status of large port loading and unloading machinery equipment

This article takes the large-scale loading and unloading machinery equipment and intelligent monitoring technology in ports as the research This article takes the large-scale loading and unloading machinery equipment and intelligent monitoring technology in ports as the research object, with the goal of rational use of intelligent detection technology. It conducts research from two dimensions of intelligent monitoring technology content and implementation, and elaborates on five technologies: Internet of Things, BP neural network, signal acquisition, data It conducts research from two dimensions of intelligent monitoring technology content and implementation and elaborates on five technologies: Internet of Things, BP neural network, signal acquisition, data transmission, and data processing; Based on the example exploration method and focusing on the operation status of equipment and actual monitoring Based on the example exploration method and focusing on the operation status of equipment and actual monitoring needs, the system development is completed using intelligent monitoring technology. The system structure and software composition are discussed. Finally, through testing and verification, the system can eliminate blind spots in equipment monitoring, realize the value of intelligent monitoring technology, and effectively improve the operability of the system. technology, and effectively improve the operability of intelligent monitoring technology for the operation status of large port loading and unloading machinery equipment. The system can eliminate blind spots in equipment monitoring, realize the value of intelligent monitoring technology, and effectively improve the operability of intelligent monitoring technology for the operation status of large port loading and unloading machinery equipment.

Progress in Research on Marine Litter-Related Monitoring Technologies

The problem of marine litter pollution is becoming increasingly serious and has become one of the important global environmental issues. From the perspective of source and sink, floating riverine litter (FRL) is the main source of floating marine litter (FML), while beach litter (BL) and FML are the source and sink of each other. It is necessary to research the transformation mechanism and collaborative governance policy among these three kinds of marine litter, based on accurate monitoring data. However, conventional monitoring methods have problems such as high labor costs, insufficient representativeness and poor comparability. Hence, developing efficient and intelligent monitoring technology is the focus of recent future. Since 2018, monitoring technique of marine litter relevant research literature has grown rapidly, showing this technique a hot spot. In this paper, the relevant research literature is divided into three litter types (BL, FML, and FRL) and two ways (fixed monitoring and mobile monitoring). Similarities and differences in research focus, platform selection, result accuracy and result application are summarized and reasoned, with a view to accelerating the research progress and better supporting BL, FML, and FRL control in China.

Preface

2023 4th International Conference on Mechanical Engineering and Materials (ICMEM 2023) has been successfully held on November 2-4, 2023, in Wuhan China. Organized by Hubei Zhongke Institute of Geology and Environment Technology, ICMEM 2023 intends to invite worldwide famous scientists, experts, scholars, and researchers for academic presentations.ICMEM 2023 proceeding is a collection of outstanding submissions in the field of Dynamics, Vibration and Sound Engineering, Materials Science and Technology, Fluids Engineering, Solid Mechanics and Design, Micro/Nano Engineering and Technology, Production Engineering, Robotics and Control, Thermal and Power Engineering, Vehicle Technology, Machine Fault Diagnostics and Prognostics, Intelligent Transport System, Intelligent Monitoring Technology, Innovative Industrial Design, etc. Around 100 articles were selected from more than 220 submissions after peer-review, and authors and guests participated in ICMEM 2023 with keynote speeches, oral presentations and posters, promoting the communication among researchers and experts from institutes and universities, including National University of Singapore, Polytechnic University of Milan, Wuhan University, China University of Geosciences (Wuhan), Shenyang Aerospace University, Okayama University, Huazhong University of Science and Technology, Dongguan University Of Technology, etc.Cordial gratitude is delivered to Technical Program Committee of ICMEM 2023 for the professional work on guarantying the high standard of conference as well as the proceedings. We believe the proceedings will enhance the exchange of ideas and state-of-art knowledge on Mechanical Engineering and Materials.Professor GUPTA Manoj National University of Singapore, SingaporeAssociate Professor Jinyang Xu Shanghai Jiao Tong University, ChinaProfessor Krishna Murari Pandey National Institute of Technology Silchar, IndiaList of Technical Committee is available in this pdf.

Research on Intelligent Monitoring Technology of Municipal Road and Bridge Engineering Construction and Quality

Abstract This study addresses quality control challenges in municipal road and bridge construction by introducing an intelligent monitoring approach. Utilizing three-dimensional laser scanning, we monitor roadbed settlement and deformation accurately. Compaction quality is assessed through vibration acceleration metrics from milling operations, applying a compaction monitoring value. Furthermore, a combination of regression models and stochastic processes in a Kriging function model evaluates the reliability of detecting bridge steel corrosion. In J city’s political road bridge analysis, we observed a differential settlement with the least affected areas showing subsidence within 250mm. In contrast, the most impacted point, B1, recorded a settlement of 2597mm in December. Compaction quality monitoring revealed that control error margins for E and CV indicators lie between −2.65% to 2.35% and −2.7% to 2.6%, respectively, demonstrating a narrower error range for E compared to CV.

Innovative security solutions: context-aware facial recognition system using VB.NET

A sophisticated VB.NET intelligent security system is presented in this paper to meet the needs of comprehensive security surveillance in varied contexts. The system's context-aware architecture lets it adapt to environmental changes and assess user activity for better security. The system uses real-time data to reduce false alarms and ensure fast security measures using reactive alerts and reaction processes. The system's novel security features, built on VB.NET, provide flexibility and quick reactions to emerging threats. This research shows that intelligent security monitoring technologies have achieved a peak, with excellent performance, and are ready for implementation in controlled-access public spaces. The system reacts to the surroundings and detects the subject behaviour. The system may now access new conceptual words for security issues that demand more focused solutions. A system with capabilities encourages security protocol innovation and maintains secure public spaces. This innovative technique increases security, scalability, and future flexibility for security systems. In the hunt for creative, secure areas with intelligent monitoring and danger detection, the example of an intelligent security system is impressive.

Material Breakthroughs in Smart Food Monitoring: Intelligent Packaging and On-Site Testing Technologies for Spoilage and Contamination Detection.

Despite extensive commercial and regulatory interventions, food spoilage and contamination continue to impose massive ramifications on human health and the global economy. Recognizing that such issues will be significantly eliminated by the accurate and timely monitoring of food quality markers, smart food sensors have garnered significant interest as platforms for both real-time, in-package food monitoring and on-site commercial testing. In both cases, the sensitivity, stability, and efficiency of the developed sensors are largely informed by underlying material design, driving focus toward the creation of advanced materials optimized for such applications. Herein, a comprehensive review of emerging intelligent materials and sensors developed in this space is provided, through the lens of three key food quality markers - biogenic amines, pH, and pathogenic microbes. Each sensing platform is presented with targeted consideration toward the contributions of the underlying metallic or polymeric substrate to the sensing mechanism and detection performance. Further, the real-world applicability of presented works is considered with respect to their capabilities, regulatory adherence, and commercial potential. Finally, a situational assessment of the current state of intelligent food monitoring technologies is provided, discussing material-centric strategies to address their existing limitations, regulatory concerns, and commercial considerations.

Intelligent Monitoring and Maintenance Technology for Next-Generation Power Electronic Systems

Next generation power converters are expected to be reduced in size, weight and cost while providing highly reliable operation over an extended lifetime with minimal ecological footprint. This work investigates how intelligent monitoring and maintenance technologies (IMMs) play a key role in realizing these typically conflicting objectives. It uniquely analyzes the economic and ecological potential, i.e. the increased average operational lifetime of converters, that can be realized via intelligent maintenance using extracted state-of-health data. Consequently, newest monitoring and maintenance technologies that address thermally induced stress and strain in power modules are discussed and categorized regarding their level of complexity and cost of implementation. This facilitates finding the necessary implementation effort and cost to achieve the defined reliability and lifetime requirements of specific applications. Finally, this work reviews the impact of artificial intelligence and cloud technologies on increasing the effectiveness of maintenance technology. Both technologies reveal a path to new business opportunities, such as “power-as-a-service”, and have the potential to transform the “hardware-oriented” power converter industry as we know it into a “data-driven” industry.

Convolutional neural network-based recognition method for volleyball movements

With the development of network technology and computer intelligent monitoring technology, a large number of video data came into being. In view of the analysis of specific targets in video, the traditional artificial analysis method can not meet the existing needs. In volleyball teaching in college physical education, because each student has different movements, intelligent processing of video data has become a key issue. Through collecting and sorting out the relevant research results on behavior recognition, it is found that in the research of deep learning, the algorithm structure applied in this paper is representative, and its strong learning ability, especially compared with the traditional algorithm, can more accurately identify human movements, natural language processing and so on, but the research on volleyball action recognition is still less. Therefore, this paper constructs a data set, improves the convolution neural network model, subsequently, new models are constructed through the neural network structure to improve the accuracy of the nonlinear expression and optimize the content of the input data. In order to more accurately analyze the effectiveness of this algorithm, the new data are obtained by grouping the volleyball games in college sports courses. Compared with the original paper, the accuracy of the improved 3D network is improved by 3.3%–88.5%, and the complexity is reduced by 33.6%.

Research on Intelligent Monitoring of Boring Bar Vibration State Based on Shuffle-BiLSTM.

Due to its low stiffness, the boring bar used in deep-hole-boring is prone to violent vibration during the cutting process. It is often inaccurate and inefficient to judge the vibration state of the boring bar through artificial experience. To detect the change of the vibration state of the boring bar over time, guide the adjustment of the processing parameters, and avoid wastage of the workpiece and the loss of equipment, it is particularly important to intelligently monitor the vibration state of the boring bar during processing. In this paper, the boring bar is taken as the research object, and an intelligent monitoring technology of the boring bar's vibration state based on deep learning is proposed. Based on grouping convolution, channel shuffle, and BiLSTM, a shuffle-BiLSTM NET model is constructed, which is both lightweight and has a high classification accuracy. The boring experiment platform is built, and 192 groups of cutting experiments are carried out. The three-way acceleration and sound pressure signals are collected, and the signals are processed by smoothed pseudo-Wigner-Ville distribution. The original signals are transformed into a 256 × 256 × 3 matrix obtained by a two-dimensional time-frequency spectrum diagram. The matrix is input into the model to recognize the boring bar's vibration state. The final classification accuracy is 91.2%. A variety of typical deep learning models are introduced for performance comparison, which proves the superiority of the models and methods used in this paper.

Application of SSD network algorithm in panoramic video image vehicle detection system

Abstract Due to the popularity of high-performance cameras and the development of computer video pattern recognition technology, intelligent video monitoring technology is widely used in all aspects of social life. It mainly includes the following: industrial control system uses video monitoring technology for remote monitoring and comprehensive monitoring; in addition, intelligent video monitoring technology is also widely used in the agricultural field, for example, farm administrators can view the activities of animals in real time through smart phones, and agricultural experts can predict future weather changes according to the growth of crops. In the implementation of intelligent monitoring system, automatic detection of vehicles in images is an important topic. The construction of China’s Intelligent Transportation System started late, especially in video traffic detection. Although there are many related studies on video traffic detection algorithms, these algorithms usually only analyze and process information from a single sensor. This article describes the application of the single-shot detector (SSD) network algorithm in a panoramic video image vehicle detection system. The purpose of this article is to investigate the effectiveness of the SSD network algorithm in a panoramic video image vehicle detection system. The experimental results show that the detection accuracy of a single convolutional neural network (CNN) algorithm is only 0.7554, the recall rate is 0.9052, and the comprehensive detection accuracy is 0.8235. The detection accuracy of SSD network algorithm is 0.8720, recall rate is 0.9397, and the comprehensive detection accuracy is 0.9046, which is higher than that of single CNN algorithm. Thus, the proposed SSD network algorithm is compared with a single convolution network algorithm. It is more suitable for vehicle detection, and it plays an important role in panoramic video image vehicle detection.

Numerical and Experimental Investigation of Slope Deformation under Stepped Excavation Equipped with Fiber Optic Sensors

The real-time evaluation of slope stability is a crucial technical issue in foundation excavation and slope construction. However, conventional monitoring methods often fall short of achieving real-time and accurate measurements, which poses challenges to the timely assessment of slope stability. To address this problem, laboratory tests and numerical simulations were jointly used to evaluate slope stability in this paper. In numerical simulations, the finite element method (FEM) results clearly illustrate the distribution and evolution of internal strain during slope excavation, and the limit equilibrium method (LEM) calculates changes in the safety factor. In laboratory tests, the fiber Bragg grating (FBG) sensing technology was employed to monitor the internal strain of the slope in real time. The distribution characteristics of the slope internal strain field under the condition of stepped excavation were analyzed, and the feasibility of strain-based evaluation of slope stability was discussed. The measurements with FBG sensing technology agree well with the numerical simulation results, indicating that FBG can effectively monitor soil strain information. Of great significance is that the maximum horizontal strain of the slope is closely related to the safety factor and can be used to evaluate slope stability. Notably, the horizontal soil strain of the slope provides insight into both the formation and evolution of the critical sliding surface during excavation. The combination of numerical simulation and intelligent monitoring technology based on FBG proposed in this paper provides a reference for capturing strain information inside the slope and realizing real-time assessment and critical warning of slope stability.

Research on Video Synopsis Based on Deep Learning Target Detection and Target Trajectory Optimization

In this era of rapid economic and technological development, monitoring is essential for security issues. However, it is also accompanied by the low efficiency of managing and viewing a large number of surveillance videos every day. Traditional methods are gradually unable to meet the requirements of application, and the video field requires algorithm technology that can better solve practical problems. Intelligent monitoring technology is gradually emerging and developing, based on theories such as computer vision, which can solve many monitoring video problems and greatly improve the efficiency of monitoring video work. Through in-depth exploration of existing video synopsis technology algorithms, this article proposes a new video synopsis method that can effectively detect and track videos, thereby greatly improving the efficiency of storage, transmission, and use of surveillance videos. Experiments have shown that this method can effectively concentrate surveillance videos, and compared with existing methods, it has a better synopsis ratio while ensuring the integrity of video information, effectively reducing collisions between targets, effectively reducing overlap between targets, and achieving good visual effects. Utilizing an improved deep learning object detection and multi object tracking algorithm with added attention mechanism to extract foreground moving targets in videos, and using a mixed Gaussian background modeling algorithm establish a background, laying a stable target foundation for subsequent video concentration. At the same time, design trajectory recombination optimization methods to ensure that the targets do not overlap as much as possible. Reasonably place the targets in the new condensed video sequence, determine the index of all targets in the new condensed video stream, and finally integrate each frame of target images into the background image according to the set index rules, ultimately obtaining the synopsis video.

Preface

2022 3rd International Conference on Mechanical Engineering and Materials (ICMEM 2022) has been held online on 18th November 2022 due to Covid-19. Organized by Hubei Zhongke Institute of Geology and Environment Technology, ICMEM 2022 intends to invite worldwide famous scientists, experts, scholars, and researchers for academic presentations. ICMEM 2022 proceeding covers all aspects of mechanical engineering and material sciences, such as Dynamics, Vibration and Sound Engineering, Materials and Technology, Fluids Engineering, Solid Mechanics and Design, Micro/Nano Engineering and Technology, Production Engineering, Robotics and Control, Thermal and Power Engineering, Vehicle Technology, Machine Fault Diagnostics and Prognostics, Intelligent Transport System, Intelligent Monitoring Technology, Metallic Alloys, Tool Materials, Ceramics and Glasses, Composite Materials, Amorphous Materials, Nanomaterials, Biomaterials, Polymers, etc.. Around 120 articles were selected from more than 290 submissions after peer-review, and authors participated in ICMEM 2022 with 19 oral presentations and 51 posters, promoting the communication among researchers and experts from institutes and universities, including Beijing Institute of Space Mechanics & Electricity, China University of Petroleum (Beijing), Tongji University, Jimei University, Harbin Engineering University, University of Science and Technology Beijing, Xiamen University, Fudan University, Chinese Academy of Sciences, etc.We would like to thank all the members of the Technical Program Committee for the success of ICMEM 2022. And we wish to express our sincere gratitude to the authors and reviewers for their valuable scientific contributions and useful feedbacks to ensure a high scientific quality of ICMEM 2022.List of Conference Committee is available in this pdf.

AgriPest-YOLO: A rapid light-trap agricultural pest detection method based on deep learning.

Light traps have been widely used for automatic monitoring of pests in the field as an alternative to time-consuming and labor-intensive manual investigations. However, the scale variation, complex background and dense distribution of pests in light-trap images bring challenges to the rapid and accurate detection when utilizing vision technology. To overcome these challenges, in this paper, we put forward a lightweight pest detection model, AgriPest-YOLO, for achieving a well-balanced between efficiency, accuracy and model size for pest detection. Firstly, we propose a coordination and local attention (CLA) mechanism for obtaining richer and smoother pest features as well as reducing the interference of noise, especially for pests with complex backgrounds. Secondly, a novel grouping spatial pyramid pooling fast (GSPPF) is designed, which enriches the multi-scale representation of pest features via fusing multiple receptive fields of different scale features. Finally, soft-NMS is introduced in the prediction layer to optimize the final prediction results of overlapping pests. We evaluated the performance of our method on a large scale multi pest image dataset containing 24 classes and 25k images. Experimental results show that AgriPest-YOLO achieves end-to-end real-time pest detection with high accuracy, obtaining 71.3% mAP on the test dataset, outperforming the classical detection models (Faster RCNN, Cascade RCNN, Dynamic RCNN,YOLOX and YOLOv4) and lightweight detection models (Mobilenetv3-YOLOv4, YOLOv5 and YOLOv4-tiny), meanwhile our method demonstrates better balanced performance in terms of model size, detection speed and accuracy. The method has good accuracy and efficiency in detecting multi-class pests from light-trap images which is a key component of pest forecasting and intelligent pest monitoring technology.

ECViST: Mine Intelligent Monitoring Based on Edge Computing and Vision Swin Transformer-YOLOv5

Mine video surveillance has a key role in ensuring the production safety of intelligent mining. However, existing mine intelligent monitoring technology mainly processes the video data in the cloud, which has problems, such as network congestion, large memory consumption, and untimely response to regional emergencies. In this paper, we address these limitations by utilizing the edge-cloud collaborative optimization framework. First, we obtained a coarse model using the edge-cloud collaborative architecture and updated this to realize the continuous improvement of the detection model. Second, we further proposed a target detection model based on the Vision Swin Transformer-YOLOv5(ViST-YOLOv5) algorithm and improved the model for edge device deployment. The experimental results showed that the object detection model based on ViST-YOLOv5, with a model size of only 27.057 MB, improved the average detection accuracy is by 25% compared to the state-of-the-art model, which makes it suitable for edge-end deployment in mining workface. For the actual mine surveillance video, the edge-cloud collaborative architecture can achieve better performance and robustness in typical application scenarios, such as weak lighting and occlusion, which verifies the feasibility of the designed architecture.

Intelligent Wireless Monitoring Technology for 10kV Overhead lines in Smart Grid Networks

Promoted by the rapid development of information technology, 10kV overhead line has been widely used in the majority of cities, and it is of great significance to monitor the distribution network effectively, in order to ensure the normal operation of the system. Most of traditional distribution network monitoring methods are based on manual work, which causes inconvenience to the distribution network fault location, repair, maintenance and real-time monitoring, and reduces the efficiency of the distribution network emergency repair and the reliability of power supply. Aiming at the automatic monitoring problem of 10kV overhead network, this paper adopts an intelligent wireless monitoring technology, where a monitoring node is employed to monitor the network transmission status through wireless links. We evaluate the system monitoring performance by using the metric of outage probability, depending on the wireless data rate over wireless channels. For the considered system, we derive analytical outage probability, in order to measure the system performance in the whole range of signal-to-noise ratio (SNR). The simulation results are finally presented to verify the analytical expressions on the system monitoring outage probability in this paper.